Using Genome-scale Models to Predict Biological Capabilities.

Constraint-based reconstruction and analysis (COBRA) methods at the genome scale have been under development since the first whole-genome sequences appeared in the mid-1990s. A few years ago, this approach began to demonstrate the ability to predict a range of cellular functions, including cellular growth capabilities on various substrates and the effect of gene knockouts at the genome scale. Thus, much interest has developed in understanding and applying these methods to areas such as metabolic engineering, antibiotic design, and organismal and enzyme evolution. This Primer will get you started.

Wdr5 mediates self-renewal and reprogramming via the embryonic stem cell core transcriptional network.

The embryonic stem (ES) cell transcriptional and chromatin-modifying networks are critical for self-renewal maintenance. However, it remains unclear whether these networks functionally interact and, if so, what factors mediate such interactions. Here, we show that WD repeat domain 5 (Wdr5), a core member of the mammalian Trithorax (trxG) complex, positively correlates with the undifferentiated state and is a regulator of ES cell self-renewal. We demonstrate that Wdr5, an "effector" of H3K4 methylation, interacts with the pluripotency transcription factor Oct4. Genome-wide protein localization and transcriptome analyses demonstrate overlapping gene regulatory functions between Oct4 and Wdr5. The Oct4-Sox2-Nanog circuitry and trxG cooperate in activating transcription of key self-renewal regulators, and furthermore, Wdr5 expression is required for the efficient formation of induced pluripotent stem (iPS) cells. We propose an integrated model of transcriptional and epigenetic control, mediated by select trxG members, for the maintenance of ES cell self-renewal and somatic cell reprogramming.

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

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.

A curated collection of Klebsiella metabolic models reveals variable substrate usage and gene essentiality.

The Klebsiella pneumoniae species complex (KpSC) is a set of seven Klebsiella taxa that are found in a variety of niches and are an important cause of opportunistic health care-associated infections in humans. Because of increasing rates of multi-drug resistance within the KpSC, there is a growing interest in better understanding the biology and metabolism of these organisms to inform novel control strategies. We collated 37 sequenced KpSC isolates isolated from a variety of niches, representing all seven taxa. We generated strain-specific genome-scale metabolic models (GEMs) for all 37 isolates and simulated growth phenotypes on 511 distinct carbon, nitrogen, sulfur, and phosphorus substrates. Models were curated and their accuracy was assessed using matched phenotypic growth data for 94 substrates (median accuracy of 96%). We explored species-specific growth capabilities and examined the impact of all possible single gene deletions using growth simulations in 145 core carbon substrates. These analyses revealed multiple strain-specific differences, within and between species, and highlight the importance of selecting a diverse range of strains when exploring KpSC metabolism. This diverse set of highly accurate GEMs could be used to inform novel drug design, enhance genomic analyses, and identify novel virulence and resistance determinants. We envisage that these 37 curated strain-specific GEMs, covering all seven taxa of the KpSC, provide a valuable resource to the Klebsiella research community.

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

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.

Predicting the primary infection source of Escherichia coli bacteremia using virulence-associated genes.

PURPOSE: To investigate the role of E. coli virulence-associated genes (VAGs) in predicting urinary tract infection (UTI) as the source of bacteremia in two distinct hospital populations, one with a large general catchment area and one dominated by referrals. METHODS: E. coli bacteremias identified at Department of Clinical Microbiology (DCM), Hvidovre Hospital and DCM, Rigshospitalet in the Capital Region of Denmark from October to December 2018. Using whole genome sequencing (WGS), we identified 358 VAGs from 224 E. coli bacteremia. For predictive analysis, VAGs were paired with clinical source of UTI from local bacteremia databases. RESULTS: VAGs strongly predicting of UTI as primary infection source of bacteremia were primarily found within the pap gene family. papX (PPV 96%, sensitivity 54%) and papGII (PPV 93%, sensitivity 56%) were found highly predictive, but showed low sensitivities. The strength of VAG predictions of UTI as source varied significantly between the two hospital populations. VAGs had weaker predictions in the tertiary referral center (Rigshospitalet), a disparity likely stemming from differences in patient population and department specialization. CONCLUSION: WGS data was used to predict the primary source of E. coli bacteremia and is an attempt on a new and different type of infection source identification. Genomic data showed potential to be utilized to predict the primary source of infection; however, discrepancy between the best performing profile of VAGs between acute care hospitals and tertiary hospitals makes it difficult to implement in clinical practice.

Cerebrospinal fluid and plasma metabolomics of acute endurance exercise.

Metabolomics has emerged as a powerful new tool in precision medicine. No studies have yet been published on the metabolomic changes in cerebrospinal fluid (CSF) produced by acute endurance exercise. CSF and plasma were collected from 19 young active adults (13 males and 6 females) before and 60 min after a 90-min monitored outdoor run. The median age, BMI, and VO2 max of subjects was 25 years (IQR 22-31), 23.2 kg/m2 (IQR 21.7-24.5), and 47 ml/kg/min (IQR 38-51), respectively. Targeted, broad-spectrum metabolomics was performed by liquid chromatography, tandem mass spectrometry (LC-MS/MS). In the CSF, purines and pyrimidines accounted for 32% of the metabolic impact after acute endurance exercise. Branch chain amino acids, amino acid neurotransmitters, fatty acid oxidation, phospholipids, and Krebs cycle metabolites traceable to mitochondrial function accounted for another 52% of the changes. A narrow but important channel of metabolic communication was identified between the brain and body by correlation network analysis. By comparing these results to previous work in experimental animal models, we found that over 80% of the changes in the CSF correlated with a cascade of mitochondrial and metabolic changes produced by ATP signaling. ATP is released as a co-neurotransmitter and neuromodulator at every synapse studied to date. By regulating brain mitochondrial function, ATP release was identified as an early step in the kinetic cascade of layered benefits produced by endurance exercise.

Metabolomic and exposomic biomarkers of risk of future neurodevelopmental delay in human milk.

BACKGROUND: The chemical composition of human milk has long-lasting effects on brain development. We examined the prognostic value of the human milk metabolome and exposome in children with the risk of neurodevelopmental delay (NDD). METHODS: This retrospective cohort study included 82 mother-infant pairs (40 male and 42 female infants). A total of 59 milk samples were from mothers with typically developing children and 23 samples were from mothers of children at risk. Milk samples were collected before 9 months of age (4.6 ± 2.5 months, mean ± SD). Neurocognitive development was assessed by maternal report at 14.2 ± 3.1 months using the Ages and Stages Questionnaires-2. RESULTS: Metabolome and exposome profiling identified 453 metabolites and 61 environmental chemicals in milk. Machine learning tools identified changes in deoxysphingolipids, phospholipids, glycosphingolipids, plasmalogens, and acylcarnitines in the milk of mothers with children at risk for future delay. A predictive classifier had a diagnostic accuracy of 0.81 (95% CI: 0.66-0.96) for females and 0.79 (95% CI: 0.62-0.94) for males. CONCLUSIONS: Once validated in larger studies, the chemical analysis of human milk might be added as an option in well-baby checks to help identify children at risk of NDD before the first symptoms appear. IMPACT: Maternal milk for infants sampled before 9 months of age contained sex-specific differences in deoxysphingolipids, sphingomyelins, plasmalogens, phospholipids, and acylcarnitines that predicted the risk of neurodevelopmental delay at 14.2 months of age. Once validated, this early biosignature in human milk might be incorporated into well-baby checks and help to identify infants at risk so early interventions might be instituted before the first symptoms appear.

Impact of insertion sequences on convergent evolution of Shigella species.

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

High-quality genome-scale metabolic network reconstruction of probiotic bacterium Escherichia coli Nissle 1917.

BACKGROUND: Escherichia coli Nissle 1917 (EcN) is a probiotic bacterium used to treat various gastrointestinal diseases. EcN is increasingly being used as a chassis for the engineering of advanced microbiome therapeutics. To aid in future engineering efforts, our aim was to construct an updated metabolic model of EcN with extended secondary metabolite representation. RESULTS: An updated high-quality genome-scale metabolic model of EcN, iHM1533, was developed based on comparison with 55 E. coli/Shigella reference GEMs and manual curation, including expanded secondary metabolite pathways (enterobactin, salmochelins, aerobactin, yersiniabactin, and colibactin). The model was validated and improved using phenotype microarray data, resulting in an 82.3% accuracy in predicting growth phenotypes on various nutrition sources. Flux variability analysis with previously published 13C fluxomics data validated prediction of the internal central carbon fluxes. A standardised test suite called Memote assessed the quality of iHM1533 to have an overall score of 89%. The model was applied by using constraint-based flux analysis to predict targets for optimisation of secondary metabolite production. Modelling predicted design targets from across amino acid metabolism, carbon metabolism, and other subsystems that are common or unique for influencing the production of various secondary metabolites. CONCLUSION: iHM1533 represents a well-annotated metabolic model of EcN with extended secondary metabolite representation. Phenotype characterisation and the iHM1533 model provide a better understanding of the metabolic capabilities of EcN and will help future metabolic engineering efforts.

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

BACKGROUND: With 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. RESULTS: Applied 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. CONCLUSIONS: These 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.

Transmission of Klebsiella strains and plasmids within and between grey-headed flying fox colonies.

The grey-headed flying fox (Pteropus poliocephalus) is an endemic Australian fruit bat, known to carry zoonotic pathogens. We recently showed they harbour bacterial pathogen Klebsiella pneumoniae and closely related species in the K. pneumoniae species complex (KpSC); however, the dynamics of KpSC transmission and gene flow within flying fox colonies are poorly understood. High-resolution genome comparisons of 39 KpSC isolates from grey-headed flying foxes identified five putative strain transmission clusters (four intra- and one inter-colony). The instance of inter-colony strain transmission of K. africana was found between two flying fox populations within flying distance, indicating either direct or indirect transmission through a common food/water source. All 11 plasmids identified within the KpSC isolates showed 73% coverage (mean) and ≥95% identity to human-associated KpSC plasmids, indicating gene flow between human clinical and grey-headed flying fox isolates. Along with strain transmission, inter-species horizontal plasmid transmission between K. pneumoniae and Klebsiella africana was also identified within a flying fox colony. Finally, genome-scale metabolic models were generated to predict and compare substrate usage to previously published KpSC models, from human and environmental sources. These models indicated no distinction on the basis of metabolic capabilities. Instead, metabolic capabilities were consistent with population structure and ST/lineage.

Genome-scale metabolic modeling reveals key features of a minimal gene set.

Mesoplasma florum, a fast-growing near-minimal organism, is a compelling model to explore rational genome designs. Using sequence and structural homology, the set of metabolic functions its genome encodes was identified, allowing the reconstruction of a metabolic network representing ˜ 30% of its protein-coding genes. Growth medium simplification enabled substrate uptake and product secretion rate quantification which, along with experimental biomass composition, were integrated as species-specific constraints to produce the functional iJL208 genome-scale model (GEM) of metabolism. Genome-wide expression and essentiality datasets as well as growth data on various carbohydrates were used to validate and refine iJL208. Discrepancies between model predictions and observations were mechanistically explained using protein structures and network analysis. iJL208 was also used to propose an in silico reduced genome. Comparing this prediction to the minimal cell JCVI-syn3.0 and its parent JCVI-syn1.0 revealed key features of a minimal gene set. iJL208 is a stepping-stone toward model-driven whole-genome engineering.

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

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

Cellular responses to reactive oxygen species are predicted from molecular mechanisms.

Catalysis using iron-sulfur clusters and transition metals can be traced back to the last universal common ancestor. The damage to metalloproteins caused by reactive oxygen species (ROS) can prevent cell growth and survival when unmanaged, thus eliciting an essential stress response that is universal and fundamental in biology. Here we develop a computable multiscale description of the ROS stress response in Escherichia coli, called OxidizeME. We use OxidizeME to explain four key responses to oxidative stress: 1) ROS-induced auxotrophy for branched-chain, aromatic, and sulfurous amino acids; 2) nutrient-dependent sensitivity of growth rate to ROS; 3) ROS-specific differential gene expression separate from global growth-associated differential expression; and 4) coordinated expression of iron-sulfur cluster (ISC) and sulfur assimilation (SUF) systems for iron-sulfur cluster biosynthesis. These results show that we can now develop fundamental and quantitative genotype-phenotype relationships for stress responses on a genome-wide basis.

Machine learning with random subspace ensembles identifies antimicrobial resistance determinants from pan-genomes of three pathogens.

The evolution of antimicrobial resistance (AMR) poses a persistent threat to global public health. Sequencing efforts have already yielded genome sequences for thousands of resistant microbial isolates and require robust computational tools to systematically elucidate the genetic basis for AMR. Here, we present a generalizable machine learning workflow for identifying genetic features driving AMR based on constructing reference strain-agnostic pan-genomes and training random subspace ensembles (RSEs). This workflow was applied to the resistance profiles of 14 antimicrobials across three urgent threat pathogens encompassing 288 Staphylococcus aureus, 456 Pseudomonas aeruginosa, and 1588 Escherichia coli genomes. We find that feature selection by RSE detects known AMR associations more reliably than common statistical tests and previous ensemble approaches, identifying a total of 45 known AMR-conferring genes and alleles across the three organisms, as well as 25 candidate associations backed by domain-level annotations. Furthermore, we find that results from the RSE approach are consistent with existing understanding of fluoroquinolone (FQ) resistance due to mutations in the main drug targets, gyrA and parC, in all three organisms, and suggest the mutational landscape of those genes with respect to FQ resistance is simple. As larger datasets become available, we expect this approach to more reliably predict AMR determinants for a wider range of microbial pathogens.

Thermodynamic favorability and pathway yield as evolutionary tradeoffs in biosynthetic pathway choice.

The structure of the metabolic network contains myriad organism-specific variations across the tree of life, but the selection basis for pathway choices in different organisms is not well understood. Here, we examined the metabolic capabilities with respect to cofactor use and pathway thermodynamics of all sequenced organisms in the Kyoto Encyclopedia of Genes and Genomes Database. We found that (i) many biomass precursors have alternate synthesis routes that vary substantially in thermodynamic favorability and energy cost, creating tradeoffs that may be subject to selection pressure; (ii) alternative pathways in amino acid synthesis are characteristically distinguished by the use of biosynthetically unnecessary acyl-CoA cleavage; (iii) distinct choices preferring thermodynamic-favorable or cofactor-use-efficient pathways exist widely among organisms; (iv) cofactor-use-efficient pathways tend to have a greater yield advantage under anaerobic conditions specifically; and (v) lysine biosynthesis in particular exhibits temperature-dependent thermodynamics and corresponding differential pathway choice by thermophiles. These findings present a view on the evolution of metabolic network structure that highlights a key role of pathway thermodynamics and cofactor use in determining organism pathway choices.

Adaptations of Escherichia coli strains to oxidative stress are reflected in properties of their structural proteomes.

BACKGROUND: The reconstruction of metabolic networks and the three-dimensional coverage of protein structures have reached the genome-scale in the widely studied Escherichia coli K-12 MG1655 strain. The combination of the two leads to the formation of a structural systems biology framework, which we have used to analyze differences between the reactive oxygen species (ROS) sensitivity of the proteomes of sequenced strains of E. coli. As proteins are one of the main targets of oxidative damage, understanding how the genetic changes of different strains of a species relates to its oxidative environment can reveal hypotheses as to why these variations arise and suggest directions of future experimental work. RESULTS: Creating a reference structural proteome for E. coli allows us to comprehensively map genetic changes in 1764 different strains to their locations on 4118 3D protein structures. We use metabolic modeling to predict basal ROS production levels (ROStype) for 695 of these strains, finding that strains with both higher and lower basal levels tend to enrich their proteomes with antioxidative properties, and speculate as to why that is. We computationally assess a strain's sensitivity to an oxidative environment, based on known chemical mechanisms of oxidative damage to protein groups, defined by their localization and functionality. Two general groups - metalloproteins and periplasmic proteins - show enrichment of their antioxidative properties between the 695 strains with a predicted ROStype as well as 116 strains with an assigned pathotype. Specifically, proteins that a) utilize a molybdenum ion as a cofactor and b) are involved in the biogenesis of fimbriae show intriguing protective properties to resist oxidative damage. Overall, these findings indicate that a strain's sensitivity to oxidative damage can be elucidated from the structural proteome, though future experimental work is needed to validate our model assumptions and findings. CONCLUSION: We thus demonstrate that structural systems biology enables a proteome-wide, computational assessment of changes to atomic-level physicochemical properties and of oxidative damage mechanisms for multiple strains in a species. This integrative approach opens new avenues to study adaptation to a particular environment based on physiological properties predicted from sequence alone.

Distinct Subpopulations of Intravalvular Methicillin-Resistant Staphylococcus aureus with Variable Susceptibility to Daptomycin in Tricuspid Valve Endocarditis.

We present a case of endocarditis wherein organisms cultured from different valve leaflets yielded different daptomycin susceptibilities from each other and from organisms obtained from peripheral blood culture. Genomic analyses showed mutations in mprF, purR, and agrA Pharmacokinetic simulations showed consistent activity of daptomycin plus beta-lactam against all subpopulations. This represents an opportunity to understand S. aureus evolution and fitness in vivo on daptomycin therapy and the role of beta-lactams to prevent the selection of daptomycin-resistant subpopulations.

High-quality genome-scale metabolic modelling of Pseudomonas putida highlights its broad metabolic capabilities.

Genome-scale reconstructions of metabolism are computational species-specific knowledge bases able to compute systemic metabolic properties. We present a comprehensive and validated reconstruction of the biotechnologically relevant bacterium Pseudomonas putida KT2440 that greatly expands computable predictions of its metabolic states. The reconstruction represents a significant reactome expansion over available reconstructed bacterial metabolic networks. Specifically, iJN1462 (i) incorporates several hundred additional genes and associated reactions resulting in new predictive capabilities, including new nutrients supporting growth; (ii) was validated by in vivo growth screens that included previously untested carbon (48) and nitrogen (41) sources; (iii) yielded gene essentiality predictions showing large accuracy when compared with a knock-out library and Bar-seq data; and (iv) allowed mapping of its network to 82 P. putida sequenced strains revealing functional core that reflect the large metabolic versatility of this species, including aromatic compounds derived from lignin. Thus, this study provides a thoroughly updated metabolic reconstruction and new computable phenotypes for P. putida, which can be leveraged as a first step toward understanding the pan metabolic capabilities of Pseudomonas.