Biology subjects

Cummings, J.

Publications and source records attributed to Cummings, J..

8 recordsLinked to original sources

bioRxiv2026

Cis-regulatory variation and transcription factor binding contribute to allelic genotype-by-environment interactions for gene expression in maize

Genotype-by-environment interactions (GxE), or differences in how genotypes perform across varying environments, are a pervasive source of phenotypic variation and underlie differences in local adaptation. Though GxE is well characterized across kingdoms of life, less is known about what causes GxE interactions, particularly at the molecular level. In this study, we use allele-specific gene expression estimates in a maize (Zea mays L.) B73 x Mo17 hybrid to isolate cis-regulatory effects on gene expression for each of the two parental alleles. The hybrid was grown in two environments, and expression differences between the parental alleles were used to characterize allele-by-environment (AxE) interactions and study the influence of gene-proximal sequence variation on transcript abundance AxE. We tested the hypothesis that gene-proximal sequence variation can cause GxE in gene expression by modifying transcription factor binding. Our results show that sequence variation in gene promoter regions has a small but consistent enrichment in genes that show transcriptional AxE. Further, we demonstrate that differential transcription factor binding potential caused by sequence variation is also enriched in AxE genes. Predictive models trained on sequence and transcription factor binding variation show that while these features contain some information about whether a gene will show transcriptional AxE, they alone are not sufficient to reliably distinguish AxE genes. These findings support the hypothesis that gene expression GxE can be caused by sequence variation that modifies transcription factor binding, while also reinforcing the complex and context-specific nature of GxE interactions.

genomics↗

bioRxiv2025

A male-transmitted B chromosome undergoes strong meiotic drag in females

Many organisms carry extra, non-essential chromosomes known as B chromosomes (Bs), which are selfishly transmitted at super-Mendelian levels to offspring. This heightened transmission, termed drive, occurs during gametogenesis, usually in one of the two parents. In some cases, Bs can experience an opposing process, drag, which reduces their transmission. If these processes occur together in the same organism, one in each parental sex, then they may facilitate the spread of Bs while countering their accumulation in the genome to harmful levels. While previous studies have elucidated mechanistic aspects of B drive, little is known about drag or other factors that govern the inheritance of these selfish genetic elements. Here we examined the inheritance of Paternal Sex Ratio (PSR), a single-copy B in the jewel wasp, Nasonia vitripennis, which is transmitted paternally to offspring. PSR drives by converting female-destined embryos into PSR-transmitting males. Using genetic manipulation, we produced exceptional PSR-carrying females, which were used to assess B transmission potential. We found that females transmit PSR at unexpectedly low levels compared to univalent chromosomes in other organisms. This reduced transmission stems from remarkable loss of PSR from the eggs nucleus upon entry into meiosis, an effect that may be caused by an absence of microtubule-based spindle fibers in meiosis I-arrested wasp eggs. We also found that PSR is strictly limited to a single copy per genome, suggesting that two PSR copies are lethal during development. Our findings reveal the successful inheritance of this selfish B chromosome involves a restriction to a single copy and hidden female meiotic drag in addition to its strong paternal drive.

genetics↗

bioRxiv2025

Single-cell digital twins identify drug targets and repurposable medicine in Alzheimers disease

Alzheimers disease (AD) is a complex and poorly understood neurodegenerative disorder without sufficiently effective treatments. Novel approaches to identify FDA-approved drugs that may hold potential for mitigating symptoms of AD hold promise for addressing this problem. One such strategy is the use of digital twins (DTs), which are virtual representations of physical entities that facilitate therapeutic target identification by accurately characterizing disease heterogeneities in real time through continuous feedback and dynamic model updates. In this study, we developed a single-cell digital twin (scDT) framework using single-nuclei RNA-seq (1,197,032 nuclei) and ATAC-seq (740,875 nuclei) data from the middle temporal gyrus of 84 donors across 4 degrees of AD neuropathological change (ADNC). We observed differential gene expression for six major cell types intensified at severe ADNC. We also constructed cell type-specific transcription factor (TF)-target gene networks by leveraging peak-to-gene linkages and motif enrichment analyses. By integrating genome-wide association study (GWAS) loci with cell type-specific cis-regulatory DNA elements (CREs), we identified 141 ADNC-associated genes. Using gene set enrichment analysis (GSEA) and network proximity analysis, we identified 13 candidate repurposable drugs that were associated with these ADNC-associated genes. In summary, we constructed a single-cell digital twin (scDT) framework cell type-specific target identification and drug repurposing in AD and other complex diseases if broadly applied.

bioinformatics↗

bioRxiv2021

Time-varying Dynamic Network Model For Dynamic Resting State Functional Connectivity in fMRI and MEG imaging

Dynamic resting state functional connectivity (RSFC) characterizes fluctuations that occurs over time in functional brain networks. Existing methods to extract dynamic RSFCs, such as sliding-window and clustering methods, have various limitations due to their inherent non-adaptive nature and high-dimensionality including an inability to reconstruct brain signals, insufficiency of data for reliable estimation, insensitivity to rapid changes in dynamics, and a lack of generalizability across multimodal functional imaging datasets. To overcome these deficiencies, we develop a novel and unifying time-varying dynamic network (TVDN) framework for examining dynamic resting state functional connectivity. TVDN includes a generative model that describes the relation between low-dimensional dynamic RSFC and the brain signals, and an inference algorithm that automatically and adaptively learns to detect dynamic state transitions in data and a low-dimensional manifold of dynamic RSFC. TVDN is generalizable to handle multimodal functional neuroimaging data (fMRI and MEG/EEG). The resulting estimated low-dimensional dynamic RSFCs manifold directly links to the frequency content of brain signals. Hence we can evaluate TVDN performance by examining whether learnt features can reconstruct observed brain signals. We conduct comprehensive simulations to evaluate TVDN under hypothetical settings. We then demonstrate the application of TVDN with real fMRI and MEG data, and compare the results with existing benchmarks. Results demonstrate that TVDN is able to correctly capture the dynamics of brain activity and more robustly detect brain state switching both in resting state fMRI and MEG data.

bioengineering↗

bioRxiv2020

Multimodal single-cell/nucleus RNA-sequencing data analysis uncovers molecular networks between disease-associated microglia and astrocytes with implications for drug repurposing in Alzheimer's disease

Systematic identification of molecular networks in disease relevant immune cells of the nervous system is critical for elucidating the underlying pathophysiology of Alzheimers disease (AD). Two key immune cell types, disease-associated microglia (DAM) and disease-associated astrocytes (DAA), are biologically involved in AD pathobiology. Therefore, uncovering molecular determinants of DAM and DAA will enhance our understanding of AD biology, potentially identifying novel therapeutic targets for AD treatment. Here, we present an integrative, network-based methodology to uncover conserved molecular networks between DAM and DAA. Specifically, we leverage single-cell and single-nucleus RNA sequencing data from both AD transgenic mouse models and AD patient brains, drug-target networks, metabolite-enzyme associations, and the human protein-protein interactome, along with large-scale patient data validation from the MarketScan Medicare Supplemental Database. We find that common and unique molecular network regulators between DAM (i.e, PAK1, MAPK14, and SYK) and DAA (i.e., NFKB1, FOS, and JUN) are significantly enriched by multiple neuro-inflammatory pathways and well-known genetic variants (i.e., BIN1) from genome-wide association studies. Further network analysis reveal shared immune pathways between DAM and DAA, including Fc gamma R-mediated phagocytosis, Th17 cell differentiation, and chemokine signaling. Furthermore, integrative metabolite-enzyme network analyses imply that fatty acids (i.e., elaidic acid) and amino acids (i.e., glutamate, serine, and phenylalanine) may trigger molecular alterations between DAM and DAA. Finally, we prioritize repurposed drug candidates for potential treatment of AD by agents that specifically reverse dysregulated gene expression of DAM or DAA, including an antithrombotic anticoagulant triflusal, a beta2-adrenergic receptor agonist salbutamol, and the steroid medications (fluticasone and mometasone). Individuals taking fluticasone (an approved anti-inflammatory and inhaled corticosteroid) displayed a significantly decreased incidence of AD (hazard ratio (HR) = 0.858, 95% confidence interval [CI] 0.829-0.888, P < 0.0001) in retrospective case-control validation. Furthermore, propensity score matching cohort studies also confirmed an association of mometasone with reduced incidence of AD in comparison to fluticasone (HR =0.921, 95% CI 0.862-0.984, P < 0.0001).

neuroscience↗

bioRxiv2020

AlzGPS: A Genome-wide Positioning Systems Platform to Catalyze Multi-omics for Alzheimer's Therapeutic Discovery

BackgroundOver15 million family members and caregivers have expended $220 billion for care of patients with AD and other dementias, and the attrition rate for AD clinical trials (2002-2012) is estimated at 99.6%. While recent DNA/RNA sequencing and other multi-omics technologies have advanced the understanding of the biology and pathophysiology of AD, no effective disease-modifying or preventive therapies, for AD have emerged in the past two decades. A new approach to integration of the genome, transcriptome, proteome, and human interactome in the drug discovery and development process is essential for this endeavor. MethodsIn this study, we developed AlzGPS (Genome-wide Positioning Systems platform for Alzheimers Therapeutic Discovery, https://alzgps.lerner.ccf.org), a comprehensive systems biology tool to enable searching, visualizing, and analyzing multi-omics, various types of heterogeneous biological networks, and clinical databases for target identification and effective prevention and treatment of AD. ResultsVia AlzGPS: (1) we curated more than 100 AD multi-omics data sets capturing DNA, RNA, protein, and small molecules profiles underlying AD pathogenesis (e.g., early vs. late stage and tau vs. amyloid endophenotype); (2) we constructed endophenotype disease modules by incorporating multi-omics findings and human protein-protein interactome networks; (3) we identified repurposable drugs from [~]3,000 FDA approved/investigational drugs for AD using state-of-the-art network proximity analyses; (4) we curated 300 literature references for highly repurposable drugs; (5) we included information from over 200 ongoing AD clinicals noting drug mechanisms and primary drug targets, and linking them to our integrated multi-omics view for targets and network analyses results for the drugs; (6) we implemented a highly interactive web-interface for database browsing and network visualization. ConclusionsNetwork visualization enabled by the AlzGPS includes brain-specific neighborhood networks for genes-of-interest, endophenotype disease module networks for data sets-of-interest, and mechanism-of-action networks for drugs targeting disease modules. By virtue of combining systems pharmacology and network-based integrative analysis of multi-omics data, the AlzGPS offers actionable systems biology tools for accelerating therapeutic development in AD.

systems biology↗

bioRxiv2020

Simple Assay, Kinetics, and Biochemical Trends for Soil Microbial Catalases

In this report, we expand upon the enzymology and biochemical ecology of soil catalases through development and application of a simple kinetic model and assay based upon volume displacement. Through this approach, we (A) directly relate apparent Michaelis-Menten terms to the catalase reaction mechanism, (B) obtain upper estimates of the intrinsic rate constants for the catalase community [Formula] and moles of catalase per 16S rRNA gene copy number, (C) utilize catalase specific activities (SAs) to obtain biomass estimates of soil and permafrost communities (LOD, ~104 copy number gdw-1), and (D) relate kinetic trends to changes in bacterial community structure. This model represents a novel approach to the kinetic treatment of soil catalases, while simultaneously incorporating barometric adjustments to afford comparisons across field measurements. As per our model, and when compared to garden soils, biological soil crusts exhibited ~2-fold lower values for [Formula], [≥]105-fold higher catalase moles per biomass (250-1200 zmol copy number-1), and ~104-fold higher SAs per biomass (74-230 fkat copy number-1). However, the highest SAs were obtained from permafrost and high-elevation soil communities (5900-6700 fkat copy number-1). In sum, these total trends suggest that microbial communities which experience higher degrees of native oxidative stress possess higher basal intracellular catalase concentrations and SAs per biomass, and that differing kinetic profiles across catalase communities are indicative of phylum and/or genus-level changes in community structure. For microbial ecology, therefore, these measures effectively serve as markers for microbial activity and abundance, and additionally provide insights into the community responses to exogenous stress. ImportanceThe efficient management of oxidative stresses arising from environmental pressures are central to the homeostasis of soil microbial communities. Among the enzymes that manage oxidative stress are catalases, which degrade hydrogen peroxide into oxygen gas and water. In this report, we detail the development and application of a simple kinetic model and assay to measure catalase reaction rates and estimate soil biomass. Our assay is based upon volume displacement, and is low-cost, field-amenable, and suitable for scientists and educators from all disciplines. Our results suggest that microbial communities that experience higher degrees of native oxidative stress possess higher basal intracellular catalase concentrations and specific activities when expressed per biomass. For microbial ecology, therefore, these measures serve as biochemical markers for microbial activity and abundance, and provide insights into the community responses to exogenous stress; thereby providing a novel means to study active microbial communities in soils and permafrost.

biochemistry↗

bioRxiv2018

Regulation of arousal via on-line neurofeedback improves human performance in a demanding sensory-motor task

Our state of arousal can significantly affect our ability to make optimal decisions, judgments, and actions in real-world dynamic environments. The Yerkes-Dodson law, which posits an inverse-U relationship between arousal and task performance, suggests that there is a state of arousal that is optimal for behavioral performance in a given task. Here we show that we can use on-line neurofeedback to shift an individuals arousal toward this optimal state. Specifically, we use a brain computer interface (BCI) that uses information in the electroencephalogram (EEG) to generate a neurofeedback signal that dynamically adjusts an individuals arousal state when they are engaged in a boundary avoidance task (BAT). The BAT is a demanding sensory-motor task paradigm that we implement as an aerial navigation task in virtual reality (VR), and which creates cognitive conditions that escalate arousal and quickly results in task failure -- e.g. missing or crashing into the boundary. We demonstrate that task performance, measured as time and distance over which the subject can navigate before failure, is significantly increased when veridical neurofeedback is provided. Simultaneous measurements of pupil dilation and heart rate variability show that the neurofeedback indeed reduces arousal. Our work is the first demonstration of a BCI system that uses on-line neurofeedback to shift arousal state and increase task performance in accordance with the Yerkes-Dodson law.

neuroscience↗