Biology subjects

Josephs-Spaulding, J.

Publications and source records attributed to Josephs-Spaulding, J..

3 recordsLinked to original sources

bioRxiv2024

Metatranscriptomics-based metabolic modeling of patient-specific urinary microbiome during infection

Urinary tract infections (UTIs) are a major health concern which incur significant socioeconomic costs in addition to substantial antibiotic prescriptions, thereby accelerating the emergence of antibiotic resistance. To address the challenge of antibiotic-resistant UTIs, our systems biology approach uncovers patient-specific uromicrobiome insights that are focused on community utilization of metabolites. By leveraging the distinct metabolic traits of patient-specific pathogens, we aim to identify metabolic dependencies of pathogens and provide suggestions for targeted interventions for future studies. Combining patient-specific metatranscriptomic data with genome-scale metabolic modeling and data from the Human Urine Metabolome, this study explores UTIs from a systems biology perspective through the reconstruction of tailored microbial community models to mirror the metabolic profiles of individual UTI patients urinary microbiomes. Delving into patient-specific bacterial gene expressions and microbial interactions, we identify metabolic signatures and propose mechanisms for UTI pathology. Our research underscores the potential of integrating metatranscriptomic data using systems biological approaches, providing insights into disease metabolic mechanisms and potential phenotypic manifestations. This contribution introduces a new method that could guide treatment options for antibiotic-resistant UTIs, aiming to lessen antibiotic use by combining the pathogens unique metabolic traits. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/586446v2_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@1074251org.highwire.dtl.DTLVardef@192d45eorg.highwire.dtl.DTLVardef@b46927org.highwire.dtl.DTLVardef@638e2a_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract Metatranscriptome sequencing was used to investigate the functional uromicrobiome across a cohort of 19 individuals; patient-specific microbiome community models were reconstructed and simulated in a virtual urine environment. Total RNA was extracted from patients urine and sequenced to assess the metatranscriptome, providing insights into patient-specific uromicrobiome microbial taxa and their associated gene expression during urinary tract infections (UTIs). These combinatory datasets derived from metatranscriptomics data were further expanded first to reconstruct species specific metabolic models that were conditioned with gene expression. Gene expression conditioned metabolic models were combined in an in silico environment with a defined urine media to construct patient-specific context-specific uromicrobiome models, enabling an understanding of each patients unique microbiome. Using this approach, we aimed to identify patient-specific microbiome dynamics and provide insight towards various metabolic features that can be utilized or validated in future studies for individualized intervention strategies. Created with www.biorender.com. C_FIG

systems biology↗

bioRxiv2023

Genomic insights into Lactobacillaceae: Analyzing the Alleleome of core pangenomes for enhanced understanding of strain diversity and revealing Phylogroup-specific unique variants

The Lactobacillaceae familys significance in food and health, combined with available strain-specific genomes, enables genome assessment through pangenome analysis. The Alleleome of the core pangenomes of the Lactobacillaceae family, which identifies natural sequence variations, was reconstructed from the amino acid and nucleotide sequences of the core genes across 2,447 strains of 26 species. It comprised 3.71 million amino acid variants in 29,448 core genes across the family. The alleleome analysis of the Lactobacillaceae family revealed key findings: 1) In the core pangenome, amino acid substitutions prevailed over rare insertions and deletions, 2) Purifying negative selection primarily influenced core gene variations in the family, with diversifying selection noted in L. helveticus. L. plantarums core alleleome was investigated due to its industrial importance. In L. plantarum, the defining characteristics of its core alleleome included: 1) It is highly conserved; 2) Among 235 isolation sources, the primary categories displaying variant prevalence were fermented food, feces, and unidentified sources; 3) It is predominantly characterized by conservative and moderately conservative mutations; and 4) Phylogroup-specific core variant gene analysis identified unique variants (DltX, FabZ1, Pts23B, CspP) in phylogroups I and B which could be used as identifier or validation markers of strain or phylogroup.

genomics↗

bioRxiv2023

Reconstructing the Transcriptional Regulatory Network of Probiotic L. reuteri is Enabled by Transcriptomics and Machine Learning

ILimosilactobacillus reuteri, a probiotic microbe instrumental to human health and sustainable food production, adapts to diverse environmental shifts via dynamic gene expression. We applied independent component analysis to 117 high-quality RNA-seq datasets to decode its transcriptional regulatory network (TRN), identifying 35 distinct signals that modulate specific gene sets. This study uncovers the fundamental properties of L. reuteris TRN, deepens our understanding of its arginine metabolism, and the co-regulation of riboflavin metabolism and fatty acid biosynthesis. It also sheds light on conditions that regulate genes within a specific biosynthetic gene cluster and the role of isoprenoid biosynthesis in L. reuteris adaptive response to environmental changes. Through the integration of transcriptomics and machine learning, we provide a systems-level understanding of L. reuteris response mechanism to environmental fluctuations, thus setting the stage for modeling the probiotic transcriptome for applications in microbial food production. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=137 SRC="FIGDIR/small/547516v1_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@118ec4corg.highwire.dtl.DTLVardef@141b0b7org.highwire.dtl.DTLVardef@1b8cf6forg.highwire.dtl.DTLVardef@3ab181_HPS_FORMAT_FIGEXP M_FIG C_FIG Comprehensive iModulon Workflow Overview. Our innovative workflow is grounded in the analysis of the LactoPRECISE compendium, a curated dataset containing 117 internally sequenced RNA-seq samples derived from a diversity of 50 unique conditions, encompassing an extensive range of 13 distinct condition types. We employ the power of Independent Component Analysis (ICA), a cutting-edge machine learning algorithm, to discern the underlying structure of iModulons within this wealth of data. In the subsequent stage of our workflow, the discovered iModulons undergo detailed scrutiny to uncover media-specific regulatory mechanisms governing metabolism, illuminate the context-dependent intricacies of gene expression, and predict pathways leading to the biosynthesis of probiotic secondary metabolites. Our workflow offers an invaluable and innovative lens through which to view probiotic strain design while simultaneously highlighting transformative approaches to data analytics in the field.

bioinformatics↗