Biology subjects

Benjamin, K. J. M.

Publications and source records attributed to Benjamin, K. J. M..

7 recordsLinked to original sources

bioRxiv2026

IsoGraph resolves coordinated transcript choice and gene abundance in the aging human brain

Relative transcript usage can change without a corresponding change in total gene expression, but resolving coordinated transcript choice across genes while separating it from abundance remains challenging. We present IsoGraph, a gene-centered framework that represents isoform usage and abundance as paired network channels, identifies gene modules, and quantifies their channel-specific contributions. In synthetic benchmarks, IsoGraph preferentially recovered network structure when coordinated transcript usage carried the planted signal, whereas abundance-based representations were more informative in abundance-dominated and RNA-degradation settings. Applied to BrainSEQ and GTEx adult brain transcriptomes, IsoGraph identified age-associated transcript-usage signals that persisted after conditioning on gene abundance and, in a region-dependent manner, after adjustment for estimated cellular composition. Modules were stable across donor subsets, and frozen module axes retained age associations across cohorts even when exact gene partitions differed. IsoGraph module context captured predictive information about held-out differential transcript usage beyond gene-wise evidence and conventional co-expression. Convergent evidence from structural annotation, short-read junction usage, independent long-read sequencing, and within-donor allelic analyses supported constituent transcript events. Disease colocalization further prioritized candidate isoform events at neurological and psychiatric disease loci, including an alternative terminal-exon event at *PRDM2* associated with amyotrophic lateral sclerosis. Together, these results establish coordinated transcript usage as a reproducible component of transcriptomic organization and provide a framework for resolving its contribution alongside gene abundance and for tracing network-level signals to candidate transcript events.

bioinformatics↗

bioRxiv2026

Local SNP-explained methylation variation reveals genetically anchored and exposure-associated methylation architecture in the human brain

Human brain DNA methylation is shaped by inherited genetic variation and cumulative environmental experience, yet how these influences partition the methylome remains poorly resolved in postmortem cohorts with modest sample sizes and limited ancestral diversity. To map this architecture in an underrepresented population, we analyzed whole-genome bisulfite sequencing and genotype array data from 168 admixed Black American adults from the BrainSEQ consortium across three brain regions. We adapted and benchmarked SNP-based elastic-net modeling to classify variably methylated regions (VMRs) by local SNP-explained methylation variation, an approach that provided stable classification at the modest sample sizes of postmortem brain cohorts, where conventional methods are underpowered. Using this framework, we partitioned 31,143 VMRs into high and low SNP-explained classes and evaluated their generalizability in a multi-ancestry cohort of Black American and non-Hispanic white American donors. High SNP-explained VMRs were concentrated in distal intergenic sequences and, at the highest heritability levels, were enriched for H3K9me3, quiescent/repressive chromatin states and LINE/L1 elements, linking genetically anchored methylation to repeat-associated repressive chromatin across the human brain. A small subset overlapping Activity-by-Contact-defined enhancers was linked to candidate immune-related genes, including MHC class II loci. By contrast, low SNP-explained VMRs were more gene-proximal and enriched for active regulatory elements. Metadata-associated VMRs showed region-, exposure-, and donor-group-dependent enrichment across SNP-explained classes, including substance use and sociodemographic variables. Together, these findings show that the most genetically anchored component of the human brain methylome is concentrated in repressive, repeat-rich chromatin compartments involved in heterochromatin maintenance and repeat silencing, distinct from the gene-proximal, exposure-associated variation less explained by nearby SNPs. By resolving this architecture in an underrepresented population, this work clarifies how inherited variation structures the brain methylome and, given the established role of these compartments in neuronal aging, informs the interpretation of epigenomic mechanisms relevant to neuropsychiatric and neurodegenerative diseases.

genetics↗

bioRxiv2024

RFMix-reader: Accelerated reading and processing for local ancestry studies

MotivationLocal ancestry inference is a powerful technique in genetics, revealing population history and the genetic basis of diseases. It is particularly valuable for improving eQTL discovery and fine-mapping in admixed populations. Despite the widespread use of the RFMix software for local ancestry inference, large-scale genomic studies face challenges of high memory consumption and processing times when handling RFMix output files. ResultsHere, I present RFMix-reader, a new Python-based parsing software, designed to streamline the analysis of large-scale local ancestry datasets. This software prioritizes computational eiciency and memory optimization, leveraging GPUs when available for additional speed boosts. By overcoming these data processing hurdles, RFMix-reader empowers researchers to unlock the full potential of local ancestry data for understanding human health and health disparities. AvailabilityRFMix-reader is freely available on PyPI at https://pypi.org/project/rfmix-reader/, implemented in Python 3, and supported on Linux, Windows, and Mac OS. ContactKynonJade.Benjamin@libd.org Supplementary informationSupplementary data are available at https://rfmix-reader.readthedocs.io/en/latest/.

bioinformatics↗

bioRxiv2024

Cell type-specific expression of angiotensin receptors in the human lung with implications for health, aging, and chronic disease

The renin-angiotensin system is a well-characterized regulator of tissue homeostasis whose clinical relevance has expanded to include lung disorders such as chronic obstructive pulmonary disease (COPD)-associated emphysema, idiopathic pulmonary fibrosis, and COVID-19. Despite this interest, the cell-specific localization of angiotensin receptors in the human lung has remained poorly defined, in part due to limitations of available antibody reagents. Here, we define the expression patterns of the two predominant angiotensin receptors, AGTR1 and AGTR2, using complementary bulk and single-nucleus transcriptomic datasets from human lung tissue. We demonstrate that these receptors exhibit mutually exclusive, compartment-specific localization, with AGTR1 expressed in lung pericytes and AGTR2 expressed in alveolar epithelial type 2 cells. AGTR1 is detectable in isolated lung pericytes, and spatial colocalization with pericyte markers confirmed within the airspace microvasculature compartment by RNAscope. Airspace pericyte abundance was reduced in an experimental emphysema model but restored by pharmacologic attenuation of AGTR1 signaling commensurate with airspace repair. In COPD lungs, AGTR1 expression showed heterogeneous, disease-associated dysregulation across stromal populations, including upregulation in alveolar fibroblasts. Bulk transcriptomics also revealed aging-associated redistribution of AGTR1 expression into stromal compartments. Angiotensin II and cigarette smoke impaired pericyte migration toward endothelial cells, while combined exposure suppressed pericyte proliferation. Together, these findings identify AGTR1 as a new highly selective marker of lung pericytes and a regulator of pericyte behaviors within the airspace microvasculature. These findings provide a cell-resolved framework for angiotensin signaling with direct relevance to airspace resilience and therapeutic targeting.

bioinformatics↗

bioRxiv2022

dRFEtools: Dynamic recursive feature elimination for omics

Technology advances have generated larger omics datasets with applications for machine learning. Even so, in many datasets, the number of measured features greatly exceeds the number of observations or experimental samples. Dynamic recursive feature elimination (RFE) provides a flexible feature elimination framework to tackle this problem and to gain biological insight by selecting feature sets that are relevant for prediction. Here, we developed dRFEtools that implements dynamic RFE, and show that it reduces computational time with high accuracy compared to RFE. Given a prediction task on a dataset, dRFEtools identifies a minimal, non-redundant, set of features and a functionally redundant set of features leading to higher prediction accuracy compared to RFE. We demonstrate dRFEtools ability to identify biologically relevant information from genomic data using RNA-Seq and genotype data from the BrainSeq Consortium. dRFEtools provides an interpretable and flexible tool to gain biological insights from omics data using machine learning.

bioinformatics↗

bioRxiv2022

Ventral forebrain organoids derived from individuals with schizophrenia recapitulate perturbed striatal gene expression dynamics of the donor's brains

Schizophrenia (SCZ) is a brain disorder originating during neurodevelopment with complex genetic and environmental etiologies. Despite decades of clinical evidence of altered striatal function in affected patients, its cellular and molecular underpinnings remain unclear. Here, to explore neurodevelopmental alterations in the striatum associated with SCZ, we established a method for the differentiation of iPS cells into ventral forebrain organoids. Given substantial genetic heterogeneity among individuals, which can obscure disease-associated phenotypes, we generated organoids from postmortem dural fibroblast-derived iPS cells of 3 patients and 4 healthy control individuals with nonoverlapping polygenic risk score (PRS) for SCZ and whose genotype and postmortem caudate transcriptomic data were profiled in the Brainseq neurogenomics consortium. Single cell RNA sequencing (scRNA-seq) analyses of the organoids revealed differences in developmental trajectory between SCZ cases and controls in which inhibitory neurons from patients exhibited accelerated maturation. Furthermore, we found a significant overlap of genes upregulated in the inhibitory neurons in SCZ organoids with upregulated genes in postmortem caudate tissues from patients with SCZ compared with control individuals, including the donors of our iPS cell cohort. Our findings suggest that striatal neurons in the patients with SCZ carry abnormalities that originated during early brain development and a ventral forebrain striatal organoid model can recapitulate those neurodevelopmental phenotypes in a dish.

neuroscience↗

bioRxiv2022

Variation in TAF1 expression in female carrier induced pluripotent stem cells and human brain ontogeny has implications for adult neostriatum vulnerability in X-linked Dystonia Parkinsonism

X-linked Dystonia-Parkinsonism (XDP) is an inherited, X-linked, adult-onset movement disorder characterized by degeneration in the neostriatum. No therapeutics alter disease progression. The mechanisms underlying regional differences in degeneration and age of onset are unknown. Developing therapeutics that target XDP-related mechanisms requires a deeper understanding of how XDP-relevant features vary in health and disease. XDP is due, in part, to either a partial loss of TAF1 function and/or a SVA-driven pathological gain of function. A disease-specific SINE-VNTR-Alu (SVA) retrotransposon insertion occurs within intron 32 of TAF1, a subunit of TFIID involved in transcription initiation. While all XDP males are usually clinically affected, females are heterozygous carriers generally not manifesting the full syndrome. As a resource for disease modeling, we characterized eight iPSC lines from XDP female carrier individuals, and identified isogenic lines where one clonal iPSC line expressed the wild-type X, and the two other clonal iPSC lines expressed the XDP haplotype. Furthermore, we characterized XDP-relevant transcript expression variation in humans, and found that SVA-F expression decreases slightly after 30 years of age in the neurotypical human brain and that TAF1 is modestly decreased in the majority of female samples. Uniquely in the caudate nucleus, TAF1 expression is not sexually dymorphic and decreased after 15 years of age. These findings indicate that regional-, age- and sex-specific mechanisms regulate TAF1, highlighting the importance of disease-relevant models and postmortem tissue analysis. We propose that the decreased TAF1 expression in the adult caudate may synergize with the XDP-specific partial loss of TAF1 function in patients, thereby passing a minimum threshold of TAF1 function, and triggering degeneration in the neostriatum. Significance StatementXDP is an inherited, X-linked, adult-onset movement disorder characterized by degeneration in the neostriatum. No therapeutics alter disease progression. Developing therapeutics requires a deeper understanding of how XDP-relevant features vary in health and disease. XDP is possibly due to a partial loss of TAF1 function. While all XDP males are usually affected, females are heterozygous carriers generally not manifesting the full syndrome. As a resource for disease modeling, we characterized eight stem cell lines from XDP female carrier individuals. Furthermore, we found that, uniquely in the caudate nucleus, TAF1 expression decreases after adolescence in healthy humans. We hypothesize that the decrease of TAF1 after adolescence in human caudate, in general, may underlie the vulnerability of the adult neostriatum in XDP.

neuroscience↗