Biology subjects

Earl, J. P.

Publications and source records attributed to Earl, J. P..

5 recordsLinked to original sources

bioRxiv2026

Identification of disease-specific alleles and gene duplications from 1,600 Haemophilus influenzae genomes using predicted protein analyses from an unsupervised language model and clinical metadata

Haemophilus influenzae, a Gram-negative bacterium that is an obligate commensal of the human nasopharynx, is associated with both acute and chronic infections of the ears, adenoids, sinuses, and lungs, as well as pelvic inflammatory disease, sepsis, and meningitis. This diverse array of clinical disease phenotypes is due to H. influenzaes natural competence providing for frequent distributed gene exchange among strains during polyclonal colonizations. We performed whole genome sequencing (WGS) on [~]1000 H. influenzae strains and combined these data with [~]600 publicly available genomes. Clinical metadata including isolation site and disease were available for all strains. Our goal was to identify variations in protein sequences inferred from the WGS that correlate with the clinical metadata. We employed the alpha-fold unsupervised machine-learning model to all protein sequences. For each unique AA sequence, a numerical vector was generated reflecting the proteins biochemical properties. Multidimensional clustering was used to group variants into clusters by distance. For gene groups with two or more clusters, clinical metadata from the strain of origin was overlayed and subsequently tested for correlation with the clustering. This allowed identification of multiple gene groups with clusters of variants that correlated significantly with the patients disease. COG category analysis showed that many of the significant gene groups were associated with antibiotic targets. The TbpA gene group type was found to be significantly correlated with disease type with three of five variant clusters having a 95% prevalence among strains that were isolated from the lungs of COPD patients or other lower pulmonary tract infections.

genomics↗

bioRxiv2025

Development of a long-read high-throughput chromatin conformation capture method for evaluating both cis and trans interactions between integrated Hepatitis B Virus and host genomes.

High-throughput chromatin conformation capture (Hi-C) has been utilized for characterizing the 3-dimensional (3D) interactome of the genome. However, short-read sequencing approaches have limited the information it provides, especially for integrated proviral DNAs . Herein we describe the development of a pipeline to investigate the 3D interactome associated with integrated Hepatitis B Virus (HBV) DNAs (iDNAs) to further understand the role of HBV iDNA in liver carcinogenesis. We employed long-read DNA sequencing combined with a novel target-pull-down library construction approach that protects against DNA shearage resulting in greatly improved sensitivity and specificity for the targeted HBV iDNA. Most importantly, we successfully increased the length of captured Hi-C library to the multi-kilobase-level and report Hi-C contacts based on a custom-developed informatics pipeline. We anticipate this combined laboratory{-}informatics pipeline will provide a more comprehensive view of HBV iDNA-host genomic interactions associated with their role in liver carcinogenesis, but even more importantly that it should be generalizable for the study of the geno-topological effects associated with any type of integrated DNA. (word count: 164/250)

genomics↗

bioRxiv2022

Evidence for the Existence of a Bacterial Etiology for Alzheimers Disease and for a Temporal-Spatial Development of a Pathogenic Microbiome in the Brain

BackgroundOver the last few decades, a growing body of evidence suggests a role for various infectious agents in Alzheimers Disease (AD) pathogenesis. Despite diverse pathogens (virus, bacteria, or fungi) being detected in AD subjects brains, most research has focused on individual pathogens and only a few studies investigated the hypothesis of a bacterial brain microbiome. We profiled the bacterial communities present in non-demented controls and AD subjects brains. ResultsWe obtained post-mortem samples from the brains of 32 individual subjects, comprising 16 AD and 16 control aged-matched subjects with a total of 130 samples from the frontal and temporal lobes and entorhinal cortex. We used full-length 16S rRNA gene amplification with Pacific Biosciences sequencing technology to identify the bacteria. We detected bacteria in the brains of both cohorts with the principal bacteria comprising Propionibacterium acnes (recently renamed Cutibacterium acnes) and two species each of Acinetobacter and Comamonas genera. We used a hierarchical Bayesian method to detect differences in relative abundance among AD and control groups. Because of large abundance variances we also employed an unconventional analysis approach that utilized Latent Dirichlet Allocation, often used in computational linguistics. This allowed us to identify 5 classes of samples, each revealing a different microbiome. Assuming that samples represented infections that potentially began at different times, we ordered these classes in time, finding that the last class exclusively explained the existence or non-existence of AD. ConclusionsThe AD-related pathogenicity of the brain microbiome seems to be based on a complex polymicrobial dynamic. The time ordering revealed a rise and fall of the abundance of Propionibacterium acnes with pathogenicity occurring for an off-peak abundance level in association with at least one other bacterium from a set of genera that included: Methylobacterium, Bacillus, Caulobacter, Delftia, and Variovorax. P. acnes may also be involved with outcompeting the Comamonas species, which were strongly associated with non-demented brain microbiome, whose early destruction could be the first stage of the disease. The statistical results are also consistent with a leaky blood brain barrier or lymphatic network that allows bacteria, viruses, fungi, or other pathogens to enter the brain.

microbiology↗

bioRxiv2018

Species-level bacterial community profiling of the healthy sinonasal microbiome using Pacific Biosciences sequencing of full-length 16S rRNA genes

BackgroundPan-bacterial 16S rRNA microbiome surveys performed with massively parallel DNA sequencing technologies have transformed community microbiological studies. Current 16S profiling methods, however, fail to provide sufficient taxonomic resolution and accuracy to adequately perform species-level associative studies for specific conditions. This is due to the amplification and sequencing of only short 16S rRNA gene regions, typically providing for only family- or genus-level taxonomy. Moreover, sequencing errors often inflate the number of taxa present. Pacific Biosciences (PacBios) long-read technology in particular suffers from high error rates per base. Herein we present a microbiome analysis pipeline that takes advantage of PacBio circular consensus sequencing (CCS) technology to sequence and error correct full-length bacterial 16S rRNA genes, which provides high-fidelity species-level microbiome data\n\nResultsAnalysis of a mock community with 20 bacterial species demonstrated 100% specificity and sensitivity. Examination of a 250-plus species mock community demonstrated correct species-level classification of >90% of taxa and relative abundances were accurately captured. The majority of the remaining taxa were demonstrated to be multiply, incorrectly, or incompletely classified. Using this methodology, we examined the microgeographic variation present among the microbiomes of six sinonasal sites, by both swab and biopsy, from the anterior nasal cavity to the sphenoid sinus from 12 subjects undergoing trans-sphenoidal hypophysectomy. We found greater variation among subjects than among sites within a subject, although significant within-individual differences were also observed. Propiniobacterium acnes (recently renamed Cutibacterium acnes [1]) was the predominant species throughout, but was found at distinct relative abundances by site.\n\nConclusionsOur microbial composition analysis pipeline for single-molecule real-time 16S rRNA gene sequencing (MCSMRT, https://github.com/jpearl01/mcsmrt) overcomes deficits of standard marker gene based microbiome analyses by using CCS of entire 16S rRNA genes to provide increased taxonomic and phylogenetic resolution. Extensions of this approach to other marker genes could help refine taxonomic assignments of microbial species and improve reference databases, as well as strengthen the specificity of associations between microbial communities and dysbiotic states.

bioinformatics↗

bioRxiv2018

Microbiome-TP53 Gene Interaction in Human Lung Cancer

BackgroundLung cancer is the leading cancer diagnosis worldwide and the number one cause of cancer deaths. Exposure to cigarette smoke, the primary risk factor in lung cancer, reduces epithelial barrier integrity and increases susceptibility to infections. Herein, we hypothesized that somatic mutations together with cigarette smoke generate a dysbiotic microbiota that is associated with lung carcinogenesis. Using lung tissue from controls (n=33) and cancer cases (n=143), we conducted 16S rRNA bacterial gene sequencing, with RNA-seq data from lung cancer cases in The Cancer Genome Atlas (n=1112) serving as the validation cohort.\n\nResultsOverall, we demonstrate a lower alpha diversity in normal lung as compared to non-tumor adjacent or tumor tissue. In squamous cell carcinoma (SCC) specifically, a separate group of taxa were identified, in which Acidovorax was enriched in smokers (P =0.0013). Acidovorax temporans was identified by fluorescent in situ hybridization within tumor sections, and confirmed by two separate 16S rRNA strategies. Further, these taxa, including Acidovorax, exhibited higher abundance among the subset of SCC cases with TP53 mutations, an association not seen in adenocarcinomas (AD).\n\nConclusionsThe results of this comprehensive study show both a microbiome-gene and microbiome-exposure interactions in SCC lung cancer tissue. Specifically, tumors harboring TP53 mutations, which can damage epithelial function, have a unique bacterial consortia which is higher in relative abundance in smoking-associated SCC. Given the significant need for clinical diagnostic tools in lung cancer, this study may provide novel biomarkers for early detection.

cancer biology↗