Biology subjects

Lamoureux, C.

Publications and source records attributed to Lamoureux, C..

7 recordsLinked to original sources

bioRxiv2025

Rapid Liquid Chromatography-Mass Spectrometry (rLC-MS) for Deep Metabolomics Analysis of Population Scale Studies

Mass spectrometry (MS)-based metabolomics is a key technology for the interrogation of exogenous and endogenous small molecule mediators that influence human health and disease. To date, however, low throughput of MS systems have largely precluded large-scale metabolomics studies of human populations, limiting power to discover physiological roles of metabolites. Here, we introduce a fully automated rapid liquid chromatography-mass spectrometry (rLC-MS) system coupled to an AI-enabled computational pipeline that enables high-throughput, reproducible, non-targeted metabolite measurements across tens of thousands of samples. This system captures thousands of polar, amphipathic and nonpolar (lipid) metabolites in a human plasma sample in 53 seconds of analytical time, enabling analysis of greater than 1,000 samples per day per instrument. To demonstrate the discovery power of the rLC-MS platform, a subset of samples from Sapients DynamiQ biorepository - comprised of 62,039 total plasma samples collected longitudinally from 11,045 individuals - were selected for deep analysis by rLC-MS to capture a rich, dynamic landscape of chemical variation that reflects both physiological processes and environmental influences. 26,042 plasma samples with matched real-world data (RWD) were chosen for the study, representing 6,935 individuals with diverse demographic backgrounds and disease profiles. Unbiased exploratory analysis revealed human metabotypes that correlate with heterogenous disease phenotypes, including key sub-populations of cardiometabolic and other human diseases. Moreover, a metabolic aging clock machine learning model trained on healthy individuals in this dataset accurately predicted accelerated aging in various chronic diseases, with dynamic reversal of metabolic aging following definitive therapy. These data demonstrate that the rLC-MS platform enables prediction of clinically relevant physiological states from plasma metabolomics at scale in human populations.

biochemistry↗

bioRxiv2023

Machine learning uncovers the Pseudomonas syringae transcriptome in microbial communities and during infection

The transcriptional regulatory network (TRN) of the phytopathogen Pseudomonas syringae pv. tomato DC3000 regulates its response to environmental stimuli, including interactions with hosts and neighboring bacteria. Despite the importance of transcriptional regulation during these agriculturally-significant interactions, a comprehensive understanding of the TRN of P. syringae is yet to be achieved. Here, we collected and decomposed a compendium of public RNA-seq data from P. syringae to obtain 45 independently modulated gene sets (iModulons) that quantitatively describe the TRN and its activity state across diverse conditions. Through iModulon analysis, we 1) untangle the complex interspecies interactions between P. syringae and other terrestrial bacteria in cocultures, 2) expand the current understanding of the Arabidopsis thaliana-P. syringae interaction, and 3) elucidate the AlgU-dependent regulation of flagellar gene expression. The modularized TRN yields a unique understanding of interaction-specific transcriptional regulation in P. syringae. ImportancePseudomonas syringae pv. tomato DC3000 is a model plant pathogen that infects tomatoes and Arabidopsis thaliana. The current understanding of global transcriptional regulation in the pathogen is limited. Here, we applied iModulon analysis to a compendium of RNA-seq data to unravel its transcriptional regulatory network. We characterize each co-regulated gene set, revealing the activity of major regulators across diverse conditions. We provide new insights on the transcriptional dynamics in interactions with the plant immune system and with other bacterial species, such as AlgU-dependent regulation of flagellar genes during plant infection and downregulation of siderophore production in the presence of a siderophore cheater. This study demonstrates the novel application of iModulons in studying temporal dynamics during host-pathogen and microbe-microbe interactions, and reveals specific insights of interest.

systems biology↗

bioRxiv2022

Lab evolution, transcriptomics, and modeling reveal mechanisms of paraquat tolerance

Relationships between the genome, transcriptome, and metabolome underlie all evolved phenotypes. However, it has proved difficult to elucidate these relationships because of the high number of variables measured. A recently developed data analytic method for characterizing the transcriptome can simplify interpretation by grouping genes into independently modulated sets (iModulons). Here, we demonstrate how iModulons reveal deep understanding of the effects of causal mutations and metabolic rewiring. We use adaptive laboratory evolution to generate E. coli strains that tolerate high levels of the redox cycling compound paraquat, which produces reactive oxygen species (ROS). We combine resequencing, iModulons, and metabolic models to elucidate six interacting stress tolerance mechanisms: 1) modification of transport, 2) activation of ROS stress responses, 3) use of ROS-sensitive iron regulation, 4) motility, 5) broad transcriptional reallocation toward growth, and 6) metabolic rewiring to decrease NADH production. This work thus reveals the genome-scale systems biology of ROS tolerance. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/521246v1_ufig1.gif" ALT="Figure 1"> View larger version (83K): org.highwire.dtl.DTLVardef@187f455org.highwire.dtl.DTLVardef@ba0798org.highwire.dtl.DTLVardef@148b175org.highwire.dtl.DTLVardef@17a8f8c_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2022

Laboratory evolution of synthetic electron transport system variants reveals a larger metabolic respiratory system and its plasticity

Respiration requires organisms to have an electron transport system (ETS) for the generation of proton motive force across the membrane that drives ATP synthase. Although the molecular details of the ETS are well studied and constitute textbook material, few studies have appeared to elucidate its systems biology. The most thermodynamically efficient ETS consists of two enzymes, an NADH: quinone oxidoreductase (NqRED) and a dioxygen reductase (O2RED), which facilitate the shuttling of electrons from NADH to oxygen. However, evolution has produced variations within ETS which modulate the overall energy efficiency of the system even within the same organism 1-3. The system-level impact of these variations and their individual physiological optimality remain poorly determined. To mimic varying ETS efficiency we generated four Escherichia coli deletion strains (named ETS-1H, 2H, 3H, and 4H) harboring unbranched ETS variants that pump 1, 2, 3, or 4 proton(s) per electron respectively. We then used a combination of synergistic methods (laboratory evolution, multi-omic analyses, and computation of proteome allocation) to characterize these ETS variants. We found that: (a) all four ETS variants evolved to a similar optimized growth rate, (b) the evolution of ETS variants was enabled by specific rewiring of major energy-generating pathways that couple to the ETS to optimize their ATP production capability, (c) proteome allocation per ATP generated was the same for all the variants, (d) the aero-type, that designates the overall ATP generation strategy 4 of a variant, remained conserved during its laboratory evolution, with the exception of the ETS-4H variant, and (e) integrated computational analysis of then data supported a proton-to-ATP ratio of 10 protons per 3 ATP for ATP synthase for all four ETS variants. We thus have defined the Aero-Type System (ATS) as a generalization of the aerobic bioenergetics, which is descriptive of the metabolic systems biology of respiration and demonstrates its plasticity.

systems biology↗

bioRxiv2022

Quantitative sequence basis for the E. coli transcriptional regulatory network

The transcriptional regulatory network (TRN) of E. coli consists of thousands of interactions between regulators and DNA sequences. Inherently the DNA sequence is the primary determinant of the TRN; however, it is well established that the presence of a DNA binding motif does not guarantee a functional regulatory protein binding site. Thus, the extent to which the TRN architecture can be predicted by the genome DNA sequence alone remains unclear. Here, we developed machine learning models that predict the TRN structure of E. coli based on genome sequence. Models were constructed successfully (cross-validation AUROC >= 0.8) for 84% (57/68) of valid E. coli regulons identified from top-down analysis of RNA-seq data. We found that: 1) While regulatory motif strength is the most important sequence feature for determining regulon membership, additional features such as DNA shape substantially influence membership; 2) complex regulons involving multiple interacting regulators could be unraveled by machine learning; 3) investigating regulons where initial ML models failed revealed new regulator-specific sequence features that improved model accuracy. Finally, while regulon structure can appear to be variable across estimation methods and strains, we found that strong regulatory sequence features underlie both the genes that appear most consistently in regulons across estimation methods as well as the core regulon genes in the Fur pan-regulon. This work develops a quantitative understanding of the sequence basis of the TRN and suggests a path towards computationally-guided control of transcriptional regulation for synthetic biology applications.

bioinformatics↗

bioRxiv2021

Machine learning uncovers a data-driven transcriptional regulatory network for the Crenarchaeal thermoacidophile Sulfolobus acidocaldarius

Dynamic cellular responses to environmental constraints are coordinated by the transcriptional regulatory network (TRN), which modulates gene expression. This network controls most fundamental cellular responses, including metabolism, motility, and stress responses. Here, we apply independent component analysis, an unsupervised machine learning approach, to 95 high-quality Sulfolobus acidocaldarius RNA-seq datasets and extract 45 independently modulated gene sets, or iModulons. Together, these iModulons contain 755 genes (32% of the genes identified on the genome) and explain over 70% of the variance in the expression compendium. We show that 5 modules represent the effects of known transcriptional regulators, and hypothesize that most of the remaining modules represent the effects of uncharacterized regulators. Further analysis of these gene sets results in: (1) the prediction of a DNA export system composed of 5 uncharacterized genes, (2) expansion of the LysM regulon, and (3) evidence for an as-yet-undiscovered global regulon. Our approach allows for a mechanistic, systems-level elucidation of an extremophiles responses to biological perturbations, which could inform research on gene-regulator interactions and facilitate regulator discovery in S. acidocaldarius. We also provide the first global TRN for S. acidocaldarius. Collectively, these results provide a roadmap towards regulatory network discovery in archaea.

systems biology↗

bioRxiv2021

Machine learning of all Mycobacterium Tuberculosis H37Rv RNA-seq data reveals complex interplay between metabolism, stress response, and infection

Mycobacterium tuberculosis is one of the most consequential human bacterial pathogens, posing a serious challenge to 21st century medicine. A key feature of its pathogenicity is its ability to adapt its transcriptional response to environmental stresses through its transcriptional regulatory network (TRN). While many studies have sought to characterize specific portions of the M. tuberculosis TRN, a systems level characterization and analysis of interactions among the controlling transcription factors remains to be achieved. Here, we applied an unsupervised machine learning method to modularize the M. tuberculosis transcriptome and describe the role of transcription factors (TFs) in the TRN. By applying Independent Component Analysis (ICA) to over 650 transcriptomic samples, we obtained 80 independently modulated gene sets known as "iModulons," many of which correspond to known regulons. These iModulons explain 61% of the variance in the organisms transcriptional response. We show that iModulons: 1) reveal the function of previously unknown regulons, 2) describe the transcriptional shifts that occur during environmental changes such as shifting carbon sources, oxidative stress, and virulence events, and 3) identify intrinsic clusters of transcriptional regulons that link several important metabolic systems, including lipid, cholesterol, and sulfur metabolism. This transcriptome-wide analysis of the M. tuberculosis TRN informs future research on effective ways to study and manipulate its transcriptional regulation, and presents a knowledge-enhanced database of all published high-quality RNA-seq data for this organism to date.

systems biology↗