Next generation sequencing technology and genomewide data analysis: Perspectives for retinal research.

Prog Retin Eye Res. 2016 Jun 10;

Authors: Chaitankar V, Karakülah G, Ratnapriya R, Giuste FO, Brooks MJ, Swaroop A

Abstract
The advent of high throughput next generation sequencing (NGS) has accelerated the pace of discovery of disease-associated genetic variants and genomewide profiling of expressed sequences and epigenetic marks, thereby permitting systems-based analyses of ocular development and disease. Rapid evolution of NGS and associated methodologies presents significant challenges in acquisition, management, and analysis of large data sets and for extracting biologically or clinically relevant information. Here we illustrate the basic design of commonly used NGS-based methods, specifically whole exome sequencing, transcriptome, and epigenome profiling, and provide recommendations for data analyses. We briefly discuss systems biology approaches for integrating multiple data sets to elucidate gene regulatory or disease networks. While we provide examples from the retina, the NGS guidelines reviewed here are applicable to other tissues/cell types as well.

PMID: 27297499 [PubMed - as supplied by publisher]

Differential integrative omic analysis for mechanism insights and biomarker discovery of abnormal Savda syndrome and its unique Munziq prescription.

Sci Rep. 2016;6:27831

Authors: Guo X, Bakri I, Abudula A, Arken K, Mijit M, Mamtimin B, Upur H

Abstract
Research has shown that many cancers have acommon pathophysiological origin and often present with similar symptoms. In terms of Traditional Uighur Medicine (TUM) Hilit (body fluid) theory, abnormal Savda syndrome (ASS) formed by abnormal Hilit is the common phenotype of complex diseases and in particular tumours. Abnormal Savda Munziq (ASMq), one representative of TUM, has been effective in the treatment of cancer since ancient times. Despite the physiopathology of ASS, the relationship between causative factors and the molecular mechanism of ASMq are not fully understood. The current study expanded upon earlier work by integrating traditional diagnostic approaches with others utilizing systems biology technology for the analysis of proteomic (iTRAQ) and metabolomic ((1)H-NMR) profiles of Uighur Medicine target organ lesion (liver) tumours. The candidate proteins were analyzed by enrichment analysis of the biological process and biomarker filters. Subsequently, 3Omics web-based tools were used to determine the relationships between proteins and appropriate metabolites. ELISA assay and IHC methods were used to verify the proteomic result; the protein von Willebrand factor (vWF) may be the "therapeutic window" of ASMq and biomarkers of ASS. This study is likely to be of great significance for the standardization and modernization of TUM.

PMID: 27296761 [PubMed - in process]

Individualized network-based drug repositioning infrastructure for precision oncology in the panomics era.

Brief Bioinform. 2016 Jun 12;

Authors: Cheng F, Hong H, Yang S, Wei Y

Abstract
Advances in next-generation sequencing technologies have generated the data supporting a large volume of somatic alterations in several national and international cancer genome projects, such as The Cancer Genome Atlas and the International Cancer Genome Consortium. These cancer genomics data have facilitated the revolution of a novel oncology drug discovery paradigm from candidate target or gene studies toward targeting clinically relevant driver mutations or molecular features for precision cancer therapy. This focuses on identifying the most appropriately targeted therapy to an individual patient harboring a particularly genetic profile or molecular feature. However, traditional experimental approaches that are used to develop new chemical entities for targeting the clinically relevant driver mutations are costly and high-risk. Drug repositioning, also known as drug repurposing, re-tasking or re-profiling, has been demonstrated as a promising strategy for drug discovery and development. Recently, computational techniques and methods have been proposed for oncology drug repositioning and identifying pharmacogenomics biomarkers, but overall progress remains to be seen. In this review, we focus on introducing new developments and advances of the individualized network-based drug repositioning approaches by targeting the clinically relevant driver events or molecular features derived from cancer panomics data for the development of precision oncology drug therapies (e.g. one-person trials) to fully realize the promise of precision medicine. We discuss several potential challenges (e.g. tumor heterogeneity and cancer subclones) for precision oncology. Finally, we highlight several new directions for the precision oncology drug discovery via biotherapies (e.g. gene therapy and immunotherapy) that target the 'undruggable' cancer genome in the functional genomics era.

PMID: 27296652 [PubMed - as supplied by publisher]

A Systems Biology Approach to Reveal Putative Host-Derived Biomarkers of Periodontitis by Network Topology Characterization of MMP-REDOX/NO and Apoptosis Integrated Pathways.

Front Cell Infect Microbiol. 2015;5:102

Authors: Zeidán-Chuliá F, Gürsoy M, Neves de Oliveira BH, Özdemir V, Könönen E, Gürsoy UK

Abstract
Periodontitis, a formidable global health burden, is a common chronic disease that destroys tooth-supporting tissues. Biomarkers of the early phase of this progressive disease are of utmost importance for global health. In this context, saliva represents a non-invasive biosample. By using systems biology tools, we aimed to (1) identify an integrated interactome between matrix metalloproteinase (MMP)-REDOX/nitric oxide (NO) and apoptosis upstream pathways of periodontal inflammation, and (2) characterize the attendant topological network properties to uncover putative biomarkers to be tested in saliva from patients with periodontitis. Hence, we first generated a protein-protein network model of interactions ("BIOMARK" interactome) by using the STRING 10 database, a search tool for the retrieval of interacting genes/proteins, with "Experiments" and "Databases" as input options and a confidence score of 0.400. Second, we determined the centrality values (closeness, stress, degree or connectivity, and betweenness) for the "BIOMARK" members by using the Cytoscape software. We found Ubiquitin C (UBC), Jun proto-oncogene (JUN), and matrix metalloproteinase-14 (MMP14) as the most central hub- and non-hub-bottlenecks among the 211 genes/proteins of the whole interactome. We conclude that UBC, JUN, and MMP14 are likely an optimal candidate group of host-derived biomarkers, in combination with oral pathogenic bacteria-derived proteins, for detecting periodontitis at its early phase by using salivary samples from patients. These findings therefore have broader relevance for systems medicine in global health as well.

PMID: 26793622 [PubMed - indexed for MEDLINE]

Changes in lipid metabolism and β-adrenergic response of adipose tissues of periparturient dairy cows affected by an energy-dense diet and nicotinic acid supplementation.

J Anim Sci. 2015 Aug;93(8):4012-22

Authors: Kenéz Á, Tienken R, Locher L, Meyer U, Rizk A, Rehage J, Dänicke S, Huber K

Abstract
Dairy cattle will mobilize large amounts of body fat during early lactation as an effect of decreased lipogenesis and increased lipolysis. Regulation of lipid metabolism involves fatty acid synthesis from acetate and β-adrenergic-stimulated phosphorylation of hormone-sensitive lipase (HSL) and perilipin in adipocytes. Although basic mechanisms of mobilizing fat storage in transition cows are understood, we lack a sufficiently detailed understanding to declare the exact regulatory network of these in a broad range of dairy cattle. The objective of the present study was to quantify 1) protein abundance of fatty acid synthase (FAS), 2) extent of phosphorylation of HSL and perilipin in vivo, and 3) β-adrenergic stimulated lipolytic response of adipose tissues in vitro at different stages of the periparturient period. We fed 20 German Holstein cows an energy-dense or an energetically adequate diet prepartum and 0 or 24 g/d nicotinic acid (NA) supplementation. Biopsy samples of subcutaneous and retroperitoneal adipose tissue were obtained at d 42 prepartum (d -42) and at d 1, 21, and 100 postpartum (d +1, d +21, d +100, respectively). To assess β-adrenergic response, tissue samples were incubated with 1 μ isoproterenol for 90 min at 37°C. The NEFA and glycerol release, as well as HSL and perilipin phosphorylation, was measured as indicators of in vitro stimulated lipolysis. In addition, protein expression of FAS and extent of HSL and perilipin phosphorylation were measured in fresh, nonincubated samples. There was no effect of dietary energy density or NA on the observed variables. The extent of HSL and perilipin phosphorylation under isoproterenol stimulation was strongly correlated with the release of NEFA and glycerol, consistent with the functional link between β-adrenergic-stimulated protein phosphorylation and lipolysis. In the nonincubated samples, FAS protein expression was decreased at d +1 and d +21, whereas HSL and perilipin phosphorylation increased from d -42 to d +1 and remained at an increased level throughout the first 100 d of lactation. In vitro lipolytic response was significant in prepartum samples at times when in vivo lipolysis was only minimally activated by phosphorylation. These data extend our understanding of the complex nature of control of lipolysis and lipogenesis in dairy cows and could be useful to the ongoing development of systems biology models of metabolism to help improve our quantitative knowledge of the cow.

PMID: 26440181 [PubMed - indexed for MEDLINE]

Systematic Mapping of Kinase Addiction Combinations in Breast Cancer Cells by Integrating Drug Sensitivity and Selectivity Profiles.

Chem Biol. 2015 Aug 20;22(8):1144-55

Authors: Szwajda A, Gautam P, Karhinen L, Jha SK, Saarela J, Shakyawar S, Turunen L, Yadav B, Tang J, Wennerberg K, Aittokallio T

Abstract
Chemical perturbation screens offer the possibility to identify actionable sets of cancer-specific vulnerabilities. However, most inhibitors of kinases or other cancer targets result in polypharmacological effects, which complicate the identification of target dependencies directly from the drug-response phenotypes. In this study, we developed a chemical systems biology approach that integrates comprehensive drug sensitivity and selectivity profiling to provide functional insights into both single and multi-target oncogenic signal addictions. When applied to 21 breast cancer cell lines, perturbed with 40 kinase inhibitors, the subtype-specific addiction patterns clustered in agreement with patient-derived subtypes, while showing considerable variability between the heterogeneous breast cancers. Experimental validation of the top predictions revealed a number of co-dependencies between kinase targets that led to unexpected synergistic combinations between their inhibitors, such as dasatinib and axitinib in the triple-negative basal-like HCC1937 cell line.

PMID: 26211361 [PubMed - indexed for MEDLINE]

Design of Probabilistic Boolean Networks Based on Network Structure and Steady-State Probabilities.

IEEE Trans Neural Netw Learn Syst. 2016 Jun 6;

Authors: Kobayashi K, Hiraishi K

Abstract
In this brief, we consider the problem of finding a probabilistic Boolean network (PBN) based on a network structure and desired steady-state properties. In systems biology and synthetic biology, such problems are important as an inverse problem. Using a matrix-based representation of PBNs, a solution method for this problem is proposed. The problem of finding a BN has been studied so far. In the problem of finding a PBN, we must calculate not only the Boolean functions, but also the probabilities of selecting a Boolean function and the number of candidates of the Boolean functions. Hence, the problem of finding a PBN is more difficult than that of finding a BN. The effectiveness of the proposed method is presented by numerical examples.

PMID: 27295690 [PubMed - as supplied by publisher]

How modeling standards, software, and initiatives support reproducibility in systems biology and systems medicine.

IEEE Trans Biomed Eng. 2016 Jun 2;

Authors: Waltemath D, Wolkenhauer O

Abstract
Only reproducible results are of significance to science. A lack of suitable standards and appropriate support of standards in software tools has led to numerous publications with irreproducible results. Our objectives are to identify the key challenges of reproducible research and to highlight existing solutions.
RESULTS: In this paper, we summarise problems concerning reproducibility in systems biology and systems medicine. We focus on initiatives, standards and software tools that aim to improve the reproducibility of simulation studies.
CONCLUSIONS: The long-term success of systems biology and systems medicine depends on trustworthy models and simulations. This requires openness to ensure reusability and transparency to enable reproducibility of results in these fields.

PMID: 27295645 [PubMed - as supplied by publisher]

Metabolomics as a Challenging Approach for Medicinal Chemistry and Personalized Medicine.

J Med Chem. 2016 Jun 13;

Authors: Frederich M, Pirotte B, Fillet M, De Tullio P

Abstract
"Omics" sciences have been developed to provide a holistic point of view of biology and to better understand the complexity of an organism as a whole. These systems biology approaches can be examined at different levels, starting from the most fundamental, i.e., the genome, and finishing with the most functional, i.e., the metabolome. Similar to how genomics is applied to the exploration of DNA, metabolomics is the qualitative and quantitative study of metabolites. This emerging field is clearly linked to genomics, transcriptomics and proteomics. In addition, metabolomics provides a unique and direct vision of the functional outcome of an organism's activities that are required for it to survive, grow and respond to internal and external stimuli or stress, e.g., pathologies and drugs. The links between metabolic changes, patient phenotype, physiological and/or pathological status and treatment are now well established and have opened a new area for the application of metabolomics in the drug discovery process and in personalized medicine.

PMID: 27295417 [PubMed - as supplied by publisher]

Unbiased Rare Event Sampling in Spatial Stochastic Systems Biology Models Using a Weighted Ensemble of Trajectories.

PLoS Comput Biol. 2016 Feb;12(2):e1004611

Authors: Donovan RM, Tapia JJ, Sullivan DP, Faeder JR, Murphy RF, Dittrich M, Zuckerman DM

Abstract
The long-term goal of connecting scales in biological simulation can be facilitated by scale-agnostic methods. We demonstrate that the weighted ensemble (WE) strategy, initially developed for molecular simulations, applies effectively to spatially resolved cell-scale simulations. The WE approach runs an ensemble of parallel trajectories with assigned weights and uses a statistical resampling strategy of replicating and pruning trajectories to focus computational effort on difficult-to-sample regions. The method can also generate unbiased estimates of non-equilibrium and equilibrium observables, sometimes with significantly less aggregate computing time than would be possible using standard parallelization. Here, we use WE to orchestrate particle-based kinetic Monte Carlo simulations, which include spatial geometry (e.g., of organelles, plasma membrane) and biochemical interactions among mobile molecular species. We study a series of models exhibiting spatial, temporal and biochemical complexity and show that although WE has important limitations, it can achieve performance significantly exceeding standard parallel simulation--by orders of magnitude for some observables.

PMID: 26845334 [PubMed - indexed for MEDLINE]

Quantitative Selection Analysis of Bacteriophage φCbK Susceptibility in Caulobacter crescentus.

J Mol Biol. 2016 Jan 29;428(2 Pt B):419-30

Authors: Christen M, Beusch C, Bösch Y, Cerletti D, Flores-Tinoco CE, Del Medico L, Tschan F, Christen B

Abstract
Classical molecular genetics uses stringent selective conditions to identify mutants with distinct phenotypic responses. Mutations giving rise to less pronounced phenotypes are often missed. However, to gain systems-level insights into complex genetic interaction networks requires genome-wide assignment of quantitative phenotypic traits. In this paper, we present a quantitative selection approach coupled with transposon sequencing (QS-TnSeq) to globally identify the cellular components that orchestrate susceptibility of the cell cycle model bacterium Caulobacter crescentus toward bacteriophage φCbK infection. We found that 135 genes representing 3.30% of the Caulobacter genome exhibit significant accumulation of transposon insertions upon φCbK selection. More than 85% thereof consist of new factors not previously associated with phage φCbK susceptibility. Using hierarchical clustering of dose-dependent TnSeq datasets, we grouped these genes into functional modules that correlate with different stages of the φCbK infection process. We assign φCbK susceptibility to eight new genes that represent novel components of the pilus secretion machinery. Further, we demonstrate that, from 86 motility genes, only seven genes encoding structural and regulatory components of the flagellar hook increase phage resistance when disrupted by transposons, suggesting a link between flagellar hook assembly and pili biogenesis. In addition, we observe high recovery of Tn5 insertions within regulatory sequences of the genes encoding the essential NADH:ubiquinone oxidoreductase complex indicating that intact proton motive force is crucial for effective phage propagation. In sum, QS-TnSeq is broadly applicable to perform quantitative and genome-wide systems-genetics analysis of complex phenotypic traits.

PMID: 26593064 [PubMed - indexed for MEDLINE]

PGSB PlantsDB: updates to the database framework for comparative plant genome research.

Nucleic Acids Res. 2016 Jan 4;44(D1):D1141-7

Authors: Spannagl M, Nussbaumer T, Bader KC, Martis MM, Seidel M, Kugler KG, Gundlach H, Mayer KF

Abstract
PGSB (Plant Genome and Systems Biology: formerly MIPS) PlantsDB (http://pgsb.helmholtz-muenchen.de/plant/index.jsp) is a database framework for the comparative analysis and visualization of plant genome data. The resource has been updated with new data sets and types as well as specialized tools and interfaces to address user demands for intuitive access to complex plant genome data. In its latest incarnation, we have re-worked both the layout and navigation structure and implemented new keyword search options and a new BLAST sequence search functionality. Actively involved in corresponding sequencing consortia, PlantsDB has dedicated special efforts to the integration and visualization of complex triticeae genome data, especially for barley, wheat and rye. We enhanced CrowsNest, a tool to visualize syntenic relationships between genomes, with data from the wheat sub-genome progenitor Aegilops tauschii and added functionality to the PGSB RNASeqExpressionBrowser. GenomeZipper results were integrated for the genomes of barley, rye, wheat and perennial ryegrass and interactive access is granted through PlantsDB interfaces. Data exchange and cross-linking between PlantsDB and other plant genome databases is stimulated by the transPLANT project (http://transplantdb.eu/).

PMID: 26527721 [PubMed - indexed for MEDLINE]

High-throughput, big data and complexity in clinical proteomics: an interview with Jasminka Godovac-Zimmermann.

Expert Rev Mol Diagn. 2015;15(10):1241-4

Authors: Godovac-Zimmermann J, Raison C

Abstract
Interview with Professor Jasminka Godovac-Zimmermann, PhD by Claire Raison (Commissioning Editor) Professor Jasminka Godovac-Zimmermann is Head of the Proteomics and Molecular Cell Dynamics Group at University College London, UK. Professor Godovac-Zimmermann trained at the Max Planck Institute of Biochemistry, Germany, and specialized in protein chemistry. Her research focuses on proteomics in cancer and systems biology. Here she talks about the clinical impact of her work and her hopes and predictions for how proteomics and diagnostics could work together in future.

PMID: 26367346 [PubMed - indexed for MEDLINE]

Plant sulfur nutrition: From Sachs to Big Data.

Plant Signal Behav. 2015;10(9):e1055436

Authors: Kopriva S

Abstract
Together with water and carbon dioxide plants require 14 essential mineral nutrients to finish their life cycle. The research in plant nutrition can be traced back to Julius Sachs, who was the first to experimentally prove the essentiality of mineral nutrients for plants. Among those elements Sachs showed to be essential is sulfur. Plant sulfur nutrition has been not as extensively studied as the nutrition of nitrogen and phosphate, probably because sulfur was not limiting for agriculture. However, with the reduction of atmospheric sulfur dioxide emissions sulfur deficiency has become common. The research in sulfur nutrition has changed over the years from using yeast and algae as experimental material to adopting Arabidopsis as the plant model as well as from simple biochemical measurements of individual parameters to system biology. Here the evolution of sulfur research from the times of Sachs to the current Big Data is outlined.

PMID: 26305261 [PubMed - indexed for MEDLINE]

Recent strategies and progress in identifying host factors involved in virus replication.

Curr Opin Microbiol. 2015 Aug;26:79-88

Authors: König R, Stertz S

Abstract
Viruses are completely dependent on their host cells for the successful production of progeny viruses. At each stage of the viral life cycle an intricate interplay between virus and host takes place with the virus aiming to usurp the host cell for its purposes and the host cell trying to block the intruder from propagation. In recent years these interactions have been studied on a global level by systems biology approaches, such as RNA interference screens, transcriptomic or proteomic methodologies, and exciting new insights into the pathogen-host relationship have been revealed. In this review, we summarize the available data, give examples for important findings from such studies and point out current limitations and potential future directions.

PMID: 26112615 [PubMed - indexed for MEDLINE]

Synechocystis sp. PCC6803 metabolic models for the enhanced production of hydrogen.

Crit Rev Biotechnol. 2015 Jun;35(2):184-98

Authors: Montagud A, Gamermann D, Fernández de Córdoba P, Urchueguía JF

Abstract
In the present economy, difficulties to access energy sources are real drawbacks to maintain our current lifestyle. In fact, increasing interests have been gathered around efficient strategies to use energy sources that do not generate high CO2 titers. Thus, science-funding agencies have invested more resources into research on hydrogen among other biofuels as interesting energy vectors. This article reviews present energy challenges and frames it into the present fuel usage landscape. Different strategies for hydrogen production are explained and evaluated. Focus is on biological hydrogen production; fermentation and photon-fuelled hydrogen production are compared. Mathematical models in biology can be used to assess, explore and design production strategies for industrially relevant metabolites, such as biofuels. We assess the diverse construction and uses of genome-scale metabolic models of cyanobacterium Synechocystis sp. PCC6803 to efficiently obtain biofuels. This organism has been studied as a potential photon-fuelled production platform for its ability to grow from carbon dioxide, water and photons, on simple culture media. Finally, we review studies that propose production strategies to weigh this organism's viability as a biofuel production platform. Overall, the work presented in this review unveils the industrial capabilities of cyanobacterium Synechocystis sp. PCC6803 to evolve interesting metabolites as a clean biofuel production platform.

PMID: 24090244 [PubMed - indexed for MEDLINE]

Advances in the integration of transcriptional regulatory information into genome-scale metabolic models.

Biosystems. 2016 Jun 7;

Authors: Vivek-Ananth RP, Samal A

Abstract
A major goal of systems biology is to build predictive computational models of cellular metabolism. Availability of complete genome sequences and wealth of legacy biochemical information has led to the reconstruction of genome-scale metabolic networks in the last 15 years for several organisms across the three domains of life. Due to paucity of information on kinetic parameters associated with metabolic reactions, the constraint-based modelling approach, flux balance analysis (FBA), has proved to be a vital alternative to investigate the capabilities of reconstructed metabolic networks. In parallel, advent of high-throughput technologies has led to the generation of massive amounts of omics data on transcriptional regulation comprising mRNA transcript levels and genome-wide binding profile of transcriptional regulators. A frontier area in metabolic systems biology has been the development of methods to integrate the available transcriptional regulatory information into constraint-based models of reconstructed metabolic networks in order to increase the predictive capabilities of computational models and understand the regulation of cellular metabolism. Here, we review the existing methods to integrate transcriptional regulatory information into constraint-based models of metabolic networks.

PMID: 27287878 [PubMed - as supplied by publisher]

Metabolic characterization of the natural progression of chronic hepatitis B.

Genome Med. 2016;8(1):64

Authors: Schoeman JC, Hou J, Harms AC, Vreeken RJ, Berger R, Hankemeier T, Boonstra A

Abstract
BACKGROUND: Worldwide, over 350 million people are chronically infected with the hepatitis B virus (HBV) and are at increased risk of developing progressive liver diseases. The confinement of HBV replication to the liver, which also acts as the central hub for metabolic and nutritional regulation, emphasizes the interlinked nature of host metabolism and the disease. Still, the metabolic processes operational during the distinct clinical phases of a chronic HBV infection-immune tolerant, immune active, inactive carrier, and HBeAg-negative hepatitis phases-remains unexplored.
METHODS: To investigate this, we conducted a targeted metabolomics approach on serum to determine the metabolic progression over the clinical phases of chronic HBV infection, using patient samples grouped based on their HBV DNA, alanine aminotransferase, and HBeAg serum levels.
RESULTS: Our data illustrate the strength of metabolomics to provide insight into the metabolic dysregulation experienced during chronic HBV. The immune tolerant phase is characterized by the speculated viral hijacking of the glycerol-3-phosphate-NADH shuttle, explaining the reduced glycerophospholipid and increased plasmalogen species, indicating a strong link to HBV replication. The persisting impairment of the choline glycerophospholipids, even during the inactive carrier phase with minimal HBV activity, alludes to possible metabolic imprinting effects. The progression of chronic HBV is associated with increased concentrations of very long chain triglycerides together with citrulline and ornithine, reflective of a dysregulated urea cycle peaking in the HBV envelope antigen-negative phase.
CONCLUSIONS: The work presented here will aid in future studies to (i) validate and understand the implication of these metabolic changes using a thorough systems biology approach, (ii) monitor and predict disease severity, as well as (iii) determine the therapeutic value of the glycerol-3-phosphate-NADH shuttle.

PMID: 27286979 [PubMed - as supplied by publisher]

Metabolic processes of Methanococcus maripaludis and potential applications.

Microb Cell Fact. 2016;15(1):107

Authors: Goyal N, Zhou Z, Karimi IA

Abstract
Methanococcus maripaludis is a rapidly growing, fully sequenced, genetically tractable model organism among hydrogenotrophic methanogens. It has the ability to convert CO2 and H2 into a useful cleaner energy fuel (CH4). In fact, this conversion enhances in the presence of free nitrogen as the sole nitrogen source due to prolonged cell growth. Given the global importance of GHG emissions and climate change, diazotrophy can be attractive for carbon capture and utilization applications from appropriately treated flue gases, where surplus hydrogen is available from renewable electricity sources. In addition, M. maripaludis can be engineered to produce other useful products such as terpenoids, hydrogen, methanol, etc. M. maripaludis with its unique abilities has the potential to be a workhorse like Escherichia coli and S. cerevisiae for fundamental and experimental biotechnology studies. More than 100 experimental studies have explored different specific aspects of the biochemistry and genetics of CO2 and N2 fixation by M. maripaludis. Its genome-scale metabolic model (iMM518) also exists to study genetic perturbations and complex biological interactions. However, a comprehensive review describing its cell structure, metabolic processes, and methanogenesis is still lacking in the literature. This review fills this crucial gap. Specifically, it integrates distributed information from the literature to provide a complete and detailed view for metabolic processes such as acetyl-CoA synthesis, pyruvate synthesis, glycolysis/gluconeogenesis, reductive tricarboxylic acid (RTCA) cycle, non-oxidative pentose phosphate pathway (NOPPP), nitrogen metabolism, amino acid metabolism, and nucleotide biosynthesis. It discusses energy production via methanogenesis and its relation to metabolism. Furthermore, it reviews taxonomy, cell structure, culture/storage conditions, molecular biology tools, genome-scale models, and potential industrial and environmental applications. Through the discussion, it develops new insights and hypotheses from experimental and modeling observations, and identifies opportunities for further research and applications.

PMID: 27286964 [PubMed - as supplied by publisher]

Comparative genome-scale modelling of Staphylococcus aureus strains identifies strain-specific metabolic capabilities linked to pathogenicity.

Proc Natl Acad Sci U S A. 2016 Jun 10;

Authors: Bosi E, Monk JM, Aziz RK, Fondi M, Nizet V, Palsson BØ

Abstract
Staphylococcus aureus is a preeminent bacterial pathogen capable of colonizing diverse ecological niches within its human host. We describe here the pangenome of S. aureus based on analysis of genome sequences from 64 strains of S. aureus spanning a range of ecological niches, host types, and antibiotic resistance profiles. Based on this set, S. aureus is expected to have an open pangenome composed of 7,411 genes and a core genome composed of 1,441 genes. Metabolism was highly conserved in this core genome; however, differences were identified in amino acid and nucleotide biosynthesis pathways between the strains. Genome-scale models (GEMs) of metabolism were constructed for the 64 strains of S. aureus These GEMs enabled a systems approach to characterizing the core metabolic and panmetabolic capabilities of the S. aureus species. All models were predicted to be auxotrophic for the vitamins niacin (vitamin B3) and thiamin (vitamin B1), whereas strain-specific auxotrophies were predicted for riboflavin (vitamin B2), guanosine, leucine, methionine, and cysteine, among others. GEMs were used to systematically analyze growth capabilities in more than 300 different growth-supporting environments. The results identified metabolic capabilities linked to pathogenic traits and virulence acquisitions. Such traits can be used to differentiate strains responsible for mild vs. severe infections and preference for hosts (e.g., animals vs. humans). Genome-scale analysis of multiple strains of a species can thus be used to identify metabolic determinants of virulence and increase our understanding of why certain strains of this deadly pathogen have spread rapidly throughout the world.

PMID: 27286824 [PubMed - as supplied by publisher]