1. Last 7 days
    1. On 2018-02-21 19:47:27, user Davidski wrote:

      Hello author,

      Unfortunately, there are some major problems with your paper.

      For one, you're basing your conclusions about the ancient samples on ADMIXTURE clusters derived from modern-day samples, and thus getting many things backwards.

      Have you thought about using ADMIXTOOLS to try and back up your conclusions with formal statistics and models based on formal statistics?

      Also, some of your inferences are based on an incomplete knowledge of the current ancient DNA record. For instance, you claim that Northern European R1a derives from the steppe north and east of the Caspian Sea, and that Southern European R1b derives from the steppe north of the Black Sea.

      Please note that the oldest recorded instance of R1a-M417, which encompasses more than 90% of modern-day R1a lineages in Europe and Asia, is on the steppe north of the Black Sea: sample Ukraine_Eneolithic ID I6561 from Mathieson et al. 2018.

      And the oldest recorded instance of R1b-M269, which encompasses more than 90% of modern-day R1b lineages in the world, is on the steppe north of the Caspian Sea: sample Yamnaya_Samara ID I0429 from Haak et al. 2015.

    1. On 2018-12-28 16:54:26, user Olga Sazonova wrote:

      Dear authors, a friendly correction: you have misreferenced the work of Khera et al - they computed a polygenic risk score for Inflammatory Bowel Disease, not Irritable Bowel Syndrome. Happy holidays :)

    1. On 2016-08-17 20:24:29, user Epigenetics guy wrote:

      While the paper is interesting, this is not "epigenetics", nor is any evidence of epigenetics presented. Epigenetics implies heritable transmission either across generations or mitotic cell divisions. This is simply transcriptional regulation.

    1. On 2019-06-07 20:21:08, user peakqi wrote:

      fully connected is plausible: each neuron of layer0 can connect to each neuron of layer1, the connection strength depends on activity time sequence mediated by molecular events. however, CNN needs to scan each kernel across the whole neurons of the upstream layer, the biological synapses do not support the moving convolution operation.

    1. On 2014-01-02 01:45:47, user Davidski wrote:

      I have a couple more observations after reading the study over again.

      It's fascinating that ANE is so widespread in the Near East, with such a high peak in the North Caucasus. I can think of a couple of reasons for this; firstly, the Indo-European expansion into the Near East from the Eastern European steppe, and perhaps secondly, the later Turkic expansion from near the Altai via Central Asia.

      However, could it be that minor South Asian admixture is artificially pushing up the levels of ANE across the Near East? In other words, in a model that doesn't account for South Asian admixture, perhaps the only way that such admixture can be expressed is via inflated levels of ANE? Indeed, an ancient DNA study of a Mesopotamian site in Syria found South Asian specific mtDNA sequences among the samples. See here...

      http://www.plosone.org/arti...

      Also, it'd be great to see a PCA of West Eurasia featuring the ancient samples which is more in tune with geography. Perhaps increasing the number of European samples might help? Here's an MDS that I did with a similar dataset to yours, except with more European individuals, and it matches geography quite well.

      http://img209.imageshack.us...

      My PCAs using the same dataset are almost identical, although from memory they have to be rotated clockwise 90 degrees. Here's an example in a PDF.

      https://docs.google.com/fil...

      Cheers

    1. On 2024-12-12 11:47:04, user FKA Arebolas wrote:

      Interesting work, that allows of all us to understand a little bit more about 'project-based science'. I do hope that a substantially revised version of this paper would eventually be published—in an internationally renowned journal. The authors have done a great job and deserve such a form of recognition.

      Still, the paper also suffers from several shortcomings that have less to do with its 'pre-print' status than with its theoretical and methodological foundations. The following is a brief exposition of its weaknesses, as I see them.

      The paper is beset with inconsistencies. To begin with, the contrast between 'ERC science' and 'Consortia science' is a non-starter. Not only do some ERC grants require grantees to set up international consortia (i.e., Synergy Grants), but, also, 'Consortia science' in this context aims to group together types of EU-awarded grants that are internally diverse, including Research and Innovation Actions (that make provision for fundamental research) and Innovation Actions (that do not). 'Consortia science', put it simply, do not exist—not even as an analytical category.

      Along these lines, at some point in the 'Introduction' section, the paper hints at an important aspect of grant funding: the constant need to look for new funding, or 'strategic anticipation'. This has to do neither with 'consortia science' nor with 'ERC science', because, in both cases, recipients are expected and must search for funding once the project is finished. Unfortunately, this rather important aspect of grant funding (or project-based funding) is nowhere considered in the presentation and discussion of results. Pursuing this line of research further would be rather illuminating and would help us disclose the real problems connected to grant funding (a topic that Merimans is concerned with in another paper, already published).

      Also critical, by this commentator's point of view, is the assimilation of 'international collaboration' to 'knowledge co-creation' and 'knowledge co-production'. This usage is clearly confusing and misguiding. 'Co-production' has a very specific meaning in Science and Technology Studies, and it has nothing whatsoever to do with getting industry involved in the project. Furthermore, the reality of RIAs and IAs do not stand to the implied meaning of such moniker. Most of the time, such involvement truly amounts to a division of labour, according to which the companies in question act as 'demonstration cases' that implement and test (or demonstrate) the technology being proposed. Nowhere can one find an instance of 'co-production'.

      I am pretty sure that the authors know a great deal about grant funding, and about EC's Framework Programmes in particular. Yet, the previous critical remarks are a glaring illustration that additional research is needed to get fully acquainted with the subtleties of such funding scheme—knowledge necessary to fully understand what is at stake in the interviews.

      To conclude with this informal review, two methodological remarks: 1) it is by no means clear which the selection of participants have been. I believe that only a minority of them have experience in ERC grants (either applying for them or, crucially, gaining them), so that framing the whole paper in terms of 'how well ERC grants are' might be, once again, distorting and misleading; 2) nowhere are details about the coding process being offered. As the research work, despite the pre-print format of the paper, seems to have concluded, this is not an absence that can be attributed to the preliminary stage at which the results have been published.

      Hoping that these indications help the authors improve the manuscript.

      With kind regards,

      L.

    1. On 2018-12-11 12:52:40, user piozaum wrote:

      Great article! The git repository is quite complete and supplementary information is really good. I also see a great potential in annotating proteoforms obtained from experiments into reactome for a more analysis. I would only add that the performance chapter should contain some validation on the model of real data and analysis of the precision of the predictions.

    1. On 2022-09-17 01:01:54, user Chengxin Zhang wrote:

      A standard approach for protein structure compression is MMTF, which is a lossless compression format supported by RCSB PDB. After including metadata, does PIC outperforms MMTF in compression rate, with and without gzip compression?

    1. On 2025-05-14 22:16:23, user Anonymous wrote:

      This paper investigates the human gut microbiota’s ability to break strong carbon-fluorine (C-F) bonds that have been introduced into the human body via pharmaceuticals and environmental pollutants. The researchers developed a 96-well colorimetric fluoride assay to screen culturable bacteria in the human body and identified dehalogenases (an enzyme that degrades environmental pollutants) in gut bacteria, including Clostridia, Bacilli, and Coriobacteriia, that<br /> hydrolyze fluorinated amino acids. This enabled the researchers to identify key amino acids important for defluorination. Then, the researchers successfully converted dechlorinating dehalogenases into defluorinating dehalogenases by substituting the carboxyl (C)-terminal 41 amino acids with those from naturally occurring defluorinating dehalogenases. Whole protein alanine scanning, molecular dynamics simulations, and chimeric protein design facilitated the<br /> identification of the role of the C-terminal region of dehalogenases in defluorination. The researchers also trained machine learning models to understand the structural and sequence differences between defluorinating and non-defluorinating dehalogenases. These novel predictive models were trained on the 41 amino acid segments of the C-termini and predicted defluorination<br /> activity with 83% accuracy and 95% accuracy when based on the full-length protein features. This study ultimately discovered that the human gut microbial enzymes are capable of cleaving C-F bonds.

      The figures in this paper are clear and well-organized, which lent themselves to effectively conveying complex data. The methods section of this paper goes into great detail, describing the different types of equipment used, explaining multiple validation steps, and noting where protocols were modified from the original workflow. The researchers provide GitHub links for their data, which shows that their methods are easily reproducible. They also have several<br /> replicates for their experiments and test why certain aspects of their experiments did not work. They also succeeded in developing a novel assay for alanine screening.

      This study is significant and relevant because of the prevalence of fluorinated drugs and environmental pollutants such as Per-and polyfluoroalkyl substances (PFAS), commonly referred to as “forever chemicals,” in our environment. This study uncovers the ability of the human gut microbiome to metabolize fluorinated compounds and gives insight into developing engineered<br /> enzymes to mitigate the effects of fluorinated pollutants and drugs in the human body. Their novel predictive model could help in the development of interventions to address environmental and human health concerns associated with fluorinated substances.

      Major points:<br /> There are a few major aspects of this paper that could be improved:<br /> 1. I think this paper could benefit from an explanation or discussion of where they speculate the fluoride ions migrate to in the human body after cleavage of the C-F bonds. This may provide more transparency between the author and the reader about what is happening in the human body, especially since this paper addresses human health concerns and environmental pollutants.<br /> 2. Also, in order to strengthen the results and increase the reproducibility of the work, I recommend a more detailed description of the statistical analysis employed by the researchers.<br /> 3. I do appreciate the acknowledgement of the limitations of the study by the authors:<br /> o The range of fluorinated compounds is limited to overexpressed recombinant<br /> dehalogenases sourced from the gut microbiota<br /> o The comparison of in vivo and pure culture experiments is lacking<br /> 4. I acknowledge that it is beyond the scope of this study, but for possible follow-up experiments, the researchers could expand the range of fluorinated compounds by testing more substrates and could conduct more experiments in vivo.

      Minor points:<br /> There are a few minor aspects of this paper that could be improved:<br /> 1. In the editing stage of this publication, it would help the reader for the authors to standardize the terminology they used. Also, there were instances where the authors used scientific jargon when introducing the study. For example, it would be helpful if the authors explained or wrote out what “LB” referred to in their methods. This would strengthen the author’s ability to communicate science to a general audience.<br /> 2. I recognize English may not be the authors’ first language, however, to increase the clarity of the presentation of this work, I suggest editing the minor typos in the second to last paragraph of the Introduction section: “Here,e” and “which us motivated…”<br /> 3. Throughout the paper, the authors switch tenses, going from past tense to present tense and then back to past tense again. There are also some grammatical errors that need to be addressed in order to improve the sentence structure. This would help with the overall flow of the paper.<br /> 4. Lastly, the figures could have color palettes that are more accessible for people who are colorblind.

      In conclusion, I would recommend this paper for publication with minor revisions. I did not find any fundamental issues with this paper that would disqualify it from publication. I acknowledge that I do not have the expertise in biochemistry to comment on the exact experimental methods utilized, however, I do not think there was experimental misconduct or unfounded claims made<br /> about the data. This work involves a novel experimental design, and this study was well thought out and executed. I think this work would be very impactful to the scientific community and human society.

    1. On 2020-03-31 21:29:20, user Einar Eftestøl wrote:

      Congratulations on an interesting paper. As I have been working in the field of load/mechanotransduction and its effect on skeletal muscle hypertrophy for quite a few years, I would like to recommend reading my paper on the subject from 2016 (PMID: 27488660). Best wishes, Einar Eftestøl

    1. On 2024-09-05 01:22:44, user GR wrote:

      Hi Authors

      Very interesting paper, I just noticed some small potential mistakes when reading. In Fig 3E, it looks like the beta-tubulin image has been put in place of gamma-tubulin, and in Figure 5A, the GO treatment image appears to be the same as the Figure 4D AA image. Hopefully this comment is helpful!

    1. On 2018-10-02 14:04:53, user BU_FALL_BI598_G5 wrote:

      Critical review #1 Target specific routing of visual information by the superior colliculus<br /> Katja Reinhard, Chen Li, Quan Do, Emily Burke, Steven Heynderickx, and Karl Farrow<br /> Group 5- Qifan Shang, Joseph Sisto, Simran Shah

      Overview

      The genesis of the paper is the ambiguity around how the retino-collicular circuits are arranged and wired for information processing after they leave the superior colliculus. As a result, it’s difficult to determine how different behaviors are initiated by the colliculus based on visual features and how the different pathways (hard-wired vs. flexible) of extracting visual information are arranged. More insight of the visual system could be shared by clarifying the arrangements of the different pathways.

      Reinhard and coauthors set out to characterize the connectivity of circuits in parallel pathways and investigated in further detail the routing of visual information through the superior colliculus (SC) by applying a combination of monosynaptic viral tracers, molecular markers and quantitative analysis. They specifically focused on two neural circuits targeting the parabigeminal nucleus (PBG; the colliculo-parabigmeinal circuit) and the pulvinar (colliculo-pulvinar circuit). The authors were able to determine that there is a dedicated set of connections between specific retinal ganglion cell types and different targets of SC.

      The authors did an excellent job by incorporating a transgenic NSTR1-GN209-Cre mouse line to specifically label neurons that project uniquely to the lateral pulvinar, and PV-Cre mice for PBG labeling (results section, line 59). In figure 1, they first performed the transsynaptic tracing by injecting the HSV, hEF1a-TVA950-T2A-rabiesG-IRES-mCherry into the PBG of the wild type mice to label the pathway from from PBG to SC and injected hEF1a-LS1L-TVA950-T2A-RabiesG-IRES-mCherry into the pulvinar of the transgenic mice to label the pathway from lateral pulvinar to the SC. They were able to observe the infection by the red fluorescence. Then, they injected Enva-coated G-deleted rabies coding for GCaMP6s to the SC to label the ganglion cells. They were able to see the ganglion cells that projected to the SC because they were expressing the green fluorescence. They used different molecular markers; SMI32, an antibody against neurofilaments and is used to distinguish alpha Ganglion Cell types in the retina, and CART, which labels three out of the four subtypes of ON/OFF retinal ganglion cells. They also used MATLAB to cluster the different subtypes of RGCs. However, the methods and results presented here merit some comments and unresolved questions/concerns.

      Major Criticisms

      First, it is unclear why the authors chose to focus on the two downstream regions PBG and pulvinar. (introduction section, line 14.) How do these two pathways contribute to the hard-wired vs. flexible pathway storyline the authors are trying to convey? They should provide a justification for the basis of this work in the introduction of the paper. Furthermore, a more in-depth explanation or discussion on the relevance of hard-wired vs flexible circuits would be appreciated. Are either of the pathways observed in this paper (PBG or PV) hard-wired or flexible? And if so, what are the further possible implications and studies of the two categories?

      Second, the images of the fluorescent labeling of the infection (such in figure 1 B, C, D, F, G, H) are too small and blurry to show the specific and successful infection by the rabies virus. Can high resolution and zoomed-in pictures of the fluorescence be provided? Please provide examples of PBG versus pulvinar injections.

      Third, the authors didn’t clarify the mechanisms of the HSV and the rabies virus and address the toxicity of the viruses. The morphology of the RGCs could be different in day 8 when they imaged due to damage of the cells due to the virus. High mag. images of the neurons while the experiment was happening to prove the neurons were indeed healthy would be helpful, for example, a image showing no destruction of dendrites. In addition, images of healthy neurons without the effects of the virus should be shown as a comparison. Moreover, it’s difficult to tell whether 8 days of incubation period is enough for the rabies virus to fill the RGCs completely. One way to control for this variable is to image the RGCs before infection and patch the cells filled with biocytin (iontophoresis). At day 8 when imaging the authors will then be able to compare and see if there’s a different pattern between the biocytin and rabies infection. Also, validation of accurate injection sites and proof that retinotopy does not influence the variability of the injections in a given pathway would be appreciated; one suggestion is to inject an inert tracer (Flurororuby or Chicago blue) in a few of the mice and determine if the injections are reproducible. Or, inject a dye (possibly Alexa 647) with the virus and quantify the intensity of the “hot spot” in the SC. Alternatively, another method is to use intrinsic optical imaging to map the anterior vs. posterior visual field and inject.

      Fourth, there is no part of the experiment to show that the cells in the SC project to both the PBG and PV. It is necessary to include this because without it, the reader cannot be sure that they are not routed from two separate areas due to the retinotopic nature of the SC, and this would go against what they suggest in the paper. One experimental method to show this is to inject HSV-GFP into the PV and mCherry into the PBG and look at the SC for any double-labeled yellow cells in the SC.

      Fifth, immunochemistry should be performed to ensure each component of the virus (G, TVA and mCherry) is being properly expressed in collicular cells. Although in the supplemental figure 1, the authors injected the SC alone with rabies without the initial infection of PBG and the pulvinar with the HSV and observed no infected cells in the retina to ensure that the rabies virus was indeed G deleted. The authors further test out the non-conditional and conditional HSV in both mouse lines to show that the conditional HSV is only expressed in the transgenic mouse line and showed that the conditional HSV is cre-dependent. To further account for the effectiveness of TVA and possible contamination of the Enva-coated G-deleted rabies virus, one suggestion is to have a negative control group by injecting the pulvinar and PBG with HSV without the TVA component and inject the same rabies virus in the SC. One should expect to see both green and red fluorescence in the SC and nothing in the retina.

      Sixth, the authors used three validation indices on MATLAB, Calinski-Harabasz, Silhouette, and Davies-Bouldin, to cluster the molecular identity of the retinal input by clustering the morphological data based on dendritic stratification and field size (figure 5). This resulted in twelve different cell type clusters. However, clearer explanations of how they performed the statistical analysis would be appreciated. There are some vague assumptions for the subtypes of the RGCs that lead to PB/PV (figure 6) For example, how accurate is the morphological evidence? It is also not clear what they used to cluster and how density distribution can show cell type grouping. It would be a good idea to label the cell types by circuit in Fig. 6.

      Minor flaws

      We suggest the authors check the article for grammatical, spelling errors and use more concise language. For example, Labeled vs labelled (results, line 136-137), “Motiont” (discussion, line 270). Ambiguity in line 150, must specify if “all cells” refer to the 54 cells or all 301 cells.

    1. On 2022-04-26 11:57:49, user Markus Löbrich wrote:

      As stated in this publication, we were asked by Dr. Benedetti to provide the phospho-specific RAD54 antibody used in our Spies et al publication and responded that we ran out of the badge used for the publication. Instead, we sent him a new company delivery that we just had received at this time and had not yet tested. This badge was used in the publication of Ghosh, Khalil and Benedetti and shown to be highly unspecific and useless for studying RAD54 phosphorylation. After we had sent the antibody to Dr. Benedetti, we had also tried it ourselves and confirmed its poor quality that prevents any useful studies.

      Since the company could not produce a new specific badge of the phospho-specific RAD54 antibody, we took a different approach to extend our studies on the physiological significance of the RAD54-Ser572 phosphorylation. Specifically, we generated RAD54-Ser572 to Ala572 mice by knock-in techology and studied DNA double-strand break repair in fibroblasts obtained from such mice. We observed a repair defect in the RAD54-S/A mutant fibroblasts which is of the same extent and epistatic to the repair defect observed after siRNA-mediated knock-down of NEK1. This result confirms and extends our conclusion of the Spies et al paper. We are currently preparing this data for publication.

    1. On 2020-12-06 14:26:03, user Dr. Nandor Ludvig wrote:

      Great paper, congratulations! As someone who first recorded monkey hippocampal place cells during their free behavior in a large space (Ludvig et al. Brain Res [2004] 1014:97-109), I still think the best way to understand the primate-specific, space-time processing hippocampal - association cortical neural circuitry (Ludvig, Physiol. Behav. [1999] 67:57-67) is to monitor this circuitry 24/7, in the monkeys' natural habitat and social setting, from spring to winter. As this is now possible -- though I was unable to raise funds from it -- I hope creative people like you or others will sooner or later expand your admirable cell-recording/analysis technologies to this direction. Regardless, congratulations again and best luck for your work! -- Nandor (nandorludvig@gmail.com)

    1. On 2018-04-21 12:37:20, user bennedose wrote:

      bennedose Vagheesh, a few others and I had a long Twitter discussion after which he requested us to post our views in detail on biorxiv. I presume this is the place for it. First - about myself - my name is Shiv Sastry and my Twitter handle is @bennedose

      I intend to make two long posts, please pardon me - some things cannot be kept short.

      I start with tacit acceptance of the finding of steppe genes as a marker for Indians.

      My only point of contention is whether the steppe migrants implied in the paper carried Indo-European languages with them to India. I am in no way questioning the migration-language connection between steppe and Europe as made in the paper. But similarity between steppe migration to Europe and to India does not mean that IE languages first arrived in India with this wave of migrants. I believe that it is not sufficient to claim this particular migration to India as a source of IE in India

      There are two important preconditions to make a claim that people from place A took their language to place B and introduced that language for the first time in place B. First there must be incontrovertible evidence that the migrants from place A actually used to speak the language that they took to place B in the pre-historic era that they migrated. Second, there must be evidence that the language they carried with them did not already exist in the "place B" to which they migrated. Anything less is conjecture. Not science. I will take these two points in reverse order.

      Any migration carrying IE language to India for the first time has to be shown to have occurred before 2500 BCE because there is powerful evidence of IE language having been in India by 2500 BCE and most likely for millennia prior to that. I will try and be as brief as possible about the evidence which in detail is enough to fill a book chapter.

      The evidence for IE in India prior to 2500 BCE falls under 2 main heads<br /> 1. Archaeoastronomy<br /> 2. Records of hydronyms, toponyms and climate related events

      I am only going to deal with the latter

      The presence of Vedic Sanskrit in India was attested in text form as recently as 150 AD or so and if attestation is the metric then all talk of Sanskrit as an ancient language is moot. However it was dubbed as ancient by Max Muller who surmised that the post Vedic texts were contemporaneous with the Buddha who was thought to have lived around 600 BC. Muller then worked backwards, assuming about 200 years for the composition of each class of post Vedic and Vedic texts and arrived at a date of 1200 to 1000 BC for the Rig Veda. However many factors have called these "guesstimated" dates into question.

      1. Rig Veda 7.95.2 speaks of a mighty river called the Saraswati flowing from mountains to the sea (RL Kashyap, Rig Veda Samhita, Mandala 10, Sakshi trust 2012)

      2. More information about the location of the "Saraswati" comes from Macdonell & Keith Vedic Index of names and Subjects 1912: "In the enumeration of rivers (evidently from east to west) in Rv. x. 75, 5, Ganga, Yamuna, Sarasvati, Sutudri, the Sarasvati comes between the Jumna and the Sutlej, the position of the modern Sarsuti (SaraswatI). which, flowing to the west of Thanesar, is joined in Patiala territory "

      These passages give a rough idea of some ancient river, real or imagined, called the Saraswati that was situated between the Yamuna river (that passes through Delhi) in the east and the Sutlej river in the west

      3.Still more geographical information comes from Macdonell and Keith again:<br /> a. Madhya­desa, the ‘Middle Country,' is, according to the Manava Dharma Sastra, the land between the Himalaya in the north, the Vindhya in the south, Vinasana in the west, and Prayaga (now Allahabad) in the east that is, between the place where the Sarasvati disappears in the desert, and the point of the confluence of the Yamuna (Jumna) and the Ganga (Ganges)

      b. The Baudhayana Dharma Sutra defines Aryavarta as the land east of Vinasana ; west of the Kalaka­vana, ‘ Black Forest,' or rather Kanakhala, near Hardvar; south of the Himalaya; and north of the Pariyatra or the Paripatra Mountains.

      c. Vinasana, 'disappearance,' is the name of the place where the Sarasvati is lost in the sands of the desert.

      Here we have references to "Vinasana" a place where a "Saraswati river" disappears in the desert. This information comes from post-Vedic texts - most likely composed centuries after the Vedas. The Himalayas in the north are mentioned. In the south are the Vindhya mountains, the traditional barrier between north and south India. The Paripatra mountains are the westernmost reaches of the Vindhyas. To the east is the Yamuna river which corresponds to what the Vedas record as being to the east of the Saraswati. West of that is the place where the Saraswati "disappears in the desert"

      So here we have mention of a river that does not exist today but located in a recognizable geographic location. The Vedas call it a mighty river that reaches the sea and later texts claim that the river dries up in the desert

      4.Now we come to more recent events and discoveries. To a man called RD Oldham must go the credit for identifying the site in the Rajasthan desert that was apparently the ancient Saraswati river. However it was his compatriot - a similarly named CF Oldham who wrote:

      “..local legends assert ( that Sarasvati ) once flowed through the desert to the sea . In confirmation of these traditions , the channel referred to , which is called Hakra or Sotra , can be traced through the Bikanir and Bhawulpur states into Sind , and thence onwards to the Rann of Kach . . . attested by the ruins everywhere overspread what is now an arid sandy waste . Throughout this tract are scattered mounds , marking the sites of cities and towns . And there are strongholds still remaining... Amongst these ruins are found, not only the huge bricks used by the Hindus in the remote past , but others of a much later make..”

      1. Next it was a British geologist called Aurel Stein who wrote, in 1942, a monograph entitled “A survey of ancient sites along the`lost' Sarasvati River” in which he identified the Ghaggar river in north India as the remnant of the Saraswati, and said that for over a distance of 100 miles (160 km) the river bed of the Ghaggar is more than 2 miles (3.2 km) wide and in many places over 4 miles (6.4 km) wide. He noted the scanty flow in the modern Ghaggar and wide ancient river bed meant that there had been a much mightier river in the remote past. Aurel Stein also noted findings of ancient settlements by the sides of this old Saraswati river bed that he connected up with the Indus Valley civilization.

      2. There is no doubt that a river existed in the site of the Saraswati as noted in old texts. But was it a mighty river that reached the sea? For that I refer you to a 2017 paper that shows results of isotopic analyses of cores from the Rann of Kutch. <br /> Tracing the Vedic Saraswati River in the Great Rann of Kachchh, Nitesh Khonde, Sunil Kumar Singh , D. M. Maurya , Vinai K. Rai , L. S. Chamyal & Liviu Giosan

      https://www.ncbi.nlm.nih.go...<br /> This study shows that until 10,000 ybp (8000 BCE) there was a great river in that area that drained into the sea. Progressive drying started after that but the paper states that their method would not be able to detect how long the river flowed all the way to the sea after that

      1. Another paper: Adaptation and human migration, and evidence of agriculture coincident with changes in the Indian summer monsoon during the Holocene Anil K. Gupta , David M. Anderson , Deep N. Pandey and Ashok K. Singhvi.

      http://repository.ias.ac.in...<br /> This paper documents a massive increase in rainfall over the entire Indian subcontinent from 10000 ypb to 7000 ybp (8000 - 5000 BCE) and mentions the effect this had on crops in Mehrgarh. Aridity started increasing after 5000 ybp.

      So what are the facts we have? The Vedas mention a mighty Saraswati river that reached the sea. Later texts clarify its location but observe that it dries up in the desert. That location correlates perfectly with the finding of a massive river bed with IVC settlements alongside. Modern isotopic studies, palaeoclimatology and palynological studies indicate a mighty river in that location, which flowed into the sea up to 8000 BCE and possibly for a little longer. Heavy monsoons swelled subcontinental rivers from 8000 BCE to 5000 BCE. Aridification started after that. These facts correspond well with the ancient records that survived in the Vedas and post-Veda texts.

      All the ancient records of events and geography, ostensibly from 8000 BCE are preserved in an "Indo-European language". That language, by the estimate of Indologist-Philologists has 96% IE words with minimal substrate of any other language. No other IE language to my knowledge has such a large proportion of IE derived words.

      The pointers towards a very ancient presence of IE language in India, long before influx of the steppe people described in this paper is strong. In a separate post I will deal with the alleged area of origin of IE language coming to India, the steppe region.

    2. On 2018-04-02 05:40:07, user Vinod nautiyal wrote:

      Is there any genetic data on the segment of the population which diverted to high himalayan region of india from central asia . I think the genetic mapping should also include data on ancient DNA of such population. There are various burial sites which have been excavatedby us durinv the last many years in kinnaur and garhwal uttarakhand

    1. On 2024-09-08 19:36:57, user Cara J. Gottardi wrote:

      Can the authors please confirm use of recombinant human WNT2 from Novus Biologicals (H00007472-P01) for their rescue experiments? The supplier says this protein is not designed to be active, and should not be used for activity-based assays (e.g., the protein is GST-tagged, not ideal for WNT proteins; also wheat germ systems do not allow for glycosylation of secreted proteins). Happy to be wrong if this protein prep really works!

    1. On 2020-03-25 17:02:59, user Sinai Immunol Review Project wrote:

      Summary: The authors use 2 neural network algorithms, NetMHCpan4 and MARIA, to identify regions within the COVID-19 genome that are presentable by HLA. They identify 405 viral epitopes that are presentable on MHC-I and MHC-II and validate using known epitopes from SARS-CoV. To determine whether immune surveillance drives viral mutations to evade MHC presentation, the authors analyzed 68 viral genomes from 4 continents. They identified 93 point mutations that occurred preferentially in regions predicted to be presented by MHC-I (p=0.02) suggesting viral evolution to evade CD8 T-cell mediated killing. 2 nonsense mutations were also identified that resulted in loss of presentation of an associated antigen (FGDSVEEVL) predicted to be good antigen for presentation across multiple HLA alleles. <br /> To identify potential sites of neutralizing antibody binding, the authors used homology modeling to the SARS-CoV’s spike protein (S protein) to determine the putative structure of the CoV2 spike protein. They used Discotope2 to identify antibody binding sites on the protein surface in both the down and up conformations of the S protein. The authors validate this approach by first identifying antibody binding site in SARS-CoV S protein. In both the down and up conformation of the CoV2 S protein, the authors identified a potential antibody binding site on the S protein receptor binding domain (RBD) of the ACE2 receptor (residues 440-460, 494-506). While RBDs in both SARS-CoV and CoV2 spike proteins may be important for antibody binding, the authors note that SARS-CoV has larger attack surfaces than CoV2. These results were later validated on published crystal structures of the CoV2 S protein RBD and human ACE2. Furthermore, analysis of 68 viral genomes did not identify any mutations in this potential antibody binding site in CoV2. <br /> Finally, the authors compile a list of potential peptide vaccine candidates across the viral genome that can be presented by multiple HLA alleles. Several of the peptides showed homology to SARS-CoV T-cell and B-cell epitopes.

      Limitations: While the authors used computational methods of validation, primarily through multiple comparisons to published SARS-CoV structures and epitopes, future work should include experimental validation of putative T-cell and B-cell epitopes.

      Importance: The authors identified potential T-cell and B-cell epitopes that may be good candidates for peptide based vaccines against CoV2. They also made interesting observations in comparing SARS-CoV and CoV2 potential antibody binding sites, noting that SARS-CoV had larger attack surfaces for potential neutralizing antibody binding. One of the highlights of this paper was the authors’ mutation analysis of 68 viral genomes from 4 continents. This analysis not only validated their computational method for identifying T-cell epitopes, but showed that immune surveillance likely drives viral mutation in MHC-I binding peptides. The smaller attack surface may point to potential mechanisms of immune evasion by CoV2. However, absence of mutations in the RBD of CoV2 and the small number of mutations in peptides presentable to T cells suggests that vaccines against multiple epitopes could still elicit robust immunity against CoV2.

    1. On 2020-04-04 13:24:25, user Jade Hawke wrote:

      Cats aren't spreading Covid-19 to humans, but they can catch it from you, and give it to other cats. There is no evidence it will go from cat to human. Please don't harm your cat, or cats you see roaming.

    1. On 2025-01-16 20:56:10, user Lisa Brents wrote:

      Nice study! Would it possible to do a more chronic study with these explants with lower concentrations of buprenorphine? I'm sure this depends primarily on how long the explants can be functionally maintained. Also, are you considering looking at whether the major metabolites of buprenorphine (norbuprenorphine, glucuronides) also can cause placental sterile inflammation? As you may know, the Concheiro paper showed higher median concentrations of these metabolites than the parent drug in placenta.

    1. On 2021-06-08 21:04:01, user Charles Warden wrote:

      Also, I am waiting for my other comment to be approved, but I think the template for Figure 5C-G was used for Supplemental Figures S6-S13 (without shifting the letters to begin with "A," for each separate cell type)?

      Thank you again for developing this tool!

    1. On 2019-11-21 14:40:18, user Manuel Martinez Garcia wrote:

      Hi all,<br /> nice results but bad that this papers does not say any word on the first published paper showing nanopore sequencing in viral metagenomics, really! https://peerj.com/articles/...<br /> and also in introduction talks about different methods to capture the viral diversity: fosmids, metagenomics AND forget one of the last one, single virus genomics, which has been very informative in marine systems. Definitely, it has different aspects to be improveb

    1. On 2017-05-16 20:00:49, user Matt wrote:

      It would be useful and appreciated to provide a supplementary table of the haplotype donations described in Figure 3, to give more understanding of how the absolute levels relate to the patterns you describe.

    1. On 2018-05-30 23:45:26, user Matt Olm wrote:

      Have you considered the effect of an unbalanced species composition on the resulting ANI distribution? For example, if you have lots of E. coli genomes, you're going to have lots of within-species E. coli comparisons, skewing the total distribution

    1. On 2023-09-29 22:35:52, user Brooke Morriswood wrote:

      Note that this preprint (v2) was updated as a result of peer review of the first version (v1). It is however non-identical to the final published version in Journal of Cell Science. For that, FigS2 was deleted, and Figure2 was moved into the supplementals; in addition, around 1000 words were cut (mostly from the Discussion) in order to comply with JCS' figure/word limits.

      As such, this v2 version of the paper is a kind of "director's cut" ;-p<br /> For the succinct version, visit the JCS version; for aficionados of this particular topic, you can enjoy the longer version here. :-)

    1. On 2020-11-10 04:09:22, user Adam Alexander Thil SMITH wrote:

      Dear authors,

      I do not have an issue with using the gene permutation approach, however the 3rd sentence of the current version of the introduction incorrectly qualifies the original GSEA implementation as being based on gene permutation, instead of sample permutation. C.F. "step 2" of the method in the referenced paper (Subramanian 2005):

      Step 2: Estimation of Significance Level of ES. We estimate the<br /> statistical significance (nominal P value) of the ES by using an<br /> empirical phenotype-based permutation test procedure that pre-<br /> serves the complex correlation structure of the gene expression<br /> data. Specifically, we permute the phenotype labels and recompute<br /> the ES of the gene set for the permuted data

      The later implementations cited do indeed use gene permutation.

      Best regards,

      -- Alex

    1. On 2018-06-11 18:35:32, user Georg Nagel wrote:

      This is a fine study on tunneling nanotubes, using<br /> fluorescence light microscopy and cryo-electron microscopy, which advances our<br /> still fragmentary understanding of these important structures to a higher<br /> level. The cryo-EM approach showed TNT bundles with individual TNT's of<br /> different transport direction which nicely explains earlier contradictory<br /> interpretations. Congratulations for great work!

    1. On 2019-12-17 01:27:50, user Alex Terzibachian wrote:

      BI 598 Group 5: Stephanie Yemane, Alex Terzibachian & Gabriela A. Rodríguez-Morales

      Review written by undergraduate and graduate students from Boston University as requirement from the BI598 class

      Summary

      Microglia are cells derived from the mesoderm that function as the macrophages of the central nervous system. Even though their function has historically been linked to the immune system, recent studies suggest that microglia might play an important role in regulating synapse development during early developmental stages through synapses pruning, mediated by the complement pathway. However, it is still unknown if microglia perform the same regulating role in synapses of adult-born cells in the olfactory bulb. To answer this question, the authors of this paper ablated microglia in the olfactory bulb of mice and looked at the functional development of abGCs.

      In figure 1, the authors observed microglia interaction with abGCs with the help of in vivo two-photon imaging. They observed that microglia interact more frequently, but not for a longer period of time, with mushroom spines of abGCs compared to filopodial spines. They also saw that the percentage of spines covered by microglial processes during interaction, both filopodial and mushroom were not different. This made them realize that microglia preferentially interact with mushroom spines on developing abGCs. The curves for both the data and offset points also seem to be very similar to one another in all the graphs, with similar cumulative probabilities for the different factors studied.

      In figure 2, researchers next wanted to determine if these microglial interactions that were defined in Figure 1, are necessarily involved for the functionality of abGCs during development. To do this, they ablated microglia using chow formulated with a CSF1R inhibitor, PLX622. This inhibitor prevents the signaling required for microglia survival. Mice were started on chow, three weeks later researchers administered lentiviral tdTomato for abGC imaging, and about five weeks post-injection, mouse brains were analyzed via two-photon microscopy where they found that this induced ablation didn’t affect overall number of adult-born neurons in the olfactory bulb, nor the overall gross morphology when PLX mice were compared to controls. They next used calcium response recordings in the dendrites of abGCs to identify differences in responses across control and PLX groups. Researchers found that PLX mice had decreased responsiveness compared to control, they found that PLX mice had dendrites that responded to less odors and that the median lifetime sparseness was decreased as well. The data from these experiments suggest that ablation of microglia induce sparser representation of odors within PLX-treated mice.

      In figure 3 they used 2-photon to image abGC dendrites in awake control and PLX-treated mice in order to compare the responsiveness, lifetime sparseness and median number of odors that elicited a response in the cells, to the results found in anesthetized mice. Results showed responses to a lower median of odors as well as lover overall responsiveness and lower lifetime sparseness. Principal component analysis also showed a significant change in response timecourse between awake and anesthetized mice. These results support the idea of an overall decrease in the proportion of responsive dendrites.

      They then wanted to look at whether microglia ablation was specific to only developing abGCs, or whether they also affect mature abGCs. To test this, they used similar techniques in figure 4, as seen in figures 2 and 3, on three-month-old labeled abGCs. The Ca2+ heat map traces of the microglia treated with PLX5622 for the 16 different odors was graphed and seen to not have a significant difference in the distribution of responses. They also observed no difference in the number of effective odors, nor in the lifetime sparseness. Hence, they concluded that microglia ablation after development has no effect on odor-evoked response.

      At this point, researchers had found that abGCs had reduced responses in the setting of ablated microglia. Next, they wanted to observe if excitatory synapses on abGCs were altered in PLX-treated mice relative to controls. Results of experiments conducted in figure 1 showed that microglia preferentially interact with the mushroom spines of the external plexiform layer of the olfactory bulb. In figure 5, they first aimed to analyze spines of apical dendrites of abGCs. They found that between PLX-treated and control mice, there was no significant difference in spine density, but they found the difference of mean head volume was somewhat significant.

      In figure 6 they looked at the electrophysiological properties of abGCs by looking at sEPSCs and sIPSCs to depict the possible effect of spine head size between control mice and mice treated with PLX. To achieve this, the authors performed whole cell recording of labeled cells 5 to 6 weeks post injection. Results showed no difference in sEPSC frequency between both experimental conditions, however the amplitude of the sEPSCs was significantly decreased in PLX-treated mice. Results from the sIPSCs showed no difference in the inhibitory events between the control mice and the ones treated with PLX. These results suggest that functional differences between both experimental conditions could be due to weaker excitatory inputs onto abGCs.

      In figure 7, the authors checked whether synaptic inputs were affected by microglia ablation. They did so by recording sEPSCs in abGCs of mice that underwent microglial ablation for three weeks after 3 months of maturation. After processing the raw traces recorded from abGCs in control and in PLX-treated mice in section B of figure 7, sections C and D show that there was no significant change in the frequency or in the amplitude of sEPSCs. This led them to believe that microglia ablation after abGC development has no effect on excitatory synaptic currents.

      Merits

      Sections C and H in Figure 1 are a great backbone to include in the figure, as it allows the reader to easily identify and differentiate between filopodial and mushroom spines of abGCs.

      Figure 2 was very thorough and the supplementary figures were useful in backing their arguments. In supplementary figures 2.1 and 2.2 they revealed that ablation was successful and sustained with continued delivery of PLX-chow; thus, proving that levels of microglia were reliable and consistent. Providing the results for supplementary figure 2.3, was imperative in showing that the number of neurons was not altered and thus any further analysis comparing responses across control and PLX groups were valid.

      Images in figure 5 were a very clear and concise depiction of the spines in question, scale bars and insets are clear.

      Major Criticisms

      The paper states that recordings performed in awake mice suggested that PLX-tretaed mice showed lower responsiveness, a lower median of odors and lower lifetime sparseness. However, only the change in fluorescence elicited by the odor was significantly different between both groups, the median of odors and lifetime sparseness was not significantly different. This begs the question if in fact there is a decrease in the number of responses dendrites or a change in the effect elicited by the odor in the responsive dendrites.

      Another major criticism for this paper would be that the authors did not identify the 16 odors used to produce the heat maps seen in figures 2 and 4, nor mention why they were used. There could be other odors that could elicit different types of activity, and not having an explanation for using these doesn’t allow the reader to be for certain that there aren’t other patterns of activity. In giving an explanation the their choices, they can eliminate more doubt.

      In the experiment in figure 5, researchers changed the experimental timeline – they administered the lentiviral injection before treatment with PLX chow, and analyzed mice brains four weeks after lentiviral administration and PLX treatment. This was odd as they suddenly flipped the protocol they were using before. Not much information was given regarding reasoning for timeline structure; could the injection have agitated microglia prior to PLX treatment? Researchers do mention that the lentiviral injection did not only target the under/developed abGCs; however, if the same protocol was used across experiments, they would have had the same baseline difference throughout – at least permitting for consistent data collection. For panels C and D in figure 5, analysis of spine head volume was presented as both averaged bar graphs and as a cumulative distribution; whereas analysis of spine density was only done via analysis of averaged spine density. The cumulative probability had reached statistical significance where the bar graph did not. Because of the statistical significance of the cumulative probability, researchers deemed there was an observed trend in increased spine density in PLX-treated mice. It is questionable as to why there was no cumulative distribution was done for differences in spine density across testing groups.

      In addition to the electrophysiology shown in figure 6, the authors could have added a figure showing the fluorophore+ cells that they recorded from.

      Overall, there should be more methodology included for the different protocol timelines used, in addition to including the motivation for the experiments conducted, and for changes in initial procedural timeline.

      Minor Criticisms

      The paper states that the animals were presented with a panel of 16 monomolecular odors, however, the data from figure 3 only includes responses to 15 odors. This should be revised to include the 16th odors or update the total number of odors included in the panel to 15.

      A minor criticism for the first figure would be to include the Wilcoxon rank sum test values next to the graphs in the figure as well, in order to make the statistical evidence clearer when presenting the data.

      In figure 2F-I, all data presented were deemed significant, although these images were depicted differently than 2H and 2I. For 2H and I, the individual data points were plotted in addition to the curve/bar graph which allows for readers to see the distribution of the individual data points in addition to the curve. However, for figures 2F and 2G the curves for control and PLX do not appear to be significantly shifted. Presenting these figures in the same manner would, or simply including the individual data points on the curve would make this figure more convincing in its efforts to depict the significance of the observed changes.

      N sizes for supplementary figures 2.1 and 2.2 were rather small. It was useful to see the sustained ablation of microglia with continued PLX delivery – however, where a maximum of three mice were observed throughout development (up to nine weeks), what is the ratio of failed/successful attempts of ablation.

      The n size for figure 5 could be larger to emphasize the differences across control and PLX groups. Researchers analyzed almost 1,000 spines in about twelve cells from each group. Although researchers confirmed the ablation of microglia by immunostaining, and confirmed the sustained depletion of microglia for up to nine weeks with continued PLX delivery, they had only confirmed doing so in 3-4 mice through up to nine weeks of constant PLX delivery (Fig2.1, Fig 2.2). If they could confirm this maintained ablation in more mice, could a larger n be used in this figure to demonstrate whether there are significant differences in spine density/volume that are outside of inherent compensatory mechanisms.

      For the electrophysiology experiments in figure 6, the experimental group should be blind to the experimenter and data taken at different times post slicing should be shown in order to neglect any contribution of recording time to electrophysiological properties of the cells.

      Another minor criticism would be to include error bars on bar graphs in all figures. This would allow the reviewer to look at the sparsity of all the raw data and visually see on the graphs how precise those averaged values are.

      Future Directions

      Researchers characterized the effects of PLX induced microglia ablation; however, it would be interesting and more revealing to observe the remaining microglia. Is there functionality the same? Does this induced depletion of microglia promote the signaling of another response pathway that could have an effect on the connectivity of these neurons in the olfactory bulb that they were observing?

      It would be interesting to see the effects of stopping ablation by discontinuing chow, and further observe the induced changes of discontinuing this ablation. This would allow for a more full picture of the role of microglia in the integration of abGCs in neuronal circuitry of the olfactory bulb during development. If chow was stopped after the developmental stages; would the deficits observed still remain? Would the re-introduction of microglia contribute to more efficient and specific responses, meaning cells could still be integrated into the circuitry and strengthen certain connections?

      Another future direction could be to look further downstream at the mitral cell level or even Piriform cortex and see what possible effects microglia ablation could have in odor representation.

    1. On 2018-01-29 13:48:04, user Mikko Rautiainen wrote:

      Addendum: String-to-graph alignment algorithms with the same time complexity have already been discovered for hypertext searching (Navarro, 2000) and approximate regular expression matching (Myers and Miller, 1989). So the main theoretical contribution of this paper is not new.

      Myers, E., and Miller, W. Approximate matching of regular expressions. Bulletin of Mathematical Biology, 1989. (http://www.sciencedirect.co... "http://www.sciencedirect.com/science/article/pii/S0092824089800461)")

      Navarro, G. Improved approximate pattern matching on hypertext, Theoretical Computer Science, 2000. (http://www.sciencedirect.co... "http://www.sciencedirect.com/science/article/pii/S0304397599003333)")

    1. On 2017-01-19 09:15:27, user Jeffrey Ross-Ibarra wrote:

      I was excited to read this, as the idea of testing the transcriptome for adaptation is a nice one. But I remain a bit confused after reading what model of adaptation is being tested. I don't think we expect all genes to show differential expression after adaptation to a new environment. Are there more plausible models of adaptation these results can definitively rule out? Are there plausible models of adaptation that it does not have the power to identify?

    1. On 2021-03-06 05:28:15, user Mohammad Aghaamoo wrote:

      In this project, we specifically targeted challenging intracellular delivery of large molecules such as >6kbp eGFP plasmid and >9kbp CRISPR-Cas9 plasmid. Our results show that our platform not only offers high delivery efficiency of large cargos, but also it can precisely control the doses delivered.

    1. On 2022-02-23 16:10:35, user Antonio Fernàndez-Guerra wrote:

      In their manuscript, "Functional and evolutionary significance of unknown genes from uncultivated taxa", Rodríguez del Río et al. share a comprehensive analysis of gene families of unknown functions by identifying such genes in publicly available Metagenome Assembled Genomes. Genes of unknown functions represent a critical gap in microbiology as they prevent deeper insights into the ecology and evolution of key microbial traits and their impact on microbial phenotypes, thus, the purpose and findings of the study are of great interest.

      While we commend their efforts to unify known and unknown gene families and generate community resources such as https://novelfams.cgmlab.org/, we regretfully report that the manuscript by Rodríguez del Río et al. fails to acknowledge extensive previous work on this topic such as FUnkFams by Wyman et al. [1], and our recent study by Vanni et al. [2], which has made available a similar resource, AGNOSTOS-DB [3]. Even though studies mentioned here have already reported many of the major findings reported in Rodríguez del Río et al., the current manuscript does not cite FUnkFams, and cites Vanni et al. only once from the Introduction, without highlighting significant parallels between the approaches and findings of the two studies, which we find unfortunate.

      Here we highlight key similarities between Rodríguez del Río et al. (first posted on bioRxiv on January 27, 2022) and Vanni et al (first posted on bioRxiv on July 01, 2020):

      • Both studies report that the largest number of gene clusters with unknown function or novel protein families (hereafter referred to as unknowns) are found in uncultivated taxa. Rodríguez del Río et al. describe their findings in the section "High content of unknown protein families in the genomes of uncultivated taxa". Vanni et al. report the same observation, "the phyla with a larger number of MAGs are enriched in GCs of unknown function" and are mainly composed of "newly described phyla such as Cand. Riflebacteria and Cand. Patescibacteria (Anantharaman et al., 2018; Brown et al., 2015; Rinke et al., 2013), both with the largest unknown to known ratio" (Figure 5D, Supp. Note 14) and that "metagenome-assembled genomes are not only unveiling new regions of the microbial universe (42% of the reference genomes in GTDB_r86), but they are also enriching the tree of life with genes of unknown function".

      • Both studies identify unknowns that are lineage-specific. Rodríguez del Río et al. identify "a core set of 980 protein family clusters synapomorphic for entire uncultivated lineages —that is, present in nearly all MAGs/SAGs from a given lineage (90% coverage) but never detected in other taxa (...) these newly discovered protein families can accurately distinguish 16 uncultivated phyla, 19 classes, and 90 orders, involving 179, 104, and 697 novel protein families, respectively.". Vanni et al. provide more than 600K lineage-specific gene clusters of unknown function within the domain Bacteria (36 at the phylum level, 428 at the class level, and 1,641 at the order level (Supp. Table 10)) and Archaea (1 phylum, 25 classes, and 378 orders (Supp. Table 13-1)).

      • Both studies conclude that there is an increase in the number of lineage-specific unknowns towards the lower levels of taxonomy (i.e., genus, and species). Rodríguez del Río et al. report these results in Figure 3C and Vanni et al. in Figure 5A.

      • Both studies report that the unknowns could be considered relevant from an evolutionary perspective. Rodríguez del Río et al. provide a set of novel gene families that are phylogenetically conserved and under purifying selection, which parallels an observation that has been described in Vanni et al. based on phylogenetic conservatism of traits: "the unknown GCs are more phylogenetically conserved (GCs shared among members of deep clades) than the known (Fig. 5B, p < 0.0001), revealing the importance of the genome's uncharacterized fraction. However, the lineage-specific unknown GCs are less phylogenetically conserved (Fig. 5B) than the known, agreeing with the large number of lineage-specific GCs observed at Genus and Species level (Fig. 5A)."

      • Both studies find unknowns that are widely distributed in the environment. Rodríguez del Río et al. report that "the majority of the new protein families (55%) are detected in more than ten samples, span at least two habitats", indicating a possible role as "core molecular functions from widespread microbial lineages, or derive from promiscuous mobile elements.". Similarly, Vanni et al. report the existence of a "pool of broadly distributed environmental unknowns", which "identified traces of potential ubiquitous organisms left uncharacterized by traditional approaches". Furthermore, the results reported by Vanni et al. also support the findings observed by Coelho et al. [4] and mentioned by Rodríguez del Río et al. “This result contrasts with the habitat-specific pattern observed for the majority of individual species-level genes” where Vanni et al. also show the narrow ecological distribution of the unknown fraction reported in Figure 4D. In addition, as shown by our colleagues in Holland-Moritz et al. [5] the majority of these dominant unknown genes are associated with mobile genetic elements in the soil.

      • Both studies report a collection of small proteins of unknown function. In Rodríguez del Río et al. they report "13,456 families of proteins shorter than 50 residues, 486 of which have been reported previously as novel functional genes" in Sberro et al. (2019). Vanni et al. report a similar finding: "12,313 high-quality gene clusters [..] encoding for small proteins (<= 50 amino acids)", the majority of which are "unknown (66%), which agrees with recent findings on novel small proteins from metagenomes (Sberro et al., 2019)."

      Parallels in major scientific insights between the two studies likely stem from parallels in computational strategies implemented to study datasets of similar nature. Overlaps between computational approaches implemented and described by Vanni et al. and Rodríguez del Río et include the following:

      • Both studies apply strict quality and novelty filters to generate the basic dataset. In Rodríguez del Río et al. Figure 1A shows the basic workflow used to compile “a collection of high-quality novel protein families from uncultivated taxa”. Similarly, Vanni et al. workflow (Supp. Fig 1) produce “highly conserved intra-homogeneous” gene clusters (Figure 1B), “both in terms of sequence similarity and domain architecture homogeneity; it exhausts any existing homology to known genes and provides a proper delimitation of the unknown genes” to provide “the best representation of the unknown space”.

      • Both studies group the predicted genes into gene clusters using the clustering workflow of the software MMseqs2 [6] with a minimum identity threshold of 30%.

      • Both studies detect and remove spurious genes searching the AntiFam [7] database.

      Both studies use a multi-search approach with different sensitivity levels to confidently identify unknowns.

      • Both studies search the unknowns against a database generated by Price et al. [8] using RB-TnSeq experiments. Rodríguez del Río et al. wrote “we mapped the protein family signatures derived from our catalog against the set of 11,779 unknown genes recently annotated based on genome-wide mutant fitness experiments, and found 69 matches to genes associated with specific growth conditions”. Vanni et al. wrote, “We searched the 37,684 genes of unknown function associated with mutant phenotypes from Price et al. (2018) [...] to identify genes of unknown function that are important for fitness under certain experimental conditions”.

      • Rodríguez del Río et al. identify “Synapomorphic protein families”, “by calculating the clade specificity and coverage of each protein family across all GTDB v202 lineages”. “Coverage was calculated as the number of genomes containing a specific protein family over the total number of genomes under the target clade. Specificity was estimated as the percentage of protein members within a family that belonged to the target clade. We considered protein families as synapomorphic if they contained at least 10 members (i.e., protein sequences from different genomes) and had a coverage higher than 0.9 and a specificity of 1.0 for a given lineage.” Vanni et al. similarly identify a gene cluster as lineage-specific if present in less than half of all genomes and at least 2 with F1-score > 0.95 using the methods described in Mendler et al. [9], where the F1-score is calculated combining trait precision and sensitivity, where Precision indicates the degree to which a trait is conserved within a lineage, and sensitivity the exclusivity of that trait to a lineage.

      Indeed, both studies also included novel findings that are not covered by either. We recognize the following findings as novel findings that are unique to the study by Rodríguez del Río et al., and are not covered by recent literature to the best of our knowledge:

      • Rodríguez del Río et al. calculate the dN/dS ratio for each protein family, showing that the majority of unknown families are under a strong purifying selection (Figure 1B).

      • Rodríguez del Río et al. also investigate the presence of potential antimicrobial peptides in their novel families and “found 965 unknown protein families in the genomic context of well-known antibiotic resistance genes, 25 of which are embedded in clear genomic islands with more than 3 resistance-related neighbor genes (as predicted by CARD) (Figure 2C).

      • Rodríguez del Río et al. report that “unknown protein families are slightly enriched in transmembrane and signal peptide-containing proteins (being 7.6% and 7.9% more frequent than in eggNOG, respectively), which suggests that they may play an important role in mediating interactions with the environment.

      • Vanni et al. pinpoint the potential of genomic context analyses to generate functional hypotheses both in Supp. Note 12 (“Next, we examined the genomic neighborhood of the broad distributed EU on the MAG contigs. Investigating the genomic neighborhood can lead to the inference of a possible function of the EU.”, Figure 12-1 C ) and in the genomic neighborhood analysis shown in Figure 6D. However, Rodríguez del Río et al. provide a much more exhaustive analysis of the genomic context of protein families of unknown function. They provide a summary of unknown protein families linked to metabolic marker genes (“Presence/absence matrix of unknown protein families forming operon-like structures with marker genes involved in energy and xenobiotic degradation KEGG pathways in Figure 2A.” and “unknown protein families tightly coupled with genes for every nitrogen cycling step (Figure 2B).”). Overall, they identify “74,356 (17.98%) novel protein families in phylogenetically conserved operon regions”, and a total of 1,344 families sharing a genomic context with “genes related to energy production or xenobiotic compound degradation pathways”.

      • Moreover, Rodríguez del Río et al. identify unknown families probability involved in “cell-cell or cell-environment interactions” and reported “502 novel families from the Patescibacteria group potentially involved in molecular transportation, 34 in adhesion, and 13 in cytokinesis”.

      Science is incremental, and significant overlaps between different studies can be seen as an opportunity to address the reproducibility crisis in science. However, failure to recognize previous work appropriately has serious implications. Not only does it make it difficult for future generations to trace the origins of novel ideas, but also impacts the careers and well-being of ECRs. We hope the authors will reconsider their omission of the previous work and cite novel findings that are already published.

      Antonio Fernandez-Guerra,<br /> On behalf of all authors of Vanni et al.

      References<br /> 1. Wyman SK, Avila-Herrera A, Nayfach S, Pollard KS. A most wanted list of conserved microbial protein families with no known domains. PLoS One. 2018;13: e0205749.<br /> 2. Vanni C, Schechter MS, Acinas SG, Barberán A, Buttigieg PL, Casamayor EO, et al. Unifying the known and unknown microbial coding sequence space. bioRxiv. 2021. p. 2020.06.30.180448. doi:10.1101/2020.06.30.180448<br /> 3. Vanni C, Schechter MS, Delmont TO, Murat Eren A, Steinegger M, Gloeckner FO, et al. AGNOSTOS-DB: a resource to unlock the uncharted regions of the coding sequence space. bioRxiv. 2021. p. 2021.06.07.447314. doi:10.1101/2021.06.07.447314<br /> 4. Coelho LP, Alves R, Del Río ÁR, Myers PN, Cantalapiedra CP, Giner-Lamia J, et al. Towards the biogeography of prokaryotic genes. Nature. 2022;601: 252–256.<br /> 5. Holland-Moritz H, Vanni C, Fernandez-Guerra A, Bissett A, Fierer N. An ecological perspective on microbial genes of unknown function in soil. bioRxiv. 2021. p. 2021.12.02.470747. doi:10.1101/2021.12.02.470747<br /> 6. Steinegger M, Söding J. MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nat Biotechnol. 2017;35: 1026–1028.<br /> 7. Eberhardt RY, Haft DH, Punta M, Martin M, O’Donovan C, Bateman A. AntiFam: a tool to help identify spurious ORFs in protein annotation. Database. 2012;2012: bas003.<br /> 8. Price MN, Wetmore KM, Waters RJ, Callaghan M, Ray J, Liu H, et al. Mutant phenotypes for thousands of bacterial genes of unknown function. Nature. 2018;557: 503–509.<br /> 9. Mendler K, Chen H, Parks DH, Lobb B, Hug LA, Doxey AC. AnnoTree: visualization and exploration of a functionally annotated microbial tree of life. Nucleic Acids Res. 2019;47: 4442–4448.

    1. On 2022-09-14 20:56:20, user Chris wrote:

      Great paper!

      Would it be possible to feed mice with ?Ch?Cs communtities with 7alpha-dehydroxylation to see if the metabolite alone is sufficient to prevent the bacterial diversity changes?

      Would it also be possible to transform 7a-dehydroxylation synthesis genes into a different bacterium other than Ch or Cs to see if that would rescue ?Ch?Cs?

      Additionally, were any obvious phenotypes noticed in ?Ch?Cs colonized mice?

    1. On 2024-11-01 14:02:04, user Cameron Thrash wrote:

      Hello. May I respectfully recommend a few relevant references for the discussion of SAR11 recombination and speciation?

      Natural variation in SAR11 marine bacterioplankton genomes inferred from metagenomic data<br /> https://biologydirect.biomedcentral.com/articles/10.1186/1745-6150-2-27

      High intraspecific recombination rate in a native population of Candidatus Pelagibacter ubique (SAR11)<br /> https://enviromicro-journals.onlinelibrary.wiley.com/doi/abs/10.1111/j.1462-2920.2007.01361.x

      The Evolutionary Success of the Marine Bacterium SAR11 Analyzed through a Metagenomic Perspective<br /> https://journals.asm.org/do...

      A comparison of homologous recombination rates in bacteria and archaea<br /> https://academic.oup.com/ismej/article/3/2/199/7588207?login=false

    1. On 2021-07-01 23:40:09, user Tania Gonzalez wrote:

      The peer-reviewed version (PMCID: PMC7571453) was published in JCEM, see: https://doi.org/10.1210/cli... and data was deposited into NCBI GEO with accessions GSE131696 (single cell RNA-seq data of 6 placenta) and GSE131874 (bulk RNA-seq of matched placenta and maternal decidua samples from 4 patients). Find me on ResearchGate if you need anything else!

    1. On 2020-05-14 04:35:59, user Lev Yampolsky wrote:

      if this mutation was indeed causative to faster spreading it would have emerged repeatedly in parallel clades each time with noticeable proliferation. It seems to have emerged twice more without any evidence of proliferation in these two other branches. (According to https://www.gisaid.org/epif... "https://www.gisaid.org/epiflu-applications/next-hcov-19-app/?fbclid=IwAR05bIEvv4kNsdhuBGXX7I4W6lx-vLkqUKGZtRezqpgrI5guycyZOOoTPBU)"). Moreover, because this substitution occurred so early (January) and the tree is unrooted it is quite hard to be confident what was the ancestral state, D or G. Maybe G was the ancestral state and it mutated to D?

    1. On 2019-02-24 17:29:03, user Anders Sejr Hansen wrote:

      Please see the updated version of this preprint: https://www.biorxiv.org/con...<br /> The numbers for CTCF in mESCs and human U2OS cells are essentially unchanged. <br /> But the estimate for cohesin (through the Rad21 subunit) has changed significantly (new estimate 109k proteins/cell; old estimate was 87k proteins/cell). <br /> Main change: estimates have now been cross-validated using calibrated FCS-imaging in collaboration with the Ellenberg lab (EMBL Heidelberg).

    1. On 2024-01-03 18:29:59, user anonymous wrote:

      Section D.3 of version 1 of the manuscript states that SPR binding affinity measurements on individual DNA variants are repeated four times: two duplicate measurements per experimental run, multiplied by two technical replicates of each run.

      In section F.3, version 1, figures 19-26 show SPR binding affinity measurements for several dozen DNA variants per antibody. The values plotted for -log10(KD)(M) appear to represent the sample mean of each set of four SPR measurements. These figures could be improved by also including error bars representing the sample standard deviation of each set of four measurements.

      Including error bars is important because the precision and accuracy of SPR experiments varies depending upon experimental context. Although some authors report standard deviations of +/-10% or better (see, for example, Table 1 in Brown, M.E. et al., (2020) "Assessing the binding properties of the anti-PD-1 antibody landscape using label-free biosensors" PLoS ONE 15(3) doi: 10.1371/journal.pone.0229206), the repeatability of SPR experiments can be impaired by many experimental artefacts: baseline drift, bulk shift discontinuities, mass transport effects, non-specific binding, manufacturing batch variation of sensor chips or other consumables, fitting to an incorrect kinetic model, etc.

      If the measurement uncertainties are large enough, then the observations of stronger binding affinities than the reference antibodies may not be statistically significant. Affinity values supposedly above the references could be explained away as random statistical noise around a true binding affinity which is actually left unchanged relative to the reference (presumably because the sequence mutations for those DNA variants ultimately had a neutral impact on the final shape of the folded antibody).

      This is a critical point, because if IgDesign does not actually produce mutant sequences with binding affinities signficantly above the reference, then it would weaken a key conclusion of the paper: in such a scenario, IgDesign would not be useful for affinity maturation.

    1. On 2020-05-13 20:00:52, user Jj TR wrote:

      It is of paramount importance to see the whole report and for it to be peer-reviewed soon. If these findings are corroborated, we cannot rely on the Abbot test as a tool for accurately quantifying Covid-19 infections. This, in turn will affect the safety measures of early reopening.

    1. On 2023-05-07 23:36:25, user Zach Hensel wrote:

      There is a negative correlation between the abundance of SARS-CoV-2 and mitochondrial material from raccoon dogs and hoary bamboo rats.

      This sentence was recently cited in an article in the NY Times with an unfortunate and wrong headline ("Why Does Bad Science on Covid’s Origin Get Hyped?" David Wallace-Wells, 3/May/2023):

      Overall, across the full database of genetic material found in the market, the presence of raccoon-dog DNA was negatively correlated with the presence of SARS-CoV-2 material: When samples had more raccoon-dog genetic material, there was actually less SARS-CoV-2 than was found in other samples.

      An article in Nature (Dyani Lewis 04/May/2023) reports that "there was no such association that made sense" and "In fact, the strongest associations were with species, such as fish, cows and goats."

      I argue that most positive and negative correlations reported in this manuscript and in the Liu et al preprint (2022) do, in fact, make sense. Liu et al reported sampling rationale (Nature 2023. Extended data tables 2 and 3). Sampling on 1/Jan was largely premised on proximity to human COVID-19 cases with disease onset in mid-to-late December 2019. Sampling on 12/Jan was for "environmental samples from stalls that sold livestock, poultry, farmed wildlife" an examination of maps sampled stalls shows that sampling had little relation to reported COVID-19 cases.

      I reproduced the visualizations in Fig 5 and colored spots based on species category in the market context (e.g. human, meat, fish, wildlife). I additionally examined correlations for data from 1/Jan only.

      Examining 1/Jan and 12/Jan separately shows little correlation. What is left for 1/Jan is largely expected positive correlation from correlated error on 1/Jan for species with the high abundance on a day when there are no samples negative for viral RNA.

      It is only when combining 1/Jan and 12/Jan data, or when combining all sampling dates, that the reported correlations are observed with all parameter combinations: positive correlations for meat and fish, and negative correlation for some wildlife species. This is an artifact of combining sampling focused on COVID-19 cases on one day, with sampling focused to a large extent on wildlife sales on subsequent days. The expectation is that later samples will be lower in viral RNA because of lack of proximity to a COVID-19 case and also sample degradation.

      Lastly, for 12/Jan there is correlation for species disproportionately found in stall 6-29/31/33, with no known link to a human COVID-19 case, and later sampling shows that this was a reason for concern in February/March 2020. This is the stall with samples analyzed by Crits-Cristoph et al (2023). The 182 animal samples reported linked to this stall include rabbit (85), hedgehog (65), snake (24), bamboo rat (5), other (3). Unfortunately, there are no reported samples of animals linked to this stall from other species: raccoon dog, malayan porcupine, palm civet, and human.

    1. On 2018-02-16 01:06:31, user Nicholas Sofroniew wrote:

      Hi, I really like your approach, it is very elegant. I was wondering if you could comment on how accurate your models are in torsion angle space? It seems to me that current state of the art is around 20-21° and 29-30° for phi and psi angles respectively using RNNs to go from sequence space to torsion space directly (for example https://www.ncbi.nlm.nih.go... "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5604354/)"). Do you achieve better than this with your 3D atom position loss function, or does having high angle errors on individual residues not prevent you from estimating good 3D positions overall? Thanks very much<br /> Nick

    1. On 2024-05-26 21:40:46, user Prof. T. K. Wood wrote:

      Line 47: this statement is false: "Though there is not yet strong evidence that backwards carbon flow can be coupled to growth in either methanogens or ANME,.." as ref 20 was seminal in reversing methanogenesis in a methanogen by cloning Mcr and showing growth for the first time on methane in the engineered methanogen.

    1. On 2021-01-20 13:48:47, user Yaniv erlich wrote:

      The paper has several problems:<br /> 1. The median calculation is off. According to Supp Table 1, the median is 0.76x and NOT 0.79x. That means that 50% of the vaccinated people loss nearly quarter of the titer activity in the presence of B.1.1.7.

      1. The paper says in the abstract that "[t]he immune sera had equivalent neutralizing titers to both variants". Again, this is not correct. Twelve out of sixteen individuals have a titer ratio below one when comparing B.1.1.7 activity to Wuhan strain. A conservative a-parametric test (coin-toss) show that the reduction is statistical significant (p<0.05). It is wrong to say that they have "equivalent" titers.

      2. The authors mention that 0.79x (sic) reduction is not biological significant. They do not report their pre-registered hypothesis about biological significance and do not contextualize this sentence. At which level they think that the reduction is alarming and why losing almost quarter of the activity is OK?

      In addition, the important thing is that the variant already have some gains. It might not escape but it might poised to escape. All of these subtle points are not communicated when tens of millions and Governments all over the world consider this vaccine as the main exit strategy.

    1. On 2016-02-27 19:55:35, user Ava wrote:

      While this tool is addressing a need in the field to improve RiboSeq data processing, the current version of the tool differs from the paper in that it does neither resolves Isoform level expression nor assigns ambiguous multi-mapped ribosome footprints based on isoforms! Even if the authors change the tool to actually resolve isoform level expression, the approach stays problematic: the authors are making the assumption that translation rate is the same as transcription rate. Hence, the ambiguous footprints can be accurately divided between isoforms depending on those isoforms abundance. There are tons of literature disagreeing with this assumption!

    1. On 2025-09-19 18:20:53, user Samantha “Pixie” Piszkiewicz wrote:

      Hi! Very cool work. Have you considered pre-conditioning your algae with salt stress in addition to osmolytes like trehalose to increase uptake of the compatible solute into the cytosol? You could add the salt and osmolyte as a bolus feed after fermentation and remove most of the salt in your pelleting step before formulation for freezing.

    1. On 2020-04-03 14:44:45, user Rob wrote:

      This is an interesting paper, which we just covered in my group's journal club. One suggestion is that the authors should consider how the faithful mapping qualities estimated according to the probabilistic model in the paper compare to the more empirically-driven MAPQ values predicted by Qtip (https://genomebiology.biome... "https://genomebiology.biomedcentral.com/articles/10.1186/s13059-017-1290-3)"). The Qtip framework also takes the computation of MAPQ values seriously, but takes a more empirical (and, perhaps, data-driven) approach based on tandem simulation.

    1. On 2025-04-22 16:52:46, user Steven Reilly wrote:

      Thanks for the suggestion and kind words Anshul! We had definitely recently noticed that finding during your groups ASHG talk and it was on the list of things to try.

      I did a brief pass through the synapse links in your great pre-print, and was wondering if you had shared your short 1kb Enformer ASE predictions anywhere? It might be great to compared to a shared set rather than redo it on our own. If not we'll still definitely repeat and recompute Enformer's scores.

      It will also be great to compare to ChromBPNet too. Since the two methods are trained on such different data modalities, it will be great to see if differences at capturing certain annotations or molecular mechanisms.

      Thanks again for the suggestion!

    1. On 2023-04-25 12:08:57, user Damien HUZARD wrote:

      Great system, congrats !<br /> I have a question concerning the system that was used to correct the identity of each mice in case of miss-identification (following fighting or nesting for example) ?<br /> thank you,<br /> Damien

    1. On 2020-11-20 08:29:17, user Abdulhadi mohamed jumaa wrote:

      First <br /> First, the aim of the study is unclear and unspecified<br /> Secondly, the issue is general and there was no allocation for a specific goal<br /> The aforementioned speech is general and does not give a clear picture of the study<br /> The researcher did not mention the period in which the changes occurred to the selected variables<br /> The study sample size was not mentioned<br /> He did not mention the type of study that was used and did not mention the time of the study. Is it in summer or winter because this thing affects the variables that are studied<br /> He also did not specify their ages, numbers and percentage for each of them<br /> No research keywords mentione

    1. On 2021-01-31 23:23:50, user Daniel Ferris wrote:

      You may be interested in related work that has a similar finding by empirical means. Peterson, S. M., & Ferris, D. P. (2019). Combined head phantom and neural mass model validation of effective connectivity measures. Journal of neural engineering, 16(2), 026010.

    1. On 2023-12-15 16:05:05, user Muhammad Ahmad wrote:

      Dear Authors, <br /> Very interesting article, I especially liked how the NPQ is induced and relaxed and differs between populations. I was looking at the method section to learn how you fit the model for NPQ and Phi PSII data. However, the link to r-scripts/code is not working. Would it be possible to update the working link? Thank you!

    1. On 2021-03-27 16:19:31, user Charles Sanders wrote:

      The final version of this article is now published with a slightly different title:<br /> Disease-linked super-trafficking of a potassium channel.<br /> Huang H, Chamness LM, Vanoye CG, Kuenze G, Meiler J, George AL Jr, Schlebach JP, Sanders CR. J Biol Chem. 2021 Feb 15:100423. doi: 10.1016/j.jbc.2021.100423.

    1. On 2015-07-12 04:27:12, user MichaelGlotzer wrote:

      This is a very important contribution and the benefits of a widely read and widely cited preprint server will eventually be evident to any active biologist. Concerning citation, a major stumbling block which is hopefully solvable, is the incorporation of BioRxiv submissions in PubMed, which is the de facto site for finding biological literature.

      An temporary alternative would be the creation of a site that searches both PubMed and BioRxiv and integrates as well into the average biologists workflow as PubMed does.

      Listing preprints in pubmed will greatly accelerate the transition.

    1. On 2018-05-24 16:35:53, user Andrew Wood wrote:

      This is a modified repost of a comment originally posted 11 days ago, which may have been missed because it is now associated with an outdated version of the manuscript.

      Congrats on a really interesting piece of work and thanks for sharing it as a preprint. I have a question that relates to your experiments involving condensin perturbation, particularly Figure 7 but possibly also Figure 4 too. Have you considered how abnormal ploidy and/or DNA damage arising from condensin-deficient mitosis might affect the interpretation of your results?

      Apologies if I missed it, but I couldn't see any data showing the chronology of<br /> SMC2 depletion relative to cell cycle exit in your fibroblast experiments. Because<br /> passage through even a single condensin-deficient mitosis will cause chromosomal instability, wouldn’t this be a straightforward explanation for the phenotypes that you attribute to direct effects of condensin on quiescence? For example, condensin depletion causes mitotic failure and polyploidy - presumably the polyploid cells would be in a G1-like state and still express cyclin D1. Couldn't this account for the increase in nuclear area and DAPI-dense puncta in G1 cells shown in Figure 7?

      Unfortunately we have found this to be an issue for the paper you cite (https://www.ncbi.nlm.nih.go... "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3025460/)") which claimed that condensin II is required for T cell quiescence in mice. Using the same mouse model we subsequently found mitotic failure to be widespread in T cell precursors and almost certainly upstream from the phenotypes attributed to condensin II functions during quiescence.

      https://www.ncbi.nlm.nih.go...

      Could you normalise your nuclear area measurements (Fig 7D) to DNA content, as we did in Figure S2A of the G&D paper above? Best of all would be to acutely deplete condensins (eg auxin-inducible degron) in post-mitotic cells, but I realise that is a lot of additional work.

      I'm interested to hear your thoughts.

    1. On 2020-12-02 19:38:41, user Enrique Flores wrote:

      Interesting work defining a new small protein regulating C/N balance in Synehcocystis. The discussion, however, may be an oversimplification, since the ornithine-ammonia cycle is just a side activity of the arginine catabolism pathway that renders glutamate as final product. The key enzyme in that cycle, called ArgZ in Synechocystis and AgrE in Anabaena, produces proline form arginine, with ornithine as an intermediate. The authors, however, do not report on proline levels, which would had been of much interest.

    1. On 2018-04-20 21:11:40, user Brock Harpur wrote:

      Exciting, more ant genomes! <br /> Out of curiosity, on line 111 you state 'A combination of fragment and jump sequences were used in combination to generate higher quality, long sequence reads.' I was curious, how much sequence depth you got and what combination did you use? No gene calls yet?

  2. jnl-biorxiv.drupal-stage-jnl-web01.highwire.org jnl-biorxiv.drupal-stage-jnl-web01.highwire.org
    1. On 2015-08-04 22:42:04, user Ann Simun wrote:

      Serious problems with your premise. Developmental Delay is NOT the same as ID in the IDEA regs. It is a special nonspecific category used differently than in edicsl diagnosis. All kids classified as Development Delay MUST be reclassified by age 6 into one of the "real" eligibility categories like Aut or ID. Many are reclassified sooner than 6. Go read the educational definition of DD.

    1. On 2020-07-12 17:55:25, user Federico N. Soria wrote:

      I'm glad to see we obtain similar results regarding increased diffusivity after extracellular matrix degradation! <br /> Regarding age and developmental stage: Did you check in older animals?

    1. On 2016-05-01 17:35:51, user kamounlab wrote:

      It's great that the authors have posted this article. It's obviously very timely given the current outbreak in Bangladesh and the Open Wheat Blast initiative.

      These are my thoughts after a first read of the paper.

      I’m not sure about naming a plant pathogen species after a specific host plant when the pathogen has additional hosts or is known to have experienced host jumps in its recent evolutionary history. There is an unfortunate tendency to do this in plant pathology.

      A more neutral name, say Pyricularia infestans (or whatever), is less likely to convey the impression that these strains can only infect wheat. What if in the future this taxon causes an epidemic on oats? The same confusion that reigns today about rice vs wheat blast would apply with such a host-specific name. What about proper quarantine of other host plants that may carry this pathogen outside Brazil and now Bangladesh? Would the authorities be more likely to ignore other potential hosts when the pathogen has such a defined name?

      I agree that a distinct name is needed for the wheat blast strains to clearly convey the message to the community and the authorities that this pathogen is distinct from the rice blast strain. There is no question that this has important implications for quarantine and disease management. But here two names are proposed. It is important to ensure that it is justified to divide the wheat isolates into two taxa. In the recently posted report by Croll and McDonald (Github 2016, attached), all wheat isolates are clustered in one well supported clade unlike Figure 1 of Castroagudin et al. Will the two taxa proposed here for the wheat blast isolates remain valid when genome-wide analyses are performed? Could the less-defined position of the “PoT” strains in Castroagudin et al. reflect genetic exchange between the main clusters of blast fungi?

      Unfortunately, Linnaean binoms are outdated. They’re becoming more and more useless and inappropriate. It’s great that the fungal community has moved to the one species-one name concept but there is still work to do. For one thing, the current scheme fails to provide stability - a key, some would say the main reason we name things. See the twitter exchange linked below for a discussion on the topic. Perhaps, virologists have figured out a solution?

      It would be unfortunate if the blast community gets bogged down in this naming issue despite what seems like a consensus that the wheat blast fungus is a distinct OTU. There are clearly more important topics to discuss and debate in relation to this invasive disease.

      Croll and McDonald. Github 2016.<br /> http://s620715531.websiteho...

      witherlinneanbinoms

      http://twitter.com/hashtag/...

      http://twitter.com/danieljm...

      Open Wheat Blast<br /> http://www.wheatblast.net

    1. On 2022-10-28 08:53:32, user Mark Banfield wrote:

      We have observed cases of domain integrations in Pikm-1 being accepted by the Pikm-2 helper. But equally, there are cases where integration results in autoactivity, like those highlighted in this work. Our goal here was to address specific cases where autoactivity arose from manipulation of the integrated domain in the Pikm-1 chassis, and to provide methods of addressing this. We are yet to determine definitive rules that describe/predict whether an integration will cause autoactivity, and as such there is an element of trial and error in the approach at present. In this regard, some pikobodies can be incorporated into the Pikm-1 chassis without autoactivity - but this isn’t contradictory, especially as shown in the supplement of Kourelis et al., where there are several different nanobodies trialled that did result in autoactive phenotypes. But yes, we agree, the use of Pikp-2 with a Pikm-1 nanobody chimera could be used to alleviate, or help lower, autoactivity caused by the integration of some nanobodies.

    1. On 2020-04-21 01:30:56, user Sinai Immunol Review Project wrote:

      Summary: The study assesses the effect of azithromycin (AZT) and its potential mechanism of action in cystic fibrosis (CF) epithelial cells beyond anti-bacterial activity and through changes in the pH of intracellular organelles of lung epithelial cells. The authors also discuss potential implications for COVID-19 therapy, as they compare AZT to hydroxychloroquine (HCQ) and chloroquine (CQ), and further suggest the use of ciprofloxacin (CPX) based on acido-tropic lipophilic weak base properties of these drugs. The study examines AZT’s mechanism of action in vitro on cystic fibrosis respiratory epithelial cells and find: <br /> - AZT elicits pH normalization in trans-Golgi network (TGN) and endosomes from their subtly increased acidification in CF epithelial cells. <br /> - AZT reduces inflammatory mediators in CF lung epithelial cells with or without bacterial products challenge: basal NF?B activation was reduced by 50% and by 40% in bacterial challenge; basal IL-8 secretion was reduced by almost 70 %, and by approximatively 33% after bacterial product challenge.<br /> - AZT corrects Furin activity in CF cells’ intracellular organelles and leads to a decrease in abundance of the profibrotic mediator TGF-beta.

      Limitations: <br /> This study is conducted in-vitro on respiratory epithelial cells affected by cystic fibrosis, and even though normal human epithelial cells were included in some experiments, they were not treated with AZT or CPX. The AZT results are derived from only 3 independent in-vitro experiments. The number of experiments supporting the CPX is not clear, nor why different cell types were used in AZT and CPX experiments. Very little is shown, except for TGN pH normalization, to suggest replication of AZT’s results with CPX. <br /> The biological mechanism linking pH increase in the TGN and the endosome and the decrease in pro-inflammatory and pro-fibrotic effect in CF epithelial cell would need to be further explored.<br /> The hypothesis of similar effect in pH shift in normal cells or in a SARS-CoV2 affected cells would need to be substantiated by experimental data.

      Findings implications: As we are desperately looking for a COVID-19 therapy, an hic and nunc approach identifying FDA-approved drugs with known safety profiles, is appealing. The preliminary in-vitro findings of this study warrant further in-vivo studies regarding these drugs. <br /> Moreover, the biological mechanism underscored in this study could provide insights in the link between SARS-Cov2 viral infection and hyperinflammatory reaction as well as anti-viral effects. In particular, the Furin activity reduction by AZT may result in hindering SARS-CoV2 entry, as its Spike protein possess a novel Furin cleavage site.

      Review by Jaime Mateus-Tique as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn school of medicine, Mount Sinai.

    1. On 2019-08-21 18:59:50, user Donal Hickey wrote:

      Yes the comment by WJR correctly surmises that the structure 'int' named<br /> .sex is a legacy variable. But no, while the code is mildly inefficient<br /> it is not grossly inefficient and nor is it incorrect. Nor is there a<br /> great deal of over-writing being done. In fact the code is multipurpose<br /> and for the uses in this paper, there is no need for the .sex flag.<br /> Nor is the quicksort subroutine ever called.

      Because the code is multipurpose, the remnant of the .sex flag can<br /> be confusing. The comment by WJR that lines 233 to 252 are slightly<br /> inefficient is correct but the comment that they are very inefficient<br /> is wrong. In this section of the code, each individual gets to produce<br /> FECUNDITY offspring. In this case FECUNDITY is 2. The individual's two<br /> offspring can be from two matings with different individuals or twice<br /> with another single individual (but not with self mating).

      The break statement at line 246 ensures that these matings occur<br /> exactly FECUNDITY times. As soon as the mating is accomplished the<br /> break statement causes the program to stop and then continue with the<br /> next mating. The progeny from each mating (of parent n[i] is stored in a<br /> new structure (m[.]) so that there is no overwriting done. It is common<br /> practice to create a second temporary structure/variable to momentarily<br /> store results.

      So, no there is no overwriting done except when the temporary<br /> structure/variable is copied back to the original. Every individual<br /> will mate at least FECUNDITY times.

      It is true that the three "if" statements that query the .sex variable<br /> are inefficient. It would be possible to rewrite the code so that it no<br /> longer has remnants of it's multi-functionality and this would slightly<br /> increase speed. But the program is already very fast and there seems no<br /> reason at all to increase the speed of a program that only takes minutes<br /> to run and by doing so would reduce the functionality of the code.

      Also I would guess that deleting the -g debug flag would increase speed<br /> a great deal more. But again there is no need.

      Brian

      Uncomment line 61 so that idum has a fixed value and you will<br /> reproducibly get the results below.

      Recompile

      run with breaks at these lines ...

      line 230: m[0]=n[0]<br /> line 231: k=88604<br /> line 244: m[0].maternal=n[88604].maternal<br /> line 246: break -> line 229


      line 230: m[1]=n[0]<br /> line 231: k=66537<br /> line 240: m[1].paternal=n[66537].maternal<br /> line 246: break -> line 229


      line 230: m[2]=n[1]<br /> line 231: k=55470<br /> line 240: m[2].paternal=n[55470].maternal<br /> line 246: break -> line 229


      line 230: m[3]=n[1]<br /> line 231: k=11152<br /> line 238: m[3].paternal=n[11152].paternal<br /> line 246: break -> line 229


      line 230: m[4]=n[2]

    1. On 2023-08-16 08:41:23, user L Scott Blankenship wrote:

      You've cited my paper - thanks! https://doi.org/10.1039/C7E...<br /> But incorrectly<br /> 1) You've cited it for the clause "With their high surface area,..." my paper makes no mention of the surface area of cigarette butts themselves. You need a better citation for this.<br /> 2) You've got my name wrong.<br /> I have no comment on the science though.

    1. On 2014-03-13 13:39:20, user hypusine wrote:

      Thanks very much for sharing your paper! This work opens a view into an important area of sequence diversity and biological function that we haven't had the tools to address systematically. I'll have a more detailed look at the paper for any subsequent comments, but did want to flag something important before I forget: your method of counting ribosomes on smORFs cannot include one in the 5' region unless there is an upstream ORF there.

      When summarizing your method, you say: "therefore on a smORF the maximum number of ribosomes associated would be 6, allowing for 5 ribosomes in the ORF, and 1 in the 5’-UTR, scanning." This is inconsistent with our understanding of eukaryotic translation initiation. It is the 40S ribosomal subunit that assembles at and scans from the 5' cap, equipped with eukaryotic initiation factors and an initiator Met-tRNA to recognize the start codon (the "preinitiation complex"). Once a start site is recognized, various biochemical steps then lead to the recruitment of the 60S subunit, yielding a nascent, translation-competent 80S (reviewed in 1). This matters for your purposes. The preinitiation complex will have a very different footprint on and dissociation kinetics from mRNA. More critically, if you are using cycloheximide to arrest ribosomes on messages, this compound binds to the E-site of intact 80S via a base on the 60S subunit (2). Cycloheximide does not to my knowledge have any effect on scanning preinitiation complexes, which makes sense given its described mode of action.

      1. http://cshperspectives.cshl...
      2. http://www.ncbi.nlm.nih.gov...

      Thanks again for the paper. Looking forward to seeing where this project goes.

      Sincerely,

      Allen Henderson<br /> Sil Laboratory<br /> MIcrobiology and Immunology<br /> UC San Francisco<br /> allen.henderson@ucsf.edu

    1. On 2019-01-28 08:23:27, user Nagasawa wrote:

      Some teleost species independently changed their reproductive manner from ‘viviparous’ to ‘ovoviviparous’ system during the evolutionary process.<br /> However, the high quality genomic sequence data of ovoviviparous fishes already published are limited to cyprinodontiformes.<br /> So, this paper is interesting in the point of sequenced the whole genome sequence of rockfish, scorpaeniformes.

      But, I think Liu, Zhang, Qi and their colleague should review this paper carefully and discuss again, because of the reasons below.

      1. There are no species which is ‘viviparity’ in teleost, but some species are ‘ovoviviparity’.
      2. There are no species fertilized by acrosomal reaction in teleost, already investigated. Their sperm are directly reached into ovarian cells through ‘micropyle’.
      3. The results of immunohistochemistry is not clear, the signals are not visible for me. I strongly recommend you to show other results, more high magnification picture and western blot.
      4. I think some sperm localized at vitelline membrane may be due to the polyspermy-blocking. The surface of vitelline membrane is sticky, since the zp-protein main component of vitelline membrane is glycoprotein.
      5. Your hypothesis can not explain the reason that ‘how single-sperm reaches to oocyte’.
      6. Many of ‘HCE-like genes’ registered in NCBI as ‘PREDICTED’ is not ‘hatching enzyme: HCE ’, but hatching enzyme paralogue. I think that HCE-like gene expressed in ovarian<br /> cells may be the paralogous gene. But I couldn’t judge because of the low-resolution<br /> figure of phylogenic tree.
    1. On 2022-10-26 10:34:06, user Mauricio P. Contreras wrote:

      Here are some potential future questions/avenues of exploration that arose in our discussion of the study:

      It would be super interesting in future works to identify the receptor/s involved in perception of plant and parasite derived PSY peptides. This would enable many new lines of questioning.

      Are MigPSY peptides triggering an immune response or mediating any sort of PTI-like response (i.e. triggering a ROS burst) in any root knot nematode hosts (i.e. rice)?

      How do plant receptors (such as Xa21) distinguish between endogenous and parasite derived PSY peptides? What are the molecular determinants for this specificity? It would be super interesting to study the potential co-evolutionary arms race between pathogen PSYs and host receptors.

      How did plant pathogens acquire PSY peptide mimics, evolutionarily speaking? Do non-plant pathogenic nematodes also have PSY peptides? Is it possible that non-plant pathogenic nematodes also produce these or similar peptides for other unrelated endogenous processes and then these were co-opted over evolutionary time to fulfil a role in pathogenesis?

    1. On 2015-11-17 16:54:09, user Ian Derrington wrote:

      Nice work! However, as with most ONT-related publications, there is an apparent blatant neglect of citing any work from Prof. Jens Gundlach's group, who has peer reviewed publications on the *initial* successes of nanopore sequencing (doi:10.1038/nbt.2171) as well as more extended publications showing species identification etc with nanopores (doi:10.1038/nbt.2950). Given that every one of ONT's patent they are using the nanopore MspA, which Gundlach et al (Including myself, Ian Derrington) pioneered the use of, it is essential that this work be cited in academic fairness. Please consider this for future publications.

    1. On 2020-09-24 15:47:02, user ZhangLab_SLU wrote:

      In our paper, we established several structural and evolutionary evidence on SARS-CoV-2 ORF8, including 1) it is novel version of immunoglobulin domain; 2) it is a fast evolving protein; 3) it shares a similar architecture as many other viral Ig proteins. Based on these sequence/structural similarity and experimental evidence on other viral Ig proteins, we made the prediction that one of the potential function of ORF8 is to disrupt immune response by interfering the MHC-I membrane presentation.

    1. On 2020-02-13 20:54:04, user Gomes Greg wrote:

      Hi Guest,

      One of the authors (Greg Gomes) here. This is one of the great reasons for posting on BioRxiv! Thanks for pointing us towards this, we are updating the manuscript accordingly!

      -Best,Greg

    1. On 2020-08-20 18:31:24, user marc verhaegen wrote:

      There's no doubt any more: australopiths were not cursorial, but were wading-climbing hominids in flooded or swamp forests, much like bonobos and lowland gorillas wading bipedally in forest swamps. For an update, google "Lucy was no human ancestor 2020 PPT Verhaegen".

    1. On 2018-06-16 16:13:58, user Andrew Belmont wrote:

      Here we were using "nuclear speckles" to refer specifically to interchromatxn granule clusters (IGCs), as defined by electron microscopy. We used the protein SON as a marker, which we have found is quite specific for these type of bodies. These IGCs have a very distinct structure- cluster of ~20 nm RNP particles between chromatin- which is distinct from other nuclear bodies. Of course, we are not trying to imply anything about the behavior of other type of nuclear bodies, including omega speckles.

    1. On 2020-03-26 12:50:53, user RedSiskin wrote:

      Would it be possible to give more precise information on origin and locality of the various specimens in the Supplementary Materials, Table S1? In particular, hagenbecki, which has a phenotype (broad white superciliaries, very broad white collar, light crown, light straw yellow coloration of flanks and upper back, small spots on flanks, large apical black wedges on upper back) matching in all respects the ones in the torquatus group, has in Table S1 given as origin "Gobi, Mongolia", with Longitude and Latitude pointing to a place where no subspecies is expected to occur at all. It would be important to make sure the feather samples were indeed hagenbecki, as its belonging to the strauchi-vlangalii group would be quite surprising. Similarly, alaschanicus has in Table S1 a Longitude and Latitude pointing to a place in Tumed Left Banner near Hohhot, not Alxa Left Banner. At this place kaingsuensis is expected to occur. Alaschanicus, however, is rather expected to occur around the Yaoba oasis region near Alxa, west of the Helan Mountain Range. The Longitude/Latitude data for vrangelii reading in Table S1 (101.50, 36.65) are in fact near Xining where typical strauchi occurs, this must be a typo. The distribution of subspecies in the Sichuan Basin is not known (with elegans, suehschanensis, decollatus, and even strauchi-like birds all reported), so it seems that the assignment of suehschanensis in Suining, far away from the Songpan, needs an explanation, are these wild birds? One should also keep in mind that the precise distribution of kiangsuensis, karpowi, and suehschanensis is very unclear to date, as are the subspecies occurring in the Sichuan Basin and the Mountain ranges north of it, and in the Ordos Plateau.

    1. On 2014-04-02 17:24:40, user Frank Albert wrote:

      Hi Casey,

      thanks a lot for your comments. Just a very brief reply for now while we're working on a more detailed response:

      1) differential measurement error on the two axes can indeed affect the slope estimates. We're currently looking into how best to incorporate this into the inferences of buffering vs reinforcement.<br /> 2 - 3) Good points, we'll take them up in the revised discussion.<br /> 4) absolutely<br /> 5) thanks!

      Best wishes,

      Frank

    1. On 2021-01-24 09:19:58, user fischmidtlab wrote:

      Wonderful story that underscores the importance of biparatopic approaches for passive immunization!

      Two questions:<br /> 1. Do you know whether Spike E484D is a genuine escape mutant (i.e. prevents neutralization with C121)? Demonstrating escape mutants with wt SARS-CoV-2 in these short infection models would be an important finding.<br /> 2. Did you get the chance to test performance of CoV-X2 against the more dramatic E484K mutant with charge reversal observed in vitro (Weisblum et al.) and in the recent variants B.1.351 (South Africa) and P.1 (Brazil)?

      Great work, looking forward to see more of this!

    1. On 2016-12-18 00:09:40, user Davidski wrote:

      Hello authors,

      This appears to be a mistake...

      "The Baltic Y. pestis genomes are genetically derived from the strain <br /> that was found in the ‘Andronovo Complex’ from the Altai region, <br /> suggesting that the disease might have spread with steppe pastoralists <br /> from Central Eurasia to Eastern and Central Europe during their massive <br /> range expansion."

      I think you mean Afanasievo, not Andronovo.

    1. On 2017-08-24 19:16:10, user David Colquhoun wrote:

      I think that this paper uses the p-less-than calculation, rather than the p-equals interpretation, which, I think, is more appropriate for interpretation of single tests. See section 10 of http://rsos.royalsocietypub... (2014) for a discussion of this point, And a more detailed discussion in section 3 of http://www.biorxiv.org/cont... (2017)

      I think that this paper might be improved if proposed an alternative to P values. My proposal (2017) is to supplement P values and CI with the prior that would be needed to produce a false positive risk of (say) 5% (Matthews reverse Bayesian approach).

    1. On 2018-03-03 19:02:42, user georgia isom wrote:

      Hi Kim,

      I think you make a good point about complementation, and that is definitely something we should do in at least some of these experiments. There is no reason why we can’t complement on the in vitro experiments, such as survival in the presence of bile, and we are currently making the plasmids!

      In the meantime… the genes in Salmonella are very similar to those found in E. coli, which we have also been working on. We find that all the phenotypes can be complemented (e.g. a pqiAB knockout can be complemented with just pqiAB on a plasmid, suggesting there are not downstream effects on ymbA). See the paper here: https://www.nature.com/arti.... Furthermore (although you’ll have to trust me on this because it’s not published!), the phenotypes of the single gene knockouts in the pqiAB-ymbA operon appear identical, and I can also fully complement a single mlaD (aka yrbD) KO with just mlaD in E. coli.

      Of course, this is E. coli and not Salmonella, but it does reduce my concern about polar effects, especially as the knockouts were made the same way in both. Also, the gene you mention downstream of yebS on SalCom, STM1849, is actually YebT – so is removed in our strains anyway.

      Obviously, this alone isn’t proof, and it is worth formally ruling out polar effects and we will add this to the manuscript. We can certainly also add the table you mentioned as supplementary.

      On a final note, there are some technical challenges to testing complementation during infections in vivo, as we can’t be sure that the plasmid is actually maintained without selection, unless we feed the animal antibiotics (which may otherwise impact the results). There are, of course, other ways around this, but each system has its flaws. However, perhaps the biggest issue comes down to whether we can justify the need for additional animals to carry out an experiment. We try to use a minimal number of animals in a study, and it isn’t really routine to do complementation (I am speaking from my experience at the institution where the work was done).

      Thank you for reading the manuscript and for your comments, very helpful!

      Georgia

    1. On 2019-07-16 06:47:15, user francois windels wrote:

      For a useful application you will need single unit resolution, there is no evidence of that in the manuscript at this stage. Can you provide more details about your spike sorting algorithm? An analysis of some spike train will also be interesting. If you use a single channel signal to record the waveform that you sort into single units you will hit the wall very quickly. We looked into that a while ago, reference below<br /> https://doi.org/10.1371/jou...

    1. On 2021-08-21 03:53:31, user Aneth David wrote:

      I found this paper really interesting and comprehensive. I liked the fact that they studied fungal communities too, I hadn't come across any study that has done this for reasons I don't understand.

      I thoroughly enjoyed reading this paper.

    1. On 2019-09-23 20:57:43, user Oliver Pescott wrote:

      Given that this paper uses R. ponticum as a case study, it's surprising that there is no mention of the partial introgression from North American Rhododendron species found by Milne & Abbott (2000) in non-native UK populations of R.p. baeticum, as this complicates some of the proposals featured in the discussion here further. Also, assuming that if it has done it once then it could do it again, additional hybridisation after a new introduction for conservation could affect the range in NW Europe, as Milne & Abbott speculate that it already has.

    1. On 2017-04-07 08:27:23, user Eran Halperin wrote:

      On behalf of the authors of ReFACTor (Rahmani et al., Nature Methods, 2016): We disagree with the message and content of this manuscript. This manuscript by Zheng et al. reiterates the authors’ claims from their recent correspondence (Zheng et al., Nature Methods, 2017) and adds several more analyses. The manuscript misses several crucial parts of our response letter; we believe that after reading carefully the supplementary information of our response letter (Rahmani et al., Nature Methods, 2017), the readers would be easily convinced that there is little evidence supporting the claims made by Zheng et al.

      Particularly, the main criticism raised by Zheng et al. incorrectly states that ReFACTor breaks down when applied to cancer tissue in an EWAS setting. In their analysis, the authors ignored our suggested guidelines in the original ReFACTor paper (Rahmani et al., Nature Methods, 2016) and in our letter (Rahmani et al., Nature Methods, 2017), which specifically explained that the feature selection step of the ReFACTor algorithm should be applied on the controls when the number of true positives is expected to be large.

    1. On 2019-06-25 16:45:15, user Thomas N. Seyfried wrote:

      In their recent study, Sperry et al conclude that the U87-MG model of glioblastoma can utilize fatty acids and ketone bodies for growth. However, their data presented in Figures 1C & 1D argue against this conclusion. It is clear that neither supplementation with fatty acids (Fig. 1C) nor B-OHB (Fig. 1D) could replace glucose as a fuel for maintaining U87 proliferation under lower glucose conditions. If fatty acids and B-OHB could be utilized for growth, then FA & B-OHB supplementation should be able to replace glucose for U87 growth under low glucose conditions. Their data do not show this.

      It is also important to mention that human GBM express abnormalities in mitochondrial number, structure, and function all of which will compromise energy production through OxPhos (DOI: 10.1177/1759091418818261). These abnormalities will force the tumor cell to rely more heavily on fermentation then on OxPhos for growth. Neither fatty acids nor ketone bodies are fermentable fuels and cannot replace either glucose or glutamine, which are fermentable through substrate level phosphorylation in the cytoplasm and mitochondria, respectively (DOI: 10.1177/1759091418818261). The data presented in Figures 1C and 1D support this view in showing that neither fatty acids nor B-OHB can replace glucose for maintaining U87 growth rate. Hence, their data do not support their main conclusion that U87 can utilize fatty acids and ketone bodies for growth.<br /> Thomas N. Seyfried

    1. On 2019-04-18 19:55:07, user UAB Bacteriology Journal Club wrote:

      Very interesting paper! We (the University of Alabama at Birmingham Bacterial Pathogenesis and Physiology Journal Club) read this paper this week, and wanted to share our comments and suggestions, in the hopes that they will be helpful to you. We had a great discussion, and definitely wanted thank you for posting it!

      Review of Limoli et al. “Interspecies signaling generated exploratory motility in Pseudomonas aeruginosa

      University of Alabama at Birmingham Bacterial Pathogenesis and Physiology Journal Club<br /> April 18, 2019

      Summary:<br /> In this work, Limoli et al. show that both type 4 pili and flagella are involved in a significant change in P. aeruginosa motility in the presence of S. aureus, in which P. aeruginosa are able to surround and disperse S. aureus microcolonies. They also report that this motility effect is modulated by the S. aureus Agr quorum sensing system and that the presence of S. aureus affects cyclic AMP levels in P. aeruginosa. Finally, they confirm that interspecies signaling occurs with both laboratory and clinical isolates of both bacterial species.

      This paper is fairly well-written, and explores an important and impactful topic. Understanding the molecular mechanisms of polymicrobial interactions is undeniably important. The microscopic movies illustrating changes in bacterial motility are powerful and compelling, and the authors do a good job of applying quantitative measures (e.g. Euclidean direction) to these otherwise qualitative observations. However, the second half of the manuscript is quite confusing, and the explorations of the role(s) of Agr and PilJ in Pseudomonas-Staphylococcus interactions lacked both depth and context. There were several instances of important conclusions that did not appear to be supported by the data presented, and in some cases the still images did not do a good job of representing the phenotypes observed in the movies (although we recognize that this is a significant challenge!).

      Overall, although it has some substantial weaknesses, we feel that this paper presents fascinating and important observations, and we recommend that, for publication, the authors focus on expanding either the Agr or cAMP/PilJ studies, saving the other for future papers. We hope the following detailed comments will be helpful:

      Major Comments:<br /> Figure 1 / Movies 1-3: Please explain (or at least cite!) why P. aeruginosa inhibits S. aureus growth. It is not clear what value the green and yellow “founder cell” markers add to Figure 1, or why the P. aeruginosa founder cell isn’t moving.

      Figure 2 / Movie 4: The disassembly of S. aureus microcolonies is shown only in a single still image. This is an important result, and should certainly be included as part of a movie or other more in-depth data presentation. “Swift motion” is very confusing, and it is unclear what the authors mean precisely by this, since it doesn’t appear to have any relationship to speed of movement. The movie presented to illustrate this was difficult to find any movement at all in. We also wondered if there is any way to distinguish between “invasion” of an S. aureus microcolony by P. aeruginosa and “climbing” onto the microcolony (and therefore moving up out of the plane of focus), since S. aureus colonies are clearly 3-dimensional piles of cells. mKO is never defined (presumably it is a red fluorescent protein).

      Figure 3 / Moves 5 – 8: The wild-type and ?flgK still images appear to be from the wrong movies (these two in particular seem to be swapped). We found several instances throughout the paper of still images which did not appear to match with movies, and this was very troubling. Please check carefully to make sure that they are showing what’s intended. It would be very helpful to have still images and movies with matching fields of view. The timescales for movies 5 – 8 are very short, and make it difficult to assess the changes in the overall motility phenotypes. It would be very useful if the authors would comment on why the ?pilA ?flgK mutant is unable to inhibit S. aureus growth. The labeling of statistical significance in Figure 3B is unconventional, and inconsistent with the use of lowercase letters as labels in other figures. Please be consistent and clear about what comparisons are being made in all figures. Finally, the motions illustrated in Figure 3C are not very clear, and at least one of the labeled cells (single cell with red arrow) does not appear to be in motion at all.

      Figure 4: The main concern with this figure was that neither twitching motility, sigB, nor Agr were introduced or explained in any detail. The rationale behind these experiments is not clear, and especially in the case of the very complex and well-studied Agr quorum sensing system, require much more information to understand the context and interpretation of these experiments. We were very curious as to why the authors did not directly test whether the Agr-dependent AIP signaling peptides are sensed by P. aeruginosa.

      Figure 5 / Movie 9: The data in Figure 5A show a much more significant effect for Agr than does Movie 9, although upon repeated viewings, we think we understand the difference between P. aeruginosa behavior in Movie 4 and Movie 9, which appears to be primarily a disassembly of the P. aeruginosa raft even at very long distances from the S. aureus microcolony. Is this what the authors mean by “avoidance”? The sentences describing these results, especially “some cells seemed to actively avoid the S. aureus colony all together”, were not clear and did not seem to be supported by the data presented. Figure 5B certainly needs a wild-type control, and a better explanation of how to interpret 5C would be appreciated.

      Figure 6: We were in general very confused by all of the results discussing PilJ and cAMP, and feel that the context, background, and interpretation of these experiments require much more explanation.

      Minor Comments:<br /> 1) In the title, we would recommend “stimulates” rather than “generates”.

      2) How has the polymicrobial nature of infections been clear since the 1600’s, when the germ theory of disease was established in the 1880s?

      3) Typo in motility results section: “Through 1.5% agar”.

      4) How close do P. aeruginosa need to be to S. aureus to experience a change in motility? Is this consistent with the known ability of small molecules / proteins to diffuse in agar?

      5) There was some concern with the use of area to measure S. aureus replication, since the S. aureus colonies appeared to be generally taller than the rafts of P. aeruginosa, which we interpreted to indicate that they were mounding up on top of one another as they divide.

      6) The twitching motility (under the agar) assay was unfamiliar to us, and we would have liked to see some experiments validating the relevance of this kind of motility to the single-cell microscopic observations the paper begins with

    1. On 2025-03-05 19:55:42, user Enrico Radaelli wrote:

      In this viral video on YouTube<br /> https://www.youtube.com/watch?v=WNuzopVDFQs <br /> The company claims that "There were no unintended consequences except adorability".<br /> However, the mice are clearly pruritic and they exhibit crusty/flaky skin in glabrous areas (especially paws and eyelids).<br /> I wonder if this is part of the phenotype or if the mice contracted infections that might have caused these lesions (mites, C. bovis, etc.).

    1. On 2021-05-09 17:25:52, user Sadegh Nabavi wrote:

      Some similarities in the concept between our manuscript (this one) and BiPOLES (published last year) may lead to a misunderstanding about the causal relation between the two works:

      1) Our work is based on a grant that was awarded in February 2018 https://lundbeckfonden.com/independent-optical-excitation-of-overlapping-neural-populations-in-behaving-animals

      2) At the same time, our institute announced a brief description of the awarded grant (note the sentence “To tackle this problem… we take advantage of the inhibitory feature of light-gated anion channels”) <br /> https://dandrite.au.dk/news/nyhed/artikel/nabavi-lab-received-2000000-dkk-from-lundbeckfonden-nih-brain-initiative-for-the-project-indepe/

      3) In the initial submission of our grant proposal, we already discussed our preliminary data on GtACR2 and redChR2 variants in flies and slices

      4) In our manuscript we referred to BiPOLES and discussed the differences

      5) We do not see there is a competition between our system and BiPOLES. They have different properties, and so serve different purposes.

    1. On 2018-11-15 14:06:15, user squad 4 lobes neuro wrote:

      BU BI598 GROUP 4

      Cell-type specific D1 dopamine receptor modulation of projection neurons and interneurons in the prefrontal cortex (Anastasiades, et al.)

      Dopaminergic signalling in PFC neurons is important for PFC-dependent behaviors and is often disrupted in neuropsychiatric disorders. However, it remains unclear what kind of neurons in the PFC express the most common dopamine receptor, D1-R. Anastasiades et al. sought to examine the neurons that expressed D1-Rs first by labeling these specific neurons and then examining the electrophysiological role of D1-R. The authors used D1-tdTomato mice to visualize the distribution of D1-R in the PFC (Figure 1). D1-tdTomato mice were injected with the viral vector AAV-CaMKII-EGFP whereas transgenic D1-tdTomato x GAD-Cre mice were injected with AAV-FLEX-EGFP to specify the neuronal population that expresses D1-R (Figure 2). Electrophysiological, D1-R+ cells have unique properties and respond to D1-R agonist and antagonist (Figure 3). The projections of D1-R+ neurons have long-range targets as verified by two distinct experiments using two different viral approaches. The first used AAV-SynaptoTag while the other utilized AAVretro-Cre-mCherry in combination with AAV-DIO-EYFP (Figure 4). Retrograde labeling with CTB revealed that long range targets project to different layers of PFC and that only a small number of the long range targets receive projections from D1-R+ neurons (Figure 5, Figure 6). Lastly, the authors crossed D1-tdTomato with either PV-, SOM- or VIP-Cre lines and then labeled the interneurons by injecting AAV-FLEX-EGFP into the PFC. D1-R overlaps with VIP+ interneurons (Figure 7) and modulates their activity (Figure 8). The authors concluded that D1-R are expressed in excitatory pyramidal neurons and inhibitory interneurons in PFC. Furthermore, D1-R ability to modulate the activity of both excitatory and inhibitory neurons emphasizes the importance of understanding the dopaminergic system in regards to PFC-dependent behaviors.

      Before addressing criticisms, we wanted to point out several strengths of this paper. In Figure 1, 2, and 4, the brain schematics with marked viral injection locations were helpful to understand both the methods and the results of the individual experiments. Additionally, Figure 1 is particularly good as there are many different magnifications of the labeling of the D1-R by td-Tomato. In Figure 3, the authors cleverly used 300 mm slices as this ensured that the slices could be used for dendritic and axonal morphology tracing and analysis. Furthermore, Figure 3E/F accurately quantified the individual neurons’ response to SKF and SCH treatment. In Figure 4D, it was important that the authors showed the location of the viral vector injection which displayed some bias towards the injection site. It is also advantageous that Figure 5 displayed one confocal image of the distribution of CTB labeled neurons in the PFC and afterwards, the distributions of all the counted retrograde-labelled neurons as it emphasized the results obtained from this figure. Lastly, Figure 8 clearly explained that VIP+ interneurons overlapped with D1-R+ neurons located in the superficial layers. Specifically, Figure 8E/F depicted the percent of VIP+ cells that were also D1+ or EGFP+, which allowed the results to be viewed from multiple perspectives decreasing possible bias due to measuring from very few neurons. In general, the results and figures supported the hypothesis. However, after performing a thorough review, we would like to suggest possible changes to improve the overall argument.

      First, in Figure 1, imaging of the tissue after in situ hybridization is recommended to verify that the tissue remained healthy post-protease treatment. Moreover, we would like to see a negative control confirming the specificity of the probe to D1-R. We suggest using a knockout animal that does not express D1-R in which application of the probe should result in no binding. In Figure 1B, the dotted outline demarcating the location of prelimbic subdivision is unclear as it also encapsulates a white matter region, which may lead some readers to become confused on the actual location of interest.

      Figure 2, localizes D1-R+ excitatory neurons largely to L5/L6 and D1-R+ inhibitory neurons to superficial layers via the injection of AAV-CaMKII-EGFP into D1-tdTomato mice and AAV-FLEX-EGFP into D1-tdTomato x GAD-Cre mice, respectively. Representative images confirming injections were made into intended targets should be shown to confirm that the viruses reached all layers of PFC and eliminate potential injection bias. Because manually counting cells carries an unavoidable subjective bias, we would like to see further explanation into how the cells were thresholded during quantification. A small change in threshold could affect results drastically.

      Figure 3 characterizes the morphology of L5/L6 pyramidal neurons via dendritic length quantification and exploration of intrinsic firing properties. We suggest a more in-depth characterization of cell morphology, such as including branch number quantifications, to sufficiently differentiate between L5/L6 pyramidal neurons with and without D1-R. Furthermore, the boxplots used to visualize dendritic length quantifications should be overlayed with dot plots so that raw data points are shown. In Figure 3E/F, we recommend showing replicates of AP firing in response to depolarization with and without SKF/SCH as well as displaying baseline data on plots instead of simply the change from baseline.

      In Figure 4, AAVretro-Cre-mCherry and AVV-DIO-EYFP injections were made into the cPFC and iPFC, respectively, allowing for the characterization of PFC projection targets across the brain. However, this technique assumes that the entire PFC has the same projection targets. Representative images labeling specific hotspots (PrL, IL, ACC, PL) where injections are made into the PFC should be shown to allow for PFC targets to be specifically subdivided by their respective start locations. Additionally, we would like to see both positive controls and negative controls to verify the specificity of viral labeling. For example, AAV-Cre-mCherry could be injected in place of AAVretro-Cre-mCherry as a negative control.

      In Figure 5, CTB was injected into the PFC targets that were characterized in the previous figure, allowing for the imaging of retrogradely labeled neurons in prelimbic PFC across all layers. Again, representative images confirming that the intended PFC targets were injected with CTB should be shown. While projection targets were divided into IT and non-IT cells, we suggest visualizing which of these cells are D1-R+ through the use of D1-tdTomato mice in order to further compare these results to those of the rest of the paper.

      In Figure 6 injection location continues to be a source of uncertainty. If the injection is not uniform across samples the overall number and location of retrograde-labelled cells could be affected. Additionally, target regions may not be as dense as the PFC which further exasperates bias in the number and location of labeled cells. Along with a visualization of injection locations, we recommended completing an optogenetic experiment to show physical and functional connectivity between the PFC and its target regions. This will help eliminate as much uncertainty as possible and strengthen the point being made.

      Finally, in Figure 9, D1-R+ VIP+ and D1-R- VIP+ neurons were classified as either irregular spiking, non-fast spiking, or fast-adaptive firing neurons using a single hyperpolarizing and depolarizing current step. Since the degree of depolarization or hyperpolarization can alter neuronal spike response, we suggest increasing the current stepwise to create an input-output curve for each of these neurons. This will allow the authors to show that spiking patterns are intrinsic properties of these cell types rather than functions of the dopamine level present in the system at the time of recording.

      The researchers concluded that D1-R are enriched in pyramidal neurons in layers 5 and 6 and are present only in VIP+ interneurons that co-express calretinin. They also found that D1-R enhance action potential firing in only a subset of these cortico-cortical neurons and VIP+ interneurons. Overall, our team thought that this paper was a well-written manuscript with minimal grammatical mistakes. Future directions include determining the function of D1 receptors in projection patterns by creating a D1-R knockout model, studying other dopamine receptor types to see if they project to similar targets, and investigating the role of D1-R in diseases like schizophrenia by comparing the expression of this receptor in both normal and transgenic mice.

    1. On 2021-09-13 13:10:09, user Anand Mayakonda wrote:

      Hello Authors,<br /> Great stuff and congratulations. Just want to point out that on the page-7, last paragraph, concerning<br /> "liftover failures", reference to Figure. 1F is being made. However, there is no Fig. 1F. I could not find the relevant figure in the supplementary material as well.<br /> Best,<br /> Anand M

    1. On 2020-04-07 20:51:11, user Sinai Immunol Review Project wrote:

      An orally bioavailable broad-spectrum antiviral inhibits SARS-CoV-2 and multiple 2 endemic, epidemic and bat coronavirus

      Sheahan et al. 2020

      Main Findings: ?-D-N4 30 –hydroxycytidine (NHC, EIDD-1931) is an orally bioavailable ribonucleoside with antiviral activity against various RNA viruses including Ebola, Influenza and CoV. NHC activity introduced mutations in the viral (but not cellular) RNA in a dose dependent manner that directly correlated with a decrease in viral titers. Authors show that NHC inhibited multiple genetically distinct Bat-CoV viruses in human primary epithelial cells without affecting cell viability even at high concentrations (100 uM). Prophylactic oral administration of NHC in C57BL/6 mice reduce lung titers of SARS-CoV and prevented weight loss and hemorrhage. Therapeutic administration of NHC in C57BL/6 mice 12 hours post infected with SARS-CoV reduced acute lung injury, viral titer, and lung hemorrhage. The degree of clinical benefit was dependent on the time of treatment initiation post infection. The authors also demonstrate that NHC reduces MERS-CoV infection titers, pathogenesis, and viral RNA in prophylactic and therapeutic settings.

      Caveats: Most of the experiments were conducted using MERS-CoV, and SARS-CoV and a few experiments were conducted using other strains of CoV as opposed to SARS-CoV-2. The authors note the core residues that make up the RNA interaction sites (which constitutes the NHC interaction sites) are highly conserved among CoV and because of this conservation their understanding is that NHC can inhibit a broad-spectrum of CoV including SARS-CoV-2.

      In addition, the temporal diminishing effectiveness of NHC on clinical outcome when NHC was used therapeutically is concerning. However, the longer window (7-10 days) for clinical disease onset in human patients from the time of infection compared to that of mice (24-48 hours), may associate with increased NHC effectiveness in the clinic.

      Importance: Prophylactic or therapeutic oral administration of NHC reduces lung titers and prevents acute lung failure in C57B\6 mice infected with CoV. Given its broad-spectrum antiviral activity, NHC could turn out to be a useful drug for treating current, emerging and future corona virus outbreaks.

      By: Luisanna Pia and PhD, Konstantina Alexandropoulos

    1. On 2020-04-23 13:02:49, user YIGUO ZHANG wrote:

      This article "Nrf1 Is Endowed with a Dominant Tumor-Repressing Effect onto the Wnt/?-Catenin-Dependent and Wnt/?-Catenin-Independent Signaling Networks in the Human Liver Cancer", by Chen J, Wang M, Xiang Y, Ru X, Ren Y, Liu X, Qiu L, Zhang Y. has been published in Oxid Med Cell Longev. 2020 Mar 23;2020:5138539. doi: 10.1155/2020/5138539.

    1. On 2023-02-17 13:22:14, user Guillaume Schwob, PhD wrote:

      Hi, thank you for sharing this preprint. After reading your manuscript, I was not sure to get clearly which compounds you'd recommend to efficiently enrich in Psychrilyobacter. Is it algae, agar powder, alginate, yeast extract or a mix of all ?

    1. On 2020-05-03 18:11:50, user jung wrote:

      Isn't it amazing? Bravo to awesome Doc. Sean Ekins. Repurposing Pyramax for the Treatment of Ebola Virus Disease: Additivity of the Lysosomotropic Pyronaridine and Non-Lysosomotropic Artesunate.

    1. On 2017-07-24 13:56:08, user Philipp Berens wrote:

      This is a long-overdue, very careful behavioral paper showing that mice can discriminate natural scenes quite well. It will be important for any study on natural mouse vision.

      We discussed the paper in our journal club and a few questions came up:

      1) The paper uses just one target image, which makes it hard to assess which strategy is used by the mice to discriminate the target from the distractors, e.g. could it be that the mice mainly look at the presence of sky at the top of the image? Fig. 4a could help to answer this question if all images were shown. If e.g. the sky was the feature the mice look at, one would expect that the percent correct is lower for all images with sky at the top that predicted by the psychometric curve. Have you considered training a small group of mice on a second target? Can the mice show that sort of flexibility?

      2) We found the behavioral consistency between the different mice surprisingly high, likely even higher than what one would expect from humans in a similar task (suitably adjusted in difficulty). Do you have any thoughts on what caused the remarkable consistency?

      3) In the first version of the article, there was a figure with a reaction time analysis. Interestingly, RTs for several mice seemed longer for images with higher performance / lower SSI. This may suggest that the mice quickly judged the high SSI images similar to the target, and therefore did not pay proper attention and made more mistakes, while they carefully investigated the obviously different images. What is your take on that and why was the RT analysis removed?

      4) Despite the fact that the rotation of the upper x-axis is noted in the figure legend, it took us a while to grasp Fig. 7b and figure out what is going on, especially since the upper x-axis is light grey.

      I hope the comments are useful!

    1. On 2020-02-27 02:37:42, user Michael wrote:

      This paper would be much stronger if it explained up front how these reagents are superior to or uniquely complement, add functionality, to existing ones. There are mentions of these aspects later and towards the end of the paper, but the abstract reads more like an introduction than a statement of the strengths of the new fluorescent proteins.

      Also, please note the endings added to two sentences pulled from the paper. Was this investigated for each of the new probes?

      Ideally, the fluorescent marker combines favorable spectroscopic properties (brightness, photostability) with specific labeling of the structure or compartment of interest **while minimally perturbing the intrinsic physiology.**

      Regardless of the application, it is crucial to use markers that show specific, crisp labeling and minimal spurious, non-specific localization **while minimally perturbing the intrinsic physiology.**

      Also, quantification of stability and brightness compared to other probes would be a great addition.

    1. On 2016-05-13 11:17:03, user Anna Need wrote:

      A really great paper, allowing us to further restrict the pool of likely contributing variants in case/ control studies of neuropsychiatric illnesses, and move a step closer to being able to provide clinical genetic diagnoses for some people with these conditions.

      Are you able to find out whether the parents carrying the ANK2 and RGL1 variants have any neuropsychiatric phenotype, and whether it was de novo or inherited in them?

      Also, when you calculated the rate of class 2 variants for schizophrenia, did you exclude the variants only seen in the EXaC patients with schizophrenia? Or should we assume that the real rate is a bit lower as some that seem to be class 2 are actually recurrent de novo mutations contributing to schizophrenia and absent in controls?

      (N.B. Not sure if this is an appropriate comment for this forum but there seems to be a typo in Supplementary table 3, the rate for congenital heart disease is given as a fraction and as a percentage for schizophrenia).

    1. On 2017-09-21 14:25:55, user Jean Manco wrote:

      Exciting new samples! Could you please clarify the location of the Alexandria sample. There are several Alexandrias in Ukraine. Your map shows the one west of the Dnieper, but your supplementary information describes the one in Kupyansk district, Kharkov region on the left bank of the river Oskol, fairly close to the border with Russia.

    1. On 2018-05-02 09:43:01, user Fredy Barneche wrote:

      Very original findings on the molecular links between Polycomb-based gene regulation and the organization of the nuclear lamina in plants, organisms that lack homologs of animal lamins. Waiting for journal publication, and more about other PWO family members!

    1. On 2018-12-17 12:42:35, user Julien Fattebert wrote:

      I think you should also look into:

      Rosenblatt et al. 'Effects of a protection gradient on carnivore density and survival: an example with leopards in the Luangwa valley, Zambia' that seems relevant to your discussion of contrasting densties across the gradient of human distrubance from settlements to reserve cores;

      and possibly Williams et al. 'Population dynamics and threats to an apex predator outside protected areas: implications for carnivore management';

      and Ramesh et al. 'Low leopard populations in protected areas of Maputaland: a<br /> consequence of poaching, habitat condition, abundance of prey, and a <br /> top predator'

    1. On 2021-04-14 13:04:34, user Martin R. Smith wrote:

      This is a very instructive and encouraging study (even if the quality of dating you have available is somewhat better than is normal for my stomping ground in the Cambrian...).

      Your simulated datasets look useful too, and really handy that they're available through Zenodo – I hope you won't mind my using them? One thing I wasn't quite clear on was the relationship between the "trees" and "samp_trees" objects: am I right in thinking that a tree reconstructed from morph\_seqs[[i]] is compared with samp\_trees[[i]]?

      One small bugbear: given the limitations and biases of the Robinson–Foulds distance, I wonder whether you might get clearer results with a more discriminating tree distance? I've reviewed some alternatives at https://doi.org/10.1093/bio... , and these are implemented in the R package TreeDist: http://ms609.github.io/Tree...

    1. On 2020-08-12 15:23:12, user Debora Marks wrote:

      Seems like a great paper Po-Ssu; Worth mentioning other generative models of proteins? Riesselman (VAE, Nature Methods 2018) and Riesselman (generate antibodies 2019)<br /> https://www.biorxiv.org/con...<br /> Oh – and the first paper on folding from coevolution – try 2011 PLoS One Marks et al <br /> In any case we’d love someone from your lab would do a journal club for us?

    1. On 2018-02-23 09:35:24, user Guillaume Devailly wrote:

      Thanks to the authors for sharing this very interesting work.

      I was catch up (a bit late) by the provocative title, and I would like to comment a few points regarding the overall interpretation of the presented results. This is not an attempt of pre-publication peer review, but only a discussion.

      1) The interpretation presented by the authors goes partially against a huge pile of literature, briefly exposed by the authors in the introduction:

      "DNA methylation of gene promoters is frequently inversely correlated with transcriptional activity,(2, 4, 5), and abolition of DNA methyltransferase activity through chemical inhibition or genetic disruption causes global demethylation and activates numerous genes (6, 7)."

      While I understand it might not be possible to do a comprehensive literature review, I would like to mention that many more papers than the 5 cited are supporting such observations.

      As the authors mentioned in the introduction:

      "However, these observations are correlative and challenging to interpret because genome-wide demethylation could have widespread and complex downstream effects upon chromatin structure and gene expression."

      Since the effect of the ZF-D3A expression increase the global level of DNA methylation (from 63% to 68.2%) by a substantial amount, it could be mentioned that:

      "The author’s observations are challenging to interpret because genome-wide hyper-methylation could have widespread and complex downstream effects."

      2) If I understood correctly (notably figure 2B), most of the effects of ZF-D3A was to put some unmethylated regions up to an intermediate methylation state (i.e. from 5% methylation up to 35% methylation). As far as I know, most of the regions of the genome are either almost fully methylated (> 85%) or almost fully unmethylated (< 15%). The authors convincingly showed that induced intermediate methylated regions are unstable and quickly return to there original, unmethylated states in their experimental system. This observation raises two additional, unresolved, questions: i) might a stronger DNA methylation induction be more stable (i.e. a complete induction of methylation, from <15% to more than >85% on a given region). This question is sadly very difficult to answer, as it is technically challenging to induce such a strong targeted hyper-methylation. ii) If one where to induce partial de-methylation of some fully methylated regions, will those newly induced intermediary methylated regions be as unstable, and will they return to a fully methylated state?

      As the induced methylation are quickly lost after the ZF-D3A expression is lost, the lack of long term gene repression is in agreement with there promoter methylation level (that was back to an unmethylated state).

      3) I found figure 5B quite misleading. I would suggest a more symmetric representation of the up-regulated and down-regulated gene numbers, such as this one (attached with the comment, or alternatively: http://image.noelshack.com/... )

      https://uploads.disquscdn.c...

      Indeed, if I understood correctly, upon expression of the ZF-D3A construct, of the 2,063 UMR gaining methylation by a delta mCG > 0.3, 63.3% of the corresponding genes where downregulated (FC < 0.9), 13.2% where stable, and 23.5% were upregulated (FC > 1.1).

      I can understand that some of those effects are either secondary events, or mediated by the DNMT3A-containing construct, and might not be due to the observed hyper-methylation.

      Nonetheless, I find it quite difficult to title the manuscript “Frequent lack of repressive capacity of promoter DNA methylation” while showing that 63.3% of the genes with an induced promoter hyper-methylation (delta-mCG > 0.3) had there expression reduced.

      Indeed, one can see the glass half empty. But granted that: 1) the hypermethylation were of limited strength (the delta-mCG is almost never greater that 0.5), 2) the apparent lack of corresponding strong chromatin state modificationd (figure 2, 3 and 4), and 3) the rapidity of the observed effect (48h after the beginning of the induction), I would personalty interpret this result as a beautiful evidence in favour of frequent repressive capacity of promoter DNA methylation in gene expression.

      Despite those remarks on result interpretation, I would like to congratulate the authors on the work done, and to thanks them for sharing those results as a pre-print.

      Guillaume Devailly

    1. On 2018-05-14 14:29:19, user Jens Staal wrote:

      Very cool!

      Hopefully this will lead to easy/efficient CRISPR/Cas9-mediated genome editing. It would be very interesting to see how different conserved innate immune signalling components (TLR, RIG-I, TRAF2, TRAF6, CARD-CC/Bcl10/MALT1, A20, CYLD, ...) influence infection/colonization by symbionts on one hand and "bleaching" on the other hand.

    1. On 2022-09-30 22:28:44, user MIT Microbiome Club wrote:

      The discussion should mention a limitation of this method is its inability to detect interactions that depend on spatial structure (it is known that higher order interactions can depend on spatial structure DOI: 10.1038/nature14485) to fast growers, and to only single carbon source (DOI: 10.1038/s41559-020-1099-4). It should also be noted that any interactions that affects flourescence rather than growth would be misinterpreted.

    1. On 2016-12-05 07:33:41, user Marc RobinsonRechavi wrote:

      The term "higher eukaryotes" is not very informative, and can mean different things to different people. Moreover only 1 species is used, so the most logical would be to title "in Arabidopsis". Alternatively, a more informative description would be something like "perform in a large eukaryotic genome with alternative splicing".

    1. On 2022-08-10 23:02:59, user Moritz Oberlander wrote:

      I was a little bit disappointed that you did not show the proteolysis of C-terminal domain at TTMV-ly1 homologs as well, at least one or two with 99% identity; for instance:

      541 KWGGDLPPMSTITNPTDQPTYVVPNNFNETTSLQNPTTRPEHFLYSFDERRGQLTEKATK TTMV-ly1: French children

      541 KWGGDLPPMSTITNPTEQPTYVIPNNFNETTSLQNPTTRPEHFLYSFDERRGQLTEKATK safia 523-10: Tanzania children

      541 KWGGDLPPMSTITNPTDQPTYVIPNNFNETTSLQNPTTRPEHFLYSFDERRGQLTEKATK xz029-anello-1: China children

      C-terminal domain changes at the TTMV-Ly1 homologs only in positions 557.aa and 563.aa

      I know that anelloviruses are “orphans” but they may have some “siblings". I think it’s important for an infectious study, scaling up VLP production, and to avoid a misleading degradation of the C-terminal domain at the TTMV-Ly1.

    1. On 2018-04-28 23:53:01, user Ruibang wrote:

      As suggested by Jared Simpson, the author of Nanopolish:

      Nanopolish is recommended that you run it over small windows (1-5Mbp) to reduce memory usage since it has to load all of the large fast5 files into memory. If you parallelize over small windows you might be able to run over the whole genome.

      The benchmark of Nanopolish will be updated in the next revision.

    1. On 2025-05-16 13:31:58, user Greggory Heller wrote:

      The article sites multiple supplementary figures (17 in fact) but they do not seem to be published with either the Elife version or the bioarxiv. How can I (and the rest of the public) access them?

    1. On 2020-02-12 19:59:48, user David Marjanovic wrote:

      The statement above to the contrary, and unlike its three preprint versions, this paper (v4) is not a preprint and has been certified by peer review by PCI Paleo (Peer Community In Paleontology). Read the editor's recommendation here.

      The trick is that the PCI journals are "journals without a journal": they peer-review and layout papers, but don't store them on their own website – instead, they leave them on preprint servers.

    1. On 2018-11-06 11:24:13, user Nibi wrote:

      Regarding the wash solution in the "DNA Preparation for NGS in RGEN-D Protocol" section at page 24-25, what composition did you use?<br /> And also, regarding the SRA data "SRP151278" at page 36, I couldn't access using the accession number. Have you deposited the data?

    1. On 2019-07-22 08:50:23, user Alexander Bruce wrote:

      Dear Amy,

      Thanks for a compelling and well conducted study - just how the Tead4/Yap complex switches from repressor and activator in a pluripotency/ differentiation related gene context is fascinating.

      ATB, Alex Bruce (Czech Republic)

    1. On 2022-02-17 17:10:34, user Peter Brodersen wrote:

      Really nice work, and a pleasant read. The suppression of the growth defect of strains with unmethylated G2922 by Nog2 T195R and H392R is beautiful. This will certainly be a new addition to the RNA modification lecture in the 2022 version of my RNA Biology course!