Rxivist uses download data on preprints from bioRxiv to help you find the papers being discussed in your field. Currently indexing 101,433 bioRxiv papers from 428,488 authors.
Most tweeted bioRxiv papers, last 7 days
386 results found. For more information, click each entry to expand.
2 tweets neuroscience
Recombinant adeno-associated virus (rAAV) has been widely used as a viral vector across mammalian biology and has been shown to be safe and effective in human gene therapy. We demonstrate that neural progenitor cells (NPCs) and immature dentate granule cells (DGCs) within the adult murine hippocampus are particularly sensitive to rAAV-induced cell death. Cell loss is dose dependent and nearly complete at experimentally relevant viral titers. rAAV-induced cell death is rapid and persistent, with loss of BrdU-labeled cells within 18 hours post-injection and no evidence of recovery of adult neurogenesis at 3 months post-injection. The remaining mature DGCs appear hyperactive 4 weeks post-injection based on immediate early gene expression, consistent with previous studies investigating the effects of attenuating adult neurogenesis. In vitro application of AAV or electroporation of AAV2 inverted terminal repeats (ITRs) is sufficient to induce cell death. Efficient transduction of the dentate gyrus (DG)-without ablating adult neurogenesis-can be achieved by injection of rAAV2-retro serotyped virus into CA3. rAAV2-retro results in efficient retrograde labeling of mature DGCs and permits in vivo 2-photon calcium imaging of dentate activity while leaving adult neurogenesis intact. These findings expand on recent reports implicating rAAV-linked toxicity in stem cells and other cell types and suggest that future work using rAAV as an experimental tool in the DG and as a gene therapy for diseases of the central nervous system (CNS) should be carefully evaluated.
2 tweets systems biology
Staphylococcus aureus ( S. aureus ) is a challenging human pathogen due to its ability to evade the immune system and resist multidrug antibiotics. These evasive strategies lead to chronic and recurrent infections. Many studies have documented that during chronic infections Myeloid Derived Suppressor Cells (MDSCs) exert immunosuppressive mechanisms on T cells. A mathematical model explains how the steady state of chronic infection can be disturbed and suggests therapeutic strategies to clear the infection. Model-driven suggestions were tested experimentally and confirmed complete clearance of S. aureus chronic infection.
2 tweets microbiology
Early administration of effective antimicrobial treatments improves the outcome of infections. Culture-based antimicrobial resistance testing allows for tailored treatments, but takes up to 96h. We present a revolutionary approach to predict resistance with unmatched speed within 24h, using calibrated logistic regression and LightGBM-classifiers trained on species-specific MALDI-TOF mass spectrometry measurements. For this analysis, we created an unprecedented large, publicly-available dataset combining mass spectra and resistance information. Our models provide highly valuable treatment guidance 12-72h earlier than classical approaches. Rejection of uncertain predictions enables quality control and clinically-applicable sensitivities and specificities for the priority pathogens Staphylococcus aureus, Escherichia coli, and Klebsiella pneumoniae. ### Competing Interest Statement The authors have declared no competing interest.
2 tweets scientific communication and education
Scientific peer review is still the most common system for fund allocation despite having been shown in multiple instances to lack accuracy in identifying the most meritorious applications among high quality ones. This study evaluates two aspects of the selection process of the top- ranked applicants to the EMBO Long-Term Fellowship program in 2007. First, the accuracy of the system is evaluated by comparing the level of career progression of the candidates in 2017 with the original award decisions made in 2007. The second aspect, explores the relationship of career progression with indicators derived from the information available to evaluators at the time of application. The results obtained suggest that the peer review system is not substantially better than random selection in identifying the best candidates once an initial pre-selection of the most promising ones is performed. Not only that, the analysis of the indicators studied, some of which have not been analyzed in detail in the past, suggests that among other potential sources of uncertainty, the information available at the time of application is not sufficiently predictive of career progression. As previously described, however, we find differences in career progression between men and women. We propose a new mixed model of fellowship evaluation in which peer review is used to select high quality applications, and random allocation of funds is subsequently used to award fellowships among these top ranked candidates.
2 tweets immunology
Michael F Cuccarese, Berton A. Earnshaw, Katie Heiser, Ben Fogelson, Chadwick T Davis, Peter F McLean, Hannah B. Gordon, Kathleen-Rose Skelly, Fiona L Weathersby, Vlad Rodic, Ian K Quigley, Elissa D. Pastuzyn, Brandon M Mendivil, Nathan H. Lazar, Carl A Brooks, Joseph Carpenter, Brandon L Probst, Pamela Jacobson, Seth W Glazier, Jes Ford, James D Jensen, Nicholas D Campbell, Michael A Statnick, Adeline S. Low, Kirk R Thomas, Anne E. Carpenter, Sharath S Hegde, Ronald W. Alfa, Mason L. Victors, Imran S Haque, Yolanda T. Chong, Christopher C Gibson
Development of accurate disease models and discovery of immune-modulating drugs is challenged by the immune system’s highly interconnected and context-dependent nature. Here we apply deep-learning-driven analysis of cellular morphology to develop a scalable “phenomics” platform and demonstrate its ability to identify dose-dependent, high-dimensional relationships among and between immunomodulators, toxins, pathogens, genetic perturbations, and small and large molecules at scale. High-throughput screening on this platform demonstrates rapid identification and triage of hits for TGF-β- and TNF-α-driven phenotypes. We deploy the platform to develop phenotypic models of active SARS-CoV-2 infection and of COVID-19-associated cytokine storm, surfacing compounds with demonstrated clinical benefit and identifying several new candidates for drug repurposing. The presented library of images, deep learning features, and compound screening data from immune profiling and COVID-19 screens serves as a deep resource for immune biology and cellular-model drug discovery with immediate impact on the COVID-19 pandemic. ### Competing Interest Statement We declare competing interests. All authors were employees of or advisors to Recursion during the course of this work. All authors have real or potential ownership interest in Recursion.
2 tweets molecular biology
The Coronaviridae is a family of positive-strand RNA viruses that includes SARS-CoV-2, the etiologic agent of the COVID-19 pandemic. Bearing the largest single-stranded RNA genomes in nature, coronaviruses are critically dependent on long-distance RNA-RNA interactions to regulate the viral transcription and replication pathways. Here we experimentally mapped the in vivo RNA-RNA interactome of the full-length SARS-CoV-2 genome and subgenomic mRNAs. We uncovered a network of RNA-RNA interactions spanning tens of thousands of nucleotides. These interactions reveal that the viral genome and subgenomes adopt alternative topologies inside cells, and engage in different interactions with host RNAs. Notably, we discovered a long-range RNA-RNA interaction - the FSE-arch - that encircles the programmed ribosomal frameshifting element. The FSE-arch is conserved in the related MERS-CoV and is under purifying selection. Our findings illuminate RNA structure based mechanisms governing replication, discontinuous transcription, and translation of coronaviruses, and will aid future efforts to develop antiviral strategies. ### Competing Interest Statement The authors have declared no competing interest.
2 tweets cell biology
Daniela Fignani, Giada Licata, Noemi Brusco, Laura Nigi, Giuseppina E. Grieco, Lorella Marselli, Lut Overbergh, Conny Gysemans, Maikel L. Colli, Piero Marchetti, Chantal Mathieu, Decio L. Eizirik, Guido Sebastiani, Francesco Dotta
Increasing evidence demonstrated that the expression of Angiotensin I-Converting Enzyme type 2 (ACE2), is a necessary step for SARS-CoV-2 infection permissiveness. In the light of the recent data highlighting an association between COVID-19 and diabetes, a detailed analysis aimed at evaluating ACE2 expression pattern distribution in human pancreas is still lacking. Here, we took advantage of INNODIA network EUnPOD biobank collection to thoroughly analyse ACE2, both at mRNA and protein level, in multiple human pancreatic tissues and using several methodologies. Using multiple reagents and antibodies, we showed that ACE2 is expressed in human pancreatic islets, where it is preferentially expressed in subsets of insulin producing β-cells. ACE2 is also is highly expressed in pancreas microvasculature pericytes and moderately expressed in rare scattered ductal cells. By using different ACE2 antibodies we showed that a recently described short-ACE2 isoform is also prevalently expressed in human β-cells. Finally, using RT-qPCR, RNA-seq and High-Content imaging screening analysis, we demonstrated that pro-inflammatory cytokines, but not palmitate, increases ACE2 expression in the β-cell line EndoC-βH1 and in primary human pancreatic islets. Taken together, our data indicate a potential link between SARS-CoV-2 and diabetes through putative infection of pancreatic microvasculature and/or ductal cells and/or through direct β-cell virus tropism. ### Competing Interest Statement The authors have declared no competing interest.
2 tweets evolutionary biology
Dating back to the last universal common ancestor (LUCA), the P-loop NTPases and Rossmanns now comprise the most ubiquitous and diverse enzyme lineages. Intriguing similarities in their overall architecture and phosphate binding motifs suggest common ancestry; however, due to a lack of sequence identity and some fundamental structural differences, these families are considered independent emergences. To address this longstanding dichotomy, we systematically searched for 'bridge proteins' with structure and sequence elements shared by both lineages. We detected homologous segments that span the first βαβ segment of both lineages and include two key functional motifs: (i) a phosphate binding loop - the 'Walker A' motif of P-loop NTPases or the Rossmann equivalent, both residing at the N-terminus of α1 ; and (ii) an Asp at the tip of β2. The latter comprises the 'Walker B' aspartate that chelates the catalytic metal in P-loop NTPases, or the canonical Rossmann β2-Asp that binds the cofactor's ribose moiety. Tubulin, a Rossmann GTPase, demonstrates the potential of the β2-Asp to take either one of these two roles. We conclude that common P-loops/Rossmann ancestry is plausible, although convergence cannot be completely ruled out. Regardless, both lineages most likely emerged from a polypeptide comprising a βαβ segment carrying the above two functional motifs, a segment that comprises the core of both enzyme families to this very day. ### Competing Interest Statement The authors have declared no competing interest.
2 tweets bioinformatics
Anna Paola Carrieri, Niina Haiminen, Sean Maudsley-Barton, Laura-Jayne Gardiner, Barry Murphy, Andrew Mayes, Sarah Paterson, Sally Grimshaw, Martyn Winn, Cameron Shand, Will Rowe, Stacy Hawkins, Ashley MacGuire-Flanagan, Jane Tazzioli, John Kenny, Laxmi Parida, Michael Hoptroff, Edward O Pyzer-Knapp
Alterations in the human microbiome have been observed in a variety of conditions such has asthma, gingivitis, dermatitis and cancer, and much remains to be learned about the links between the microbiome and human health. The fusion of artificial intelligence with rich microbiome datasets can offer an improved understanding of the microbiome's role in our health. To gain actionable insights it is essential to consider both the predictive power and the transparency of the models by providing explanations for the predictions. We combine the effort of collecting a corpus of leg skin microbiome samples of two healthy cohorts of women with the development of an explainable artificial intelligence (EAI) approach that provides accurate predictions of phenotypes and explanations. The explanations are expressed in terms of variations in the abundance of key microbes that drive the predictions. We predict skin hydration, subject's age, pre/post-menopausal status and smoking status from the leg skin microbiome. The key changes in microbial composition linked to skin hydration can accelerate the development of personalised treatments for healthy skin, while those associated with age may offer insights into the skin aging process. The leg microbiome signatures associated with smoking and menopausal status are consistent with previous findings from oral/respiratory tract microbiomes and vaginal microbiomes respectively. This suggests that easily accessible microbiome samples could be used to investigate health-related phenotypes, offering potential for non-invasive diagnosis and condition monitoring. Our EAI approach sets the stage for new work focused on understanding the complex relationships between microbial communities and phenotypes. Our approach can be applied to predict any conditions from microbiome samples and has the potential to accelerate the development of microbiome-based personalised therapeutics and non-invasive diagnostics. ### Competing Interest Statement The authors were employed by private or academic organizations as described in the author affiliations at the time this work was completed.
2 tweets epidemiology
The discrepancy between the protective effect of early surgery of breast cancer and the poor benefits of mammography screening programs in the long term can be explained if mammography induces breast cancer at a much higher rate than anticipated. Mammography screening is associated in most countries with a higher incidence of breast cancer, attributed to overdiagnosis. X-ray-induced cancers can be distinguished from overdiagnosed cancers by the fact that their incidence depends on the number of previous mammograms, whereas overdiagnosis solely depends on the last screening mammogram, leading to diagnosis. The unbiased relationship between the number of mammograms and breast cancer incidence was evaluated from the data of the NHS Breast Cancer screening programme in women aged from 50 to 64 years in the United Kingdom. The delay between mammography and increased breast cancer incidence was confirmed from the data of the Age trial, a randomized trial of annual screening starting at age 40 in the UK. In women aged 50-64 attending screening at the NHS Breast Cancer programme, in situ breast cancer incidence increased linearly from 1993 to 2005 as a function of the number of mammograms. Incidence did not increase anymore after 2005 when the number of mammograms and the delay after screening was stable. Invasive breast cancer incidence increased more specifically in the 60-69 age group. The risk of breast cancer almost doubled after 15 years of screening. Additional cancers began to occur less than 6 years after mammography. These results are evidence that X-ray-induced carcinogenesis, rather than overdiagnosis, is the cause of the increase in breast cancer incidence.
2 tweets microbiology
Toxoplasma gondii is an intracellular protozoan pathogen of humans that causes severe disease in immunocompromised patients and in the developing fetus. T. gondii specifically alters production of the immunomodulatory chemokine CCL22 in human placental cells during infection. Using a combination of bioinformatics and molecular genetics, we have now identified T. gondii GRA28 as the gene product required for CCL22 induction. GRA28 is strongly co-regulated at the transcriptional level along with other known secreted effectors and their chaperones. GRA28 is secreted into the host cell where it localizes to the nucleus, and deletion of this gene results in reduced CCL22 secretion from human monocytes and second trimester placental explants. The impact of GRA28 on CCL22 is also conserved in mouse immune and placental cells and the deletion of GRA28 results in increased inflammatory responses and reduced CNS burden during mouse infections ### Competing Interest Statement The authors have declared no competing interest.
2 tweets genetics
Despite considerable progress on pathogenicity scores prioritizing both coding and non-coding variants for Mendelian disease, little is known about the utility of these pathogenicity scores for common disease. Here, we sought to assess the informativeness of Mendelian disease-derived pathogenicity scores for common disease, and to improve upon existing scores. We first applied stratified LD score regression to assess the informativeness of annotations defined by top variants from published Mendelian disease-derived pathogenicity scores across 41 independent common diseases and complex traits (average N = 320K). Several of the resulting annotations were informative for common disease, even after conditioning on a broad set of coding, conserved, regulatory and LD-related annotations from the baseline-LD model. We then improved upon the published pathogenicity scores by developing AnnotBoost, a gradient boosting-based framework to impute and denoise pathogenicity scores using functional annotations from the baseline-LD model. AnnotBoost substantially increased the informativeness for common disease of both previously uninformative and previously informative pathogenicity scores, implying pervasive variant-level overlap between Mendelian disease and common disease. The boosted scores also produced significant improvements in heritability model fit and in classifying disease-associated, fine-mapped SNPs. Our boosted scores have high potential to improve candidate gene discovery and fine-mapping for common disease. ### Competing Interest Statement The authors have declared no competing interest.
2 tweets bioinformatics
The regulation of mRNA controls both overall gene expression as well as the distribution of mRNA isoforms encoded by the gene. Current algorithmic approaches focus on characterization of significant differential expression or alternative splicing events or isoform distribution without integrating both events. Here, we present Hierarchical Bayesian Analysis of Differential Expression and ALternative SPlicing (HBA-DEALS), which simultaneously characterizes differential expression and splicing in cohorts. HBA-DEALS attains state of the art or better performance for both expression and splicing, and allows genes to be characterized as having differential gene expression (DGE), differential alternative splicing (DAST), both, or neither. Based on an analysis of Genotype-Tissue Expression (GTEx) data we demonstrate the existence of sets of genes that show predominant DGE or DAST across a comparison of 20 tissue types, and show that these sets have pervasive differences with respect to gene structure, function, membership in protein complexes, and promoter architecture.
2 tweets bioinformatics
Hi-C experiments have been widely adopted to study chromatin spatial organization, which plays an essential role in genome function. We have recently identified frequently interacting regions(FIREs) and found that they are closely associated with cell type-specific gene regulation. However, computational tools for detecting FIREs from Hi-C data are still lacking. In this work, we present FIREcaller, a stand-alone, user-friendly R package for detecting FIREs from Hi-C data. FIREcaller takes raw Hi-C contact matrices as input, performs within-sample and crosssample normalization, and outputs continuous FIRE scores, dichotomous FIREs, and super-FIREs. Applying FIREcaller to Hi-C data from various human tissues, we demonstrate that FIREs and superFIREs identified, in a tissue specific manner, are closely related to transcription regulation, are enriched in enhancer-promoter (E-P) interactions, tend to co-localize with regions exhibiting epigenomic signatures of regulatory roles, and aid the interpretation of prioritization of GWAS variants. The FIREcaller package is implemented in R and freely available at https://yunliweb.its.unc.edu/FIREcaller. ### Competing Interest Statement The authors have declared no competing interest.
2 tweets bioinformatics
Motivation: Integrative genomic analysis is a powerful tool to study the biological mechanisms underlying a complex disease or trait across multiplatform high-dimensional data, such as DNA methylation, copy number variation (CNV), and gene expression. It is common to perform large-scale genome-wide association analysis of an outcome for each data type separately and combine the results ad hoc, leading to loss of statistical power and uncontrolled overall false discovery rate (FDR). Results: We propose a multivariate mixture model framework (IMIX) that integrates multiple types of genomic data and allows examining and relaxing the commonly adopted conditional independence assumption. We investigate across-data-type FDR control in IMIX, and show the gain in lower misclassification rates at controlled overall FDR compared with established individual data type analysis strategies, such as Benjamini-Hochberg FDR control, the q-value, and the local FDR control by extensive simulations. IMIX features statistically-principled model selection, FDR control, and computational efficiency. Applications to the Cancer Genome Atlas (TCGA) data provide novel multi-omic insights into the luminal/basal subtyping of bladder cancer and the prognosis of pancreatic cancer. Software: We have implemented our method in R package "IMIX" with instructions and examples available at https://github.com/ziqiaow/IMIX. ### Competing Interest Statement The authors have declared no competing interest.
2 tweets evolutionary biology
Classical evolutionary theory maintains that mutation rate variation between genes should be random with respect to fitness and evolutionary optimization of genic mutation rates remains controversial. However, it has now become known that cytogenetic (DNA sequence + epigenomic) features influence local mutation probabilities, which is predicted by more recent theory to be a prerequisite for beneficial mutation rates between different classes of genes to readily evolve. To test this possibility, we used de novo mutations in Arabidopsis thaliana to create a high resolution predictive model of mutation rates as a function of cytogenetic features across the genome. As expected, mutation rates are significantly predicted by features such as GC content, histone modifications, and chromatin accessibility. Deeper analyses of predicted mutation rates reveal effects of introns and untranslated exon regions in distancing coding sequences from mutational hotspots at the start and end of transcribed regions in A. thaliana . Finally, predicted coding region mutation rates are significantly lower in genes where mutations are more likely to be deleterious, supported by numerous estimates of evolutionary and functional constraint. These findings contradict neutral expectations that mutation probabilities are independent of fitness consequences. Instead they are consistent with the evolution of lower mutation rates in functionally constrained loci due to cytogenetic features, with important implications for evolutionary biology. ### Competing Interest Statement The authors have declared no competing interest.
2 tweets immunology
Modern immunologic research increasingly requires high-dimensional analyses in order to understand the complex milieu of cell-types that comprise the tissue microenvironments of disease. To achieve this, we developed Infinity Flow combining hundreds of overlapping flow cytometry panels using machine learning to enable the simultaneous analysis of the co-expression patterns of 100s of surface-expressed proteins across millions of individual cells. In this study, we demonstrate that this approach allows the comprehensive analysis of the cellular constituency of the steady-state murine lung and to identify novel cellular heterogeneity in the lungs of melanoma metastasis bearing mice. We show that by using supervised machine learning, Infinity Flow enhances the accuracy and depth of clustering or dimensionality reduction algorithms. Infinity Flow is a highly scalable, low-cost and accessible solution to single cell proteomics in complex tissues. ### Competing Interest Statement E.W.N. is a co-founder, advisor and shareholder of ImmunoScape Pte. Ltd. and an advisor for Neogene Therapeutics and Nanostring Technologies. R.G. declares ownership in CellSpace Biosciences.
2 tweets neuroscience
The hippocampus is a medial temporal lobe brain structure that contains circuitry and neural representations capable of supporting declarative memory. Hippocampal place cells fire in one or few restricted spatial locations in a given environment. Between environmental contexts, place cell firing fields remap (turning on/off or moving to a new spatial location), providing a unique population-wide neural code for context specificity. However, the manner by which features associated with a given context combine to drive place cell remapping remains a matter of debate. Here we show that remapping of neural representations in region CA1 of the hippocampus is strongly driven by prior beliefs about the frequency of certain contexts, and that remapping is equivalent to an optimal estimate of the identity of the current context under that prior. This prior-driven remapping is learned early in training and remains robust to changes in behavioral task-demands. Furthermore, a simple associative learning mechanism is sufficient to reproduce these results. Our findings demonstrate that place cell remapping is a generalization of representing an animal's location. Rather than simply representing location in physical space, the hippocampus represents an optimal estimate of location in a multi-dimensional stimulus space.
2 tweets neuroscience
From hand tools to cyborgs, humans have long been fascinated by the opportunities afforded by augmenting ourselves. Here, we studied how motor augmentation with an extra robotic thumb (the Third Thumb) impacts the biological hand representation in the brains of able-bodied people. Participants were tested on a variety of behavioural and neuroimaging tests designed to interrogate the augmented hand's representation before and after 5-days of semi-intensive training. Training improved the Thumb's motor control, dexterity and hand-robot coordination, even when cognitive load was increased or when vision was occluded, and resulted in increased sense of embodiment over the robotic Thumb. Thumb usage also weakened natural kinematic hand synergies. Importantly, brain decoding of the augmented hand's motor representation demonstrated mild collapsing of the canonical hand structure following training, suggesting that motor augmentation may disrupt the biological hand representation. Together, our findings unveil critical neurocognitive considerations for designing human body augmentation. ### Competing Interest Statement The authors have declared no competing interest.
2 tweets microbiology
Ho Sing Lo, Kenrie Pui Yan Hui, Hei-Ming Lai, Khadija Shahed Khan, Simranjeet Kaur, Junzhe Huang, Zhongqi Li, Anthony K. N. Chan, Hayley Hei-Yin Cheung, Ka-Chun Ng, John Chi Wang Ho, Yu Wai Chen, Bowen Ma, Peter Man-Hin Cheung, Donghyuk Shin, Kaidao Wang, Meng-Hsuan Lee, Barbara Selisko, Cecilia Eydoux, Jean-Claude Guillemot, Bruno Canard, Kuen-Phon Wu, Po-Huang Liang, Ivan Dikic, Zhong Zuo, Francis K. L. Chan, David S. C. Hui, Vincent C. T. Mok, Kam-Bo Wong, Ho Ko, Wei Shen Aik, Michael Chi Wai Chan, Wai-Lung Ng
The outbreak of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a global threat to human health. Using a multidisciplinary approach, we identified and validated the hepatitis C virus (HCV) protease inhibitor simeprevir as an especially promising repurposable drug for treating COVID-19. Simeprevir potently reduces SARS-CoV-2 viral load by multiple orders of magnitude and synergizes with remdesivir in vitro . Mechanistically, we showed that simeprevir inhibits the main protease (Mpro) and unexpectedly the RNA-dependent RNA polymerase (RdRp). Our results thus reveal the viral protein targets of simeprevir, and provide preclinical rationale for the combination of simeprevir and remdesivir for the pharmacological management of COVID-19 patients. One Sentence Summary Discovery of simeprevir as a potent suppressor of SARS-CoV-2 viral replication that synergizes with remdesivir. ### Competing Interest Statement CUHK and HKU have filed a US provisional patent application based on the finding of this manuscript. W.L.N., M.C.W.C., K.P.Y.H., H.S.L., K.S.K., H.K. and H.M.L. are inventors of the patent. F.K.L.C. has served as a consultant to Eisai, Pfizer, Takeda and Otsuka, and has been paid lecture fees by Eisai, Pfizer, AstraZeneca and Takeda.
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