Rxivist combines preprints from bioRxiv with data from Twitter to help you find the papers being discussed in your field. Currently indexing 42,904 bioRxiv papers from 193,370 authors.
Most tweeted bioRxiv papers, last 24 hours
258 results found. For more information, click each entry to expand.
1 tweet bioinformatics
The PTSD Biomarker Database (PTSDDB) is a database that provides a landscape view of physiological markers being studied as putative biomarkers in the current post-traumatic stress disorder (PTSD) literature to enable researchers to quickly explore and compare findings. The PTSDDB currently contains over 900 biomarkers and their relevant information from 109 original articles published from 1997 to 2017. Further, the curated content stored in this database is complemented by a web application consisting of multiple interactive visualizations that enable the investigation of biomarker knowledge in PTSD (e.g., clinical study metadata, biomarker findings, experimental methods, etc.) by compiling results from biomarker studies to visualize the level of evidence for single biomarkers and across functional categories. This resource is the first attempt, to the best of our knowledge, to capture and organize biomarker and metadata in the area of PTSD for storage in a comprehensive database that may, in turn, facilitate future analysis and research in the field.
1 tweet scientific communication and education
Researchers in the life sciences are posting their work to preprint servers at an unprecedented and increasing rate, sharing papers online before (or instead of) publication in peer-reviewed journals. Though the popularity and practical benefits of preprints are driving policy changes at journals and funding organizations, there is little bibliometric data available to measure trends in their usage. Here, we collected and analyzed data on all 37,648 preprints that were uploaded to bioRxiv.org, the largest biology-focused preprint server, in its first five years. We find that preprints on bioRxiv are being read more than ever before (1.1 million downloads in October 2018 alone) and that the rate of preprints being posted has increased to a recent high of more than 2,100 per month. We also find that two-thirds of bioRxiv preprints posted in 2016 or earlier were later published in peer-reviewed journals, and that the majority of published preprints appeared in a journal less than six months after being posted. We evaluate which journals have published the most preprints, and find that preprints with more downloads are likely to be published in journals with a higher impact factor. Lastly, we developed Rxivist.org, a website for downloading and interacting programmatically with indexed metadata on bioRxiv preprints.
1 tweet cell biology
To promote chromosome bi-orientation, Aurora B kinase weakens and disrupts aberrant kinetochore-MT interaction. It has long been debated how Aurora B halts this action when bi-orientation is established and tension is applied across sister kinetochores. Pertinent to this debate, it was shown that Bir1 (yeast Survivin), which recruits Ipl1-Sli15 (yeast Aurora B-INCENP) to centromeres, is dispensable for bi-orientation, raising the possibility that Aurora B localization at centromeres is not required for bi-orientation. Here, we show that the COMA inner kinetochore sub-complex physically interacts with Sli15, recruits Ipl1-Sli15 to the inner kinetochore and promotes chromosome bi-orientation, independently of Bir1, in budding yeast. Moreover, using an engineered recruitment of Ipl1-Sli15 to the inner kinetochore when both Bir1 and COMA are defective, we show that localization of Ipl1-Sli15 at centromeres/inner kinetochores is essential for bi-orientation, refuting the above possibility. Our results give important insight into how Aurora B disrupts kinetochore-MT interaction in a tension-dependent manner, to promote chromosome bi-orientation.
1 tweet genomics
Background: Trials testing the effect of vitamin D or omega-3 fatty acids (n3-PUFA) supplementation on major depressive disorder (MDD) reported conflicting findings. These trials were boosted by epidemiological evidence suggesting an inverse association of circulating 25-hydroxyvitamin D (25-OH-D) and n3-PUFA levels with MDD. Observational associations may emerge from unresolved confounding, shared genetic risk, or direct causal relationships. We explored the nature of these associations exploiting data and statistical tools from genomics. Methods: Results from GWAS on 25-OH-D (N=79366), n3-PUFA (N=24925) and MDD (135458 cases, 344901 controls) were applied to individual-level data (>2,000 subjects with measures of genotype, DSM-IV lifetime MDD diagnoses and circulating 25-OH-D and n3-PUFA) and summary-level data analyses. Shared genetic risk between traits was tested by polygenic risk scores (PRS). Two-sample Mendelian Randomization (2SMR) analyses tested the potential bidirectional causality between traits. Outcome: In individual-level data, PRS were associated with the phenotype of the same trait (PRS 25-OH-D p=1.4e-20, PRS N3-PUFA p=9.3e-6, PRS MDD p=1.4e-4), but not with the other phenotypes, suggesting a lack of shared genetic effects. In summary-level data, 2SMR analyses provided no evidence of a causal role on MDD of 25-OH-D (p=0.50) or n3-PUFA (p=0.16), or for a causal role of MDD on 25-OH-D (p=0.25) or n3-PUFA (p=0.66). Conclusions: Applying genomics tools indicated that that shared genetic risk or direct causality between 25-OH-D, n3-PUFA and MDD is unlikely: unresolved confounding may explain the associations reported in observational studies. These findings represent a cautionary tale for testing supplementation of these compounds in preventing or treating MDD.
1 tweet biochemistry
DNA and RNA nucleases play a critical role in a growing number of cellular processes ranging from DNA repair to immune surveillance. Nevertheless, many nucleases have unknown or poorly characterized activities. Elucidating nuclease substrate specificities and regulatory components can support a more definitive understanding of cellular mechanisms in physiology and disease. Using fluorescence-based methods, we have developed a quick, safe, reproducible, cost-effective, and real-time nuclease assay toolkit that could be used for small- and large- scale experimental assays. Additionally, these data can be analysed to determine each reaction's unique enzyme kinetics. We have designed a library of substrates that can be used to study catalytic rates, directionality, and substrate preferences. The assay is sensitive enough to detect kinetics of repair enzymes when confronted with DNA mismatches or DNA methylation sites. We have also extended this assay to consider analysing the kinetics of human single-strand DNA nuclease TREX2, DNA polymerases, and RNA:DNA nucleases, which are also involved in DNA repair and immune regulation, and have been associated with various disease conditions, including cancer and immune disorders.
1 tweet cell biology
Intrinsically disordered regions (IDRs) often are fast evolving protein domains of low sequence complexity that can drive phase transitions and are commonly found in many proteins associated with neurodegenerative diseases, including the RNA processing factor TDP43. Yet, how phase separation contributes to the physiological functions of TDP43 in cells remains enigmatic. We combined systematic mutagenesis guided by evolutionary sequence analysis with a live-cell reporter assay of TDP43 phase dynamics to identify regularly-spaced hydrophobic motifs separated by flexible, hydrophilic segments in the IDR as a key determinant of TDP43 phase properties. This heuristic framework allowed us to customize the material properties of TDP43 condensates to determine effects on splicing function. Remarkably, mutants with increased or decreased phase dynamics, and even mutants that failed to phase separate, could mediate the splicing of a quantitative, single-cell splicing reporter and endogenous targets. We conclude that phase separation is not required for the function of TDP43 in splicing.
1 tweet neuroscience
Algorithmic reconstruction of neurons from volume electron microscopy data traditionally requires training machine learning models on dataset-specific ground truth annotations that are expensive and tedious to acquire. We enhanced the training procedure of an unsupervised image-to-image translation method with additional components derived from an automated neuron segmentation approach. We show that this method, Segmentation-Enhanced CycleGAN (SECGAN), enables near perfect reconstruction accuracy on a benchmark connectomics segmentation dataset despite operating in a "zero-shot" setting in which the segmentation model was trained using only volumetric labels from a different dataset and imaging method. By reducing or eliminating the need for novel ground truth annotations, SECGANs alleviate one of the main practical burdens involved in pursuing automated reconstruction of volume electron microscopy data.
1 tweet neuroscience
Over the last decade there has been growing interest in understanding the brain activity in the absence of any task or stimulus captured by the resting-state functional magnetic resonance imaging (rsfMRI). These resting state patterns are not static, but exhibit complex spatio-temporal dynamics. In the recent years substantial effort has been put to characterize different FC configurations while brain states makes transitions over time. The dynamics governing this transitions and their relationship with stationary functional connectivity remains elusive. Over the last years a multitude of methods has been proposed to discover and characterize FC dynamics and one of the most accepted method is sliding window approach. Moreover, as these FC configurations are observed to be cyclically repeating in time there was further motivation to use of a generic clustering scheme to identify latent states of dynamics. We discover the underlying lower-dimensional manifold of the temporal structure which is further parameterized as a set of local density distributions, or latent transient states. We propose an innovative method that learns parameters specific to these latent states using a graph-theoretic model (temporal Multiple Kernel Learning, tMKL) and finally predicts the grand average functional connectivity (FC) of the unseen subjects by leveraging a state transition Markov model. tMKL thus learns a mapping between the underlying anatomical network and the temporal structure. Training and testing were done using the rs-fMRI data of 46 healthy participants and the results establish the viability of the proposed solution. Parameters of the model are learned via state-specific optimization formulations and yet the model performs at par or better than state-of-the-art models for predicting the grand average FC. Moreover, the model shows sensitivity towards subject-specific anatomy. The proposed model performs significantly better than the established models of predicting resting state functional connectivity based on whole-brain dynamic mean-field model, single diffusion kernel model and another version of multiple kernel learning model. In summary, We provide a novel solution that does not make strong assumption about underlying data and is generally applicable to resting or task data to learn subject specific state transitions and successful characterization of SC-dFC-FC relationship through an unifying framework.
1 tweet cell biology
The emergence of eukaryotes from ancient prokaryotic lineages was accompanied by a remarkable increase in cellular complexity. While prokaryotes use simple systems to connect DNA to the segregation machinery during cell division, eukaryotes use a highly complex protein assembly known as the kinetochore. Although conceptually similar, prokaryotic segregation systems and eukaryotic kinetochore proteins share no homology, raising the question of the origins of the latter. Using large-scale gene family reconstruction, sensitive profile-versus-profile homology detection and protein structural comparisons, we here reveal that the kinetochore of the last eukaryotic common ancestor (LECA) consisted of 52 proteins that share deep evolutionary histories with proteins involved in a few prokaryotic processes and a multitude of eukaryotic processes, including ubiquitination, chromatin regulation and flagellar as well as vesicular transport systems. We find that gene duplications played a major role in shaping the kinetochore: roughly half of LECA kinetochore proteins have other kinetochore proteins as closest homologs. Some of these (e.g. subunits of the Mis12 complex) have no detectable homology to any other eukaryotic protein, suggesting they arose as kinetochore-specific proteins de novo before LECA. We propose that the primordial kinetochore evolved from proteins involved in various (pre-)eukaryotic systems as well as novel proteins, after which a subset duplicated to give rise to the complex kinetochore of LECA.
1 tweet immunology
IgA production depends on gut colonization by a diverse microbiota. However, the bacterial strains that drive gut IgA production remain largely unknown. By accessing the IgA-inducing capacity of a diverse set of human gut microbial strains, we identified Bacteroides ovatus as the species that best induced gut IgA production. However, this induction varied biomodally across different B. ovatus strains. The high IgA-inducing B. ovatus strains preferentially elicited more IgA production in the large intestine through both T-cell-dependent and T-cell-independent B cell-activation pathways. Remarkably, a low-IgA phenotype in mice could be robustly and consistently converted into a high-IgA phenotype by transplanting a multiplex cocktail of high IgA-inducing B. ovatus strains but not individual ones. Thus, microbial strain specificity is essential for the optimal induction of high-IgA responses in the gut. Our results highlight the critical importance of microbial strains in driving phenotype variation in the mucosal immune system and provide a strategy to robustly modify a given gut immune phenotype, including IgA production.
1 tweet genomics
Identifying differentially expressed (DE) genes from RNA sequencing (RNAseq) studies is among the most common analyses in genomics. However, RNAseq DE analysis presents several statistical and computational challenges, including over-dispersed read counts and, in some settings, sample non-independence. Previous count-based methods rely on simple hierarchical Poisson models (e.g., negative binomial) to model independent over-dispersion, but do not account for sample non-independence due to relatedness, population structure and/or hidden confounders. Here, we present a Poisson mixed model with two random effects terms that account for both independent over-dispersion and sample non-independence. We also develop a scalable sampling-based inference algorithm using a latent variable representation of the Poisson distribution. With simulations, we show that our method properly controls for type I error and is generally more powerful than other widely used approaches, except in small samples (n<15) with other unfavorable properties (e.g., small effect sizes). We also apply our method to three real data sets that contain related individuals, population stratification, or hidden confounders. Our results show that our method increases power in all three data compared to other approaches, though the power gain is smallest in the smallest sample (n=6). Our method is implemented in MACAU, freely available at www.xzlab.org/software.html.
1 tweet evolutionary biology
The uneven distribution of species in the tree of life is rooted in unequal speciation and extinction among groups. Yet the causes of differential diversification are little known despite their relevance for sustaining biodiversity into the future. Here we investigate rates of species diversification across extant Mammalia, a compelling system that includes our own closest relatives. We develop a new phylogeny of nearly all ~6000 species using a 31-gene supermatrix and fossil node- and tip-dating approaches to establish a robust evolutionary timescale for mammals. Our findings link the causes of uneven modern species richness with ecologically-driven variation in diversification rates, including 24 detected rate shifts. Speciation rates are a stronger predictor of among-clade richness than clade age, countering claims of clock-like speciation in large phylogenies. Surprisingly, rate heterogeneity in recent radiations shows limited association with latitude, despite the well-known richness increase toward the equator. Instead, we find a deeper-time association where clades of high-latitude species have the highest speciation rates, suggesting that species durations are shorter outside than inside the tropics. At shallower timescales (i.e., young clades), diurnality and low vagility are both linked to greater speciation rates and extant richness. High turnover among small-ranged allopatric species may erase the signal of vagility in older clades, while diurnality may adaptively reduce competition and extinction. These findings highlight the underappreciated joint roles of ephemeral (turnover-based) and adaptive (persistence-based) diversification processes, which manifest as speciation gradients in recent and more ancient radiations to explain the evolution of mammal diversity.
1 tweet animal behavior and cognition
The objective was to determine the effects of sleep or lying deprivation on the behavior of dairy cows. Data were collected from 8 multi- and 4 primiparous cows (DIM = 199 ± 44 (mean ± SD); days pregnant = 77 ± 30). Using a crossover design, each cow experienced: 1) sleep deprivation implemented by noise or physical contact when their posture suggested sleep, and 2) lying deprivation imposed by a grid placed on the pen floor. One day before treatment (baseline), and treatment day (treatment) were followed by a 12-d washout period. Study days were organized from 2100 to 2059. During habituation (d -3 and -2 before treatment), baseline (d -1), and trt (d 0), housing was individual boxstalls (mattress with no bedding). After treatment, cows returned to sand-bedded freestalls for a 7-d recovery period (d 1 to 7) where data on lying behaviors were collected. Daily lying time, number lying bouts, bout duration, and number of steps were recorded by dataloggers attached to the hind leg of cows throughout the study period. Data were analyzed using a mixed model in SAS including fixed effects of treatment (sleep deprivation vs. sleep and lying deprivation), day, and their interaction with significant main effects separated using a PDIFF statement (P ≤ 0.05). Interactions between treatment and day were detected for daily lying time and the number of bouts. Lying time was lower for both treatments during the treatment period compared to baseline. Lying time increased during the recovery period for both lying and sleep deprived cows. However, it took 4 d for the lying deprived cows to fully recover their lying time after treatment, whereas it took the sleep deprived cows 2 d for their lying time to return to baseline levels. Results suggest that both sleep and lying deprivation can have impact cow behavior. Management factors that limit freestall access likely reduce lying time and sleep, causing negative welfare implications for dairy cows.
1 tweet molecular biology
In order to explore the mechanisms employed by living cells to deal with DNA alterations, we have developed a method by which we insert a modified DNA into a specific site of the yeast genome. This is achieved by the site-specific integration of a modified plasmid at a chosen locus of the genome of Saccharomyces cerevisiae, through the use of the Cre/lox recombination system. In the present work, we have used our method to insert a single UV lesion into the yeast genome, and studied how the balance between error-free and error-prone lesion bypass is regulated. We show that the inhibition of homologous recombination, either directly (by the inactivation of rad51 recombinase) or through its control by preventing the poly-ubiquitination of PCNA (ubc13 mutant), leads to a strong increase in the use of TLS. Such regulatory aspects of the DNA damage tolerance could not have been observed with previous strategies using plasmid or randomly distributed lesions, which shows the advantage of our new method. The very robust and precise integration of any modified DNA at any chosen locus of the yeast genome that we describe here is a powerful tool that will allow exploration of many biological processes related to replication and repair of modified DNA.
1 tweet genomics
Mutations that cause genetic diseases can be difficult to identify if the mutation does not affect the sequence of the protein, but the splice form of the transcript. However, the prediction of deleterious changes caused by genomic variants that affect splicing has been shown to be accurate using information theory-based methods. We made several such predictions of potential splicing changes that could be caused by SNPs which were found to cause natural and/or cryptic splice site strength changes. We evaluated a selected set of 22 SNPs that we predicted by information analysis to affect splicing, validated these with targeted expression analysis, and compared the results with genome-scale interpretation of RNAseq data from tumors. Abundance of natural and predicted splice isoforms were quantified by q-RT-PCR and with probeset intensities from exon microarrays using RNA isolated from HapMap lymphoblastoid cell lines containing the predicted deleterious variants. These SNPs reside within the following genes: XRCC4, IL19, C21orf2, UBASH3A, TTC3, PRAME, EMID1, ARFGAP3, GUSBP11 (Fλ8), WBP2NL, LPP, IFI44L, CFLAR, FAM3B, CYB5R3, COL6A2, BCR, BACE2, CLDN14, TMPRSS3 and DERL3. 15 of these SNPs showed a significant change in the use of the affected splice site. Individuals homozygous for the stronger allele had higher transcription of the associated gene than individuals with the weaker allele in 3 of these SNPs. 13 SNPs had a direct effect on exon inclusion, while 10 altered cryptic site use. In 4 genes, individuals of the same genotype had high expression variability caused by alternate factors which masked potential effects of the SNP. Targeted expression analyses for 8 SNPs in this study were confirmed by results of genome-wide information theory and expression analyses.
1 tweet bioinformatics
High Performance Computing (HPC) Best Practice offers opportunities to implement lessons learned in areas such as computational chemistry and physics in genomics workflows, specifically Next-Generation Sequencing (NGS) workflows. In this study we will briefly describe how distributed-memory parallelism can be an important enhancement to the performance and resource utilization of NGS workflows. We will illustrate this point by showing results on the parallelization of the Inchworm module of the Trinity RNA-Seq pipeline for de novo transcriptome assembly. We show that these types of applications can scale to thousands of cores. Time scaling as well as memory scaling will be discussed at length using two RNA-Seq datasets, targeting the Mus musculus (mouse) and the Axolotl (Mexican salamander). Details about the efficient MPI communication and the impact on performance will also be shown. We hope to demonstrate that this type of parallelization approach can be extended to most types of bioinformatics workflows, with substantial benefits. The efficient, distributed-memory parallel implementation eliminates memory bottlenecks and dramatically accelerates NGS analysis. We further include a summary of programming paradigms available to the bioinformatics community, such as C++/MPI.
1 tweet bioinformatics
A number of recent studies have highlighted the findings that certain lncRNAs are associated with alternative splicing (AS) in tumorigenesis and progression. Although existing work showed the importance of linking certain misregulations of RNA splicing with lncRNAs, a primary concern is the lack of genome-wide comprehensive analysis for their associations. We analyzed an extensive collection of RNA-seq data, quantified 198,619 isoform expressions, and found systematic isoform usage changes between hepatocellular carcinoma (HCC) and normal liver tissue. We identified a total of 1375 splicing switched isoforms and further analyzed their biological functions. To predict which lncRNAs are associated with these AS genes, we integrated the co-expression networks and epigenetic interaction networks collected from text mining and database searching, linking lncRNA modulators such as splicing factors, transcript factors, and miRNAs with their targeted AS genes in HCC. To model the heterogeneous networks in a single framework, we developed a multi-graphic random walk (RWMG) network method to prioritize the lncRNAs associated with AS in HCC. RWMG showed a good performace evaluated by ROC curve based on cross-validation and bootstrapping strategy. As a summary, we identified 31 AS-related lncRNAs including MALAT1 and HOXA11-AS, which have been reported before, as well as some novel lncRNAs such as DNM1P35, HAND2-AS1, and DLX6-AS1. Survival analysis further confirmed the clinical significance of identified lncRNAs.
1 tweet molecular biology
Iart Luca Shytaj, Bojana Lucic, Mattia Forcato, James M Billingsley, Steven Bosinger, Mia Stanic, Francesco Gregoretti, Laura Antonelli, Gennaro Oliva, Christian Frese, Alexandra Trifunovic, Bruno Galy, Clarissa Eibl, Guido Silvestri, Silvio Bicciato, Andrea Savarino, Marina Lusic
Metabolic alterations, such as oxidative stress, are hallmarks of HIV-1 infection. However, their influence on the development of viral latency, and thus on HIV-1 persistence during antiretroviral therapy (ART), have just begun to be explored. We analyzed omics profiles of in-vitro and in-vivo models of infection by HIV-1 and its simian homolog SIVmac. We found that cells survive retroviral replication by upregulating antioxidant pathways and intertwined iron import pathways. These changes are associated with remodeling of the redox sensitive promyelocytic leukemia protein nuclear bodies (PML NBs), an important constituent of nuclear architecture and a marker of HIV-1 latency. We found that PML is depleted in productively infected cells and restored by ART. Moreover, we identified intracellular iron as a key link between oxidative stress and PML depletion, thus supporting iron metabolism modulators as pharmacological tools to impair latency establishment.
1 tweet developmental biology
The spikelet is the basic unit of the grass inflorescence. In this study, we show that wheat MADS-box genes VRN1 and FUL2 play critical and redundant roles in the determination of spikelet meristem identity. Combined loss-of-function mutations of these two genes (vrn1ful2-null) were sufficient to revert lateral spikelet meristems into vegetative meristems in the spikes of tetraploid wheat. These plants were also unable to form a terminal spikelet and had an indeterminate inflorescence. The single vrn1-null and ful2-null mutants showed increased number of spikelets per spike, likely due to a delayed formation of the terminal spikelet. Mutations in these two genes and in the more distantly related paralog FUL3, also affected wheat heading time and stem elongation. The ful2-null mutant also showed a higher number of florets per spikelet, which together with a higher number of spikelets, resulted in a significant increase in the number of grains per spike in the field. Our results suggest that a better understanding of the basic mechanisms underlying wheat spike development can inform future strategies to improve grain yield in wheat.
1 tweet neuroscience
Traditional neurobiological theories of musical emotions explain well why extreme music such as punk, hardcore or metal, whose vocal and instrumental characteristics share much similarity with acoustic threat signals, should evoke unpleasant feelings for a large proportion of listeners. Why it doesn't for metal music fans, however, remains a theoretical challenge: metal fans may differ from non-fans in how they process acoustic threat signals at the sub-cortical level, showing deactivated or reconditioned responses that differ from controls. Alternatively, it is also possible that appreciation for metal depends on the inhibition by cortical circuits of a normal low-order response to auditory threat. In a series of three experiments, we show here that, at a sensory level, metal fans actually react equally negatively, equally fast and even more accurately to cues of auditory threat in vocal and instrumental contexts than non-fans. Conversely, cognitive load somewhat appears to reduce fans' appreciation of metal to the level reported by non-fans. Taken together, these results are not compatible with the idea that extreme music lovers do so because of a different low-level response to threat, but rather, highlight a critical contribution of higher-order cognition to the aesthetic experience. These results are discussed in the light of recent higher-order theories of emotional consciousness, which we argue should be generalized to the emotional experience of music across musical genres.
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