Biology subjects

Johnson, E. K.

Publications and source records attributed to Johnson, E. K..

4 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↗

bioRxiv2021

Bayesian estimation of population size and overlap from random subsamples

Counting the number of species, items, or genes that are shared between two sets is a simple calculation when sampling is complete. However, when only partial samples are available, quantifying the overlap between two sets becomes an estimation problem. Furthermore, to calculate normalized measures of {beta}-diversity, such as the Jaccard and Sorenson-Dice indices, one must also estimate the total sizes of the sets being compared. Previous efforts to address these problems have assumed knowledge of total population sizes and then used Bayesian methods to produce unbiased estimates with quantified uncertainty. Here, we address populations of unknown size and show that this produces systematically better estimates--both in terms of central estimates and quantification of uncertainty in those estimates. We further show how to use species count data to refine estimates of population size in a Bayesian joint model of populations and overlap.

ecology↗

bioRxiv2021

Identification of Structures for Ion Channel Kinetic Models

Markov models of ion channel dynamics have evolved as experimental advances have improved our understanding of channel function. Past studies have examined various topologies for Markov models of channel dynamics. We present a systematic method for identification of all possible Markov model topologies using experimental data for two types of native voltage-gated ion channel currents: mouse atrial sodium and human left ventricular fast transient outward potassium currents. In addition to optional biophysically inspired restrictions on the number of connections from a state and elimination of long-range connections, this study further suggests successful models have more than minimum number of connections for set number of states. When working with topologies with more than the minimum number of connections, the topologies with three and four connections to the open state tend to serve well as Markov models of ion channel dynamics. Significance StatementHere, we present a computational routine to thoroughly search for Markov model topologies for simulating whole-cell currents given an experimental dataset. We test this method on two distinct types of voltage-gated ion channels that function in the generation of cardiac action potentials. Particularly successful models have more than one connection between an open state and the rest of the model, and large models may benefit from having even more connections between the open state and the rest of the other states.

biophysics↗