Contribution of network connectivity in determining the relationship between gene expression and metabolite concentration changes

Enhancement of production of glycoalkaloids by elicitors along with characterization of gene expression of pathways in Solanum xanthocarpum

Solanum xanthocarpum fruits are used in the treatment of cough, fever, and heart disorders. It possesses antipyretic, hypotensive, antiasthmatic, aphrodisiac and antianaphylactic properties. In the present study, 24 elicitors (both biotic and abiotic) were used to enhance the production of glycoalkaloids in cell cultures of S. xanthocarpum. Four concentrations of elicitors were added into the MS culture medium. The maximum accumulation (5.56-fold higher than control) of demissidine was induced by sodium nitroprusside at 50 mM concentration whereas the highest growth of cell biomass (4.51-fold higher than control) stimulated by systemin at 30 mM concentration. A total of 17 genes of biosynthetic pathways of glycoalkaloids were characterized from the cells of S. xanthocarpum. The greater accumulation of demissidine was confirmed with the expression analysis of 11 key biosynthetic pathway enzymes e.g., acetoacetic-CoA thiolase, 3- hydroxy 3-methyl glutaryl synthase, β-hydroxy β-methylglutaryl CoA reductase, mevalonate kinase, farnesyl diphosphate synthase, squalene synthase, squalene epoxidase, squalene-2,3- epoxide cyclase, cycloartenol synthase, UDP-glucose: solanidine glucosyltransferase and UDP-rhamnose: solanidine rhamno-galactosyl transferase. The maximum expression levels of UDP-rhamnose: solanidine rhamno-galactosyl transferase gene was recorded in this study.

Mass Spectrometry-Based Multiomics Identifies Metabolic Signatures of Sarcopenia in Rhesus Monkey Skeletal Muscle

Sarcopenia is a progressive disorder characterized by age-related loss of skeletal muscle mass and function. Although significant progress has been made over the years to identify the molecular determinants of sarcopenia, the precise mechanisms underlying the age-related loss of contractile function remains unclear. Advances in "omics" technologies, including mass spectrometry-based proteomic and metabolomic analyses, offer great opportunities to better understand sarcopenia. Herein, we performed mass spectrometry-based analyses of the vastus lateralis from young, middle-aged, and older rhesus monkeys to identify molecular signatures of sarcopenia. In our proteomic analysis, we identified proteins that change with age, including those involved in adenosine triphosphate and adenosine monophosphate metabolism as well as fatty acid beta oxidation. In our untargeted metabolomic analysis, we identified metabolites that changed with age largely related to energy metabolism including fatty acid beta oxidation. Pathway analysis of age-responsive proteins and metabolites revealed changes in muscle structure and contraction as well as lipid, carbohydrate, and purine metabolism. Together, this study discovers new metabolic signatures and offers new insights into the molecular mechanisms underlying sarcopenia for the evaluation and monitoring of a therapeutic treatment of sarcopenia.

Metabolomics, Transcriptome and Single-Cell RNA Sequencing Analysis of the Metabolic Heterogeneity between Oral Cancer Stem Cells and Differentiated Cancer Cells

Understanding the distinct metabolic characteristics of cancer stem cells (CSC) may allow us to better cope with the clinical challenges associated with them. In this study, OSCC cell lines (CAL27 and HSC3) and multicellular tumor spheroid (MCTS) models were used to generate CSC-like cells. Quasi-targeted metabolomics and RNA sequencing were used to explore altered metabolites and metabolism-related genes. Pathview was used to display the metabolites and transcriptome data in a KEGG pathway. The single-cell RNA sequencing data of six patients with oral cancer were analyzed to characterize in vivo CSC metabolism. The results showed that 19 metabolites (phosphoethanolamine, carbamoylphosphate, etc.) were upregulated and 109 metabolites (2-aminooctanoic acid, 7-ketocholesterol, etc.) were downregulated in both MCTS cells. Integration pathway analysis revealed altered activity in energy production (glycolysis, citric cycle, fatty acid oxidation), macromolecular synthesis (purine/pyrimidine metabolism, glycerophospholipids metabolism) and redox control (glutathione metabolism). Single-cell RNA sequencing analysis confirmed altered glycolysis, glutathione and glycerophospholipid metabolism in in vivo CSC. We concluded that CSCs are metabolically inactive compared with differentiated cancer cells. Thus, oral CSCs may resist current metabolic-related drugs. Our result may be helpful in developing better therapeutic strategies against CSC.

Temperature differences between sites lead to altered phenylpropanoid metabolism in a varietal dependent manner

Elevated temperature has already caused a significant loss of wine growing areas and resulted in inferior fruit quality, particularly in arid and semi-arid regions. The existence of broad genetic diversity in V. vinifera is key in adapting viticulture to climate change; however, a lack of understanding on the variability in berry metabolic response to climate change remains a major challenge to build ad-hoc strategies for quality fruit production. In the present study, we examined the impact of a consistent temperature difference between two vineyards on polyphenol metabolism in the berries of 20 red V. vinifera cultivars across three consecutive seasons (2017-2019). The results emphasize a varietal specific response in the content of several phenylpropanoid metabolites; the interaction factor between the variety and the vineyard location was also found significant. Higher seasonal temperatures were coupled with lower flavonol and anthocyanin contents, but such reductions were not related with the level of expression of phenylpropanoid related genes. Hierarchical clustering analyses of the metabolic data revealed varieties with a location specific response, exceptional among them was Tempranillo, suggesting a greater susceptibility to temperature of this cultivar. In conclusion, our results indicate that the extensive genetic capacity of V. vinifera bears a significant potential to withstand temperature increase associated with climate change.

Proteins and metabolites fingerprints of gestational diabetes mellitus forming protein-metabolite interactomes are its potential biomarkers

Gestational diabetes mellitus (GDM) is a consequence of glucose intolerance with an inadequate production of insulin that happens during pregnancy and leads to adverse health consequences for both mother and fetus. GDM patients are at higher risk for preeclampsia, and developing diabetes mellitus type 2 in later life, while the child born to GDM mothers are more prone to macrosomia, and hypoglycemia. The universally accepted diagnostic criteria for GDM are lacking, therefore there is a need for a diagnosis of GDM that can identify GDM at its early stage (first trimester). We have reviewed the literature on proteins and metabolites fingerprints of GDM. Further, we have performed protein-protein, metabolite-metabolite, and protein-metabolite interaction network studies on GDM proteins and metabolites fingerprints. Notably, some proteins and metabolites fingerprints are forming strong interaction networks at high confidence scores. Therefore, we have suggested that those proteins and metabolites that are forming protein-metabolite interactomes are the potential biomarkers of GDM. The protein-metabolite biomarkers interactome may help in a deep understanding of the prognosis, pathogenesis of GDM, and also detection of GDM. The protein-metabolites interactome may be further applied in planning future therapeutic strategies to promote long-term health benefits in GDM mothers and their children.

Integrating transcriptomics, metabolomics, and GWAS helps reveal molecular mechanisms for metabolite levels and disease risk

Transcriptomics data have been integrated with genome-wide association studies (GWASs) to help understand disease/trait molecular mechanisms. The utility of metabolomics, integrated with transcriptomics and disease GWASs, to understand molecular mechanisms for metabolite levels or diseases has not been thoroughly evaluated. We performed probabilistic transcriptome-wide association and locus-level colocalization analyses to integrate transcriptomics results for 49 tissues in 706 individuals from the GTEx project, metabolomics results for 1,391 plasma metabolites in 6,136 Finnish men from the METSIM study, and GWAS results for 2,861 disease traits in 260,405 Finnish individuals from the FinnGen study. We found that genetic variants that regulate metabolite levels were more likely to influence gene expression and disease risk compared to the ones that do not. Integrating transcriptomics with metabolomics results prioritized 397 genes for 521 metabolites, including 496 previously identified gene-metabolite pairs with strong functional connections and suggested 33.3% of such gene-metabolite pairs shared the same causal variants with genetic associations of gene expression. Integrating transcriptomics and metabolomics individually with FinnGen GWAS results identified 1,597 genes for 790 disease traits. Integrating transcriptomics and metabolomics jointly with FinnGen GWAS results helped pinpoint metabolic pathways from genes to diseases. We identified putative causal effects of UGT1A1/UGT1A4 expression on gallbladder disorders through regulating plasma (E,E)-bilirubin levels, of SLC22A5 expression on nasal polyps and plasma carnitine levels through distinct pathways, and of LIPC expression on age-related macular degeneration through glycerophospholipid metabolic pathways. Our study highlights the power of integrating multiple sets of molecular traits and GWAS results to deepen understanding of disease pathophysiology.

Dynamic proteome allocation regulates the profile of interaction of auxotrophic bacterial consortia

Microbial ecosystems are composed of multiple species in constant metabolic exchange. A pervasive interaction in microbial communities is metabolic cross-feeding and occurs when the metabolic burden of producing costly metabolites is distributed between community members, in some cases for the benefit of all interacting partners. In particular, amino acid auxotrophies generate obligate metabolic inter-dependencies in mixed populations and have been shown to produce a dynamic profile of interaction that depends upon nutrient availability. However, identifying the key components that determine the pair-wise interaction profile remains a challenging problem, partly because metabolic exchange has consequences on multiple levels, from allocating proteomic resources at a cellular level to modulating the structure, function and stability of microbial communities. To evaluate how ppGpp-mediated resource allocation drives the population-level profile of interaction, here we postulate a multi-scale mathematical model that incorporates dynamics of proteome partition into a population dynamics model. We compare our computational results with experimental data obtained from co-cultures of auxotrophic Escherichia coli K12 strains under a range of amino acid concentrations and population structures. We conclude by arguing that the stringent response promotes cooperation by inhibiting the growth of fast-growing strains and promoting the synthesis of metabolites essential for other community members.

Transcriptomics and Metabolomics Reveal the Possible Mechanism by which 1-MCP Regulates the Postharvest Senescence of Zizania latifolia

To understand the mechanism governing the postharvest senescence of Zizania latifolia, and the regulatory mechanism induced by 1-methylcyclopropene (1-MCP) during storage at 25°C, physiobiochemical and conjoint analyses of the transcriptome and metabolome were performed. The results indicated that 1-MCP treatment engendered changes in the expression of genes and metabolites during the postharvest storage of Z. latifolia. The 1-MCP treatment maintained a good visual appearance, preserved the cell structure, and membrane integrity of Z. latifolia by keeping the expression of membranes-related lipolytic enzymes (and related genes) low and the amount of phosphatidylethanolamine high. Compared to the control group, 1-MCP treatment enhanced the activities of antioxidant enzymes, resulting in a decrease of reactive oxygen species (ROS) and malondialdehyde (MDA) contents, and thus inhibition of oxidative damage and loss of membrane integrity. In addition, 1-MCP treatment retarded the senescence of Z. latifolia by down-regulating the expression of ethylene biosynthesis-related genes and promoting up-regulation of brassinosteroid insensitive 1 (BRI1) kinase inhibitor 1, calmodulin (CaM), glutathione reductase, jasmonate amino acid synthase, and mitogen-activated protein kinase (MAPK)-related genes. Moreover, 1-MCP retarded Z. latifolia senescence by inducing the activity of ATP-biosynthesis related genes and metabolites. Our findings should facilitate future research on the postharvest storage of Z. latifolia, and could help delay senescence and prolong the storage time for commercial applications.

Multiomic Metabolic Enrichment Network Analysis Reveals Metabolite-Protein Physical Interaction Subnetworks Altered in Cancer

Metabolism is recognized as an important driver of cancer progression and other complex diseases, but global metabolite profiling remains a challenge. Protein expression profiling is often a poor proxy since existing pathway enrichment models provide an incomplete mapping between the proteome and metabolism. To overcome these gaps, we introduce multiomic metabolic enrichment network analysis (MOMENTA), an integrative multiomic data analysis framework for more accurately deducing metabolic pathway changes from proteomics data alone in a gene set analysis context by leveraging protein interaction networks to extend annotated metabolic models. We apply MOMENTA to proteomic data from diverse cancer cell lines and human tumors to demonstrate its utility at revealing variation in metabolic pathway activity across cancer types, which we verify using independent metabolomics measurements. The novel metabolic networks we uncover in breast cancer and other tumors are linked to clinical outcomes, underscoring the pathophysiological relevance of the findings.

Stable Isotopes for Tracing Cardiac Metabolism in Diseases

Although metabolic remodeling during cardiovascular diseases has been well-recognized for decades, the recent development of analytical platforms and mathematical tools has driven the emergence of assessing cardiac metabolism using tracers. Metabolism is a critical component of cellular functions and adaptation to stress. The pathogenesis of cardiovascular disease involves metabolic adaptation to maintain cardiac contractile function even in advanced disease stages. Stable-isotope tracer measurements are a powerful tool for measuring flux distributions at the whole organism level and assessing metabolic changes at a systems level in vivo. The goal of this review is to summarize techniques and concepts for in vivo or ex vivo stable isotope labeling in cardiovascular research, to highlight mathematical concepts and their limitations, to describe analytical methods at the tissue and single-cell level, and to discuss opportunities to leverage metabolic models to address important mechanistic questions relevant to all patients with cardiovascular disease.

Age and sex dependent changes of free circulating blood metabolite and lipid abundances, correlations and ratios

In this study, we investigated how the concentrations, pairwise correlations and ratios of 202 free circulating blood metabolites and lipids vary with age in a panel of n = 1 882 participants with an age range from 48 to 94 years. We report a statistically significant sex-dependent association with age of a panel of metabolites and lipids involving, in women, linoleic acid, α-linoleic acid, and carnitine, and, in men, monoacylglycerols and lysophosphatidylcholines. Evaluating the association of correlations among metabolites and/or lipids with age, we found that phosphatidylcholines correlations tend to have a positive trend associated with age in women, and monoacylglycerols and lysophosphatidylcholines correlations tend to have a negative trend associated with age in men. The association of ratio between molecular features with age reveals that decanoyl-l-carnitine/lysophosphatidylcholine ratio in women “decrease” with age, while l-carnitine/phosphatidylcholine and l-acetylcarnitine/phosphatidylcholine ratios in men “increase” with age. These results suggest an age-dependent remodeling of lipid metabolism that induces changes in cell membrane bilayer composition and cell cycle mechanisms. Furthermore, we conclude that lipidome is directly involved in this age-dependent differentiation. Our results demonstrate that, using a comprehensive approach focused on the changes of concentrations and relationships of blood metabolites and lipids, as expressed by their correlations and ratios, it is possible to obtain relevant information about metabolic dynamics associated with age.

On the optimality of the enzyme-substrate relationship in bacteria

Much recent progress has been made to understand the impact of proteome allocation on bacterial growth; much less is known about the relationship between the abundances of the enzymes and their substrates, which jointly determine metabolic fluxes. Here, we report a correlation between the concentrations of enzymes and their substrates in Escherichia coli. We suggest this relationship to be a consequence of optimal resource allocation, subject to an overall constraint on the biomass density: For a cellular reaction network composed of effectively irreversible reactions, maximal reaction flux is achieved when the dry mass allocated to each substrate is equal to the dry mass of the unsaturated (or “free”) enzymes waiting to consume it. Calculations based on this optimality principle successfully predict the quantitative relationship between the observed enzyme and metabolite abundances, parameterized only by molecular masses and enzyme–substrate dissociation constants (K_m). The corresponding organizing principle provides a fundamental rationale for cellular investment into different types of molecules, which may aid in the design of more efficient synthetic cellular systems.

This study shows that in E. coli, the cellular mass of each metabolite approximately equals the combined mass of the free enzymes waiting to consume it; this simple relationship arises from the optimal utilization of cellular dry mass, and quantitatively describes available experimental data.

Secondary Metabolites of Pseudomonas aeruginosa LV Strain Decrease Asian Soybean Rust Severity in Experimentally Infected Plants

Asian Soybean Rust (ASR), a disease caused by Phakopsora pachyrhizi, causing yield losses up to 90%. The control is based on the fungicides which may generate resistant fungi. The activation of the plant defense system, should help on ASR control. In this study, secondary metabolites of Pseudomonas aeruginosa LV strain were applied on spore germination and the expression of defense genes in infected soybean plants. The F4A fraction and the pure metabolites were used. In vitro, 10 µg mL⁻¹ of F4A reduced spore germination by 54%, while 100 µg mL⁻¹ completely inhibited. Overexpression of phenylalanine ammonia lyase (PAL), O-methyltransferase (OMT) and pathogenesis related protein-2 (PR-2; glucanases) defense-related genes were detected 24 and 72 h after soybean sprouts were sprayed with an organocopper antimicrobial compound (OAC). Under greenhouse conditions, the best control was observed in plants treated with 60 µg mL⁻¹ of PCA, which reduced ASR severity and lesion frequency by 75% and 43%, respectively. Plants sprayed with 2 and 20 µg mL⁻¹ of F4A also decreased severity (41%) and lesion frequency (32%). The significant reduction in spore germination ASR in plant suggested that the strain of these metabolites are effective against P. pachyrhizi, and they can be used for ASR control.

Combining QTL Mapping and Transcriptomics to Decipher the Genetic Architecture of Phenolic Compounds Metabolism in the Conifer White Spruce

Conifer forests worldwide are becoming increasingly vulnerable to the effects of climate change. Although the production of phenolic compounds (PCs) has been shown to be modulated by biotic and abiotic stresses, the genetic basis underlying the variation in their constitutive production level remains poorly documented in conifers. We used QTL mapping and RNA-Seq to explore the complex polygenic network underlying the constitutive production of PCs in a white spruce (Picea glauca) full-sib family for 2 years. QTL detection was performed for nine PCs and differentially expressed genes (DEGs) were identified between individuals with high and low PC contents for five PCs exhibiting stable QTLs across time. A total of 17 QTLs were detected for eight metabolites, including one major QTL explaining up to 91.3% of the neolignan-2 variance. The RNA-Seq analysis highlighted 50 DEGs associated with phenylpropanoid biosynthesis, several key transcription factors, and a subset of 137 genes showing opposite expression patterns in individuals with high levels of the flavonoids gallocatechin and taxifolin glucoside. A total of 19 DEGs co-localized with QTLs. Our findings represent a significant step toward resolving the genomic architecture of PC production in spruce and facilitate the functional characterization of genes and transcriptional networks responsible for differences in constitutive production of PCs in conifers.

Cyanobacterial blooms in wastewater treatment facilities: Significance and emerging monitoring strategies

Municipal wastewater treatment facilities (WWTFs) are prone to the proliferation of cyanobacterial species which thrive in stable, nutrient-rich environments. Dense cyanobacterial blooms frequently disrupt treatment processes and the supply of recycled water due to their production of extracellular polymeric substances, which hinder microfiltration, and toxins, which pose a health risk to end-users. A variety of methods are employed by water utilities for the identification and monitoring of cyanobacteria and their toxins in WWTFs, including microscopy, flow cytometry, ELISA, chemoanalytical methods, and more recently, molecular methods. Here we review the literature on the occurrence and significance of cyanobacterial blooms in WWTFs and discuss the pros and cons of the various strategies for monitoring these potentially hazardous events. Particular focus is directed towards next-generation metagenomic sequencing technologies for the development of site-specific cyanobacterial bloom management strategies. Long-term multi-omic observations will enable the identification of indicator species and the development of site-specific bloom dynamics models for the mitigation and management of cyanobacterial blooms in WWTFs. While emerging metagenomic tools could potentially provide deep insight into the diversity and flux of problematic cyanobacterial species in these systems, they should be considered a complement to, rather than a replacement of, quantitative chemoanalytical approaches.

Recent advances in constraint and machine learning-based metabolic modeling by leveraging stoichiometric balances, thermodynamic feasibility and kinetic law formalisms

Understanding the governing principles behind organisms' metabolism and growth underpins their effective deployment as bioproduction chassis. A central objective of metabolic modeling is predicting how metabolism and growth are affected by both external environmental factors and internal genotypic perturbations. The fundamental concepts of reaction stoichiometry, thermodynamics, and mass action kinetics have emerged as the foundational principles of many modeling frameworks designed to describe how and why organisms allocate resources towards both growth and bioproduction. This review focuses on the latest algorithmic advancements that have integrated these foundational principles into increasingly sophisticated quantitative frameworks.

Evaluation of Single Sample Network Inference Methods for Metabolomics-Based Systems Medicine

Networks and network analyses are fundamental tools of systems biology. Networks are built by inferring pair-wise relationships among biological entities from a large number of samples such that subject-specific information is lost. The possibility of constructing these sample (individual)-specific networks from single molecular profiles might offer new insights in systems and personalized medicine and as a consequence is attracting more and more research interest. In this study, we evaluated and compared LIONESS (Linear Interpolation to Obtain Network Estimates for Single Samples) and ssPCC (single sample network based on Pearson correlation) in the metabolomics context of metabolite–metabolite association networks. We illustrated and explored the characteristics of these two methods on (i) simulated data, (ii) data generated from a dynamic metabolic model to simulate real-life observed metabolite concentration profiles, and (iii) 22 metabolomic data sets and (iv) we applied single sample network inference to a study case pertaining to the investigation of necrotizing soft tissue infections to show how these methods can be applied in metabolomics. We also proposed some adaptations of the methods that can be used for data exploration. Overall, despite some limitations, we found single sample networks to be a promising tool for the analysis of metabolomics data.

Metabolic Responses to Arsenite Exposure Regulated through Histidine Kinases PhoR and AioS in Agrobacterium tumefaciens 5A

Arsenite (As^III) oxidation is a microbially-catalyzed transformation that directly impacts arsenic toxicity, bioaccumulation, and bioavailability in environmental systems. The genes for As^III oxidation (aio) encode a periplasmic As^III sensor AioX, transmembrane histidine kinase AioS, and cognate regulatory partner AioR, which control expression of the As^III oxidase AioBA. The aio genes are under ultimate control of the phosphate stress response via histidine kinase PhoR. To better understand the cell-wide impacts exerted by these key histidine kinases, we employed ¹H nuclear magnetic resonance (¹H NMR) and liquid chromatography-coupled mass spectrometry (LC-MS) metabolomics to characterize the metabolic profiles of ΔphoR and ΔaioS mutants of Agrobacterium tumefaciens 5A during As^III oxidation. The data reveals a smaller group of metabolites impacted by the ΔaioS mutation, including hypoxanthine and various maltose derivatives, while a larger impact is observed for the ΔphoR mutation, influencing betaine, glutamate, and different sugars. The metabolomics data were integrated with previously published transcriptomics analyses to detail pathways perturbed during As^III oxidation and those modulated by PhoR and/or AioS. The results highlight considerable disruptions in central carbon metabolism in the ΔphoR mutant. These data provide a detailed map of the metabolic impacts of As^III, PhoR, and/or AioS, and inform current paradigms concerning arsenic-microbe interactions and nutrient cycling in contaminated environments.

Growth and alkaloid production along with expression profiles of biosynthetic pathway genes in two contrasting morphotypes of prickly and prickleless Solanum viarum Dunal

Growth and production kinetics of three important glycoalkaloids viz. α-solanine, solanidine, and solasodine in two contrasting prickly and prickleless plants of Solanum viarum Dunal were evaluated under in vitro conditions. The prickleless plants showed improved accumulation of total glycoalkaloid content [7.11 and 6.85 mg g^-1 dry weight (DW)] and growth (GI = 11.08 and 19.26) after 45 and 50 days of culture cycle, respectively. For higher biomass (91.18 g l^-1) as well as glycoalkaloid (52.56 mg l^-1) recovery, the prickleless plants served as highly profitable platform. All the three studied glycoalkaloids were identified and quantified by mass spectrometry and HPLC. All the three studied glycoalkaloids accumulated in age-dependent manner. The presence of two constituents, i.e., solasodine and solanidine mainly contributed for higher accumulation of total glycoalkaloid content in the prickleless plants. However, the synthesis of α-solanine was highly age specific and could be detected after 4 to 5 weeks of culture cycle in both prickle containing as well as prickleless plants of S. viarum. The higher accumulation of glycoalkaloids in prickleless plants was also supported with the expression analysis of six key pathway enzymes viz. mevalonate kinase (MVK), 3-hydroxy-3-methyl-glutaryl coenzyme A reductase (HMGR), farnesyl diphosphate synthase (FPS), UDP-galactose/solanidine galactosyltransferase (SGT1), UDP-glucose/solanidine glucosyltransferase (SGT2), and cytochrome P450 monooxygenase (CYP). The results indicated that the plants harvested after 45 and 50 days of culture cycle accumulated maximum bioactive in-demand glycoalkaloids in the prickly and prickleless plants of S. viarum Dunal, respectively.

The developmental transcriptome for Lytechinus variegatus exhibits temporally punctuated gene expression changes

Embryonic development is arguably the most complex process an organism undergoes during its lifetime, and understanding this complexity is best approached with a systems-level perspective. The sea urchin has become a highly valuable model organism for understanding developmental specification, morphogenesis, and evolution. As a non-chordate deuterostome, the sea urchin occupies an important evolutionary niche between protostomes and vertebrates. Lytechinus variegatus (Lv) is an Atlantic species that has been well studied, and which has provided important insights into signal transduction, patterning, and morphogenetic changes during embryonic and larval development. The Pacific species, Strongylocentrotus purpuratus (Sp), is another well-studied sea urchin, particularly for gene regulatory networks (GRNs) and cis-regulatory analyses. A well-annotated genome and transcriptome for Sp are available, but similar resources have not been developed for Lv. Here, we provide an analysis of the Lv transcriptome at 11 timepoints during embryonic and larval development. Temporal analysis suggests that the gene regulatory networks that underlie specification are well-conserved among sea urchin species. We show that the major transitions in variation of embryonic transcription divide the developmental time series into four distinct, temporally sequential phases. Our work shows that sea urchin development occurs via sequential intervals of relatively stable gene expression states that are punctuated by abrupt transitions.

Identification of key genes involved in the biosynthesis of triterpenic acids in the mint family

Triterpenic acids (TAs), a large group of natural compounds with diverse biological activity, are produced by several plant taxa. Betulinic, oleanolic, and ursolic acids are the most medicinally important TAs and are mainly found in plants of the mint family. Metabolic engineering is strongly dependent on identifying the key genes in biosynthetic pathways toward the products of interest. In this study, gene expression tracking was performed by transcriptome mining, co-expression network analysis, and tissue-specific metabolite-expression analysis in order to identify possible key genes involved in TAs biosynthetic pathways. To this end, taxa-specific degenerate primers of six important genes were designed using an effective method based on the MEME algorithm in a phylogenetically related group of sequences and successfully applied in three members of the Lamiaceae (Rosmarinus officinalis, Salvia officinalis, and Thymus persicus). Based on the results of in-depth data analysis, genes encoding squalene epoxidase and oxido squalene cyclases are proposed as targets for boosting triterpene production. The results emphasize the importance of identifying key genes in triterpene biosynthesis, which may facilitate genetic manipulation or overexpression of target genes.

Integration of Metabolomic and Other Omics Data in Population-Based Study Designs: An Epidemiological Perspective

It is not controversial that study design considerations and challenges must be addressed when investigating the linkage between single omic measurements and human phenotypes. It follows that such considerations are just as critical, if not more so, in the context of multi-omic studies. In this review, we discuss (1) epidemiologic principles of study design, including selection of biospecimen source(s) and the implications of the timing of sample collection, in the context of a multi-omic investigation, and (2) the strengths and limitations of various techniques of data integration across multi-omic data types that may arise in population-based studies utilizing metabolomic data.

Human Systems Biology and Metabolic Modelling: A Review-From Disease Metabolism to Precision Medicine

In cell and molecular biology, metabolism is the only system that can be fully simulated at genome scale. Metabolic systems biology offers powerful abstraction tools to simulate all known metabolic reactions in a cell, therefore providing a snapshot that is close to its observable phenotype. In this review, we cover the 15 years of human metabolic modelling. We show that, although the past five years have not experienced large improvements in the size of the gene and metabolite sets in human metabolic models, their accuracy is rapidly increasing. We also describe how condition-, tissue-, and patient-specific metabolic models shed light on cell-specific changes occurring in the metabolic network, therefore predicting biomarkers of disease metabolism. We finally discuss current challenges and future promising directions for this research field, including machine/deep learning and precision medicine. In the omics era, profiling patients and biological processes from a multiomic point of view is becoming more common and less expensive. Starting from multiomic data collected from patients and N-of-1 trials where individual patients constitute different case studies, methods for model-building and data integration are being used to generate patient-specific models. Coupled with state-of-the-art machine learning methods, this will allow characterizing each patient's disease phenotype and delivering precision medicine solutions, therefore leading to preventative medicine, reduced treatment, and in silico clinical trials.

Enhanced flux prediction by integrating relative expression and relative metabolite abundance into thermodynamically consistent metabolic models

The ever-increasing availability of transcriptomic and metabolomic data can be used to deeply analyze and make ever-expanding predictions about biological processes, as changes in the reaction fluxes through genome-wide pathways can now be tracked. Currently, constraint-based metabolic modeling approaches, such as flux balance analysis (FBA), can quantify metabolic fluxes and make steady-state flux predictions on a genome-wide scale using optimization principles. However, relating the differential gene expression or differential metabolite abundances in different physiological states to the differential flux profiles remains a challenge. Here we present a novel method, named REMI (Relative Expression and Metabolomic Integrations), that employs genome-scale metabolic models (GEMs) to translate differential gene expression and metabolite abundance data obtained through genetic or environmental perturbations into differential fluxes to analyze the altered physiology for any given pair of conditions. REMI allows for gene-expression, metabolite abundance, and thermodynamic data to be integrated into a single framework, then uses optimization principles to maximize the consistency between the differential gene-expression levels and metabolite abundance data and the estimated differential fluxes and thermodynamic constraints. We applied REMI to integrate into the Escherichia coli GEM publicly available sets of expression and metabolomic data obtained from two independent studies and under wide-ranging conditions. The differential flux distributions obtained from REMI corresponding to the various perturbations better agreed with the measured fluxomic data, and thus better reflected the different physiological states, than a traditional model. Compared to the similar alternative method that provides one solution from the solution space, REMI was able to enumerate several alternative flux profiles using a mixed-integer linear programming approach. Using this important advantage, we performed a high-frequency analysis of common genes and their associated reactions in the obtained alternative solutions and identified the most commonly regulated genes across any two given conditions. We illustrate that this new implementation provides more robust and biologically relevant results for a better understanding of the system physiology.

Metabolic profiling of cancer cells reveals genome-wide crosstalk between transcriptional regulators and metabolism

Transcriptional reprogramming of cellular metabolism is a hallmark of cancer. However, systematic approaches to study the role of transcriptional regulators (TRs) in mediating cancer metabolic rewiring are missing. Here, we chart a genome-scale map of TR-metabolite associations in human cells using a combined computational-experimental framework for large-scale metabolic profiling of adherent cell lines. By integrating intracellular metabolic profiles of 54 cancer cell lines with transcriptomic and proteomic data, we unraveled a large space of associations between TRs and metabolic pathways. We found a global regulatory signature coordinating glucose- and one-carbon metabolism, suggesting that regulation of carbon metabolism in cancer may be more diverse and flexible than previously appreciated. Here, we demonstrate how this TR-metabolite map can serve as a resource to predict TRs potentially responsible for metabolic transformation in patient-derived tumor samples, opening new opportunities in understanding disease etiology, selecting therapeutic treatments and in designing modulators of cancer-related TRs.

Aberrant gene expression in cancer coincides with drastic changes in metabolism. Here, the authors combined metabolome, transcriptome and proteome data in 54 cancer cell lines to uncover a genome-scale network of associations between transcriptional regulators and metabolites.

Extreme pathway analysis reveals the organizing rules of metabolic regulation

Cellular systems shift metabolic states by adjusting gene expression and enzyme activities to adapt to physiological and environmental changes. Biochemical and genetic studies are identifying how metabolic regulation affects the selection of metabolic phenotypes. However, how metabolism influences its regulatory architecture still remains unexplored. We present a new method of extreme pathway analysis (the minimal set of conically independent metabolic pathways) to deduce regulatory structures from pure pathway information. Applying our method to metabolic networks of human red blood cells and Escherichia coli, we shed light on how metabolic regulation are organized by showing which reactions within metabolic networks are more prone to transcriptional or allosteric regulation. Applied to a human genome-scale metabolic system, our method detects disease-associated reactions. Thus, our study deepens the understanding of the organizing principle of cellular metabolic regulation and may contribute to metabolic engineering, synthetic biology, and disease treatment.

Yeast Genome-Scale Metabolic Models for Simulating Genotype-Phenotype Relations

Understanding genotype-phenotype dependency is a universal aim for all life sciences. While the complete genotype-phenotype relations remain challenging to resolve, metabolic phenotypes are moving within the reach through genome-scale metabolic model simulations. Genome-scale metabolic models are available for commonly investigated yeasts, such as model eukaryote and domesticated fermentation species Saccharomyces cerevisiae, and automatic reconstruction methods facilitate obtaining models for any sequenced species. The models allow for investigating genotype-phenotype relations through simulations simultaneously considering the effects of nutrient availability, and redox and energy homeostasis in cells. Genome-scale models also offer frameworks for omics data integration to help to uncover how the translation of genotypes to the apparent phenotypes is regulated at different levels. In this chapter, we provide an overview of the yeast genome-scale metabolic models and the simulation approaches for using these models to interrogate genotype-phenotype relations. We review the methodological approaches according to the underlying biological reasoning in order to inspire formulating novel questions and applications that the genome-scale metabolic models could contribute to. Finally, we discuss current challenges and opportunities in the genome-scale metabolic model simulations.

A diurnal flux balance model of Synechocystis sp. PCC 6803 metabolism

Phototrophic organisms such as cyanobacteria utilize the sun's energy to convert atmospheric carbon dioxide into organic carbon, resulting in diurnal variations in the cell's metabolism. Flux balance analysis is a widely accepted constraint-based optimization tool for analyzing growth and metabolism, but it is generally used in a time-invariant manner with no provisions for sequestering different biomass components at different time periods. Here we present CycleSyn, a periodic model of Synechocystis sp. PCC 6803 metabolism that spans a 12-hr light/12-hr dark cycle by segmenting it into 12 Time Point Models (TPMs) with a uniform duration of two hours. The developed framework allows for the flow of metabolites across TPMs while inventorying metabolite levels and only allowing for the utilization of currently or previously produced compounds. The 12 TPMs allow for the incorporation of time-dependent constraints that capture the cyclic nature of cellular processes. Imposing bounds on reactions informed by temporally-segmented transcriptomic data enables simulation of phototrophic growth as a single linear programming (LP) problem. The solution provides the time varying reaction fluxes over a 24-hour cycle and the accumulation/consumption of metabolites. The diurnal rhythm of metabolic gene expression driven by the circadian clock and its metabolic consequences is explored. Predicted flux and metabolite pools are in line with published studies regarding the temporal organization of phototrophic growth in Synechocystis PCC 6803 paving the way for constructing time-resolved genome-scale models (GSMs) for organisms with a circadian clock. In addition, the metabolic reorganization that would be required to enable Synechocystis PCC 6803 to temporally separate photosynthesis from oxygen-sensitive nitrogen fixation is also explored using the developed model formalism.

Soybean leaves transcriptomic data dissects the phenylpropanoid pathway genes as a defence response against Phakopsora pachyrhizi

Asian soybean rust (ASR), caused by the obligate biotrophic fungus Phakopsora pachyrhizi, is responsible for severe yield losses of up to 90% in all soybean producing countries. Till today, eight resistance to Phakopsora pachyrhizi (Rpp) loci have been mapped in soybean. Their resistance mechanism is race specific but largely unknown. The transcriptomes of susceptible BRS184 and Rpp3 with ASR isolates T1-2 at 24 h after inoculation (hai) and without ASR inoculation (mock) were annotated by similarity searching with different databases. A total of 4518 differentially expressed genes were identified. We found 70.89%, 56.61%, 32.13%, and 56.04% genes in the protein family databases (PFAM), Gene Ontology (GO), Eukaryotic clusters of Orthologous Groups (KOG), and Kyoto Encyclopedia of Genes and Genomes Pathway (KEGG), respectively. KEGG disclosed that 52% of the phenylpropanoid pathway related genes were up-regulated. The relative gene expression study for selected genes of that pathway was conducted by RT-qPCR using Rpp1-Rpp4 carrying lines with T1-2 infection. The RT-qPCR results revealed that the Rpp lines utilized these genes in a rate limiting manner as a defence response. With the exception of glycinol 4-dimethylallyltransferase (G4DT) and chalcone reductase (CHR), all the genes showed the greatest expression at 12 hai, but the gene expressions which occur between 24 and 96 hai make these Rpp lines unique to their respective ASR isolates. Moreover, functional coordination of arogenate dehydratase 6 (ADT6) and 4-hydroxy-3-methylbut-2-enyl diphosphate synthase (ispG), chalcone synthase (CHS) and CHR, and G4DT and phytyltransferase 3 (PT3) may have a great impact on soybean resistance against ASR.

Machine Learning Predicts the Yeast Metabolome from the Quantitative Proteome of Kinase Knockouts

A challenge in solving the genotype-to-phenotype relationship is to predict a cell’s metabolome, believed to correlate poorly with gene expression. Using comparative quantitative proteomics, we found that differential protein expression in 97 Saccharomyces cerevisiae kinase deletion strains is non-redundant and dominated by abundance changes in metabolic enzymes. Associating differential enzyme expression landscapes to corresponding metabolomes using network models provided reasoning for poor proteome-metabolome correlations; differential protein expression redistributes flux control between many enzymes acting in concert, a mechanism not captured by one-to-one correlation statistics. Mapping these regulatory patterns using machine learning enabled the prediction of metabolite concentrations, as well as identification of candidate genes important for the regulation of metabolism. Overall, our study reveals that a large part of metabolism regulation is explained through coordinated enzyme expression changes. Our quantitative data indicate that this mechanism explains more than half of metabolism regulation and underlies the interdependency between enzyme levels and metabolism, which renders the metabolome a predictable phenotype.

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The proteome of kinase knockouts is dominated by enzyme abundance changes

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The enzyme expression profiles of kinase knockouts are non-redundant

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Metabolism is regulated by many expression changes acting in concert

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Machine learning accurately predicts the metabolome from enzyme abundance

IntLIM: integration using linear models of metabolomics and gene expression data

BACKGROUND

Integration of transcriptomic and metabolomic data improves functional interpretation of disease-related metabolomic phenotypes, and facilitates discovery of putative metabolite biomarkers and gene targets. For this reason, these data are increasingly collected in large (> 100 participants) cohorts, thereby driving a need for the development of user-friendly and open-source methods/tools for their integration. Of note, clinical/translational studies typically provide snapshot (e.g. one time point) gene and metabolite profiles and, oftentimes, most metabolites measured are not identified. Thus, in these types of studies, pathway/network approaches that take into account the complexity of transcript-metabolite relationships may neither be applicable nor readily uncover novel relationships. With this in mind, we propose a simple linear modeling approach to capture disease-(or other phenotype) specific gene-metabolite associations, with the assumption that co-regulation patterns reflect functionally related genes and metabolites.

RESULTS

The proposed linear model, metabolite ~ gene + phenotype + gene:phenotype, specifically evaluates whether gene-metabolite relationships differ by phenotype, by testing whether the relationship in one phenotype is significantly different from the relationship in another phenotype (via a statistical interaction gene:phenotype p-value). Statistical interaction p-values for all possible gene-metabolite pairs are computed and significant pairs are then clustered by the directionality of associations (e.g. strong positive association in one phenotype, strong negative association in another phenotype). We implemented our approach as an R package, IntLIM, which includes a user-friendly R Shiny web interface, thereby making the integrative analyses accessible to non-computational experts. We applied IntLIM to two previously published datasets, collected in the NCI-60 cancer cell lines and in human breast tumor and non-tumor tissue, for which transcriptomic and metabolomic data are available. We demonstrate that IntLIM captures relevant tumor-specific gene-metabolite associations involved in known cancer-related pathways, including glutamine metabolism. Using IntLIM, we also uncover biologically relevant novel relationships that could be further tested experimentally.

CONCLUSIONS

IntLIM provides a user-friendly, reproducible framework to integrate transcriptomic and metabolomic data and help interpret metabolomic data and uncover novel gene-metabolite relationships. The IntLIM R package is publicly available in GitHub ( https://github.com/mathelab/IntLIM ) and includes a user-friendly web application, vignettes, sample data and data/code to reproduce results.

Polyamines in the life of Arabidopsis: profiling the expression of S-adenosylmethionine decarboxylase (SAMDC) gene family during its life cycle

BACKGROUND

Arabidopsis has 5 paralogs of the S-adenosylmethionine decarboxylase (SAMDC) gene. Neither their specific role in development nor the role of positive/purifying selection in genetic divergence of this gene family is known. While some data are available on organ-specific expression of AtSAMDC1, AtSAMDC2, AtSAMDC3 and AtSAMDC4, not much is known about their promoters including AtSAMDC5, which is believed to be non-functional.

RESULTS

(1) Phylogenetic analysis of the five AtSAMDC genes shows similar divergence pattern for promoters and coding sequences (CDSs), whereas, genetic divergence of 5'UTRs and 3'UTRs was independent of the promoters and CDSs; (2) while AtSAMDC1 and AtSAMDC4 promoters exhibit high activity (constitutive in the former), promoter activities of AtSAMDC2, AtSAMDC3 and AtSAMDC5 are moderate to low in seedlings (depending upon translational or transcriptional fusions), and are localized mainly in the vascular tissues and reproductive organs in mature plants; (3) based on promoter activity, it appears that AtSAMDC5 is both transcriptionally and translationally active, but based on it's coding sequence it seems to produce a non-functional protein; (4) though 5'-UTR based regulation of AtSAMDC expression through upstream open reading frames (uORFs) in the 5'UTR is well known, no such uORFs are present in AtSAMDC4 and AtSAMDC5; (5) the promoter regions of all five AtSAMDC genes contain common stress-responsive elements and hormone-responsive elements; (6) at the organ level, the activity of AtSAMDC enzyme does not correlate with the expression of specific AtSAMDC genes or with the contents of spermidine and spermine.

CONCLUSIONS

Differential roles of positive/purifying selection were observed in genetic divergence of the AtSAMDC gene family. All tissues express one or more AtSAMDC gene with significant redundancy, and concurrently, there is cell/tissue-specificity of gene expression, particularly in mature organs. This study provides valuable information about AtSAMDC promoters, which could be useful in future manipulation of crop plants for nutritive purposes, stress tolerance or bioenergy needs. The AtSAMDC1 core promoter might serve the need of a strong constitutive promoter, and its high expression in the gametophytic cells could be exploited, where strong male/female gametophyte-specific expression is desired; e.g. in transgenic modification of crop varieties.

Designing and interpreting 'multi-omic' experiments that may change our understanding of biology

Most biological mechanisms involve more than one type of biomolecule, and hence operate not solely at the level of either genome, transcriptome, proteome, metabolome or ionome. Datasets resulting from single-omic analysis are rapidly increasing in throughput and quality, rendering multi-omic studies feasible. These should offer a comprehensive, structured and interactive overview of a biological mechanism. However, combining single-omic datasets in a meaningful manner has so far proved challenging, and the discovery of new biological information lags behind expectation. One reason is that experiments conducted in different laboratories can typically not to be combined without restriction. Second, the interpretation of multi-omic datasets represents a significant challenge by nature, as the biological datasets are heterogeneous not only for technical, but also for biological, chemical, and physical reasons. Here, multi-layer network theory and methods of artificial intelligence might contribute to solve these problems. For the efficient application of machine learning however, biological datasets need to become more systematic, more precise - and much larger. We conclude our review with basic guidelines for the successful set-up of a multi-omic experiment.

A computational method using differential gene expression to predict altered metabolism of multicellular organisms

Altered metabolism is often identified as a cause or an effect of physiology and pathogenesis. But it is difficult to predict the metabolic flux distributions of multicellular organisms due to the lack of an explicit metabolic objective function. Here we present a computational method which can successfully describe the differences in metabolism between two different conditions on a large scale. By integrating gene expression data with an existing comprehensive reconstruction of the global human metabolic network, we qualitatively predicted significantly differential fluxes without prior knowledge or the rate of metabolite uptake and secretion. Therefore, this method can be applied for both microorganisms and multicellular organisms. Different from traditional enrichment analysis methods and constraint-based models, we consider conditions and interactions within the metabolic network simultaneously. To apply the proposed method, we predicted altered fluxes for E. coli strains and clear cell renal cell carcinoma, while the E. coli strains are growing aerobically in a chemostat with different dilution rates and clear cell renal cell carcinoma is compared with normal kidney cells. Then we map the significantly differential reactions to metabolic subsystems defined in the original metabolic network for ccRCC to observe the altered metabolism. In contrast with existing studies, our results show a high accuracy of the E. coli experiment and a more reasonable prediction of the ccRCC experiment. The method presented here provides a computational approach for the genome-wide study of altered metabolism under pairs of conditions for both microorganisms and multicellular organisms.

Inference of cancer mechanisms through computational systems analysis

Large amounts of metabolomics data have been accumulated to study metabolic alterations in cancer that allow cancer cells to synthesize molecular materials necessary for cell growth and proliferation. Although metabolic reprogramming in cancer was discovered almost a century ago, the underlying biochemical mechanisms are still unclear. We show that metabolomics data can be used to infer likely biochemical mechanisms associated with cancer. The proposed inference method is data-driven and quite generic; its efficacy is demonstrated by the analysis of changes in purine metabolism of human renal cell carcinoma. The method and results are essentially unbiased and tolerate noise in the data well. The proposed method correctly identified and accurately quantified primary enzymatic alterations in cancer, and these account for over 80% of the metabolic alterations in the investigated carcinoma. Interestingly, the two primary action sites are not the most sensitive reaction steps in purine metabolism, which implies that sensitivity analysis is not a valid approach for identifying cancer targets. The proposed method exhibits statistically high precision and robustness even for analyses of moderately incomplete metabolomics data. By permitting analyses of individual metabolic profiles, the method may become a tool of personalized precision medicine.

Systems biology solutions for biochemical production challenges

There is an urgent need to significantly accelerate the development of microbial cell factories to produce fuels and chemicals from renewable feedstocks in order to facilitate the transition to a biobased society. Methods commonly used within the field of systems biology including omics characterization, genome-scale metabolic modeling, and adaptive laboratory evolution can be readily deployed in metabolic engineering projects. However, high performance strains usually carry tens of genetic modifications and need to operate in challenging environmental conditions. This additional complexity compared to basic science research requires pushing systems biology strategies to their limits and often spurs innovative developments that benefit fields outside metabolic engineering. Here we survey recent advanced applications of systems biology methods in engineering microbial production strains for biofuels and -chemicals.

Making life difficult for Clostridium difficile: augmenting the pathogen's metabolic model with transcriptomic and codon usage data for better therapeutic target characterization

BACKGROUND

Clostridium difficile is a bacterium which can infect various animal species, including humans. Infection with this bacterium is a leading healthcare-associated illness. A better understanding of this organism and the relationship between its genotype and phenotype is essential to the search for an effective treatment. Genome-scale metabolic models contain all known biochemical reactions of a microorganism and can be used to investigate this relationship.

RESULTS

We present icdf834, an updated metabolic network of C. difficile that builds on iMLTC806cdf and features 1227 reactions, 834 genes, and 807 metabolites. We used this metabolic network to reconstruct the metabolic landscape of this bacterium. The standard metabolic model cannot account for changes in the bacterial metabolism in response to different environmental conditions. To account for this limitation, we also integrated transcriptomic data, which details the gene expression of the bacterium in a wide array of environments. Importantly, to bridge the gap between gene expression levels and protein abundance, we accounted for the synonymous codon usage bias of the bacterium in the model. To our knowledge, this is the first time codon usage has been quantified and integrated into a metabolic model. The metabolic fluxes were defined as a function of protein abundance. To determine potential therapeutic targets using the model, we conducted gene essentiality and metabolic pathway sensitivity analyses and calculated flux control coefficients. We obtained 92.3% accuracy in predicting gene essentiality when compared to experimental data for C. difficile R20291 (ribotype 027) homologs. We validated our context-specific metabolic models using sensitivity and robustness analyses and compared model predictions with literature on C. difficile. The model predicts interesting facets of the bacterium's metabolism, such as changes in the bacterium's growth in response to different environmental conditions.

CONCLUSIONS

After an extensive validation process, we used icdf834 to obtain state-of-the-art predictions of therapeutic targets for C. difficile. We show how context-specific metabolic models augmented with codon usage information can be a beneficial resource for better understanding C. difficile and for identifying novel therapeutic targets. We remark that our approach can be applied to investigate and treat against other pathogens.

Systematic Analysis of Transcriptional and Post-transcriptional Regulation of Metabolism in Yeast

Cells react to extracellular perturbations with complex and intertwined responses. Systematic identification of the regulatory mechanisms that control these responses is still a challenge and requires tailored analyses integrating different types of molecular data. Here we acquired time-resolved metabolomics measurements in yeast under salt and pheromone stimulation and developed a machine learning approach to explore regulatory associations between metabolism and signal transduction. Existing phosphoproteomics measurements under the same conditions and kinase-substrate regulatory interactions were used to in silico estimate the enzymatic activity of signalling kinases. Our approach identified informative associations between kinases and metabolic enzymes capable of predicting metabolic changes. We extended our analysis to two studies containing transcriptomics, phosphoproteomics and metabolomics measurements across a comprehensive panel of kinases/phosphatases knockouts and time-resolved perturbations to the nitrogen metabolism. Changes in activity of transcription factors, kinases and phosphatases were estimated in silico and these were capable of building predictive models to infer the metabolic adaptations of previously unseen conditions across different dynamic experiments. Time-resolved experiments were significantly more informative than genetic perturbations to infer metabolic adaptation. This difference may be due to the indirect nature of the associations and of general cellular states that can hinder the identification of causal relationships. This work provides a novel genome-scale integrative analysis to propose putative transcriptional and post-translational regulatory mechanisms of metabolic processes.

Phosphorylation is a broad regulatory mechanism with implications in nearly all processes of the cell. However, a global understanding of possible regulatory mechanisms remains elusive. In this study, we examined the potential regulatory role of kinases, phosphatases and transcription-factors in yeast metabolism across a variety of steady-state and dynamic conditions. The main novelty of our analysis was to infer putative regulatory interactions from in silico estimated activity of transcription-factors and kinases/phosphatases. This provided functional information about the proteins important for the experimental conditions at hand that had not been uncovered before. We showed that activity profiles are predictive features to estimate metabolite changes in dynamic experiments, while the same was not visible in steady-state conditions. We also showed that dynamic experiments could be used to recapitulate and provide novel TFs-metabolite and K/Ps-metabolite regulatory associations. We believe these findings illustrates the usefulness of this approach for future integrative studies interested in studying metabolic regulation.

Frontiers of high-throughput metabolomics

Large scale metabolomics studies are increasingly used to investigate genetically different individuals and time-dependent responses to environmental stimuli. New mass spectrometric approaches with at least an order of magnitude more rapid analysis of small molecules within the cell's metabolome are now paving the way towards true high-throughput metabolomics, opening new opportunities in systems biology, functional genomics, drug discovery, and personalized medicine. Here we discuss the impact and advantages of the progress made in profiling large cohorts and dynamic systems with high temporal resolution and automated sampling. In both areas, high-throughput metabolomics is gaining traction because it can generate hypotheses on molecular mechanisms and metabolic regulation. We conclude with the current status of the less mature single cell analyses where high-throughput analytics will be indispensable to resolve metabolic heterogeneity in populations and compartmentalization of metabolites.

The role of tumor metabolism as a driver of prostate cancer progression and lethal disease: results from a nested case-control study

Background

Understanding the biologic mechanisms underlying the development of lethal prostate cancer is critical for improved therapeutic and prevention strategies. In this study we explored the role of tumor metabolism in prostate cancer progression using mRNA expression profiling of seven metabolic pathways; fatty acid metabolism, glycolysis/gluconeogenesis, oxidative phosphorylation, pentose phosphate, purine metabolism, pyrimidine metabolism and the tricarboxylic acid cycle.

Methods

The study included 404 men with archival formalin-fixed, paraffin-embedded prostate tumor tissue from the prospective Health Professionals Follow-up Study and Physicians’ Health Study. Lethal cases (n = 113) were men who experienced a distant metastatic event or died of prostate cancer during follow-up. Non-lethal controls (n = 291) survived at least 8 years post-diagnosis without metastases. Of 404 men, 202 additionally had matched normal tissue (140 non-lethal, 62 lethal). Analyses compared expression levels between tumor and normal tissue, by Gleason grade and by lethal status. Secondary analyses considered the association with biomarkers of cell proliferation, apoptosis and angiogenesis.

Results

Oxidative phosphorylation and pyrimidine metabolism were identified as the most dysregulated pathways in lethal tumors (p < 0.007), and within these pathways, a number of novel differentially expressed genes were identified including POLR2K and APT6V1A. The associations were tumor specific as there was no evidence any pathways were altered in the normal tissue of lethal compared to non-lethal cases.

Conclusions

The results suggest prostate cancer progression and lethal disease are associated with alterations in key metabolic signaling pathways. Pathways supporting proliferation appeared to be of particular importance in prostate tumor aggressiveness.

Electronic supplementary material

The online version of this article (doi:10.1186/s40170-016-0161-9) contains supplementary material, which is available to authorized users.

New views on the selection acting on genetic polymorphism in central metabolic genes

Studies of the polymorphism of central metabolic genes as a source of fitness variation in natural populations date back to the discovery of allozymes in the 1960s. The unique features of these genes and their enzymes and our knowledge base greatly facilitates the systems-level study of this group. The expectation that pathway flux control is central to understanding the molecular evolution of genes is discussed, as well as studies that attempt to place gene-specific molecular evolution and polymorphism into a context of pathway and network architecture. There is an increasingly complex picture of the metabolic genes assuming additional roles beyond their textbook anabolic and catabolic reactions. In particular, this review emphasizes the potential role of these genes as part of the energy-sensing machinery. It is underscored that the concentrations of key cellular metabolites are the reflections of cellular energy status and nutritional input. These metabolites are the top-down signaling messengers that set signaling through signaling pathways that are involved in energy economy. I propose that the polymorphisms in central metabolic genes shift metabolite concentrations and in that fashion act as genetic modifiers of the energy-state coupling to the transcriptional networks that affect physiological trade-offs with significant fitness consequences.

Transition of an Anaerobic Escherichia coli Culture to Aerobiosis: Balancing mRNA and Protein Levels in a Demand-Directed Dynamic Flux Balance Analysis

The facultative anaerobic bacterium Escherichia coli is frequently forced to adapt to changing environmental conditions. One important determinant for metabolism is the availability of oxygen allowing a more efficient metabolism. Especially in large scale bioreactors, the distribution of oxygen is inhomogeneous and individual cells encounter frequent changes. This might contribute to observed yield losses during process upscaling. Short-term gene expression data exist of an anaerobic E. coli batch culture shifting to aerobic conditions. The data reveal temporary upregulation of genes that are less efficient in terms of energy conservation than the genes predicted by conventional flux balance analyses. In this study, we provide evidence for a positive correlation between metabolic fluxes and gene expression. We then hypothesize that the more efficient enzymes are limited by their low expression, restricting flux through their reactions. We define a demand that triggers expression of the demanded enzymes that we explicitly include in our model. With these features we propose a method, demand-directed dynamic flux balance analysis, dddFBA, bringing together elements of several previously published methods. The introduction of additional flux constraints proportional to gene expression provoke a temporary demand for less efficient enzymes, which is in agreement with the transient upregulation of these genes observed in the data. In the proposed approach, the applied objective function of growth rate maximization together with the introduced constraints triggers expression of metabolically less efficient genes. This finding is one possible explanation for the yield losses observed in large scale bacterial cultivations where steady oxygen supply cannot be warranted.

The space of enzyme regulation in HeLa cells can be inferred from its intracellular metabolome

During the transition from a healthy state to a cancerous one, cells alter their metabolism to increase proliferation. The underlying metabolic alterations may be caused by a variety of different regulatory events on the transcriptional or post-transcriptional level whose identification contributes to the rational design of therapeutic targets. We present a mechanistic strategy capable of inferring enzymatic regulation from intracellular metabolome measurements that is independent of the actual mechanism of regulation. Here, enzyme activities are expressed by the space of all feasible kinetic constants (k-cone) such that the alteration between two phenotypes is given by their corresponding kinetic spaces. Deriving an expression for the transformation of the healthy to the cancer k-cone we identified putative regulated enzymes between the HeLa and HaCaT cell lines. We show that only a few enzymatic activities change between those two cell lines and that this regulation does not depend on gene transcription but is instead post-transcriptional. Here, we identify phosphofructokinase as the major driver of proliferation in HeLa cells and suggest an optional regulatory program, associated with oxidative stress, that affects the activity of the pentose phosphate pathway.

The Landscape of Evolution: Reconciling Structural and Dynamic Properties of Metabolic Networks in Adaptive Diversifications

The network of the interactions among genes, proteins, and metabolites delineates a range of potential phenotypic diversifications in a lineage, and realized phenotypic changes are the result of differences in the dynamics of the expression of the elements and interactions in this deterministic network. Regulatory mechanisms, such as hormones, mediate the relationship between the structural and dynamic properties of networks by determining how and when the elements are expressed and form a functional unit or state. Changes in regulatory mechanisms lead to variable expression of functional states of a network within and among generations. Functional properties of network elements, and the magnitude and direction of evolutionary change they determine, depend on their location within a network. Here, we examine the relationship between network structure and the dynamic mechanisms that regulate flux through a metabolic network. We review the mechanisms that control metabolic flux in enzymatic reactions and examine structural properties of the network locations that are targets of flux control. We aim to establish a predictive framework to test the contributions of structural and dynamic properties of deterministic networks to evolutionary diversifications.

Constructing kinetic models of metabolism at genome-scales: A review

Constraint-based modeling of biological networks (metabolism, transcription and signal transduction), although used successfully in many applications, suffer from specific limitations such as the lack of representation of metabolite concentrations and enzymatic regulation, which are necessary for a complete physiologically relevant model. Kinetic models conversely overcome these shortcomings and enable dynamic analysis of biological systems for enhanced in silico hypothesis generation. Nonetheless, kinetic models also have limitations for modeling at genome-scales chiefly due to: (i) model non-linearity; (ii) computational tractability; (iii) parameter identifiability; (iv) estimability; and (v) uncertainty. In order to support further development of kinetic models as viable alternatives to constraint-based models, this review presents a brief description of the existing obstacles towards building genome-scale kinetic models. Specific kinetic modeling frameworks capable of overcoming these obstacles are covered in this review. The tractability and physiological feasibility of these models are discussed with the objective of using available in vivo experimental observations to define the model parameter space. Among the different methods discussed, Monte Carlo kinetic models of metabolism stand out as potentially tractable methods to model genome scale networks while also addressing in vivo parameter uncertainty.

A method for analysis and design of metabolism using metabolomics data and kinetic models: Application on lipidomics using a novel kinetic model of sphingolipid metabolism

We present a model-based method, designated Inverse Metabolic Control Analysis (IMCA), which can be used in conjunction with classical Metabolic Control Analysis for the analysis and design of cellular metabolism. We demonstrate the capabilities of the method by first developing a comprehensively curated kinetic model of sphingolipid biosynthesis in the yeast Saccharomyces cerevisiae. Next we apply IMCA using the model and integrating lipidomics data. The combinatorial complexity of the synthesis of sphingolipid molecules, along with the operational complexity of the participating enzymes of the pathway, presents an excellent case study for testing the capabilities of the IMCA. The exceptional agreement of the predictions of the method with genome-wide data highlights the importance and value of a comprehensive and consistent engineering approach for the development of such methods and models. Based on the analysis, we identified the class of enzymes regulating the distribution of sphingolipids among species and hydroxylation states, with the D-phospholipase SPO14 being one of the most prominent. The method and the applications presented here can be used for a broader, model-based inverse metabolic engineering approach.

Metabolomics: beyond biomarkers and towards mechanisms

Metabolomics, which is the profiling of metabolites in biofluids, cells and tissues, is routinely applied as a tool for biomarker discovery. Owing to innovative developments in informatics and analytical technologies, and the integration of orthogonal biological approaches, it is now possible to expand metabolomic analyses to understand the systems-level effects of metabolites. Moreover, because of the inherent sensitivity of metabolomics, subtle alterations in biological pathways can be detected to provide insight into the mechanisms that underlie various physiological conditions and aberrant processes, including diseases.

Functional elucidation of the non-coding RNAs of Kluyveromyces marxianus in the exponential growth phase

BACKGROUND

Non-coding RNAs (ncRNAs), which perform diverse regulatory roles, have been found in organisms from all superkingdoms of life. However, there have been limited numbers of studies on the functions of ncRNAs, especially in nonmodel organisms such as Kluyveromyces marxianus that is widely used in the field of industrial biotechnology.

RESULTS

In this study, we measured changes in transcriptome at three time points during the exponential growth phase of K. marxianus by using strand-specific RNA-seq. We found that approximately 60% of the transcriptome consists of ncRNAs transcribed from antisense and intergenic regions of the genome that were transcribed at lower levels than mRNA. In the transcriptome, a substantial number of long antisense ncRNAs (lancRNAs) are differentially expressed and enriched in carbohydrate and energy metabolism pathways. Furthermore, this enrichment is evolutionarily conserved, at least in yeast. Particularly, the mode of regulation of mRNA/lancRNA pairs is associated with mRNA transcription levels; the correlation between the pairs is positive at high mRNA transcriptional levels and negative at low levels. In addition, significant induction of mRNA and coverage of more than half of the mRNA sequence by a lancRNA strengthens the positive correlation between mRNA/lancRNA pairs.

CONCLUSIONS

Transcriptome sequencing of K. marxianus in the exponential growth phase reveals pervasive transcription of ncRNAs with evolutionarily conserved functions. Studies of the mode of regulation of mRNA/lancRNA pairs suggest that induction of lancRNA may be associated with switch-like behavior of mRNA/lancRNA pairs and efficient regulation of the carbohydrate and energy metabolism pathways in the exponential growth phase of K. marxianus being used in industrial applications.