Biology subjects

Love, M. I.

Publications and source records attributed to Love, M. I..

At least 37 records · Page 2Linked to original sources

bioRxiv2023

Genetics of cell-type-specific post-transcriptional gene regulation during human neurogenesis

The function of some genetic variants associated with brain-relevant traits has been explained through colocalization with expression quantitative trait loci (eQTL) conducted in bulk post-mortem adult brain tissue. However, many brain-trait associated loci have unknown cellular or molecular function. These genetic variants may exert context-specific function on different molecular phenotypes including post-transcriptional changes. Here, we identified genetic regulation of RNA-editing and alternative polyadenylation (APA), within a cell-type-specific population of human neural progenitors and neurons. More RNA-editing and isoforms utilizing longer polyadenylation sequences were observed in neurons, likely due to higher expression of genes encoding the proteins mediating these post-transcriptional events. We also detected hundreds of cell-type-specific editing quantitative trait loci (edQTLs) and alternative polyadenylation QTLs (apaQTLs). We found colocalizations of a neuron edQTL in CCDC88A with educational attainment and a progenitor apaQTL in EP300 with schizophrenia, suggesting genetically mediated post-transcriptional regulation during brain development lead to differences in brain function.

genomics↗

bioRxiv2023

Cross-site reproducibility of human cortical organoids reveals consistent cell type composition and architecture

BackgroundReproducibility of human cortical organoid (hCO) phenotypes remains a concern for modeling neurodevelopmental disorders. While guided hCO protocols reproducibly generate cortical cell types in multiple cell lines at one site, variability across sites using a harmonized protocol has not yet been evaluated. We present an hCO cross-site reproducibility study examining multiple phenotypes. MethodsThree independent research groups generated hCOs from one induced pluripotent stem cell (iPSC) line using a harmonized miniaturized spinning bioreactor protocol. scRNA-seq, 3D fluorescent imaging, phase contrast imaging, qPCR, and flow cytometry were used to characterize the 3 month differentiations across sites. ResultsIn all sites, hCOs were mostly cortical progenitor and neuronal cell types in reproducible proportions with moderate to high fidelity to the in vivo brain that were consistently organized in cortical wall-like buds. Cross-site differences were detected in hCO size and morphology. Differential gene expression showed differences in metabolism and cellular stress across sites. Although iPSC culture conditions were consistent and iPSCs remained undifferentiated, primed stem cell marker expression prior to differentiation correlated with cell type proportions in hCOs. ConclusionsWe identified hCO phenotypes that are reproducible across sites using a harmonized differentiation protocol. Previously described limitations of hCO models were also reproduced including off-target differentiations, necrotic cores, and cellular stress. Improving our understanding of how stem cell states influence early hCO cell types may increase reliability of hCO differentiations. Cross-site reproducibility of hCO cell type proportions and organization lays the foundation for future collaborative prospective meta-analytic studies modeling neurodevelopmental disorders in hCOs.

neuroscience↗

bioRxiv2023

Diffsig: Associating Risk Factors With Mutational Signatures

Somatic mutational signatures elucidate molecular vulnerabilities to therapy and therefore detecting signatures and classifying tumors with respect to signatures has clinical value. However, identifying the etiology of the mutational signatures remains a statistical challenge, with both small sample sizes and high variability in classification algorithms posing barriers. As a result, few signatures have been strongly linked to particular risk factors. Here we present Diffsig, a model and R package for estimating the association of risk factors with mutational signatures, suggesting etiologies for the pre-defined mutational signatures. Diffsig is a Bayesian Dirichlet-multinomial hierarchical model that allows testing of any type of risk factor while taking into account the uncertainty associated with samples with a low number of observations. In simulation, we found that our method can accurately estimate risk factor-mutational signal associations. We applied Diffsig to breast cancer data to assess relationships between five established breast-relevant mutational signatures and etiologic variables, confirming known mechanisms of cancer development. Diffsig is implemented as an R package available at: https://github.com/jennprk/diffsig.

cancer biology↗

bioRxiv2023

WNT activity reveals context-specific genetic effects on gene regulation in neural progenitors

Gene regulatory effects in bulk-post mortem brain tissues are undetected at many non-coding brain trait-associated loci. We hypothesized that context-specific genetic variant function during stimulation of a developmental signaling pathway would explain additional regulatory mechanisms. We measured chromatin accessibility and gene expression following activation of the canonical Wnt pathway in primary human neural progenitors from 82 donors. TCF/LEF motifs, brain structure-, and neuropsychiatric disorder-associated variants were enriched within Wnt-responsive regulatory elements (REs). Genetically influenced REs were enriched in genomic regions under positive selection along the human lineage. Stimulation of the Wnt pathway increased the detection of genetically influenced REs/genes by 66.2%/52.7%, and led to the identification of 397 REs primed for effects on gene expression. Context-specific molecular quantitative trait loci increased brain-trait colocalizations by up to 70%, suggesting that genetic variant effects during early neurodevelopmental patterning lead to differences in adult brain and behavioral traits.

genetics↗

bioRxiv2022

excluderanges: exclusion sets for T2T-CHM13, GRCm39, and other genome assemblies

SummaryExclusion regions are sections of reference genomes with abnormal pileups of short sequencing reads. Removing reads overlapping them improves biological signal, and these benefits are most pronounced in differential analysis settings. Several labs created exclusion region sets, available primarily through ENCODE and Github. However, the variety of exclusion sets creates uncertainty which sets to use. Furthermore, gap regions (e.g., centromeres, telomeres, short arms) create additional considerations in generating exclusion sets. We generated exclusion sets for the latest human T2T-CHM13 and mouse GRCm39 genomes and systematically assembled and annotated these and other sets in the excluderanges R/Bioconductor data package, also accessible via the BEDbase.org API. The package provides unified access to 82 GenomicRanges objects covering six organisms, multiple genome assemblies and types of exclusion regions. For human hg38 genome assembly, we recommend hg38.Kundaje.GRCh38_unified_blacklist as the most well-curated and annotated, and sets generated by the Blacklist tool for other organisms. Availability and implementationhttps://bioconductor.org/packages/excluderanges/ ContactMikhail G. Dozmorov (mdozmorov@vcu.edu) Supplementary informationPackage website: https://dozmorovlab.github.io/excluderanges/

bioinformatics↗

bioRxiv2022

Context-Aware Transcript Quantification from Long Read RNA-Seq data with Bambu

Most approaches to transcript quantification rely on fixed reference annotations. However, the transcriptome is dynamic, and depending on the context, such static annotations contain inactive isoforms for some genes while they are incomplete for others. To address this, we have developed Bambu, a method that performs machine-learning based transcript discovery to enable quantification specific to the context of interest using long-read RNA-Seq data. To identify novel transcripts, Bambu employs a precision-focused threshold referred to as the novel discovery rate (NDR), which replaces arbitrary per-sample thresholds with a single interpretable parameter. Bambu retains the full-length and unique read counts, enabling accurate quantification in presence of inactive isoforms. Compared to existing methods for transcript discovery, Bambu achieves greater precision without sacrificing sensitivity. We show that context-aware annotations improve abundance estimates for both novel and known transcripts. We apply Bambu to human embryonic stem cells to quantify isoforms from repetitive HERVH-LTR7 retrotransposons, demonstrating the ability to estimate transcript expression specific to the context of interest.

bioinformatics↗

bioRxiv2022

Uncovering Cross-Cohort Molecular Features with Multi-Omics Integration Analysis

Integrative approaches that simultaneously model multi-omics data have gained increasing popularity because they provide holistic system biology views of multiple or all components in a biological system of interest. Canonical correlation analysis (CCA) is a correlation-based integrative method. It was initially designed to extract latent features shared between two assays by finding the linear combinations of features - referred to as canonical vectors (CVs) - within each assay that achieve maximal across-assay correlation. Sparse multiple CCA (SMCCA), a widely-used derivative of CCA, allows more than two assays but can result in non-orthogonal CVs when applied to high-dimensional data. Here, we incorporated a variation of the Gram-Schmidt (GS) algorithm with SMCCA to improve orthogonality among CVs. Applying our SMCCA-GS method to proteomics and methylomics data from the Multi-Ethnic Study of Atherosclerosis (MESA) and Jackson Heart Study (JHS), we identified strong associations between blood cell counts and protein abundance. This finding suggests that adjustment of blood cell composition should be considered in protein-based association studies. Importantly, CVs obtained from two independent cohorts demonstrate transferability across the cohorts. For example, proteomic CVs learned from JHS explain similar amounts of blood cell count phenotypic variance in MESA, explaining 39.0% ~ 50.0% variation in JHS and 38.9% ~ 49.1% in MESA, similar transferability was observed for other omics-CV-trait pairs. This suggests that biologically meaningful and cohort-agnostic variation is captured by CVs. We further developed Sparse Supervised Multiple CCA (SSMCCA) to allow supervised integration analysis for more than two assays. We anticipate that applying our SMCCA-GS and SSMCCA on various cohorts would help identify cohort-agnostic biologically meaningful relationships between multi-omics data and phenotypic traits. Author SummaryComprehensive understanding of human complex traits may benefit from incorporation of molecular features from multiple biological layers such as genome, epigenome, transcriptome, proteome, and metabolome. CCA is a correlation-based method for multi-omics data which reduces the dimension of each omic assay to several orthogonal components - commonly referred to as canonical vectors (CVs). The widely-used SMCCA method allows effective dimension reduction and integration of multi-omics data, but suffers from potentially highly correlated CVs when applied to high-dimensional omics data. Here, we improve the statistical independence among the CVs by adopting a variation of the GS algorithm. We applied our SMCCA-GS method to proteomic and methylomic data from two cohort studies, MESA and JHS. Our results reveal a pronounced effect of blood cell counts on protein abundance, strongly suggesting blood cell composition adjustment in protein-based association studies may be necessary. Finally, we present SSMCCA which allows supervised CCA analysis for the association between one phenotype of interest and more than two assays. We anticipate that SMCCA-GS would help reveal meaningful system-level factors from biological processes involving features from multiple assays; and SSMCCA would further empower interrogation of these factors for phenotypic traits related to health and diseases.

genomics↗

bioRxiv2022

TreeTerminus - Creating transcript trees using inferential replicate counts

The accuracy and robustness of many types of analyses performed using RNA-seq data are directly impacted by the quality of the transcript and gene abundance estimates inferred from this data. However, a certain degree of uncertainty is always associated with the transcript abundance estimates. This uncertainty may make many downstream analyses, such as differential testing, difficult for certain transcripts. Conversely, gene-level analysis, though less ambiguous, is often too coarse-grained. To circumvent this problem, methods have proposed grouping transcripts together into distinct inferential units that should be used as a base unit for analysis. However, these methods dont take downstream analysis into account. We introduce TreeTerminus, a data-driven approach for grouping transcripts into a tree structure where leaves represent individual transcripts and internal nodes represent an aggregation of a transcript set. TreeTerminus constructs trees such that, on average, the inferential uncertainty decreases as we ascend the tree topology. The tree provides the flexibility to analyze data at nodes that are at different levels of resolution in the tree and can be tuned depending on the analysis of interest. To obtain fixed groups for the downstream analysis, we provide a dynamic programming (DP) approach that can be used to find a cut through the tree that optimizes one of several different objectives. We evaluated TreeTerminus on two simulated and two experimental datasets, and observed an improved performance compared to transcripts (leaves) and other methods under several different metrics.

bioinformatics↗

bioRxiv2022

Chromatin loop dynamics during cellular differentiation are associated with changes to both anchor and internal regulatory features

3D chromatin structure has been shown to play a role in regulating gene transcription during biological transitions. While our understanding of loop formation and maintenance is rapidly improving, much less is known about the mechanisms driving changes in looping and the impact of differential looping on gene transcription. One limitation has been a lack of well powered differential looping data sets. To address this, we conducted a deeply sequenced Hi-C time course of megakaryocyte development comprising 4 biological replicates and 6 billion reads per time point. Statistical analysis revealed 1,503 differential loops. Gained loops were enriched for AP-1 occupancy and correlated with increased expression of genes at their anchors. Lost loops were characterized by increases in expression of genes within the loop boundaries. Linear modeling revealed that changes in histone H3 K27 acetylation, chromatin accessibility, and JUN binding in between the loop anchors were as predictive of changes in loop strength as changes to CTCF and/or cohesin occupancy at loop anchors. Finally, we built linear models and found that incorporating the dynamics of enhancer acetylation and loop strength increased accuracy of gene expression predictions.

genomics↗

bioRxiv2022

bootRanges: Flexible generation of null sets of genomic ranges for hypothesis testing

bootRanges provides fast functions for generation of bootstrapped genomic ranges representing the null sets in enrichment analysis. We show that shuffling or permutation schemes may result in overly narrow test statistics null distributions, while creating new ranges sets with a block bootstrap preserves local genomic correlation structure and generates more reliable null distributions. It can also be used in more complex analyses, such as accessing correlations between cis-regulatory elements (CREs) and genes across cell types or providing optimized thresholds, e.g. log fold change (logFC) from differential analysis. The bootRanges functions are available in the R/Bioconductor package nullranges at https://bioconductor.org/packages/nullranges.

bioinformatics↗

bioRxiv2022

Detecting isoform-level allelic imbalance accounting for inferential uncertainty

Allelic imbalance (AI) of gene expression in heterozygous individuals is a hallmark of cis-genetic regulation, revealing mechanisms underlying the association of non-coding genetic variation with downstream traits, as in GWAS. Most methods for detecting AI from RNA-sequencing (RNA-seq) data examine allelic expression per exonic SNP, which may obscure imbalance in expression of individual isoforms. Detecting AI at the isoform level requires accounting for inferential uncertainty (IU) of expression estimates, caused by multi-mapping of RNA-seq reads to isoforms and alleles. Swish, a method developed previously for differential transcript expression accounting for IU, can be applied in a paired setting to detect AI. However, in AI analysis, most transcripts will have high IU across alleles such that even methods like Swish will lose power. Our proposed method, SEESAW, offers AI analysis at various level of resolution, including gene level, isoform level, and optionally aggregating isoforms within a gene based on their transcription start site (TSS). This TSS-based aggregation strategy strengthens the signal for transcripts that may have high IU with respect to allelic quantification. SEESAW is primarily designed for experiments with multiple replicates or conditions of organisms with the same genotype, as in an F1 cross or time course experiments of cell lines. Additionally, we introduce a new test for detecting AI that changes across a continuous covariate, as in a time course experiment. The SEESAW suite of methods is evaluated both on simulated data and applied to an RNA-seq dataset of differentiating F1 mouse osteoblasts.

bioinformatics↗

bioRxiv2022

matchRanges: Generating null hypothesis genomic ranges via covariate-matched sampling

Deriving biological insights from genomic data commonly requires comparing attributes of selected genomic loci to a null set of loci. The selection of this null set is non trivial, as it requires careful consideration of potential covariates, a problem that is exacerbated by the non-uniform distribution of genomic features including genes, enhancers, and transcription factor binding sites. Propensity score-based covariate matching methods allow selection of null sets from a pool of possible items while controlling for multiple covariates; however, existing packages do not operate on genomic data classes and can be slow for large data sets making them difficult to integrate into genomic workflows. To address this, we developed matchRanges, a propensity score-based covariate matching method for the efficient and convenient generation of matched null ranges from a set of background ranges within the Bioconductor framework.

bioinformatics↗

bioRxiv2022

Temporal analysis suggests a reciprocal relationship between 3D chromatin structure and transcription

To infer potential causal relationships between 3D chromatin structure, enhancers, and gene transcription, we mapped each feature in a genome-wide fashion across eight narrowly-spaced timepoints of macrophage activation. Enhancers and genes connected by loops exhibited stronger correlations between histone H3K27 acetylation and expression than can be explained by genomic distance or physical proximity alone. Changes in acetylation at looped distal enhancers preceded changes in gene expression. Changes in gene expression exhibit a directional bias at differential loop anchors; gained loops are associated with increased expression of genes oriented away from the center of the loop, while lost loops were often accompanied by high levels of transcription with the loop boundaries themselves. Taken together, these results are consistent with a reciprocal relationship in which loops can facilitate increased transcription by connecting promoters to distal enhancers while high levels of transcription can impede loop formation. HIGHLIGHTSO_LILPS + IFN{gamma} triggers genome-wide changes in chromatin looping, enhancer acetylation, and gene expression C_LIO_LILooped enhancer-promoter pairs exhibit ordered and correlated changes in acetylation and expression C_LIO_LIChanges in gene expression exhibit a directional bias at differential loop anchors C_LIO_LILost loops are associated with high levels of transcription within loop boundaries C_LI

genomics↗

bioRxiv2022

Inferring cell-type-specific causal gene regulatory networks during human neurogenesis

BackgroundGenetic variation influences both chromatin accessibility, assessed in chromatin accessibility quantitative trait loci (caQTL) studies, and gene expression, assessed in expression QTL (eQTL) studies. Genetic variants can impact either nearby genes (local eQTLs) or distal genes (trans eQTLs). Colocalization between caQTL and eQTL, or local- and distant-eQTLs suggests that they share causal variants. However, pairwise colocalization between these molecular QTLs does not guarantee a causal relationship. Mediation analysis can be applied to assess the evidence supporting causality versus independence between molecular QTLs. Given that the function of QTLs can be cell-type-specific, we performed mediation analyses to find epigenetic and distal regulatory causal pathways for genes within two major cell types of the developing human cortex, progenitors and neurons. ResultsWe found that expression of 168 and 38 genes were mediated by chromatin accessibility in progenitors and neurons, respectively. We also found that the expression of 781 and 200 downstream genes were mediated by upstream genes in progenitors and neurons. Moreover, we discovered that a genetic locus associated with inter-individual differences in brain structure showed evidence for mediation of SLC26A7 through chromatin accessibility, identifying molecular mechanisms of a common variant association to a brain trait. ConclusionsIn this study, we identified cell-type-specific causal gene regulatory networks whereby the impacts of variants on gene expression were mediated by chromatin accessibility or distal gene expression. Identification of these causal paths will enable identifying and prioritizing actionable regulatory targets perturbing these key processes during neurodevelopment.

genomics↗

bioRxiv2021

Airpart: Interpretable statistical models for analyzing allelic imbalance in single-cell datasets

MotivationAllelic expression analysis aids in detection of cis-regulatory mechanisms of genetic variation which produce allelic imbalance (AI) in heterozygotes. Measuring AI in bulk data lacking time or spatial resolution has the limitation that cell-type-specific (CTS), spatial-, or time-dependent AI signals may be dampened or not detected. ResultsWe introduce a statistical method airpart for identifying differential CTS AI from single-cell RNA-sequencing (scRNA-seq) data, or other spatially- or time-resolved datasets. airpart outputs discrete partitions of data, pointing to groups of genes and cells under common mechanisms of cis-genetic regulation. In order to account for low counts in single-cell data, our method uses a Generalized Fused Lasso with Binomial likelihood for partitioning groups of cells by AI signal, and a hierarchical Bayesian model for AI statistical inference. In simulation, airpart accurately detected partitions of cell types by their AI and had lower RMSE of allelic ratio estimates than existing methods. In real data, airpart identified differential AI patterns across cell states and could be used to define trends of AI signal over spatial or time axes. AvailabilityThe airpart package is available as an R/Bioconductor package at https://bioconductor.org/packages/airpart.

bioinformatics↗

bioRxiv2021

Inference of cell-type specific imprinted regulatory elements and genes during human neuronal differentiation

Genomic imprinting results in gene expression biased by parental chromosome of origin and occurs in genes with important roles during human brain development. However, the cell-type and temporal specificity of imprinting during human neurogenesis is generally unknown. By detecting within-donor allelic biases in chromatin accessibility and gene expression that are unrelated to cross-donor genotype, we inferred imprinting in both primary human neural progenitor cells (phNPCs) and their differentiated neuronal progeny from up to 85 donors. We identified 43/20 putatively imprinted regulatory elements (IREs) in neurons/progenitors, and 133/79 putatively imprinted genes in neurons/progenitors. Though 10 IREs and 42 genes were shared between neurons and progenitors, most imprinting was only detected within specific cell types. In addition to well-known imprinted genes and their promoters, we inferred novel IREs and imprinted genes. We found IREs overlapped with CpG islands more than non-imprinted regulatory elements. Consistent with DNA methylation-based regulation of imprinted expression, some putatively imprinted regulatory elements also overlapped with differentially methylated regions on the maternal germline. Finally, we identified a progenitor-specific putatively imprinted gene overlap with copy number variation that is associated with uniparental disomy-like phenotypes. Our results can therefore be useful in interpreting the function of variants identified in future parent-of-origin association studies.

genomics↗

bioRxiv2021

Plotgardener: Cultivating precise multi-panel figures in R

The R programming language is one of the most widely used programming languages for transforming raw genomic data sets into meaningful biological conclusions through analysis and visualization, which has been largely facilitated by infrastructure and tools developed by the Bioconductor project. However, existing plotting packages rely on relative positioning and sizing of plots, which is often sufficient for exploratory analysis but is poorly suited for the creation of publication-quality multi-panel images inherent to scientific manuscript preparation. We present plotgardener, a coordinate-based genomic data visualization package that offers a new paradigm for multi-plot figure generation in R. Plotgardener allows precise, programmatic control over the placement, aesthetics, and arrangements of plots while maximizing user experience through fast and memory-efficient data access, support for a wide variety of data and file types, and tight integration with the Bioconductor environment. Plotgardener also allows precise placement and sizing of ggplot2 plots, making it an invaluable tool for R users and data scientists from virtually any discipline. AvailabilityPackage: https://bioconductor.org/packages/plotgardener Code: https://github.com/PhanstielLab/plotgardener Documentation: https://phanstiellab.github.io/plotgardener/

bioinformatics↗

bioRxiv2021

Distribution-based comprehensive evaluation ofmethods for differential expression analysis inmetatranscriptomics

Understanding the function of the human microbiome is important; however, the development of statistical methods specifically for the microbial gene expression (i.e., metatranscriptomics) is in its infancy. Many currently employed differential expression analysis methods have been designed for different data types and have not been evaluated in metatranscriptomics settings. To address this gap, we undertook a comprehensive evaluation and benchmarking of ten differential analysis methods for metatranscriptomics data. We used a combination of real and simulated data to evaluate performance (i.e., model fit, type I error, false discovery rate, and sensitivity) of the methods: log-normal (LN), logistic-beta (LB), MAST, DESeq2, metagenomeSeq, ANCOM-BC, LEfSe, ALDEx2, Kruskal-Wallis, and two-part Kruskal-Wallis. The simulation was informed by supragingival biofilm microbiome data from 300 preschool-age children enrolled in a study of early childhood caries (ECC), whereas validations were sought in two additional datasets from an ECC study and an inflammatory bowel disease (IBD) study. The LB test showed the highest sensitivity in both small and large samples and reasonably controlled type I error. Contrarily, MAST was hampered by inflated type I error. Upon application of the LN and LB tests in the ECC study, we found that genes C8PHV7 and C8PEV7, harbored by the lactate-producing Campylobacter gracilis, had the strongest association with childhood dental diseases. This comprehensive model evaluation offer practical guidance for selection of appropriate methods for rigorous analyses of differential expression in metatranscriptomics. Selection of an optimal method increases the possibility of detecting true signals while minimizing the chance of claiming false ones.

bioinformatics↗