Biology subjects

Knight, R.

Publications and source records attributed to Knight, R..

At least 37 records · Page 2Linked to original sources

bioRxiv2025

Environmental and Maternal Imprints on Infant Gut Metabolic Programming

Early life is a critical period for immune and metabolic programming, but developmental patterns remain underexplored in populations from low- and middle-income countries. Here, we profiled the microbiome and metabolome of 55 Bangladeshi mother-infant dyads over the first six months of life. Importantly, we observed an increase in microbially-derived bile amidates and N-acyl lipids with age in conjunction with reads matching the bile salt hydrolase/transferase (bsh) gene. While microbial source tracking confirmed maternal fecal seeding, a substantial environmental contribution was also highlighted. Differences in infant fecal metabolic profiles were associated with delivery mode, maternal milk composition, household assets, and household-level water treatment. C-section delivery and untreated drinking water were linked to transient metabolic differences, including increases in bile amidates, N-acyl lipids, and other host-microbe co-metabolic products, including acylcarnitines. Multi-omics analysis revealed specific microbial-metabolite relationships, highlighting how early environmental and maternal living circumstances shape metabolic gut programming through the microbiome.

microbiology↗

bioRxiv2025

Ultra-fast and Efficient Network Embedding for Gigascale Biological Datasets

Graph/network representation learning (or graph/network embedding) is a widely used machine learning technique in industry recommending systems and has recently been applied in computational biology. Popular network representation learning algorithms include random walk and matrix factorization methods, but they do not scale well to large networks. To accommodate the fast growth of real-world network datasets, especially biological datasets, we engineered and improved several network embedding algorithms via intensive computational optimization (e.g., randomized-generalized singular value decomposition/SVD, efficient sketching via ProbMinHash including edge weights) and parallelization to allow ultra-fast and accurate embedding of large- scale networks. We present GraphEmbed, a computer program for scalable, memory-efficient network embedding. GraphEmbed can perform embedding for large-scale networks with several billion nodes in less than 2 hours on a commodity computing cluster. We benchmark it against standard datasets and demonstrate consistent speed and accuracy advantages over state-of-the- art techniques. We also propose centric AUC, a new metric for evaluating link-prediction accuracy in network embedding. It corrects the bias in conventional AUC caused by the highly skewed node degree distributions, which are typically found in real-world networks, especially biological networks. Taken together, GraphEmbed solves a major challenge in large-scale network representation learning for networks in general and biological networks in particular.

bioinformatics↗

bioRxiv2025

Open-science discovery of DNDI-6510, a compound that addresses genotoxic and metabolic liabilities of the COVID Moonshot SARS-CoV-2 Mpro lead inhibitor

The 2020 SARS-CoV-2 coronavirus pandemic highlighted the urgent need for novel small molecule antiviral drugs. (S)-x38 DNDI-6510 is a non-covalent SARS-CoV-2 main protease inhibitor developed by the open science collaboration COVID Moonshot. Here, we report on the metabolic and toxicologic optimization of the lead series previously disclosed by the COVID Moonshot Initiative, leading up to the selection of (S)-x38 DNDI-6510 as the preclinical candidate. We describe the thorough profiling of the series, identifying key risks such as formation of genotoxic metabolites and high clearance, which were successfully addressed during lead optimization. In addition, we disclose the in vitro and in vivo evaluation of (S)-x38 DNDI-6510 in pharmacokinetic and pharmacodynamic models, exploring multiple approaches to ameliorate rodent-specific metabolic clearance, and show that both co-dosing of (S)-x38 DNDI-6510 with an ABT inhibitor and utilizing a metabolically humanized mouse model (8HUM) achieve significant improvements in exposure. Through comparisons of ABT co-dosing and humanized mouse models in efficacy experiments, we demonstrate that continuous exposure over cellular EC90 is required for SARS-CoV-2 antiviral efficacy in vivo in an antiviral model using a mouse-adapted SARS-CoV-2 strain. Finally, (S)-x38 DNDI-6510 was assessed in maximum tolerated dose experiments in two species, demonstrating significant in vivo PXR-linked auto-induction of metabolism, leading to the discontinuation of this compound. In summary, we report the successful effort to overcome series-specific AMES liabilities in a lead development program. Downstream optimization of existing series will require in-depth optimization of rodent-specific liabilities and metabolic induction profile.

pharmacology and toxicology↗

bioRxiv2025

A Multi-Organ Murine Metabolomics Atlas Reveals Molecular Dysregulations in Alzheimer's Disease

The etiology of Alzheimers Disease (AD) remains largely unclear but is likely driven by gene-environment interactions. Here, we present a multi-organ untargeted metabolomics dataset (2,271 samples) generated from five tissue types in two genetic AD mouse models under colonized or germ-free conditions, complemented by shotgun metagenomics sequencing data (666 samples). Systems-level analyses of 3xTg and 5xFAD mice reveal clusters of dysregulated molecular classes across tissues including carnitines, bile acids, B vitamins, and neurotransmitters. This signature, coupled with microbiome profiles, suggests increased oxidative stress via mitochondrial dysfunction. Molecular feature tracking via tissueMASST, a mass spectrometry search tool we developed to bridge animal model findings with human data, identifies microbially-modulated phenylacetyl-carnitine as positively associated with aging and cognitive impairment across human AD studies. With hundreds of yet-to-be-characterized metabolites, this public resource and its associated tools will aid future research in the pathophysiology of AD.

microbiology↗

bioRxiv2025

Novel insights into the genetic architecture and mechanisms of host/microbiome interactions from a multi-cohort analysis of outbred laboratory rats

The intestinal microbiome influences health and disease. Its composition is affected by host genetics and environmental exposures. Understanding host genetic effects is critical but challenging in humans, due to the difficulty of detecting, mapping and interpreting them. To address this, we analysed host genetic effects in four cohorts of outbred laboratory rats exposed to distinct but controlled environments. We found that polygenic host genetic effects were consistent across environments. We identified three replicated microbiome-associated loci, one of which involved a sialyltransferase gene and Paraprevotella. We found a similar association in a human cohort, between ST6GAL1 and Paraprevotella, both of which have been linked with immune and infectious diseases. Moreover, we found evidence of indirect genetic effects on microbiome phenotypes, which substantially increased their total genetic variance. Finally, we identified a novel mechanism whereby indirect genetic effects can contribute to "missing heritability".

genetics↗

bioRxiv2025

Ecological relationships between human gut bacteria predicted from analysis of dense microbiome time series data from US travelers in Bangladesh

Gut microbiomes provide critical host homeostatic functions, resulting from a complex web of ecological interactions among community members. We studied these interactions using a time-lagged correlational strategy of dense longitudinal sequence data from Western individuals traveling abroad to Bangladesh who experienced diarrhea. We identified both negative (140) and positive (78) relationships between bacterial pairs. Positive relationships occurred in pairs that were significantly more phylogenetically distant, such as inter-order associations between Clostridiales and Bacteroidales, while negative relationships were more between more phylogenetically related pairs. Further analysis of computationally predicted genome content and metabolic pathways revealed that cooperative bacterial pairs overlapped less in function and offered each other metabolic support, while competitive pairs were more likely to compete for the same resources. Predicted levels of B vitamins (B5 and B3), enoyl acyl- carrier protein (acp) reductase II (FabK) and its metabolites, and nucleotide/nucleoside derivatives were able to differentiate negatively and positively associated microbe pairs. Ultimately, our findings show that combining time-series analysis with metabolic/genomic network analysis can identify relationships between bacteria with plausible causal mechanisms that are consistent with existing ecological and biochemical observations. IMPORTANCEUnderstanding how microbes in the gut interact with each other is important for devising strategies to target the human gut microbiome therapeutically. For instance, understanding competitive relationships, where a shared need of similar limited resources limits the degree to which two microbes can co-exist, can inform strategies for limiting colonization of undesirable microbes. Understanding cooperative relationships, where one microbe provides the other with substrates needed for growth, can inform strategies to promote desirable microbes. By evaluating dense time-series gut microbiome data from individuals who experienced diarrhea while traveling, we were able to predict both cooperative and competitive relationships among human gut microbes as those whose abundances were significantly related within an individual over time. Strikingly, in subsequent analyses performed using inferred genomic information, pairs with negative associations from the time series analysis were predicted to compete over more metabolic substrates, and pairs with positive associations had significantly more metabolic complementarity. These predictions regarding the underlying molecular bases of interactions could inform how nutritional environment will impact interactions between gut microbiome community members.

microbiology↗

bioRxiv2025

Community assembly modeling of microbial evolution within Barrett's esophagus and esophageal adenocarcinoma

Mathematical modeling of somatic evolution, a process impacting both host cells and microbial communities in the human body, can capture important dynamics driving carcinogenesis. Here we considered models for esophageal adenocarcinoma (EAC), a cancer that has dramatically increased in incidence over the past few decades in Western populations, with high case fatality rates due to late-stage diagnoses. Despite advancements in genomic analyses of the precursor Barretts esophagus (BE), prevention of late-stage EAC remains a significant clinical challenge. Previous microbiome studies in BE and EAC have focused on quantifying static microbial abundance differences rather than evolutionary dynamics. Using whole genome sequencing data from esophageal tissues, we first applied a robust bioinformatics pipeline to extract non-host DNA reads, mapped these putative reads to microbial taxa, and retained those taxa with high genomic coverage. When applying mathematical models of microbial evolution to sequential stages of progression to EAC, we observed evidence of neutral dynamics in community assembly within normal esophageal tissue and BE, but not EAC. In a case-control study of BE patients who progressed to EAC cancer outcomes (CO) versus those who had non-cancer outcomes (NCO) during follow-up (mean=10.5 years), we found that Helicobacter pylori deviated significantly from the neutral expectation in BE NCO, suggesting that factors related to H. pylori or H. pylori infection itself may influence EAC risk. Additionally, simulations incorporating selection recapitulated non-neutral behaviors observed in the datasets. Formally modeling dynamics during progression holds promise in clinical applications by offering a deeper understanding of microbial involvement in cancer development.

cancer biology↗

bioRxiv2024

Corticospinal excitability is facilitated during coordinative action observation and motor imagery of adapted single-leg sit-to-stand movements in young healthy adults

Combined action observation and motor imagery (AOMI) facilitates corticospinal excitability (CSE). This study used single-pulse transcranial magnetic stimulation (TMS) to explore changes in CSE for coordinative AOMI of a single-leg sit-to-stand (SL-STS) movement. Twenty-one healthy adults completed two testing sessions, where they engaged with baseline (BL), action observation (AO), and motor imagery (MI) control conditions, and three experimental conditions where they observed a slow-paced SL-STS while simultaneously imagining a slow- (AOMIHICO), medium- (AOMIMOCO), or fast-paced (AOMILOCO) SL-STS. A TMS pulse was delivered to the right leg representation of the left primary motor cortex at three stimulation timepoints aligned with peak EMG activity of the knee extensor muscle group for the slow-paced (T3), medium-paced (T2), and fast-paced (T1) SL-STS during each condition. Motor evoked potential (MEP) amplitudes were recorded from the knee extensor muscle group as a marker of CSE for all stimulation timepoints and conditions. A main effect for experimental condition was reported for all stimulation timepoints. MEP amplitudes were significantly greater for AOMIHICO at T1 and T3, and AOMIMOCO and AOMILOCO at all stimulation timepoints, when compared with control conditions. This study provides neurophysiological evidence supporting the use of coordinative AOMI as an alternative method for movement (re)learning.

neuroscience↗

bioRxiv2024

Gut Microbiota and Derived Metabolites Mediate Obstructive Sleep Apnea Induced Atherosclerosis

BackgroundObstructive sleep apnea (OSA) is characterized by intermittent hypoxia/hypercapnia (IHC), affects predominantly obese individuals, and increases atherosclerosis risk. Since we and others have implicated gut microbiota and metabolites in atherogenesis, we dissected their contributions to OSA-induced atherosclerosis. ResultsAtherosclerotic lesions were compared between conventionally-reared specific pathogen free (SPF) and germ-free (GF) ApoE-/- mice following a high fat high cholesterol diet (HFHC), with and without IHC conditions. The fecal microbiota and metabolome were profiled using 16S rRNA gene amplicon sequencing and untargeted tandem mass spectrometry (LC-MS/MS) respectively. Phenotypic data showed that HFHC significantly increased atherosclerosis as compared to regular chow (RC) in both aorta and pulmonary artery (PA) of SPF mice. IHC exacerbated lesions in addition to HFHC. Differential abundance analysis of gut microbiota identified an enrichment of Akkermansiaceae and a depletion of Muribaculaceae (formerly S24-7) family members in the HFHC-IHC group. LC-MS/MS showed a dysregulation of bile acid profiles with taurocholic acid, taurodeoxycholic acid, and 12-ketodeoxycholic acid enriched in the HFHC-IHC group, long-chain N-acyl amides, and phosphatidylcholines. Interestingly, GF ApoE-/- mice markedly reduced atherosclerotic formation relative to SPF ApoE-/- mice in the aorta under HFHC/IHC conditions. In contrast, microbial colonization did not show a significant impact on the atherosclerotic progression in PA. ConclusionsIn summary, this research demonstrated that (1) IHC acts cooperatively with HFHC to induce atherosclerosis; (2) gut microbiota modulate atherogenesis, induced by HFHC/IHC, in the aorta not in PA; (3) different analytical methods suggest that a specific imbalance between Akkermansiaceae and Muribaculaceae bacterial families mediate OSA-induced atherosclerosis; and (4) derived bile acids, such as deoxycholic acid and lithocholic acid, regulate atherosclerosis in OSA. The knowledge obtained provides novel insights into the potential therapeutic approaches to prevent and treat OSA-induced atherosclerosis.

microbiology↗

bioRxiv2024

To impute or not to impute in untargeted metabolomics - that is the compositional question

Untargeted metabolomics often produce large datasets with missing values, arising from biological or technical factors, which can undermine statistical analyses and lead to biased biological interpretations. Imputation methods, such as k-Nearest Neighbors (kNN) and Random Forest (RF) regression are commonly used but their effects vary depending on the type of missing data e.g. Missing Completely At Random (MCAR) and Missing Not At Random (MNAR). Here, we determined the impacts of degree and type of missing data on the accuracy of kNN and RF imputation using two datasets: a targeted metabolomic dataset with spiked-in standards and an untargeted metabolomic dataset. We also assessed the effect of compositional data approaches (CoDA), such as the centered log-ratio (CLR) transform, on data interpretation, since these methods are increasingly being used in metabolomics. Overall, we found that kNN and RF performed more accurately when the proportion of missing data across samples for a metabolic feature was low. However, these imputations could not handle MNAR data and generated wildly inflated values or imputed values where none should exist. Furthermore, we show that the proportion of missing values had a strong impact on the accuracy of imputation which affected the interpretation of the results. Our results suggest extreme caution should be used with imputation even with modestly levels of missing data or when the type of missingness is unknown.

bioinformatics↗

bioRxiv2024

A three-country analysis of the gut microbiome indicates taxon associations with diet vary by location and strain

Emerging research suggests that diet plays a vital role in shaping the composition and function of the gut microbiota. While significant efforts have been made to identify general patterns linking diet to the gut microbiome, much of this research lacks representation from low- and middle-income countries such as Mexico. Additionally, both diet and the gut microbiome have highly complex and individualized configurations, and there is growing evidence that tailoring diets to individual gut microbiota profiles may optimize the path toward improving or maintaining health and preventing disease. Using fecal metagenomic data from 1,291 individuals across three countries, we examine two bacterial genera prevalent in the human gut, Prevotella and Faecalibacterium, which have gained significant attention due to their potential roles in human health. We find that they show significant associations with many aspects of diet, but that these associations vary in scale and direction, depending on the level of metagenomic resolution and the contextual population. These results highlight the growing importance of assembling metagenomic datasets that are standardized, comprehensive, and representative of diverse populations to increase our ability to tease apart the complex relationship between diet and the microbiome.

microbiology↗

bioRxiv2024

Human untargeted metabolomics in the gut microbiome era: ethanol vs methanol

Untargeted metabolomics is frequently performed on human fecal samples in conjunction with sequencing to unravel gut microbiome functionality. As sample collection efforts rapidly expand, with individuals often collecting specimens at home, metabolomics experiments should adapt to accommodate the safety and needs of bulk off-site collections. Here, we show that a 95% ethanol, safe to be shipped and handled, extraction pipeline recovers comparable amounts of metabolites as a validated 50% methanol extraction, preserving metabolic profiles differences between subjects. Additionally, the fecal metabolome remains relatively stable when stored in 95% ethanol for up to a week at room temperature. Finally, we suggest a metabolomics data analysis workflow using robust centered log ratio transformation, which removes variance introduced by different sample weights, allowing for reliable and integration-ready untargeted metabolomics experiments in gut microbiome studies.

biochemistry↗

bioRxiv2024

Macrophage-derived RNAseT2 stimulates muscle stem cell fusion via SLK/N-WASP/actin bundling

Muscle stem cells (MuSCs) fuse to form myofibers to repair skeletal muscle after injury. Within the regenerative MuSC niche, restorative macrophages stimulate MuSC fusion, although the molecular mechanisms involved are largely unknown. Here, we show that restorative macrophages secrete ribonuclease T2 (RNAseT2) to stimulate MuSC fusion. RNAseT2 entered MuSCs via the mannose receptor and induced the formation of actin bundles in MuSCs, enabling cell/cell fusion. Mechanistically, RNAseT2 bound to Ste20-like kinase (SLK), which itself triggered the phosphorylation-mediated activation of N-WASP, through Paxillin phosphorylation, allowing actin bundling necessary for MuSC fusion. In vivo, overexpressing RNAseT2 in regenerating muscle increased fusion in newly formed myofibers in mouse and zebrafish while macrophages deficient for RNAseT2 gene led to fusion defect and smaller myofibers. This study reveals a new function for the highly conserved RNAseT2 and provides a new molecular mechanism by which restorative macrophages support MuSC fusion during muscle repair.

cell biology↗

bioRxiv2024

Pathogenic Bacteroides fragilis strains can emerge from gut-resident commensals

Bacteroides fragilis is a prominent member of the human gut microbiota, playing crucial roles in maintaining gut homeostasis and host health. Although it primarily functions as a beneficial commensal, B. fragilis can become pathogenic. To determine the genetic basis of its duality, we conducted a comparative genomic analysis of 813 B. fragilis strains, representing both commensal and pathogenic origins. Our findings reveal that pathogenic strains emerge across diverse phylogenetic lineages, due in part to rapid gene exchange and the adaptability of the accessory genome. We identified 16 phylogenetic groups, differentiated by genes associated with capsule composition, interspecies competition, and host interactions. A microbial genome-wide association study identified 44 genes linked to extra-intestinal survival and pathogenicity. These findings reveal how genomic diversity within commensal species can lead to the emergence of pathogenic traits, broadening our understanding of microbial evolution in the gut.

microbiology↗

bioRxiv2024

Weighted variance component test for the integrative multi-omics analysis of microbiome data

Metabolic dysregulation and alterations have been linked to various diseases and conditions. Innovations in high-throughput technology now allow rapid profiling of the metabolome and metagenome -- often the gene content of bacterial populations -- for characterizing metabolism. Due to the small sample sizes and high dimensionality of the data, pathway analysis (wherein the effect of multiple genes or metabolites on an outcome is cumulatively assessed) of metabolomic data is commonly conducted and also represents a standard for metagenomic analysis. However, how to integrate both data types remains unclear. Recognizing that a metabolic pathway can be complementarily characterized by both metagenomics and metabolomics, we propose a weighted variance components framework to test if the joint effect of genes and metabolites in a biological pathway is associated with outcomes. The approach allows analytic p-value calculation, correlation between data types, and optimal weighting. Power simulations show that our approach often outperforms other strategies while maintaining type I error. The approach is illustrated on real data.

bioinformatics↗

bioRxiv2024

The microbiome shapes immunity in a sex-specific manner in mouse models of Alzheimer's disease

INTRODUCTION: Preclinical studies reveal that the microbiome broadly affects immune responses and deposition and/or clearance of amyloid-beta (A{beta}) in mouse models of Alzheimers disease (AD). Whether the microbiome shapes central and peripheral immune profiles in AD models remains unknown. METHODS: We examined adaptive immune responses in two mouse models containing AD- related genetic predispositions (3xTg and 5xFAD) in the presence or absence of the microbiome. RESULTS: T and B cells were altered in brain-associated and systemic immune tissues between genetic models and wildtype mice, with earlier signs of immune activity in females. Systemic immune responses were modulated by the microbiome and differed by sex. Further, the absence of a microbiome in germ-free mice resulted in reduced cognitive deficits, primarily in females. DISCUSSION: These data reveal sexual dimorphism in early signs of immune activity and microbiome effects, and highlight an interesting interaction between sex and the microbiome in mouse models of AD.

microbiology↗

bioRxiv2024

Gut microbiome is associated with recurrence-free survival in patients with resected Stage IIIB-D or Stage IV melanoma treated with immune checkpoint inhibitors

The gut microbiome (GMB) has been associated with outcomes of immune checkpoint blockade therapy in melanoma, but there is limited consensus on the specific taxa involved, particularly across different geographic regions. We analyzed pre-treatment stool samples from 674 melanoma patients participating in a phase-III trial of adjuvant nivolumab plus ipilimumab versus nivolumab, across three continents and five regions. Longitudinal analysis revealed that GMB was largely unchanged following treatment, offering promise for lasting GMB-based interventions. In region-specific and cross-region meta-analyses, we identified pre-treatment taxonomic markers associated with recurrence, including Eubacterium, Ruminococcus, Firmicutes, and Clostridium. Recurrence prediction by these markers was best achieved across regions by matching participants on GMB compositional similarity between the intra-regional discovery and external validation sets. AUCs for prediction ranged from 0.83-0.94 (depending on the initial discovery region) for patients closely matched on GMB composition (e.g., JSD [≤]0.11). This evidence indicates that taxonomic markers for prediction of recurrence are generalizable across regions, for individuals of similar GMB composition. HighlightsO_LIOverall gut microbiome (GMB) composition is largely unchanged during ICB treatment. C_LIO_LIGMB composition varies by geographic region C_LIO_LIWe identified gut bacterial markers associated with recurrence in region-specific analyses. C_LIO_LIRegion-identified markers are generalizable if GMB composition is taken into account by matching. C_LI

cancer biology↗

bioRxiv2024

Medication Use is Associated with Distinct Microbial Features in Anxiety and Depression

This study investigated the relationship between gut microbiota and neuropsychiatric disorders (NPDs), specifically anxiety disorder (ANXD) and/or major depressive disorder (MDD), as defined by DSM-IV or V criteria. The study also examined the influence of medication use, particularly antidepressants and/or anxiolytics, classified through the Anatomical Therapeutic Chemical (ATC) Classification System, on the gut microbiota. Both 16S rRNA gene amplicon sequencing and shallow shotgun sequencing were performed on DNA extracted from 666 fecal samples from the Tulsa-1000 and NeuroMAP CoBRE cohorts. The results highlight the significant influence of medication use; antidepressant use is associated with significant differences in gut microbiota beta diversity and has a larger effect size than NPD diagnosis. Next, specific microbes were associated with ANXD and MDD, highlighting their potential for non-pharmacological intervention. Finally, the study demonstrated the capability of Random Forest classifiers to predict diagnoses of NPD and medication use from microbial profiles, suggesting a promising direction for the use of gut microbiota as biomarkers for NPD. The findings suggest that future research on the gut microbiotas role in NPD and its interactions with pharmacological treatments are needed.

microbiology↗