Biology subjects

Adkins, J.

Publications and source records attributed to Adkins, J..

8 recordsLinked to original sources

bioRxiv2026

Habituation drives elevated antimicrobial resistance, functional, and compositional shifts in the black bear gut microbiome

Wildlife habituation across the globe poses threats to human and animal safety and alters regular ecological function, yet the wildlife, environmental, and public health ramifications are poorly characterized. The gut microbiome is a valuable proxy for these factors as it influences host health, is affected by anthropogenic exposure, and may contribute to the environmental spread of virulence factors. Despite their importance, wildlife gut microbiomes are largely uncharacterized, both in composition and function. We characterized American black bear (Ursus americanus) gut microbiomes across a spectrum of lifestyles including wild (conflict-free), habituated (conflict-prone), and captive individuals. 16S rRNA gene sequencing was used for compositional analyses and whole genome short-read sequencing (WGS) was performed on a subset of bears to investigate microbiome function and virulence factors. We found that habituated, wild, and captive bears have unique gut microbiome compositions that are indicative of their respective lifestyles. In particular, captive bears are enriched in carnivory-associated taxa and habituated bears in inflammatory taxa. WGS reveals wild bears have increased fungi and soil-associated bacteria, as well as higher proportions of unclassified reads. Habituated bears have significantly increased levels of antimicrobial resistance genes, and captive bears are enriched in enterotoxins. Our results demonstrate previously undescribed wildlife physiological and One Health consequences of habituation.

ecology↗

bioRxiv2026

A GENOMIC BASIS FOR TRANS-OCEANIC SEA TURTLE MIGRATION

Long-distance migration has evolved repeatedly across the animal kingdom, yet the underlying processes giving rise to and maintaining these complex eco-behavioral phenotypes remain poorly understood. Here, we present the first evidence of genomic determinants of migratory phenotypes in sea turtles, using whole genome resequencing to demonstrate that complex genomic architecture underlies divergent migratory destinations and reproductive timing in the critically endangered western Pacific leatherback turtle (Dermochelys coriacea). Individuals from this admixed population that navigate to foraging grounds on opposite sides of the Pacific Ocean have a putative inversion on chromosome 2 encompassing one gene, potentially conferring pleiotropic physiological effects and supporting magnetoreception. Genomic architecture underlying divergent reproductive timing is more dispersed, aligned with reduced gene flow, and is associated with genes that may influence reproductive success. Genes underlying both traits suggest a role for neurodevelopment and memory. Our study adds to the increasing evidence of at least partial genomic control of migratory traits in wild populations, with important potential implications for conservation measures such as translocation and genetic rescue. Our results align with a growing body of work describing complex genomic architecture and structural variants underlying key eco-behavioral traits, advancing the understanding of evolution of long-distance migration across taxa.

ecology↗

bioRxiv2026

Genomic indicators of risk and resilience in global leatherback turtle populations

Understanding the drivers of genomic health and their consequences for population viability is often overlooked but potentially important to effective conservation amidst the biodiversity crisis of the Anthropocene. Leatherback turtle (Dermochelys coriacea) populations have declined globally due to anthropogenic factors, with some populations losing over 90% of their abundance over the past 30-50 years. While conservation efforts have been successful in stabilizing some populations, others continue to decline, and the reasons for these differential trajectories remain unclear. To assess how recent demographic factors, such as population size and decline, influence population genomic health, we combined population monitoring information with medium depth whole-genome and reduced representation resequencing data from globally representative populations. We found that small-stable populations have lower genomic diversity and higher inbreeding than large declining populations, reflecting prolonged small population sizes and limited gene flow. Yet, small-stable populations also show evidence of deleterious allele purging, suggesting genetic resilience. This, combined with lack of detectable genomic erosion over the study period, provides hope for potential recovery of healthy leatherback populations provided that anthropogenic threats are effectively mitigated. However, potential time lags and possible recent increases in inbreeding among close relatives in recently declined populations warrant continued monitoring and assessment. Genomic and abundance-based metrics were less aligned following rapid population declines, emphasizing the different timescales of the evolutionary and demographic processes they reflect, respectively, and the strength in their complementary, integrative use for extinction risk assessments. This also supports that it is not too late to turn the tide for recently declined leatherback populations and that continued investment in conservation efforts and threat reductions are warranted. Collectively, our results highlight how recent and historical demography shapes current genomic health and recovery potential in leatherback turtles, aids understanding of current risks and informs future conservation and management strategies.

ecology↗

bioRxiv2021

LungMAP Portal Ecosystem: Systems-Level Exploration of the Lung

An improved understanding of the human lung necessitates advanced systems models informed by an ever-increasing repertoire of molecular omics, cellular, imaging and pathological datasets. To centralize and standardize information across broad lung research efforts we expanded the LungMAP.net website into a gateway portal. This portal connects a broad-spectrum of research networks, bulk and single-cell multi-omics data and a diverse collection of image data that span mammalian lung development and disease. The data are standardized across species and technologies using harmonized data and metadata models that leverage recent advances including those from the Human Cell Atlas, diverse ontologies, and the LungMAP CellCards initiative. To cultivate future discoveries, we have aggregated a diverse collection of single-cell atlases for multiple species (human, rhesus, mouse), to enable consistent queries across technologies, cohorts, age, disease and drug treatment. These atlases are provided as independent and integrated queriable datasets, with an emphasis on dynamic visualization, figure generation and reference-based classification of user-provided datasets (Azimuth). As this resource grows, we intend to increase the breadth of available interactive interfaces, data portals and datasets from LungMAP and external research efforts.

systems biology↗

bioRxiv2019

Tumor cell phenotype and heterogeneity differences in IDH1 mutant vs wild-type gliomas

Glioma is recognized to be a highly heterogeneous CNS malignancy, whose diverse cellular composition and cellular interactions have not been well characterized. To gain new clinical- and biological-insights into the genetically-bifurcated IDH1 mutant (mt) vs wildtype (wt) forms of glioma, we integrated multiplexed immunofluorescence single cell data for 43 protein markers across cancer hallmarks, in addition to cell spatial metrics, genomic sequencing and magnetic resonance imaging (MRI) quantitative features. Molecular and spatial heterogeneity scores for angiogenesis and cell invasion differ between IDHmt and wt gliomas irrespective of prior treatment and tumor grade; these differences also persisted in the MR imaging features of peritumoral edema and contrast enhancement volumes. Longer overall survival for IDH1mt glioma patients may reflect generalized altered cellular, molecular, spatial heterogeneity which manifest in discernable radiological manifestations.

genomics↗

bioRxiv2019

ACValidator: a novel assembly-based approach for in silico validation of circular RNAs

Circular RNAs (circRNAs) are evolutionarily conserved RNA species that are formed when exons back-splice to each other. Current computational algorithms to detect these back-splicing junctions produce divergent results, and hence there is a need for a method to distinguish true positive circRNAs. To this end, we developed ACValidator (Assembly based CircRNA Validator) for in silico validation of circRNAs. ACValidator extracts reads from a user-defined window on either side of the circRNA junction and assembles them to generate contigs. These contigs are aligned against the circRNA sequence to find contigs spanning the backspliced junction. When evaluated on simulated datasets, ACValidator achieved over 80% sensitivity and specificity on datasets with an average of 10 circRNA-supporting reads and with read lengths of at least 100 bp. In experimental datasets, ACValidator produced higher validation percentages for samples treated with ribonuclease R compared to non-treated samples. Our workflow is applicable to non-polyA-selected RNAseq datasets and can also be used as a candidate selection strategy for experimental validations. All workflow scripts are freely accessible on our github page https://github.com/tgen/ACValidator along with detailed instructions to set up and run ACValidator. Author summaryCircular RNAs (circRNAs) are a recent addition to the class of non-coding RNAs and are produced when exons back-splice and form closed circular loops. Although several computational algorithms have been developed to detect circRNAs from RNA sequencing (RNAseq) data, they produce divergent results. We hence developed the software Assembly based Circular RNA Validator (ACValidator) as an orthogonal strategy to separately validate predicted circRNAs in silico. ACValidator takes as input a sequence alignment mapping (SAM) file and the circRNA coordinate(s) to be validated. Reads surrounding the circRNA junction are extracted from the SAM file and assembled to generate contigs. These contigs are then aligned against the circRNA sequence to identify contigs that span the back-spliced junction. We evaluated our workflow on simulated as well as experimental datasets to demonstrate the utility of our approach. ACValidator is implemented in python and is highly computationally efficient, with a run time of less than 2 minutes for an 8 GB SAM file. This workflow is applicable to non-polyA-selected RNAseq datasets and can also be used as a candidate selection strategy for experimental validations.

bioinformatics↗

bioRxiv2018

Joint analysis of matched tumor samples with varying tumor contents improves somatic variant calling in the absence of a germline sample

Archival tumor samples represent a potential rich resource of annotated specimens for translational genomics research. However, standard variant calling approaches require a matched normal sample from the same individual, which is often not available in the retrospective setting, making it difficult to distinguish between true somatic variants and germline variants that are private to the individual. Archival sections often contain adjacent normal tissue, but this normal tissue can include infiltrating tumor cells. Comparative somatic variant callers are designed to exclude variants present in the normal sample, so a novel approach is required to leverage sequencing of adjacent normal tissue for somatic variant calling. Here we present LumosVar 2.0, a software package designed to jointly analyze multiple samples from the same patient. The approach is based on the concept that the allelic fraction of somatic variants, but not germline variants, would be reduced in samples with low tumor content. LumosVar 2.0 estimates allele specific copy number and tumor sample fractions from the data, and uses the model to determine expected allelic fractions for somatic and germline variants and classify variants accordingly. To evaluate using LumosVar 2.0 to jointly call somatic variants with tumor and adjacent normal samples, we used a glioblastoma dataset with matched high tumor content, low tumor content, and germline exome sequencing data (to define true somatic variants) available for each patient. We show that both sensitivity and positive predictive value are improved by analyzing the high tumor and low tumor samples jointly compared to analyzing the samples individually or compared to in-silico pooling of the two samples. Finally, we applied this approach to a set of breast and prostate archival tumor samples for which normal samples were not available for germline sequencing, but tumor blocks containing adjacent normal tissue were available for sequencing. Joint analysis using LumosVar 2.0 detected several variants, including known cancer hotspot mutations that were not detected by standard somatic variant calling tools using the adjacent normal as a reference. Together, these results demonstrate the potential utility of leveraging paired tissue samples to improve somatic variant calling when a constitutional DNA sample is not available.

bioinformatics↗

bioRxiv2018

Circular RNA expression and regulatory network prediction in posterior cingulate astrocytes in elderly subjects

BackgroundCircular RNAs (circRNAs) are a novel class of endogenous, non-coding RNAs that form covalently closed continuous loops and are both highly conserved and abundant in the mammalian brain. A role for circRNAs in sponging microRNAs (miRNAs) has been proposed, but the circRNA-miRNA-mRNA interaction networks in human brain cells have not been defined. Therefore, we identified circRNAs in RNA sequencing data previously generated from astrocytes microdissected from the posterior cingulate (PC) of Alzheimers disease (AD) patients (N=10) and healthy elderly controls (N=10) using four circRNA prediction algorithms - CIRI, CIRCexplorer, find_circ and KNIFE.\n\nResultsOverall, utilizing these four tools, we identified a union of 4,438 unique circRNAs across all samples, of which 70.3% were derived from exonic regions. Notably, the widely reported CDR1as circRNA was detected in all samples across both groups by find_circ. Given the putative miRNA regulatory function of circRNAs, we identified potential miRNA targets of circRNAs, and further, delineated circRNA-miRNA-mRNA networks using in silico methods. Pathway analysis of the genes regulated by these miRNAs identified significantly enriched immune response pathways, which is consistent with the known function of astrocytes as immune sensors in the brain.\n\nConclusionsIn this study, we performed circRNA detection on cell-specific transcriptomic data and identified potential circRNA-miRNA-mRNA regulatory networks in PC astrocytes. Given the known function of astrocytes in cerebral innate immunity and our identification of significantly enriched immune response pathways, the circRNAs we identified may be associated with such key functions. While we did not detect recurrent differentially expressed circRNAs in the context of healthy controls or Alzheimers, we report for the first time circRNAs and their potential regulatory impact in a cell-specific and region-specific manner in aged subjects. These predicted regulatory network and pathway analyses may help provide new insights into transcriptional regulation in the brain.

neuroscience↗