Leveraging genome-scale metabolic models for human health applications.

Genome-scale metabolic modeling is a scalable and extensible computational method for analyzing and predicting biological function. With the ongoing improvements in computational methods and experimental capabilities, genome-scale metabolic models (GEMs) are demonstrating utility in addressing human health applications. The initial areas of highest impact are likely to be health applications where disease states involve metabolic changes. In this review, we focus on recent application of GEMs to studying cancer and the human microbiome by describing the enabling methodologies and outcomes of these studies. We conclude with proposing some areas of research that are likely to arise as a result of recent methodological advances.

Computational Modeling of the Human Microbiome.

The impact of microorganisms on human health has long been acknowledged and studied, but recent advances in research methodologies have enabled a new systems-level perspective on the collections of microorganisms associated with humans, the human microbiome. Large-scale collaborative efforts such as the NIH Human Microbiome Project have sought to kick-start research on the human microbiome by providing foundational information on microbial composition based upon specific sites across the human body. Here, we focus on the four main anatomical sites of the human microbiome: gut, oral, skin, and vaginal, and provide information on site-specific background, experimental data, and computational modeling. Each of the site-specific microbiomes has unique organisms and phenomena associated with them; there are also high-level commonalities. By providing an overview of different human microbiome sites, we hope to provide a perspective where detailed, site-specific research is needed to understand causal phenomena that impact human health, but there is equally a need for more generalized methodology improvements that would benefit all human microbiome research.

Recent advances on constraint-based models by integrating machine learning.

Research that meaningfully integrates constraint-based modeling with machine learning is at its infancy but holds much promise. Here, we consider where machine learning has been implemented within the constraint-based modeling reconstruction framework and highlight the need to develop approaches that can identify meaningful features from large-scale data and connect them to biological mechanisms to establish causality to connect genotype to phenotype. We motivate the construction of iterative integrative schemes where machine learning can fine-tune the input constraints in a constraint-based model or contrarily, constraint-based model simulation results are analyzed by machine learning and reconciled with experimental data. This can iteratively refine a constraint-based model until there is consistency between experimental data, machine learning results, and constraint-based model simulations.

Metabolic characterization of the chitinolytic bacterium Serratia marcescens using a genome-scale metabolic model.

BACKGROUND: Serratia marcescens is a chitinolytic bacterium that can potentially be used for consolidated bioprocessing to convert chitin to value-added chemicals. Currently, S. marcescens is poorly characterized and studies on intracellular metabolic and regulatory mechanisms would expedite development of bioprocessing applications. RESULTS: In this study, our goal was to characterize the metabolic profile of S. marcescens to provide insight for metabolic engineering applications and fundamental biological studies. Hereby, we constructed a constraint-based genome-scale metabolic model (iSR929) including 929 genes, 1185 reactions and 1164 metabolites based on genomic annotation of S. marcescens Db11. The model was tested by comparing model predictions with experimental data and analyzed to identify essential aspects of the metabolic network (e.g. 138 essential genes predicted). The model iSR929 was refined by integrating RNAseq data of S. marcescens growth on three different carbon sources (glucose, N-acetylglucosamine, and glycerol). Significant differences in TCA cycle utilization were found for growth on the different carbon substrates, For example, for growth on N-acetylglucosamine, S. marcescens exhibits high pentose phosphate pathway activity and nucleotide synthesis but low activity of the TCA cycle. CONCLUSIONS: Our results show that S. marcescens model iSR929 can provide reasonable predictions and can be constrained to fit with experimental values. Thus, our model may be used to guide strain designs for metabolic engineering to produce chemicals such as 2,3-butanediol, N-acetylneuraminic acid, and n-butanol using S. marcescens.

The vaginal microbiome and preterm birth.

The incidence of preterm birth exceeds 10% worldwide. There are significant disparities in the frequency of preterm birth among populations within countries, and women of African ancestry disproportionately bear the burden of risk in the United States. In the present study, we report a community resource that includes 'omics' data from approximately 12,000 samples as part of the integrative Human Microbiome Project. Longitudinal analyses of 16S ribosomal RNA, metagenomic, metatranscriptomic and cytokine profiles from 45 preterm and 90 term birth controls identified harbingers of preterm birth in this cohort of women predominantly of African ancestry. Women who delivered preterm exhibited significantly lower vaginal levels of Lactobacillus crispatus and higher levels of BVAB1, Sneathia amnii, TM7-H1, a group of Prevotella species and nine additional taxa. The first representative genomes of BVAB1 and TM7-H1 are described. Preterm-birth-associated taxa were correlated with proinflammatory cytokines in vaginal fluid. These findings highlight new opportunities for assessment of the risk of preterm birth.

Racioethnic diversity in the dynamics of the vaginal microbiome during pregnancy.

The microbiome of the female reproductive tract has implications for women's reproductive health. We examined the vaginal microbiome in two cohorts of women who experienced normal term births: a cross-sectionally sampled cohort of 613 pregnant and 1,969 non-pregnant women, focusing on 300 pregnant and 300 non-pregnant women of African, Hispanic or European ancestry case-matched for race, gestational age and household income; and a longitudinally sampled cohort of 90 pregnant women of African or non-African ancestry. In these women, the vaginal microbiome shifted during pregnancy toward Lactobacillus-dominated profiles at the expense of taxa often associated with vaginal dysbiosis. The shifts occurred early in pregnancy, followed predictable patterns, were associated with simplification of the metabolic capacity of the microbiome and were significant only in women of African or Hispanic ancestry. Both genomic and environmental factors are likely contributors to these trends, with socioeconomic status as a likely environmental influence.

Engineering microbial consortia by division of labor.

During microbial applications, metabolic burdens can lead to a significant drop in cell performance. Novel synthetic biology tools or multi-step bioprocessing (e.g., fermentation followed by chemical conversions) are therefore needed to avoid compromised biochemical productivity from over-burdened cells. A possible solution to address metabolic burden is Division of Labor (DoL) via natural and synthetic microbial consortia. In particular, consolidated bioprocesses and metabolic cooperation for detoxification or cross feeding (e.g., vitamin C fermentation) have shown numerous successes in industrial level applications. However, distributing a metabolic pathway among proper hosts remains an engineering conundrum due to several challenges: complex subpopulation dynamics/interactions with a short time-window for stable production, suboptimal cultivation of microbial communities, proliferation of cheaters or low-producers, intermediate metabolite dilution, transport barriers between species, and breaks in metabolite channeling through biosynthesis pathways. To develop stable consortia, optimization of strain inoculations, nutritional divergence and crossing feeding, evolution of mutualistic growth, cell immobilization, and biosensors may potentially be used to control cell populations. Another opportunity is direct integration of non-bioprocesses (e.g., microbial electrosynthesis) to power cell metabolism and improve carbon efficiency. Additionally, metabolic modeling and 13C-metabolic flux analysis of mixed culture metabolism and cross-feeding offers a computational approach to complement experimental research for improved consortia performance.

Cloning and characterization of a chitinase from Thermobifida fusca reveals Tfu_0580 as a thermostable and acidic endochitinase.

Being capable of hydrolyzing chitin, chitinases have various applications such as production of N-acetylchitooligosaccharides (COSs) and N-acetylglucosamine (GlcNAc), degrading chitin as a consolidated bioprocessing, and bio-control of fungal phytopathogens. Here, a putative chitinase in Thermobifida fusca, Tfu_0580, is characterized. Tfu_0580 was purified by homogeneity with a molecular weight of 44.9 kDa by sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) analysis. Tfu_0580 displayed a clear activity against colloidal chitin, which is comparable to a commercial Streptomyces griseus chitinase. Enzyme activities against p-nitrophenyl ß-D-N,N',N''-triacetylchitotriose (p-NP-(GlcNAc)3), N,N'-diacetyl-ß-D-chitobioside (p-NP-(GlcNAc)2) and p-nitrophenyl N-acetyl-ß-D-glucosaminide (p-NP-(GlcNAc)) showed that Tfu_0580 exhibited highest activity against p-NP-(GlcNAc)3. Further optimization of the enzyme activity conditions showed: 1) an optimum catalytic activity at pH 6.0 and 30 °C; 2) activity over broad pH (4.8-7.5) and temperature (20-55 °C); 3) stimulation of activity by the metallic ions Ca2+ and Mn2+.

Design and modularized optimization of one-step production of N-acetylneuraminic acid from chitin in Serratia marcescens.

Chitin is an abundant, biorenewable, nitrogen-rich biomass feedstock that can be potentially developed for biochemical production; however, efficient bioprocesses have yet to be established. Here, we demonstrate an engineered bioprocess to produce N-acetylneuraminic acid (Neu5Ac) directly from chitin using the chitinolytic bacterium, Serratia marcescens by selecting and characterizing promoters, characterization of heterologous enzyme activity, and optimization of pathway fluxes. By generating RNASeq data for S. marcescens growth in different carbon-limited conditions (glucose, N-acetylglucosamine, and glycerol), 12 promoters with varying strength were identified and characterized to implement for transcriptional control. Neu5Ac production was initially engineered into S. marcescens through heterologous expression of N-acetylglucosamine 2-epimerase (slr1975) and N-acetylneuraminic acid aldolase (nanA). The activity of both genes was characterized in vitro for kinetics and in vivo expression using promoters identified in this study. Optimization of Neu5Ac production was accomplished by balancing pathways fluxes through promoter swapping and replacing the reversible nanA with the irreversible gene neuB. The optimized recombinant strain P T5 -slr1975-P rplJ -neuB was able to produce 0.48 g/L Neu5Ac from 20 g/L N-acetylglucosamine, and 0.30 g/L Neu5Ac from 5 g/L crystal chitin. These results represent the first demonstration of direct conversion of crystal chitin to N-acetylneuraminic acid.

Study of in vitro transcriptional binding effects and noise using constitutive promoters combined with UP element sequences in Escherichia coli.

BACKGROUND: UP elements (upstream element) are DNA sequences upstream of a promoter that interact with the α-subunit of RNA polymerase (RNAP) and can affect transcription by altering the binding RNAP to DNA. However, details of UP element and binding affinity effects on transcriptional strength are unclear. RESULTS: Here, we investigated the effects of UP element sequences on gene transcription, binding affinity, and gene expression noise. Addition of UP elements resulted in increased gene expression (maximum 95.7-fold increase) and reduced gene expression noise (8.51-fold reduction). Half UP element sequences at the proximal subsite has little effect on transcriptional strength despite increasing binding affinity by 2.28-fold. In vitro binding assays were used to determine dissociation constants (Kd) and in the in vitro system, the full range of gene expression occurs in a small range of dissociation constants (25 nM < Kd < 45 nM) indicating that transcriptional strength is highly sensitive to small changes in binding affinity. CONCLUSIONS: These results demonstrate the utility of UP elements and provide mechanistic insight into the functional relationship between binding affinity and transcription. Given the centrality of gene expression via transcription to biology, additional insight into transcriptional mechanisms can foster both fundamental and applied research. In particular, knowledge of the DNA sequence-specific effects on expression strength can aid in promoter engineering for different organisms and for metabolic engineering to balance pathway fluxes.

Metabolic Profile of the Cellulolytic Industrial Actinomycete Thermobifida fusca.

Actinomycetes have a long history of being the source of numerous valuable natural products and medicinals. To expedite product discovery and optimization of biochemical production, high-throughput technologies can now be used to screen the library of compounds present (or produced) at a given time in an organism. This not only facilitates chemical product screening, but also provides a comprehensive methodology to the study cellular metabolic networks to inform cellular engineering. Here, we present some of the first metabolomic data of the industrial cellulolytic actinomycete Thermobifida fusca generated using LC-MS/MS. The underlying objective of conducting global metabolite profiling was to gain better insight on the innate capabilities of T. fusca, with a long-term goal of facilitating T. fusca-based bioprocesses. The T. fusca metabolome was characterized for growth on two cellulose-relevant carbon sources, cellobiose and Avicel. Furthermore, the comprehensive list of measured metabolites was computationally integrated into a metabolic model of T. fusca, to study metabolic shifts in the network flux associated with carbohydrate and amino acid metabolism.

Challenges and Advances for Genetic Engineering of Non-model Bacteria and Uses in Consolidated Bioprocessing.

Metabolic diversity in microorganisms can provide the basis for creating novel biochemical products. However, most metabolic engineering projects utilize a handful of established model organisms and thus, a challenge for harnessing the potential of novel microbial functions is the ability to either heterologously express novel genes or directly utilize non-model organisms. Genetic manipulation of non-model microorganisms is still challenging due to organism-specific nuances that hinder universal molecular genetic tools and translatable knowledge of intracellular biochemical pathways and regulatory mechanisms. However, in the past several years, unprecedented progress has been made in synthetic biology, molecular genetics tools development, applications of omics data techniques, and computational tools that can aid in developing non-model hosts in a systematic manner. In this review, we focus on concerns and approaches related to working with non-model microorganisms including developing molecular genetics tools such as shuttle vectors, selectable markers, and expression systems. In addition, we will discuss: (1) current techniques in controlling gene expression (transcriptional/translational level), (2) advances in site-specific genome engineering tools [homologous recombination (HR) and clustered regularly interspaced short palindromic repeats (CRISPR)], and (3) advances in genome-scale metabolic models (GSMMs) in guiding design of non-model species. Application of these principles to metabolic engineering strategies for consolidated bioprocessing (CBP) will be discussed along with some brief comments on foreseeable future prospects.

Study of ChiR function in Serratia marcescens and its application for improving 2,3-butanediol from crystal chitin.

Microbial utilization of chitin, a potential renewable biomass feedstock, is being pursued as a means of developing novel consolidated bioprocessing for the production of chemicals. Serratia marcescens is a gram-negative bacterium that is known for its chitinolytic capability and as a native 2,3-butanediol producer. In S. marcescens, ChiR has been suggested to be a positive regulator of chitinase production. In this study, we aim to understand the effect of ChiR in regulating nine chitinase-related genes in S. marcescens Db11 and demonstrate manipulation of chiR as a useful and efficient genetic target to enhance chitin utilization. First, a chiR overexpression (chiROE) strain and a chiR deletion (ΔchiR) strain were generated and characterized in terms of cellular growth, chitinase activity, and total secreted protein. Compared to the wild-type Db11 strain, the S. marcescens chiROE strain showed an increase in chitinase activity (2.14- to 6.31-fold increase). Increased transcriptional expression of chitinase-related genes was measured using real-time PCR, showing 2.12- to 10.93-fold increases. The S. marcescens ΔchiR strain showed decreases in chitinase activity (4.5- to 25-fold decrease), confirming ChiR's role as a positive regulator of chitinase expression. Finally, chiR overexpression was investigated as a means of increasing biochemical production (2,3-butanediol) from crystal chitin. The chiROE strain produced 1.13 ± 0.08 g/L 2,3-butanediol from 2% crystal chitin, a 2.83-fold improvement from the wild-type strain, indicating ChiR is an important and useful genetic engineering target for enhancing chitin utilization in S. marcescens.

Single sample resolution of rare microbial dark matter in a marine invertebrate metagenome.

Direct, untargeted sequencing of environmental samples (metagenomics) and de novo genome assembly enable the study of uncultured and phylogenetically divergent organisms. However, separating individual genomes from a mixed community has often relied on the differential-coverage analysis of multiple, deeply sequenced samples. In the metagenomic investigation of the marine bryozoan Bugula neritina, we uncovered seven bacterial genomes associated with a single B. neritina individual that appeared to be transient associates, two of which were unique to one individual and undetectable using certain "universal" 16S rRNA primers and probes. We recovered high quality genome assemblies for several rare instances of "microbial dark matter," or phylogenetically divergent bacteria lacking genomes in reference databases, from a single tissue sample that was not subjected to any physical or chemical pre-treatment. One of these rare, divergent organisms has a small (593 kbp), poorly annotated genome with low GC content (20.9%) and a 16S rRNA gene with just 65% sequence similarity to the closest reference sequence. Our findings illustrate the importance of sampling strategy and de novo assembly of metagenomic reads to understand the extent and function of bacterial biodiversity.

Lack of Overt Genome Reduction in the Bryostatin-Producing Bryozoan Symbiont "Candidatus Endobugula sertula".

The uncultured bacterial symbiont "Candidatus Endobugula sertula" is known to produce cytotoxic compounds called bryostatins, which protect the larvae of its host, Bugula neritina The symbiont has never been successfully cultured, and it was thought that its genome might be significantly reduced. Here, we took a shotgun metagenomics and metatranscriptomics approach to assemble and characterize the genome of "Ca Endobugula sertula." We found that it had specific metabolic deficiencies in the biosynthesis of certain amino acids but few other signs of genome degradation, such as small size, abundant pseudogenes, and low coding density. We also identified homologs to genes associated with insect pathogenesis in other gammaproteobacteria, and these genes may be involved in host-symbiont interactions and vertical transmission. Metatranscriptomics revealed that these genes were highly expressed in a reproductive host, along with bry genes for the biosynthesis of bryostatins. We identified two new putative bry genes fragmented from the main bry operon, accounting for previously missing enzymatic functions in the pathway. We also determined that a gene previously assigned to the pathway, bryS, is not expressed in reproductive tissue, suggesting that it is not involved in the production of bryostatins. Our findings suggest that "Ca Endobugula sertula" may be able to live outside the host if its metabolic deficiencies are alleviated by medium components, which is consistent with recent findings that it may be possible for "Ca Endobugula sertula" to be transmitted horizontally. IMPORTANCE: The bryostatins are potent protein kinase C activators that have been evaluated in clinical trials for a number of indications, including cancer and Alzheimer's disease. There is, therefore, considerable interest in securing a renewable supply of these compounds, which is currently only possible through aquaculture of Bugula neritina and total chemical synthesis. However, these approaches are labor-intensive and low-yielding and thus preclude the use of bryostatins as a viable therapeutic agent. Our genome assembly and transcriptome analysis for "Ca Endobugula sertula" shed light on the metabolism of this symbiont, potentially aiding isolation and culturing efforts. Our identification of additional bry genes may also facilitate efforts to express the complete pathway heterologously.

Metabolic Burden: Cornerstones in Synthetic Biology and Metabolic Engineering Applications.

Engineering cell metabolism for bioproduction not only consumes building blocks and energy molecules (e.g., ATP) but also triggers energetic inefficiency inside the cell. The metabolic burdens on microbial workhorses lead to undesirable physiological changes, placing hidden constraints on host productivity. We discuss cell physiological responses to metabolic burdens, as well as strategies to identify and resolve the carbon and energy burden problems, including metabolic balancing, enhancing respiration, dynamic regulatory systems, chromosomal engineering, decoupling cell growth with production phases, and co-utilization of nutrient resources. To design robust strains with high chances of success in industrial settings, novel genome-scale models (GSMs), (13)C-metabolic flux analysis (MFA), and machine-learning approaches are needed for weighting, standardizing, and predicting metabolic costs.

Systematic analysis of intracellular mechanisms of propanol production in the engineered Thermobifida fusca B6 strain.

Thermobifida fusca is a moderately thermophilic actinobacterium naturally capable of utilizing lignocellulosic biomass. The B6 strain of T. fusca was previously engineered to produce 1-propanol directly on lignocellulosic biomass by expressing a bifunctional butyraldehyde/alcohol dehydrogenase (adhE2). To characterize the intracellular mechanisms related to the accumulation of 1-propanol, the engineered B6 and wild-type (WT) strains were systematically compared by analysis of the transcriptome and intracellular metabolome during exponential growth on glucose, cellobiose, and Avicel. Of the 18 known cellulases in T. fusca, 10 cellulase genes were transcriptionally expressed on all three substrates along with three hemicellulases. Transcriptomic analysis of cellodextrin and cellulose transport revealed that Tfu_0936 (multiple sugar transport system permease) was the key enzyme regulating the uptake of sugars in T. fusca. For both WT and B6 strains, it was found that growth in oxygen-limited conditions resulted in a blocked tricarboxylic acid (TCA) cycle caused by repressed expression of Tfu_1925 (aconitate hydratase). Further, the transcriptome suggested a pathway for synthesizing succinyl-CoA: oxaloacetate to malate (by malate dehydrogenase), malate to fumarate (by fumarate hydratase), and fumarate to succinate (by succinate dehydrogenase/fumarate reductase) which was ultimately converted to succinyl-CoA by succinyl-CoA synthetase. Both the transcriptome and the intracellular metabolome confirmed that 1-propanol was produced through succinyl-CoA, L-methylmalonyl-CoA, D-methylmalonyl-CoA, and propionyl-CoA in the B6 strain.

Genome-scale resources for Thermoanaerobacterium saccharolyticum.

BACKGROUND: Thermoanaerobacterium saccharolyticum is a hemicellulose-degrading thermophilic anaerobe that was previously engineered to produce ethanol at high yield. A major project was undertaken to develop this organism into an industrial biocatalyst, but the lack of genome information and resources were recognized early on as a key limitation. RESULTS: Here we present a set of genome-scale resources to enable the systems level investigation and development of this potentially important industrial organism. Resources include a complete genome sequence for strain JW/SL-YS485, a genome-scale reconstruction of metabolism, tiled microarray data showing transcription units, mRNA expression data from 71 different growth conditions or timepoints and GC/MS-based metabolite analysis data from 42 different conditions or timepoints. Growth conditions include hemicellulose hydrolysate, the inhibitors HMF, furfural, diamide, and ethanol, as well as high levels of cellulose, xylose, cellobiose or maltodextrin. The genome consists of a 2.7 Mbp chromosome and a 110 Kbp megaplasmid. An active prophage was also detected, and the expression levels of CRISPR genes were observed to increase in association with those of the phage. Hemicellulose hydrolysate elicited a response of carbohydrate transport and catabolism genes, as well as poorly characterized genes suggesting a redox challenge. In some conditions, a time series of combined transcription and metabolite measurements were made to allow careful study of microbial physiology under process conditions. As a demonstration of the potential utility of the metabolic reconstruction, the OptKnock algorithm was used to predict a set of gene knockouts that maximize growth-coupled ethanol production. The predictions validated intuitive strain designs and matched previous experimental results. CONCLUSION: These data will be a useful asset for efforts to develop T. saccharolyticum for efficient industrial production of biofuels. The resources presented herein may also be useful on a comparative basis for development of other lignocellulose degrading microbes, such as Clostridium thermocellum.