Biology subjects

Benner, C.

Publications and source records attributed to Benner, C..

36 records · Page 2Linked to original sources

bioRxiv2022

Type I IFN promotes pathogenic inflammatory monocyte maturation during H5N1 infection

Ly6Chi inflammatory monocytes show high IFN responses, and contribute to both protective and pathogenic functions following influenza virus infection. In order to understand the significance of IFN responses in this subset, we examined monocytes during infection with a lethal H5N1 virus that induces high levels of IFN and a low-pathogenicity H1N1 virus that induces low levels of IFN. We show that H5N1 infection results in early recruitment of high numbers of Ly6Chi monocytes and induction of chemokines and Ifnb1. Using unbiased transcriptomic and proteomic approaches, we also find that monocytes are significantly enriched during H5N1 infection and are associated with chemokine and IFN signatures in mice, and with severity of symptoms after influenza virus infection in humans. Recruited Ly6Chi monocytes subsequently become infected in the lung, produce IFN-{beta}, and mature into FasL+ monocyte-derived cells (FasL+MCs) expressing dendritic cell markers. Both Ccr2-/- and Faslgld mice are protected from lethal infection, indicating that monocytes contribute to pathogenesis. Global loss of type I and type III IFN signaling in Stat2-/- mice results in loss of monocyte recruitment, likely reflecting a requirement for IFN-dependent chemokine induction. Here we show that IFN is not directly required for monocyte recruitment on an IFN-sufficient background, but is required for maturation to FasL+MCs. Loss of IFN signaling skews to a Ly6Clo phenotype associated with tissue repair, suggesting that IFN signaling in monocytes is a critical determinant of influenza virus pathogenesis.

immunology↗

bioRxiv2021

Transcriptional regulatory features associated with Coccidioides immitis phase transition

Coccidioidomycosis (Valley Fever) is an emerging endemic fungal infection with a rising incidence and an expanding geographic range. It is caused by Coccidiodes, which are thermally dimorphic fungi that grow as mycelia in soil but transition in the lung to form pathogenic spherules. The regulatory mechanisms underlying this transition are not understood. Exploiting capped small (cs)RNA-seq, which identifies actively initiated stable and unstable transcripts and thereby detects acute changes in gene regulation with remarkable sensitivity, here we report the changes in architectural organization and key sequence features underlying phase transition of this highly pathogenic fungus. Spherule transition was accompanied by large-scale transcriptional reprogramming, functional changes in transcript isoforms, and a massive increase in promoter-distal transcription of ncRNAs. Analysis of spherule-activated regulatory elements revealed a motif predicted to recruit a WOPR family transcription factor, which are known regulators of virulence in other fungi. We identify CIMG_02671 as a C. immitis WOPR homologue and show that it activates transcription in a WOPR motif-dependent manner, suggesting it is an important regulator of pathogenic phase transition. Collectively, this also highlights csRNA-seq as a powerful means to identify transcriptional mechanisms that control pathogenesis.

immunology↗

bioRxiv2020

A Chinese hamster transcription start site atlas that enables targeted editing of CHO cells

Chinese hamster ovary (CHO) cells, with their human-compatible glycosylation and high protein titers, are the most widely used cells for producing biopharmaceuticals. Engineering gene expression in CHO is key to improving drug quality and affordability. However, engineering gene expression or activating silent genes requires accurate annotation of the underlying regulatory elements and transcription start sites (TSSs). Unfortunately, most TSSs in the Chinese hamster genome were computationally predicted and are frequently inaccurate. Here, we revised TSS annotations for 15,308 Chinese hamster genes and 4,478 non-coding RNAs based on experimental data from CHO-K1 cells and 10 hamster tissues. The experimental realignment and discovery of TSSs now expose previously hidden motifs, such as the TATA box. We further demonstrate, by targeting the glycosyltransferase gene Mgat3, how accurate annotations readily facilitate activating silent genes by CRISPRa to obtain more human-like glycosylation. Together, we envision our annotation and data will provide a rich resource for the CHO community, improve genome engineering efforts and aid comparative and evolutionary studies.

genomics↗

bioRxiv2020

Functional Landscape of SARS-CoV-2 Cellular Restriction

A deficient interferon response to SARS-CoV-2 infection has been implicated as a determinant of severe COVID-19. To identify the molecular effectors that govern interferon control of SARS-CoV-2 infection, we conducted a large-scale gain-of-function analysis that evaluated the impact of human interferon stimulated genes (ISGs) on viral replication. A limited subset of ISGs were found to control viral infection, including endosomal factors that inhibited viral entry, nucleic acid binding proteins that suppressed viral RNA synthesis, and a highly enriched cluster of ER and Golgi-resident ISGs that inhibited viral translation and egress. These included the type II integral membrane protein BST2/tetherin, which was found to impede viral release, and is targeted for immune evasion by SARS-CoV-2 Orf7a protein. Overall, these data define the molecular basis of early innate immune control of viral infection, which will facilitate the understanding of host determinants that impact disease severity and offer potential therapeutic strategies for COVID-19.

microbiology↗

bioRxiv2020

An Effector Index to Predict Causal Genes at GWAS Loci

Drug development and biological discovery require effective strategies to map existing genetic associations to causal genes. To approach this problem, we began by identifying a set of positive control genes for 12 common diseases and traits that cause a Mendelian form of the disease or are the target of a medicine used for disease treatment. We then identified a widely-available set of genomic features enriching GWAS-associated single nucleotide variants (SNVs) for these positive control genes. Using these features, we trained and validated the Effector Index (Ei), a causal gene mapping algorithm using the 12 common diseases and traits. The area under Eis receiver operator curve to identify positive control genes was 80% and area under the precision recall curve was 29%. Using an enlarged set of independently curated positive control genes for type 2 diabetes which included genes identified by large-scale exome sequencing, these areas increased to 85% and 61%, respectively. The best predictors were coding or transcript altering SNVs, distance to gene and open chromatin-based metrics. We provide the Ei algorithm for its widespread use and have created a web-portal to facilitate understanding of results. This work outlines a simple, understandable approach to prioritize genes at GWAS loci for functional follow-up and drug development. Author summaryIn order to derive biological insight, or develop drugs based on genome-wide association studies (GWAS) data, causal genes at associated loci need to be identified. GWAS usually identify large genome regions containing many genes, but seldomly identifies specific causal genes. We have developed an algorithm to predict which genes in a region of disease association are likely causal and have named this algorithm the Effector Index. The Effector Index was optimized on diseases that have known causal or drug target genes, and further validated to predict these types of genes in independent datasets. The Effector Index formalizes these predictive features into a tool that can be used by researchers, and results from the traits and diseases studied here are available via the Accelerating Medicine Partnership web-portal at http://hugeamp.org/effectorgenes.html.

bioinformatics↗

bioRxiv2020

Computationally efficient whole genome regression for quantitative and binary traits

Genome-wide association analysis of cohorts with thousands of phenotypes is computationally expensive, particularly when accounting for sample relatedness or population structure. Here we present a novel machine learning method called REGENIE for fitting a whole genome regression model that is orders of magnitude faster than alternatives, while maintaining statistical efficiency. The method naturally accommodates parallel analysis of multiple phenotypes, and only requires local segments of the genotype matrix to be loaded in memory, in contrast to existing alternatives which must load genomewide matrices into memory. This results in substantial savings in compute time and memory usage. The method is applicable to both quantitative and binary phenotypes, including rare variant analysis of binary traits with unbalanced case-control ratios where we introduce a fast, approximate Firth logistic regression test. The method is ideally suited to take advantage of distributed computing frameworks. We demonstrate the accuracy and computational benefits of this approach compared to several existing methods using quantitative and binary traits from the UK Biobank dataset with up to 407,746 individuals.

genetics↗

bioRxiv2020

A Large-scale Drug Repositioning Survey for SARS-CoV-2 Antivirals

The emergence of novel SARS coronavirus 2 (SARS-CoV-2) in 2019 has triggered an ongoing global pandemic of severe pneumonia-like disease designated as coronavirus disease 2019 (COVID-19). To date, more than 2.1 million confirmed cases and 139,500 deaths have been reported worldwide, and there are currently no medical countermeasures available to prevent or treat the disease. As the development of a vaccine could require at least 12-18 months, and the typical timeline from hit finding to drug registration of an antiviral is >10 years, repositioning of known drugs can significantly accelerate the development and deployment of therapies for COVID-19. To identify therapeutics that can be repurposed as SARS-CoV-2 antivirals, we profiled a library of known drugs encompassing approximately 12,000 clinical-stage or FDA-approved small molecules. Here, we report the identification of 30 known drugs that inhibit viral replication. Of these, six were characterized for cellular dose-activity relationships, and showed effective concentrations likely to be commensurate with therapeutic doses in patients. These include the PIKfyve kinase inhibitor Apilimod, cysteine protease inhibitors MDL-28170, Z LVG CHN2, VBY-825, and ONO 5334, and the CCR1 antagonist MLN-3897. Since many of these molecules have advanced into the clinic, the known pharmacological and human safety profiles of these compounds will accelerate their preclinical and clinical evaluation for COVID-19 treatment.

microbiology↗

bioRxiv2020

MAGGIE: leveraging genetic variation to identify DNA sequence motifs mediating transcription factor binding and function

Genetic variation in regulatory elements can alter transcription factor (TF) binding by mutating a TF binding motif, which in turn may affect the activity of the regulatory elements. However, it is unclear which TFs are prone to be affected by a given variant. Current motif analysis tools either prioritize TFs based on motif enrichment without linking to a function or are limited in their applications due to the assumption of linearity between motifs and their functional effects. Here, we present MAGGIE, a novel method for identifying motifs mediating TF binding and function. By leveraging measurements from diverse genotypes, MAGGIE uses a statistical approach to link mutation of a motif to changes of an epigenomic feature without assuming a linear relationship. We benchmark MAGGIE across various applications using both simulated and biological datasets and demonstrate its improvement in sensitivity and specificity compared to the state-of-the-art motif analysis approaches. We use MAGGIE to reveal insights into the divergent functions of distinct NF-{kappa}B factors in the pro-inflammatory macrophages, showing its promise in discovering novel functions of TFs. The Python package for MAGGIE is freely available at https://github.com/zeyang-shen/maggie.

bioinformatics↗

bioRxiv2019

An expanded analysis framework for multivariate GWAS connects inflammatory biomarkers to functional variants and disease

Multivariate methods are known to increase the statistical power of association detection, but they have lacked essential follow-up analysis tools necessary for understanding the biology underlying these associations. We developed a novel computational workflow for multivariate GWAS follow-up analyses, including fine-mapping and identification of the subset of traits driving associations (driver traits). Many follow-up tools require univariate regression coefficients which are lacking from multivariate results. Our method overcomes this problem by using Canonical Correlation Analysis to turn each multivariate association into its optimal univariate Linear Combination Phenotype (LCP). This enables an LCP-GWAS, which in turn generates the statistics required for follow-up analyses. We implemented our method on 12 highly correlated inflammatory biomarkers in a Finnish population-based study. Altogether, we identified 11 associations, four of which (F5, ABO, C1orf140 and PDGFRB) were not detected by biomarker-specific analyses. Fine-mapping identified 19 signals within the 11 loci and driver trait analysis determined the traits contributing to the associations. A phenome-wide association study on the 19 putative causal variants from the signals in 176,899 individuals from the FinnGen study revealed 53 disease associations (p < 1x10-4). Several reported pQTLs in the 11 loci provided orthogonal evidence for the biologically relevant functions of the putative causal variants. Our novel multivariate analysis workflow provides a powerful addition to standard univariate GWAS analyses by enabling multivariate GWAS follow-up and thus promoting the advancement of powerful multivariate methods in genomics.

genetics↗

bioRxiv2019

Functionally-informed fine-mapping and polygenic localization of complex trait heritability

Fine-mapping aims to identify causal variants impacting complex traits. Several recent methods improve fine-mapping accuracy by prioritizing variants in enriched functional annotations. However, these methods can only use information at genome-wide significant loci (or a small number of functional annotations), severely limiting the benefit of functional data. We propose PolyFun, a computationally scalable framework to improve fine-mapping accuracy using genome-wide functional data for a broad set of coding, conserved, regulatory and LD-related annotations. PolyFun prioritizes variants in enriched functional annotations by specifying prior causal probabilities for fine-mapping methods such as SuSiE or FINEMAP, employing special procedures to ensure robustness to model misspecification and winners curse. In simulations with in-sample LD, PolyFun + SuSiE and PolyFun + FINEMAP were well-calibrated and identified >20% more variants with posterior causal probability >0.95 than their non-functionally informed counterparts (and >33% more fine-mapped variants than previous functionally-informed fine-mapping methods). In simulations with mismatched reference LD, PolyFun + SuSiE remained well-calibrated when reducing the maximum number of assumed causal SNPs per locus, which reduces absolute power but still produces large relative improvements. In analyses of 49 UK Biobank traits (average N=318K) with in-sample LD, PolyFun + SuSiE identified 3,025 fine-mapped variant-trait pairs with posterior causal probability >0.95, a >32% improvement vs. SuSiE; 223 variants were fine-mapped for multiple genetically uncorrelated traits, indicating pervasive pleiotropy. We used posterior mean per-SNP heritabilities from PolyFun + SuSiE to perform polygenic localization, constructing minimal sets of common SNPs causally explaining 50% of common SNP heritability; these sets ranged in size from 28 (hair color) to 3,400 (height) to 2 million (number of children). In conclusion, PolyFun prioritizes variants for functional follow-up and provides insights into complex trait architectures.

genetics↗

bioRxiv2019

Polygenic Hyperlipidemias and Coronary Artery Disease Risk

BackgroundHyperlipidemia is a highly heritable risk factor for coronary artery disease (CAD). Monogenic familial hypercholesterolemia associates with higher increase in CAD risk than expected from a single LDL-C measurement, likely due to lifelong cumulative exposure to high LDL-C. It remains unclear to what extent a high polygenic load of LDL-C or TG-increasing variants associates with increased CAD risk.\n\nMethods and ResultsWe derived polygenic risk scores (PRS) with [~]6M variants for LDL-C and TG with weights from a UK biobank-based genome-wide association study with [~]500K samples. We evaluated the impact of polygenic hypercholesterolemia and hypertriglyceridemia to lipid levels in 27 039 individuals from the FINRISK cohort, and to CAD risk in 135 300 individuals (13 695 CAD cases) from the FinnGen project.\n\nIn FINRISK, LDL-C ranged from 2.83 (95% CI 2.79-2.89) to 3.80 (3.72-3.88) and TG from 0.99 (0.95-1.01) to 1.52 (1.48-1.58) mmol/l between the lowest and highest 5% of the respective PRS distributions. The corresponding CAD prevalences ranged from 8.2% to 12.7% for the LDL-C PRS and from 8.2% to 12.1% for the TG PRS in FinnGen. Furthermore, CAD risk was 1.36-fold higher (OR, 95% CI 1.24-1.49) for the LDL-C PRS and 1.31-fold higher (1.20-1.44) for the TG PRS for those with the PRS >95th percentile vs those without. These estimates were only slightly attenuated when adjusting for a CAD PRS (OR 1.26 [95% CI 1.15-1.39] for LDL-C and 1.21 [1.10-1.32] for TG PRS).\n\nConclusionsThe CAD risk associated with a high polygenic load for lipid-increasing variants was proportional to their impact on lipid levels and mostly independent of a CAD PRS. In contrast with a PRS for CAD, the lipid PRSs point to known and directly modifiable risk factors providing more direct guidance for clinical translation.

genomics↗

bioRxiv2019

LSD1-mediated enhancer silencing enables endocrine cell development through attenuation of retinoic acid signalling.

Developmental progression depends on temporally defined changes in gene expression mediated by transient exposure of lineage intermediates to signals in the progenitor niche. To identify a possible contribution of cell-intrinsic epigenetic mechanisms to signal-induced transcriptional responses, we manipulated the signalling environment and activity of the histone demethylase LSD1 during stepwise differentiation of gut tube intermediates into pancreatic endocrine cells. Analysis of enhancer and transcriptome landscapes revealed that lineage progression and endocrine cell differentiation requires LSD1-mediated silencing of transiently active retinoic acid (RA)-induced enhancers. In the absence of LSD1, early RA-responsive enhancers remain partially active despite RA removal, resulting in perduring expression of RA-induced genes, and failure to progress in development. Our findings identify LSD1-mediated enhancer silencing as a cell-intrinsic epigenetic feedback mechanism by which the duration of the transcriptional response to a developmental signal is limited. Given LSD1s requirement in numerous developmental contexts, the here-described mechanism would be broadly relevant.

developmental biology↗

bioRxiv2019

Discovery Of UFO Proteins: Human-Virus Chimeric Proteins Generated During Influenza Virus Infection

Influenza A virus (IAV) is a threat to mankind because it generates yearly epidemics and poorly predictable sporadic pandemics with catastrophic potential. IAV has a small RNA genome composed of 8 mini-chromosomes (segments) that constitute a 5UTR followed by a coding region and a 3UTR. Transcription of IAV RNA into mRNA depends on host mRNA, as the viral polymerase cleaves 5m7G-capped nascent transcripts to use as primers to initiate viral mRNA synthesis. We hypothesized that captured host transcripts bearing AUG could drive the expression of upstream ORFs in the viral segments, a phenomenon that would depend on the translatability of the viral 5UTRs. Here we report the existence of this mechanism, which generates host-virus chimeric proteins. We label these proteins as Upstream Flu ORFs (UFO). Depending on the frame, two types of host-virus UFO proteins are made: canonical viral proteins with human-derived N term extensions or novel uncharacterized proteins. Here we show that both types are made during IAV infection. Sequences that enable chimeric protein synthesis are conserved across IAV strains, indicating that selection allowed the expansion of the proteome diversity of IAV in infected cells to include multiple human-virus proteins.

microbiology↗

bioRxiv2019

Genomic decoding of neuronal depolarization by stimulus-specific NPAS4 heterodimers

WITHDRAWAL STATEMENTThis manuscript has been withdrawn after a formal request from The Office of Research Integrity of the U.S. Department of Health & Human Services, with the support of the corresponding author, Brenda Bloodgood. More information can be found at https://ori.hhs.gov/content/case-summary-brigidi-gian-stefano.

neuroscience↗

bioRxiv2018

Diverse motif ensembles specify non-redundant DNA binding activities of AP-1 family members in macrophages

Mechanisms by which members of the AP-1 family of transcription factors play both redundant and non-redundant biological roles despite recognizing the same DNA sequence remain poorly understood. To address this question, we investigated the molecular functions and genome-wide DNA binding patterns of AP-1 family members in macrophages. ChIP-sequencing showed overlapping and distinct binding profiles for each factor that were remodeled following TLR4 ligation. Development of a machine learning approach that jointly weighs hundreds of DNA recognition elements yielded dozens of motifs predicted to drive factor-specific binding profiles. Machine learning-based predictions were confirmed by analysis of the effects of mutations in genetically diverse mice and by loss of function experiments. These findings provide evidence that non-redundant genomic locations of different AP-1 family members in macrophages largely result from collaborative interactions with diverse, locus-specific ensembles of transcription factors and suggest a general mechanism for encoding functional specificities of their common recognition motif.

genomics↗

bioRxiv2018

Refining fine-mapping: effect sizes and regional heritability

Recent statistical approaches have shown that the set of all available genetic variants explains considerably more phenotypic variance of complex traits and diseases than the individual variants that are robustly associated with these phenotypes. However, rapidly increasing sample sizes constantly improve detection and prioritization of individual variants driving the associations between genomic regions and phenotypes. Therefore, it is useful to routinely estimate how much phenotypic variance the detected variants explain for each region by taking into account the correlation structure of variants and the uncertainty in their causal status. Here we extend the FINEMAP software to estimate the effect sizes and regional heritability under the probabilistic model that assumes a handful of causal variants per each region. Using the UK Biobank data to simulate GWAS regions with only a few causal variants, we demonstrate that FINEMAP provides higher precision and enables more detailed decomposition of regional heritability into individual variants than the variance component model implemented in BOLT or the fixed-effect model implemented in HESS. Using data from 51 serum biomarkers and four lipid traits from the FINRISK study, we estimate that FINEMAP captures on average 24% more regional heritability than the variant with the lowest P-value alone and 20% less than BOLT. Our simulations suggest how an upward bias of BOLT and a downward bias of FINEMAP could together explain the observed difference between the methods. We conclude that FINEMAP enables computationally efficient estimation of effect sizes and regional heritability in the era of biobank scale data.

genetics↗

bioRxiv2018

Interrogation of human hematopoiesis at single-cell and single-variant resolution

Incomplete annotation of cell-to-cell state variance and widespread linkage disequilibrium in the human genome represent significant challenges to elucidating mechanisms of trait-associated genetic variation. Here, using data from the UK Biobank, we perform genetic fine-mapping for 16 blood cell traits to quantify posterior probabilities of association while allowing for multiple independent signals per region. We observe an enrichment of fine-mapped variants in accessible chromatin of lineage-committed hematopoietic progenitor cells. Further, we develop a novel analytic framework that identifies \"core gene\" cell type enrichments and show that this approach uniquely resolves relevant cell types within closely related populations. Applying our approach to single cell chromatin accessibility data, we discover significant heterogeneity within classically defined multipotential progenitor populations. Finally, using several lines of empirical evidence, we identify relevant cell types, predict target genes, and propose putative causal mechanisms for fine-mapped variants. In total, our study provides an analytic framework for single-variant and single-cell analyses to elucidate putative causal variants and cell types from GWAS and high-resolution epigenomic assays.

genetics↗

bioRxiv2016

biMM: Efficient estimation of genetic variances andcovariances for cohorts with high-dimensional phenotype measurements

Genetic research utilizes a decomposition of trait variances and covariances into genetic and environmental parts. Our software package biMM is a computationally efficient implementation of a bivariate linear mixed model for settings where hundreds of traits have been measured on partially overlapping sets of individuals.\n\nAvailabilityImplementation in R freely available at www.iki.fi/mpirinen.

genetics↗