Kazim Yalcin Arga

Genome-scale reconstruction of metabolic network for a halophilic extremophile, Chromohalobacter salexigens DSM 3043

BackgroundChromohalobacter salexigens (formerly Halomonas elongata DSM 3043) is a halophilic extremophile with a very broad salinity range and is used as a model organism to elucidate prokaryotic osmoadaptation due to its strong euryhaline phenotype.ResultsC. salexigens DSM 3043s metabolism was reconstructed based on genomic, biochemical and physiological information via a non-automated but iterative process. This manually-curated reconstruction accounts for 584 genes, 1386 reactions, and 1411 metabolites. By using flux balance analysis, the model was extensively validated against literature data on the C. salexigens phenotypic features, the transport and use of different substrates for growth as well as against experimental observations on the uptake and accumulation of industrially important organic osmolytes, ectoine, betaine, and its precursor choline, which play important roles in the adaptive response to osmotic stress.ConclusionsThis work presents the first comprehensive genome-scale metabolic model of a halophilic bacterium. Being a useful guide for identification and filling of knowledge gaps, the reconstructed metabolic network i OA584 will accelerate the research on halophilic bacteria towards application of systems biology approaches and design of metabolic engineering strategies.

The role of protein interaction networks in systems biomedicine

The challenging task of studying and modeling complex dynamics of biological systems in order to describe various human diseases has gathered great interest in recent years. Major biological processes are mediated through protein interactions, hence there is a need to understand the chaotic network that forms these processes in pursuance of understanding human diseases. The applications of protein interaction networks to disease datasets allow the identification of genes and proteins associated with diseases, the study of network properties, identification of subnetworks, and network-based disease gene classification. Although various protein interaction network analysis strategies have been employed, grand challenges are still existing. Global understanding of protein interaction networks via integration of high-throughput functional genomics data from different levels will allow researchers to examine the disease pathways and identify strategies to control them. As a result, it seems likely that more personalized, more accurate and more rapid disease gene diagnostic techniques will be devised in the future, as well as novel strategies that are more personalized. This mini-review summarizes the current practice of protein interaction networks in medical research as well as challenges to be overcome.

Triple Negative Breast Cancer: A Multi-Omics Network Discovery Strategy for Candidate Targets and Driving Pathways

Triple negative breast cancer (TNBC) represents approximately 15% of breast cancers and is characterized by lack of expression of both estrogen receptor (ER) and progesterone receptor (PR), together with absence of human epidermal growth factor 2 (HER2). TNBC has attracted considerable attention due to its aggressiveness such as large tumor size, high proliferation rate, and metastasis. The absence of clinically efficient molecular targets is of great concern in treatment of patients with TNBC. In light of the complexity of TNBC, we applied a systematic and integrative transcriptomics and interactomics approach utilizing transcriptional regulatory and protein-protein interaction networks to discover putative transcriptional control mechanisms of TNBC. To this end, we identified TNBC-driven molecular pathways such as the Janus kinase-signal transducers, and activators of transcription (JAK-STAT) and tumor necrosis factor (TNF) signaling pathways. The multi-omics molecular target and biomarker discovery approach presented here can offer ways forward on novel diagnostics and potentially help to design personalized therapeutics for TNBC in the future.

The Stimulatory Effect of Mannitol on Levan Biosynthesis: Lessons from Metabolic Systems Analysis of Halomonas smyrnensis AAD6 T

Halomonas smyrnensis AADT is a halophilic, gram‐negative bacterium that can efficiently produce levan from sucrose as carbon source via levansucrase activity. However, systems‐based approaches are required to further enhance its metabolic performance for industrial application. As an important step toward this goal, the genome‐scale metabolic network of Chromohalobacter salexigens DSM3043, which is considered a model organism for halophilic bacteria, has been reconstructed based on its genome annotation, physiological information, and biochemical information. In the present work, the genome‐scale metabolic network of C. salexigens was recruited, and refined via integration of the available biochemical, physiological, and phenotypic features of H. smyrnensis AAD6T. The generic metabolic model, which comprises 1,393 metabolites and 1,108 reactions, was then systematically analyzed in silico using constraints‐based simulations. To elucidate the relationship between levan biosynthesis and other metabolic processes, an enzyme‐graph representation of the metabolic network and a graph decomposition technique were employed. Using the concept of control effective fluxes, significant links between several metabolic processes and levan biosynthesis were estimated. The major finding was the elucidation of the stimulatory effect of mannitol on levan biosynthesis, which was further verified experimentally via supplementation of mannitol to the fermentation medium. The optimal concentration of 30 g/L mannitol supplemented to the 50 g/L sucrose‐based medium resulted in a twofold increase in levan production in parallel with increased sucrose hydrolysis rate, accumulated extracellular glucose, and decreased fructose uptake rate.

Draft Genome Sequence of Halomonas smyrnensis AAD6T

Halomonas smyrnensis AAD6(T) is a Gram-negative, aerobic, exopolysaccharide-producing, and moderately halophilic bacterium that produces levan, a fructose homopolymer with many potential uses in various industries. We report the draft genome sequence of H. smyrnensis AAD6(T), which will accelerate research on the rational design and optimization of microbial levan production.

A system based network approach to ethanol tolerance in Saccharomyces cerevisiae

BackgroundSaccharomyces cerevisiae has been widely used for bio-ethanol production and development of rational genetic engineering strategies leading both to the improvement of productivity and ethanol tolerance is very important for cost-effective bio-ethanol production. Studies on the identification of the genes that are up- or down-regulated in the presence of ethanol indicated that the genes may be involved to protect the cells against ethanol stress, but not necessarily required for ethanol tolerance.ResultsIn the present study, a novel network based approach was developed to identify candidate genes involved in ethanol tolerance. Protein-protein interaction (PPI) network associated with ethanol tolerance (tETN) was reconstructed by integrating PPI data with Gene Ontology (GO) terms. Modular analysis of the constructed networks revealed genes with no previously reported experimental evidence related to ethanol tolerance and resulted in the identification of 17 genes with previously unknown biological functions. We have randomly selected four of these genes and deletion strains of two genes (YDR307W and YHL042W) were found to exhibit improved tolerance to ethanol when compared to wild type strain.The genome-wide transcriptomic response of yeast cells to the deletions of YDR307W and YHL042W in the absence of ethanol revealed that the deletion of YDR307W and YHL042W genes resulted in the transcriptional re-programming of the metabolism resulting from a mis-perception of the nutritional environment. Yeast cells perceived an excess amount of glucose and a deficiency of methionine or sulfur in the absence of YDR307W and YHL042W, respectively, possibly resulting from a defect in the nutritional sensing and signaling or transport mechanisms. Mutations leading to an increase in ribosome biogenesis were found to be important for the improvement of ethanol tolerance. Modulations of chronological life span were also identified to contribute to ethanol tolerance in yeast.ConclusionsThe system based network approach developed allows the identification of novel gene targets for improved ethanol tolerance and supports the highly complex nature of ethanol tolerance in yeast.

Genomic analysis reveals the biotechnological and industrial potential of levan producing halophilic extremophile, Halomonas smyrnensis AAD6T

Halomonas smyrnensis AAD6T is a gram negative, aerobic, and moderately halophilic bacterium, and is known to produce high levels of levan with many potential uses in foods, feeds, cosmetics, pharmaceutical and chemical industries due to its outstanding properties. Here, the whole-genome analysis was performed to gain more insight about the biological mechanisms, and the whole-genome organization of the bacterium. Industrially crucial genes, including the levansucrase, were detected and the genome-scale metabolic model of H. smyrnensis AAD6T was reconstructed. The bacterium was found to have many potential applications in biotechnology not only being a levan producer, but also because of its capacity to produce Pel exopolysaccharide, polyhydroxyalkanoates, and osmoprotectants. The genomic information presented here will not only provide additional information to enhance our understanding of the genetic and metabolic network of halophilic bacteria, but also accelerate the research on systematical design of engineering strategies for biotechnology applications.

Molecular signatures of ovarian diseases: Insights from network medicine perspective

ABSTRACT Dysfunctions and disorders in the ovary lead to a host of diseases including ovarian cancer, ovarian endometriosis, and polycystic ovarian syndrome (PCOS). Understanding the molecular mechanisms behind ovarian diseases is a great challenge. In the present study, we performed a meta-analysis of transcriptome data for ovarian cancer, ovarian endometriosis, and PCOS, and integrated the information gained from statistical analysis with genome-scale biological networks (protein-protein interaction, transcriptional regulatory, and metabolic). Comparative and integrative analyses yielded reporter biomolecules (genes, proteins, metabolites, transcription factors, and micro-RNAs), and unique or common signatures at protein, metabolism, and transcription regulation levels, which might be beneficial to uncovering the underlying biological mechanisms behind the diseases. These signatures were mostly associated with formation or initiation of cancer development, and pointed out the potential tendency of PCOS and endometriosis to tumorigenesis. Molecules and pathways related to MAPK signaling, cell cycle, and apoptosis were the mutual determinants in the pathogenesis of all three diseases. To our knowledge, this is the first report that screens these diseases from a network medicine perspective. This study provides signatures which could be considered as potential therapeutic targets and/or as medical prognostic biomarkers in further experimental and clinical studies. Abbreviations DAVID: Database for Annotation, Visualization and Integrated Discovery; DEGs: differentially expressed genes; GEO: Gene Expression Omnibus; KEGG: Kyoto Encyclopedia of Genes and Genomes; LIMMA: Linear Models for Microarray Data; MBRole: Metabolite Biological Role; miRNA: micro-RNA; PCOS: polycystic ovarian syndrome; PPI: protein-protein interaction; RMA: Robust Multi-Array Average; TF: transcription factor

Integration of multiple biological features yields high confidence human protein interactome.

The biological function of a protein is usually determined by its physical interaction with other proteins. Protein-protein interactions (PPIs) are identified through various experimental methods and are stored in curated databases. The noisiness of the existing PPI data is evident, and it is essential that a more reliable data is generated. Furthermore, the selection of a set of PPIs at different confidence levels might be necessary for many studies. Although different methodologies were introduced to evaluate the confidence scores for binary interactions, a highly reliable, almost complete PPI network of Homo sapiens is not proposed yet. The quality and coverage of human protein interactome need to be improved to be used in various disciplines, especially in biomedicine. In the present work, we propose an unsupervised statistical approach to assign confidence scores to PPIs of H. sapiens. To achieve this goal PPI data from six different databases were collected and a total of 295,288 non-redundant interactions between 15,950 proteins were acquired. The present scoring system included the context information that was assigned to PPIs derived from eight biological attributes. A high confidence network, which included 147,923 binary interactions between 13,213 proteins, had scores greater than the cutoff value of 0.80, for which sensitivity, specificity, and coverage were 94.5%, 80.9%, and 82.8%, respectively. We compared the present scoring method with others for evaluation. Reducing the noise inherent in experimental PPIs via our scoring scheme increased the accuracy significantly. As it was demonstrated through the assessment of process and cancer subnetworks, this study allows researchers to construct and analyze context-specific networks via valid PPI sets and one can easily achieve subnetworks around proteins of interest at a specified confidence level.

Assessment of high-confidence protein-protein interactome in yeast.

The identification of protein-protein interactions (PPIs) and their networks is vitally important to systemically define and understand the roles of proteins in biological systems. In spite of development of numerous experimental systems to detect PPIs and diverse research on assessment of the quality of the obtained data, a consensus--highly reliable, almost complete--interactome of Saccharomyces cerevisiae is not presented yet. In this work, we proposed an unsupervised statistical approach to create a high-confidence yeast PPI network. For this, we assembled databases of interacting protein pairs for yeast and obtained an extremely large PPI dataset which comprises of 135,154 non-redundant interactions between 6191 yeast proteins. A scoring scheme considering eight heterogeneous biological features resulted with a broad score distribution and a highly reliable network consisting of 29,046 physical interactions with scores higher than the threshold value of 0.85, for which sensitivity, specificity and coverage were 86%, 68%, and 72%, respectively. We evaluated our method by comparing it with other scoring schemes and showed that reducing the noise inherent in experimental PPIs via our scoring scheme further increased the accuracy. Current study is expected to increase the efficiency of the methodologies in biological research which make use of protein interaction networks.