Biology subjects

Moody, T.

Publications and source records attributed to Moody, T..

7 recordsLinked to original sources

bioRxiv2026

Characterization of Influenza A HA and NA Subtypes Using Protein Sequences

MotivationInfluenza is an infectious disease associated with excess human deaths. For influenza A virus (IAV), the surface antigens hemagglutinin (HA) and neuraminidase (NA) define the specific subtype. The high mutation rate of IAV can make clinical testing and assigning subtype after sequencing challenging. Accurate subtype classification is important for tracking circulating IAV strains that may impact human health. ResultsWe analyzed a large IAV protein sequence dataset with known HA and NA subtypes. Using logistic regression and random forest approaches, we identified a small set of subtype-associated amino acids, which were then used to develop a subtype characterization method with near-optimal accuracy. Further analysis indicated that signal sequence peptide variation and indels in HA and NA explained the unique combination of subtype-associated amino acids.

bioinformatics↗

bioRxiv2026

Gut microbiome-dependent IL-1 signaling is a mediator of ACVR1R206H-driven heterotopic ossification

Inflammatory diseases cause significant morbidity and mortality, but their pathobiology is often difficult to dissect due to complex genetic-environmental interactions. Genetic forms of heterotopic ossification, such as fibrodysplasia ossificans progressiva (FOP), reduce genetic variability, allowing careful dissection of non-genetic drivers of inflammation. While >95% of FOP patients harbor the ACVR1R206H mutation, patients exhibit significant variability in disease progression, suggesting a role of environmental drivers. Here, we identify the gut microbiome as a regulator of inflammation-driven HO in FOP. Metagenomic profiling of cohabitating FOP/unaffected sibling pairs revealed a pathogenic gut microbiome profile in FOP patients (Bray-Curtis, p < 0.05). In Pdgfr-Cre/Acvr1R206H (FOP) mice, gut microbiome ablation by antibiotics reduced spontaneous HO formation (47.4% reduction, p < 0.05) and reduced plasma IL-1 pathway activity. IL-1{beta} blockade in FOP mice suppressed trauma-induced HO formation. These findings identify a gut microbiome-IL-1-HO axis with modifiable targets for developing treatments for HO and related inflammatory conditions. One Sentence SummaryAntibiotic disruption of the gut microbiome reduces HO in FOP mice via an IL-1 mediated pathway.

immunology↗

bioRxiv2024

MCSPACE: inferring microbiome spatiotemporal dynamics from high-throughput co-localization data

Recent advances in high-throughput approaches for estimating co-localization of microbes, such as SAMPL-seq, allow characterization of the biogeography of the gut microbiome longitudinally and at unprecedented scale. However, these high-dimensional data are complex and have unique noise properties. To address these challenges, we developed MCSPACE, a probabilistic AI method that infers from microbiome co-localization data spatially coherent assemblages of taxa, their dynamics over time, and their responses to perturbations. To evaluate MCSPACEs capabilities, we generated the largest longitudinal microbiome co-localization dataset to date, profiling spatial relationships of microbes in the guts of mice subjected to serial dietary perturbations over 76 days. Analyses of these data and an existing human longitudinal dataset demonstrated superior benchmarking performance of MCSPACE over existing methods, and moreover yielded insights into spatiotemporal structuring of the gut microbiome, including identifying temporally persistent and dynamic microbial assemblages in the human gut, and shifts in assemblages in the murine gut induced by specific dietary components. Our results highlight the utility of our method, which we make available to the community as an open-source software tool, for elucidating dynamics of microbiome biogeography and gaining insights into the role of spatial relationships in host-microbial ecosystem function.

microbiology↗

bioRxiv2024

BioStructNet: Structure-Based Network with Transfer Learning for Predicting Biocatalyst Functions

Enzyme-substrate interactions are essential to both biological processes and industrial applications. Advanced machine learning techniques have significantly accelerated biocatalysis research, revolutionizing the prediction of biocatalytic activities and facilitating the discovery of novel biocatalysts. However, the limited availability of data for specific enzyme functions, such as conversion efficiency and stereoselectivity, presents challenges for prediction accuracy. In this study, we developed BioStructNet, a structure-based deep learning network that integrates both protein and ligand structural data to capture the complexity of enzyme-substrate interactions. Benchmarking studies with the different algorithms showed the enhanced predictive accuracy of BioStructNet. To further optimize the prediction accuracy for the small dataset, we implemented transfer learning in the framework, training a source model on a large dataset and fine-tuning it on a small, function-specific dataset, using the CalB dataset as a case study. The model performance was validated by comparing the attention heat maps generated by the BioStructNet interaction module, with the enzyme substrate interactions revealed by enzyme-substrate complexes revealed from molecular simulations. BioStructNet would accelerate the discovery of functional enzymes for industrial use, particularly in cases where the training datasets for machine learning are small.

bioinformatics↗

bioRxiv2024

SAMPL-seq reveals micron-scale spatial hubs in the human gut microbiome

The local arrangement of microbes can profoundly impact community assembly, function, and stability. To date, little is known about the spatial organization of the human gut microbiome. Here, we describe a high-throughput and streamlined method, dubbed SAMPL-seq, that samples microbial composition of micron-scale sub-communities with split-and-pool barcoding to capture spatial colocalization in a complex consortium. SAMPL-seq analysis of the gut microbiome of healthy humans identified bacterial taxa pairs that consistently co-occurred both over time and across multiple individuals. These colocalized microbes organize into spatially distinct groups or "spatial hubs" dominated by Bacteroideceae, Ruminococceae, and Lachnospiraceae families. From a dietary perturbation using inulin, we observed reversible spatial rearrangement of the gut microbiome, where specific taxa form new local partnerships. Spatial metagenomics using SAMPL-seq can unlock new insights to improve the study of microbial communities. One Sentence SummaryHigh throughput micron-scale subcommunity sampling and sequencing identifies distinct spatial associations of gut bacteria within and across individuals.

systems biology↗

bioRxiv2023

A highly conserved and globally prevalent cryptic plasmid is among the most numerous mobile genetic elements in the human gut

Plasmids are extrachromosomal genetic elements that often encode fitness enhancing features. However, many bacteria carry cryptic plasmids that do not confer clear beneficial functions. We identified one such cryptic plasmid, pBI143, which is ubiquitous across industrialized gut microbiomes, and is 14 times as numerous as crAssphage, currently established as the most abundant genetic element in the human gut. The majority of mutations in pBI143 accumulate in specific positions across thousands of metagenomes, indicating strong purifying selection. pBI143 is monoclonal in most individuals, likely due to the priority effect of the version first acquired, often from ones mother. pBI143 can transfer between Bacteroidales and although it does not appear to impact bacterial host fitness in vivo, can transiently acquire additional genetic content. We identified important practical applications of pBI143, including its use in identifying human fecal contamination and its potential as an inexpensive alternative for detecting human colonic inflammatory states.

microbiology↗

bioRxiv2021

ACVR1R206H increases osteogenic/ECM gene expression and impairs myofiber formation in human skeletal muscle stem cells

Abnormalities in skeletal muscle repair lead to poor function and complications such as scarring or heterotopic ossification (HO). Here, we use fibrodysplasia ossificans progressiva (FOP), a disease of progressive HO caused by ACVR1R206H (Activin receptor type-1 receptor) mutation, to elucidate how ACVR1 affects skeletal muscle repair. Rare and unique primary FOP human muscle stem cells (Hu-MuSCs) isolated from cadaveric skeletal muscle demonstrated increased ECM marker expression, and showed skeletal muscle-specific impaired engraftment and regeneration ability. Human induced pluripotent stem cell (iPSC)-derived muscle stem/progenitor cells (iMPCs) Single cell transcriptome analyses from FOP also revealed unusually increased ECM and osteogenic marker expression compared to control iMPCs. These results show that iMPCs can recapitulate many aspects of Hu-MuSCs for detailed in vitro study, that ACVR1 is a key regulator of Hu-MuSC function and skeletal muscle repair; and that ACVR1 activation in iMPCs or Hu-MuSCs contributes to HO by changing the local tissue environment.

cell biology↗