Biology subjects

Palsson, B.

Publications and source records attributed to Palsson, B..

At least 37 records · Page 2Linked to original sources

bioRxiv2021

Regulatory perturbations of ribosome allocation results in a trade-off between fast growth and adaptation capacity

Bacteria regulate their cellular resource allocation to enable fast growth-adaptation to a variety of environmental niches. We studied the ribosomal allocation, growth and expression profiles of two sets of fast-growing mutants of Escherichia coli K-12 MG1655 in glucose minimal medium. Mutants with only 3 of the seven copies of ribosomal RNA operons grew faster than the wild-type strain in minimal media and show similar phenotype to previously studied rpoB mutants. Higher growth rates due to increased ribosome content affected resource allocation. Expression profiles of fast-growing mutants shared downregulation of hedging functions and upregulated growth functions. Mutants showed longer diauxic shifts and reduced activity of gluconeogenic promoters during glucose-acetate shifts, suggesting reduced availability of the RNA Polymerase for expressing hedging proteome. These results show that the regulation of ribosomal allocation underlies the growth/hedging phenotypes obtained from laboratory evolution experiments. We show how two different regulatory perturbations (rRNA promoters or rpoB mutations) reshape the proteome for growth with a concomitant fitness cost HighlightsMutants with only 3 ribosomal operons grow faster than wild-type in minimal medium {Delta}4 rrn and rpoB mutants share phenotypic traits Faster growth of mutants is achieved by increased ribosome content Fast-growing mutants display reduced hedging expression and adaptation trade-offs Despite similar ribosomal content in rich medium the mutants present growth defects

systems biology↗

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 Pseudomonas aeruginosa transcriptomes identifies independently modulated sets of genes associated with known transcriptional regulators

The transcriptional regulatory network (TRN) of Pseudomonas aeruginosa plays a critical role in coordinating numerous cellular processes. We extracted and quality controlled all publicly available RNA-sequencing datasets for P. aeruginosa to find 281 high-quality transcriptomes. We produced 83 new RNAseq data sets under critical conditions to generate a comprehensive compendium of 364 transcriptomes. We used this compendium to reconstruct the TRN of P. aeruginosa using independent component analysis (ICA). We identified 104 independently modulated sets of genes (called iModulons), among which 81 (78%) reflect the effects of known transcriptional regulators. We show that iModulons: 1) play an important role in defining the genomic boundaries of biosynthetic gene clusters (BGCs); 2) show increased expression of the BGCs and associated secretion systems in conditions that emulate cystic fibrosis (CF); 3) show the presence of a novel BGC named RiPP (bacteriocin producer) which might have a role in worsening CF outcomes; 4) exhibit the interplay of amino acid metabolism regulation and central metabolism across carbon sources, and 5) clustered according to their activity changes to define iron and sulfur stimulons. Finally, we compare the iModulons of P. aeruginosa with those of E. coli to observe conserved regulons across two gram negative species. This comprehensive TRN framework covers almost every aspect of the transcriptional regulatory machinery in P. aeruginosa, and thus could prove foundational for future research of its physiological functions.

bioinformatics↗

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↗

bioRxiv2021

A systems-guided approach to discover the intracellular target of a novel evolution-drug lead

Elucidating intracellular drug targets has been a difficult problem. While machine learning analysis of omics data has been a promising approach, going from large-scale trends to specific targets remains a challenge. Here, we developed a systems-guided hierarchic workflow that utilizes metabolic and structural analysis to narrow in on specific targets suggested by statistical and machine learning analysis of metabolomics data. Utilizing a novel multi-valent DHFR-targeting antibiotic compound, CD15-3, as a case study, we first measured global metabolomics and applied statistics and machine learning to locate broad areas of metabolic perturbation under antibiotic stress. We then tested the ability of suggested compounds to rescue growth and performed metabolic modelling to identify pathways whose inhibition was consistent with growth rescue patterns. Next, we utilized protein structural similarity to further prioritize candidate drug targets within these pathways. Overexpression and in vitro activity assays of a top candidate target, HPPK (folK), showed complete recovery from drug induced growth inhibition and with microscopy. As interest in white-box machine learning methods continues to grow, this study demonstrates how established machine learning methods can be combined with mechanistic analyses to improve the resolution of drug target finding workflows.

systems biology↗

bioRxiv2020

Environmental conditions dictate differential evolution of vancomycin resistance in Staphylococcus aureus

While microbiological resistance to vancomycin in Staphylococcus aureus is rare, clinical vancomycin treatment failures are common, and methicillin-resistant S. aureus (MRSA) strains isolated from patients after prolonged vancomycin treatment failure remain susceptible. Adaptive laboratory evolution was utilized to uncover mutational mechanisms associated with MRSA vancomycin resistance in a bacteriological medium used in clinical susceptibility testing and a physiological medium. Sequencing of resistant clones revealed shared and media-specific mutational outcomes, with an overlap in cell wall regulons (walKRyycHI, vraSRT). Evolved strains displayed similar genetic and phenotypic traits to resistant clinical isolates. Importantly, resistant phenotypes that developed in physiological media did not translate into resistance in bacteriological media. Further, a bacteriological media-specific mechanism for vancomycin resistance enabled by a mutated mprF was confirmed. This study bridges the gap of understanding between clinical and microbiological vancomycin resistance in S. aureus and expands the number of allelic variants that result in vancomycin resistance phenotypes.

microbiology↗

bioRxiv2020

Reconstruction of Fur pan-regulon uncovers the complexity and diversity of transcriptional regulation in E. coli

Regulons for many transcription factors have been elucidated in model strains leading to an understanding of their role in producing physiological states. Comparative analysis of a regulon and its target genes between different strains of the same species is lacking. Ferric uptake regulator (Fur), involved in iron homeostasis, is one of the most conserved TFs, and is present in a wide range of bacteria. Using ChIP-exo experiments, we performed a comprehensive study of Fur binding sites in nine Escherichia coli strains with different lifestyles. 79 of the 431 target genes (18%) found belong to Fur core regulon, comprising genes involved in ion transport and metabolism, energy production and conversion, and amino acid metabolism and transport. 179 of the target genes (42%) comprise the accessory regulon, most of which were related to cell wall structure and biogenesis, and virulence factor pathways. The remaining target genes (173 or 40%) were in the unique regulon, with gene functions that were largely unknown. Furthermore, deletion of the fur gene led to distinct phenotypes in growth, motility, antibiotic resistance, and the change of siderophore production. These results provide a more complete understanding of how Fur regulates a set of target genes with surprising variation in closely related bacteria.

systems biology↗

bioRxiv2020

Characterizing the response of Acinetobacter baumannii ATCC 17978 to azithromycin in multiple in vitro growth conditions

Multi-drug resistant (MDR) Acinetobacter baumannii is one of the most concerning pathogens in hospital infections. A. baumannii is categorized as an "Urgent Threat" by the U.S. Centers for Disease Control and the highest priority pathogen by the World Health Organization due to its propensity for broad antibiotic resistance and its associated high mortality rates. New treatment options are urgently needed for MDR A. baumannii infections. Our prior studies have demonstrated an unappreciated utility of the macrolide azithromycin (AZM) against MDR A. baumannii in tissue-culture medium. This finding is all the more surprising since AZM has no appreciable activity against A. baumannii in standard bacteriological media. The basis for this media-dependent activity of AZM against A. baumannii is not fully defined. In this study, we utilize a variety of techniques (growth dynamics, bacterial cytological profiling, RNA sequencing, and LC/MS) to profile the response of MDR A. baumannii to AZM in both standard bacteriological and more physiological relevant mammalian tissue-culture medium. [Table 1]

microbiology↗

bioRxiv2020

Laboratory evolution of multiple E. coli strains reveals unifying principles of adaptation but diversity in driving genotypes

Fitness landscapes are a central concept in evolutionary biology and have been thoroughly detailed in terms of genotypes. However, our understanding of the selected metabolic and gene expression adaptations, and their dependence on genetic background, remains limited. Here, we reveal multi-scale adaptation principles in the E. coli species by taking multi-omics measurements of six different strains throughout their adaptive evolution to glucose minimal media. Statistics and matrix factorization is applied to yield four key results. First, analysis of the metabolic and physiological data shows evolutionary convergence in growth rate, glucose uptake rate, glycolytic ATP and NADH production but divergence in NADPH production strategies. Second, factorization-based analysis of the transcriptome revealed six conserved transcriptomic adaptations describing increased expression of ribosome and amino acid biosynthetic genes and decreased expression of stress response and structural genes. Third, correlation analysis identifies five tradeoffs underlying the transcriptomic profiles. Fourth, statistical tests leveraging ALE design identify four mutation-flux correlates and eight mutation-transcriptomic correlates that link mutations to systems level adaptation principles. Our total results reveal the dominant metabolic and regulatory constraints governing E. coli growth adaptation that either distinguish strains or are conserved principles.

systems biology↗

bioRxiv2020

Decomposition of transcriptional responses provides insights into differential antibiotic susceptibility

Responses of bacteria to antibiotic treatments depend on their environments. Differences between in vitro testing conditions and the physiological environments inside patients have resulted in poor antibiotic susceptibility predictions, contributing to treatment failures in the clinic. Here, we investigate how media composition affects antibiotic susceptibility in the laboratory strain E. coli K-12 MG1655, and contextualize these changes through machine learning of transcriptomics data. We show that complex transcriptional changes induced by different media or antibiotic treatment can be traced back to a few key regulators. Integration of results from machine learning with biochemical knowledge reveals fundamental shifts in respiration and iron availability that may explain media-dependent differential susceptibility to antibiotics. The data generation and analytical workflow used here can interrogate the regulatory state of a pathogen under any condition, and can be extended to additional strains and organisms for which data is available.

microbiology↗

bioRxiv2020

Profiling the effect of nafcillin on HA-MRSA D592 using bacteriological and physiological media

Staphylococcus aureus is a leading human pathogen associated with both hospital-acquired and community-acquired infections. The bacterium has steadily gained resistance to {beta}-lactams and other important first-line antibiotics culminating in its categorization as an urgent threat by the U.S. Centers for Disease Control and Prevention. Observations of a varying response to antimicrobial exposure as a function of media type has revealed that clinical susceptibility testing performed in standard bacteriological media might not adequately represent pharmacological responses in the patient. Such observations have encouraged research designed to identify media types that more closely mimic the in vivo environment. In this study, we examine the response of a hospital-acquired USA100 lineage methicillin-resistant, vancomycin-intermediate S. aureus (MRSA/VISA) strain (D592) to nafcillin in a bacteriological compared to a more physiological tissue culture-based medium. We performed multi-dimensional analysis including growth and bacterial cytological profiling, RNA sequencing, and exo-metabolomics measurements (both HPLC and LC/MS) to shed light on the media-dependent activity of the commonly prescribed {beta}-lactam antibiotic nafcillin. O_TBL View this table: org.highwire.dtl.DTLVardef@85b95borg.highwire.dtl.DTLVardef@14c1504org.highwire.dtl.DTLVardef@1f7e976org.highwire.dtl.DTLVardef@100598dorg.highwire.dtl.DTLVardef@1f770e9_HPS_FORMAT_FIGEXP M_TBL C_TBL

microbiology↗

bioRxiv2020

Machine learning uncovers independently regulated modules in the Bacillus subtilis transcriptome

The transcriptional regulatory network (TRN) of Bacillus subtilis coordinates cellular functions of fundamental interest, including metabolism, biofilm formation, and sporulation. Here, we use unsupervised machine learning to modularize the transcriptome and quantitatively describe regulatory activity under diverse conditions, creating an unbiased summary of gene expression. We obtain 83 independently modulated gene sets that explain most of the variance in expression, and demonstrate that 76% of them represent the effects of known regulators. The TRN structure and its condition-dependent activity uncover novel or recently discovered roles for at least 5 regulons, such as a relationship between histidine utilization and quorum sensing. The TRN also facilitates quantification of population-level sporulation states, revealing a putative anaerobic metabolism role for SigG. As this TRN covers the majority of the transcriptome and concisely characterizes the global expression state, it could inform research on nearly every aspect of transcriptional regulation in B. subtilis.

systems biology↗

bioRxiv2020

Computation of condition-dependent proteome allocation reveals variability in the macro and micro nutrient requirements for growth

Sustaining a robust metabolic network requires a balanced and fully functioning proteome. In addition to amino acids, many enzymes require cofactors (coenzymes and engrafted prosthetic groups) to function properly. Extensively validated genome-scale models of metabolism and gene expression (ME-models) have the unique ability to compute an optimal proteome composition underlying a metabolic phenotype, including the provision of all required cofactors. Here we use the ME-model for Escherichia coli K-12 MG1655 to computationally examine how environmental conditions change the proteome and its accompanying cofactor usage. We found that: (1) The cofactor requirements computed by the ME model mostly agree with the standard biomass objective function used in models of metabolism alone (M models); (2) ME-model computations reveal non-intuitive variability in cofactor use under different growth conditions; (3) An analysis of ME-model predicted protein use in aerobic and anaerobic conditions suggests an enrichment in the use of prebiotic amino acids in the proteins used to sustain anaerobic growth (4) The ME-model could describe how limitation in key protein components affect the metabolic state of E. coli. Genome-scale models have thus reached a level of sophistication where they reveal intricate properties of functional proteomes and how they support different E. coli lifestyles.

systems biology↗

bioRxiv2020

Revealing 29 sets of independently modulated genes in Staphylococcus aureus, their regulators and role in key physiological responses

The ability of Staphylococcus aureus to infect many different tissue sites is enabled, in part, by its Transcriptional Regulatory Network (TRN) that coordinates its gene expression to respond to different environments. We elucidated the organization and activity of this TRN by applying Independent Component Analysis (ICA) to a compendium of 108 RNAseq expression profiles from two S. aureus clinical strains (TCH1516 and LAC). ICA decomposed the S. aureus transcriptome into 29 independently modulated sets of genes (i-modulons) that revealed (1) high confidence associations between 21 i-modulons and known regulators; (2) an association between an i-modulon and {sigma}S, whose regulatory role was previously undefined; (3) the regulatory organization of 65 virulence factors in the form of three i-modulons associated with AgrR, SaeR and Vim-3, (4) the roles of three key transcription factors (codY, Fur and ccpA) in coordinating the metabolic and regulatory networks; and (5) a low dimensional representation, involving the function of few transcription factors, of changes in gene expression between two laboratory media (RPMI, CAMHB) and two physiological media (blood and serum). This representation of the TRN covers 842 genes representing 76% of the variance in gene expression that provides a quantitative reconstruction of transcriptional modules in S. aureus, and a platform enabling its full elucidation. Significance StatementStaphylococcus aureus infections impose an immense burden on the healthcare system. To establish a successful infection in a hostile host environment, S. aureus must coordinate its gene expression to respond to a wide array of challenges. This balancing act is largely orchestrated by the Transcriptional Regulatory Network (TRN). Here, we present a model of 29 independently modulated sets of genes that form the basis for a segment of the TRN in clinical USA300 strains of S. aureus. Using this model, we demonstrate the concerted role of various cellular systems (e.g. metabolism, virulence and stress response) underlying key physiological responses, including response during blood infection.

microbiology↗

bioRxiv2019

Impact of insertion sequences on convergent evolution of Shigella species

Shigella species are specialised lineages of Escherichia coli that have converged to become human-adapted and cause dysentery by invading human gut epithelial cells. Most studies of Shigella evolution have been restricted to comparisons of single representatives of each species; and population genomic studies of individual Shigella species have focused on genomic variation caused by single nucleotide variants and ignored the contribution of insertion sequences (IS) which are highly prevalent in Shigella genomes. Here, we investigate the distribution and evolutionary dynamics of IS within populations of Shigella dysenteriae Sd1, Shigella sonnei and Shigella flexneri. We find that five IS (IS1, IS2, IS4, IS600 and IS911) have undergone expansion in all Shigella species, creating substantial strain-to-strain variation within each population and contributing to convergent patterns of functional gene loss within and between species. We find that IS expansion and genome degradation are most advanced in S. dysenteriae and least advanced in S. sonnei; and using genome-scale models of metabolism we show that Shigella species display convergent loss of core E. coli metabolic capabilities, with S. sonnei and S. flexneri following a similar trajectory of metabolic streamlining to that of S. dysenteriae. This study highlights the importance of IS to the evolution of Shigella and provides a framework for the investigation of IS dynamics and metabolic reduction in other bacterial species.

microbiology↗

bioRxiv2018

Memote: A community-driven effort towards a standardized genome-scale metabolic model test suite

Several studies have shown that neither the formal representation nor the functional requirements of genome-scale metabolic models (GEMs) are precisely defined. Without a consistent standard, comparability, reproducibility, and interoperability of models across groups and software tools cannot be guaranteed.\n\nHere, we present memote (https://github.com/opencobra/memote) an open-source software containing a community-maintained, standardized set of metabolic model tests. The tests cover a range of aspects from annotations to conceptual integrity and can be extended to include experimental datasets for automatic model validation. In addition to testing a model once, memote can be configured to do so automatically, i.e., while building a GEM. A comprehensive report displays the models performance parameters, which supports informed model development and facilitates error detection.\n\nMemote provides a measure for model quality that is consistent across reconstruction platforms and analysis software and simplifies collaboration within the community by establishing workflows for publicly hosted and version controlled models.

systems biology↗

bioRxiv2018

ALEdb 1.0: A Database of Mutations from Adaptive Laboratory Evolution

Full genomic sequences are readily available, but their functional interpretation remains a fundamental challenge. Adaptive Laboratory Evolution (ALE) has emerged as an experimental approach to discover causal mutations that confer desired phenotypic functions. Thus, ALE not only represents a controllable experimental approach to systematically discover genotype-phenotype relationships, but it also allows for the revelation of the series of genetic alterations required to acquire the new phenotype. Numerous ALE studies have appeared in the literature providing a strong impetus for developing structured databases to warehouse experimental evolution information and make it retrievable for large-scale analysis. Here, the first step towards establishing this capability is presented: ALEdb (http://aledb.org). This initial release contains over 11,000 mutations that have been discovered in ALE experiments. ALEdb is the first of its kind; (1) it is a web-based platform that comprehensively reports on ALE acquired mutations and their conditions, (2) it reports key mutations using previously established trends, (3) it enables a search-driven workflow to enhance user mutation functional analysis, (4) it allows exporting of mutation query results for custom analysis, (5) it has a bibliome that describes the underlying published literature, and (6) contains experimental evolution mutations from multiple model organisms. Thus, ALEdb is an informative platform which will become increasingly revealing as the number of reported ALE experiments and identified mutations continue to expand.

bioinformatics↗

bioRxiv2018

Temperature-dependent estimation of Gibbs energies using an updated group contribution method

Reaction equilibrium constants determine the mass action ratios necessary to drive flux through metabolic pathways. Group contribution methods offer a way to estimate reaction equilibrium constants at wide coverage across the metabolic network. Here, we present an updated group contribution method with: 1) additional curated thermodynamic data used in fitting; and 2) capabilities to calculate equilibrium constants as a function of temperature. We first collected and curated aqueous thermo-dynamic data, including reaction equilibrium constants, enthalpies of reaction, Gibbs free energies of formation, enthalpies of formation, entropies change of formation of compounds, and proton and metal ion binding constants. We further estimated magnesium binding constants for 618 compounds using a linear regression model validated against measured data. Next, we formulated the calculation of equilibrium constants as a function of temperature and calculated necessary parameters, including standard entropy change of formation ({Delta}f S{circ}) and standard entropy change of reaction ({Delta}rS{circ}), using a model based on molecular properties. The median absolute errors in estimating {Delta}f S{circ} and {Delta}rS{circ} were 0.010 kJ/K/mol and 0.018 kJ/K/mol, respectively. The efforts here fill in gaps for thermodynamic calculations under various conditions, specifically different temperatures and metal ion concentrations. These results support the study of thermodynamic driving forces underlying the metabolic function of organisms living under diverse conditions.

biophysics↗