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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2020

The evolution of human SIRT1 for multi-specificity is enabled by active-site adaptation

Many enzymes that catalyze protein post-translational modifications (PTM) can specifically modify multiple target proteins. However, little is known regarding the molecular basis and evolution of multi-specificity in these enzymes. Here, we used bioinformatics and experimental approaches to investigate the molecular basis and evolution of multi-specificity in the sirtuin-1 (SIRT1) deacetylase. Guided by bioinformatics analysis of SIRT1 orthologues and substrates, we identified and examined key mutations that have occurred during the evolution of human SIRT1. We found that while these mutants maintain high activity toward conserved histone substrates they exhibit a dramatic loss of activity toward acetylated p53 protein that appeared only in Metazoa. These results demonstrate that active site substitutions in SIRT1 are essential to enable the inclusion of p53 substrate. Our results are consistent with a model in which promiscuous ancestral activity can provide an evolutionary starting point from which multispecificity toward a number of cellular substrates can develop.

biochemistry↗
bioRxiv2020

Quantitative single cell heterogeneity profiling of patient derived tumor initiating gliomaspheres reveals unique signatures of malignancy and response

BackgroundGlioblastoma is a deadly brain tumor with median patient survival of 14.6 months. At the core of this malignancy are rare, highly heterogenous malignant stem-like tumor initiating cells. Aberrant signaling across the EGFR-PTEN-AKT-mTOR signal transduction pathways are common oncogenic drivers in these cells. Though gene-level clustering has determined the importance of the EGFR signaling pathway as a treatment indicator, multiparameter protein-level analyses are necessary to discern functional attributes of signal propagation. Multiparameter single cell analyses is emerging as particularly useful in identifying such attributes. MethodsSingle cell targeted proteomic analysis of EGFR-PTEN-AKT-mTOR proteins profiled heterogeneity in a panel of fifteen patient derived gliomaspheres. A microfluidic cell array chip tool served as a low cost methodology to derive high quality quantitative single cell analytical outputs. Chip design specifications produced extremely high signal-to-noise ratios and brought experimental efficiencies of cell control and minimal cell use to accommodate experimentation with these rare and often slow-growing cell populations. Quantitative imaging software generated datasets to observe similarities and differences within and between cells and patients. Bioinformatic self-organizing maps (SOMs) and hierarchical clustering stratified patients into malignancy and responder groups which were validated by phenotypic and statistical analyses. ResultsFifteen patient dissociated gliomaspheres produced 59,464 data points from 14,866 cells. Forty-nine molecularly defined signaling phenotypes were identified across samples. Bioinformatics resolved two clusters diverging on EGFR expression (p = 0.0003) and AKT/TORC1 activation (p = 0.08 and p = 0.09 respectively). TCGA status of a subset showed genetic heterogeneity with proneural, classical and mesenchymal subtypes represented in both clusters. Phenotypic validation measures indicated drug responsive phenotypes to EGFR blocking were found in the EGFR expressing cluster. EGFR expression in the subset of drug-treated lines was statistically significant (p<.05). The EGFR expressing cluster was of lower tumor initiating potential in comparison to the AKT/TORC1 activated cluster. Though not statistically significant, EGFR expression trended with improved patient prognosis while AKT/TORC1 activated samples trended with poorer outcomes. ConclusionsQuantitative single cell heterogeneity profiling resolves signaling diversity into meaningful non-obvious phenotypic groups suggesting EGFR is decoupled from AKT/TORC1 signalling while identifying potentially valuable targets for personalized therapeutic approaches for deadly tumor-initiating cell populations.

cancer biology↗
bioRxiv2020

KALRN Mutations Promote Anti-tumor Immunity and Immunotherapy Response in Cancer

BackgroundKALRN (kalirin RhoGEF kinase) is mutated in a wide range of cancers. Nevertheless, the association between KALRN mutations and the pathogenesis of cancer remains unexplored. The identification of biomarkers for cancer immunotherapy response is important considering that immunotherapies show beneficial effects only in a subset of cancer patients. MethodsWe explored the correlation between KALRN mutations and anti-tumor immunity in 10 cancer cohorts from The Cancer Genome Atlas (TCGA) program by the bioinformatics approach. Moreover, we verified the findings from bioinformatics analysis by in vitro experiments. Furthermore, we explored the correlation between KALRN mutations and immunotherapy response in four cancer cohorts receiving immune checkpoint blockade therapy. ResultsWe found that anti-tumor immune signatures were stronger in KALRN-mutated than in KALRN-wildtype cancers. Moreover, KALRN mutations correlated with increased tumor mutation burden and the microsatellite instability or DNA damage repair deficiency genomic properties which may explain the elevated anti-tumor immunity in KALRN-mutated cancers. Furthermore, we found that PD-L1 expression was significantly upregulated in KALRN-mutated versus KALRN-wildtype cancers. The enhanced anti-tumor immune signatures and PD-L1 expression in KALRN-mutated cancers may favor the response to immune checkpoint blockade therapy in this cancer subtype, as evidenced in four cancer cohorts receiving anti-PD-1/PD-L1/CTLA-4 immunotherapy. We further revealed that the significant association between KALRN mutations and increased anti-tumor immunity was attributed to that KALRN mutations compromised the function of KALRN target Rho GTPases on regulating DNA damage repair pathways. ConclusionsThe KALRN mutation is a useful biomarker for predicting the response to immunotherapy in cancer patients.

immunology↗
bioRxiv2020

Wheat pathogen Zymoseptoria tritici N-myristoyltransferase inhibitors: on-target antifungal activity and an unusual metabolic defense mechanism

Zymoseptoria tritici is the causative agent of Septoria tritici blotch (STB), which costs billions of dollars annually to major wheat-producing countries in terms of both fungicide use and crop loss. Agricultural pathogenic fungi have acquired resistance to most commercially available fungicide classes, and the rate of discovery and development of new fungicides has stalled, demanding new approaches and insights. Here we investigate a potential mechanism of targeting an important wheat pathogen Z. tritici via inhibition of N-myristoyltransferase (NMT). We characterize Z. tritici NMT biochemically for the first time, profile the in vivo Z. tritici myristoylated proteome and identify and validate the first Z. tritici NMT inhibitors. Proteomic investigation of the downstream effects of NMT inhibition identified an unusual and novel mechanism of defense against chemical toxicity in Z. tritici through the application of comparative bioinformatics to deconvolute function from the previously largely unannotated Z. tritici proteome. Research into novel fungicidal modes-of-action is essential to satisfy an urgent unmet need for novel fungicide targets, and we anticipate that this study will serve as a useful proteomics and bioinformatics resource for researchers studying Z. tritici.

cell biology↗
bioRxiv2020

Generating realistic null hypothesis of cancer mutational landscapes using SigProfilerSimulator

Performing a statistical test requires a null hypothesis. In cancer genomics, a key challenge is the fast generation of accurate somatic mutational landscapes that can be used as a realistic null hypothesis for making biological discoveries. Here we present SigProfilerSimulator, a powerful tool that is capable of simulating the mutational landscapes of thousands of cancer genomes at different resolutions within seconds. Applying SigProfilerSimulator to 2,144 whole-genome sequenced cancers reveals: (i) that most doublet base substitutions are not due to two adjacent single base substitutions but likely occur as single genomic events; (ii) that an extended sequencing context of +/-2bp is required to more completely capture the patterns of substitution mutational signatures in human cancer; (iii) information on false-positive discovery rate of commonly used bioinformatics tools for detecting driver genes. SigProfilerSimulators breadth of features allows one to construct a tailored null hypothesis and use it for evaluating the accuracy of other bioinformatics tools or for downstream statistical analysis for biological discoveries.

genomics↗
bioRxiv2020

New pathogenic variants and insights into pathogenic mechanisms in GRK1-related Oguchi disease.

PurposeBiallelic mutations in G-Protein coupled receptor kinase 1 (GRK1) cause Oguchi disease, a rare subtype of congenital stationary night blindness (CSNB). The purpose of this study was to identify pathogenic GRK1 variants and use in-depth bioinformatic analyses to evaluate how their impact on protein structure could lead to pathogenicity. MethodsPatients genomic DNA was sequenced by whole genome, whole exome or focused exome sequencing. Pathogenic variants, published and novel, were compared to nondisease associated missense variants. The impact of GRK1 missense variants at the protein level were then predicted using a series of computational tools. ResultsWe identified eleven previously unpublished cases with biallelic pathogenic GRK1 variants, including seven novel variants, and reviewed all GRK1 pathogenic variants. Further structure-based scoring revealed a hotspot for missense variants in the kinase domain. Additionally, to aid future clinical interpretation, we identified the bioinformatics tools best able to differentiate pathogenic from non-pathogenic variants. ConclusionWe identified new GRK1 pathogenic variants in Oguchi disease patients and investigated how disease-causing variants may impede protein function, giving new insights into the mechanisms of pathogenicity. All pathogenic GRK1 variants described to date have been collated into a Leiden Open Variation Database (http://dna2.leeds.ac.uk/GRK1_LOVD/genes/GRK1).

genetics↗
bioRxiv2020

Sensitive, highly multiplexed sequencing of microhaplotypes from the Plasmodium falciparum heterozygome

BackgroundTargeted next generation sequencing offers the potential for consistent, deep coverage of information rich genomic regions to characterize polyclonal Plasmodium falciparum infections. However, methods to identify and sequence these genomic regions are currently limited. MethodsA bioinformatic pipeline and multiplex methods were developed to identify and simultaneously sequence 100 targets and applied to dried blood spot (DBS) controls and field isolates from Mozambique. For comparison, WGS data were generated for the same controls. ResultsUsing publicly available genomes, 4465 high diversity genomic regions suited for targeted sequencing were identified, representing the P. falciparum heterozygome. For this study, 93 microhaplotypes with high diversity (median HE = 0.7) were selected along with 7 drug resistance loci. The sequencing method achieved very high coverage (median 99%), specificity (99.8%) and sensitivity (90% for haplotypes with 5% within sample frequency in DBS with 100 parasites/{micro}L). In silico analyses revealed that microhaplotypes provided much higher resolution to discriminate related from unrelated polyclonal infections than biallelic SNP barcodes. DiscussionThe bioinformatic and laboratory methods outlined here provide a flexible tool for efficient, low-cost, high throughput interrogation of the P. falciparum genome, and can be tailored to simultaneously address multiple questions of interest in various epidemiological settings.

molecular biology↗
bioRxiv2020

Rapid metagenomic characterization of a case of imported COVID-19 in Cambodia

Rapid production and publication of pathogen genome sequences during emerging disease outbreaks provide crucial public health information. In resource-limited settings, especially near an outbreak epicenter, conventional deep sequencing or bioinformatics are often challenging. Here we successfully used metagenomic next generation sequencing on an iSeq100 Illumina platform paired with an open-source bioinformatics pipeline to quickly characterize Cambodias first case of COVID-2019.

microbiology↗
bioRxiv2020

A Comprehensive Multi-Center Cross-platform Benchmarking Study of Single-cell RNA Sequencing Using Reference Samples

Single-cell RNA sequencing (scRNA-seq) has become a very powerful technology for biomedical research and is becoming much more affordable as methods continue to evolve, but it is unknown how reproducible different platforms are using different bioinformatics pipelines, particularly the recently developed scRNA-seq batch correction algorithms. We carried out a comprehensive multi-center cross-platform comparison on different scRNA-seq platforms using standard reference samples. We compared six pre-processing pipelines, seven bioinformatics normalization procedures, and seven batch effect correction methods including CCA, MNN, Scanorama, BBKNN, Harmony, limma and ComBat to evaluate the performance and reproducibility of 20 scRNA-seq data sets derived from four different platforms and centers. We benchmarked scRNA-seq performance across different platforms and testing sites using global gene expression profiles as well as some cell-type specific marker genes. We showed that there were large batch effects; and the reproducibility of scRNA-seq across platforms was dictated both by the expression level of genes selected and the batch correction methods used. We found that CCA, MNN, and BBKNN all corrected the batch variations fairly well for the scRNA-seq data derived from biologically similar samples across platforms/sites. However, for the scRNA-seq data derived from or consisting of biologically distinct samples, limma and ComBat failed to correct batch effects, whereas CCA over-corrected the batch effect and misclassified the cell types and samples. In contrast, MNN, Harmony and BBKNN separated biologically different samples/cell types into correspondingly distinct dimensional subspaces; however, consistent with this algorithms logic, MNN required that the samples evaluated each contain a shared portion of highly similar cells. In summary, we found a great cross-platform consistency in separating two distinct samples when an appropriate batch correction method was used. We hope this large cross-platform/site scRNA-seq data set will provide a valuable resource, and that our findings will offer useful advice for the single-cell sequencing community.

genomics↗
bioRxiv2020

Computational Recipe for Designing Antibodies against the Ebola Virus

A conceptual basis for antiviral therapy is to deliver a synthetic antibody that binds to a viral surface protein, and thus prevents the virus from deploying its cell-entry mechanism. The fast and untraceable virus mutations take lives of thousands of people before the immune system can produce the inhibitory antibody. In this paper, we devised a computational recipe to predict both the viral escape mutations and the possible inhibitory synthetic antibodies. We combined bioinformatics, structural biology, and molecular dynamics (MD) simulations to explore the most likely viral mutations and the candidate antibodies that can inhibit those escape mutations. Specifically, using the crystal structures of the Sudan and Zaire Ebola viral GPs in complex to their respective antibodies (ABs), we have performed an extensive set of MD simulations, both on the wild-type structures and on a large array of additional complexes designed and generated through combinatorial mutations. We discovered that our methods enabled the successful redesign of antibody sequences to essentially all likely glycoprotein mutations. Our findings and the computational methodology developed here for general antibody design can facilitate therapy of current and possibly next generations of viruses. Significance of the ManuscriptThis manuscript has high significance both methodologically and in potential biomedical application. In methodology, the manuscript combines molecular dynamics, Monte Carlo calculations, and bioinformatics in a novel way to simulate the evolutionary arms race between an evolving viral coat protein and a counter-evolving antibody against the virus. This simulation is shown to provide a method for designing a synthetic antibody against the newly emerging viral strains. This work is done in the context of ongoing work in other laboratories in which cells can be induced to produce synthetic antibodies and those synthetic antibodies can be edited (via, for example, CRISPR) to have an arbitrary sequence in the region that binds the viral coat protein. Putting those experimental methods together with the computational methods we present in this paper has the potential to provide a important approach to produce antibodies-on-demand against evolving viruses.

immunology↗
bioRxiv2020

Significant expression of FURIN and ACE2 on oral epithelial cells may facilitate the efficiency of 2019-nCov entry

Background Leading to a sustained epidemic spread with >40,000 confirmed human infections, including >10,000 deaths, COVID-19 was caused by 2019-nCov and resulted in acute respiratory distress syndrome (ARDS) and sepsis, which brought more challenges to the patient’s treatment. The S-glycoprotein, which recognized as the key factor for the entry of 2019-nCov into the cell, contains two functional domains: an ACE2 receptor binding domain and a second domain necessary for fusion of the coronavirus and cell membranes. FURIN activity, exposes the binding and fusion domains, is essential for the zoonotic transmission of 2019-nCov. Moreover, it has been reported that ACE2 is likely to be the receptor for 2019-nCoV. In addition, FURIN enzyme and ACE2 receptor were expressed in airway epithelia, cardiac tissue, and enteric canals, which considered as the potential target organ of the virus. However, report about the expression of FURIN and ACE2 in oral tissues was limited.Methods In order to investigate the potential infective channel of new coronavirus in oral cavity, we analyze the expression of ACE2 and FURIN that mediate the new coronavirus entry into host cells in oral mucosa using the public single-cell sequence datasets. Furthermore, immunohistochemical staining experiment was performed to confirm the expression of ACE2 and FURIN in the protein level.Results The bioinformatics results indicated the differential expression of ACE2 and FURIN on epithelial cells of different oral mucosal tissues and the proportion of FURIN-positive cells was obviously higher than that of ACE2-positive cells. IHC experiments revealed that both the ACE2-positive and FURIN-positive cells in the target tissues were mainly positioned in the epithelial layers, partly expressed in fibroblasts, which further confirm the bioinformatics results.Conclusions Based on these findings, we speculated that 2019-nCov could effectively invade oral mucosal cells though two possible routes: binding to the ACE2 receptor and fusion with cell membrane activated by FURIN protease. Our results indicated that oral mucosa tissues are susceptible to 2019-nCov, which provides valuable information for virus-prevention strategy in clinical care as well as daily life.Competing Interest StatementThe authors have declared no competing interest.View Full Text

pathology↗
bioRxiv2020

LncRNA s Landscape in the patients of primary gout by Microarray Analysis

To determine the differential profiles of long non-coding RNAs (lncRNAs) in Peripheral blood mononuclear cells (PBMCs) among acute gout (AG) patients, intercritical gout (IG) patients and healthy control subjects, and to explore the specific biomarkers for acute gout diagnosis and treatment in future. Human lncRNA microarrays were used to identify the differentially expressed lncRNAs and mRNAs in primary AG (n=3), IG (n=3) and healthy subjects (n=3). For comparison Bioinformatics analyses were performed to predict the roles of abberrantly expressed lncRNAs and mRNAs. Quantitative real-time polymerase chain reaction (qRT-PCR) was applied to validate the results in 32 AG, 32 IG patients and 32 healthy control subjects. A receiver operating characteristic (ROC) curve was constructed to evaluate the diagnostic value of the lncRNAs identified in gout. The microarray analysis identified 3421 and 1739 differentially expressed lncRNAs, 2240 and 1027 differentially expressed mRNAs in AG and IG (fold change>1.5, P<0.05; respectively), respectively. qRT-PCR results of 9 dysregulated genes were consistent with the microarray data. The bioinformatic analysis indicated that the differentially expressed lncRNAs regulated the abnormally expressed mRNAs, which were involved in the pathogenesis of gout through several different pathways. The expression levels of TCONS_00004393 and ENST00000566457 were significantly increased in the AG group than those in the IG group or healthy subjects (P<0.01, respectively). Moreover, the areas under the ROC curve were 0.955 and 0.961 for TCONS_00004393 and ENST00000566457,respectively. Our results provide novel insight into the mechanisms of the primary gout, and reveal that TCONS_00004393 and ENST00000566457 might be as candidate diagnostic biomarkers and targets for the treatment of acute gout.

genetics↗
bioRxiv2020

Optimal protocols for sequence-based characterization of the human vaginal microbiome

The vaginal microbiome has been connected to a wide range of health outcomes. This has led to a thriving research environment, but also to the use of conflicting methodologies to study its microbial composition. Here we systematically assess best practices for the sequencing-based characterization of the human vaginal microbiome. As far as 16S rRNA gene sequencing is concerned, the V1-V3 region has the best theoretical properties, but limitations of current sequencing technologies mean that the V3-V4 region performs equally well. Both of these approaches present very good agreement with qPCR quantification of key taxa, provided an appropriate bioinformatic pipeline is used. Shotgun metagenomic sequencing presents an interesting alternative to 16S amplification and sequencing, but it is not without its challenges. We have assessed different tools for the removal of host reads and the taxonomic annotation of metagenomic reads, including a new, easy-to-build and - use, reference database of vaginal taxa. This strategy performed as well as the best performing previously published strategies. Despite the many advantages of shotgun sequencing none of the shotgun approaches assessed here had as good agreement with the qPCR data as 16S rRNA gene sequencing. ImportanceThe vaginal microbiome has been connected to a wide range of health outcomes, from susceptibility to sexually transmitted infections to gynecological cancers and pregnancy outcomes. This has led to a thriving research environment, but also to conflicting available methodologies, including many studies that do not report their molecular biological and bioinformatic methods in sufficient detail for them to be considered reproducible. This can lead to conflicting messages and delay progress from descriptive to intervention studies. By systematically assessing best practices for the characterization of the human vaginal microbiome, this study will enable past studies to be assessed more critically and assist future studies in the selection of appropriate methods for their specific research questions.

molecular biology↗
bioRxiv2020

Efficiently processing amplicon sequencing data for microbial ecology with dadasnake, a DADA2 implementation in Snakemake

BackgroundAmplicon sequencing of phylogenetic marker genes, e.g. 16S, 18S or ITS rRNA sequences, is still the most commonly used method to determine the composition of microbial communities. Microbial ecologists often have expert knowledge on their biological question and data analysis in general, and most research institutes have computational infrastructures to employ the bioinformatics command line tools and workflows for amplicon sequencing analysis, but requirements of bioinformatics skills often limit the efficient and up-to-date use of computational resources. Resultsdadasnake wraps pre-processing of sequencing reads, delineation of exact sequence variants using the favorably benchmarked, widely-used the DADA2 algorithm, taxonomic classification and post-processing of the resultant tables, and hand-off in standard formats, into a user-friendly, one-command Snakemake pipeline. The suitability of the provided default configurations is demonstrated using mock-community data from bacteria and archaea, as well as fungi. ConclusionsBy use of Snakemake, dadasnake makes efficient use of high-performance computing infrastructures. Easy user configuration guarantees flexibility of all steps, including the processing of data from multiple sequencing platforms. dadasnake facilitates easy installation via conda environments. dadasnake is available at https://github.com/a-h-b/dadasnake.

microbiology↗
bioRxiv2020

Whole Genome Sequencing of SARS-CoV-2: Adapting Illumina Protocols for Quick and Accurate Outbreak Investigation During a Pandemic

The COVID-19 pandemic spread very fast around the world. A few days after the first detected case in South Africa, an infection started a large hospital outbreak in Durban, KwaZulu-Natal. Phylogenetic analysis of SARS-CoV-2 genomes can be used to trace the path of transmission within a hospital. It can also identify the source of the outbreak and provide lessons to improve infection prevention and control strategies. In this manuscript, we outline the obstacles we encountered in order to genotype SARS-CoV-2 in real-time during an urgent outbreak investigation. In this process, we encountered problems with the length of the original genotyping protocol, reagent stockout and sample degradation and storage. However, we managed to set up three different library preparation methods for sequencing in Illumina. We also managed to decrease the hands on library preparation time from twelve to three hours, which allowed us to complete the outbreak investigation in just a few weeks. We also fine-tuned a simple bioinformatics workflow for the assembly of high-quality genomes in real-time. In order to allow other laboratories to learn from our experience, we released all of the library preparation and bioinformatics protocols publicly and distributed them to other laboratories of the South African Network for Genomics Surveillance (SANGS) consortium.

genomics↗
bioRxiv2020

Identification of a lymphocyte minor histocompatibility antigen in Mauritian cynomolgus macaques

Allogeneic hematopoietic stem cell transplantation can lead to dramatic reductions in human immunodeficiency virus (HIV) reservoirs. This effect is mediated in part by donor T cells that recognize lymphocyte-expressed minor histocompatibility antigens (mHAgs). The potential to mark malignant and latently infected cells for destruction makes mHAgs attractive targets for cellular immunotherapies. However, testing such HIV reservoir reduction strategies will likely require preclinical studies in nonhuman primates (NHPs). In this study, we used a combination of alloimmunization, whole exome sequencing, and bioinformatics to identify a mHAg in Mauritian cynomolgus macaques (MCMs). We mapped the minimal optimal epitope to a 10-mer peptide (SW10) in apolipoprotein B mRNA editing enzyme catalytic polypeptide-like 3C (APOBEC3) and determined the major histocompatibility complex class I restriction element as Mafa-A1*063, which is expressed in almost 90% of MCMs. APOBEC3C SW10-specific CD8+ T cells recognized immortalized B cells but not fibroblasts from a mHAg positive MCM. These results collectively provide a framework for identifying mHAgs in a nontransplant setting and suggest that APOBEC3C SW10 could be used as a lymphocyte-restricted model antigen in NHPs to test various mHAg-targeted immunotherapies. ImportanceCellular immunotherapies developed to treat blood cancers may also be effective against latent HIV. Preclinical studies of such immunotherapies are hindered by a lack of known target antigens. We used a combination of alloimmunization, basic immune assays, whole exome sequencing, and bioinformatics to identify a lymphocyte-restricted minor histocompatibility antigen in a genetically related population of nonhuman primates. This minor histocompatibility antigen provides an actionable target for piloting cellular immunotherapies designed to reduce or eliminate latent reservoirs of HIV.

immunology↗
bioRxiv2020

Computational approaches identify novel transcription factor combinations that promote corticospinal axon growth following injury

Transcription factors (TFs) act as powerful levers to regulate neural physiology and can be targeted to improve cellular responses to injury or disease. Because TFs often depend on cooperative activity, a major challenge is to identify and deploy optimal sets. Here we developed a novel bioinformatics pipeline, centered on TF co-occupancy of regulatory DNA, and used it to predict factors that potentiate the effects of pro-regenerative Klf6. High content screens of neurite outgrowth identified cooperative activity by 12 candidates, and systematic testing in an animal model of corticospinal tract (CST) damage substantiated three novel instances of pairwise cooperation. Combined Klf6 and Nr5a2 drove the strongest growth, and transcriptional profiling of CST neurons identified Klf6/Nr5a2-responsive gene networks involved in macromolecule biosynthesis and DNA repair. These data identify novel TF combinations that promote enhanced CST growth, clarify the transcriptional correlates, and provide a bioinformatics roadmap to detect TF synergy.

neuroscience↗
bioRxiv2020

Strengthening The Organizing and Reporting of Microbiome Studies (STORMS)

BackgroundHuman microbiome research is a growing field with the potential for improving our understanding and treatment of diseases and other conditions. The field is interdisciplinary, making concise organization and reporting of results across different styles of epidemiology, biology, bioinformatics, translational medicine, and statistics a challenge. Commonly used reporting guidelines for observational or genetic epidemiology studies lack key features specific to microbiome studies. MethodsA multidisciplinary group of microbiome epidemiology researchers reviewed elements of available reporting guidelines for observational and genetic studies and adapted these for application to culture-independent human microbiome studies. New reporting elements were developed for laboratory, bioinformatic, and statistical analyses tailored to microbiome studies, and other parts of these checklists were streamlined to keep reporting manageable. ResultsSTORMS is a 17-item checklist for reporting on human microbiome studies, organized into six sections covering typical sections of a scientific publication, presented as a table with space for author-provided details and intended for inclusion in supplementary materials. ConclusionsSTORMS provides guidance for authors and standardization for interdisciplinary microbiome studies, facilitating complete and concise reporting and augments information extraction for downstream applications. AvailabilityThe STORMS checklist is available as a versioned spreadsheet from https://www.stormsmicrobiome.org/.

genomics↗