Methods for integration of transcriptomic data in genome-scale metabolic models

MitoMAMMAL: a genome scale model of mammalian mitochondria predicts cardiac and BAT metabolism

Motivation

Mitochondria are essential for cellular metabolism and are inherently flexible to allow correct function in a wide range of tissues. Consequently, dysregulated mitochondrial metabolism affects different tissues in different ways leading to challenges in understanding the pathology of mitochondrial diseases. System-level metabolic modelling is useful in studying tissue-specific mitochondrial metabolism, yet despite the mouse being a common model organism in research, no mouse specific mitochondrial metabolic model is currently available.

Results

Building upon the similarity between human and mouse mitochondrial metabolism, we present mitoMammal, a genome-scale metabolic model that contains human and mouse specific gene-product reaction rules. MitoMammal is able to model mouse and human mitochondrial metabolism. To demonstrate this, using an adapted E-Flux algorithm, we integrated proteomic data from mitochondria of isolated mouse cardiomyocytes and mouse brown adipocyte tissue, as well as transcriptomic data from in vitro differentiated human brown adipocytes and modelled the context specific metabolism using flux balance analysis. In all three simulations, mitoMammal made mostly accurate, and some novel predictions relating to energy metabolism in the context of cardiomyocytes and brown adipocytes. This demonstrates its usefulness in research in cardiac disease and diabetes in both mouse and human contexts.

Availability and implementation

The MitoMammal Jupyter Notebook is available at: https://gitlab.com/habermann_lab/mitomammal.

Review on computer-assisted biosynthetic capacities elucidation to assess metabolic interactions and communication within microbial communities

Microbial communities thrive through interactions and communication, which are challenging to study as most microorganisms are not cultivable. To address this challenge, researchers focus on the extracellular space where communication events occur. Exometabolomics and interactome analysis provide insights into the molecules involved in communication and the dynamics of their interactions. Advances in sequencing technologies and computational methods enable the reconstruction of taxonomic and functional profiles of microbial communities using high-throughput multi-omics data. Network-based approaches, including community flux balance analysis, aim to model molecular interactions within and between communities. Despite these advances, challenges remain in computer-assisted biosynthetic capacities elucidation, requiring continued innovation and collaboration among diverse scientists. This review provides insights into the current state and future directions of computer-assisted biosynthetic capacities elucidation in studying microbial communities.

Integrating Molecular Perspectives: Strategies for Comprehensive Multi-Omics Integrative Data Analysis and Machine Learning Applications in Transcriptomics, Proteomics, and Metabolomics

With the advent of high-throughput technologies, the field of omics has made significant strides in characterizing biological systems at various levels of complexity. Transcriptomics, proteomics, and metabolomics are the three most widely used omics technologies, each providing unique insights into different layers of a biological system. However, analyzing each omics data set separately may not provide a comprehensive understanding of the subject under study. Therefore, integrating multi-omics data has become increasingly important in bioinformatics research. In this article, we review strategies for integrating transcriptomics, proteomics, and metabolomics data, including co-expression analysis, metabolite-gene networks, constraint-based models, pathway enrichment analysis, and interactome analysis. We discuss combined omics integration approaches, correlation-based strategies, and machine learning techniques that utilize one or more types of omics data. By presenting these methods, we aim to provide researchers with a better understanding of how to integrate omics data to gain a more comprehensive view of a biological system, facilitating the identification of complex patterns and interactions that might be missed by single-omics analyses.

Machine learning for the advancement of genome-scale metabolic modeling

Constraint-based modeling (CBM) has evolved as the core systems biology tool to map the interrelations between genotype, phenotype, and external environment. The recent advancement of high-throughput experimental approaches and multi-omics strategies has generated a plethora of new and precise information from wide-ranging biological domains. On the other hand, the continuously growing field of machine learning (ML) and its specialized branch of deep learning (DL) provide essential computational architectures for decoding complex and heterogeneous biological data. In recent years, both multi-omics and ML have assisted in the escalation of CBM. Condition-specific omics data, such as transcriptomics and proteomics, helped contextualize the model prediction while analyzing a particular phenotypic signature. At the same time, the advanced ML tools have eased the model reconstruction and analysis to increase the accuracy and prediction power. However, the development of these multi-disciplinary methodological frameworks mainly occurs independently, which limits the concatenation of biological knowledge from different domains. Hence, we have reviewed the potential of integrating multi-disciplinary tools and strategies from various fields, such as synthetic biology, CBM, omics, and ML, to explore the biochemical phenomenon beyond the conventional biological dogma. How the integrative knowledge of these intersected domains has improved bioengineering and biomedical applications has also been highlighted. We categorically explained the conventional genome-scale metabolic model (GEM) reconstruction tools and their improvement strategies through ML paradigms. Further, the crucial role of ML and DL in omics data restructuring for GEM development has also been briefly discussed. Finally, the case-study-based assessment of the state-of-the-art method for improving biomedical and metabolic engineering strategies has been elaborated. Therefore, this review demonstrates how integrating experimental and in silico strategies can help map the ever-expanding knowledge of biological systems driven by condition-specific cellular information. This multiview approach will elevate the application of ML-based CBM in the biomedical and bioengineering fields for the betterment of society and the environment.

Biocontrol in built environments to reduce pathogen exposure and infection risk

The microbiome of the built environment comprises bacterial, archaeal, fungal, and viral communities associated with human-made structures. Even though most of these microbes are benign, antibiotic-resistant pathogens can colonize and emerge indoors, creating infection risk through surface transmission or inhalation. Several studies have catalogued the microbial composition and ecology in different built environment types. These have informed in vitro studies that seek to replicate the physicochemical features that promote pathogenic survival and transmission, ultimately facilitating the development and validation of intervention techniques used to reduce pathogen accumulation. Such interventions include using Bacillus-based cleaning products on surfaces or integrating bacilli into printable materials. Though this work is in its infancy, early research suggests the potential to use microbial biocontrol to reduce hospital- and home-acquired multidrug-resistant infections. Although these techniques hold promise, there is an urgent need to better understand the microbial ecology of built environments and to determine how these biocontrol solutions alter species interactions. This review covers our current understanding of microbial ecology of the built environment and proposes strategies to translate that knowledge into effective biocontrol of antibiotic-resistant pathogens.

A multi-organ maize metabolic model connects temperature stress with energy production and reducing power generation

Climate change has adversely affected maize productivity. Thereby, a holistic understanding of metabolic crosstalk among its organs is important to address this issue. Thus, we reconstructed the first multi-organ maize metabolic model, iZMA6517, and contextualized it with heat and cold stress transcriptomics data using expression distributed reaction flux measurement (EXTREAM) algorithm. Furthermore, implementing metabolic bottleneck analysis on contextualized models revealed differences between these stresses. While both stresses had reducing power bottlenecks, heat stress had additional energy generation bottlenecks. We also performed thermodynamic driving force analysis, revealing thermodynamics-reducing power-energy generation axis dictating the nature of temperature stress responses. Thus, a temperature-tolerant maize ideotype can be engineered by leveraging the proposed thermodynamics-reducing power-energy generation axis. We experimentally inoculated maize root with a beneficial mycorrhizal fungus, Rhizophagus irregularis, and as a proof-of-concept demonstrated its efficacy in alleviating temperature stress. Overall, this study will guide the engineering effort of temperature stress-tolerant maize ideotypes.

Genome-scale modeling predicts metabolic differences between macrophage subtypes in colorectal cancer

Colorectal cancer (CRC) shows high incidence and mortality, partly due to the tumor microenvironment (TME), which is viewed as an active promoter of disease progression. Macrophages are among the most abundant cells in the TME. These immune cells are generally categorized as M1, with inflammatory and anti-cancer properties, or M2, which promote tumor proliferation and survival. Although the M1/M2 subclassification scheme is strongly influenced by metabolism, the metabolic divergence between the subtypes remains poorly understood. Therefore, we generated a suite of computational models that characterize the M1- and M2-specific metabolic states. Our models show key differences between the M1 and M2 metabolic networks and capabilities. We leverage the models to identify metabolic perturbations that cause the metabolic state of M2 macrophages to more closely resemble M1 cells. Overall, this work increases understanding of macrophage metabolism in CRC and elucidates strategies to promote the metabolic state of anti-tumor macrophages.

Flux balance analysis-based metabolic modeling of microbial secondary metabolism: Current status and outlook

In microorganisms, different from primary metabolism for cellular growth, secondary metabolism is for ecological interactions and stress responses and an important source of natural products widely used in various areas such as pharmaceutics and food additives. With advancements of sequencing technologies and bioinformatics tools, a large number of biosynthetic gene clusters of secondary metabolites have been discovered from microbial genomes. However, due to challenges from the difficulty of genome-scale pathway reconstruction and the limitation of conventional flux balance analysis (FBA) on secondary metabolism, the quantitative modeling of secondary metabolism is poorly established, in contrast to that of primary metabolism. This review first discusses current efforts on the reconstruction of secondary metabolic pathways in genome-scale metabolic models (GSMMs), as well as related FBA-based modeling techniques. Additionally, potential extensions of FBA are suggested to improve the prediction accuracy of secondary metabolite production. As this review posits, biosynthetic pathway reconstruction for various secondary metabolites will become automated and a modeling framework capturing secondary metabolism onset will enhance the predictive power. Expectedly, an improved FBA-based modeling workflow will facilitate quantitative study of secondary metabolism and in silico design of engineering strategies for natural product production.

Ensemble-based genome-scale modeling predicts metabolic differences between macrophage subtypes in colorectal cancer

1Colorectal cancer (CRC) shows high incidence and mortality, partly due to the tumor microenvironment, which is viewed as an active promoter of disease progression. Macrophages are among the most abundant cells in the tumor microenvironment. These immune cells are generally categorized as M1, with inflammatory and anti-cancer properties, or M2, which promote tumor proliferation and survival. Although the M1/M2 subclassification scheme is strongly influenced by metabolism, the metabolic divergence between the subtypes remains poorly understood. Therefore, we generated a suite of computational models that characterize the M1- and M2-specific metabolic states. Our models show key differences between the M1 and M2 metabolic networks and capabilities. We leverage the models to identify metabolic perturbations that cause the metabolic state of M2 macrophages to more closely resemble M1 cells. Overall, this work increases understanding of macrophage metabolism in CRC and elucidates strategies to promote the metabolic state of anti-tumor macrophages.

A Genome-Scale Metabolic Model of Marine Heterotroph Vibrio splendidus Strain 1A01

While Vibrio splendidus is best known as an opportunistic pathogen in oysters, Vibrio splendidus strain 1A01 was first identified as an early colonizer of synthetic chitin particles incubated in seawater. To gain a better understanding of its metabolism, a genome-scale metabolic model (GSMM) of V. splendidus 1A01 was reconstructed. GSMMs enable us to simulate all metabolic reactions in a bacterial cell using flux balance analysis. A draft model was built using an automated pipeline from BioCyc. Manual curation was then performed based on experimental data, in part by gap-filling metabolic pathways and tailoring the model's biomass reaction to V. splendidus 1A01. The challenges of building a metabolic model for a marine microorganism like V. splendidus 1A01 are described. IMPORTANCE A genome-scale metabolic model of V. splendidus 1A01 was reconstructed in this work. We offer solutions to the technical problems associated with model reconstruction for a marine bacterial strain like V. splendidus 1A01, which arise largely from the high salt concentration found in both seawater and culture media that simulate seawater.

Computational Approaches to Assess Abnormal Metabolism in Alzheimer's Disease Using Transcriptomics

Transcriptome-integrated human genome-scale metabolic models (GEMs) have been used widely to assess alterations in metabolism in response to disease. Transcriptome integration leads to identification of metabolic reactions that are differentially inactivated in the tissue of interest. Among the methods available for mapping transcriptome data on GEMs, we focus here on an Integrative Metabolic Analysis Tool (iMAT), which we have recently applied to the analysis of Alzheimer's disease (AD). We provide a detailed protocol for applying iMAT to create models of personalized metabolic networks, which can be further processed to identify reactions associated with abnormal metabolism.

Tuning a high performing multiplexed-CRISPRi Pseudomonas putida strain to further enhance indigoidine production

In this study, a 14-gene edited Pseudomonas putida KT2440 strain for heterologous indigoidine production was examined using three distinct omic datasets. Transcriptomic data indicated that CRISPR/dCpf1-interference (CRISPRi) mediated multiplex repression caused global gene expression changes, implying potential undesirable changes in metabolic flux. ¹³C-metabolic flux analysis (¹³C-MFA) revealed that the core P. putida flux network after CRISPRi repression was conserved, with moderate reduction of TCA cycle and pyruvate shunt activity along with glyoxylate shunt activation during glucose catabolism. Metabolomic results identified a change in intracellular TCA metabolites and extracellular metabolite secretion profiles (sugars and succinate overflow) in the engineered strains. These omic analyses guided further strain engineering, with a random mutagenesis screen first identifying an optimal ribosome binding site (RBS) for Cpf1 that enabled stronger product-substrate pairing (1.6-fold increase). Then, deletion strains were constructed with excision of the PHA operon (ΔphaAZC-IID) resulting in a 2.2-fold increase in indigoidine titer over the optimized Cpf1-RBS construct at the end of the growth phase (∼6 h). The maximum indigoidine titer (at 72 h) in the ΔphaAZC-IID strain had a 1.5-fold and 1.8-fold increase compared to the optimized Cpf1-RBS construct and the original strain, respectively. Overall, this study demonstrated that integration of omic data types is essential for understanding responses to complex metabolic engineering designs and directly quantified the effect of such modifications on central metabolism.

Fuzzy optimization for identifying anti-cancer targets with few side effects in constraint-based models of head and neck cancer

Computer-aided methods can be used to screen potential candidate targets and to reduce the time and cost of drug development. In most of these methods, synthetic lethality is used as a therapeutic criterion to identify drug targets. However, these methods do not consider the side effects during the identification stage. This study developed a fuzzy multi-objective optimization for identifying anti-cancer targets that not only evaluated cancer cell mortality, but also minimized side effects due to treatment. We identified potential anti-cancer enzymes and antimetabolites for the treatment of head and neck cancer (HNC). The identified one- and two-target enzymes were primarily involved in six major pathways, namely, purine and pyrimidine metabolism and the pentose phosphate pathway. Most of the identified targets can be regulated by approved drugs; thus, these drugs are potential candidates for drug repurposing as a treatment for HNC. Furthermore, we identified antimetabolites involved in pathways similar to those identified using a gene-centric approach. Moreover, HMGCR knockdown could not block the growth of HNC cells. However, the two-target combinations of (UMPS, HMGCR) and (CAD, HMGCR) could achieve cell mortality and improve metabolic deviation grades over 22% without reducing the cell viability grade.

What are housekeeping genes?

The concept of "housekeeping gene" has been used for four decades but remains loosely defined. Housekeeping genes are commonly described as "essential for cellular existence regardless of their specific function in the tissue or organism", and "stably expressed irrespective of tissue type, developmental stage, cell cycle state, or external signal". However, experimental support for the tenet that gene essentiality is linked to stable expression across cell types, conditions, and organisms has been limited. Here we use genome-scale functional genomic screens together with bulk and single-cell sequencing technologies to test this link and optimize a quantitative and experimentally validated definition of housekeeping gene. Using the optimized definition, we identify, characterize, and provide as resources, housekeeping gene lists extracted from several human datasets, and 10 other animal species that include primates, chicken, and C. elegans. We find that stably expressed genes are not necessarily essential, and that the individual genes that are essential and stably expressed can considerably differ across organisms; yet the pathways enriched among these genes are conserved. Further, the level of conservation of housekeeping genes across the analyzed organisms captures their taxonomic groups, showing evolutionary relevance for our definition. Therefore, we present a quantitative and experimentally supported definition of housekeeping genes that can contribute to better understanding of their unique biological and evolutionary characteristics.

Genome-Wide Analysis of Yeast Metabolic Cycle through Metabolic Network Models Reveals Superiority of Integrated ATAC-seq Data over RNA-seq Data

Saccharomyces cerevisiae undergoes robust oscillations to regulate its physiology for adaptation and survival under nutrient-limited conditions. Environmental cues can induce rhythmic metabolic alterations in order to facilitate the coordination of dynamic metabolic behaviors. Of such metabolic processes, the yeast metabolic cycle enables adaptation of the cells to varying nutritional status through oscillations in gene expression and metabolite production levels. In this process, yeast metabolism is altered between diverse cellular states based on changing oxygen consumption levels: quiescent (reductive charging [RC]), growth (oxidative [OX]), and proliferation (reductive building [RB]) phases. We characterized metabolic alterations during the yeast metabolic cycle using a variety of approaches. Gene expression levels are widely used for condition-specific metabolic simulations, whereas the use of epigenetic information in metabolic modeling is still limited despite the clear relationship between epigenetics and metabolism. This prompted us to investigate the contribution of epigenomic information to metabolic predictions for progression of the yeast metabolic cycle. In this regard, we determined altered pathways through the prediction of regulated reactions and corresponding model genes relying on differential chromatin accessibility levels. The predicted metabolic alterations were confirmed via data analysis and literature. We subsequently utilized RNA sequencing (RNA-seq) and assay for transposase-accessible chromatin using sequencing (ATAC-seq) data sets in the contextualization of the yeast model. The use of ATAC-seq data considerably enhanced the predictive capability of the model. To the best of our knowledge, this is the first attempt to use genome-wide chromatin accessibility data in metabolic modeling. The preliminary results showed that epigenomic data sets can pave the way for more accurate metabolic simulations.

IMPORTANCE Dynamic chromatin organization mediates the emergence of condition-specific phenotypes in eukaryotic organisms. Saccharomyces cerevisiae can alter its metabolic profile via regulation of genome accessibility and robust transcriptional oscillations under nutrient-limited conditions. Thus, both epigenetic information and transcriptomic information are crucial in the understanding of condition-specific metabolic behavior in this organism. Based on genome-wide alterations in chromatin accessibility and transcription, we investigated the yeast metabolic cycle, which is a remarkable example of coordinated and dynamic yeast behavior. In this regard, we assessed the use of ATAC-seq and RNA-seq data sets in condition-specific metabolic modeling. To our knowledge, this is the first attempt to use chromatin accessibility data in the reconstruction of context-specific metabolic models, despite the extensive use of transcriptomic data. As a result of comparative analyses, we propose that the incorporation of epigenetic information is a promising approach in the accurate prediction of metabolic dynamics.

Cancer Recurrence and Omics: Metabolic Signatures of Cancer Dormancy Revealed by Transcriptome Mapping of Genome-Scale Networks

A major problem in medicine and oncology is cancer recurrence through the activation of dormant cancer cells. A system scale examination of metabolic dysregulations associated with the cancer dormancy offers promise for the discovery of novel molecular targets for cancer precision medicine, and importantly, for the prevention of cancer recurrence. In this study, we mapped the total mRNA sequencing-based transcriptomic data from dormant cancer cell lines and nondormant cancer controls onto a human genome-scale metabolic network by using a graph-based approach, and two mass balance-based approaches with one based on reaction activity/inactivity and the other one on flux changes. The gene expression datasets were accessed from Gene Expression Omnibus (GSE83142 and GSE114012). This analysis included two diverse cancer types, a liquid and a solid cancer, namely, acute lymphoblastic leukemia and colorectal cancer. For the dormant cancer state, we observed changes in major adenosine triphosphate-producing pathways, including the citric acid cycle, oxidative phosphorylation, and glycolysis/gluconeogenesis, indicating a reprogramming in the metabolism of dormant cells away from Warburg-based energy metabolism. All three computational approaches unanimously predicted that folate metabolism, pyruvate metabolism, and glutamate metabolism, as well as valine/leucine/isoleucine metabolism are likely dysregulated in cancer dormancy. These findings provide new insights and molecular pathway targets on cancer dormancy, comprehensively catalog dormancy-associated metabolic pathways, and inform future research aimed at prevention of cancer recurrence in particular.

Dual RNA Sequencing of Mycobacterium tuberculosis-Infected Human Splenic Macrophages Reveals a Strain-Dependent Host-Pathogen Response to Infection

Tuberculosis (TB) is caused by Mycobacterium tuberculosis (Mtb), leading to pulmonary and extrapulmonary TB, whereby Mtb is disseminated to many other organs and tissues. Dissemination occurs early during the disease, and bacteria can be found first in the lymph nodes adjacent to the lungs and then later in the extrapulmonary organs, including the spleen. The early global gene expression response of human tissue macrophages and intracellular clinical isolates of Mtb has been poorly studied. Using dual RNA-seq, we have explored the mRNA profiles of two closely related clinical strains of the Latin American and Mediterranean (LAM) family of Mtb in infected human splenic macrophages (hSMs). This work shows that these pathogens mediate a distinct host response despite their genetic similarity. Using a genome-scale host–pathogen metabolic reconstruction to analyze the data further, we highlight that the infecting Mtb strain also determines the metabolic response of both the host and pathogen. Thus, macrophage ontogeny and the genetic-derived program of Mtb direct the host–pathogen interaction.

Exploring the associations between transcript levels and fluxes in constraint-based models of metabolism

BACKGROUND

Several computational methods have been developed that integrate transcriptomics data with genome-scale metabolic reconstructions to increase accuracy of inferences of intracellular metabolic flux distributions. Even though existing methods use transcript abundances as a proxy for enzyme activity, each method uses a different hypothesis and assumptions. Most methods implicitly assume a proportionality between transcript levels and flux through the corresponding function, although these proportionality constant(s) are often not explicitly mentioned nor discussed in any of the published methods. E-Flux is one such method and, in this algorithm, flux bounds are related to expression data, so that reactions associated with highly expressed genes are allowed to carry higher flux values.

RESULTS

Here, we extended E-Flux and systematically evaluated the impact of an assumed proportionality constant on model predictions. We used data from published experiments with Escherichia coli and Saccharomyces cerevisiae and we compared the predictions of the algorithm to measured extracellular and intracellular fluxes.

CONCLUSION

We showed that detailed modelling using a proportionality constant can greatly impact the outcome of the analysis. This increases accuracy and allows for extraction of better physiological information.

Path to improving the life cycle and quality of genome-scale models of metabolism

Genome-scale models of metabolism (GEMs) are key computational tools for the systems-level study of metabolic networks. Here, we describe the "GEM life cycle," which we subdivide into four stages: inception, maturation, specialization, and amalgamation. We show how different types of GEM reconstruction workflows fit in each stage and proceed to highlight two fundamental bottlenecks for GEM quality improvement: GEM maturation and content removal. We identify common characteristics contributing to increasing quality of maturing GEMs drawing from past independent GEM maturation efforts. We then shed some much-needed light on the latent and unrecognized but pervasive issue of content removal, demonstrating the substantial effects of model pruning on its solution space. Finally, we propose a novel framework for content removal and associated confidence-level assignment which will help guide future GEM development efforts, reduce duplication of effort across groups, potentially aid automated reconstruction platforms, and boost the reproducibility of model development.

Knowledge extraction from literature and enzyme sequences complements FBA analysis in metabolic engineering

Flux balance analysis (FBA) using genome-scale metabolic model (GSM) is a useful method for improving the bio-production of useful compounds. However, FBA often does not impose important constraints such as nutrients uptakes, by-products excretions and gases (oxygen and carbon dioxide) transfers. Furthermore, important information on metabolic engineering such as enzyme amounts, activities, and characteristics caused by gene expression and enzyme sequences is basically not included in GSM. Therefore, simple FBA is often not sufficient to search for metabolic manipulation strategies that are useful for improving the production of target compounds. In this study, we proposed a method using literature and enzyme search to complement the FBA-based metabolic manipulation strategies. As a case study, this method was applied to shikimic acid production by Corynebacterium glutamicum to verify its usefulness. As unique strategies in literature-mining, overexpression of the transcriptional regulator SugR and gene disruption related to by-products productions were complemented. In the search for alternative enzyme sequences, it was suggested that those candidates are searched for from various species based on features captured by deep learning, which are not simply homologous to amino acid sequences of the base enzymes.

Advances in flux balance analysis by integrating machine learning and mechanism-based models

The availability of multi-omics data sets and genome-scale metabolic models for various organisms provide a platform for modeling and analyzing genotype-to-phenotype relationships. Flux balance analysis is the main tool for predicting flux distributions in genome-scale metabolic models and various data-integrative approaches enable modeling context-specific network behavior. Due to its linear nature, this optimization framework is readily scalable to multi-tissue or -organ and even multi-organism models. However, both data and model size can hamper a straightforward biological interpretation of the estimated fluxes. Moreover, flux balance analysis simulates metabolism at steady-state and thus, in its most basic form, does not consider kinetics or regulatory events. The integration of flux balance analysis with complementary data analysis and modeling techniques offers the potential to overcome these challenges. In particular machine learning approaches have emerged as the tool of choice for data reduction and selection of most important variables in big data sets. Kinetic models and formal languages can be used to simulate dynamic behavior. This review article provides an overview of integrative studies that combine flux balance analysis with machine learning approaches, kinetic models, such as physiology-based pharmacokinetic models, and formal graphical modeling languages, such as Petri nets. We discuss the mathematical aspects and biological applications of these integrated approaches and outline challenges and future perspectives.

Guided extraction of genome-scale metabolic models for the integration and analysis of omics data

Omics data can be integrated into a reference model using various model extraction methods (MEMs) to yield context-specific genome-scale metabolic models (GEMs). How to chose the appropriate MEM, thresholding rule and threshold remains a challenge. We integrated mouse transcriptomic data from a Cyp51 knockout mice diet experiment (GSE58271) using five MEMs (GIMME, iMAT, FASTCORE, INIT an tINIT) in a combination with a recently published mouse GEM iMM1865. Except for INIT and tINIT, the size of extracted models varied with the MEM used (t-test: p-value < 0.001). The Jaccard index of iMAT models ranged from 0.27 to 1.0. Out of the three factors under study in the experiment (diet, gender and genotype), gender explained most of the variability ( > 90%) in PC1 for FASTCORE. In iMAT, each of the three factors explained less than 40% of the variability within PC1, PC2 and PC3. Among all the MEMs, FASTCORE captured the most of the true variability in the data by clustering samples by gender. Our results show that for the efficient use of MEMs in the context of omics data integration and analysis, one should apply various MEMs, thresholding rules, and thresholding values to select the MEM and its configuration that best captures the true variability in the data. This selection can be guided by the methodology as proposed and used in this paper. Moreover, we describe certain approaches that can be used to analyse the results obtained with the selected MEM and to put these results in a biological context.

Resolving trained immunity with systems biology

Trained immunity is characterized by long-term functional reprogramming of innate immune cells following challenge with pathogens or microbial ligands during infection or vaccination. This cellular reprogramming leads to increased responsiveness upon restimulation, and is mediated through epigenetic and metabolic modifications. In this review, we describe how molecular mechanisms underlying trained immunity, for example, induced by β-glucan or Bacille Calmette-Guérin (BCG) vaccination, can be investigated by using and integrating different layers of information including genome, epigenome, transcriptome, proteome, metabolome, microbiome, immune cell phenotyping, and function. We also describe the most commonly used experimental and computational techniques. Finally, we provide a number of examples of how a systems biology approach was applied to study trained immunity to understand interindividual variation or the complex interplay between molecular layers. In conclusion, trained immunity represents an opportunity for regulating innate immune function, and understanding the complex interplay of mechanisms that mediate trained immunity might enable us to employ it as a clinical tool in the future.

Low Entropy Sub-Networks Prevent the Integration of Metabolomic and Transcriptomic Data

The constantly and rapidly increasing amount of the biological data gained from many different high-throughput experiments opens up new possibilities for data- and model-driven inference. Yet, alongside, emerges a problem of risks related to data integration techniques. The latter are not so widely taken account of. Especially, the approaches based on the flux balance analysis (FBA) are sensitive to the structure of a metabolic network for which the low-entropy clusters can prevent the inference from the activity of the metabolic reactions. In the following article, we set forth problems that may arise during the integration of metabolomic data with gene expression datasets. We analyze common pitfalls, provide their possible solutions, and exemplify them by a case study of the renal cell carcinoma (RCC). Using the proposed approach we provide a metabolic description of the known morphological RCC subtypes and suggest a possible existence of the poor-prognosis cluster of patients, which are commonly characterized by the low activity of the drug transporting enzymes crucial in the chemotherapy. This discovery suits and extends the already known poor-prognosis characteristics of RCC. Finally, the goal of this work is also to point out the problem that arises from the integration of high-throughput data with the inherently nonuniform, manually curated low-throughput data. In such cases, the over-represented information may potentially overshadow the non-trivial discoveries.

Assessment of transcriptomic constraint-based methods for central carbon flux inference

MOTIVATION

Determining intracellular metabolic flux through isotope labeling techniques such as 13C metabolic flux analysis (13C-MFA) incurs significant cost and effort. Previous studies have shown transcriptomic data coupled with constraint-based metabolic modeling can determine intracellular fluxes that correlate highly with 13C-MFA measured fluxes and can achieve higher accuracy than constraint-based metabolic modeling alone. These studies, however, used validation data limited to E. coli and S. cerevisiae grown on glucose, with significantly similar flux distribution for central metabolism. It is unclear whether those results apply to more diverse metabolisms, and therefore further, extensive validation is needed.

RESULTS

In this paper, we formed a dataset of transcriptomic data coupled with corresponding 13C-MFA flux data for 21 experimental conditions in different unicellular organisms grown on varying carbon substrates and conditions. Three computational flux-balance analysis (FBA) methods were comparatively assessed. The results show when uptake rates of carbon sources and key metabolites are known, transcriptomic data provides no significant advantage over constraint-based metabolic modeling (average correlation coefficients, transcriptomic E-Flux2 0.725 and SPOT 0.650 vs non-transcriptomic pFBA 0.768). When uptake rates are unknown, however, predictions obtained utilizing transcriptomic data are generally good and significantly better than those obtained using constraint-based metabolic modeling alone (E-Flux2 0.385 and SPOT 0.583 vs pFBA 0.237). Thus, transcriptomic data coupled with constraint-based metabolic modeling is a promising method to obtain intracellular flux estimates in microorganisms, particularly in cases where uptake rates of key metabolites cannot be easily determined, such as for growth in complex media or in vivo conditions.

StanDep: Capturing transcriptomic variability improves context-specific metabolic models

Diverse algorithms can integrate transcriptomics with genome-scale metabolic models (GEMs) to build context-specific metabolic models. These algorithms require identification of a list of high confidence (core) reactions from transcriptomics, but parameters related to identification of core reactions, such as thresholding of expression profiles, can significantly change model content. Importantly, current thresholding approaches are burdened with setting singular arbitrary thresholds for all genes; thus, resulting in removal of enzymes needed in small amounts and even many housekeeping genes. Here, we describe StanDep, a novel heuristic method for using transcriptomics to identify core reactions prior to building context-specific metabolic models. StanDep clusters gene expression data based on their expression pattern across different contexts and determines thresholds for each cluster using data-dependent statistics, specifically standard deviation and mean. To demonstrate the use of StanDep, we built hundreds of models for the NCI-60 cancer cell lines. These models successfully increased the inclusion of housekeeping reactions, which are often lost in models built using standard thresholding approaches. Further, StanDep also provided a transcriptomic explanation for inclusion of lowly expressed reactions that were otherwise only supported by model extraction methods. Our study also provides novel insights into how cells may deal with context-specific and ubiquitous functions. StanDep, as a MATLAB toolbox, is available at https://github.com/LewisLabUCSD/StanDep.

Modeling Plant Metabolism: From Network Reconstruction to Mechanistic Models

Mathematical modeling of plant metabolism enables the plant science community to understand the organization of plant metabolism, obtain quantitative insights into metabolic functions, and derive engineering strategies for manipulation of metabolism. Among the various modeling approaches, metabolic pathway analysis can dissect the basic functional modes of subsections of core metabolism, such as photorespiration, and reveal how classical definitions of metabolic pathways have overlapping functionality. In the many studies using constraint-based modeling in plants, numerous computational tools are currently available to analyze large-scale and genome-scale metabolic networks. For ¹³C-metabolic flux analysis, principles of isotopic steady state have been used to study heterotrophic plant tissues, while nonstationary isotope labeling approaches are amenable to the study of photoautotrophic and secondary metabolism. Enzyme kinetic models explore pathways in mechanistic detail, and we discuss different approaches to determine or estimate kinetic parameters. In this review, we describe recent advances and challenges in modeling plant metabolism.

Single-Cell RNA Sequencing Maps Endothelial Metabolic Plasticity in Pathological Angiogenesis

Endothelial cell (EC) metabolism is an emerging target for anti-angiogenic therapy in tumor angiogenesis and choroidal neovascularization (CNV), but little is known about individual EC metabolic transcriptomes. By single-cell RNA sequencing 28,337 murine choroidal ECs (CECs) and sprouting CNV-ECs, we constructed a taxonomy to characterize their heterogeneity. Comparison with murine lung tumor ECs (TECs) revealed congruent marker gene expression by distinct EC phenotypes across tissues and diseases, suggesting similar angiogenic mechanisms. Trajectory inference predicted that differentiation of venous to angiogenic ECs was accompanied by metabolic transcriptome plasticity. ECs displayed metabolic transcriptome heterogeneity during cell-cycle progression and in quiescence. Hypothesizing that conserved genes are important, we used an integrated analysis, based on congruent transcriptome analysis, CEC-tailored genome-scale metabolic modeling, and gene expression meta-analysis in cross-species datasets, followed by in vitro and in vivo validation, to identify SQLE and ALDH18A1 as previously unknown metabolic angiogenic targets.

Rewiring of Metabolic Network in Mycobacterium tuberculosis During Adaptation to Different Stresses

Metabolic adaptation of Mycobacterium tuberculosis (M. tuberculosis) to microbicidal intracellular environment of host macrophages is fundamental to its pathogenicity. However, an in-depth understanding of metabolic adjustments through key reaction pathways and networks is limited. To understand how such changes occur, we measured the cellular metabolome of M. tuberculosis subjected to four microbicidal stresses using liquid chromatography-mass spectrometric multiple reactions monitoring (LC-MRM/MS). Overall, 87 metabolites were identified. The metabolites best describing the separation between stresses were identified through multivariate analysis. The coupling of the metabolite measurements with existing genome-scale metabolic model, and using constraint-based simulation led to several new concepts and unreported observations in M. tuberculosis; such as (i) the high levels of released ammonia as an adaptive response to acidic stress was due to increased flux through L-asparaginase rather than urease activity; (ii) nutrient starvation-induced anaplerotic pathway for generation of TCA intermediates from phosphoenolpyruvate using phosphoenolpyruvate kinase; (iii) quenching of protons through GABA shunt pathway or sugar alcohols as possible mechanisms of early adaptation to acidic and oxidative stresses; and (iv) usage of alternate cofactors by the same enzyme as a possible mechanism of rewiring metabolic pathways to overcome stresses. Besides providing new leads and important nodes that can be used for designing intervention strategies, the study advocates the strength of applying flux balance analyses coupled with metabolomics to get a global picture of complex metabolic adjustments.

Consumer Safety Considerations of Skin and Oral Microbiome Perturbation

Microbiomes associated with human skin and the oral cavity are uniquely exposed to personal care regimes. Changes in the composition and activities of the microbial communities in these environments can be utilized to promote consumer health benefits, for example, by reducing the numbers, composition, or activities of microbes implicated in conditions such as acne, axillary odor, dandruff, and oral diseases. It is, however, important to ensure that innovative approaches for microbiome manipulation do not unsafely disrupt the microbiome or compromise health, and where major changes in the composition or activities of the microbiome may occur, these require evaluation to ensure that critical biological functions are unaffected. This article is based on a 2-day workshop held at SEAC Unilever, Sharnbrook, United Kingdom, involving 31 specialists in microbial risk assessment, skin and oral microbiome research, microbial ecology, bioinformatics, mathematical modeling, and immunology. The first day focused on understanding the potential implications of skin and oral microbiome perturbation, while approaches to characterize those perturbations were discussed during the second day. This article discusses the factors that the panel recommends be considered for personal care products that target the microbiomes of the skin and the oral cavity.

MOOMIN - Mathematical explOration of 'Omics data on a MetabolIc Network

MOTIVATION

Analysis of differential expression of genes is often performed to understand how the metabolic activity of an organism is impacted by a perturbation. However, because the system of metabolic regulation is complex and all changes are not directly reflected in the expression levels, interpreting these data can be difficult.

RESULTS

In this work, we present a new algorithm and computational tool that uses a genome-scale metabolic reconstruction to infer metabolic changes from differential expression data. Using the framework of constraint-based analysis, our method produces a qualitative hypothesis of a change in metabolic activity. In other words, each reaction of the network is inferred to have increased, decreased, or remained unchanged in flux. In contrast to similar previous approaches, our method does not require a biological objective function and does not assign on/off activity states to genes. An implementation is provided and it is available online. We apply the method to three published datasets to show that it successfully accomplishes its two main goals: confirming or rejecting metabolic changes suggested by differentially expressed genes based on how well they fit in as parts of a coordinated metabolic change, as well as inferring changes in reactions whose genes did not undergo differential expression.

AVAILABILITY AND IMPLEMENTATION

github.com/htpusa/moomin.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Seeing the wood for the trees: a forest of methods for optimization and omic-network integration in metabolic modelling

Metabolic modelling has entered a mature phase with dozens of methods and software implementations available to the practitioner and the theoretician. It is not easy for a modeller to be able to see the wood (or the forest) for the trees. Driven by this analogy, we here present a 'forest' of principal methods used for constraint-based modelling in systems biology. This provides a tree-based view of methods available to prospective modellers, also available in interactive version at http://modellingmetabolism.net, where it will be kept updated with new methods after the publication of the present manuscript. Our updated classification of existing methods and tools highlights the most promising in the different branches, with the aim to develop a vision of how existing methods could hybridize and become more complex. We then provide the first hands-on tutorial for multi-objective optimization of metabolic models in R. We finally discuss the implementation of multi-view machine learning approaches in poly-omic integration. Throughout this work, we demonstrate the optimization of trade-offs between multiple metabolic objectives, with a focus on omic data integration through machine learning. We anticipate that the combination of a survey, a perspective on multi-view machine learning and a step-by-step R tutorial should be of interest for both the beginner and the advanced user.

Condition-specific series of metabolic sub-networks and its application for gene set enrichment analysis

MOTIVATION

Genome-scale metabolic networks and transcriptomic data represent complementary sources of knowledge about an organism's metabolism, yet their integration to achieve biological insight remains challenging.

RESULTS

We investigate here condition-specific series of metabolic sub-networks constructed by successively removing genes from a comprehensive network. The optimal order of gene removal is deduced from transcriptomic data. The sub-networks are evaluated via a fitness function, which estimates their degree of alteration. We then consider how a gene set, i.e. a group of genes contributing to a common biological function, is depleted in different series of sub-networks to detect the difference between experimental conditions. The method, named metaboGSE, is validated on public data for Yarrowia lipolytica and mouse. It is shown to produce GO terms of higher specificity compared to popular gene set enrichment methods like GSEA or topGO.

AVAILABILITY AND IMPLEMENTATION

The metaboGSE R package is available at https://CRAN.R-project.org/package=metaboGSE.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

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.

Prediction of metabolic fluxes from gene expression data with Huber penalty convex optimization function

As one of the critical parameters of a metabolic pathway, the metabolic flux in a metabolic network serves as an essential role in physiology and pathology. Constraint-based metabolic models are the widely used frameworks for predicting metabolic fluxes in genome-scale metabolic networks. Integrating the transcriptomic data into the constraint-based metabolic models can effectively predict context-specific fluxes across different conditions. However, these methods always need user-defined thresholds to identify the expression levels of metabolic genes or restrain the rate of biomass production, and the predictive results are sensitive to the thresholds. In this work, we present the Huber penalty convex optimization function (HPCOF) combined with the flux minimization principle to predict metabolic fluxes. Our HPCOF method integrates gene expression profiles into the genome-scale metabolic models (GEMs) to reduce the sensitivity to outliers, and uses continuous expression data to avoid selection of arbitrary threshold parameters. In the case studies of Saccharomyces cerevisiae (S. cerevisiae) and Escherichia coli (E. coli) strains under different conditions, the results show that our HPCOF method has a better fit to the experimentally measured values, and has a higher Pearson correlation coefficient, a smaller P-value and a lower sum of squared error than other methods. The HPCOF code can be freely downloaded from for academic users.

Endothelial Cell Metabolism

Endothelial cells (ECs) are more than inert blood vessel lining material. Instead, they are active players in the formation of new blood vessels (angiogenesis) both in health and (life-threatening) diseases. Recently, a new concept arose by which EC metabolism drives angiogenesis in parallel to well-established angiogenic growth factors (e.g., vascular endothelial growth factor). 6-Phosphofructo-2-kinase/fructose-2,6-bisphosphatase-3-driven glycolysis generates energy to sustain competitive behavior of the ECs at the tip of a growing vessel sprout, whereas carnitine palmitoyltransferase 1a-controlled fatty acid oxidation regulates nucleotide synthesis and proliferation of ECs in the stalk of the sprout. To maintain vascular homeostasis, ECs rely on an intricate metabolic wiring characterized by intracellular compartmentalization, use metabolites for epigenetic regulation of EC subtype differentiation, crosstalk through metabolite release with other cell types, and exhibit EC subtype-specific metabolic traits. Importantly, maladaptation of EC metabolism contributes to vascular disorders, through EC dysfunction or excess angiogenesis, and presents new opportunities for anti-angiogenic strategies. Here we provide a comprehensive overview of established as well as newly uncovered aspects of EC metabolism.

Computational Systems Biology of Metabolism in Infection

A systems approach to elucidate the effect of infection on cell metabolism provides several opportunities from a better understanding of molecular mechanisms to the identification of potential biomarkers and drug targets. This is obvious from the fact that we have witnessed the accelerated use of computational systems biology in the last five years to study metabolic changes in pathogen and/or host cells in response to infection. In this chapter, we aim to present a comprehensive review of the recent research by focusing on genome-scale metabolic network models of pathogen-host systems and genome-wide metabolomics and fluxomics analysis of infected cells.

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.

Genome-scale modeling of yeast: chronology, applications and critical perspectives

Over the last 15 years, several genome-scale metabolic models (GSMMs) were developed for different yeast species, aiding both the elucidation of new biological processes and the shift toward a bio-based economy, through the design of in silico inspired cell factories. Here, an historical perspective of the GSMMs built over time for several yeast species is presented and the main inheritance patterns among the metabolic reconstructions are highlighted. We additionally provide a critical perspective on the overall genome-scale modeling procedure, underlining incomplete model validation and evaluation approaches and the quest for the integration of regulatory and kinetic information into yeast GSMMs. A summary of experimentally validated model-based metabolic engineering applications of yeast species is further emphasized, while the main challenges and future perspectives for the field are finally addressed.

More than just a gut feeling: constraint-based genome-scale metabolic models for predicting functions of human intestinal microbes

The human gut is colonized with a myriad of microbes, with substantial interpersonal variation. This complex ecosystem is an integral part of the gastrointestinal tract and plays a major role in the maintenance of homeostasis. Its dysfunction has been correlated to a wide array of diseases, but the understanding of causal mechanisms is hampered by the limited amount of cultured microbes, poor understanding of phenotypes, and the limited knowledge about interspecies interactions. Genome-scale metabolic models (GEMs) have been used in many different fields, ranging from metabolic engineering to the prediction of interspecies interactions. We provide showcase examples for the application of GEMs for gut microbes and focus on (i) the prediction of minimal, synthetic, or defined media; (ii) the prediction of possible functions and phenotypes; and (iii) the prediction of interspecies interactions. All three applications are key in understanding the role of individual species in the gut ecosystem as well as the role of the microbiota as a whole. Using GEMs in the described fashions has led to designs of minimal growth media, an increased understanding of microbial phenotypes and their influence on the host immune system, and dietary interventions to improve human health. Ultimately, an increased understanding of the gut ecosystem will enable targeted interventions in gut microbial composition to restore homeostasis and appropriate host-microbe crosstalk.

Principles of proteome allocation are revealed using proteomic data and genome-scale models

Integrating omics data to refine or make context-specific models is an active field of constraint-based modeling. Proteomics now cover over 95% of the Escherichia coli proteome by mass. Genome-scale models of Metabolism and macromolecular Expression (ME) compute proteome allocation linked to metabolism and fitness. Using proteomics data, we formulated allocation constraints for key proteome sectors in the ME model. The resulting calibrated model effectively computed the "generalist" (wild-type) E. coli proteome and phenotype across diverse growth environments. Across 15 growth conditions, prediction errors for growth rate and metabolic fluxes were 69% and 14% lower, respectively. The sector-constrained ME model thus represents a generalist ME model reflecting both growth rate maximization and "hedging" against uncertain environments and stresses, as indicated by significant enrichment of these sectors for the general stress response sigma factor σS. Finally, the sector constraints represent a general formalism for integrating omics data from any experimental condition into constraint-based ME models. The constraints can be fine-grained (individual proteins) or coarse-grained (functionally-related protein groups) as demonstrated here. This flexible formalism provides an accessible approach for narrowing the gap between the complexity captured by omics data and governing principles of proteome allocation described by systems-level models.

Integrative analysis of human omics data using biomolecular networks

High-throughput '-omics' technologies have given rise to an increasing abundance of genome-scale data detailing human biology at the molecular level. Although these datasets have already made substantial contributions to a more comprehensive understanding of human physiology and diseases, their interpretation becomes increasingly cryptic and nontrivial as they continue to expand in size and complexity. Systems biology networks offer a scaffold upon which omics data can be integrated, facilitating the extraction of new and physiologically relevant information from the data. Two of the most prevalent networks that have been used for such integrative analyses of omics data are genome-scale metabolic models (GEMs) and protein-protein interaction (PPI) networks, both of which have demonstrated success among many different omics and sample types. This integrative approach seeks to unite 'top-down' omics data with 'bottom-up' biological networks in a synergistic fashion that draws on the strengths of both strategies. As the volume and resolution of high-throughput omics data continue to grow, integrative network-based analyses are expected to play an increasingly important role in their interpretation.

E-Flux2 and SPOT: Validated Methods for Inferring Intracellular Metabolic Flux Distributions from Transcriptomic Data

Background

Several methods have been developed to predict system-wide and condition-specific intracellular metabolic fluxes by integrating transcriptomic data with genome-scale metabolic models. While powerful in many settings, existing methods have several shortcomings, and it is unclear which method has the best accuracy in general because of limited validation against experimentally measured intracellular fluxes.

Results

We present a general optimization strategy for inferring intracellular metabolic flux distributions from transcriptomic data coupled with genome-scale metabolic reconstructions. It consists of two different template models called DC (determined carbon source model) and AC (all possible carbon sources model) and two different new methods called E-Flux2 (E-Flux method combined with minimization of l² norm) and SPOT (Simplified Pearson cOrrelation with Transcriptomic data), which can be chosen and combined depending on the availability of knowledge on carbon source or objective function. This enables us to simulate a broad range of experimental conditions. We examined E. coli and S. cerevisiae as representative prokaryotic and eukaryotic microorganisms respectively. The predictive accuracy of our algorithm was validated by calculating the uncentered Pearson correlation between predicted fluxes and measured fluxes. To this end, we compiled 20 experimental conditions (11 in E. coli and 9 in S. cerevisiae), of transcriptome measurements coupled with corresponding central carbon metabolism intracellular flux measurements determined by ¹³C metabolic flux analysis (¹³C-MFA), which is the largest dataset assembled to date for the purpose of validating inference methods for predicting intracellular fluxes. In both organisms, our method achieves an average correlation coefficient ranging from 0.59 to 0.87, outperforming a representative sample of competing methods. Easy-to-use implementations of E-Flux2 and SPOT are available as part of the open-source package MOST (http://most.ccib.rutgers.edu/).

Conclusion

Our method represents a significant advance over existing methods for inferring intracellular metabolic flux from transcriptomic data. It not only achieves higher accuracy, but it also combines into a single method a number of other desirable characteristics including applicability to a wide range of experimental conditions, production of a unique solution, fast running time, and the availability of a user-friendly implementation.

Reconstruction of genome-scale human metabolic models using omics data

The impact of genome-scale human metabolic models on human systems biology and medical sciences is becoming greater, thanks to increasing volumes of model building platforms and publicly available omics data. The genome-scale human metabolic models started with Recon 1 in 2007, and have since been used to describe metabolic phenotypes of healthy and diseased human tissues and cells, and to predict therapeutic targets. Here we review recent trends in genome-scale human metabolic modeling, including various generic and tissue/cell type-specific human metabolic models developed to date, and methods, databases and platforms used to construct them. For generic human metabolic models, we pay attention to Recon 2 and HMR 2.0 with emphasis on data sources used to construct them. Draft and high-quality tissue/cell type-specific human metabolic models have been generated using these generic human metabolic models. Integration of tissue/cell type-specific omics data with the generic human metabolic models is the key step, and we discuss omics data and their integration methods to achieve this task. The initial version of the tissue/cell type-specific human metabolic models can further be computationally refined through gap filling, reaction directionality assignment and the subcellular localization of metabolic reactions. We review relevant tools for this model refinement procedure as well. Finally, we suggest the direction of further studies on reconstructing an improved human metabolic model.

OM-FBA: Integrate Transcriptomics Data with Flux Balance Analysis to Decipher the Cell Metabolism

Constraint-based metabolic modeling such as flux balance analysis (FBA) has been widely used to simulate cell metabolism. Thanks to its simplicity and flexibility, numerous algorithms have been developed based on FBA and successfully predicted the phenotypes of various biological systems. However, their phenotype predictions may not always be accurate in FBA because of using the objective function that is assumed for cell metabolism. To overcome this challenge, we have developed a novel computational framework, namely omFBA, to integrate multi-omics data (e.g. transcriptomics) into FBA to obtain omics-guided objective functions with high accuracy. In general, we first collected transcriptomics data and phenotype data from published database (e.g. GEO database) for different microorganisms such as Saccharomyces cerevisiae. We then developed a “Phenotype Match” algorithm to derive an objective function for FBA that could lead to the most accurate estimation of the known phenotype (e.g. ethanol yield). The derived objective function was next correlated with the transcriptomics data via regression analysis to generate the omics-guided objective function, which was next used to accurately simulate cell metabolism at unknown conditions. We have applied omFBA in studying sugar metabolism of S. cerevisiae and found that the ethanol yield could be accurately predicted in most of the cases tested (>80%) by using transcriptomics data alone, and revealed valuable metabolic insights such as the dynamics of flux ratios. Overall, omFBA presents a novel platform to potentially integrate multi-omics data simultaneously and could be incorporated with other FBA-derived tools by replacing the arbitrary objective function with the omics-guided objective functions.

Reconstruction of Tissue-Specific Metabolic Networks Using CORDA

Human metabolism involves thousands of reactions and metabolites. To interpret this complexity, computational modeling becomes an essential experimental tool. One of the most popular techniques to study human metabolism as a whole is genome scale modeling. A key challenge to applying genome scale modeling is identifying critical metabolic reactions across diverse human tissues. Here we introduce a novel algorithm called Cost Optimization Reaction Dependency Assessment (CORDA) to build genome scale models in a tissue-specific manner. CORDA performs more efficiently computationally, shows better agreement to experimental data, and displays better model functionality and capacity when compared to previous algorithms. CORDA also returns reaction associations that can greatly assist in any manual curation to be performed following the automated reconstruction process. Using CORDA, we developed a library of 76 healthy and 20 cancer tissue-specific reconstructions. These reconstructions identified which metabolic pathways are shared across diverse human tissues. Moreover, we identified changes in reactions and pathways that are differentially included and present different capacity profiles in cancer compared to healthy tissues, including up-regulation of folate metabolism, the down-regulation of thiamine metabolism, and tight regulation of oxidative phosphorylation.

Identification of candidate network hubs involved in metabolic adjustments of rice under drought stress by integrating transcriptome data and genome-scale metabolic network

In this study, we have integrated a rice genome-scale metabolic network and the transcriptome of a drought-tolerant rice line, DK151, to identify the major transcriptional regulators involved in metabolic adjustments necessary for adaptation to drought. This was achieved by examining the differential expressions of transcription factors and metabolic genes in leaf, root and young panicle of rice plants subjected to drought stress during tillering, booting and panicle elongation stages. Critical transcription factors such as AP2/ERF, bZIP, MYB and NAC that control the important nodes in the gene regulatory pathway were identified through correlative analysis of the patterns of spatio-temporal expression and cis-element enrichment. We showed that many of the candidate transcription factors involved in metabolic adjustments were previously linked to phenotypic variation for drought tolerance. This approach represents the first attempt to integrate models of transcriptional regulation and metabolic pathways for the identification of candidate regulatory genes for targeted selection in rice breeding.