Biology subjects

Thompson, K. N.

Publications and source records attributed to Thompson, K. N..

8 recordsLinked to original sources

bioRxiv2026

Enhanced multi-omic viral profiling from microbial community sequencing with BAQLaVa

Viruses are crucial components of microbial communities, both phage that infect bacterial community members as well as pathogenic and other eukaryotic viruses. However, they remain unobserved by most current technologies, due to combinations of experimental and analytical factors. To address the latter, we developed the BAQLaVa algorithm for high-resolution profiling of >120,000 viral species (viral genome bins, VGBs) via reference-based metagenome (MGX) or metatranscriptome (MTX) alignment to complementary nucleotide markers and proteome sets. In comprehensive benchmarking, BAQLaVa substantially outperformed alternatives, achieving species-level recall and precision regularly over 90%. We applied BAQLaVa to MGX and MTX samples from the HMP2 IBDMDB cohort to identify previously undescribed viral perturbations in inflammatory bowel diseases. Most notably, virome diversity was reduced in tandem with bacterial diversity during inflammation, in contrast to previous findings based on a narrower range of viral detection. A subset of viruses were enriched during IBD, associated with carriage of abortive infection anti-defense systems such as AbiL and PD-{lambda}-2, as well as genes involved in the regulation of lysogeny. Leveraging the corresponding viral profiles, we also inferred phage-host relationships using scalable co-occurrence and covariation signals, even in the absence of host references or genome annotations. By enabling high sensitivity and specificity viral profiling from metagenomes or metatranscriptomes, BAQLaVa provides a scalable framework for virome epidemiology and systematic analysis of virus-host interactions.

bioinformatics↗

bioRxiv2025

Quantifying Metagenomic Strain Associations fromMicrobiomes with Anpan

Genetic and genomic variation among microbial strains can dramatically influence their phenotypes and environmental impact, including on human health. However, inferential methods for quantifying these differences have been lacking. Strain-level metagenomic profiling data has several features that make traditional statistical methods challenging to use, including high dimensionality, extreme variation among samples, and complex phylogenetic relatedness. We present Anpan, a set of quantitative methods addressing three key challenges in microbiome strain epidemiology. First, adaptive filtering designed to interrogate microbial strain gene carriage is combined with linear models to identify strain-specific genetic elements associated with host health outcomes and other phenotypes. Second, phylogenetic generalized linear mixed models are used to characterize the association of sub-species lineages with such phenotypes. Finally, random effects models are used to identify pathways more likely to be retained or lost by outcome-associated strains. We validated our methods by simulation, showing that we achieve more accurate effect size estimation and a lower false positive rate compared to alternative methodologies. We then applied our methods to a dataset of 1,262 colorectal cancer patients, identifying functionally adaptive genes and strong phylogenetic effects associated with CRC status, sometimes complementing and sometimes extending known species-level microbiome CRC biomarkers. Anpans methods have been implemented as a publicly available R library to support microbial community strain and genetic epidemiology in a variety of contexts, environments, and phenotypes.

bioinformatics↗

bioRxiv2024

Evaluating metagenomic analyses for undercharacterized environments: what's needed to light up the microbial dark matter?

Non-human-associated microbial communities play important biological roles, but they remain less understood than human-associated communities. Here, we assess the impact of key environmental sample properties on a variety of state-of-the-art metagenomic analysis methods. In simulated datasets, all methods performed similarly at high taxonomic ranks, but newer marker-based methods incorporating metagenomic assembled genomes outperformed others at lower taxonomic levels. In real environmental data, taxonomic profiles assigned to the same sample by different methods showed little agreement at lower taxonomic levels, but the methods agreed better on community diversity estimates and estimates of the relationships between environmental parameters and microbial profiles.

microbiology↗

bioRxiv2024

Genetic removal of Nlrp3 protects against sporadic and R345W Efemp1-induced basal laminar deposit formation

Chronic, unresolved inflammation has long been speculated to serve as an initiating and propagating factor in numerous neurodegenerative diseases, including a leading cause of irreversible blindness in the elderly, age-related macular degeneration (AMD). Intracellular multiprotein complexes called inflammasomes in combination with activated caspases facilitate production of pro-inflammatory cytokines such as interleukin 1 beta. Specifically, the nucleotide-binding oligomerization (NOD)-like receptor protein 3 (NLRP3) has received heightened attention due to the wide range of stimuli to which it can respond and its potential involvement in AMD. In this study, we directly tested the role of Nlrp3 and its downstream effector, caspase 1 (Casp1) in mediating early AMD-like pathology (i.e., basal laminar deposits [BLamDs]) in wild-type (WT) mice and the Malattia Leventinese/Doyne honeycomb retinal dystrophy (ML/DHRD) mouse model (p.R345W mutation in Efemp1). Compared to aged-matched controls, R345W+/+ knockin mice demonstrated increased Muller cell gliosis, subretinal Iba-1+ microglial cells, higher Nlrp3 immunoreactivity in the retina, as well as significant transcriptional upregulation of complement component 3, Nlrp3, pro-Il1b, pro-caspase-1, and tissue inhibitor of matrix metalloproteinase 3 in the retinal pigmented epithelium (RPE)/choroid. These findings were accompanied by an age-related increase in BLamD formation in the R345W+/+ mice. Genetic elimination of either Nlrp3 or Casp1 significantly reduced both the size and coverage of BLamDs in the R345W+/+ background, highlighting an important and underappreciated pathway that could affect ML/DHRD onset and progression. Moreover, Nlrp3 knockout reduced spontaneous, idiopathic BLamDs in WT mice, suggesting translatability of our findings not only to rare inherited retinal dystrophies, but also potentially to AMD itself.

immunology↗

bioRxiv2023

GSK3 inhibition reduces ECM production and prevents age-related macular degeneration-like pathology

Malattia Leventinese/Doyne Honeycomb Retinal Dystrophy (ML/DHRD) is an age-related macular degeneration (AMD)-like retinal dystrophy caused by an autosomal dominant R345W mutation in the secreted glycoprotein, fibulin-3 (F3). To identify new small molecules that reduce F3 production from retinal pigmented epithelium (RPE) cells, we knocked-in a luminescent peptide tag (HiBiT) into the endogenous F3 locus which enabled simple, sensitive, and high throughput detection of the protein. The GSK3 inhibitor, CHIR99021 (CHIR), significantly reduced F3 burden (expression, secretion, and intracellular levels) in immortalized RPE and non-RPE cells. Low-level, long-term CHIR treatment promoted remodeling of the RPE extracellular matrix (ECM), reducing sub-RPE deposit-associated proteins (e.g., amelotin, complement component 3, collagen IV, and fibronectin), while increasing RPE differentiation factors (e.g., tyrosinase, and pigment epithelium derived factor). In vivo, treatment of 8 mo R345W+/+ knockin mice with CHIR (25 mg/kg i.p., 1 mo) was well tolerated and significantly reduced R345W F3-associated AMD-like basal laminar deposit number and size, thereby preventing the main pathological feature in these mice. This is the first demonstration of small molecule-based prevention of AMD-like pathology in ML/DHRD mice and may herald a rejuvenation of interest in GSK3 inhibition for the treatment of neurodegenerative diseases, including, potentially AMD itself.

neuroscience↗

bioRxiv2023

Metastatic breast cancer cells have reduced calcium and actin response after ATP-P2Y2 signaling

The tumor microenvironment and wound healing after injury both contain extremely high concentrations of the extracellular signaling molecule, adenosine triphosphate (ATP) compared to normal tissue. P2Y2 receptor, an ATP-activated purinergic receptor, is typically associated with pulmonary, endothelial, and neurological cell signaling. Here we report its role and importance in breast epithelial cell signaling and how it is altered in metastatic breast cancer. In response to ATP activation, P2Y2 receptor signaling causes an increase of intracellular Ca2+ in non-tumorigenic breast epithelial cells, while their tumorigenic and metastatic counterparts have significantly reduced Ca2+ responses. The non-tumorigenic cells respond to increased Ca2+ with actin polymerization and localization to cell edges, while the metastatic cells remained unaffected. The increase in intracellular Ca2+ after ATP stimulation was blunted using a P2Y2 antagonist, which also prevented actin mobilization and caused cell dissemination from spheroids in non-tumorigenic breast epithelial cells. Furthermore, the lack of Ca2+ concentration changes and actin mobilization in the metastatic breast cancer cells could be due to reduced P2Y2 expression, which correlates with poorer overall survival in breast cancer patients. This study elucidates rapid changes that occur after elevated intracellular Ca2+ in breast epithelial cells and how metastatic cancer cells have adapted to evade this cellular response.

cancer biology↗

bioRxiv2022

Extending and improving metagenomic taxonomic profiling with uncharacterized species with MetaPhlAn 4

Metagenomic assembly enables novel organism discovery from microbial communities, but from most metagenomes it can only capture few abundant organisms. Here, we present a method - MetaPhlAn 4 - to integrate information from both metagenome assemblies and microbial isolate genomes for improved and more comprehensive metagenomic taxonomic profiling. From a curated collection of 1.01M prokaryotic reference and metagenome-assembled genomes, we defined unique marker genes for 26,970 species-level genome bins, 4,992 of them taxonomically unidentified at the species level. MetaPhlAn 4 explains [~]20% more reads in most international human gut microbiomes and >40% in less-characterized environments such as the rumen microbiome, and proved more accurate than available alternatives on synthetic evaluations while also reliably quantifying organisms with no cultured isolates. Application of the method to >24,500 metagenomes highlighted previously undetected species to be strong biomarkers for host conditions and lifestyles in human and mice microbiomes, and showed that even previously uncharacterized species can be genetically profiled at the resolution of single microbial strains. MetaPhlAn 4 thus integrates the novelty of metagenomic assemblies with the sensitivity and fidelity of reference-based analyses, providing efficient metagenomic profiling of uncharacterized species and enabling deeper and more comprehensive microbiome biomarker detection.

bioinformatics↗

bioRxiv2021

{-}Multivariable Association Discovery in Population-scale Meta-omics Studies

It is challenging to associate features such as human health outcomes, diet, environmental conditions, or other metadata to microbial community measurements, due in part to their quantitative properties. Microbiome multi-omics are typically noisy, sparse (zero-inflated), high-dimensional, extremely non-normal, and often in the form of count or compositional measurements. Here we introduce an optimized combination of novel and established methodology to assess multivariable association of microbial community features with complex metadata in population-scale observational studies. Our approach, MaAsLin 2 (Microbiome Multivariable Associations with Linear Models), uses general linear models to accommodate a wide variety of modern epidemiological studies, including cross-sectional and longitudinal designs, as well as a variety of data types (e.g. counts and relative abundances) with or without covariates and repeated measurements. To construct this method, we conducted a large-scale evaluation of a broad range of scenarios under which straightforward identification of meta-omics associations can be challenging. These simulation studies reveal that MaAsLin 2s linear model preserves statistical power in the presence of repeated measures and multiple covariates, while accounting for the nuances of meta-omics features and controlling false discovery. We also applied MaAsLin 2 to a microbial multi-omics dataset from the Integrative Human Microbiome (HMP2) project which, in addition to reproducing established results, revealed a unique, integrated landscape of inflammatory bowel disease (IBD) across multiple time points and omics profiles.

microbiology↗