Publications by authors named "Bernhard Ø Palsson"

425 Publications

Elucidating the Regulatory Elements for Transcription Termination and Posttranscriptional Processing in the Streptomyces clavuligerus Genome.

mSystems 2021 May 4;6(3). Epub 2021 May 4.

Department of Biological Sciences, Korea Advanced Institute of Science and Technology, Daejeon, South Korea

Identification of transcriptional regulatory elements in the GC-rich genome is essential for the production of novel biochemicals from secondary metabolite biosynthetic gene clusters (smBGCs). Despite many efforts to understand the regulation of transcription initiation in smBGCs, information on the regulation of transcription termination and posttranscriptional processing remains scarce. In this study, we identified the transcriptional regulatory elements in β-lactam antibiotic-producing ATCC 27064 by determining a total of 1,427 transcript 3'-end positions (TEPs) using the term-seq method. Termination of transcription was governed by three classes of TEPs, of which each displayed unique sequence features. The data integration with transcription start sites and transcriptome data generated 1,648 transcription units (TUs) and 610 transcription unit clusters (TUCs). TU architecture showed that the transcript abundance in TU isoforms of a TUC was potentially affected by the sequence context of their TEPs, suggesting that the regulatory elements of TEPs could control the transcription level in additional layers. We also identified TU features of a xenobiotic response element (XRE) family regulator and DUF397 domain-containing protein, particularly showing the abundance of bidirectional TEPs. Finally, we found that 189 noncoding TUs contained potential - and -regulatory elements that played a major role in regulating the 5' and 3' UTR. These findings highlight the role of transcriptional regulatory elements in transcription termination and posttranscriptional processing in sp. sp. is a great source of bioactive secondary metabolites, including antibiotics, antifungal agents, antiparasitic agents, immunosuppressant compounds, and other drugs. Secondary metabolites are synthesized via multistep conversions of the precursor molecules from primary metabolism, governed by multicomplex enzymes from secondary metabolite biosynthetic gene clusters. As their production is closely related with the growth phase and dynamic cellular status in response to various intra- and extracellular signals, complex regulatory systems tightly control the gene expressions related to secondary metabolism. In this study, we determined genome-wide transcript 3'-end positions and transcription units in the β-lactam antibiotic producer ATCC 27064 to elucidate the transcriptional regulatory elements in transcription termination and posttranscriptional processing by integration of multiomics data. These unique features, such as transcript 3'-end sequence, potential riboregulators, and potential 3'-untranslated region (UTR) -regulatory elements, can be potentially used to design engineering tools that can regulate the transcript abundance of genes for enhancing secondary metabolite production.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1128/mSystems.01013-20DOI Listing
May 2021

Restoration of fitness lost due to dysregulation of the pyruvate dehydrogenase complex is triggered by ribosomal binding site modifications.

Cell Rep 2021 Apr;35(1):108961

Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA; Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet, Building 220, 2800 Kongens, Lyngby, Denmark. Electronic address:

Pyruvate dehydrogenase complex (PDC) functions as the main determinant of the respiro-fermentative balance because it converts pyruvate to acetyl-coenzyme A (CoA), which then enters the TCA (tricarboxylic acid cycle). PDC is repressed by the pyruvate dehydrogenase complex regulator (PdhR) in Escherichia coli. The deletion of the pdhR gene compromises fitness in aerobic environments. We evolve the E. coli pdhR deletion strain to examine its achievable growth rate and the underlying adaptive strategies. We find that (1) optimal proteome allocation to PDC is critical in achieving optimal growth rate; (2) expression of PDC in evolved strains is reduced through mutations in the Shine-Dalgarno sequence; (3) rewiring of the TCA flux and increased reactive oxygen species (ROS) defense occur in the evolved strains; and (4) the evolved strains adapt to an efficient biomass yield. Together, these results show how adaptation can find alternative regulatory mechanisms for a key cellular process if the primary regulatory mode fails.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.celrep.2021.108961DOI Listing
April 2021

Discovery of novel secondary metabolites encoded in actinomycete genomes through coculture.

J Ind Microbiol Biotechnol 2021 Jan 25. Epub 2021 Jan 25.

Department of Biological Sciences and KI for the BioCentury, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea.

Actinomycetes are a rich source of bioactive natural products important for novel drug leads. Recent genome mining approaches have revealed an enormous number of secondary metabolite biosynthetic gene clusters (smBGCs) in actinomycetes. However, under standard laboratory culture conditions, many smBGCs are silent or cryptic. To activate these dormant smBGCs, several approaches, including culture-based or genetic engineering-based strategies, have been developed. Above all, coculture is a promising approach to induce novel secondary metabolite production from actinomycetes by mimicking an ecological habitat where cryptic smBGCs may be activated. In this review, we introduce coculture studies that aim to expand the chemical diversity of actinomycetes, by categorizing the cases by the type of coculture partner. Furthermore, we discuss the current challenges that need to be overcome to support the elicitation of novel bioactive compounds from actinomycetes.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/jimb/kuaa001DOI Listing
January 2021

Experimentally Validated Reconstruction and Analysis of a Genome-Scale Metabolic Model of an Anaerobic Neocallimastigomycota Fungus.

mSystems 2021 Feb 16;6(1). Epub 2021 Feb 16.

Department of Chemical Engineering, University of California Santa Barbara, Santa Barbara, California, USA

Anaerobic gut fungi in the phylum Neocallimastigomycota typically inhabit the digestive tracts of large mammalian herbivores, where they play an integral role in the decomposition of raw lignocellulose into its constitutive sugar monomers. However, quantitative tools to study their physiology are lacking, partially due to their complex and unresolved metabolism that includes the largely uncharacterized fungal hydrogenosome. Modern omics approaches combined with metabolic modeling can be used to establish an understanding of gut fungal metabolism and develop targeted engineering strategies to harness their degradation capabilities for lignocellulosic bioprocessing. Here, we introduce a high-quality genome of the anaerobic fungus from which we constructed the first genome-scale metabolic model of an anaerobic fungus. Relative to its size (200 Mbp, sequenced at 62× depth), it is the least fragmented publicly available gut fungal genome to date. Of the 1,788 lignocellulolytic enzymes annotated in the genome, 585 are associated with the fungal cellulosome, underscoring the powerful lignocellulolytic potential of The genome-scale metabolic model captures the primary metabolism of and accurately predicts experimentally validated substrate utilization requirements. Additionally, metabolic flux predictions are verified by C metabolic flux analysis, demonstrating that the model faithfully describes the underlying fungal metabolism. Furthermore, the model clarifies key aspects of the hydrogenosomal metabolism and can be used as a platform to quantitatively study these biotechnologically important yet poorly understood early-branching fungi. Recent genomic analyses have revealed that anaerobic gut fungi possess both the largest number and highest diversity of lignocellulolytic enzymes of all sequenced fungi, explaining their ability to decompose lignocellulosic substrates, e.g., agricultural waste, into fermentable sugars. Despite their potential, the development of engineering methods for these organisms has been slow due to their complex life cycle, understudied metabolism, and challenging anaerobic culture requirements. Currently, there is no framework that can be used to combine multi-omic data sets to understand their physiology. Here, we introduce a high-quality PacBio-sequenced genome of the anaerobic gut fungus Beyond identifying a trove of lignocellulolytic enzymes, we use this genome to construct the first genome-scale metabolic model of an anaerobic gut fungus. The model is experimentally validated and sheds light on unresolved metabolic features common to gut fungi. Model-guided analysis will pave the way for deepening our understanding of anaerobic gut fungi and provides a systematic framework to guide strain engineering efforts of these organisms for biotechnological use.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1128/mSystems.00002-21DOI Listing
February 2021

Independent component analysis recovers consistent regulatory signals from disparate datasets.

PLoS Comput Biol 2021 Feb 2;17(2):e1008647. Epub 2021 Feb 2.

Department of Bioengineering, University of California San Diego, La Jolla, California, United States of America.

The availability of bacterial transcriptomes has dramatically increased in recent years. This data deluge could result in detailed inference of underlying regulatory networks, but the diversity of experimental platforms and protocols introduces critical biases that could hinder scalable analysis of existing data. Here, we show that the underlying structure of the E. coli transcriptome, as determined by Independent Component Analysis (ICA), is conserved across multiple independent datasets, including both RNA-seq and microarray datasets. We subsequently combined five transcriptomics datasets into a large compendium containing over 800 expression profiles and discovered that its underlying ICA-based structure was still comparable to that of the individual datasets. With this understanding, we expanded our analysis to over 3,000 E. coli expression profiles and predicted three high-impact regulons that respond to oxidative stress, anaerobiosis, and antibiotic treatment. ICA thus enables deep analysis of disparate data to uncover new insights that were not visible in the individual datasets.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1371/journal.pcbi.1008647DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7888660PMC
February 2021

MASSpy: Building, simulating, and visualizing dynamic biological models in Python using mass action kinetics.

PLoS Comput Biol 2021 01 28;17(1):e1008208. Epub 2021 Jan 28.

Department of Bioengineering, University of California San Diego, La Jolla, California, United States of America.

Mathematical models of metabolic networks utilize simulation to study system-level mechanisms and functions. Various approaches have been used to model the steady state behavior of metabolic networks using genome-scale reconstructions, but formulating dynamic models from such reconstructions continues to be a key challenge. Here, we present the Mass Action Stoichiometric Simulation Python (MASSpy) package, an open-source computational framework for dynamic modeling of metabolism. MASSpy utilizes mass action kinetics and detailed chemical mechanisms to build dynamic models of complex biological processes. MASSpy adds dynamic modeling tools to the COnstraint-Based Reconstruction and Analysis Python (COBRApy) package to provide an unified framework for constraint-based and kinetic modeling of metabolic networks. MASSpy supports high-performance dynamic simulation through its implementation of libRoadRunner: the Systems Biology Markup Language (SBML) simulation engine. Three examples are provided to demonstrate how to use MASSpy: (1) a validation of the MASSpy modeling tool through dynamic simulation of detailed mechanisms of enzyme regulation; (2) a feature demonstration using a workflow for generating ensemble of kinetic models using Monte Carlo sampling to approximate missing numerical values of parameters and to quantify biological uncertainty, and (3) a case study in which MASSpy is utilized to overcome issues that arise when integrating experimental data with the computation of functional states of detailed biological mechanisms. MASSpy represents a powerful tool to address challenges that arise in dynamic modeling of metabolic networks, both at small and large scales.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1371/journal.pcbi.1008208DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7872247PMC
January 2021

Pangenome Analytics Reveal Two-Component Systems as Conserved Targets in ESKAPEE Pathogens.

mSystems 2021 Jan 26;6(1). Epub 2021 Jan 26.

Systems Biology Research Group, Department of Bioengineering, University of California, San Diego, San Diego, California, USA

The two-component system (TCS) helps bacteria sense and respond to environmental stimuli through histidine kinases and response regulators. TCSs are the largest family of multistep signal transduction processes, and they are involved in many important cellular processes such as antibiotic resistance, pathogenicity, quorum sensing, osmotic stress, and biofilms. Here, we perform the first comprehensive study to highlight the role of TCSs as potential drug targets against ESKAPEE (, , , , , spp., and ) pathogens through annotation, mapping, pangenomic status, gene orientation, and sequence variation analysis. The distribution of the TCSs is group specific with regard to Gram-positive and Gram-negative bacteria, except for KdpDE. The TCSs among ESKAPEE pathogens form closed pangenomes, except for Furthermore, their conserved nature due to closed pangenomes might make them good drug targets. Fitness score analysis suggests that any mutation in some TCSs such as BaeSR, ArcBA, EvgSA, and AtoSC, etc., might be lethal to the cell. Taken together, the results of this pangenomic assessment of TCSs reveal a range of strategies deployed by the ESKAPEE pathogens to manifest pathogenicity and antibiotic resistance. This study further suggests that the conserved features of TCSs might make them an attractive group of potential targets with which to address antibiotic resistance. The ESKAPEE pathogens are the leading cause of health care-associated infections worldwide. Two-component systems (TCSs) can be used as effective targets against pathogenic bacteria since they are ubiquitous and manage various vital functions such as antibiotic resistance, virulence, biofilms, quorum sensing, and pH balance, among others. This study provides a comprehensive overview of the pangenomic status of the TCSs among ESKAPEE pathogens. The annotation and pangenomic analysis of TCSs show that they are significantly distributed and conserved among the pathogens, as most of them form closed pangenomes. Furthermore, our analysis also reveals that the removal of the TCSs significantly affects the fitness of the cell. Hence, they may be used as promising drug targets against bacteria.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1128/mSystems.00981-20DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7842365PMC
January 2021

Bacterial fitness landscapes stratify based on proteome allocation associated with discrete aero-types.

PLoS Comput Biol 2021 01 19;17(1):e1008596. Epub 2021 Jan 19.

Department of Bioengineering, University of California, San Diego, La Jolla, California, United States of America.

The fitness landscape is a concept commonly used to describe evolution towards optimal phenotypes. It can be reduced to mechanistic detail using genome-scale models (GEMs) from systems biology. We use recently developed GEMs of Metabolism and protein Expression (ME-models) to study the distribution of Escherichia coli phenotypes on the rate-yield plane. We found that the measured phenotypes distribute non-uniformly to form a highly stratified fitness landscape. Systems analysis of the ME-model simulations suggest that this stratification results from discrete ATP generation strategies. Accordingly, we define "aero-types", a phenotypic trait that characterizes how a balanced proteome can achieve a given growth rate by modulating 1) the relative utilization of oxidative phosphorylation, glycolysis, and fermentation pathways; and 2) the differential employment of electron-transport-chain enzymes. This global, quantitative, and mechanistic systems biology interpretation of fitness landscape formed upon proteome allocation offers a fundamental understanding of bacterial physiology and evolution dynamics.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1371/journal.pcbi.1008596DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7846111PMC
January 2021

Identifying the effect of vancomycin on health care-associated methicillin-resistant Staphylococcus aureus strains using bacteriological and physiological media.

Gigascience 2021 Jan;10(1)

Department of Bioengineering, University of California, 9500 Gilman Dr, La Jolla, CA 92093, USA.

Background: The evolving antibiotic-resistant behavior of health care-associated methicillin-resistant Staphylococcus aureus (HA-MRSA) USA100 strains are of major concern. They are resistant to a broad class of antibiotics such as macrolides, aminoglycosides, fluoroquinolones, and many more.

Findings: The selection of appropriate antibiotic susceptibility examination media is very important. Thus, we use bacteriological (cation-adjusted Mueller-Hinton broth) as well as physiological (R10LB) media to determine the effect of vancomycin on USA100 strains. The study includes the profiling behavior of HA-MRSA USA100 D592 and D712 strains in the presence of vancomycin through various high-throughput assays. The US100 D592 and D712 strains were characterized at sub-inhibitory concentrations through growth curves, RNA sequencing, bacterial cytological profiling, and exo-metabolomics high throughput experiments.

Conclusions: The study reveals the vancomycin resistance behavior of HA-MRSA USA100 strains in dual media conditions using wide-ranging experiments.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/gigascience/giaa156DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7794652PMC
January 2021

Systems and synthetic biology to elucidate secondary metabolite biosynthetic gene clusters encoded in genomes.

Nat Prod Rep 2021 Jan 4. Epub 2021 Jan 4.

Department of Biological Sciences, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea. and Innovative Biomaterials Centre, KI for the BioCentury, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea and Novo Nordisk Foundation Centre for Biosustainability, Technical University of Denmark, Lyngby, 2800, Denmark.

Covering: 2010 to 2020Over the last few decades, Streptomyces have been extensively investigated for their ability to produce diverse bioactive secondary metabolites. Recent advances in Streptomyces research have been largely supported by improvements in high-throughput technology 'omics'. From genomics, numerous secondary metabolite biosynthetic gene clusters were predicted, increasing their genomic potential for novel bioactive compound discovery. Additional omics, including transcriptomics, translatomics, interactomics, proteomics and metabolomics, have been applied to obtain a system-level understanding spanning entire bioprocesses of Streptomyces, revealing highly interconnected and multi-layered regulatory networks for secondary metabolism. The comprehensive understanding derived from this systematic information accelerates the rational engineering of Streptomyces to enhance secondary metabolite production, integrated with the exploitation of the highly efficient 'Design-Build-Test-Learn' cycle in synthetic biology. In this review, we describe the current status of omics applications in Streptomyces research to better understand the organism and exploit its genetic potential for higher production of valuable secondary metabolites and novel secondary metabolite discovery.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1039/d0np00071jDOI Listing
January 2021

DeepTFactor: A deep learning-based tool for the prediction of transcription factors.

Proc Natl Acad Sci U S A 2021 01;118(2)

Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 Plus Program), Korea Advanced Institute of Science and Technology, 34141 Daejeon, Republic of Korea;

A transcription factor (TF) is a sequence-specific DNA-binding protein that modulates the transcription of a set of particular genes, and thus regulates gene expression in the cell. TFs have commonly been predicted by analyzing sequence homology with the DNA-binding domains of TFs already characterized. Thus, TFs that do not show homologies with the reported ones are difficult to predict. Here we report the development of a deep learning-based tool, DeepTFactor, that predicts whether a protein in question is a TF. DeepTFactor uses a convolutional neural network to extract features of a protein. It showed high performance in predicting TFs of both eukaryotic and prokaryotic origins, resulting in 1 scores of 0.8154 and 0.8000, respectively. Analysis of the gradients of prediction score with respect to input suggested that DeepTFactor detects DNA-binding domains and other latent features for TF prediction. DeepTFactor predicted 332 candidate TFs in K-12 MG1655. Among them, 84 candidate TFs belong to the y-ome, which is a collection of genes that lack experimental evidence of function. We experimentally validated the results of DeepTFactor prediction by further characterizing genome-wide binding sites of three predicted TFs, YqhC, YiaU, and YahB. Furthermore, we made available the list of 4,674,808 TFs predicted from 73,873,012 protein sequences in 48,346 genomes. DeepTFactor will serve as a useful tool for predicting TFs, which is necessary for understanding the regulatory systems of organisms of interest. We provide DeepTFactor as a stand-alone program, available at https://bitbucket.org/kaistsystemsbiology/deeptfactor.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1073/pnas.2021171118DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7812831PMC
January 2021

The Expanding Computational Toolbox for Engineering Microbial Phenotypes at the Genome Scale.

Microorganisms 2020 Dec 21;8(12). Epub 2020 Dec 21.

Department of Bioengineering, University of California, San Diego, San Diego, CA 92093, USA.

Microbial strains are being engineered for an increasingly diverse array of applications, from chemical production to human health. While traditional engineering disciplines are driven by predictive design tools, these tools have been difficult to build for biological design due to the complexity of biological systems and many unknowns of their quantitative behavior. However, due to many recent advances, the gap between design in biology and other engineering fields is closing. In this work, we discuss promising areas of development of computational tools for engineering microbial strains. We define five frontiers of active research: (1) Constraint-based modeling and metabolic network reconstruction, (2) Kinetics and thermodynamic modeling, (3) Protein structure analysis, (4) Genome sequence analysis, and (5) Regulatory network analysis. Experimental and machine learning drivers have enabled these methods to improve by leaps and bounds in both scope and accuracy. Modern strain design projects will require these tools to be comprehensively applied to the entire cell and efficiently integrated within a single workflow. We expect that these frontiers, enabled by the ongoing revolution of big data science, will drive forward more advanced and powerful strain engineering strategies.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3390/microorganisms8122050DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7767376PMC
December 2020

Blood donor exposome and impact of common drugs on red blood cell metabolism.

JCI Insight 2021 02 8;6(3). Epub 2021 Feb 8.

Department of Biochemistry and Molecular Genetics, University of Colorado Denver - Anschutz Medical Campus, Aurora, Colorado, USA.

Computational models based on recent maps of the RBC proteome suggest that mature erythrocytes may harbor targets for common drugs. This prediction is relevant to RBC storage in the blood bank, in which the impact of small molecule drugs or other xenometabolites deriving from dietary, iatrogenic, or environmental exposures ("exposome") may alter erythrocyte energy and redox metabolism and, in so doing, affect red cell storage quality and posttransfusion efficacy. To test this prediction, here we provide a comprehensive characterization of the blood donor exposome, including the detection of common prescription and over-the-counter drugs in blood units donated by 250 healthy volunteers in the Recipient Epidemiology and Donor Evaluation Study III Red Blood Cell-Omics (REDS-III RBC-Omics) Study. Based on high-throughput drug screenings of 1366 FDA-approved drugs, we report that approximately 65% of the tested drugs had an impact on erythrocyte metabolism. Machine learning models built using metabolites as predictors were able to accurately predict drugs for several drug classes/targets (bisphosphonates, anticholinergics, calcium channel blockers, adrenergics, proton pump inhibitors, antimetabolites, selective serotonin reuptake inhibitors, and mTOR), suggesting that these drugs have a direct, conserved, and substantial impact on erythrocyte metabolism. As a proof of principle, here we show that the antacid ranitidine - though rarely detected in the blood donor population - has a strong effect on RBC markers of storage quality in vitro. We thus show that supplementation of blood units stored in bags with ranitidine could - through mechanisms involving sphingosine 1-phosphate-dependent modulation of erythrocyte glycolysis and/or direct binding to hemoglobin - improve erythrocyte metabolism and storage quality.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1172/jci.insight.146175DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7934844PMC
February 2021

Genome-scale determination of 5´ and 3´ boundaries of RNA transcripts in Streptomyces genomes.

Sci Data 2020 12 15;7(1):436. Epub 2020 Dec 15.

Department of Biological Sciences and KI for the BioCentury, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea.

Streptomyces species are gram-positive bacteria with GC-rich linear genomes and they serve as dominant reservoirs for producing clinically and industrially important secondary metabolites. Genome mining of Streptomyces revealed that each Streptomyces species typically encodes 20-50 secondary metabolite biosynthetic gene clusters (smBGCs), emphasizing their potential for novel compound discovery. Unfortunately, most of smBGCs are uncharacterized in terms of their products and regulation since they are silent under laboratory culture conditions. To translate the genomic potential of Streptomyces to practical applications, it is essential to understand the complex regulation of smBGC expression and to identify the underlying regulatory elements. To progress towards these goals, we applied two Next-Generation Sequencing methods, dRNA-Seq and Term-Seq, to industrially relevant Streptomyces species to reveal the 5´ and 3´ boundaries of RNA transcripts on a genome scale. This data provides a fundamental resource to aid our understanding of Streptomyces' regulation of smBGC expression and to enhance their potential for secondary metabolite synthesis.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41597-020-00775-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7738537PMC
December 2020

Machine learning uncovers independently regulated modules in the Bacillus subtilis transcriptome.

Nat Commun 2020 12 11;11(1):6338. Epub 2020 Dec 11.

Department of Bioengineering, University of California San Diego, La Jolla, CA, 92093, USA.

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 putative or recently discovered roles for at least five regulons, such as a relationship between histidine utilization and quorum sensing. The TRN also facilitates quantification of population-level sporulation states. 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.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41467-020-20153-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7732839PMC
December 2020

High-Quality Genome-Scale Models From Error-Prone, Long-Read Assemblies.

Front Microbiol 2020 12;11:596626. Epub 2020 Nov 12.

Exobiology Branch, Space Science and Astrobiology Division, NASA Ames Research Center, Moffett Field, CA, United States.

Advances in nanopore-based sequencing techniques have enabled rapid characterization of genomes and transcriptomes. An emerging application of this sequencing technology is point-of-care characterization of pathogenic bacteria. However, genome assessments alone are unable to provide a complete understanding of the pathogenic phenotype. Genome-scale metabolic reconstruction and analysis is a bottom-up Systems Biology technique that has elucidated the phenotypic nuances of antimicrobial resistant (AMR) bacteria and other human pathogens. Combining these genome-scale models (GEMs) with point-of-care nanopore sequencing is a promising strategy for combating the emerging health challenge of AMR pathogens. However, the sequencing errors inherent to the nanopore technique may negatively affect the quality, and therefore the utility, of GEMs reconstructed from nanopore assemblies. Here we describe and validate a workflow for rapid construction of GEMs from nanopore (MinION) derived assemblies. Benchmarking the pipeline against a high-quality reference GEM of -12 resulted in nanopore-derived models that were >99% complete even at sequencing depths of less than 10× coverage. Applying the pipeline to clinical isolates of pathogenic bacteria resulted in strain-specific GEMs that identified canonical AMR genome content and enabled simulations of strain-specific microbial growth. Additionally, we show that treating the sequencing run as a mock metagenome did not degrade the quality of models derived from metagenome assemblies. Taken together, this study demonstrates that combining nanopore sequencing with GEM construction pipelines enables rapid, characterization of microbial metabolism.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3389/fmicb.2020.596626DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7688782PMC
November 2020

Elucidation of Regulatory Modes for Five Two-Component Systems in Escherichia coli Reveals Novel Relationships.

mSystems 2020 Nov 10;5(6). Epub 2020 Nov 10.

Department of Bioengineering, University of California, San Diego, San Diego, California, USA

uses two-component systems (TCSs) to respond to environmental signals. TCSs affect gene expression and are parts of 's global transcriptional regulatory network (TRN). Here, we identified the regulons of five TCSs in MG1655: BaeSR and CpxAR, which were stimulated by ethanol stress; KdpDE and PhoRB, induced by limiting potassium and phosphate, respectively; and ZraSR, stimulated by zinc. We analyzed RNA-seq data using independent component analysis (ICA). ChIP-exo data were used to validate condition-specific target gene binding sites. Based on these data, we do the following: (i) identify the target genes for each TCS; (ii) show how the target genes are transcribed in response to stimulus; and (iii) reveal novel relationships between TCSs, which indicate noncognate inducers for various response regulators, such as BaeR to iron starvation, CpxR to phosphate limitation, and PhoB and ZraR to cell envelope stress. Our understanding of the TRN in is thus notably expanded. is a common commensal microbe found in the human gut microenvironment; however, some strains cause diseases like diarrhea, urinary tract infections, and meningitis. 's two-component systems (TCSs) modulate target gene expression, especially related to virulence, pathogenesis, and antimicrobial peptides, in response to environmental stimuli. Thus, it is of utmost importance to understand the transcriptional regulation of TCSs to infer bacterial environmental adaptation and disease pathogenicity. Utilizing a combinatorial approach integrating RNA sequencing (RNA-seq), independent component analysis, chromatin immunoprecipitation coupled with exonuclease treatment (ChIP-exo), and data mining, we suggest five different modes of TCS transcriptional regulation. Our data further highlight noncognate inducers of TCSs, which emphasizes the cross-regulatory nature of TCSs in and suggests that TCSs may have a role beyond their cognate functionalities. In summary, these results can lead to an understanding of the metabolic capabilities of bacteria and correctly predict complex phenotype under diverse conditions, especially when further incorporated with genome-scale metabolic models.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1128/mSystems.00980-20DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7657598PMC
November 2020

Systems biology analysis of the Clostridioides difficile core-genome contextualizes microenvironmental evolutionary pressures leading to genotypic and phenotypic divergence.

NPJ Syst Biol Appl 2020 10 20;6(1):31. Epub 2020 Oct 20.

Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA.

Hospital acquired Clostridioides (Clostridium) difficile infection is exacerbated by the continued evolution of C. difficile strains, a phenomenon studied by multiple laboratories using stock cultures specific to each laboratory. Intralaboratory evolution of strains contributes to interlaboratory variation in experimental results adding to the challenges of scientific rigor and reproducibility. To explore how microevolution of C. difficile within laboratories influences the metabolic capacity of an organism, three different laboratory stock isolates of the C. difficile 630 reference strain were whole-genome sequenced and profiled in over 180 nutrient environments using phenotypic microarrays. The results identified differences in growth dynamics for 32 carbon sources including trehalose, fructose, and mannose. An updated genome-scale model for C. difficile 630 was constructed and used to contextualize the 28 unique mutations observed between the stock cultures. The integration of phenotypic screens with model predictions identified pathways enabling catabolism of ethanolamine, salicin, arbutin, and N-acetyl-galactosamine that differentiated individual C. difficile 630 laboratory isolates. The reconstruction was used as a framework to analyze the core-genome of 415 publicly available C. difficile genomes and identify areas of metabolism prone to evolution within the species. Genes encoding enzymes and transporters involved in starch metabolism and iron acquisition were more variable while C. difficile distinct metabolic functions like Stickland fermentation were more consistent. A substitution in the trehalose PTS system was identified with potential implications in strain virulence. Thus, pairing genome-scale models with large-scale physiological and genomic data enables a mechanistic framework for studying the evolution of pathogens within microenvironments and will lead to predictive modeling to combat pathogen emergence.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41540-020-00151-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7576604PMC
October 2020

iModulonDB: a knowledgebase of microbial transcriptional regulation derived from machine learning.

Nucleic Acids Res 2021 01;49(D1):D112-D120

Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA.

Independent component analysis (ICA) of bacterial transcriptomes has emerged as a powerful tool for obtaining co-regulated, independently-modulated gene sets (iModulons), inferring their activities across a range of conditions, and enabling their association to known genetic regulators. By grouping and analyzing genes based on observations from big data alone, iModulons can provide a novel perspective into how the composition of the transcriptome adapts to environmental conditions. Here, we present iModulonDB (imodulondb.org), a knowledgebase of prokaryotic transcriptional regulation computed from high-quality transcriptomic datasets using ICA. Users select an organism from the home page and then search or browse the curated iModulons that make up its transcriptome. Each iModulon and gene has its own interactive dashboard, featuring plots and tables with clickable, hoverable, and downloadable features. This site enhances research by presenting scientists of all backgrounds with co-expressed gene sets and their activity levels, which lead to improved understanding of regulator-gene relationships, discovery of transcription factors, and the elucidation of unexpected relationships between conditions and genetic regulatory activity. The current release of iModulonDB covers three organisms (Escherichia coli, Staphylococcus aureus and Bacillus subtilis) with 204 iModulons, and can be expanded to cover many additional organisms.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/nar/gkaa810DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7778901PMC
January 2021

Gastrointestinal Surgery for Inflammatory Bowel Disease Persistently Lowers Microbiome and Metabolome Diversity.

Inflamm Bowel Dis 2021 Apr;27(5):603-616

Division of Gastroenterology, Department of Medicine, University of California, San Diego, CA, USA.

Background: Many studies have investigated the role of the microbiome in inflammatory bowel disease (IBD), but few have focused on surgery specifically or its consequences on the metabolome that may differ by surgery type and require longitudinal sampling. Our objective was to characterize and contrast microbiome and metabolome changes after different surgeries for IBD, including ileocolonic resection and colectomy.

Methods: The UC San Diego IBD Biobank was used to prospectively collect 332 stool samples from 129 subjects (50 ulcerative colitis; 79 Crohn's disease). Of these, 21 with Crohn's disease had ileocolonic resections, and 17 had colectomies. We used shotgun metagenomics and untargeted liquid chromatography followed by tandem mass spectrometry metabolomics to characterize the microbiomes and metabolomes of these patients up to 24 months after the initial sampling.

Results: The species diversity and metabolite diversity both differed significantly among groups (species diversity: Mann-Whitney U test P value = 7.8e-17; metabolomics, P-value = 0.0043). Escherichia coli in particular expanded dramatically in relative abundance in subjects undergoing surgery. The species profile was better able to classify subjects according to surgery status than the metabolite profile (average precision 0.80 vs 0.68).

Conclusions: Intestinal surgeries seem to reduce the diversity of the gut microbiome and metabolome in IBD patients, and these changes may persist. Surgery also further destabilizes the microbiome (but not the metabolome) over time, even relative to the previously established instability in the microbiome of IBD patients. These long-term effects and their consequences for health outcomes need to be studied in prospective longitudinal trials linked to microbiome-involved phenotypes.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/ibd/izaa262DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8047854PMC
April 2021

Genome-scale metabolic models highlight stage-specific differences in essential metabolic pathways in Trypanosoma cruzi.

PLoS Negl Trop Dis 2020 10 6;14(10):e0008728. Epub 2020 Oct 6.

Skaggs School of Pharmacy and Pharmaceutical Sciences, UC San Diego, La Jolla, California, United States of America.

Chagas disease is a neglected tropical disease and a leading cause of heart failure in Latin America caused by a protozoan called Trypanosoma cruzi. This parasite presents a complex multi-stage life cycle. Anti-Chagas drugs currently available are limited to benznidazole and nifurtimox, both with severe side effects. Thus, there is a need for alternative and more efficient drugs. Genome-scale metabolic models (GEMs) can accurately predict metabolic capabilities and aid in drug discovery in metabolic genes. This work developed an extended GEM, hereafter referred to as iIS312, of the published and validated T. cruzi core metabolism model. From iIS312, we then built three stage-specific models through transcriptomics data integration, and showed that epimastigotes present the most active metabolism among the stages (see S1-S4 GEMs). Stage-specific models predicted significant metabolic differences among stages, including variations in flux distribution in core metabolism. Moreover, the gene essentiality predictions suggest potential drug targets, among which some have been previously proven lethal, including glutamate dehydrogenase, glucokinase and hexokinase. To validate the models, we measured the activity of enzymes in the core metabolism of the parasite at different stages, and showed the results were consistent with model predictions. Our results represent a potential step forward towards the improvement of Chagas disease treatment. To our knowledge, these stage-specific models are the first GEMs built for the stages Amastigote and Trypomastigote. This work is also the first to present an in silico GEM comparison among different stages in the T. cruzi life cycle.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1371/journal.pntd.0008728DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7567352PMC
October 2020

The Bitome: digitized genomic features reveal fundamental genome organization.

Nucleic Acids Res 2020 10;48(18):10157-10163

Department of Bioengineering, University of California San Diego, La Jolla, CA 92093, USA.

A genome contains the information underlying an organism's form and function. Yet, we lack formal framework to represent and study this information. Here, we introduce the Bitome, a matrix composed of binary digits (bits) representing the genomic positions of genomic features. We form a Bitome for the genome of Escherichia coli K-12 MG1655. We find that: (i) genomic features are encoded unevenly, both spatially and categorically; (ii) coding and intergenic features are recapitulated at high resolution; (iii) adaptive mutations are skewed towards genomic positions with fewer features; and (iv) the Bitome enhances prediction of adaptively mutated and essential genes. The Bitome is a formal representation of a genome and may be used to study its fundamental organizational properties.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/nar/gkaa774DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7544223PMC
October 2020

Reconstructing organisms in silico: genome-scale models and their emerging applications.

Nat Rev Microbiol 2020 Sep 21. Epub 2020 Sep 21.

Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA.

Escherichia coli is considered to be the best-known microorganism given the large number of published studies detailing its genes, its genome and the biochemical functions of its molecular components. This vast literature has been systematically assembled into a reconstruction of the biochemical reaction networks that underlie E. coli's functions, a process which is now being applied to an increasing number of microorganisms. Genome-scale reconstructed networks are organized and systematized knowledge bases that have multiple uses, including conversion into computational models that interpret and predict phenotypic states and the consequences of environmental and genetic perturbations. These genome-scale models (GEMs) now enable us to develop pan-genome analyses that provide mechanistic insights, detail the selection pressures on proteome allocation and address stress phenotypes. In this Review, we first discuss the overall development of GEMs and their applications. Next, we review the evolution of the most complete GEM that has been developed to date: the E. coli GEM. Finally, we explore three emerging areas in genome-scale modelling of microbial phenotypes: collections of strain-specific models, metabolic and macromolecular expression models, and simulation of stress responses.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41579-020-00440-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7981288PMC
September 2020

Kinetic profiling of metabolic specialists demonstrates stability and consistency of in vivo enzyme turnover numbers.

Proc Natl Acad Sci U S A 2020 09 1;117(37):23182-23190. Epub 2020 Sep 1.

Department of Bioengineering, University of California San Diego, La Jolla, CA 92093;

Enzyme turnover numbers (s) are essential for a quantitative understanding of cells. Because s are traditionally measured in low-throughput assays, they can be inconsistent, labor-intensive to obtain, and can miss in vivo effects. We use a data-driven approach to estimate in vivo s using metabolic specialist strains that resulted from gene knockouts in central metabolism followed by metabolic optimization via laboratory evolution. By combining absolute proteomics with fluxomics data, we find that in vivo s are robust against genetic perturbations, suggesting that metabolic adaptation to gene loss is mostly achieved through other mechanisms, like gene-regulatory changes. Combining machine learning and genome-scale metabolic models, we show that the obtained in vivo s predict unseen proteomics data with much higher precision than in vitro s. The results demonstrate that in vivo s can solve the problem of inconsistent and low-coverage parameterizations of genome-scale cellular models.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1073/pnas.2001562117DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7502767PMC
September 2020

Genome-Scale Metabolic Model of pv. : An Approach to Elucidate Pathogenicity at the Metabolic Level.

Front Genet 2020 11;11:837. Epub 2020 Aug 11.

Laboratory of Molecular Interactions of Agricultural Microbes, LIMMA, Department of Biological Sciences, Universidad de Los Andes, Bogotá, Colombia.

pv. () is the causal agent of cassava bacterial blight, the most important bacterial disease in this crop. There is a paucity of knowledge about the metabolism of and its relevance in the pathogenic process, with the exception of the elucidation of the xanthan biosynthesis route. Here we report the reconstruction of the genome-scale model of metabolism and the insights it provides into plant-pathogen interactions. The model, iXpm1556, displayed 1,556 reactions, 1,527 compounds, and 890 genes. Metabolic maps of central amino acid and carbohydrate metabolism, as well as xanthan biosynthesis of , were reconstructed using Escher (https://escher.github.io/) to guide the curation process and for further analyses. The model was constrained using the RNA-seq data of a mutant of for quorum sensing (QS), and these data were used to construct context-specific models (CSMs) of the metabolism of the two strains (wild type and QS mutant). The CSMs and flux balance analysis were used to get insights into pathogenicity, xanthan biosynthesis, and QS mechanisms. Between the CSMs, 653 reactions were shared; unique reactions belong to purine, pyrimidine, and amino acid metabolism. Alternative objective functions were used to demonstrate a trade-off between xanthan biosynthesis and growth and the re-allocation of resources in the process of biosynthesis. Important features altered by QS included carbohydrate metabolism, NAD(P) balance, and fatty acid elongation. In this work, we modeled the xanthan biosynthesis and the QS process and their impact on the metabolism of the bacterium. This model will be useful for researchers studying host-pathogen interactions and will provide insights into the mechanisms of infection used by this and other species.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3389/fgene.2020.00837DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7432306PMC
August 2020

System-level understanding of gene expression and regulation for engineering secondary metabolite production in Streptomyces.

J Ind Microbiol Biotechnol 2020 Oct 10;47(9-10):739-752. Epub 2020 Aug 10.

Department of Biological Sciences and KI for the BioCentury, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea.

The gram-positive bacterium, Streptomyces, is noticed for its ability to produce a wide array of pharmaceutically active compounds through secondary metabolism. To discover novel bioactive secondary metabolites and increase the production, Streptomyces species have been extensively studied for the past decades. Among the cellular components, RNA molecules play important roles as the messengers for gene expression and diverse regulations taking place at the RNA level. Thus, the analysis of RNA-level regulation is critical to understanding the regulation of Streptomyces' metabolism and secondary metabolite production. A dramatic advance in Streptomyces research was made recently, by exploiting high-throughput technology to systematically understand RNA levels. In this review, we describe the current status of the system-wide investigation of Streptomyces in terms of RNA, toward expansion of its genetic potential for secondary metabolite synthesis.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1007/s10295-020-02298-0DOI Listing
October 2020

Synthetic cross-phyla gene replacement and evolutionary assimilation of major enzymes.

Nat Ecol Evol 2020 10 10;4(10):1402-1409. Epub 2020 Aug 10.

Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA.

The ability of DNA to produce a functional protein even after transfer to a foreign host is of fundamental importance in both evolutionary biology and biotechnology, enabling horizontal gene transfer in the wild and heterologous expression in the lab. However, the influence of genetic particulars on DNA functionality in a new host is poorly understood, as are the evolutionary mechanisms of assimilation and refinement. Here, we describe an automation-enabled large-scale experiment wherein Escherichia coli strains were evolved in parallel after replacement of the genes pgi or tpiA with orthologous DNA from donor species spanning all domains of life, from humans to hyperthermophilic archaea. Via analysis of hundreds of clones evolved for 50,000+ cumulative generations across dozens of independent lineages, we show that orthogene-upregulating mutations can completely mitigate fitness defects that result from initial non-functionality, with coding sequence changes unnecessary. Gene target, donor species and genomic location of the swap all influenced outcomes-both the nature of adaptive mutations (often synonymous) and the frequency with which strains successfully evolved to assimilate the foreign DNA. Additionally, time series DNA sequencing and replay evolution experiments revealed transient copy number expansions, the contingency of lineage outcome on first-step mutations and the ability for strains to escape from suboptimal local fitness maxima. Overall, this study establishes the influence of various DNA and protein features on cross-species genetic interchangeability and evolutionary outcomes, with implications for both horizontal gene transfer and rational strain design.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41559-020-1271-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7529951PMC
October 2020

Causal mutations from adaptive laboratory evolution are outlined by multiple scales of genome annotations and condition-specificity.

BMC Genomics 2020 Jul 25;21(1):514. Epub 2020 Jul 25.

Department of Bioengineering, University of California, San Diego, La Jolla, CA, 92093, USA.

Background: Adaptive Laboratory Evolution (ALE) has emerged as an experimental approach to discover mutations that confer phenotypic functions of interest. However, the task of finding and understanding all beneficial mutations of an ALE experiment remains an open challenge for the field. To provide for better results than traditional methods of ALE mutation analysis, this work applied enrichment methods to mutations described by a multiscale annotation framework and a consolidated set of ALE experiment conditions. A total of 25,321 unique genome annotations from various sources were leveraged to describe multiple scales of mutated features in a set of 35 Escherichia coli based ALE experiments. These experiments totalled 208 independent evolutions and 2641 mutations. Additionally, mutated features were statistically associated across a total of 43 unique experimental conditions to aid in deconvoluting mutation selection pressures.

Results: Identifying potentially beneficial, or key, mutations was enhanced by seeking coding and non-coding genome features significantly enriched by mutations across multiple ALE replicates and scales of genome annotations. The median proportion of ALE experiment key mutations increased from 62%, with only small coding and non-coding features, to 71% with larger aggregate features. Understanding key mutations was enhanced by considering the functions of broader annotation types and the significantly associated conditions for key mutated features. The approaches developed here were used to find and characterize novel key mutations in two ALE experiments: one previously unpublished with Escherichia coli grown on glycerol as a carbon source and one previously published with Escherichia coli tolerized to high concentrations of L-serine.

Conclusions: The emergent adaptive strategies represented by sets of ALE mutations became more clear upon observing the aggregation of mutated features across small to large scale genome annotations. The clarification of mutation selection pressures among the many experimental conditions also helped bring these strategies to light. This work demonstrates how multiscale genome annotation frameworks and data-driven methods can help better characterize ALE mutations, and thus help elucidate the genotype-to-phenotype relationship of the studied organism.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12864-020-06920-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7382830PMC
July 2020

Independent component analysis of E. coli's transcriptome reveals the cellular processes that respond to heterologous gene expression.

Metab Eng 2020 09 22;61:360-368. Epub 2020 Jul 22.

Department of Bioengineering, University of California, San Diego, La Jolla, USA; Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet, Building 220, 2800, Kgs. Lyngby, Denmark. Electronic address:

Achieving the predictable expression of heterologous genes in a production host has proven difficult. Each heterologous gene expressed in the same host seems to elicit a different host response governed by unknown mechanisms. Historically, most studies have approached this challenge by manipulating the properties of the heterologous gene through methods like codon optimization. Here we approach this challenge from the host side. We express a set of 45 heterologous genes in the same Escherichia coli strain, using the same expression system and culture conditions. We collect a comprehensive RNAseq set to characterize the host's transcriptional response. Independent Component Analysis of the RNAseq data set reveals independently modulated gene sets (iModulons) that characterize the host response to heterologous gene expression. We relate 55% of variation of the host response to: Fear vs Greed (16.5%), Metal Homeostasis (19.0%), Respiration (6.0%), Protein folding (4.5%), and Amino acid and nucleotide biosynthesis (9.0%). If these responses can be controlled, then the success rate with predicting heterologous gene expression should increase.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.ymben.2020.07.002DOI Listing
September 2020

Redefining fundamental concepts of transcription initiation in bacteria.

Nat Rev Genet 2020 11 14;21(11):699-714. Epub 2020 Jul 14.

Programa de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Morelos, Cuernavaca, México.

Despite enormous progress in understanding the fundamentals of bacterial gene regulation, our knowledge remains limited when compared with the number of bacterial genomes and regulatory systems to be discovered. Derived from a small number of initial studies, classic definitions for concepts of gene regulation have evolved as the number of characterized promoters has increased. Together with discoveries made using new technologies, this knowledge has led to revised generalizations and principles. In this Expert Recommendation, we suggest precise, updated definitions that support a logical, consistent conceptual framework of bacterial gene regulation, focusing on transcription initiation. The resulting concepts can be formalized by ontologies for computational modelling, laying the foundation for improved bioinformatics tools, knowledge-based resources and scientific communication. Thus, this work will help researchers construct better predictive models, with different formalisms, that will be useful in engineering, synthetic biology, microbiology and genetics.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41576-020-0254-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7990032PMC
November 2020