Osbaldo Resendis-Antonio

Regulation by transcription factors in bacteria: beyond description

Transcription is an essential step in gene expression and its understanding has been one of the major interests in molecular and cellular biology. By precisely tuning gene expression, transcriptional regulation determines the molecular machinery for developmental plasticity, homeostasis and adaptation. In this review, we transmit the main ideas or concepts behind regulation by transcription factors and give just enough examples to sustain these main ideas, thus avoiding a classical ennumeration of facts. We review recent concepts and developments: cis elements and trans regulatory factors, chromosome organization and structure, transcriptional regulatory networks (TRNs) and transcriptomics. We also summarize new important discoveries that will probably affect the direction of research in gene regulation: epigenetics and stochasticity in transcriptional regulation, synthetic circuits and plasticity and evolution of TRNs. Many of the new discoveries in gene regulation are not extensively tested with wetlab approaches. Consequently, we review this broad area in Inference of TRNs and Dynamical Models of TRNs. Finally, we have stepped backwards to trace the origins of these modern concepts, synthesizing their history in a timeline schema.

Modeling Core Metabolism in Cancer Cells: Surveying the Topology Underlying the Warburg Effect

Background Alterations on glucose consumption and biosynthetic activity of amino acids, lipids and nucleotides are metabolic changes for sustaining cell proliferation in cancer cells. Irrevocable evidence of this fact is the Warburg effect which establishes that cancer cells prefers glycolysis over oxidative phosphorylation to generate ATP. Regulatory action over metabolic enzymes has opened a new window for designing more effective anti-cancer treatments. This enterprise is not trivial and the development of computational models that contribute to identifying potential enzymes for breaking the robustness of cancer cells is a priority. Methodology/Principal Findings This work presents a constraint-base modeling of the most experimentally studied metabolic pathways supporting cancer cells: glycolysis, TCA cycle, pentose phosphate, glutaminolysis and oxidative phosphorylation. To evaluate its predictive capacities, a growth kinetics study for Hela cell lines was accomplished and qualitatively compared with in silico predictions. Furthermore, based on pure computational criteria, we concluded that a set of enzymes (such as lactate dehydrogenase and pyruvate dehydrogenase) perform a pivotal role in cancer cell growth, findings supported by an experimental counterpart. Conclusions/Significance Alterations on metabolic activity are crucial to initiate and sustain cancer phenotype. In this work, we analyzed the phenotype capacities emerged from a constructed metabolic network conformed by the most experimentally studied pathways sustaining cancer cell growth. Remarkably, in silico model was able to resemble the physiological conditions in cancer cells and successfully identified some enzymes currently studied by its therapeutic effect. Overall, we supplied evidence that constraint-based modeling constitutes a promising computational platform to: 1) integrate high throughput technology and establish a crosstalk between experimental validation and in silico prediction in cancer cell phenotype; 2) explore the fundamental metabolic mechanism that confers robustness in cancer; and 3) suggest new metabolic targets for anticancer treatments. All these issues being central to explore cancer cell metabolism from a systems biology perspective.

Systems biology of bacterial nitrogen fixation: High-throughput technology and its integrative description with constraint-based modeling

BackgroundBacterial nitrogen fixation is the biological process by which atmospheric nitrogen is uptaken by bacteroids located in plant root nodules and converted into ammonium through the enzymatic activity of nitrogenase. In practice, this biological process serves as a natural form of fertilization and its optimization has significant implications in sustainable agricultural programs. Currently, the advent of high-throughput technology supplies with valuable data that contribute to understanding the metabolic activity during bacterial nitrogen fixation. This undertaking is not trivial, and the development of computational methods useful in accomplishing an integrative, descriptive and predictive framework is a crucial issue to decoding the principles that regulated the metabolic activity of this biological process.ResultsIn this work we present a systems biology description of the metabolic activity in bacterial nitrogen fixation. This was accomplished by an integrative analysis involving high-throughput data and constraint-based modeling to characterize the metabolic activity in Rhizobium etli bacteroids located at the root nodules of Phaseolus vulgaris ( bean plant). Proteome and transcriptome technologies led us to identify 415 proteins and 689 up-regulated genes that orchestrate this biological process. Taking into account these data, we: 1) extended the metabolic reconstruction reported for R. etli; 2) simulated the metabolic activity during symbiotic nitrogen fixation; and 3) evaluated the in silico results in terms of bacteria phenotype. Notably, constraint-based modeling simulated nitrogen fixation activity in such a way that 76.83% of the enzymes and 69.48% of the genes were experimentally justified. Finally, to further assess the predictive scope of the computational model, gene deletion analysis was carried out on nine metabolic enzymes. Our model concluded that an altered metabolic activity on these enzymes induced different effects in nitrogen fixation, all of these in qualitative agreement with observations made in R. etli and other Rhizobiaceas.ConclusionsIn this work we present a genome scale study of the metabolic activity in bacterial nitrogen fixation. This approach leads us to construct a computational model that serves as a guide for 1) integrating high-throughput data, 2) describing and predicting metabolic activity, and 3) designing experiments to explore the genotype-phenotype relationship in bacterial nitrogen fixation.

Modeling metabolism: A window toward a comprehensive interpretation of networks in cancer

Given the multi-factorial nature of cancer, uncovering its metabolic alterations and evaluating their implications is a major challenge in biomedical sciences that will help in the optimal design of personalized treatments. The advance of high-throughput technologies opens an invaluable opportunity to monitor the activity at diverse biological levels and elucidate how cancer originates, evolves and responds under drug treatments. To this end, researchers are confronted with two fundamental questions: how to interpret high-throughput data and how this information can contribute to the development of personalized treatment in patients. A variety of schemes in systems biology have been suggested to characterize the phenotypic states associated with cancer by utilizing computational modeling and high-throughput data. These theoretical schemes are distinguished by the level of complexity of the biological mechanisms that they represent and by the computational approaches used to simulate them. Notably, these theoretical approaches have provided a proper framework to explore some distinctive metabolic mechanisms observed in cancer cells such as the Warburg effect. In this review, we focus on presenting a general view of some of these approaches whose application and integration will be crucial in the transition from local to global conclusions in cancer studies. We are convinced that multidisciplinary approaches are required to construct the bases of an integrative and personalized medicine, which has been and remains a fundamental task in the medicine of this century.

Filling Kinetic Gaps: Dynamic Modeling of Metabolism Where Detailed Kinetic Information Is Lacking

Background Integrative analysis between dynamical modeling of metabolic networks and data obtained from high throughput technology represents a worthy effort toward a holistic understanding of the link among phenotype and dynamical response. Even though the theoretical foundation for modeling metabolic network has been extensively treated elsewhere, the lack of kinetic information has limited the analysis in most of the cases. To overcome this constraint, we present and illustrate a new statistical approach that has two purposes: integrate high throughput data and survey the general dynamical mechanisms emerging for a slightly perturbed metabolic network. Methodology/Principal Findings This paper presents a statistic framework capable to study how and how fast the metabolites participating in a perturbed metabolic network reach a steady-state. Instead of requiring accurate kinetic information, this approach uses high throughput metabolome technology to define a feasible kinetic library, which constitutes the base for identifying, statistical and dynamical properties during the relaxation. For the sake of illustration we have applied this approach to the human Red blood cell metabolism (hRBC) and its capacity to predict temporal phenomena was evaluated. Remarkable, the main dynamical properties obtained from a detailed kinetic model in hRBC were recovered by our statistical approach. Furthermore, robust properties in time scale and metabolite organization were identify and one concluded that they are a consequence of the combined performance of redundancies and variability in metabolite participation. Conclusions/Significance In this work we present an approach that integrates high throughput metabolome data to define the dynamic behavior of a slightly perturbed metabolic network where kinetic information is lacking. Having information of metabolite concentrations at steady-state, this method has significant relevance due its potential scope to analyze others genome scale metabolic reconstructions. Thus, I expect this approach will significantly contribute to explore the relationship between dynamic and physiology in other metabolic reconstructions, particularly those whose kinetic information is practically nulls. For instances, I envisage that this approach can be useful in genomic medicine or pharmacogenomics, where the estimation of time scales and the identification of metabolite organization may be crucial to characterize and identify (dis)functional stages.

Evolution of Centrality Measurements for the Detection of Essential Proteins in Biological Networks

Current breakthroughs in high-throughput technologies have propelled the development of databases that systematically store knowledge of how genes, proteins, and metabolites interact. To elucidate the mechanisms of molecular interaction, such data can be represented through networks where nodes are biological entities (e.g., gene, protein, miRNA, transcription factor, and metabolites) and edges are associations/interactions between them (e.g., co-expression, signaling, regulation, and physical interaction). One approach to use such networks is to analyze their topological structure and try to relate this to biological function. Topological analysis hints at the possible behavior of a network in the regulation of biological processes or phenotypes and help in unveiling the core mechanisms. Broadly speaking, topological parameters can be used to explore: (1) collective behaviors (global properties such as diameter, small-world and scale-free properties of a network), (2) subnetwork behaviors (functional motif discovery), and (3) individual behaviors (prioritization of important nodes by centrality indices) of various network components (Ma and Gao, 2012). One of the first attempts found in the literature considered centrality related to lethality, and is known as the centrality–lethality rule proposed by Jeong et al. indicating a positive correlation between connectivity and indispensability in the yeast protein-protein interaction map (Jeong et al., 2001). Similarly, Wagner and Fell analyzed the structure of a large metabolic network of E. coli using metabolite node degree and shortest mean path length and observed small world like properties that follow power-law distributions (Wagner and Fell, 2001). In these two comprehensive studies, an old metric system (centrality index) was applied with different strategies, aiming to answer the following question: Do centrality indices predict the essential nodes in the biological networks? Remarkably, topological analyses carried out in transcriptional regulatory (TR) and metabolic networks have been a valuable guide to identify those biological components, called essential nodes, that play a major role in vital functional activities for some microorganisms (Resendis-Antonio et al., 2005, 2012). The relationship between nodes topological features, such as their degree, and their essentiality remains however debated (Coulomb et al., 2005). Prediction of essential proteins is a challenging task because it needs experimental approaches that are expensive, time-consuming, and laborious (Zhong et al., 2013; Li et al., 2014). To optimize the search of essential nodes in biological networks, a series of computational methods that include topological criteria have been proposed. In this paper, we review the cutting edge computational methods by categorizing them according to their underlying strategies to identify essential components. In each case, we discuss their predictive experimental power and identify shortcomings.

Systems biology of cancer: moving toward the integrative study of the metabolic alterations in cancer cells.

One of the main objectives in systems biology is to understand the biological mechanisms that give rise to the phenotype of a microorganism by using high-throughput technologies (HTs) and genome-scale mathematical modeling. The computational modeling of genome-scale metabolic reconstructions is one systemic and quantitative strategy for characterizing the metabolic phenotype associated with human diseases and potentially for designing drugs with optimal clinical effects. The purpose of this short review is to describe how computational modeling, including the specific case of constraint-based modeling, can be used to explore, characterize, and predict the metabolic capacities that distinguish the metabolic phenotype of cancer cell lines. As we show herein, this computational framework is far from a pure theoretical description, and to ensure proper biological interpretation, it is necessary to integrate high-throughput data and generate predictions for later experimental assessment. Hence, genome-scale modeling serves as a platform for the following: (1) the integration of data from HTs, (2) the assessment of how metabolic activity is related to phenotype in cancer cell lines, and (3) the design of new experiments to evaluate the outcomes of the in silico analysis. By combining the functions described above, we show that computational modeling is a useful methodology to construct an integrative, systemic, and quantitative scheme for understanding the metabolic profiles of cancer cell lines, a first step to determine the metabolic mechanism by which cancer cells maintain and support their malignant phenotype in human tissues.

Functional Modules, Structural Topology, and Optimal Activity in Metabolic Networks

Modular organization in biological networks has been suggested as a natural mechanism by which a cell coordinates its metabolic strategies for evolving and responding to environmental perturbations. To understand how this occurs, there is a need for developing computational schemes that contribute to integration of genomic-scale information and assist investigators in formulating biological hypotheses in a quantitative and systematic fashion. In this work, we combined metabolome data and constraint-based modeling to elucidate the relationships among structural modules, functional organization, and the optimal metabolic phenotype of Rhizobium etli, a bacterium that fixes nitrogen in symbiosis with Phaseolus vulgaris. To experimentally characterize the metabolic phenotype of this microorganism, we obtained the metabolic profile of 220 metabolites at two physiological stages: under free-living conditions, and during nitrogen fixation with P. vulgaris. By integrating these data into a constraint-based model, we built a refined computational platform with the capability to survey the metabolic activity underlying nitrogen fixation in R. etli. Topological analysis of the metabolic reconstruction led us to identify modular structures with functional activities. Consistent with modular activity in metabolism, we found that most of the metabolites experimentally detected in each module simultaneously increased their relative abundances during nitrogen fixation. In this work, we explore the relationships among topology, biological function, and optimal activity in the metabolism of R. etli through an integrative analysis based on modeling and metabolome data. Our findings suggest that the metabolic activity during nitrogen fixation is supported by interacting structural modules that correlate with three functional classifications: nucleic acids, peptides, and lipids. More fundamentally, we supply evidence that such modular organization during functional nitrogen fixation is a robust property under different environmental conditions.

Personalized Prediction of Proliferation Rates and Metabolic Liabilities in Cancer Biopsies

Cancer is a heterogeneous disease and its genetic and metabolic mechanism may manifest differently in each patient. This creates a demand for studies that can characterize phenotypic traits of cancer on a per-sample basis. Combining two large data sets, the NCI60 cancer cell line panel, and The Cancer Genome Atlas, we used a linear interaction model to predict proliferation rates for more than 12,000 cancer samples across 33 different cancers from The Cancer Genome Atlas. The predicted proliferation rates are associated with patient survival and cancer stage and show a strong heterogeneity in proliferative capacity within and across different cancer panels. We also show how the obtained proliferation rates can be incorporated into genome-scale metabolic reconstructions to obtain the metabolic fluxes for more than 3000 cancer samples that identified specific metabolic liabilities for nine cancer panels. Here we found that affected pathways coincided with the literature, with pentose phosphate pathway, retinol, and branched-chain amino acid metabolism being the most panel-specific alterations and fatty acid metabolism and ROS detoxification showing homogeneous metabolic activities across all cancer panels. The presented strategy has potential applications in personalized medicine since it can leverage gene expression signatures for cell line based prediction of additional metabolic properties which might help in constraining personalized metabolic models and improve the identification of metabolic alterations in cancer for individual patients.

Synthesis of multi-omic data and community metabolic models reveals insights into the role of hydrogen sulfide in colon cancer

Multi-omic data and genome-scale microbial metabolic models have allowed us to examine microbial communities, community function, and interactions in ways that were not available to us historically. Now, one of our biggest challenges is determining how to integrate data and maximize data potential. Our study demonstrates one way in which to test a hypothesis by combining multi-omic data and community metabolic models. Specifically, we assess hydrogen sulfide production in colorectal cancer based on stool, mucosa, and tissue samples collected on and off the tumor site within the same individuals. 16S rRNA microbial community and abundance data were used to select and inform the metabolic models. We then used MICOM, an open source platform, to track the metabolic flux of hydrogen sulfide through a defined microbial community that either represented on-tumor or off-tumor sample communities. We also performed targeted and untargeted metabolomics, and used the former to quantitatively evaluate our model predictions. A deeper look at the models identified several unexpected but feasible reactions, microbes, and microbial interactions involved in hydrogen sulfide production for which our 16S and metabolomic data could not account. These results will guide future in vitro, in vivo, and in silico tests to establish why hydrogen sulfide production is increased in tumor tissue.