Biology subjects

Nelson, M. R.

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

9 recordsLinked to original sources

bioRxiv2026

The Evolving Bileome: A Pan-Repository Landscape of Expanded Bile Acid and Related Steroidal Signatures

Bile acids and related steroidal metabolites are among the most studied biomolecules, yet recent work has revealed far greater structural diversity than previously recognized, much of it shaped by the microbiome. We previously introduced a repository-scale bile acid MS/MS library built from queries targeting C24 bile acid fragmentation, limited to Orbitrap Q Exactive datasets within GNPS/MassIVE. Here we announce a major expansion of this library: a bile acid and steroidal MS/MS spectral library assembled across three major metabolomics repositories, now capturing C23, C24 (including alkamine), C26 (C-C bonded aceto derivatives), and C27 bile acids, as well as C28 bile alcohols. The expanded libraries comprise 640,141 unique MS/MS spectra, of which 120,293 (18.8%) match 2,529 reference spectra, leaving most of this steroidal chemical space structurally unassigned. Linked to harmonized pan-repository metadata, the library provides a starting point for classifying MS/MS spectra as likely bile acids or related steroids, with two example analyses illustrated here. This resource allows researchers to flag candidate bile acid and steroidal signatures in their own data for further characterization.

bioinformatics↗

bioRxiv2026

Alkamines reveal a hidden layer of steroid and drug metabolism

Biomedical research overlooks most genes in favor of a well-studied minority, yet whether analogous blind spots exist in metabolomics remains unknown. We show that reductive amination, forming secondary amines from aldehydes or ketones and amines, generates a previously hidden class of metabolites we term alkamines. Multiplexed synthesis of 8,475 alkamines combined with MS/MS searches across 1.7 billion spectra identified 1,626 candidates across multiple species and organs. Of these, 56 were confirmed in biological samples, including 27 steroid- and 12 drug-derived alkamines matching prescription patterns. Notably, 77% of synthesized alkamines are absent from PubChem. This combinatorial logic likely explains why alkamines have evaded detection and suggests drug metabolism frameworks substantially underestimate drug-derived metabolite diversity. Reductive amination is an overlooked route modifying steroids, bile acids, and xenobiotics.

biochemistry↗

bioRxiv2023

Microglia Depletion Reduces Human Neuronal APOE4-Driven Pathologies in a Chimeric Alzheimer's Disease Model

Despite strong evidence supporting the involvement of both apolipoprotein E4 (APOE4) and microglia in Alzheimers Disease (AD) pathogenesis, the effects of microglia on neuronal APOE4-driven AD pathogenesis remain elusive. Here, we examined such effects utilizing microglial depletion in a chimeric model with human neurons in mouse hippocampus. Specifically, we transplanted homozygous APOE4, isogenic APOE3, and APOE-knockout (APOE-KO) induced pluripotent stem cell (iPSC)-derived human neurons into the hippocampus of human APOE3 or APOE4 knock-in mice, and depleted microglia in half the chimeric mice. We found that both neuronal APOE and microglial presence were important for the formation of A{beta} and tau pathologies in an APOE isoform-dependent manner (APOE4 > APOE3). Single-cell RNA-sequencing analysis identified two pro-inflammatory microglial subtypes with high MHC-II gene expression that are enriched in chimeric mice with human APOE4 neuron transplants. These findings highlight the concerted roles of neuronal APOE, especially APOE4, and microglia in AD pathogenesis. HIGHLIGHTSO_LITransplanted human APOE4 neurons generate A{beta} and p-tau aggregates in APOE4-KI mouse hippocampus. C_LIO_LIHuman neuronal APOE4 promotes the formation of dense-core A{beta} plaques and p-tau aggregates. C_LIO_LIMicroglia is required for human neuronal APOE4-driven formation of p-tau aggregates. C_LIO_LIscRNA-seq reveals enrichment of MHC-II microglia in mice with human APOE4 neuron transplants. C_LI

neuroscience↗

bioRxiv2019

A Multi-Niche Microvascularized Human Bone-Marrow-on-a-Chip

The human bone marrow (hBM) is a complex organ critical for hematopoietic and immune homeostasis, and where many cancers metastasize. Yet, understanding the fundamental biology of the hBM in health and diseases remain difficult due to complexity of studying or manipulating the BM in humans. Accurate in vitro models of the hBM microenvironment are critical to further our understanding of the BM niche and advancing new clinical interventions. Although, in vitro culture models that recapitulate some key components of the BM niche have been reported, there are no examples of a fully human, in vitro, organoid platform that incorporates the various niches of the hBM - specifically the endosteal, central marrow, and perivascular niches - thus limiting their physiological relevance. Here we report an hBM-on-a-chip that incorporates these three niches in a single micro-physiological device. Osteogenic differentiation of hMSCs produced robust mineralization on the PDMS surface ("bone layer") and subsequent seeding of endothelial cells and hMSCs in a hydrogel network ("central marrow") created an interconnected vascular network ("perivascular niche") on top. We show that this multi-niche hBM accurately mimics the ECM composition, allows hematopoietic progenitor cell proliferation and migration, and is affected by radiation. A key finding is that the endosteal niche significantly contributes to hBM physiology. Taken together, this multi-niche micro-physiological system opens up new opportunities in hBM research and therapeutics development, and can be used to better understand hBM physiology, normal and impaired hematopoiesis, and hBM pathologies, including cancer metastasis, multiple myelomas, and BM failures.

bioengineering↗

bioRxiv2019

Identification of putative effector genes across the GWAS Catalog using molecular quantitative trait loci from 68 tissues and cell types

Identifying the effector genes from genome-wide association studies (GWAS) is a crucial step towards understanding the biological mechanisms underlying complex traits and diseases. Colocalization of expression and protein quantitative trait loci (eQTL and pQTL, hereafter collectively called \"xQTL\") can be effective for mapping associations to genes in many loci. However, existing colocalization methods require full single-variant summary statistics which are often not readily available for many published GWAS or xQTL studies. Here, we present PICCOLO, a method that uses minimum SNP p-values within a locus to determine if pairs of genetic associations are colocalized. This method greatly expands the number of GWAS and xQTL datasets that can be tested for colocalization. We applied PICCOLO to 10,759 genome-wide significant associations across the NHGRI-EBI GWAS Catalog with xQTLs from 28 studies. We identified at least one colocalized gene-xQTL in at least one tissue for 30% of associations, and we pursued multiple lines of evidence to demonstrate that these mappings are biologically meaningful. PICCOLO genes are significantly enriched for biologically relevant tissues, and 4.3-fold enriched for targets of approved drugs.

genetics↗

bioRxiv2019

Phenome-wide Mendelian randomization mapping the influence of the plasma proteome on complex diseases

The human proteome is a major source of therapeutic targets. Recent genetic association analyses of the plasma proteome enable systematic evaluation of the causal consequences of variation in plasma protein levels. Here, we estimated the effects of 1002 proteins on 225 phenotypes using two-sample Mendelian randomization (MR) and colocalization. Of 413 associations supported by evidence from MR, 130 (31.5%) were not supported by results of colocalization analyses, suggesting that genetic confounding due to linkage disequilibrium (LD) is widespread in naive phenome-wide association studies of proteins. Combining MR and colocalization evidence in cis-only analyses, we identified 111 putatively causal effects between 65 proteins and 52 disease-related phenotypes (www.epigraphdb.org/pqtl/). Evaluation of data from historic drug development programmes showed that target-indication pairs with MR and colocalization support were more likely to be approved, evidencing the value of our approach in identifying and prioritising potential therapeutic targets.

genetics↗

bioRxiv2019

Whole exome sequencing and characterization of coding variation in 49,960 individuals in the UK Biobank

The UK Biobank is a prospective study of 502,543 individuals, combining extensive phenotypic and genotypic data with streamlined access for researchers around the world. Here we describe the first tranche of large-scale exome sequence data for 49,960 study participants, revealing approximately 4 million coding variants (of which ~98.4% have frequency < 1%). The data includes 231,631 predicted loss of function variants, a >10-fold increase compared to imputed sequence for the same participants. Nearly all genes (>97%) had [≥]1 predicted loss of function carrier, and most genes (>69%) had [≥]10 loss of function carriers. We illustrate the power of characterizing loss of function variation in this large population through association analyses across 1,741 phenotypes. In addition to replicating a range of established associations, we discover novel loss of function variants with large effects on disease traits, including PIEZO1 on varicose veins, COL6A1 on corneal resistance, MEPE on bone density, and IQGAP2 and GMPR on blood cell traits. We further demonstrate the value of exome sequencing by surveying the prevalence of pathogenic variants of clinical significance in this population, finding that 2% of the population has a medically actionable variant. Additionally, we leverage the phenotypic data to characterize the relationship between rare BRCA1 and BRCA2 pathogenic variants and cancer risk. Exomes from the first 49,960 participants are now made accessible to the scientific community and highlight the promise offered by genomic sequencing in large-scale population-based studies.

genomics↗

bioRxiv2018

The Medical Genome Reference Bank: Whole genomes and phenotype of 2,570 healthy elderly

Population health research is increasingly focused on the genetic determinants of healthy ageing, but there is no public resource of whole genome sequences and phenotype data from healthy elderly individuals. Here we describe the Medical Genome Reference Bank (MGRB), comprising whole genome sequence and phenotype of 2,570 elderly Australians depleted for cancer, cardiovascular disease, and dementia. We analysed the MGRB for single-nucleotide, indel and structural variation in the nuclear and mitochondrial genomes. Individuals in the MGRB had fewer disease-associated common and rare germline variants, relative to both cancer cases and the gnomAD and UK BioBank cohorts, consistent with risk depletion. Pervasive age-related somatic changes were correlated with grip strength in men, suggesting blood-derived whole genomes may also provide a biologic measure of age-related functional deterioration. The MGRB provides a broadly applicable reference cohort for clinical genetics and genomic association studies, and for understanding the genetics of healthy ageing. This research has been conducted using the UK Biobank Resource under Application Number 17984.

genomics↗

bioRxiv2018

Predicting clinically promising therapeutic hypotheses using tensor factorization

Determining which target to pursue is a challenging and error-prone first step in developing a therapeutic treatment for a disease, where missteps are potentially very costly given the long-time frames and high expenses of drug development. We identified examples of successes and failures of target-indication pairs in clinical trials across 875 targets and 574 disease indications to build a gold-standard data set of 6,140 known clinical outcomes. We used information from Open Targets and others databases that covered 17 different sources of evidence for target-indication association and represented the data as a matrix of 21,437x2,211x17 with over two million non-null values. We designed and executed three benchmarking strategies to examine the performance of multiple machine learning models: Logistic Regression, Elasticnet, Random Forest, Tensor Factorization and Gradient Boosting Machine. With ten-fold cross validation, tensor factorization achieved AUROC=0.82{+/-}0.02 and AUPRC=0.71{+/-}0.03. Across multiple validation schemes, this was comparable or better than other methods. Tensor factorization is a general form of matrix factorization that has been successfully exploited in recommendation systems that suggest items to users based on their existing preference on a small number of items. Our application, using Bayesian probabilistic modelling, extends the capacity of matrix factorization to model multiple relationships between and among targets and indications. We use the model to show that our predicted probabilities of success correlate with clinical phases, and within clinical phase we can predict which trials are most likely to succeed.

bioinformatics↗