Biology subjects

Mell, J. C.

Publications and source records attributed to Mell, J. C..

14 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↗

bioRxiv2024

Scorpio : Enhancing Embeddings to Improve Downstream Analysis of DNA sequences

Analysis of genomic and metagenomic sequences is inherently more challenging than that of amino acid sequences due to the higher divergence among evolutionarily related nucleotide sequences, variable k-mer and codon usage within and among genomes of diverse species, and poorly understood selective constraints. We introduce Scorpio, a versatile framework designed for nucleotide sequences that employs contrastive learning to improve embeddings. By leveraging pre-trained genomic language models and k-mer frequency embeddings, Scorpio demonstrates competitive performance in diverse applications, including taxonomic and gene classification, antimicrobial resistance (AMR) gene identification, and promoter detection. A key strength of Scorpio is its ability to generalize to novel DNA sequences and taxa, addressing a significant limitation of alignment-based methods. Scorpio has been tested on multiple datasets with DNA sequences of varying lengths (long and short) and shows robust inference capabilities. Additionally, we provide an analysis of the biological information underlying this representation, including correlations between codon adaptation index as a gene expression factor, sequence similarity, and taxonomy, as well as the functional and structural information of genes.

genomics↗

bioRxiv2023

Linked-Pair Long-Read Sequencing Strategy for Targeted Resequencing and Enrichment

In this report, we present linked-pair sequencing, a novel strategy to construct a long-read sequencing library such that adjacent fragments are linked with end-terminal duplications. We use the CRISPR-Cas9 nickase enzyme and a pool of multiple sgRNAs to perform non-random fragmentation of targeted long DNA molecules (>300kb) into smaller library-sized fragments (about 20 kbp) in a manner so as to retain physical linkage information (up to 1000 bp) between adjacent fragments. DNA molecules targeted for fragmentation are preferentially ligated with adaptors for sequencing, so this method can enrich targeted regions while taking advantage of the long-read sequencing platforms. This enables the sequencing of target regions with significantly lower total coverage, and the genome sequence within linker regions provides information for assembly and phasing. We demonstrated the validity and efficacy of the method first using phage and then by sequencing a panel of 100 full-length cancer-related genes (including both exons and introns) in the human genome. When the designed linkers contained heterozygous genetic variants, long haplotypes could be established. This sequencing strategy can be readily applied in both PacBio and Oxford Nanopore platforms. This economically viable approach is useful for targeted enrichment of hundreds of target genomic regions and where long no-gap contigs need deep sequencing.

genomics↗

bioRxiv2022

Mitochondrially targeted proximity biotinylation and proteomic analysis in Plasmodium falciparum

Despite ongoing efforts to control malaria infection, progress in lowering the number of deaths and infections appears to have stalled. The continued high incidence of malaria infection and mortality is in part due to emergence of parasites resistant to frontline antimalarials. This highlights the need for continued identification of novel protein drug targets. Mitochondrial functions in Plasmodium falciparum, the deadliest species of human malaria parasite, are targets of validated antimalarials including atovaquone and proguanil (Malarone). Thus, there has been great interest in identifying other essential mitochondrial proteins as candidates for novel drug targets. Garnering an increased understanding of the proteomic landscape inside the P. falciparum mitochondrion will also allow us to learn about the basic biology housed within this unique organelle. We employed a proximity biotinylation technique and mass spectrometry to identify novel P. falciparum proteins putatively targeted to the mitochondrion. We fused the leader sequence of a mitochondrially targeted chaperone, Hsp60, to the promiscuous biotin ligase TurboID. Through these experiments, we generated a list of 122 "putative mitochondrial" proteins. To verify whether these proteins were indeed mitochondrial, we chose five candidate proteins of interest for localization studies using ectopic expression and tagging of each full-length protein. This allowed us to localize four candidate proteins of unknown function to the mitochondrion, three of which have previously been assessed to be essential. We suggest that phenotypic characterization of these and other proteins from this list of 122 could be fruitful in understanding the basic mitochondrial biology of these parasites and aid antimalarial drug discovery efforts.

microbiology↗

bioRxiv2021

Pre-vaccination Frequency of Circulatory Tfh is associated with Robust Immune Response to TV003 Dengue Vaccine

It has been estimated that more than 390 million people are infected with Dengue virus every year; around 96 millions of these infections result in clinical pathologies. To date, there is only one licensed viral vector-based Dengue virus vaccine CYD-TDV approved for use in dengue endemic areas. While initially approved for administration independent of serostatus, the current guidance only recommends the use of this vaccine for seropositive individuals. Therefore, there is a critical need for investigating the influence of Dengue virus serostatus and immunological mechanisms that influence vaccine outcome. Here, we provide comprehensive evaluation of sero-status and host immune factors that correlate with robust immune responses to a Dengue virus vector based tetravalent vaccine (TV003) in a Phase II clinical cohort of human participants. We observed that sero-positive individuals demonstrate a much stronger immune response to the TV003 vaccine. Our multi-layered immune profiling revealed that sero-positive subjects have increased baseline/pre-vaccination frequencies of circulating T follicular helper (cTfh) cells and the Tfh related chemokine CXCL13/BLC. Importantly, this baseline/pre-vaccination cTfh profile correlated with the vaccinees ability to launch neutralizing antibody response against all four sero-types of Dengue virus, an important endpoint for Dengue vaccine clinical trials. Overall, we provide novel insights into the favorable cTfh related immune status that persists in Dengue virus sero-positive individuals that correlate with their ability to mount robust vaccine specific immune responses. Such detailed interrogation of cTfh cell biology in the context of clinical vaccinology will help uncover mechanisms and targets for favorable immuno-modulatory agents. Author summaryDengue virus (DENV) is a worldwide threat that causes significant health and economic burden. Currently, there are several challenges in the development of a DENV vaccine including the existence of four different serotypes all; capable of causing disease and antibody dependent enhancement (ADE). For complete protection, a vaccine must be able to generate neutralizing antibodies against all 4 serotypes to avoid ADE. Currently, there is one licensed DENV vaccine, CYD-TDV (DENGVAXIATM). However, this vaccine is only efficacious in protecting against severe disease in DENV seropositive individuals therefore serostatus effect must be further studied for optimal vaccine design. A subset of CD4+ T cells called T-follicular helper (Tfh) cells have been well known to play a major role in aiding high affinity antibody production. Therefore, we chose to look at subsets of Tfh and the cytokines they produce in human blood that can serve as biomarkers for effective vaccine design. We found that DENV sero-positive participants had increased pre-vaccination frequencies of Tfh cells and higher levels of the Tfh related chemokine CXCL13/BLC that plays a role in directing antigen-specific responses. This pre-vaccination Tfh profile and CXCL13/BLC are then correlated positively with the vaccinees ability to produce neutralizing antibody against all four sero-types (breadth of the Response) of DENV, an important goal for all DENV vaccine trials.

immunology↗

bioRxiv2021

Comparative Analysis of Emerging B.1.1.7+E484K SARS-CoV-2 isolates from Pennsylvania

Rapid whole genome sequencing of SARS-CoV-2 has presented the ability to detect new emerging variants of concern in near real time. Here we report the genome of a virus isolated in Pennsylvania in March 2021 that was identified as lineage B.1.1.7 (VOC-202012/01) that also harbors the E484K spike mutation, which has been shown to promote "escape" from neutralizing antibodies in vitro. We compare this sequence to the only 5 other B.1.1.7+E484K genomes from Pennsylvania, all of which were isolated in mid March. Beginning in February 2021, only a small number (n=60) of isolates with this profile have been detected in the US, and only a total of 253 have been reported globally (first in the UK in December 2020). Comparative genomics of all currently available high coverage B.1.1.7+E484K genomes (n=235) available on GISAID suggested the existence of 7 distinct groups or clonal complexes (CC; as defined by GNUVID) bearing the E484K mutation raising the possibility of 7 independent acquisitions of the E484K spike mutation in each background. Phylogenetic analysis suggested the presence of at least 3 distinct clades of B.1.1.7+E484K circulating in the US, with the Pennsylvanian isolates belonging to two distinct clades. Increased genomic surveillance will be crucial for detection of emerging variants of concern that can escape natural and vaccine induced immunity.

genomics↗

bioRxiv2019

Genome-wide analysis of DNA uptake by naturally competent Haemophilus influenzae

The genomes of naturally competent Pasteurellaceae and Neisseriaceae have many short uptake sequences (USS), which allow them to distinguish self-DNA from foreign DNA. To fully characterize this preference we developed genome-wide maps of DNA uptake using both a sequence-based computational model and genomic DNA that had been sequenced after uptake by and recovery from competent Haemophilus influenzae cells. When DNA fragments were shorter than the average USS spacing of ~1000 bp, sharp peaks of uptake were centered at USS and separated by valleys with 1000-fold lower uptake. Long DNA fragments (1.5-17 kb) gave much less variation, with 90% of positions having uptake within two-fold of the mean. All detectable uptake biases arose from sequences that fit the USS uptake motif. Simulated competition predicted that, in its respiratory tract environment, H. influenzae will efficiently take up its own DNA even when human DNA is present in 100-fold excess.

microbiology↗

bioRxiv2019

Themetagenomics: Exploring Thematic Structure and Predicted Functionality of 16s rRNA Amplicon Data

Analysis of microbiome data involves identifying co-occurring groups of taxa associated with sample features of interest (e.g., disease state). Elucidating such relations is often difficult as microbiome data are compositional, sparse, and have high dimensionality. Also, the configuration of co-occurring taxa may represent overlapping subcommunities that contribute to sample characteristics such as host status. Preserving the configuration of co-occurring microbes rather than detecting specific indicator species is more likely to facilitate biologically meaningful interpretations. Additionally, analyses that use taxonomic relative abundances to predict the abundances of different gene functions aggregate predicted functional profiles across taxa. This precludes straightforward identification of predicted functional components associated with subsets of co-occurring taxa. We provide an approach to explore co-occurring taxa using \"topics\" generated via a topic model and link these topics to specific sample features (e.g., disease state). Rather than inferring predicted functional content based on overall taxonomic relative abundances, we instead focus on inference of functional content within topics, which we parse by estimating interactions between topics and pathways through a multilevel, fully Bayesian regression model. We apply our methods to three publicly available 16S amplicon sequencing datasets: an inflammatory bowel disease dataset from Gevers et al., an oral cancer dataset from Schmidt et al., and a time-series dataset from David et al. Using our topic model approach to uncover latent structure in 16S rRNA amplicon surveys, investigators can (1) capture groups of co-occurring taxa termed topics; (2) uncover within-topic functional potential; (3) link taxa co-occurrence, gene function, and environmental/host features; and (4) explore the way in which sets of co-occurring taxa behave and evolve over time. These methods have been implemented in a freely available R package: https://github.com/EESI/themetagenomics.

bioinformatics↗

bioRxiv2019

Complete genome sequence of Escherichia coli C - an old model organism with a new application in biofilm research

Escherichia coli C forms more robust biofilms than the other laboratory strains. Biofilm formation and cell aggregation under a high shear force depends on temperature and salt concentrations. It is the last of five E. coli strains (C, K12, B, W, Crooks) designated as safe for laboratory purposes whose genome has not been sequenced. Here we present the complete genomic sequence of this strain in which we utilized both long-read PacBio-based sequencing and high resolution optical mapping to confirm a large inversion in comparison to the other laboratory strains. Notably, DNA sequence comparison revealed the absence of several genes thought to be involved in biofilm formation, including antigen 43, waaSBOJYZUL for LPS synthesis, and cpsB for curli synthesis. The first main difference we identified that likely affects biofilm formation is the presence of an IS3-like insertion sequence in front of the carbon storage regulator csrA gene. This insertion is located 86 bp upstream of the csrA start codon inside the -35 region of P4 promoter and blocks the transcription from the sigma32 and sigma70 promoters P1-P3 located further upstream. The second is the presence of an IS5/IS1182 in front of the csgD gene, which may drive its overexpression in biofilm. And finally, E. coli C encodes an additional sigma70 subunit overexpressed in biofilm and driven by the same IS3-like insertion sequence. Promoter analyses using GFP gene fusions and total expression profiles using RNA-seq analyses comparing planktonic and biofilm envirovars provided insights into understanding this regulatory pathway in E. coli. IMPORTANCEBiofilms are crucial for bacterial survival, adaptation, and dissemination in natural, industrial, and medical environments. Most laboratory strains of E. coli grown for decades in vitro have evolved and lost their ability to form biofilm, while environmental isolates that can cause infections and diseases are not safe to work with. Here, we show that the historic laboratory strain of E. coli C produces a robust biofilm and can be used as a model organism for multicellular bacterial research. Furthermore, we ascertained the full genomic sequence as well as gene expression profiles of both the biofilm and planktonic envirovars of this classic strain, which provide for a base level of characterization and make it useful for many biofilm-based applications.

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

A competence-regulated toxin-antitoxin system in Haemophilus influenzae

Natural competence allows bacteria to respond to environmental and nutritional cues by taking up free DNA from their surroundings, thus gaining nutrients and genetic information. In the Gram-negative bacterium Haemophilus influenae, the DNA uptake machinery is induced by the CRP and Sxy transcription factors in response to lack of preferred carbon sources and nucleotide precursors. Here we show that HI0659--which is absolutely required for DNA uptake-- encodes the antitoxin of a competence-regulated toxin-antitoxin operon ( toxTA), likely acquired by horizontal gene transfer from a Streptococcus species. Deletion of the toxin restores uptake to the antitoxin mutant. In addition to the expected Sxy-and CRP-dependent-competence promoter, transcript analysis using RNA-seq identified an internal antitoxin-repressed promoter whose transcription starts within toxT and will yield nonfunctional protein. We present evidence that the most likely effect of unopposed toxin expression is non-specific cleavage of mRNAs and arrest or death of competent cells in the culture, and we show that the toxin gene has been inactivated by deletion in many H. influenzae strains. We suggest that this competence-regulated toxin-antitoxin system may facilitate downregulation of protein synthesis and recycling of nucleotides under starvation conditions, or alternatively be a simple genetic parasite.

microbiology↗

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↗

bioRxiv2017

Evaluating a topic model approach for parsing microbiome data structure

The increasing availability of microbiome survey data has led to the use of complex machine learning and statistical approaches to measure taxonomic diversity and extract relationships between taxa and their host or environment. However, many approaches inadequately account for the difficulties inherent to microbiome data. These difficulties include (1) insufficient sequencing depth resulting in sparse count data, (2) a large feature space relative to sample space, resulting in data prone to overfitting, (3) library size imbalance, requiring normalization strategies that lead to compositional artifacts, and (4) zero-inflation. Recent work has used probabilistic topics models to more appropriately model microbiome data, but a thorough inspection of just how well topic models capture underlying microbiome signal is lacking. Also, no work has determined whether library size or variance normalization improves model fitting. Here, we assessed a topic model approach on 16S rRNA gene survey data. Through simulation, we show, for small sample sizes, library-size or variance normalization is unnecessary prior to fitting the topic model. In addition, by exploiting topic-to-topic correlations, the topic model successfully captured dynamic time-series behavior of simulated taxonomic subcommunities. Lastly, when the topic model was applied to the David et al. time-series dataset, three distinct gut configurations emerged. However, unlike the David et al. approach, we characterized the events in terms of topics, which captured taxonomic co-occurrence, and posterior uncertainty, which facilitated the interpretation of how the taxonomic configurations evolved over time.

bioinformatics↗

bioRxiv2017

Exploring thematic structure in 16S rRNA marker gene surveys

BackgroundAnalysis of microbiome data involves identifying co-occurring groups of taxa associated with sample features of interest (e.g., disease state). But elucidating key associations is often difficult since microbiome data are compositional, high dimensional, and sparse. Also, the configuration of co-occurring taxa may represent overlapping subcommunities that contribute to, for example, host status. Preserving the configuration of co-occurring microbes rather than detecting specific indicator species is more likely to facilitate biologically meaningful interpretations. In addition, analyses that utilize both taxonomic and predicted functional abundances typically independently characterize the taxonomic and functional profiles before linking them to sample information. This prevents investigators from identifying the specific functional components associate with which subsets of co-occurring taxa.\n\nResultsWe provide an approach to explore co-occurring taxa using \"topics\" generated via a topic model and then link these topics to specific sample classes (e.g., diseased versus healthy). Rather than inferring predicted functional content independently from taxonomic abundances, we instead focus on inference of functional content within topics, which we parse by estimating pathway-topic interactions through a multilevel, fully Bayesian regression model. We apply our methods to two large publically available 16S amplicon sequencing datasets: an inflammatory bowel disease (IBD) dataset from Gevers et al. and data from the American Gut (AG) project. When applied to the Gevers et al. IBD study, we demonstrate that a topic highly associated with Crohns disease (CD) diagnosis is (1) dominated by a cluster of bacteria known to be linked with CD and (2) uniquely enriched for a subset of lipopolysaccharide (LPS) synthesis genes. In the AG data, our approach found that individuals with plant-based diets were enriched with Lachnospiraceae, Roseburia, Blautia, and Ruminococcaceae, as well as fluorobenzoate degradation pathways, whereas pathways involved in LPS biosynthesis were depleted.\n\nConclusionsWe introduce an approach for uncovering latent thematic structure in the context of sample features for 16S rRNA surveys. Using our topic-model approach, investigators can (1) capture groups of co-occurring taxa termed topics, (2) uncover within-topic functional potential, and (3) identify gene sets that may guide future inquiry. These methods have been implemented in a freely available R package https://github.com/EESI/themetagenomics.

bioinformatics↗