Biology subjects

Hood, L. E.

Publications and source records attributed to Hood, L. E..

7 recordsLinked to original sources

bioRxiv2025

Protection against reinfection with Mycobacterium tuberculosis extends across heterologous Mtb lineages

Immunological memory elicited either through previous or ongoing M. tuberculosis (Mtb) infection provides a critical mechanism by which hosts protect against re-infection and disease progression upon Mtb re-exposure. Conversely, the uneven competition between distinct Mtb strains suggest certain bacterial clades have enhanced ability to spread across communities and circulate globally, potentially by evading memory responses gained by prior infection with genomically different strains. To address whether memory responses induced by one strain can protect against a genetically distinct strain, we conducted a heterologous reinfection study in cynomolgus macaques involving primary infection by a Lineage 4 Erdman Mtb strain and subsequent re-challenge by a Lineage 2 strain, HT-L2. Recent epidemiologic studies have shown that the clade to which HT-L2 belongs has been spreading successfully over the last decade in Lima, Peru. Here, through microbiologic, PET-CT imaging and sequencing of Mtb genomic barcodes, we show that reinfected animals developed fewer lung lesions and controlled both pulmonary and disseminated forms of infection better than naive animals without prior exposure to Mtb. Our data support that protection against reinfection is not limited by Mtb lineage, providing optimism that vaccines can be effective across populations and geographic locations.

immunology↗

bioRxiv2024

CD4 T cells and CD8α+ lymphocytes are necessary for intravenous BCG-induced protection against tuberculosis in macaques

Tuberculosis (TB) is a major cause of morbidity and mortality worldwide despite widespread intradermal (ID) BCG vaccination in newborns. We previously demonstrated that changing the route and dose of BCG vaccination from 5{xi}105 CFU ID to 5{xi}107 CFU intravenous (IV) resulted in prevention of infection and disease in a rigorous, highly susceptible non-human primate model of TB. Identifying the immune mechanisms of protection for IV BCG will facilitate development of more effective vaccines against TB. Here, we depleted select lymphocyte subsets in IV BCG vaccinated macaques prior to Mtb challenge to determine the cell types necessary for that protection. Depletion of CD4 T cells or all CD8 expressing lymphoycytes (both innate and adaptive) resulted in loss of protection in most macaques, concomitant with increased bacterial burdens ([~]4-5 log10 thoracic CFU) and dissemination of infection. In contrast, depletion of only adaptive CD8{beta}+ T cells did not significantly reduce protection against disease. Our results demonstrate that CD4 T cells and innate CD8+ lymphocytes are critical for IV BCG-induced protection, supporting investigation of how eliciting these cells and their functions can improve future TB vaccines. One Sentence SummaryAntibody depletion of lymphocytes in rhesus macques demonstrates key roles for CD4 T cells and innate-like CD8+ lymphocytes in conferring sterilizing immunity against tuberculosis following intravenous BCG vaccination.

immunology↗

bioRxiv2020

Altered Huntingtin-Chromatin Interactions Predict Transcriptional and Epigenetic Changes in Huntingtons Disease

Progressive striatal gene expression changes and epigenetic alterations are a prominent feature of Huntingtons disease (HD), but the mechanistic basis remains poorly understood. Using chromatin immunoprecipitation and sequencing (ChIP-seq), we show that the huntingtin protein (HTT) reproducibly occupies specific locations in the mouse genome. Moreover, many genomic loci were differentially occupied by HTT in striatal tissue from a homozygous knock-in mouse model of HD (B6.HttQ111/Q111) when compared to wildtype controls. Huntingtin ChIP-seq peaks were enriched in the coding regions of cell identity genes important for striatal function, with many of these genes found to have reduced expression in the striata of HD patients and mouse models, as well as reduced HTT occupancy in HttQ111/Q111mice compared to controls. Conversely, HTT ChIP-seq peaks were depleted near genes that are up-regulated in HD. ChIP-seq of bulk striatal histone modifications, generated in parallel, revealed genotype-specific colocalization of HTT with active chromatin marks (H3K4me3 and H3K27ac), and with enhancer of zeste homolog 2 (EZH2), a key enzymatic component of the PRC2 complex. Near genes that are differentially regulated in HD, greater HTT occupancy in HttQ111/Q111 vs. wildtype mice was associated with increased EZH2 binding, increased histone H3 lysine 4 (H3K4me3), and decreased histone H3 lysine 27 (H3K27me3). Our study suggests that huntingtin-chromatin interactions may play a direct role in organizing chromatin and promoting cell type-specific gene expression, with loss of HTT occupancy predicting decreased gene expression in HD.

neuroscience↗

bioRxiv2018

Scale-invariant geometric data analysis (SIGDA) provides robust, detailed visualizations of human ancestry specific to individuals and populations

Scale invariance is a common property of physical laws and a key concept in perspective drawing, which aims to provide a meaningful two-dimensional representation of a more complex, three-dimensional scene. Here we describe Scale Invariant Geometric Data Analysis (SIGDA), a new, general exploratory data analysis (EDA) method based on normalization of data to scale invariance. We discuss similarities and differences between SIGDA and two widely-used EDA methods, Correspondence Analysis (CA) and Principal Components Analysis (PCA). We then illustrate SIGDAs ability to analyze and visualize population structure relationships within the data that inspired its development: genetic marker data, in which context PCA is considered a standard method. We show that SIGDA provides significant advantages over PCA of the same data, including: (a) robust detection and separation of a larger number of population axes, leading to (b) better separation of annotated populations; (c) separation of an independent allele frequency axis interpretable as a proxy for allele age, (d) visualization of marker flow between populations (population history), and (d) robust detection and visualization of relationships between closely-related individuals and among family groups. Although this illustration focuses on a specific task, SIGDA is a general-purpose EDA method and derives its advantages from its novel approach to fundamental issues in data analysis, rather than clever sampling or other task-specific methodology.\n\nOne Sentence SummaryWe illustrate the advantages of Scale Invariant Geometric Data Analysis (SIGDA), a new exploratory data analysis method similar to PCA, by applying SIGDA to derive detailed, robust visualizations of the complex history of human population structure from a large sample of single nucleotide variants.

bioinformatics↗

bioRxiv2018

BDQC: a general-purpose analytics tool for domain-blind validation of Big Data

Translational biomedical research is generating exponentially more data: thousands of whole-genome sequences (WGS) are now available; brain data are doubling every two years. Analyses of Big Data, including imaging, genomic, phenotypic, and clinical data, present qualitatively new challenges as well as opportunities. Among the challenges is a proliferation in ways analyses can fail, due largely to the increasing length and complexity of processing pipelines. Anomalies in input data, runtime resource exhaustion or node failure in a distributed computation can all cause pipeline hiccups that are not necessarily obvious in the output. Flaws that can taint results may persist undetected in complex pipelines, a danger amplified by the fact that research is often concurrent with the development of the software on which it depends. On the positive side, the huge sample sizes increase statistical power, which in turn can shed new insight and motivate innovative analytic approaches. We have developed a framework for Big Data Quality Control (BDQC) including an extensible set of heuristic and statistical analyses that identify deviations in data without regard to its meaning (domain-blind analyses). BDQC takes advantage of large sample sizes to classify the samples, estimate distributions and identify outliers. Such outliers may be symptoms of technology failure (e.g., truncated output of one step of a pipeline for a single genome) or may reveal unsuspected \" signal\" in the data (e.g., evidence of aneuploidy in a genome). We have applied the framework to validate real-world WGS analysis pipelines. BDQC successfully identified data outliers representing various failure classes, including genome analyses missing a whole chromosome or part thereof, hidden among thousands of intermediary output files. These failures could then be resolved by reanalyzing the affected samples. BDQC both identified hidden flaws as well as yielded new insights into the data. BDQC is designed to complement quality software development practices. There are multiple benefits from the application of BDQC at all pipeline stages. By verifying input correctness, it can help avoid expensive computations on flawed data. Analysis of intermediary and final results facilitates recovery from aberrant termination of processes. All these computationally inexpensive verifications reduce cryptic analytical artifacts that could seriously preclude clinical-grade genome interpretation. BDQC is available at https://github.com/ini-bdds/bdqc.

bioinformatics↗

bioRxiv2017

Ultrafast comparison of personal genomes

We present an ultra-fast method for comparing personal genomes. We transform the standard genome representation (lists of variants relative to a reference) into genome fingerprints that can be readily compared across sequencing technologies and reference versions. Because of their reduced size, computation on the genome fingerprints is fast and requires little memory. This enables scaling up a variety of important genome analyses, including quantifying relatedness, recognizing duplicative sequenced genomes in a set, population reconstruction, and many others. The original genome representation cannot be reconstructed from its fingerprint; the method thus has significant implications for privacy-preserving genome analytics.

bioinformatics↗

bioRxiv2016

Genome-scale transcriptional regulatory network models for the mouse and human striatum predict roles for SMAD3 and other transcription factors in Huntington’s disease

Transcriptional changes occur presymptomatically and throughout Huntingtons Disease (HD), motivating the study of transcriptional regulatory networks (TRNs) in HD. We reconstructed a genome-scale model for the target genes of 718 TFs in the mouse striatum by integrating a model of the genomic binding sites with transcriptome profiling of striatal tissue from HD mouse models. We identified 48 differentially expressed TF-target gene modules associated with age- and Htt allele-dependent gene expression changes in the mouse striatum, and replicated many of these associations in independent transcriptomic and proteomic datasets. Strikingly, many of these predicted target genes were also differentially expressed in striatal tissue from human disease. We experimentally validated a key model prediction that SMAD3 regulates HD-related gene expression changes using chromatin immunoprecipitation and deep sequencing (ChIP-seq) of mouse striatum. We found Htt allele-dependent changes in the genomic occupancy of SMAD3 and confirmed our models prediction that many SMAD3 target genes are down-regulated early in HD. Importantly, our study provides a mouse and human striatal-specific TRN and prioritizes a hierarchy of transcription factor drivers in HD.

systems biology↗