Biology subjects

Langston, M. A.

Publications and source records attributed to Langston, M. A..

4 recordsLinked to original sources

bioRxiv2025

Liver sinusoid constraints make random search of CD8 T cells for Plasmodium parasites efficient

A relatively small number of liver-localized CD8 T cells can provide sterilizing protection against exposure to Plasmodium sporozoites. Previously published mathematical model-based simulations of randomly searching for an infection T cells, assuming that T cells move in open (3D) space or on a regular lattice, suggested that some degree of attraction is needed for cells to find the infection. Yet, in our previous experiments we failed to find evidence of attraction towards the infection in most T cells. Because the movement of liver-localized T cells is confined to sinusoids, the blood vessels of the liver, T cells may in fact be attracted to the infection site via the sinusoids. In new experiments we imaged Plasmodium-specific CD8 T cells, sporozoites, and liver sinusoids simultaneously. We also developed a new method to model imaged sinusoids as graphs and to simulate movement of T cells on graphs, and new tests to detect biased cell movement on graphs. As before, our previously published open space/3D-based metric suggested that few T cells are attracted to the infection site in presence of moderate cluster of T cells. However, with our new graph-based metric we could not detect attraction when constraining T cell movement to sinusoids. Surprisingly, simulations of T cells searching for an infection via liver sinusoids showed that a relatively small number of T cells (1 cell/imaging volume or <10^5 cell/liver), moving without attraction at experimentally observed speeds, is sufficient to find the infection within 24 hours, well below the expected lifespan of the Plasmodium liver stage in mice. Our results suggest that the constrained environment of the tissue structure makes a random search more effective, and that detecting T cell attraction towards specific areas may arise as an artifact of metrics designed for open space/3D. Our results thus call for a re-evaluation of the implicit assumption that attraction is necessary for T cells to find infections in tissues.

immunology↗

bioRxiv2020

Finding human gene-disease associations using a Network Enhanced Similarity Search (NESS) of multi-species heterogeneous functional genomics data

Disease diagnosis and treatment is challenging in part due to the misalignment of diagnostic categories with the underlying biology of disease. The evaluation of large-scale genomic experimental datasets is a compelling approach to refining the classification of biological concepts, such as disease. Well-established approaches, some of which rely on information theory or network analysis, quantitatively assess relationships among biological entities using gene annotations, structured vocabularies, and curated data sources. However, the gene annotations used in these evaluations are often sparse, potentially biased due to uneven study and representation in the literature, and constrained to the single species from which they were derived. In order to overcome these deficiencies inherent in the structure and sparsity of these annotated datasets, we developed a novel Network Enhanced Similarity Search (NESS) tool which takes advantage of multi-species networks of heterogeneous data to bridge sparsely populated datasets. NESS employs a random walk with restart algorithm across harmonized multi-species data, effectively compensating for sparsely populated and noisy genomic studies. We further demonstrate that it is highly resistant to spurious or sparse datasets and generates significantly better recapitulation of ground truth biological pathways than other similarity metrics alone. Furthermore, since NESS has been deployed as an embedded tool in the GeneWeaver environment, it can rapidly take advantage of curated multi-species networks to provide informative assertions of relatedness of any pair of biological entities or concepts, e.g., gene-gene, gene-disease, or phenotype-disease associations. NESS ultimately enables multi-species analysis applications to leverage model organism data to overcome the challenge of data sparsity in the study of human disease. Availability and ImplementationImplementation available at https://geneweaver.org/ness. Source code freely available at https://github.com/treynr/ness. Author summaryFinding consensus among large-scale genomic datasets is an ongoing challenge in the biomedical sciences. Harmonizing and analyzing such data is important because it allows researchers to mitigate the idiosyncrasies of experimental systems, alleviate study biases, and augment sparse datasets. Additionally, it allows researchers to utilize animal model studies and cross-species experiments to better understand biological function in health and disease. Here we provide a tool for integrating and analyzing heterogeneous functional genomics data using a graph-based model. We show how this type of analysis can be used to identify similar relationships among biological entities such as genes, processes, and disease through shared genomic associations. Our results indicate this approach is effective at reducing biases caused by sparse and noisy datasets. We show how this type of analysis can be used to aid the classification gene function and prioritization of genes involved in substance use disorders. In addition, our analysis reveals genes and biological pathways with shared association to multiple, co-occurring substance use disorders.

bioinformatics↗

bioRxiv2018

Integration of heterogeneous functional genomics data in gerontology research identifies genes and pathway underlying aging across species

Understanding the biological mechanisms behind aging, lifespan and healthspan is becoming increasingly important as the proportion of the world's population over the age of 65 grows, along with the cost and complexity of their care. BigData oriented approaches and analysis methods for integrative functional genomics enable current and future bio-gerontologists to synthesize, distill and interpret vast, heterogeneous data. GeneWeaver is an analysis system for integration of data that allows investigators to store, search, and analyze immense amounts of data including user-submitted experimental data, data from primary publications, and data in other databases. Aging related genome-wide gene sets from primary publications were curated into this system in concert with data from other model-organism and aging-specific databases, and used in several application using GeneWeavers analysis tools. For example, we identified Cd63 as a frequently represented gene among aging-related genome-wide results. To evaluate the role of Cd63 in aging, we performed RNAi knockdown of the C. elegans ortholog, tsp-7, demonstrating that this manipulation is capable of extending lifespan. The tools in GeneWeaver enable aging researchers to make new discoveries into the associations between the genes, normal biological processes, and diseases that affect aging, healthspan, and lifespan.

bioinformatics↗

bioRxiv2018

Systems genetic discovery of host-microbiome interactions reveals mechanisms of microbial involvement in disease

The role of the microbiome in health and disease involves complex networks of host genetics, genomics, microbes and environment. Identifying the mechanisms of these interactions has remained challenging. Systems genetics in the laboratory mouse enables data-driven discovery of network components and mechanisms of host-microbial interactions underlying multiple disease phenotypes. To examine the interplay among the whole host genome, transcriptome and microbiome, we mapped quantitative trait loci and correlated the abundance of cecal mRNA, luminal microflora, physiology and behavior in incipient strains of the highly diverse Collaborative Cross mouse population. The relationships that are extracted can be tested experimentally to ascribe causality among host and microbe in behavior and physiology, providing insight into disease. Application of this strategy in the Collaborative Cross population revealed experimentally validated mechanisms of microbial involvement in models of autism, inflammatory bowel disease and sleep disorder.\n\neTOC BlurbHost genetic diversity provides a variable selection environment and physiological context for microbiota and their interaction with host physiology. Using a highly diverse mouse population Bubier et al. identified a variety of host, microbe and potentially disease interactions.\n\nHighlights* 18 significant species-specific QTL regulating microbial abundance were identified\n* Cis and trans eQTL for 1,600 cecal transcripts were mapped in the Collaborative Cross\n* Sleep phenotypes were highly correlated with the abundance of B.P. Odoribacter\n* Elimination of sleep-associated microbes restored normal sleep patterns in mice.

genetics↗