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5,814 results on '"Bernhard, O."'

1. Modulating bacterial function utilizing A knowledge base of transcriptional regulatory modules

Author

Shin, Jongoh, Zielinski, Daniel C, and Palsson, Bernhard O

Subjects
Biochemistry and Cell Biology, Biological Sciences, Genetics, Biotechnology, Generic health relevance, Environmental Sciences, Information and Computing Sciences, Developmental Biology, Biological sciences, Chemical sciences, Environmental sciences
Abstract

Synthetic biology enables the reprogramming of cellular functions for various applications. However, challenges in scalability and predictability persist due to context-dependent performance and complex circuit-host interactions. This study introduces an iModulon-based engineering approach, utilizing machine learning-defined co-regulated gene groups (iModulons) as design parts containing essential genes for specific functions. This approach identifies the necessary components for genetic circuits across different contexts, enhancing genome engineering by improving target selection and predicting module behavior. We demonstrate several distinct uses of iModulons: (i) discovery of unknown iModulons to increase protein productivity, heat tolerance and fructose utilization; (ii) an iModulon boosting approach, which amplifies the activity of specific iModulons, improved cell growth under osmotic stress with minimal host regulation disruption; (iii) an iModulon rebalancing strategy, which adjusts the activity levels of iModulons to balance cellular functions, significantly increased oxidative stress tolerance while minimizing trade-offs and (iv) iModulon-based gene annotation enabled natural competence activation by predictably rewiring iModulons. Comparative experiments with traditional methods showed our approach offers advantages in efficiency and predictability of strain engineering. This study demonstrates the potential of iModulon-based strategies to systematically and predictably reprogram cellular functions, offering refined and adaptable control over complex regulatory networks.

Published
2024

2. Deciphering Nutritional Stress Responses via Knowledge-Enriched Transcriptomics for Microbial Engineering

Author

Shin, Jongoh, Zielinski, Daniel C, and Palsson, Bernhard O

Subjects
Microbiology, Biological Sciences, Nutrition, Genetics, Biotechnology, Independent component analysis, Machine learning, Nutritional response, Systems biology, Vibrio natriegens, iModulon, Industrial Biotechnology, Biochemistry and cell biology, Industrial biotechnology
Abstract

Understanding diverse bacterial nutritional requirements and responses is foundational in microbial research and biotechnology. In this study, we employed knowledge-enriched transcriptomic analytics to decipher complex stress responses of Vibrio natriegens to supplied nutrients, aiming to enhance microbial engineering efforts. We computed 64 independently modulated gene sets that comprise a quantitative basis for transcriptome dynamics across a comprehensive transcriptomics dataset containing a broad array of nutrient conditions. Our approach led to the i) identification of novel transporter systems for diverse substrates, ii) a detailed understanding of how trace elements affect metabolism and growth, and iii) extensive characterization of nutrient-induced stress responses, including osmotic stress, low glycolytic flux, proteostasis, and altered protein expression. By clarifying the relationship between the acetate-associated regulon and glycolytic flux status of various nutrients, we have showcased its vital role in directing optimal carbon source selection. Our findings offer deep insights into the transcriptional landscape of bacterial nutrition and underscore its significance in tailoring strain engineering strategies, thereby facilitating the development of more efficient and robust microbial systems for biotechnological applications.

Published
2024

3. CyuR is a dual regulator for L-cysteine dependent antimicrobial resistance in Escherichia coli

Author

Irina A. Rodionova, Hyun Gyu Lim, Ye Gao, Dmitry A. Rodionov, Ying Hutchison, Richard Szubin, Christopher Dalldorf, Jonathan Monk, and Bernhard O. Palsson

Subjects
Biology (General), QH301-705.5
Abstract

Abstract Hydrogen sulfide (H2S), mainly produced from L-cysteine (Cys), renders bacteria highly resistant to oxidative stress and potentially increases antimicrobial resistance (AMR). CyuR is a Cys-dependent transcription regulator, responsible for the activation of the cyuPA operon and generation of H2S. Despite its potential importance, its regulatory network remains poorly understood. In this study, we investigate the roles of the CyuR regulon in a Cys-dependent AMR mechanism in E. coli strains. We show: (1) Generation of H2S from Cys affects the sensitivities to growth inhibitors; (2) Cys supplementation decreases stress responses; (3) CyuR negatively controls the expression of mdlAB encoding a potential transporter for antibiotics; (4) CyuR binds to a DNA sequence motif ‘GAAwAAATTGTxGxxATTTsyCC’ in the absence of Cys; and (5) CyuR may regulate 25 additional genes which were not reported previously. Collectively, our findings expand the understanding of the biological roles of CyuR relevant to antibiotic resistance associated with Cys.

Published
2024
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7. Proteome allocation is linked to transcriptional regulation through a modularized transcriptome

Author

Arjun Patel, Dominic McGrosso, Ying Hefner, Anaamika Campeau, Anand V. Sastry, Svetlana Maurya, Kevin Rychel, David J. Gonzalez, and Bernhard O. Palsson

Subjects
Science
Abstract

Abstract It has proved challenging to quantitatively relate the proteome to the transcriptome on a per-gene basis. Recent advances in data analytics have enabled a biologically meaningful modularization of the bacterial transcriptome. We thus investigate whether matched datasets of transcriptomes and proteomes from bacteria under diverse conditions can be modularized in the same way to reveal novel relationships between their compositions. We find that; (1) the modules of the proteome and the transcriptome are comprised of a similar list of gene products, (2) the modules in the proteome often represent combinations of modules from the transcriptome, (3) known transcriptional and post-translational regulation is reflected in differences between two sets of modules, allowing for knowledge-mapping when interpreting module functions, and (4) through statistical modeling, absolute proteome allocation can be inferred from the transcriptome alone. Quantitative and knowledge-based relationships can thus be found at the genome-scale between the proteome and transcriptome in bacteria.

Published
2024
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8. Systems biology of competency in Vibrio natriegens is revealed by applying novel data analytics to the transcriptome

Author

Shin, Jongoh, Rychel, Kevin, and Palsson, Bernhard O

Subjects
Biochemistry and Cell Biology, Genetics, Biological Sciences, Human Genome, 1.1 Normal biological development and functioning, Underpinning research, Transcriptome, Systems Biology, Data Science, Vibrio, Bacterial Proteins, CP: Genomics, CP: Microbiology, ICA, Vibrio natriegens, iModulon, independent component analysis, machine learning, natural competence, regulon discovery, systems biology, transcriptional regulation, Machine learning, Independent component analysis &#x28, ICA&#x29, Vibrio natrigens, Regulon discovery, Medical Physiology, Biological sciences
Abstract

Vibrio natriegens regulates natural competence through the TfoX and QstR transcription factors, which are involved in external DNA capture and transport. However, the extensive genetic and transcriptional regulatory basis for competency remains unknown. We used a machine-learning approach to decompose Vibrio natriegens's transcriptome into 45 groups of independently modulated sets of genes (iModulons). Our findings show that competency is associated with the repression of two housekeeping iModulons (iron metabolism and translation) and the activation of six iModulons; including TfoX and QstR, a novel iModulon of unknown function, and three housekeeping iModulons (representing motility, polycations, and reactive oxygen species [ROS] responses). Phenotypic screening of 83 gene deletion strains demonstrates that loss of iModulon function reduces or eliminates competency. This database-iModulon-discovery cycle unveils the transcriptomic basis for competency and its relationship to housekeeping functions. These results provide the genetic basis for systems biology of competency in this organism.

Published
2023

9. A model industrial workhorse: Bacillus subtilis strain 168 and its genome after a quarter of a century

Author

Bremer, Erhard, Calteau, Alexandra, Danchin, Antoine, Harwood, Colin, Helmann, John D, Médigue, Claudine, Palsson, Bernhard O, Sekowska, Agnieszka, Vallenet, David, Zuniga, Abril, and Zuniga, Cristal

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Genetics, Human Genome, Generic health relevance, Bacillus subtilis, Genomics, Genome, Bacterial, Microbiology, Industrial biotechnology
Abstract

The vast majority of genomic sequences are automatically annotated using various software programs. The accuracy of these annotations depends heavily on the very few manual annotation efforts that combine verified experimental data with genomic sequences from model organisms. Here, we summarize the updated functional annotation of Bacillus subtilis strain 168, a quarter century after its genome sequence was first made public. Since the last such effort 5 years ago, 1168 genetic functions have been updated, allowing the construction of a new metabolic model of this organism of environmental and industrial interest. The emphasis in this review is on new metabolic insights, the role of metals in metabolism and macromolecule biosynthesis, functions involved in biofilm formation, features controlling cell growth, and finally, protein agents that allow class discrimination, thus allowing maintenance management, and accuracy of all cell processes. New 'genomic objects' and an extensive updated literature review have been included for the sequence, now available at the International Nucleotide Sequence Database Collaboration (INSDC: AccNum AL009126.4).

Published
2023

10. Empowering drug off-target discovery with metabolic and structural analysis.

Author

Chowdhury, Sourav, Zielinski, Daniel C, Dalldorf, Christopher, Rodrigues, Joao V, Palsson, Bernhard O, and Shakhnovich, Eugene I

Subjects
Tetrahydrofolate Dehydrogenase, Proteins, Anti-Bacterial Agents, Metabolomics, Power, Psychological, 2.1 Biological and endogenous factors, 5.1 Pharmaceuticals, Development of treatments and therapeutic interventions, Aetiology, Generic health relevance, Good Health and Well Being
Abstract

Elucidating intracellular drug targets is 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 develop a hierarchic workflow to focus on specific targets based on analysis of metabolomics data and growth rescue experiments. We deploy this framework to understand the intracellular molecular interactions of the multi-valent dihydrofolate reductase-targeting antibiotic compound CD15-3. We analyse global metabolomics data utilizing machine learning, metabolic modelling, and protein structural similarity to prioritize candidate drug targets. Overexpression and in vitro activity assays confirm one of the predicted candidates, HPPK (folK), as a CD15-3 off-target. This study demonstrates how established machine learning methods can be combined with mechanistic analyses to improve the resolution of drug target finding workflows for discovering off-targets of a metabolic inhibitor.

Published
2023

11. Machine‐Learning Analysis of Streptomyces coelicolor Transcriptomes Reveals a Transcription Regulatory Network Encompassing Biosynthetic Gene Clusters

Author

Yongjae Lee, Donghui Choe, Bernhard O. Palsson, and Byung‐Kwan Cho

Subjects
biosynthetic gene cluster, independent component analysis, machine learning, secondary metabolites, streptomyces, transcriptional regulatory network, Science
Abstract

Abstract Streptomyces produces diverse secondary metabolites of biopharmaceutical importance, yet the rate of biosynthesis of these metabolites is often hampered by complex transcriptional regulation. Therefore, a fundamental understanding of transcriptional regulation in Streptomyces is key to fully harness its genetic potential. Here, independent component analysis (ICA) of 454 high‐quality gene expression profiles of the model species Streptomyces coelicolor is performed, of which 249 profiles are newly generated for S. coelicolor cultivated on 20 different carbon sources and 64 engineered strains with overexpressed sigma factors. ICA of the transcriptome dataset reveals 117 independently modulated groups of genes (iModulons), which account for 81.6% of the variance in the dataset. The genes in each iModulon are involved in specific cellular responses, which are often transcriptionally controlled by specific regulators. Also, iModulons accurately predict 25 secondary metabolite biosynthetic gene clusters encoded in the genome. This systemic analysis leads to reveal the functions of previously uncharacterized genes, putative regulons for 40 transcriptional regulators, including 30 sigma factors, and regulation of secondary metabolism via phosphate‐ and iron‐dependent mechanisms in S. coelicolor. ICA of large transcriptomic datasets thus enlightens a new and fundamental understanding of transcriptional regulation of secondary metabolite synthesis along with interconnected metabolic processes in Streptomyces.

Published
2024
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12. iModulonMiner and PyModulon: Software for unsupervised mining of gene expression compendia.

Author

Anand V Sastry, Yuan Yuan, Saugat Poudel, Kevin Rychel, Reo Yoo, Cameron R Lamoureux, Gaoyuan Li, Joshua T Burrows, Siddharth Chauhan, Zachary B Haiman, Tahani Al Bulushi, Yara Seif, Bernhard O Palsson, and Daniel C Zielinski

Subjects
Biology (General), QH301-705.5
Abstract

Public gene expression databases are a rapidly expanding resource of organism responses to diverse perturbations, presenting both an opportunity and a challenge for bioinformatics workflows to extract actionable knowledge of transcription regulatory network function. Here, we introduce a five-step computational pipeline, called iModulonMiner, to compile, process, curate, analyze, and characterize the totality of RNA-seq data for a given organism or cell type. This workflow is centered around the data-driven computation of co-regulated gene sets using Independent Component Analysis, called iModulons, which have been shown to have broad applications. As a demonstration, we applied this workflow to generate the iModulon structure of Bacillus subtilis using all high-quality, publicly-available RNA-seq data. Using this structure, we predicted regulatory interactions for multiple transcription factors, identified groups of co-expressed genes that are putatively regulated by undiscovered transcription factors, and predicted properties of a recently discovered single-subunit phage RNA polymerase. We also present a Python package, PyModulon, with functions to characterize, visualize, and explore computed iModulons. The pipeline, available at https://github.com/SBRG/iModulonMiner, can be readily applied to diverse organisms to gain a rapid understanding of their transcriptional regulatory network structure and condition-specific activity.

Published
2024
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13. Diagnosis and mitigation of the systemic impact of genome reduction in Escherichia coli DGF-298

Author

Antoine Champie, Jean-Christophe Lachance, Anand Sastry, Dominick Matteau, Colton J. Lloyd, Frédéric Grenier, Cameron R. Lamoureux, Simon Jeanneau, Adam M. Feist, Pierre-Étienne Jacques, Bernhard O. Palsson, and Sébastien Rodrigue

Subjects
Escherichia coli, systems biology, genome-scale metabolic model, simplified genome, oxidative stress, Microbiology, QR1-502
Abstract

ABSTRACT Microorganisms with simplified genomes represent interesting cell chassis for systems and synthetic biology. However, genome reduction can lead to undesired traits, such as decreased growth rate and metabolic imbalances. To investigate the impact of genome reduction on Escherichia coli strain DGF-298, a strain in which ~ 36% of the genome has been removed, we reconstructed a strain-specific metabolic model (iAC1061), investigated the regulation of gene expression using iModulon-based transcriptome analysis, and performed adaptive laboratory evolution to let the strain correct potential imbalances that arose during its simplification. The model notably predicted that the removal of all three key pathways for glycolaldehyde disposal in this microorganism would lead to a metabolic bottleneck through folate starvation. Glycolaldehyde is also known to cause self-generation of reactive oxygen species, as evidenced by the increased expression of oxidative stress resistance genes in the SoxS iModulon. The reintroduction of the aldA gene, responsible for one native glycolaldehyde disposal route, alleviated the constitutive oxidative stress response. Our results suggest that systems-level approaches and adaptive laboratory evolution have additive benefits when trying to repair and optimize genome-engineered strains.IMPORTANCEGenomic streamlining can be employed in model organisms to reduce complexity and enhance strain predictability. One of the most striking examples is the bacterial strain Escherichia coli DGF-298, notable for having over one-third of its genome deleted. However, such extensive genome modifications raise the question of how similar this simplified cell remains when compared with its parent, and what are the possible unintended consequences of this simplification. In this study, we used metabolic modeling along with iModulon-based transcriptomic analysis in different growth conditions to assess the impact of genome reduction on metabolism and gene regulation. We observed little impact of genomic reduction on the regulatory network of E. coli DGF-298 and identified a potential metabolic bottleneck leading to the constitutive activity of the SoxS iModulon. We then leveraged the model's predictions to successfully restore SoxS activity to the basal level.

Published
2024
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14. A novel simple disposition index (SPINA‐DI) from fasting insulin and glucose concentration as a robust measure of carbohydrate homeostasis

Author

Johannes W. Dietrich, Assjana Abood, Riddhi Dasgupta, Shajith Anoop, Felix K. Jebasingh, R. Spurgeon, Nihal Thomas, and Bernhard O. Boehm

Subjects
disposition index, dynamical compensation, insulin‐glucose homeostasis, Diseases of the endocrine glands. Clinical endocrinology, RC648-665
Abstract

Abstract Aims The widely used dynamic disposition index, derived from oral glucose tolerance testing, is an integrative measure of the homeostatic performance of the insulin‐glucose feedback control. Its collection is, however, time consuming and expensive. We, therefore, pursued the question if such a measure can be calculated at baseline/fasting conditions using plasma concentrations of insulin and glucose. Methods A new fasting‐based disposition index (structure parameter inference approach‐disposition index [SPINA‐DI]) was calculated as the product of the reconstructed insulin receptor gain (SPINA‐GR) times the secretory capacity of pancreatic beta cells (SPINA‐GBeta). The novel index was evaluated in computer simulations and in three independent, multiethnic cohorts. The objectives were distribution in various populations, diagnostic performance, reliability and correlation to established physiological biomarkers of carbohydrate metabolism. Results Mathematical and in‐silico analysis demonstrated SPINA‐DI to mirror the hyperbolic relationship between insulin sensitivity and beta‐cell function and to represent an optimum of the homeostatic control. It significantly correlates to the oral glucose tolerance test based disposition index and other important physiological parameters. Furthermore, it revealed higher discriminatory power for the diagnosis of (pre)diabetes and superior retest reliability than other static and dynamic function tests of glucose homeostasis. Conclusions SPINA‐DI is a novel simple reliable and inexpensive marker of insulin‐glucose homeostasis suitable for screening purposes and a wider clinical application.

Published
2024
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15. A genome-scale metabolic model of a globally disseminated hyperinvasive M1 strain of Streptococcus pyogenes

Author

Yujiro Hirose, Daniel C. Zielinski, Saugat Poudel, Kevin Rychel, Jonathon L. Baker, Yoshihiro Toya, Masaya Yamaguchi, Almut Heinken, Ines Thiele, Shigetada Kawabata, Bernhard O. Palsson, and Victor Nizet

Subjects
Streptococcus pyogenes, metabolic modeling, genome-scale model, auxotrophy, carbon sources, essential gene, Microbiology, QR1-502
Abstract

ABSTRACT Streptococcus pyogenes is responsible for a range of diseases in humans contributing significantly to morbidity and mortality. Among more than 200 serotypes of S. pyogenes, serotype M1 strains hold the greatest clinical relevance due to their high prevalence in severe human infections. To enhance our understanding of pathogenesis and discovery of potential therapeutic approaches, we have developed the first genome-scale metabolic model (GEM) for a serotype M1 S. pyogenes strain, which we name iYH543. The curation of iYH543 involved cross-referencing a draft GEM of S. pyogenes serotype M1 from the AGORA2 database with gene essentiality and autotrophy data obtained from transposon mutagenesis-based and growth screens. We achieved a 92.6% (503/543 genes) accuracy in predicting gene essentiality and a 95% (19/20 amino acids) accuracy in predicting amino acid auxotrophy. Additionally, Biolog Phenotype microarrays were employed to examine the growth phenotypes of S. pyogenes, which further contributed to the refinement of iYH543. Notably, iYH543 demonstrated 88% accuracy (168/190 carbon sources) in predicting growth on various sole carbon sources. Discrepancies observed between iYH543 and the actual behavior of living S. pyogenes highlighted areas of uncertainty in the current understanding of S. pyogenes metabolism. iYH543 offers novel insights and hypotheses that can guide future research efforts and ultimately inform novel therapeutic strategies.IMPORTANCEGenome-scale models (GEMs) play a crucial role in investigating bacterial metabolism, predicting the effects of inhibiting specific metabolic genes and pathways, and aiding in the identification of potential drug targets. Here, we have developed the first GEM for the S. pyogenes highly virulent serotype, M1, which we name iYH543. The iYH543 achieved high accuracy in predicting gene essentiality. We also show that the knowledge obtained by substituting actual measurement values for iYH543 helps us gain insights that connect metabolism and virulence. iYH543 will serve as a useful tool for rational drug design targeting S. pyogenes metabolism and computational screening to investigate the interplay between inhibiting virulence factor synthesis and growth.

Published
2024
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16. Metabolic homeostasis and growth in abiotic cells

Author

Akbari, Amir and Palsson, Bernhard O

Subjects
Citric Acid Cycle, Metabolic Networks and Pathways, Carbon Cycle, Homeostasis, Carbon, origin of metabolism, carbon-fixing cycles, alkaline hydrothermal vents, metabolic homeostasis, emergent properties
Abstract

Metabolism constitutes the core chemistry of life. How it began on the early Earth and whether it had a cellular origin are still uncertain. A leading hypothesis for life's origins postulates that metabolism arose from geochemical CO2-fixing pathways, driven by inorganic catalysts and energy sources, long before enzymes or genes existed. The acetyl-CoA pathway and the reductive tricarboxylic acid cycle are considered ancient reaction networks that hold relics of early carbon-fixing pathways. Although transition metals can promote many steps of these pathways, whether they form a functional metabolic network in abiotic cells has not been demonstrated. Here, we formulate a nonenzymatic carbon-fixing network from these pathways and determine its functional feasibility in abiotic cells by imposing fundamental physicochemical constraints. Using first principles, we show that abiotic cells can sustain a steady carbon-fixing cycle that performs a systemic function over a relatively narrow range of conditions. Furthermore, we find that in all feasible steady states, the operation of the cycle elevates the osmotic pressure, leading to volume expansion. These results suggest that achieving homeostatic metabolic states under prebiotic conditions was possible, but challenging, and volume growth was a fundamental property of early metabolism.

Published
2023

17. The Escherichia coli Fur pan-regulon has few conserved but many unique regulatory targets

Author

Gao, Ye, Bang, Ina, Seif, Yara, Kim, Donghyuk, and Palsson, Bernhard O

Subjects
Genetics, Escherichia coli, Gene Expression Regulation, Bacterial, Iron, Regulon, Repressor Proteins, Escherichia coli Proteins, Transcription Factors, Environmental Sciences, Biological Sciences, Information and Computing Sciences, Developmental Biology
Abstract

While global transcription factors (TFs) have been studied extensively in Escherichia coli model strains, conservation and diversity in TF regulation between strains is still unknown. Here we use a combination of ChIP-exo-to define ferric uptake regulator (Fur) binding sites-and differential gene expression-to define the Fur regulon in nine E. coli strains. We then define a pan-regulon consisting of 469 target genes that includes all Fur target genes in all nine strains. The pan-regulon is then divided into the core regulon (target genes found in all the strains, n = 36), the accessory regulon (target found in two to eight strains, n = 158) and the unique regulon (target genes found in one strain, n = 275). Thus, there is a small set of Fur regulated genes common to all nine strains, but a large number of regulatory targets unique to a particular strain. Many of the unique regulatory targets are genes unique to that strain. This first-established pan-regulon reveals a common core of conserved regulatory targets and significant diversity in transcriptional regulation amongst E. coli strains, reflecting diverse niche specification and strain history.

Published
2023

18. Whole-genome sequences from wild-type and laboratory-evolved strains define the alleleome and establish its hallmarks

Author

Catoiu, Edward Alexander, Phaneuf, Patrick, Monk, Jonathan, and Palsson, Bernhard O

Subjects
Genetics, Human Genome, Biotechnology, Genetic Variation, Mutation, Escherichia coli, Genome, Bacterial, Amino Acid Sequence, alignment, allele, laboratory evolution, mutation, sequence
Abstract

The genomic diversity across strains of a species forms the genetic basis for differences in their behavior. A large-scale assessment of sequence variation has been made possible by the growing availability of strain-specific whole-genome sequences (WGS) and with the advent of large-scale databases of laboratory-acquired mutations. We define the Escherichia coli "alleleome" through a genome-scale assessment of amino acid (AA) sequence diversity in open reading frames across 2,661 WGS from wild-type strains. We observe a highly conserved alleleome enriched in mutations unlikely to affect protein function. In contrast, 33,000 mutations acquired in laboratory evolution experiments result in more severe AA substitutions that are rarely achieved by natural selection. Large-scale assessment of the alleleome establishes a method for the quantification of bacterial allelic diversity, reveals opportunities for synthetic biology to explore novel sequence space, and offers insights into the constraints governing evolution.

Published
2023

19. Advancing the scale of synthetic biology via cross-species transfer of cellular functions enabled by iModulon engraftment

Author

Donghui Choe, Connor A. Olson, Richard Szubin, Hannah Yang, Jaemin Sung, Adam M. Feist, and Bernhard O. Palsson

Subjects
Science
Abstract

Abstract Machine learning applied to large compendia of transcriptomic data has enabled the decomposition of bacterial transcriptomes to identify independently modulated sets of genes, such iModulons represent specific cellular functions. The identification of iModulons enables accurate identification of genes necessary and sufficient for cross-species transfer of cellular functions. We demonstrate cross-species transfer of: 1) the biotransformation of vanillate to protocatechuate, 2) a malonate catabolic pathway, 3) a catabolic pathway for 2,3-butanediol, and 4) an antimicrobial resistance to ampicillin found in multiple Pseudomonas species to Escherichia coli. iModulon-based engineering is a transformative strategy as it includes all genes comprising the transferred cellular function, including genes without functional annotation. Adaptive laboratory evolution was deployed to optimize the cellular function transferred, revealing mutations in the host. Combining big data analytics and laboratory evolution thus enhances the level of understanding of systems biology, and synthetic biology for strain design and development.

Published
2024
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20. Whole-genome sequencing of multiple related individuals with type 2 diabetes reveals an atypical likely pathogenic mutation in the PAX6 gene

Author

Boehm, Bernhard O, Kratzer, Wolfgang, and Bansal, Vikas

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Genetics, Human Genome, Pediatric, Diabetes, Clinical Research, Autoimmune Disease, Genetic Testing, Aetiology, 2.1 Biological and endogenous factors, Metabolic and endocrine, Humans, Diabetes Mellitus, Type 2, PAX6 Transcription Factor, Case-Control Studies, Mutation, Eye Abnormalities, Aniridia, Homeodomain Proteins, Eye Proteins, Pedigree, Clinical Sciences, Genetics & Heredity, Clinical sciences
Abstract

Pathogenic variants in more than 14 genes have been implicated in monogenic diabetes; however, a significant fraction of individuals with young-onset diabetes and a strong family history of diabetes have unknown genetic etiology. To identify novel pathogenic alleles for monogenic diabetes, we performed whole-genome sequencing (WGS) on four related individuals with type 2 diabetes - including one individual diagnosed at the age of 31 years - that were negative for mutations in known monogenic diabetes genes. The individuals were ascertained from a large case-control study and had a multi-generation family history of diabetes. Identity-by-descent (IBD) analysis revealed that the four individuals represent two sib-pairs that are third-degree relatives. A novel missense mutation (p.P81S) in the PAX6 gene was one of eight rare coding variants across the genome shared IBD by all individuals and was inherited from affected mothers in both sib-pairs. The mutation affects a highly conserved amino acid located in the paired-domain of PAX6 - a hotspot for missense mutations that cause aniridia and other eye abnormalities. However, no eye-related phenotype was observed in any individual. The well-established functional role of PAX6 in glucose-induced insulin secretion and the co-segregation of diabetes in families with aniridia provide compelling support for the pathogenicity of this mutation for diabetes. The mutation could be classified as "likely pathogenic" with a posterior probability of 0.975 according to the ACMG/AMP guidelines. This is the first PAX6 missense mutation that is likely pathogenic for autosomal-dominant adult-onset diabetes without eye abnormalities.

Published
2023

21. Experimental Evolution Reveals Unifying Systems-Level Adaptations but Diversity in Driving Genotypes

Author

Kavvas, Erol S, Long, Christopher P, Sastry, Anand, Poudel, Saugat, Antoniewicz, Maciek R, Ding, Yang, Mohamed, Elsayed T, Szubin, Richard, Monk, Jonathan M, Feist, Adam M, and Palsson, Bernhard O

Subjects
Biotechnology, Human Genome, Genetics, 2.1 Biological and endogenous factors, Aetiology, Escherichia coli, NADP, Adaptation, Physiological, Genotype, Mutation, transcriptional regulatory network, adaptive evolution, metabolism, regulatory network, systems biology
Abstract

Genotype-fitness maps of evolution have been well characterized for biological components, such as RNA and proteins, but remain less clear for systems-level properties, such as those of metabolic and transcriptional regulatory networks. Here, we take multi-omics measurements of 6 different E. coli strains throughout adaptive laboratory evolution (ALE) to maximal growth fitness. The results show the following: (i) convergence in most overall phenotypic measures across all strains, with the notable exception of divergence in NADPH production mechanisms; (ii) conserved transcriptomic adaptations, describing increased expression of growth promoting genes but decreased expression of stress response and structural components; (iii) four groups of regulatory trade-offs underlying the adjustment of transcriptome composition; and (iv) correlates that link causal mutations to systems-level adaptations, including mutation-pathway flux correlates and mutation-transcriptome composition correlates. We thus show that fitness landscapes for ALE can be described with two layers of causation: one based on system-level properties (continuous variables) and the other based on mutations (discrete variables). IMPORTANCE Understanding the mechanisms of microbial adaptation will help combat the evolution of drug-resistant microbes and enable predictive genome design. Although experimental evolution allows us to identify the causal mutations underlying microbial adaptation, it remains unclear how causal mutations enable increased fitness and is often explained in terms of individual components (i.e., enzyme rate) as opposed to biological systems (i.e., pathways). Here, we find that causal mutations in E. coli are linked to systems-level changes in NADPH balance and expression of stress response genes. These systems-level adaptation patterns are conserved across diverse E. coli strains and thus identify cofactor balance and proteome reallocation as dominant constraints governing microbial adaptation.

Published
2022

22. Pan-Genome Analysis of Transcriptional Regulation in Six Salmonella enterica Serovar Typhimurium Strains Reveals Their Different Regulatory Structures

Author

Yuan, Yuan, Seif, Yara, Rychel, Kevin, Yoo, Reo, Chauhan, Siddharth, Poudel, Saugat, Al-bulushi, Tahani, Palsson, Bernhard O, and Sastry, Anand V

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Biomedical and Clinical Sciences, Genetics, Microbiology, Clinical Sciences, Vaccine Related, Infectious Diseases, Digestive Diseases, Emerging Infectious Diseases, Human Genome, Prevention, Biotechnology, Biodefense, Foodborne Illness, 2.2 Factors relating to the physical environment, Aetiology, Infection, Humans, Salmonella enterica, Serogroup, Salmonella typhimurium, Gene Expression Regulation, Gene Expression Profiling, Salmonella, ICA, pan-genomics, pan-genome, Salmonella Typhimurium, systems biology, transcriptomics, gene regulation
Abstract

Establishing transcriptional regulatory networks (TRNs) in bacteria has been limited to well-characterized model strains. Using machine learning methods, we established the transcriptional regulatory networks of six Salmonella enterica serovar Typhimurium strains from their transcriptomes. By decomposing a compendia of RNA sequencing (RNA-seq) data with independent component analysis, we obtained 400 independently modulated sets of genes, called iModulons. We (i) performed pan-genome analysis of the phylogroup structure of S. Typhimurium and analyzed the iModulons against this background, (ii) revealed different genetic signatures in pathogenicity islands that explained phenotypes, (iii) discovered three transport iModulons linked to antibiotic resistance, (iv) described concerted responses to cationic antimicrobial peptides, and (v) uncovered new regulons. Thus, by combining pan-genome and transcriptomic analytics, we revealed variations in TRNs across six strains of serovar Typhimurium. IMPORTANCE Salmonella enterica serovar Typhimurium is a pathogen involved in human nontyphoidal infections. Treating S. Typhimurium infections is difficult due to the species's dynamic adaptation to its environment, which is dictated by a complex transcriptional regulatory network (TRN) that is different across strains. In this study, we describe the use of independent component analysis to characterize the differential TRNs across the S. Typhimurium pan-genome using a compendium of high-quality RNA-seq data. This approach provided unprecedented insights into the differences between regulation of key cellular functions and pathogenicity in the different strains. The study provides an impetus to initiate a large-scale effort to reveal the TRN differences between the major phylogroups of the pathogenic bacteria, which could fundamentally impact personalizing treatments of bacterial pathogens.

Published
2022

23. Comparative pangenomics: analysis of 12 microbial pathogen pangenomes reveals conserved global structures of genetic and functional diversity

Author

Hyun, Jason C, Monk, Jonathan M, and Palsson, Bernhard O

Subjects
Infectious Diseases, Human Genome, Genetics, Biotechnology, Aetiology, 2.2 Factors relating to the physical environment, Phylogeny, Pangenome, Core genome, Comparative genomics, Multispecies, Heaps' law, Functional diversity, Sequence diversity, Protein domains, Aminoacyl-tRNA synthetases, Heaps’ law, Biological Sciences, Information and Computing Sciences, Medical and Health Sciences, Bioinformatics
Abstract

BackgroundWith the exponential growth of publicly available genome sequences, pangenome analyses have provided increasingly complete pictures of genetic diversity for many microbial species. However, relatively few studies have scaled beyond single pangenomes to compare global genetic diversity both within and across different species. We present here several methods for "comparative pangenomics" that can be used to contextualize multi-pangenome scale genetic diversity with gene function for multiple species at multiple resolutions: pangenome shape, genes, sequence variants, and positions within variants.ResultsApplied to 12,676 genomes across 12 microbial pathogenic species, we observed several shared resolution-specific patterns of genetic diversity: First, pangenome openness is associated with species' phylogenetic placement. Second, relationships between gene function and frequency are conserved across species, with core genomes enriched for metabolic and ribosomal genes and accessory genomes for trafficking, secretion, and defense-associated genes. Third, genes in core genomes with the highest sequence diversity are functionally diverse. Finally, certain protein domains are consistently mutation enriched across multiple species, especially among aminoacyl-tRNA synthetases where the extent of a domain's mutation enrichment is strongly function-dependent.ConclusionsThese results illustrate the value of each resolution at uncovering distinct aspects in the relationship between genetic and functional diversity across multiple species. With the continued growth of the number of sequenced genomes, these methods will reveal additional universal patterns of genetic diversity at the pangenome scale.

Published
2022

24. The hallmarks of a tradeoff in transcriptomes that balances stress and growth functions

Author

Christopher Dalldorf, Kevin Rychel, Richard Szubin, Ying Hefner, Arjun Patel, Daniel C. Zielinski, and Bernhard O. Palsson

Subjects
transcriptional regulation, sigma factors, ribosomes, Escherichia coli, Microbiology, QR1-502
Abstract

ABSTRACT Fast growth phenotypes are achieved through optimal transcriptomic allocation, in which cells must balance tradeoffs in resource allocation between diverse functions. One such balance between stress readiness and unbridled growth in E. coli has been termed the fear versus greed (f/g) tradeoff. Two specific RNA polymerase (RNAP) mutations observed in adaptation to fast growth have been previously shown to affect the f/g tradeoff, suggesting that genetic adaptations may be primed to control f/g resource allocation. Here, we conduct a greatly expanded study of the genetic control of the f/g tradeoff across diverse conditions. We introduced 12 RNA polymerase (RNAP) mutations commonly acquired during adaptive laboratory evolution (ALE) and obtained expression profiles of each. We found that these single RNAP mutation strains resulted in large shifts in the f/g tradeoff primarily in the RpoS regulon and ribosomal genes, likely through modifying RNAP-DNA interactions. Two of these mutations additionally caused condition-specific transcriptional adaptations. While this tradeoff was previously characterized by the RpoS regulon and ribosomal expression, we find that the GAD regulon plays an important role in stress readiness and ppGpp in translation activity, expanding the scope of the tradeoff. A phylogenetic analysis found the greed-related genes of the tradeoff present in numerous bacterial species. The results suggest that the f/g tradeoff represents a general principle of transcriptome allocation in bacteria where small genetic changes can result in large phenotypic adaptations to growth conditions.IMPORTANCETo increase growth, E. coli must raise ribosomal content at the expense of non-growth functions. Previous studies have linked RNAP mutations to this transcriptional shift and increased growth but were focused on only two mutations found in the protein’s central region. RNAP mutations, however, commonly occur over a large structural range. To explore RNAP mutations’ impact, we have introduced 12 RNAP mutations found in laboratory evolution experiments and obtained expression profiles of each. The mutations nearly universally increased growth rates by adjusting said tradeoff away from non-growth functions. In addition to this shift, a few caused condition-specific adaptations. We explored the prevalence of this tradeoff across phylogeny and found it to be a widespread and conserved trend among bacteria.

Published
2024
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25. Bottom-up parameterization of enzyme rate constants: Reconciling inconsistent data

Author

Daniel C. Zielinski, Marta R.A. Matos, James E. de Bree, Kevin Glass, Nikolaus Sonnenschein, and Bernhard O. Palsson

Subjects
Biotechnology, TP248.13-248.65, Biology (General), QH301-705.5
Abstract

Kinetic models of metabolism are promising platforms for studying complex metabolic systems and designing production strains. Given the availability of enzyme kinetic data from historical experiments and machine learning estimation tools, a straightforward modeling approach is to assemble kinetic data enzyme by enzyme until a desired scale is reached. However, this type of ‘bottom up’ parameterization of kinetic models has been difficult due to a number of issues including gaps in kinetic parameters, the complexity of enzyme mechanisms, inconsistencies between parameters obtained from different sources, and in vitro-in vivo differences. Here, we present a computational workflow for the robust estimation of kinetic parameters for detailed mass action enzyme models while taking into account parameter uncertainty. The resulting software package, termed MASSef (the Mass Action Stoichiometry Simulation Enzyme Fitting package), can handle standard ‘macroscopic’ kinetic parameters, including Km, kcat, Ki, Keq, and nh, as well as diverse reaction mechanisms defined in terms of mass action reactions and ‘microscopic’ rate constants. We provide three enzyme case studies demonstrating that this approach can identify and reconcile inconsistent data either within in vitro experiments or between in vitro and in vivo enzyme function. We further demonstrate how parameterized enzyme modules can be used to assemble pathway-scale kinetic models consistent with in vivo behavior. This work builds on the legacy of knowledge on kinetic behavior of enzymes by enabling robust parameterization of enzyme kinetic models at scale utilizing the abundance of historical literature data and machine learning parameter estimates.

Published
2024
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26. Advanced transcriptomic analysis reveals the role of efflux pumps and media composition in antibiotic responses of Pseudomonas aeruginosa

Author

Rajput, Akanksha, Tsunemoto, Hannah, Sastry, Anand V, Szubin, Richard, Rychel, Kevin, Chauhan, Siddharth M, Pogliano, Joe, and Palsson, Bernhard O

Subjects
Lung, Antimicrobial Resistance, Rare Diseases, Infectious Diseases, Cystic Fibrosis, Genetics, 2.2 Factors relating to the physical environment, Development of treatments and therapeutic interventions, 5.1 Pharmaceuticals, Aetiology, 2.1 Biological and endogenous factors, Infection, Anti-Bacterial Agents, Bacterial Proteins, Biofilms, Gene Expression Profiling, Humans, Pseudomonas Infections, Pseudomonas aeruginosa, beta-Lactams, Environmental Sciences, Biological Sciences, Information and Computing Sciences, Developmental Biology
Abstract

Pseudomonas aeruginosa is an opportunistic pathogen and major cause of hospital-acquired infections. The virulence of P. aeruginosa is largely determined by its transcriptional regulatory network (TRN). We used 411 transcription profiles of P. aeruginosa from diverse growth conditions to construct a quantitative TRN by identifying independently modulated sets of genes (called iModulons) and their condition-specific activity levels. The current study focused on the use of iModulons to analyze the biofilm production and antibiotic resistance of P. aeruginosa. Our analysis revealed: (i) 116 iModulons, 81 of which show strong association with known regulators; (ii) novel roles of regulators in modulating antibiotics efflux pumps; (iii) substrate-efflux pump associations; (iv) differential iModulon activity in response to beta-lactam antibiotics in bacteriological and physiological media; (v) differential activation of 'Cell Division' iModulon resulting from exposure to different beta-lactam antibiotics and (vi) a role of the PprB iModulon in the stress-induced transition from planktonic to biofilm lifestyle. In light of these results, the construction of an iModulon-based TRN provides a transcriptional regulatory basis for key aspects of P. aeruginosa infection, such as antibiotic stress responses and biofilm formation. Taken together, our results offer a novel mechanistic understanding of P. aeruginosa virulence.

Published
2022

27. Biological and genetic determinants of glycolysis: Phosphofructokinase isoforms boost energy status of stored red blood cells and transfusion outcomes

Author

Nemkov, Travis, Stephenson, Daniel, Earley, Eric J., Keele, Gregory R., Hay, Ariel, Key, Alicia, Haiman, Zachary B., Erickson, Christopher, Dzieciatkowska, Monika, Reisz, Julie A., Moore, Amy, Stone, Mars, Deng, Xutao, Kleinman, Steven, Spitalnik, Steven L., Hod, Eldad A., Hudson, Krystalyn E., Hansen, Kirk C., Palsson, Bernhard O., Churchill, Gary A., Roubinian, Nareg, Norris, Philip J., Busch, Michael P., Zimring, James C., Page, Grier P., and D’Alessandro, Angelo

Published
2024
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28. GWAS of random glucose in 476,326 individuals provide insights into diabetes pathophysiology, complications and treatment stratification

Author

Lagou, Vasiliki, Jiang, Longda, Ulrich, Anna, Zudina, Liudmila, González, Karla Sofia Gutiérrez, Balkhiyarova, Zhanna, Faggian, Alessia, Maina, Jared G., Chen, Shiqian, Todorov, Petar V., Sharapov, Sodbo, David, Alessia, Marullo, Letizia, Mägi, Reedik, Rujan, Roxana-Maria, Ahlqvist, Emma, Thorleifsson, Gudmar, Gao, Ηe, Εvangelou, Εvangelos, Benyamin, Beben, Scott, Robert A., Isaacs, Aaron, Zhao, Jing Hua, Willems, Sara M., Johnson, Toby, Gieger, Christian, Grallert, Harald, Meisinger, Christa, Müller-Nurasyid, Martina, Strawbridge, Rona J., Goel, Anuj, Rybin, Denis, Albrecht, Eva, Jackson, Anne U., Stringham, Heather M., Corrêa, Jr., Ivan R., Farber-Eger, Eric, Steinthorsdottir, Valgerdur, Uitterlinden, André G., Munroe, Patricia B., Brown, Morris J., Schmidberger, Julian, Holmen, Oddgeir, Thorand, Barbara, Hveem, Kristian, Wilsgaard, Tom, Mohlke, Karen L., Wang, Zhe, Shmeliov, Aleksey, den Hoed, Marcel, Loos, Ruth J. F., Kratzer, Wolfgang, Haenle, Mark, Koenig, Wolfgang, Boehm, Bernhard O., Tan, Tricia M., Tomas, Alejandra, Salem, Victoria, Barroso, Inês, Tuomilehto, Jaakko, Boehnke, Michael, Florez, Jose C., Hamsten, Anders, Watkins, Hugh, Njølstad, Inger, Wichmann, H.-Erich, Caulfield, Mark J., Khaw, Kay-Tee, van Duijn, Cornelia M., Hofman, Albert, Wareham, Nicholas J., Langenberg, Claudia, Whitfield, John B., Martin, Nicholas G., Montgomery, Grant, Scapoli, Chiara, Tzoulaki, Ioanna, Elliott, Paul, Thorsteinsdottir, Unnur, Stefansson, Kari, Brittain, Evan L., McCarthy, Mark I., Froguel, Philippe, Sexton, Patrick M., Wootten, Denise, Groop, Leif, Dupuis, Josée, Meigs, James B., Deganutti, Giuseppe, Demirkan, Ayse, Pers, Tune H., Reynolds, Christopher A., Aulchenko, Yurii S., Kaakinen, Marika A., Jones, Ben, and Prokopenko, Inga

Published
2023
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29. Positively charged mineral surfaces promoted the accumulation of organic intermediates at the origin of metabolism.

Author

Akbari, Amir and Palsson, Bernhard O

Subjects
Minerals, Ion Channels, Biological Evolution, Artificial Cells, Mathematical Sciences, Biological Sciences, Information and Computing Sciences, Bioinformatics
Abstract

Identifying plausible mechanisms for compartmentalization and accumulation of the organic intermediates of early metabolic cycles in primitive cells has been a major challenge in theories of life's origins. Here, we propose a mechanism, where positive membrane potentials elevate the concentration of the organic intermediates. Positive membrane potentials are generated by positively charged surfaces of protocell membranes due to accumulation of transition metals. We find that (i) positive membrane potentials comparable in magnitude to those of modern cells can increase the concentration of the organic intermediates by several orders of magnitude; (ii) generation of large membrane potentials destabilize ion distributions; (iii) violation of electroneutrality is necessary to induce nonzero membrane potentials; and (iv) violation of electroneutrality enhances osmotic pressure and diminishes reaction efficiency, resulting in an evolutionary driving force for the formation of lipid membranes, specialized ion channels, and active transport systems.

Published
2022

30. Adaptive laboratory evolution and independent component analysis disentangle complex vancomycin adaptation trajectories

Author

Fait, Anaëlle, Seif, Yara, Mikkelsen, Kasper, Poudel, Saugat, Wells, Jerry M, Palsson, Bernhard O, and Ingmer, Hanne

Subjects
Infectious Diseases, Antimicrobial Resistance, Emerging Infectious Diseases, Genetics, Biotechnology, Aetiology, 2.2 Factors relating to the physical environment, Infection, Anti-Bacterial Agents, Evolution, Molecular, Humans, Methicillin-Resistant Staphylococcus aureus, Microbial Sensitivity Tests, Oxacillin, Staphylococcal Infections, Staphylococcus aureus, Vancomycin, Vancomycin Resistance, Virulence, antibiotic resistance, adaptive laboratory evolution, transcriptional regulation, virulence
Abstract

Human infections with methicillin-resistant Staphylococcus aureus (MRSA) are commonly treated with vancomycin, and strains with decreased susceptibility, designated as vancomycin-intermediate S. aureus (VISA), are associated with treatment failure. Here, we profiled the phenotypic, mutational, and transcriptional landscape of 10 VISA strains adapted by laboratory evolution from one common MRSA ancestor, the USA300 strain JE2. Using functional and independent component analysis, we found that: 1) despite the common genetic background and environmental conditions, the mutational landscape diverged between evolved strains and included mutations previously associated with vancomycin resistance (in vraT, graS, vraFG, walKR, and rpoBCD) as well as novel adaptive mutations (SAUSA300_RS04225, ssaA, pitAR, and sagB); 2) the first wave of mutations affected transcriptional regulators and the second affected genes involved in membrane biosynthesis; 3) expression profiles were predominantly strain-specific except for sceD and lukG, which were the only two genes significantly differentially expressed in all clones; 4) three independent virulence systems (φSa3, SaeR, and T7SS) featured as the most transcriptionally perturbed gene sets across clones; 5) there was a striking variation in oxacillin susceptibility across the evolved lineages (from a 10-fold increase to a 63-fold decrease) that also arose in clinical MRSA isolates exposed to vancomycin and correlated with susceptibility to teichoic acid inhibitors; and 6) constitutive expression of the VraR regulon explained cross-susceptibility, while mutations in walK were associated with cross-resistance. Our results show that adaptation to vancomycin involves a surprising breadth of mutational and transcriptional pathways that affect antibiotic susceptibility and possibly the clinical outcome of infections.

Published
2022

31. General and abdominal adiposity and hypertension in eight world regions: a pooled analysis of 837 population-based studies with 7·5 million participants

Author

Zhou, Bin, Bennett, James E, Wickham, Aidan P, Singleton, Rosie K, Mishra, Anu, Carrillo-Larco, Rodrigo M, Ikeda, Nayu, Jain, Lakshya, Barradas-Pires, Ana, Heap, Rachel A, Lhoste, Victor PF, Sheffer, Kate E, Phelps, Nowell H, Rayner, Archie W, Gregg, Edward W, Woodward, Mark, Stevens, Gretchen A, Iurilli, Maria LC, Danaei, Goodarz, Di Cesare, Mariachiara, Aguilar-Salinas, Carlos A, Ahmad, Noor Ani, Bovet, Pascal, Chen, Zhengming, Damasceno, Albertino, Filippi, Sarah L, Janszky, Imre, Kengne, Andre P, Khang, Young-Ho, Khunti, Kamlesh, Laxmaiah, Avula, Lim, Lee-Ling, Lissner, Lauren, Margozzini, Paula, Mbanya, Jean Claude N, McGarvey, Stephen T, Shaw, Jonathan E, Söderberg, Stefan, Soto-Mota, Luis Adrián, Wang, Junyang, Zaccardi, Francesco, Abarca-Gómez, Leandra, Abbasi-Kangevari, Mohsen, Abdrakhmanova, Shynar, Abdul Ghaffar, Suhaila, Abdul Rahim, Hanan F, Abdurrahmonova, Zulfiya, Abu-Rmeileh, Niveen M, Acosta-Cazares, Benjamin, Adam, Ishag, Adamczyk, Marzena, Aekplakorn, Wichai, Agdeppa, Imelda A, Aghazadeh-Attari, Javad, Agyemang, Charles, Ahmad, Mohamad Hasnan, Ahmadi, Ali, Ahmadi, Naser, Ahmadi, Nastaran, Ahmed, Soheir H, Ahrens, Wolfgang, Aitmurzaeva, Gulmira, Ajlouni, Kamel, Al-Hazzaa, Hazzaa M, Al-Hinai, Halima, Al-Lawati, Jawad A, Al-Raddadi, Rajaa, Al Asfoor, Deena, Al Hourani, Huda M, Alarouj, Monira, AlBuhairan, Fadia, AlDhukair, Shahla, Ali, Mohamed M, Alieva, Anna V, Alkandari, Abdullah, Alkhatib, Buthaina M, Aly, Eman, Amarapurkar, Deepak N, Amiano Etxezarreta, Pilar, Amougou, Norbert, Andersen, Lars Bo, Anderssen, Sigmund A, Androutsos, Odysseas, Anjana, Ranjit Mohan, Ansari-Moghaddam, Alireza, Anufrieva, Elena, Aounallah-Skhiri, Hajer, Aris, Tahir, Arku, Raphael E, Arlappa, Nimmathota, Aryal, Krishna K, Assah, Felix K, Assembekov, Batyrbek, Assunção, Maria Cecília F, Auvinen, Juha, Avdičová, Mária, Azad, Kishwar, Azevedo, Ana, Azimi-Nezhad, Mohsen, Azizi, Fereidoun, Bacopoulou, Flora, Bahijri, Suhad, Bajramovic, Izet, Balakrishna, Nagalla, Bamoshmoosh, Mohamed, Banach, Maciej, Bandosz, Piotr, Banegas, José R, Baran, Rafał, Barbagallo, Carlo M, Barbosa Filho, Valter, Barceló, Alberto, Baretić, Maja, Barnoya, Joaquin, Barrera, Lena, Barros, Aluisio JD, Barros, Mauro Virgílio Gomes, Basit, Abdul, Bastos, Joao Luiz, Batieha, Anwar M, Batista, Aline P, Batista, Rosangela L, Battakova, Zhamilya, Baur, Louise A, Bayauli, Pascal M, Bel-Serrat, Silvia, Belavendra, Antonisamy, Ben Romdhane, Habiba, Benedek, Theodora, Benedics, Judith, Benet, Mikhail, Benitez Rolandi, Gilda Estela, Benzeval, Michaela, Bere, Elling, Berger, Nicolas, Bergh, Ingunn Holden, Berkinbayev, Salim, Bernabe-Ortiz, Antonio, Bettiol, Heloísa, Beybey, Augustin F, Bezerra, Jorge, Bhagyalaxmi, Aroor, Bhargava, Santosh K, Bika Lele, Elysée Claude, Bikbov, Mukharram M, Bista, Bihungum, Bjelica, Dusko J, Bjerregaard, Peter, Bjertness, Espen, Bjertness, Marius B, Björkelund, Cecilia, Bloch, Katia V, Blokstra, Anneke, Bo, Simona, Bobak, Martin, Boddy, Lynne M, Boehm, Bernhard O, Boggia, Jose G, Bogova, Elena, Bonaccio, Marialaura, Bonilla-Vargas, Alice, Borghs, Herman, Botomba, Steve, Bourne, Rupert, Boymatova, Khadichamo, Braeckman, Lutgart, Braithwaite, Tasanee, Brajkovich, Imperia, Branca, Francesco, Brenner, Hermann, Brewster, Lizzy M, Briceño, Yajaira, Brinduse, Lacramioara, Bringolf-Isler, Bettina, Brito, Miguel, Brug, Johannes, Bugge, Anna, Buntinx, Frank, Buoncristiano, Marta, Burns, Con, Cabrera de León, Antonio, Caixeta, Roberta B, Cama, Tilema, Can, Günay, Cândido, Ana Paula C, Cañete, Felicia, Capanzana, Mario V, Čapková, Naděžda, Capuano, Eduardo, Capuano, Rocco, Capuano, Vincenzo, Cardoso, Viviane C, Carlsson, Axel C, Casanueva, Felipe F, Casas, Maribel, Censi, Laura, Cervantes‐Loaiza, Marvin, Chamnan, Parinya, Chamukuttan, Snehalatha, Chan, Queenie, Chaturvedi, Nish, Chen, Fangfang, Chen, Huashuai, Chen, Long-Sheng, Cheng, Yiling J, Cheraghian, Bahman, Chetrit, Angela, Chikova-Iscener, Ekaterina, Chinapaw, Mai JM, Chinnock, Anne, Chiolero, Arnaud, Chirita-Emandi, Adela, Chirlaque, María-Dolores, Chong, Chean Lin, Christofaro, Diego G, Chudek, Jerzy, Cifkova, Renata, Cirillo, Massimo, Claessens, Frank, Clare, Philip, Cohen, Emmanuel, Confortin, Susana C, Coppinger, Tara C, Cortés, Lilia Yadira, Cosmin, Cojocaru R, Costanzo, Simona, Cowan, Melanie J, Cowell, Chris, Crampin, Amelia C, Cross, Amanda J, Crujeiras, Ana B, Cruz, Juan J, Cucu, Alexandra M, Cureau, Felipe V, Cuschieri, Sarah, D'Arrigo, Graziella, d'Orsi, Eleonora, da Silva-Ferreira, Haroldo, Dahm, Christina C, Dallongeville, Jean, Dankner, Rachel, Davletov, Kairat, de Assis Guedes de Vasconcelos, Francisco, de Assis, Maria Alice Altenburg, De Bacquer, Dirk, De Bacquer, Jaco, de Bont, Jeroen, De Curtis, Amalia, de Fragas Hinnig, Patrícia, de Gaetano, Giovanni, De Henauw, Stefaan, De Miguel-Etayo, Pilar, de Oliveira, Paula Duarte, de Paiva, Karina Mary, De Ridder, Karin, de Valois Correia Júnior, Marco Aurélio, Deepa, Mohan, DeGennaro, Vincent Jr, Demarest, Stefaan, Dennison, Elaine, Deschamps, Valérie, Dhimal, Meghnath, Díez Ripollés, María Pilar, Dika, Zivka, Djalalinia, Shirin, Dominguez, Liria, Donati, Maria Benedetta, Donfrancesco, Chiara, Dong, Guanghui, Donoso, Silvana P, Dorobantu, Maria, Dörr, Marcus, Dragano, Nico, Drygas, Wojciech, Du, Shufa, Duante, Charmaine A, Duboz, Priscilla, Duda, Rosemary B, Duleva, Vesselka L, Dushpanova, Anar, Dyussupova, Azhar, Dziankowska-Zaborszczyk, Elzbieta, Ebrahimi, Narges, Echeverría, Guadalupe, Eddie, Ricky, Eftekhar, Ebrahim, Efthymiou, Vasiliki, Egbagbe, Eruke E, Eghtesad, Sareh, Ekelund, Ulf, El-Khateeb, Mohammad, El Ati, Jalila, Elosua, Roberto, Enang, Ofem, Erasmus, Rajiv T, Erem, Cihangir, Ergor, Gul, Eriksen, Louise, Eriksson, Johan G, Escobedo-de la Peña, Jorge, Esmaeili, Ali, Evans, Roger G, Fakhradiyev, Ildar, Fakhretdinova, Albina A, Fall, Caroline H, Faramarzi, Elnaz, Farjam, Mojtaba, Farzadfar, Farshad, Farzi, Yosef, Fattahi, Mohammad Reza, Fawwad, Asher, Felix-Redondo, Francisco J, Ferguson, Trevor S, Fernandes, Romulo A, Fernández-Bergés, Daniel, Fernando, Desha R, Ferrante, Daniel, Ferrari, Gerson, Ferrari, Marika, Ferreccio, Catterina, Ferrer, Eldridge, Figueiró, Thamara Hubler, Fijalkowska, Anna, Fink, Günther, Fisberg, Mauro, Forsner, Maria, Fottrell, Edward F, Fouad, Heba M, Francis, Damian K, Frontera, Guillermo, Fuchs, Flavio D, Fuchs, Sandra C, Furdela, Viktoriya, Furusawa, Takuro, Gabriela, Stefan Adela, Gaciong, Zbigniew, Galán Cuesta, Manuel, Galbarczyk, Andrzej, Galcheva, Sonya V, Galfo, Myriam, Garcia-de-la-Hera, Manoli, Garcia, Pablo, Garnett, Sarah P, Gasull, Magda, Gazzinelli, Andrea, Gehring, Ulrike, Gerdts, Eva, Ghaderi, Ebrahim, Ghamari, Seyyed-Hadi, Ghanbari, Ali, Ghasemi, Erfan, Gheorghe-Fronea, Oana-Florentina, Ghimire, Anup, Gialluisi, Alessandro, Giampaoli, Simona, Gianfagna, Francesco, Gironella, Glen, Giwercman, Aleksander, Gkiouras, Konstantinos, Glushkova, Natalya, Godara, Ramesh, Godos, Justyna, Goldberg, Marcel, Gómez, Georgina, Gómez Gómez, Jesús Humberto, Gomez, Luis F, Gómez, Santiago F, Gomula, Aleksandra, Gonçalves Cordeiro da Silva, Bruna, Gonçalves, Helen, Gonçalves, Mauer, González-Alvarez, Ana D, Gonzalez-Chica, David A, González-Gil, Esther M, Gonzalez-Gross, Marcela, González-Rivas, Juan P, Gonzalez, Angel R, Gottrand, Frederic, Grafnetter, Dušan, Grajda, Aneta, Grammatikopoulou, Maria G, Grodzicki, Tomasz, Grøholt, Else Karin, Grøntved, Anders, Guajardo, Viviana, Guallar-Castillón, Pilar, Guerchet, Maëlenn, Guerrero, Ramiro, Guimaraes, Andre L, Gujral, Unjali P, Gulliford, Martin C, Gunter, Marc J, Gupta, Rajeev, Gureje, Oye, Gurinović, Mirjana A, Gurzkowska, Beata, Gutierrez, Laura, Gwee, Xinyi, Haghshenas, Rosa, Hakimi, Hamid, Halkjær, Jytte, Hambleton, Ian R, Hamzeh, Behrooz, Hanekom, Willem A, Hange, Dominique, Hanif, Abu AM, Hantunen, Sari, Hao, Jie, Hardman, Carla Menêses, Hardy, Louise, Hari Kumar, Rachakulla, Harooni, Javad, Hashemi-Shahri, Seyed Mohammad, Hassapidou, Maria, Hata, Jun, Haugsgjerd, Teresa, Heinen, Mirjam, Hendriks, Marleen Elisabeth, Henrique, Rafael dos Santos, Henriques, Ana, Hernandez Cadena, Leticia, Herrala, Sauli, Herrera-Cuenca, Marianella, Herrera, Victor M, Herter-Aeberli, Isabelle, Herzig, Karl-Heinz, Heshmat, Ramin, Hill, Allan G, Ho, Sai Yin, Holdsworth, Michelle, Homayounfar, Reza, Homs, Clara, Hoogendijk, Emiel O, Horimoto, Andrea RVR, Hormiga, Claudia M, Horta, Bernardo L, Houti, Leila, Howitt, Christina, Htay, Thein Thein, Htet, Aung Soe, Htike, Maung Maung Than, Huerta, José María, Huhtaniemi, Ilpo Tapani, Huiart, Laetitia, Huidumac Petrescu, Constanta, Huisman, Martijn, Husseini, Abdullatif, Huybrechts, Inge, Hwalla, Nahla, Iacoviello, Licia, Iakupova, Ellina M, Iannone, Anna G, Igland, Jannicke, Ijoma, Chinwuba, Iotova, Violeta, Irazola, Vilma E, Ishida, Takafumi, Isiguzo, Godsent C, Islam, Muhammad, Islam, Sheikh Mohammed Shariful, Islek, Duygu, Ittermann, Till, Ivanova-Pandourska, Ivaila Y, Iwasaki, Masanori, Jääskeläinen, Tuija, Jackson, Rod T, Jaddou, Hashem Y, Jadoul, Michel, Jafar, Tazeen, Jan, Nataša, Janus, Edward, Jarani, Juel, Jarnig, Gerald, Jarvelin, Marjo-Riitta, Jasienska, Grazyna, Jelaković, Ana, Jelaković, Bojan, Jha, Anjani Kumar, Jimenez, Ramon O, Jöckel, Karl-Heinz, Joffres, Michel, Jokelainen, Jari J, Jonas, Jost B, Joshi, Pradeep, Joshi, Rohina, Josipović, Josipa, Joukar, Farahnaz, Jóźwiak, Jacek J, Juolevi, Anne, Juresa, Vesna, Jureša, Vesna, Kaaks, Rudolf, Kaducu, Felix O, Kadvan, Agnes L, Kafatos, Anthony, Kajantie, Eero O, Kakutia, Natia, Kállayová, Daniela, Kalmatayeva, Zhanna, Kalter-Leibovici, Ofra, Kannan, Srinivasan, Kapantais, Efthymios, Karaglani, Eva, Karakosta, Argyro, Karki, Khem B, Kassi Anicet, Adoubi, Katibeh, Marzieh, Katulanda, Prasad, Katzmarzyk, Peter T, Kauhanen, Jussi, Kazakbaeva, Gyulli M, Kaze, François F, Ke, Calvin, Keinänen-Kiukaanniemi, Sirkka, Kelishadi, Roya, Kelleher, Cecily, Kemper, Han CG, Keramati, Maryam, Kersting, Mathilde, Khader, Yousef Saleh, Khaledifar, Arsalan, Khalili, Davood, Kheiri, Bahareh, Kheradmand, Motahareh, Khosravi, Alireza, Kiechl-Kohlendorfer, Ursula, Kiechl, Sophia J, Kiechl, Stefan, Kim, Hyeon Chang, Klakk, Heidi, Klanarong, Suntara, Klanova, Jana, Klimek, Magdalena, Knoflach, Michael, Kobel, Susanne, Koirala, Bhawesh, Kolle, Elin, Kolsteren, Patrick, König, Jürgen, Korpelainen, Raija, Korrovits, Paul, Korzycka, Magdalena, Kos, Jelena, Koskinen, Seppo, Koussoh Simone, Malik, Kovács, Éva, Kovalskys, Irina, Kowlessur, Sudhir, Koziel, Slawomir, Kratenova, Jana, Kratzer, Wolfgang, Kriemler, Susi, Kristensen, Peter Lund, Krizan, Helena, Kroker-Lobos, Maria F, Krokstad, Steinar, Kruger, Herculina S, Kruger, Ruan, Kryst, Łukasz, Kubinova, Ruzena, Kujala, Urho M, Kujundzic, Enisa, Kulaga, Zbigniew, Kulimbet, Mukhtar, Kumari, Meena, Kunešová, Marie, Kurjata, Pawel, Kyobutungi, Catherine, La, Quang Ngoc, Labadarios, Demetre, Lachat, Carl, Lai, Daphne, Laid, Youcef, Lall, Lachmie, Landaeta Jimenez, Maritza, Landais, Edwige, Lankila, Tiina, Lanska, Vera, Lappas, Georg, Larijani, Bagher, Lateva, Mina P, Latt, Tint Swe, Laurenzi, Martino, Lazo-Porras, Maria, Le Coroller, Gwenaëlle, Le Nguyen Bao, Khanh, Lehtimäki, Terho, Lemogoum, Daniel, Leong, Elvynna, Leszczak, Justyna, Leung, Gabriel M, Li, Yanping, Liivak, Merike, Lim, Charlie, Lim, Wei-Yen, Lima-Costa, M Fernanda, Lin, Hsien-Ho, Lind, Lars, Litwin, Mieczyslaw, Liu, Liping, Liu, Xiaotian, Longo Abril, Guadalupe, Lopes, Oscar, Lopez-Garcia, Esther, López-Gil, José Francisco, Lopez, Tania, Lozano, José Eugenio, Lukrafka, Janice L, Luksiene, Dalia, Lundqvist, Annamari, Lunet, Nuno, Lunogelo, Charles, Lustigová, Michala, M'Buyamba-Kabangu, Jean-René, Machado-Coelho, George LL, Machado-Rodrigues, Aristides M, Macia, Enguerran, Madar, Ahmed A, Maestre, Gladys E, Maggi, Stefania, Magliano, Dianna J, Magnacca, Sara, Magriplis, Emmanuella, Mahasampath, Gowri, Maire, Bernard, Makdisse, Marcia, Malekpour, Mohammad-Reza, Malekzadeh, Fatemeh, Malekzadeh, Reza, Mallikharjuna Rao, Kodavanti, Malyutina, Sofia, Maniego, Lynell V, Manios, Yannis, Mann, Jim I, Mansour-Ghanaei, Fariborz, Manzato, Enzo, Mapatano, Mala Ali, Maria-Magdalena, Rosu, Mariño, Joany, Markaki, Anastasia, Marques, Larissa Pruner, Marrugat, Jaume, Martorell, Reynaldo, Maruszczak, Katharina, Masala, Giovanna, Mascarenhas, Luis P, Masimango Imani, Mannix, Masinaei, Masoud, Mathiesen, Ellisiv B, Matijasevich, Alicia, Matłosz, Piotr, Matsha, Tandi E, Matsudo, Victor, Matteo, Giletta, Maulik, Pallab K, Mavrogianni, Christina, Mc Donald Posso, Anselmo J, McFarlane, Shelly R, McLean, Rachael M, Mediene Benchekor, Sounnia, Mehlig, Kirsten, Mehrparvar, Amir Houshang, Melgarejo, Jesus D, Méndez, Fabián, Mendivil, Carlos O, Mendoza Montano, Carlos, Menezes, Ana Maria B, Mensink, Gert BM, Mereke, Alibek, Meshram, Indrapal I, Meto, Diane T, Meyer, Haakon E, Mi, Jie, Miłkowska, Karolina, Miller, Jody C, Milushkina, Olga, Minderico, Cláudia S, Mini, GK, Miquel, Juan Francisco, Miranda, J Jaime, Mirjalili, Mohammad Reza, Mišigoj-Duraković, Marjeta, Mistretta, Antonio, Mocanu, Veronica, Modesti, Pietro A, Moghaddam, Sahar Saeedi, Mohammad, Kazem, Mohammadi, Mohammad Reza, Mohammadi, Zahra, Mohammadifard, Noushin, Mohammadpourhodki, Reza, Mohan, Viswanathan, Mohd Yusoff, Muhammad Fadhli, Mohebbi, Iraj, Møller, Niels C, Molnár, Dénes, Momenan, Amirabbas, Mondo, Charles K, Monroy-Valle, Michele M, Montenegro Mendoza, Roger A, Monterrubio-Flores, Eric, Monyeki, Kotsedi Daniel K, Moon, Jin Soo, Moosazadeh, Mahmood, Moradpour, Farhad, Moreira, Leila B, Morejon, Alain, Moreno, Luis A, Morgan, Karen, Moschonis, George, Moslem, Alireza, Mosquera, Mildrey, Mossakowska, Malgorzata, Mostafa, Aya, Mostafavi, Seyed-Ali, Motlagh, Mohammad Esmaeel, Motta, Jorge, Moura-dos-Santos, Marcos André, Mridha, Malay K, Msyamboza, Kelias P, Mu, Thet Thet, Muca, Florian, Mugoša, Boban, Munroe, Patricia B, Mursu, Jaakko, Musa, Kamarul Imran, Musić Milanović, Sanja, Musil, Vera, Musinguzi, Geofrey, Mustafa, Norlaila, Muyer, Muel Telo Marie-Claire, Nabipour, Iraj, Naidu, Balkish M, Najafi, Farid, Nalecz, Hanna, Námešná, Jana, Narayan, KM Venkat, Naseri, Take, Nathalie, Michels, Neelapaichit, Nareemarn, Nejatizadeh, Azim, Nenko, Ilona, Nervi, Flavio, Neuhauser, Hannelore K, Ng, Tze Pin, Nguyen, Chung T, Nguyen, Quang V, Nguyen, Quang Ngoc, Ni, Michael Y, Nie, Peng, Nieto-Martínez, Ramfis E, Niiranen, Teemu J, Ninomiya, Toshiharu, Nishi, Nobuo, Nishtar, Sania, Noale, Marianna, Noboa, Oscar A, Nogueira, Helena, Norton, Kevin I, Noto, Davide, Nowak-Szczepanska, Natalia, Nsour, Mohannad Al, Nuhoğlu, Irfan, Nurk, Eha, Nuwaha, Fred, Nyirenda, Moffat, O'Neill, Terence W, Ochimana, Caleb, Ochoa-Avilés, Angélica M, Oda, Eiji, Odili, Augustine N, Oh, Kyungwon, Ohtsuka, Ryutaro, Oldenburg, Brian, Olié, Valérie, Omar, Mohd Azahadi, Omar, Saeed M, Onat, Altan, Ong, Sok King, Onland-Moret, N Charlotte, Ono, Lariane M, Onodugo, Obinna, Ordunez, Pedro, Ornelas, Rui, Ortiz, Ana P, Ortiz, Pedro J, Osmond, Clive, Ostojic, Sergej M, Ostovar, Afshin, Otero, Johanna A, Ottendahl, Charlotte B, Otu, Akaninyene, Overvad, Kim, Owusu-Dabo, Ellis, Padez, Cristina P, Pagkalos, Ioannis, Pajula, Natalja, Palloni, Alberto, Palmieri, Luigi, Pan, Wen-Harn, Panza, Francesco, Paoli, Mariela, Papadopoulou, Sousana K, Pareja, Rossina G, Park, Soon-Woo, Park, Suyeon, Parnell, Winsome R, Parsaeian, Mahboubeh, Pascanu, Ionela M, Pasquet, Patrick, Patel, Nikhil D, Pavlyshyn, Halyna, Pechlaner, Raimund, Pećin, Ivan, Pedro, João M, Peixoto, Sergio Viana, Peltonen, Markku, Pereira, Alexandre C, Peres, Karen GDA, Peres, Marco A, Perez-Londoño, Agustín, Pérez, Cynthia M, Peterkova, Valentina, Petrovna Kovtun, Olga, Peykari, Niloofar, Pham, Son Thai, Pichardo, Rafael N, Pierre-Marie, Preux, Pikhart, Hynek, Pilav, Aida, Piler, Pavel, Piwonska, Aleksandra, Pizarro, Andreia N, Plata, Silvia, Pop, Raluca M, Popkin, Barry M, Popovic, Stevo R, Porta, Miquel, Poudyal, Anil, Pourfarzi, Farhad, Pourshams, Akram, Poustchi, Hossein, Pradeepa, Rajendra, Price, Alison J, Prista, Antonio, Providencia, Rui, Puder, Jardena J, Pudule, Iveta, Puhakka, Soile, Puiu, Maria, Punab, Margus, Qorbani, Mostafa, Quialheiro, Anna, Quintana, Hedley K, Quiroga-Padilla, Pedro J, Quoc Bao, Tran, Rach, Stefan, Rahimikazerooni, Salar, Rahman, Mahmudur, Raitakari, Olli, Rakhmatulloev, Sherali, Rakovac, Ivo, Ramachandran, Ambady, Ramadan, Otim PC, Ramirez-Zea, Manuel, Ramos, Rafel, Rampal, Lekhraj, Rampal, Sanjay, Ramsay, Sheena E, Rangel Junior, João FLB, Rangel Reina, Daniel A, Rangelova, Lalka S, Rarra, Vayia, Rashidi, Mohammad-Mahdi, Rech, Cassiano Ricardo, Redon, Josep, Regecová, Valéria, Renner, Jane DP, Repasy, Judit A, Reuter, Cézane P, Revilla, Luis, Reynolds, Andrew, Rezaei, Negar, Rezaianzadeh, Abbas, Riboli, Elio, Rigo, Fernando, Rigotti, Attilio, Riley, Leanne M, Rinke de Wit, Tobias F, Risérus, Ulf, Ritti-Dias, Raphael M, Roa, Reina G, Roccaldo, Romana, Rodríguez-Artalejo, Fernando, Rodriguez-Perez, María del Cristo, Rodríguez-Villamizar, Laura A, Rodríguez, Andrea Y, Roggenbuck, Ulla, Rohloff, Peter, Rojas-Martinez, Rosalba, Romeo, Elisabetta L, Rosario, Rafaela V, Rosengren, Annika, Rouse, Ian, Rubinstein, Adolfo, Ruiz-Betancourt, Blanca Sandra, Ruiz-Castell, Maria, Ruiz Moreno, Emma, Rusakova, Iuliia A, Rusek, Wojciech, Rust, Petra, Rutkowski, Marcin, Saamel, Marge, Sabbaghi, Hamideh, Sachdev, Harshpal S, Sadjadi, Alireza, Safarpour, Ali Reza, Safi, Sare, Saghi, Mohammad Hossien, Saidi, Olfa, Saieva, Calogero, Sakata, Satoko, Saki, Nader, Šalaj, Sanja, Salazar Martinez, Eduardo, Salkhanova, Akkumis, Salonen, Jukka T, Samoutian, Margarita, Sánchez-Abanto, Jose, Sánchez Rodríguez, Inés, Santos, Diana A, Santos, Ina S, Santos, Maria Paula, Santos, Tamara R, Saramies, Jouko L, Sardinha, Luis B, Sarganas, Giselle, Sarrafzadegan, Nizal, Saum, Kai-Uwe, Savin, Stefan, Sbaraini, Mariana, Scazufca, Marcia, Schaan, Beatriz D, Schienkiewitz, Anja, Schindler, Karin, Schipf, Sabine, Schmidt, Amand Floriaan, Schmidt, Börge, Schmidt, Carsten O, Schöttker, Ben, Schramm, Sara, Schramm, Stine, Schröder, Helmut, Schultsz, Constance, Schutte, Aletta E, Sebert, Sylvain, Sedaghattalab, Moslem, Sein, Aye Aye, Sen, Abhijit, Sepanlou, Sadaf G, Sequera, Guillermo, Ševčíková, Ľudmila, Sewpaul, Ronel, Shamah-Levy, Teresa, Shamshirgaran, Seyed Morteza, Sharafkhah, Maryam, Sharma, Sanjib K, Sharman, Almaz, Shayanrad, Amaneh, Shayesteh, Ali Akbar, Shengelia, Lela, Shibuya, Kenji, Shimizu-Furusawa, Hana, Shiri, Rahman, Shoranov, Marat, Shrestha, Namuna, Si-Ramlee, Khairil, Sibai, Abla M, Sidossis, Labros S, Silva, Antonio M, Silva, Caroline Ramos de Moura, Silva, Diego Augusto Santos, Silva, Kelly Samara, Sim, Xueling, Simon, Mary, Sjöström, Michael, Skoblina, Natalia A, Slowikowska-Hilczer, Jolanta, Slusarczyk, Przemysław, Smeeth, Liam, Smith, Lee, Soares, Fernanda Cunha, Sobek, Grzegorz, Sobngwi, Eugène, Sodemann, Morten, Soemantri, Agustinus, Solfrizzi, Vincenzo, Somi, Mohammad Hossein, Sørgjerd, Elin P, Sorić, Maroje, Soto-Rojas, Victoria E, Soumaré, Aïcha, Sousa-Poza, Alfonso, Spiroski, Igor, Staessen, Jan A, Stang, Andreas, Steene-Johannessen, Jostein, Stehle, Peter, Stein, Aryeh D, Stergiou, George S, Stokwiszewski, Jakub, Stoyanova, Ekaterina, Stratton, Gareth, Stronks, Karien, Sturua, Lela, Suarez-Ortegón, Milton F, Suebsamran, Phalakorn, Sulo, Gerhard, Sundström, Johan, Suriyawongpaisal, Paibul, Swinburn, Boyd A, Sylva, René Charles, Szponar, Lucjan, Tai, E Shyong, Tambalis, Konstantinos D, Tamosiunas, Abdonas, Tanabayev, Baimakhan, Tanrygulyyeva, Maya, Tarawneh, Mohammed Rasoul, Tarp, Jakob, Tarqui-Mamani, Carolina B, Taxová Braunerová, Radka, Te Velde, Saskia, Tebar, William R, Tell, Grethe S, Tello, Tania, Thankappan, KR, Theodoridis, Xenophon, Thirunavukkarasu, Sathish, Thomas, Nihal, Thrift, Amanda G, Tichá, Ľubica, Timmermans, Erik J, Tjandrarini, Dwi Hapsari, Tjonneland, Anne, Tolstrup, Janne S, Topbas, Murat, Torres-Collado, Laura, Touloumi, Giota, Traissac, Pierre, Triantafyllou, Areti, Trivedi, Atul, Tshepo, Lechaba, Tsintavis, Panagiotis, Tuitele, John, Tuliakova, Azaliia M, Tulloch-Reid, Marshall K, Tullu, Fikru, Tuomainen, Tomi-Pekka, Turley, Maria L, Tzala, Evangelia, Tzotzas, Themistoklis, Tzourio, Christophe, Ueda, Peter, Ugel, Eunice, Ukoli, Flora AM, Usupova, Zhamyila, Uusitalo, Hannu MT, Uysal, Nalan, Valdivia, Gonzalo, Valvi, Damaskini, van Dam, Rob M, van den Born, Bert-Jan, Van der Heyden, Johan, van der Schouw, Yvonne T, Van Lippevelde, Wendy, Van Minh, Hoang, Van Schoor, Natasja M, van Valkengoed, Irene GM, Vanderschueren, Dirk, Vanuzzo, Diego, Varela-Moreiras, Gregorio, Vargas, Luz Nayibe, Vasan, Senthil K, Vasques, Daniel G, Vega, Tomas, Velasquez-Melendez, Gustavo, Velika, Biruta, Verdot, Charlotte, Verloigne, Maïté, Veronesi, Giovanni, Verschuren, WM Monique, Verstraeten, Roosmarijn, Viet, Lucie, Vik, Frøydis N, Vilar, Monica, Villalpando, Salvador, Vioque, Jesus, Virtanen, Jyrki K, Visser, Marjolein, Viswanathan, Bharathi, Vladulescu, Mihaela, Völzke, Henry, Voutilainen, Ari, Vrijheid, Martine, Wade, Alisha N, Wan Bebakar, Wan Mohamad, Wan Mohamud, Wan Nazaimoon, Wanderley Júnior, Rildo de Souza, Wang, Chongjian, Wang, Huijun, Wang, Ningli, Wang, Qian, Wang, Xiangjun, Wang, Ya Xing, Wang, Ying-Wei, Wannamethee, S Goya, Wareham, Nicholas, Wartha, Olivia, Weber, Adelheid, Webster-Kerr, Karen, Wedderkopp, Niels, Weghuber, Daniel, Wei, Wenbin, Westbury, Leo, Whincup, Peter H, Wickramasinghe, Kremlin, Widhalm, Kurt, Widyahening, Indah S, Więcek, Andrzej, Wilks, Rainford J, Willeit, Karin, Willeit, Peter, Williams, Julianne, Wilsgaard, Tom, Wojtyniak, Bogdan, Wong-McClure, Roy A, Wong, Andrew, Wong, Emily B, Wu, Frederick C, Wyszyńska, Justyna, Xu, Haiquan, Xu, Liang, Yaacob, Nor Azwany, Yan, Li, Yan, Weili, Yang, Yang, Yépez García, Martha, Yoosefi, Moein, Yoshihara, Akihiro, Younger-Coleman, Novie O, Yu, Yu-Ling, Yu, Yunjiang, Yusoff, Ahmad Faudzi, Zafiropulos, Vassilis, Zainuddin, Ahmad A, Zamani, Farhad, Zambon, Sabina, Zampelas, Antonis, Zapata, Maria Elisa, Zaw, Ko Ko, Zdrojewski, Tomasz, Żegleń, Magdalena, Zejglicova, Kristyna, Zeljkovic Vrkic, Tajana, Zhang, Bing, Zhang, Zhen-Yu, Zhecheva, Yanitsa V, Zholdin, Bekbolat, Zimmet, Paul, Zins, Marie, Zuñiga Cisneros, Julio, Zuziak, Monika, and Ezzati, Majid

Published
2024
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36. Reconstructing the transcriptional regulatory network of probiotic L. reuteri is enabled by transcriptomics and machine learning

Author

Jonathan Josephs-Spaulding, Akanksha Rajput, Ying Hefner, Richard Szubin, Archana Balasubramanian, Gaoyuan Li, Daniel C. Zielinski, Leonie Jahn, Morten Sommer, Patrick Phaneuf, and Bernhard O. Palsson

Subjects
L. reuteri, probiotic, machine learning, transcriptome, systems biology, Microbiology, QR1-502
Abstract

ABSTRACTLimosilactobacillus reuteri, a probiotic microbe instrumental to human health and sustainable food production, adapts to diverse environmental shifts via dynamic gene expression. We applied the independent component analysis (ICA) to 117 RNA-seq data sets to decode its transcriptional regulatory network (TRN), identifying 35 distinct signals that modulate specific gene sets. Our findings indicate that the ICA provides a qualitative advancement and captures nuanced relationships within gene clusters that other methods may miss. This study uncovers the fundamental properties of L. reuteri’s TRN and deepens our understanding of its arginine metabolism and the co-regulation of riboflavin metabolism and fatty acid conversion. It also sheds light on conditions that regulate genes within a specific biosynthetic gene cluster and allows for the speculation of the potential role of isoprenoid biosynthesis in L. reuteri’s adaptive response to environmental changes. By integrating transcriptomics and machine learning, we provide a system-level understanding of L. reuteri’s response mechanism to environmental fluctuations, thus setting the stage for modeling the probiotic transcriptome for applications in microbial food production.IMPORTANCEWe have studied Limosilactobacillus reuteri, a beneficial probiotic microbe that plays a significant role in our health and production of sustainable foods, a type of foods that are nutritionally dense and healthier and have low-carbon emissions compared to traditional foods. Similar to how humans adapt their lifestyles to different environments, this microbe adjusts its behavior by modulating the expression of genes. We applied machine learning to analyze large-scale data sets on how these genes behave across diverse conditions. From this, we identified 35 unique patterns demonstrating how L. reuteri adjusts its genes based on 50 unique environmental conditions (such as various sugars, salts, microbial cocultures, human milk, and fruit juice). This research helps us understand better how L. reuteri functions, especially in processes like breaking down certain nutrients and adapting to stressful changes. More importantly, with our findings, we become closer to using this knowledge to improve how we produce more sustainable and healthier foods with the help of microbes.

Published
2024
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37. Machine learning analysis of RB-TnSeq fitness data predicts functional gene modules in Pseudomonas putida KT2440

Author

Andrew J. Borchert, Alissa C. Bleem, Hyun Gyu Lim, Kevin Rychel, Keven D. Dooley, Zoe A. Kellermyer, Tracy L. Hodges, Bernhard O. Palsson, and Gregg T. Beckham

Subjects
transposon insertion sequencing, RB-TnSeq, independent component analysis, machine learning, Pseudomonas putida, aromatic catabolism, Microbiology, QR1-502
Abstract

ABSTRACTThere is growing interest in engineering Pseudomonas putida KT2440 as a microbial chassis for the conversion of renewable and waste-based feedstocks, and metabolic engineering of P. putida relies on the understanding of the functional relationships between genes. In this work, independent component analysis (ICA) was applied to a compendium of existing fitness data from randomly barcoded transposon insertion sequencing (RB-TnSeq) of P. putida KT2440 grown in 179 unique experimental conditions. ICA identified 84 independent groups of genes, which we call fModules (“functional modules”), where gene members displayed shared functional influence in a specific cellular process. This machine learning-based approach both successfully recapitulated previously characterized functional relationships and established hitherto unknown associations between genes. Selected gene members from fModules for hydroxycinnamate metabolism and stress resistance, acetyl coenzyme A assimilation, and nitrogen metabolism were validated with engineered mutants of P. putida. Additionally, functional gene clusters from ICA of RB-TnSeq data sets were compared with regulatory gene clusters from prior ICA of RNAseq data sets to draw connections between gene regulation and function. Because ICA profiles the functional role of several distinct gene networks simultaneously, it can reduce the time required to annotate gene function relative to manual curation of RB-TnSeq data sets.IMPORTANCEThis study demonstrates a rapid, automated approach for elucidating functional modules within complex genetic networks. While Pseudomonas putida randomly barcoded transposon insertion sequencing data were used as a proof of concept, this approach is applicable to any organism with existing functional genomics data sets and may serve as a useful tool for many valuable applications, such as guiding metabolic engineering efforts in other microbes or understanding functional relationships between virulence-associated genes in pathogenic microbes. Furthermore, this work demonstrates that comparison of data obtained from independent component analysis of transcriptomics and gene fitness datasets can elucidate regulatory-functional relationships between genes, which may have utility in a variety of applications, such as metabolic modeling, strain engineering, or identification of antimicrobial drug targets.

Published
2024
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38. Systems biology approach to functionally assess the Clostridioides difficile pangenome reveals genetic diversity with discriminatory power

Author

Norsigian, Charles J, Danhof, Heather A, Brand, Colleen K, Midani, Firas S, Broddrick, Jared T, Savidge, Tor C, Britton, Robert A, Palsson, Bernhard O, Spinler, Jennifer K, and Monk, Jonathan M

Subjects
Genetics, Infectious Diseases, Human Genome, Infection, Clostridioides, Clostridioides difficile, Cross Infection, Genetic Variation, Humans, Systems Biology, systems biology, pangenomics, strain typing, metabolic profile
Abstract

Combatting Clostridioides difficile infections, a dominant cause of hospital-associated infections with incidence and resulting deaths increasing worldwide, is complicated by the frequent emergence of new virulent strains. Here, we employ whole-genome sequencing, high-throughput phenotypic screenings, and genome-scale models of metabolism to evaluate the genetic diversity of 451 strains of C. difficile. Constructing the C. difficile pangenome based on this set revealed 9,924 distinct gene clusters, of which 2,899 (29%) are defined as core, 2,968 (30%) are defined as unique, and the remaining 4,057 (41%) are defined as accessory. We develop a strain typing method, sequence typing by accessory genome (STAG), that identifies 176 genetically distinct groups of strains and allows for explicit interrogation of accessory gene content. Thirty-five strains representative of the overall set were experimentally profiled on 95 different nutrient sources, revealing 26 distinct growth profiles and unique nutrient preferences; 451 strain-specific genome scale models of metabolism were constructed, allowing us to computationally probe phenotypic diversity in 28,864 unique conditions. The models create a mechanistic link between the observed phenotypes and strain-specific genetic differences and exhibit an ability to correctly predict growth in 76% of measured cases. The typing and model predictions are used to identify and contextualize discriminating genetic features and phenotypes that may contribute to the emergence of new problematic strains.

Published
2022

39. Machine learning from Pseudomonas aeruginosa transcriptomes identifies independently modulated sets of genes associated with known transcriptional regulators

Author

Rajput, Akanksha, Tsunemoto, Hannah, Sastry, Anand V, Szubin, Richard, Rychel, Kevin, Sugie, Joseph, Pogliano, Joe, and Palsson, Bernhard O

Subjects
Genetics, Rare Diseases, Lung, Biotechnology, Cystic Fibrosis, Aetiology, 2.2 Factors relating to the physical environment, Bacterial Proteins, Escherichia coli, Gene Expression Regulation, Bacterial, Machine Learning, Pseudomonas aeruginosa, Transcription Factors, Transcriptome, Environmental Sciences, Biological Sciences, Information and Computing Sciences, Developmental Biology
Abstract

The transcriptional regulatory network (TRN) of Pseudomonas aeruginosa coordinates cellular processes in response to stimuli. We used 364 transcriptomes (281 publicly available + 83 in-house generated) to reconstruct the TRN of P. aeruginosa using independent component analysis. We identified 104 independently modulated sets of genes (iModulons) among which 81 reflect the effects of known transcriptional regulators. We identified iModulons that (i) play an important role in defining the genomic boundaries of biosynthetic gene clusters (BGCs), (ii) show increased expression of the BGCs and associated secretion systems in nutrient conditions that are important in cystic fibrosis, (iii) show the presence of a novel ribosomally synthesized and post-translationally modified peptide (RiPP) BGC which might have a role in P. aeruginosa virulence, (iv) exhibit interplay of amino acid metabolism regulation and central metabolism across different carbon sources and (v) clustered according to their activity changes to define iron and sulfur stimulons. Finally, we compared the identified iModulons of P. aeruginosa with those previously described in Escherichia coli to observe conserved regulons across two Gram-negative species. This comprehensive TRN framework encompasses the majority of the transcriptional regulatory machinery in P. aeruginosa, and thus should prove foundational for future research into its physiological functions.

Published
2022

40. Regulatory perturbations of ribosome allocation in bacteria reshape the growth proteome with a trade-off in adaptation capacity

Author

Hidalgo, David, Martínez-Ortiz, César A, Palsson, Bernhard O, Jiménez, José I, and Utrilla, José

Subjects
Microbiology, Biological Sciences, Genetics, Bacteriology, Bacterium, Cell biology, ribosomes, rRNA, Resource Allocation, fitness, RNA polymerase
Abstract

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. Mutants with only three 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 fast-growing rpoB mutants. Comparing these two different regulatory perturbations (rRNA promoters or rpoB mutations), we show how they reshape the proteome for growth with a concomitant fitness cost. The fast-growing mutants shared downregulation of hedging functions and upregulated growth functions. They 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.

Published
2022

41. Global pathogenomic analysis identifies known and candidate genetic antimicrobial resistance determinants in twelve species

Author

Jason C. Hyun, Jonathan M. Monk, Richard Szubin, Ying Hefner, and Bernhard O. Palsson

Subjects
Science
Abstract

Abstract Surveillance programs for managing antimicrobial resistance (AMR) have yielded thousands of genomes suited for data-driven mechanism discovery. We present a workflow integrating pangenomics, gene annotation, and machine learning to identify AMR genes at scale. When applied to 12 species, 27,155 genomes, and 69 drugs, we 1) find AMR gene transfer mostly confined within related species, with 925 genes in multiple species but just eight in multiple phylogenetic classes, 2) demonstrate that discovery-oriented support vector machines outperform contemporary methods at recovering known AMR genes, recovering 263 genes compared to 145 by Pyseer, and 3) identify 142 AMR gene candidates. Validation of two candidates in E. coli BW25113 reveals cases of conditional resistance: ΔcycA confers ciprofloxacin resistance in minimal media with D-serine, and frdD V111D confers ampicillin resistance in the presence of ampC by modifying the overlapping promoter. We expect this approach to be adaptable to other species and phenotypes.

Published
2023
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42. Functional annotation of enzyme-encoding genes using deep learning with transformer layers

Author

Gi Bae Kim, Ji Yeon Kim, Jong An Lee, Charles J. Norsigian, Bernhard O. Palsson, and Sang Yup Lee

Subjects
Science
Abstract

Abstract Functional annotation of open reading frames in microbial genomes remains substantially incomplete. Enzymes constitute the most prevalent functional gene class in microbial genomes and can be described by their specific catalytic functions using the Enzyme Commission (EC) number. Consequently, the ability to predict EC numbers could substantially reduce the number of un-annotated genes. Here we present a deep learning model, DeepECtransformer, which utilizes transformer layers as a neural network architecture to predict EC numbers. Using the extensively studied Escherichia coli K-12 MG1655 genome, DeepECtransformer predicted EC numbers for 464 un-annotated genes. We experimentally validated the enzymatic activities predicted for three proteins (YgfF, YciO, and YjdM). Further examination of the neural network’s reasoning process revealed that the trained neural network relies on functional motifs of enzymes to predict EC numbers. Thus, DeepECtransformer is a method that facilitates the functional annotation of uncharacterized genes.

Published
2023
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43. Worldwide trends in underweight and obesity from 1990 to 2022: a pooled analysis of 3663 population-representative studies with 222 million children, adolescents, and adults

Author

Phelps, Nowell H, Singleton, Rosie K, Zhou, Bin, Heap, Rachel A, Mishra, Anu, Bennett, James E, Paciorek, Christopher J, Lhoste, Victor PF, Carrillo-Larco, Rodrigo M, Stevens, Gretchen A, Rodriguez-Martinez, Andrea, Bixby, Honor, Bentham, James, Di Cesare, Mariachiara, Danaei, Goodarz, Rayner, Archie W, Barradas-Pires, Ana, Cowan, Melanie J, Savin, Stefan, Riley, Leanne M, Aguilar-Salinas, Carlos A, Baker, Jennifer L, Barkat, Amina, Bhutta, Zulfiqar A, Branca, Francesco, Caixeta, Roberta B, Cuschieri, Sarah, Farzadfar, Farshad, Ganapathy, Shubash, Ikeda, Nayu, Iotova, Violeta, Kengne, Andre P, Khang, Young-Ho, Laxmaiah, Avula, Lin, Hsien-Ho, Ma, Jun, Mbanya, Jean Claude N, Miranda, J Jaime, Pradeepa, Rajendra, Rodríguez-Artalejo, Fernando, Sorić, Maroje, Turley, Maria, Wang, Limin, Webster-Kerr, Karen, Aarestrup, Julie, Abarca-Gómez, Leandra, Abbasi-Kangevari, Mohsen, Abdeen, Ziad A, Abdrakhmanova, Shynar, Abdul Ghaffar, Suhaila, Abdul Rahim, Hanan F, Abdurrahmonova, Zulfiya, 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Alexander D, DeGennaro, Vincent Jr, Delisle, Hélène, Delpeuch, Francis, Demarest, Stefaan, Dennison, Elaine, Dereń, Katarzyna, Deschamps, Valérie, Devrishov, Ruslan D, Dhimal, Meghnath, Di Castelnuovo, Augusto, Dias-da-Costa, Juvenal Soares, Díaz-Sánchez, María Elena, Diaz, Alejandro, Díaz Fernández, Pedro, Díez Ripollés, María Pilar, Dika, Zivka, Djalalinia, Shirin, Djordjic, Visnja, Do, Ha TP, Dobson, Annette J, Dominguez, Liria, Donati, Maria Benedetta, Donfrancesco, Chiara, Dong, Guanghui, Dong, Yanhui, Donoso, Silvana P, Döring, Angela, Dorobantu, Maria, Dorosty, Ahmad Reza, Dörr, Marcus, Doua, Kouamelan, Dragano, Nico, Drygas, Wojciech, Du, Shufa, Duan, Jia Li, Duante, Charmaine A, Duboz, Priscilla, Duleva, Vesselka L, Dulskiene, Virginija, Dumith, Samuel C, Dushpanova, Anar, Dwyer, Terence, Dyussupova, Azhar, Dzerve, Vilnis, Dziankowska-Zaborszczyk, Elzbieta, Ebrahimi, Narges, Echeverría, Guadalupe, Eddie, Ricky, Eftekhar, Ebrahim, Efthymiou, Vasiliki, Egbagbe, Eruke E, 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Fottrell, Edward F, Fouad, Heba M, Francis, Damian K, Franco, Maria do Carmo, Fras, Zlatko, Fraser, Brooklyn, Frontera, Guillermo, Fuchs, Flavio D, Fuchs, Sandra C, Fujiati, Isti I, Fujita, Yuki, Fumihiko, Matsuda, Furdela, Viktoriya, Furusawa, Takuro, Gabriela, Stefan Adela, Gaciong, Zbigniew, Gafencu, Mihai, Galán Cuesta, Manuel, Galbarczyk, Andrzej, Galcheva, Sonya V, Galenkamp, Henrike, Galeone, Daniela, Galfo, Myriam, Galvano, Fabio, Gao, Jingli, Gao, Pei, Garcia-de-la-Hera, Manoli, García Mérida, María José, García Solano, Marta, Gareta, Dickman, Garnett, Sarah P, Gaspoz, Jean-Michel, Gasull, Magda, Gaya, Adroaldo Cesar Araujo, Gaya, Anelise Reis, Gazzinelli, Andrea, Gehring, Ulrike, Geiger, Harald, Geleijnse, Johanna M, George, Ronnie, Gerdts, Eva, Ghaderi, Ebrahim, Ghamari, Seyyed-Hadi, Ghanbari, Ali, Ghasemi, Erfan, Gheorghe-Fronea, Oana-Florentina, Gialluisi, Alessandro, Giampaoli, Simona, Gianfagna, Francesco, Gieger, Christian, Gill, Tiffany K, Giovannelli, Jonathan, 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2024
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44. Is the kinetome conserved?

Author

Palsson, Bernhard O and Yurkovich, James T

Subjects
Biochemistry and Cell Biology, Biological Sciences, Computer Simulation, Kinetics, Models, Biological, Other Biological Sciences, Bioinformatics, Biochemistry and cell biology
Abstract

Computational biologists have labored for decades to produce kinetic models to mechanistically explain complex metabolic phenomena. The estimation of numerical values for the large number of kinetic parameters required for constructing large-scale models has been a major challenge. This collection of kinetic constants has recently been termed the kinetome (Nilsson et al, 2017). In this Commentary, we discuss the recent advances in the field that suggest that the kinetome may be more conserved than expected. A conserved kinetome will accelerate the development of future kinetic models of integrated cellular functions and expand their scope and usability in many fields of biology and biomedicine.

Published
2022

45. proChIPdb: a chromatin immunoprecipitation database for prokaryotic organisms

Author

Decker, Katherine T, Gao, Ye, Rychel, Kevin, Bulushi, Tahani Al, Chauhan, Siddharth M, Kim, Donghyuk, Cho, Byung-Kwan, and Palsson, Bernhard O

Subjects
Biotechnology, Genetics, Human Genome, 1.1 Normal biological development and functioning, Underpinning research, Generic health relevance, Binding Sites, Chromatin, Chromatin Immunoprecipitation, Databases, Genetic, Genome, Prokaryotic Cells, Protein Binding, Transcription Factors, Environmental Sciences, Biological Sciences, Information and Computing Sciences, Developmental Biology
Abstract

The transcriptional regulatory network in prokaryotes controls global gene expression mostly through transcription factors (TFs), which are DNA-binding proteins. Chromatin immunoprecipitation (ChIP) with DNA sequencing methods can identify TF binding sites across the genome, providing a bottom-up, mechanistic understanding of how gene expression is regulated. ChIP provides indispensable evidence toward the goal of acquiring a comprehensive understanding of cellular adaptation and regulation, including condition-specificity. ChIP-derived data's importance and labor-intensiveness motivate its broad dissemination and reuse, which is currently an unmet need in the prokaryotic domain. To fill this gap, we present proChIPdb (prochipdb.org), an information-rich, interactive web database. This website collects public ChIP-seq/-exo data across several prokaryotes and presents them in dashboards that include curated binding sites, nucleotide-resolution genome viewers, and summary plots such as motif enrichment sequence logos. Users can search for TFs of interest or their target genes, download all data, dashboards, and visuals, and follow external links to understand regulons through biological databases and the literature. This initial release of proChIPdb covers diverse organisms, including most major TFs of Escherichia coli, and can be expanded to support regulon discovery across the prokaryotic domain.

Published
2022

46. System-Level Analysis of Transcriptional and Translational Regulatory Elements in Streptomyces griseus

Author

Hwang, Soonkyu, Lee, Namil, Choe, Donghui, Lee, Yongjae, Kim, Woori, Kim, Ji Hun, Kim, Gahyeon, Kim, Hyeseong, Ahn, Neung-Ho, Lee, Byoung-Hee, Palsson, Bernhard O, and Cho, Byung-Kwan

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Genetics, Underpinning research, 1.1 Normal biological development and functioning, Generic health relevance, Streptomyces, differential gene expression, regulatory elements, sigma factor, transcription factor, transcript 3 '-end positions, transcript 3′-end positions, Other Biological Sciences, Biomedical Engineering, Medical Biotechnology, Industrial biotechnology, Medical biotechnology, Biomedical engineering
Abstract

Bacteria belonging to Streptomyces have the ability to produce a wide range of secondary metabolites through a shift from primary to secondary metabolism regulated by complex networks activated after vegetative growth terminates. Despite considerable effort to understand the regulatory elements governing gene expression related to primary and secondary metabolism in Streptomyces, system-level information remains limited. In this study, we integrated four multi-omics datasets from Streptomyces griseus NBRC 13350: RNA-seq, ribosome profiling, dRNA-seq, and Term-Seq, to analyze the regulatory elements of transcription and translation of differentially expressed genes during cell growth. With the functional enrichment of gene expression in different growth phases, one sigma factor regulon and four transcription factor regulons governing differential gene transcription patterns were found. In addition, the regulatory elements of transcription termination and post-transcriptional processing at transcript 3'-end positions were elucidated, including their conserved motifs, stem-loop RNA structures, and non-terminal locations within the polycistronic operons, and the potential regulatory elements of translation initiation and elongation such as 5'-UTR length, RNA structures at ribosome-bound sites, and codon usage were investigated. This comprehensive genetic information provides a foundational genetic resource for strain engineering to enhance secondary metabolite production in Streptomyces.

Published
2022

47. A systems approach discovers the role and characteristics of seven LysR type transcription factors in Escherichia coli

Author

Rodionova, Irina A, Gao, Ye, Monk, Jonathan, Hefner, Ying, Wong, Nicholas, Szubin, Richard, Lim, Hyun Gyu, Rodionov, Dmitry A, Zhang, Zhongge, Saier, Milton H, and Palsson, Bernhard O

Subjects
Microbiology, Biological Sciences, Genetics, Aetiology, 2.2 Factors relating to the physical environment, Underpinning research, 1.1 Normal biological development and functioning, Infection, Bacterial Proteins, Escherichia coli, Escherichia coli K12, Escherichia coli Proteins, Gene Expression Regulation, Bacterial, Nitrogen, Operon, Systems Analysis, Transcription Factors
Abstract

Although Escherichia coli K-12 strains represent perhaps the best known model bacteria, we do not know the identity or functions of all of their transcription factors (TFs). It is now possible to systematically discover the physiological function of TFs in E. coli BW25113 using a set of synergistic methods; including ChIP-exo, growth phenotyping, conserved gene clustering, and transcriptome analysis. Among 47 LysR-type TFs (LTFs) found on the E. coli K-12 genome, many regulate nitrogen source utilization or amino acid metabolism. However, 19 LTFs remain unknown. In this study, we elucidated the regulation of seven of these 19 LTFs: YbdO, YbeF, YcaN, YbhD, YgfI, YiaU, YneJ. We show that: (1) YbdO (tentatively re-named CitR) regulation has an effect on bacterial growth at low pH with citrate supplementation. CitR is a repressor of the ybdNM operon and is implicated in the regulation of citrate lyase genes (citCDEFG); (2) YgfI (tentatively re-named DhfA) activates the dhaKLM operon that encodes the phosphotransferase system, DhfA is involved in formate, glycerol and dihydroxyacetone utilization; (3) YiaU (tentatively re-named LpsR) regulates the yiaT gene encoding an outer membrane protein, and waaPSBOJYZU operon is also important in determining cell density at the stationary phase and resistance to oxacillin microaerobically; (4) YneJ, re-named here as PtrR, directly regulates the expression of the succinate-semialdehyde dehydrogenase, Sad (also known as YneI), and is a predicted regulator of fnrS (a small RNA molecule). PtrR is important for bacterial growth in the presence of L-glutamate and putrescine as nitrogen/energy sources; and (5) YbhD and YcaN regulate adjacent y-genes on the genome. We have thus established the functions for four LTFs and identified the target genes for three LTFs.

Published
2022

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

Author

Anand, Amitesh, Patel, Arjun, Chen, Ke, Olson, Connor A, Phaneuf, Patrick V, Lamoureux, Cameron, Hefner, Ying, Szubin, Richard, Feist, Adam M, and Palsson, Bernhard O

Subjects
Biological Sciences, Industrial Biotechnology, Biotechnology, Lung, Affordable and Clean Energy, Electron Transport, Escherichia coli, Proteome, Respiratory System, Systems Biology
Abstract

The bacterial respiratory electron transport system (ETS) is branched to allow condition-specific modulation of energy metabolism. There is a detailed understanding of the structural and biochemical features of respiratory enzymes; however, a holistic examination of the system and its plasticity is lacking. Here we generate four strains of Escherichia coli harboring unbranched ETS that pump 1, 2, 3, or 4 proton(s) per electron and characterized them using a combination of synergistic methods (adaptive laboratory evolution, multi-omic analyses, and computation of proteome allocation). We report that: (a) all four ETS variants evolve to a similar optimized growth rate, and (b) the laboratory evolutions generate specific rewiring of major energy-generating pathways, coupled to the ETS, to optimize ATP production capability. We thus define an Aero-Type System (ATS), which is a generalization of the aerobic bioenergetics and is a metabolic systems biology description of respiration and its inherent plasticity.

Published
2022

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