Biology subjects

Nelson, T. M.

Publications and source records attributed to Nelson, T. M..

10 recordsLinked to original sources

bioRxiv2026

Modulator Resolves Transcript-, Allele-, and Condition-associated RNA Modification Differences from Direct RNA Sequencing

Nanopore direct-RNA sequencing (DRS) reports RNA modifications on single molecules, but there is no comprehensive algorithm for processing, aligning, and differentiating base modifications across reads that are often 5'-truncated. Here we present Modulator, which assigns every read, truncated or not, to a fragmentform, defined by a shared transcript 3'-end and the splice-junctions upstream of it. Of note, Modulator builds fragmentforms from genomic alignments without a reference transcriptome, estimates per-base modification stoichiometry within each fragmentform, and classifies each stoichiometry difference as transcript-, allele-, or condition-dependent. In the control HG002 transcriptome, Modulator built 23,746 fragmentforms, with 15,620 candidate novel isoforms that carried poly(A) signals as often as previously annotated transcripts. Also, assigning 5'-truncated reads to fragmentforms nearly doubled the number of testable and differentially modified sites. m6A sites within 100 nucleotides (nt) of a splice junction or within 1 kb of a poly(A) site were less modified in fragmentforms that use that junction or poly(A) site, indicating that local transcript context shapes where m6A is deposited. From the same reads, Modulator called SNPs with 93% precision and no phasing errors against the Genome in a Bottle benchmark. It identified 131 SNPs that create or remove a modifiable nucleotide and 1,358 allele-specific modification differences, most involving SNPs more than 50 nt from the modified site. In cells infected with Zika virus, which remodels gene expression relative to uninfected cells, Modulator identified 1,253 transcript-independent modification stoichiometry changes. Of the m6A changes at sites measured by GLORI, 96% agreed in direction with single-nucleotide-resolution GLORI maps of the same samples. Thus, Modulator accurately separates changes in RNA modification from transcript-associated and allele-associated differences and can improve the utility of nanopore DRS. Availability and Implementation: Modulator is free and open source: https://github.com/Theo-Nelson/Modulator. It is written in Python and runs on Linux OS.

bioinformatics↗

bioRxiv2026

Alternative polyadenylation drives isoform-dependent m6A remodeling during Zika virus infection

Alternative RNA processing generates extensive transcript diversity, yet how transcript architecture influences selective m6A deposition is incompletely understood. Exon-junction-based models explain where m6A is excluded, but a positive determinant of m6A accumulation remains undefined. Here, we leverage Zika virus-induced changes in m6A deposition to uncover determinants of transcript-selective methylation. By integrating GLORI-seq, native METTL3 RNA immunoprecipitation, and nanopore direct RNA sequencing, we generate a single-nucleotide, isoform-resolved map of m6A dynamics during infection. We identify over 2,000 dynamic m6A sites, many arising from changes in transcript architecture, and pinpoint proximal polyadenylation sites as positive determinants of m6A accumulation. The cleavage stimulation factors CSTF2 and CSTF2T drive this remodeling through two routes: redundant induction of intronic polyadenylation, which converts internal exons into terminal exons that expose DRACH motifs to METTL3, and non-redundant, cleavage-independent recruitment of METTL3 near proximal polyadenylation sites, establishing alternative polyadenylation as a key architectural determinant of the m6A landscape.

molecular biology↗

bioRxiv2026

Diverse high-fat diets drive multi-omic reprogramming that persists after dietary reversal

Dietary fat composition modulates host physiology and the gut microbiome, but the long-term effects of specific fat sources and the extent to which these changes resolve after dietary reversal remain incompletely defined. Here, we present a longitudinal multi-omic resource of mice maintained for one year on a purified control diet, seven high-fat diets differing in predominant fat source, or reversal regimens in which animals were switched from high-fat to control diet after 4 or 9 months. We further incorporated two cohorts with distinct pre-existing microbiome configurations to determine how baseline community structure shapes diet-induced remodeling of the gut microbiome ecosystem. By integrating longitudinal phenotyping, fecal metagenomics, fecal metabolomics, plasma metabolomics and lipidomics, and intestinal single-cell RNA sequencing, we defined the shared and dietary fat-specific responses across host and microbiome compartments. Baseline microbiome composition strongly influenced microbial responses to diet, indicating that pre-existing community structure is a major determinant of dietary ecosystem remodeling. Although many altered features shifted toward baseline after dietary reversal, only approximately half of diet-associated microbial changes recovered within the study window. A subset of taxa exhibited persistent alterations, including sustained depletion of Lactobacillus johnsonii and Bifidobacterium pseudolongum and sustained enrichment of Alistipes finegoldii, consistent with a "microbiome memory" of prior high-fat diet exposure. This memory effect is mirrored in the host, by sustained suppression of major histocompatibility complex class II (MHC-II) gene expression in intestinal epithelial cells after dietary reversal. These findings indicate that dietary fats leave a lasting imprint on the host-microbiome interactome that survives dietary intervention. Together, these data establish a resource for defining how dietary fat source, baseline microbiome composition, and dietary history shape host-microbiome states. The entire resource is available online as an RShiny app.

systems biology↗

bioRxiv2026

Whole-genome benchmarking reveals context-specific error rates in the Ultima UG100 and Illumina NovaSeqX Platforms.

Whole genome sequencing (WGS) is becoming more common in research and clinical applications, but there is a paucity of comparative data between high-throughput WGS platforms. We benchmarked the Ultima Genomics UG100 system against Illumina NovaSeqX using the Genome in a Bottle (GIAB) WGS reference set sample HG002. Across NIST v4.2.1 benchmark regions, UG100 exhibited higher total variant-calling errors (27x), primarily driven by indel false negatives. Restricting to Ultima Genomics high confidence regions (UG HCR v3.1, 90.3% of GRCh38) reduced the error burden by 89.6%, indicating most errors lie outside these regions. Strong degradation of variant calling performances occurred in homopolymer tracts > 10 bp, and base calling error rates increased sharply after position 200 bp within each read, whereas Illuminas error rates were higher earlier in the read. Coverage dropouts in UG100 data were pronounced in GC-rich regions. Of note, 2.24% of ClinVar Pathogenic and Likely Pathogenic variants and 22.6% of a common catalogue of polymorphic STRs were found to be excluded from UG HCR v3.1, indicating potential impact to clinical applications. These results highlight context-specific genotyping errors of the UG100 and NovaSeqX platforms, and underscore the importance of whole genome benchmarking for adjudicating accuracy of NGS platforms beyond the current HG002 reference set.

genomics↗

bioRxiv2025

A computational framework for mapping isoform landscape and regulatory mechanisms from spatial transcriptomics data

Transcript diversity including splicing and alternative 3 end usage is crucial for cellular identity and adaptation, yet its spatial coordination remains poorly understood. Here, we present SPLISOSM (SpatiaL ISOform Statistical Modeling), a computational framework for detecting isoform-resolution patterns from spatial transcriptomics data. SPLISOSM leverages multivariate testing to account for spot- and isoform-level dependencies, demonstrating robust and theoretically grounded performance on sparse data. In the mouse brain, we identify over 1,000 spatially variable transcript diversity events, primarily in synaptic signaling pathways linked to neuropsychiatric disorders, and uncover both known and novel regulatory relationships with region-specific RNA binding proteins. We further show that these patterns are evolutionarily conserved between mouse and human prefrontal cortex. Analysis of human glioblastoma highlights pervasive transcript diversity in antigen presentation and adhesion genes associated with specific microenvironmental conditions. Together, we present a comprehensive spatial splicing analysis in the brain under normal and neoplastic conditions.

bioinformatics↗

bioRxiv2024

Pharmacogenomics Guided Spaceflight: the intersection between space-flown drugs and space genes

Ten years ago, it was predicted that the multi-omics revolution would also revolutionize space pharmacogenomics. Current barriers related to the findable, accessible, interoperable, and reproducible use of space-flown pharmaceutical data have contributed to a lack of progress beyond application of earth-based principles. To directly tackle these challenges, we have produced a novel database of all the drugs flown into space, compiled from publicly available ontological and spaceflight-related datasets, to exemplify analyses for describing significant spaceflight-related targets. By focusing on mechanisms perturbed by spaceflight, we have provided a novel avenue for identifying the most relevant changes within the drug absorption, distribution, metabolism, and excretion pathways. We suggest a set of space genes, by necessity limited to available tissue types, that can be expanded and modified based on future tissue-specific and mechanistic-specific high-throughput assays. In sum, we provide the justification and a definitive starting point for pharmacogenomics guided spaceflight as a foundation of precision medicine, which will enable long-term human habitation of the Moon, Mars, and beyond. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=147 SRC="FIGDIR/small/575951v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@48f739org.highwire.dtl.DTLVardef@5ecdb0org.highwire.dtl.DTLVardef@121c93org.highwire.dtl.DTLVardef@1122b3f_HPS_FORMAT_FIGEXP M_FIG C_FIG

pharmacology and toxicology↗

bioRxiv2020

Microbial function is related to behavior of an invasive anuran

Gut bacterial communities influence, and are influenced by, the behaviour and ecology of their hosts. Those interactions have been studied primarily in humans and model organisms, but we need field research to understand the relationship between an organisms gut bacteria and its ecological challenges, such as those imposed by rapid range expansion (as in invasive species) and the presence of host-manipulating parasites. Cane toads (Rhinella marina) provide an excellent model system in this respect, because the species ongoing colonization of Australia has enforced major changes in phenotypic traits (including behaviour), and lungworm parasites (Rhabdias pseudosphaerocephala) modify host gut function in ways that enhance the viability of lungworm larvae. We collected female toads from across the species invasive range and studied their morphology, behaviour, parasite infection status and gut bacterial community. Range-core versus range-edge toads differed in morphology, behaviour, gut bacterial composition and predicted gut bacterial function but did not differ in the occurrence of parasite infection nor in the intensity of infection. Toads infected with lungworms differed from uninfected conspecifics in gut bacterial composition and diversity. Our study demonstrates strong associations between gut bacterial community and host ecology and behaviour.

microbiology↗

bioRxiv2020

Wine terroir and the soil microbiome: an amplicon sequencing-based assessment of the Barossa Valley and its sub-regions

Soil is an important factor that contributes to the uniqueness of a wine produced by vines grown in specific conditions. Recent data shows that the composition, diversity and function of soil microbial communities may play important roles in determining wine quality and indirectly affect its economic value. Here, we evaluated the impact of environmental variables on the soil microbiomes of 22 Barossa Valley vineyard sites based on the 16S rRNA gene hypervariable region 4. In this study, we report that environmental heterogeneity (soil plant-available P content, elevation, rainfall, temperature, spacing between row and spacing between vine) caused more microbial dissimilarity than geographic distance. Vineyards located in cooler and wetter regions showed lower beta diversity and a higher ratio of dominant taxa. Differences in microbial community composition were significantly associated with differences in fruit traits and in wine chemical and metabolomic profiles, highlighting the potential influence of microbial communities on the phenotype of grapevines. Our results suggest that environmental factors affect wine terroir, and this may be mediated by changes in microbial structure, thus providing a basic understanding of how growing conditions affect interactions between plants and their soil microbiomes.

microbiology↗

bioRxiv2017

Burly1 is a mouse QTL for lean body mass that maps to a 0.8-Mb region on chromosome 2

Our goal was to fine map a mouse QTL for lean body mass (Burly1) using information from several populations including newly created congenic mice derived from the B6 (host) and 129 (donor) strains. The results from each mapping population were concordant and showed that Burly1 is likely a single QTL in a 0.8-Mb region at 151.9-152.7 Mb (rs33197365 to rs3700604) on mouse chromosome 2. Results from mice of all the mapping populations we studied including intercrossed, backcrossed, consomic, and congenic strains indicate that lean body mass was increased by the B6-derived allele relative to the 129-derived allele. We determined that the congenic region harboring Burly1 contains 26 protein-coding genes, 11 noncoding RNA elements (e.g., lncRNA), and 4 pseudogenes, with 1949 predicted functional variants. The effect of the Burly1 locus on lean body weight was apparent at all ages measured and did not affect food intake or locomotor activity. However, congenic mice with the B6-allele produced more heat per kilogram of lean body weight than did controls, pointing to a genotype effect on lean mass metabolism. These results show the value of integrating information from several mapping populations to refine the map location of body composition QTLs.

genetics↗

bioRxiv2017

Adiposity QTL Adip20 decomposes into at least four loci when dissected using congenic strains

An average mouse in midlife weighs between 25 and 30 g, with about a gram of tissue in the largest adipose depot (gonadal), and the weight of this depot differs between inbred strains. Specifically, C57BL/6ByJ mice have heavier gonadal depots on average than do 129P3/J mice. To understand the genetic contributions to this trait, we mapped several quantitative trait loci (QTLs) for gonadal depot weight in an F2 intercross population. Our goal here was to fine-map one of these QTLs, Adip20 (formerly Adip5), on mouse chromosome 9. To that end, we analyzed the weight of the gonadal adipose depot from newly created congenic strains. Results from the sequential comparison method indicated at least four rather than one QTL; two of the QTLs were less than 0.5 Mb apart, with opposing directions of allelic effect. Different types of evidence (missense and regulatory genetic variation, human adiposity/body mass index orthologues, and differential gene expression) implicated numerous candidate genes from the four QTL regions. These results highlight the value of mouse congenic strains and the value of this sequential method to dissect challenging genetic architecture.

genetics↗