Abnormal Bone Acquisition with Early-Life HIV Infection: Role of Immune Activation and Senescent Osteogenic Precursors.

J Bone Miner Res. 2016 Jun 10;

Authors: Manavalan JS, Arpadi S, Tharmarajah S, Shah J, Zhang CA, Foca M, Neu N, Bell DL, Nishiyama KK, Kousteni S, Yin MT

Abstract
INTRODUCTION: Chronic immune activation associated with HIV infection may have negative consequences on bone acquisition in individuals infected with HIV early in life.
METHODS: Bone mineral density (BMD) and microarchitecture were characterized in 38 HIV-infected men on antiretroviral therapy (18 perinatally-infected, 20 adolescence-infected) and 20 uninfected men aged 20-25 years by dual energy x-ray absorptiometry (DXA), high resolution peripheral quantitative computed tomography (HRpQCT). Flow cytometry was utilized to measure CD4 + /CD8+ activation (HLADR + CD38 +) and senescence (CD28-CD57 +) and to quantify circulating osteogenic precursor (COP) cells in peripheral blood mononuclear cells using antibodies to Runx2 and osteocalcin (OCN). Telomere lengths were measured in sorted COP cells using qPCR.
RESULTS: DXA derived areal BMD Z-scores and HRpQCT derived volumetric BMD (vBMD) measures were lower in HIV-infected than uninfected men. Proportion of activated and senescent CD4+ and CD8+ T cells were higher in HIV-infected than uninfected men. The percentage of COP cells (Mean ± SEM) was lower in HIV-infected than uninfected (0.19 ± 0.02% vs 0.43 ± 0.06%; p < 0.0001) men, and also lower in perinatally-infected than adolescence-infected men (0.15 ± 0.02% vs 0.22 ± 0.03%; p < 0.04). Higher proportion of COP cells correlated with higher bone stiffness, a measure of bone strength, while higher proportion of activated CD4+ T cells correlated with lower BMD and stiffness and lower proportion of COP cells.
CONCLUSION: T cell activation with HIV-infection was associated with decreased numbers of osteogenic precursors as well as lower peak bone mass and bone strength. This article is protected by copyright. All rights reserved.

PMID: 27283956 [PubMed - as supplied by publisher]

SMT and TOFT: Why and How They are Opposite and Incompatible Paradigms.

Acta Biotheor. 2016 Jun 9;

Authors: Bizzarri M, Cucina A

Abstract
The Somatic Mutation Theory (SMT) has been challenged on its fundamentals by the Tissue Organization Field Theory of Carcinogenesis (TOFT). However, a recent publication has questioned whether TOFT could be a valid alternative theory of carcinogenesis to that presented by SMT. Herein we critically review arguments supporting the irreducible opposition between the two theoretical approaches by highlighting differences regarding the philosophical, methodological and experimental approaches on which they respectively rely. We conclude that SMT has not explained carcinogenesis due to severe epistemological and empirical shortcomings, while TOFT is gaining momentum. The main issue is actually to submit SMT to rigorous testing. This concern includes the imperatives to seek evidence for disproving one's hypothesis, and to consider the whole, and not just selective evidence.

PMID: 27283400 [PubMed - as supplied by publisher]

Systems Biology Approaches for Understanding Genome Architecture.

Methods Mol Biol. 2016;1431:109-26

Authors: Sewitz S, Lipkow K

Abstract
The linear and three-dimensional arrangement and composition of chromatin in eukaryotic genomes underlies the mechanisms directing gene regulation. Understanding this organization requires the integration of many data types and experimental results. Here we describe the approach of integrating genome-wide protein-DNA binding data to determine chromatin states. To investigate spatial aspects of genome organization, we present a detailed description of how to run stochastic simulations of protein movements within a simulated nucleus in 3D. This systems level approach enables the development of novel questions aimed at understanding the basic mechanisms that regulate genome dynamics.

PMID: 27283305 [PubMed - in process]

Systems biology of viral infection.

Virus Res. 2016 Jun 15;218:1

Authors: Kaderali L, Thiel V

PMID: 27282286 [PubMed - in process]

[Stability Analysis of Susceptible-Infected-Recovered Epidemic Model].

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2015 Oct;32(5):1013-8

Authors: Pan D, Shi H, Huang M, Yuan D

Abstract
With the range of application of computational biology and systems biology gradually expanding, the complexity of the bioprocess models is also increased. To address this difficult problem, it is required to introduce positive alternative analysis method to cope with it. Taking the dynamic model of the epidemic control process as research object, we established an evaluation model in our laboratory. Firstly, the model was solved with nonlinear programming method. The results were shown to be good. Based on biochemical systems theory, the ODE dynamic model was transformed into S-system. The eigen values of the model showed that the system was stable and contained oscillation phenomenon. Next the sensitivities of rate constant and logarithmic gains of the three key parameters were analyzed, as well as the robust of the system. The result indicated that the biochemical systems theory could be applied in different fields more widely.

PMID: 26964304 [PubMed - indexed for MEDLINE]

BIG: A large-scale data integration tool for renal physiology.

Am J Physiol Renal Physiol. 2016 Jun 8;:ajprenal.00249.2016

Authors: Zhao Y, Yang CR, Raghuram V, Parulekar J, Knepper MA

Abstract
Due to recent advances in high throughput techniques, we and others have generated multiple proteomic and transcriptomic databases to describe and quantify gene expression, protein abundance, or cellular signaling on the scale of the whole genome/proteome in kidney cells. The existence of so much data from diverse sources raises the following question: "How can researchers find information efficiently for a given gene product over all of these data sets without searching each data set individually?" This is the type of problem that has motivated the "Big-Data" revolution in Data Science, which has driven progress in fields such as marketing. Here we present an online Big-Data tool called BIG (Biological Information Gatherer) that allows users to submit a single online query to obtain all relevant information from all indexed databases. BIG is accessible at http://big.nhlbi.nih.gov/.

PMID: 27279488 [PubMed - as supplied by publisher]

A systems biology and proteomics-based approach identifies SRC and VEGFA as biomarkers in risk factor mediated coronary heart disease.

Mol Biosyst. 2016 Jun 9;

Authors: V A, Nayar PG, Murugesan R, S S, Krishnan J, Ahmed SS

Abstract
Coronary heart disease (CHD) is the most common cause of death worldwide. The burden of CHD increases with risk factors such as smoking, hypertension, obesity and diabetes. Several studies have demonstrated the association of these classical risk factors with CHD. However, the mechanisms of these associations remain largely unclear due to the complexity of disease pathophysiology and the lack of an integrative approach that fails to provide a definite understanding of molecular linkage. To overcome these problems, we propose a novel systems biology approach that relates causative genes, interactomes and pathways to elucidate the risk factors mediating the molecular mechanisms and biomarkers for feasible diagnosis. The literature was mined to retrieve the causative genes of each risk factor and CHD to construct protein interactomes. The interactomes were examined to identify 298 common molecular signatures. The common signatures were mapped to the tissue network to synthesize a sub-network consisting of 82 proteins. Further, the dissection of the sub-network provides functional modules representing a diverse range of molecular functions, including the AKT/p13k, MAPK and wnt pathways. Also, the prioritization of functional modules identifies SRC, VEGFA and HIF1A as potential candidate markers. Further, we validate these candidates with the existing markers CRP, NOS3 and VCAM1 in the serum of 63 individuals, 33 with CHD and 30 controls, using ELISA. SRC, VEGFA, H1F1A, CRP and NOS3 were significantly altered in patients compared to controls. These results support the utility of these candidate markers for the diagnosis of CHD. Overall, our molecular observations indicate the influence of risk factors in the pathophysiology of CHD and identify serum markers for diagnosis.

PMID: 27279347 [PubMed - as supplied by publisher]

Global, quantitative and dynamic mapping of protein subcellular localization.

Elife. 2016 Jun 9;5

Authors: Itzhak DN, Tyanova S, Cox J, Borner GH

Abstract
Subcellular localization critically influences protein function, and cells control protein localization to regulate biological processes. We have developed and applied Dynamic Organellar Maps, a proteomic method that allows global mapping of protein translocation events. We initially used maps statically to generate a database with localization and absolute copy number information for over 8,700 proteins from HeLa cells, approaching comprehensive coverage. All major organelles were resolved, with exceptional prediction accuracy (estimated at >92%). Combining spatial and abundance information yielded an unprecedented quantitative view of HeLa cell anatomy and organellar composition, at the protein level. We subsequently demonstrated the dynamic capabilities of the approach by capturing translocation events following EGF stimulation, which we integrated into a quantitative model. Dynamic Organellar Maps enable the proteome-wide analysis of physiological protein movements, without requiring any reagents specific to the investigated process, and will thus be widely applicable in cell biology.

PMID: 27278775 [PubMed - as supplied by publisher]

Applying NGS Data to Find Evolutionary Network Biomarkers from the Early and Late Stages of Hepatocellular Carcinoma.

Biomed Res Int. 2015;2015:391475

Authors: Wong YH, Wu CC, Lin CL, Chen TS, Chang TH, Chen BS

Abstract
Hepatocellular carcinoma (HCC) is a major liver tumor (~80%), besides hepatoblastomas, angiosarcomas, and cholangiocarcinomas. In this study, we used a systems biology approach to construct protein-protein interaction networks (PPINs) for early-stage and late-stage liver cancer. By comparing the networks of these two stages, we found that the two networks showed some common mechanisms and some significantly different mechanisms. To obtain differential network structures between cancer and noncancer PPINs, we constructed cancer PPIN and noncancer PPIN network structures for the two stages of liver cancer by systems biology method using NGS data from cancer cells and adjacent noncancer cells. Using carcinogenesis relevance values (CRVs), we identified 43 and 80 significant proteins and their PPINs (network markers) for early-stage and late-stage liver cancer. To investigate the evolution of network biomarkers in the carcinogenesis process, a primary pathway analysis showed that common pathways of the early and late stages were those related to ordinary cancer mechanisms. A pathway specific to the early stage was the mismatch repair pathway, while pathways specific to the late stage were the spliceosome pathway, lysine degradation pathway, and progesterone-mediated oocyte maturation pathway. This study provides a new direction for cancer-targeted therapies at different stages.

PMID: 26366411 [PubMed - indexed for MEDLINE]

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Polysaccharide degradation systems of the saprophytic bacterium Cellvibrio japonicus.

World J Microbiol Biotechnol. 2016 Jul;32(7):121

Authors: Gardner JG

Abstract
Study of recalcitrant polysaccharide degradation by bacterial systems is critical for understanding biological processes such as global carbon cycling, nutritional contributions of the human gut microbiome, and the production of renewable fuels and chemicals. One bacterium that has a robust ability to degrade polysaccharides is the Gram-negative saprophyte Cellvibrio japonicus. A bacterium with a circuitous history, C. japonicus underwent several taxonomy changes from an initially described Pseudomonas sp. Most of the enzymes described in the pre-genomics era have also been renamed. This review aims to consolidate the biochemical, structural, and genetic data published on C. japonicus and its remarkable ability to degrade cellulose, xylan, and pectin substrates. Initially, C. japonicus carbohydrate-active enzymes were studied biochemically and structurally for their novel polysaccharide binding and degradation characteristics, while more recent systems biology approaches have begun to unravel the complex regulation required for lignocellulose degradation in an environmental context. Also included is a discussion for the future of C. japonicus as a model system, with emphasis on current areas unexplored in terms of polysaccharide degradation and emerging directions for C. japonicus in both environmental and biotechnological applications.

PMID: 27263016 [PubMed - as supplied by publisher]

Towards Consensus Gene Ages.

Genome Biol Evol. 2016 Jun 3;

Authors: Liebeskind BJ, McWhite CD, Marcotte EM

Abstract
Correctly estimating the age of a gene or gene family is important for a variety of fields, including molecular evolution, comparative genomics, and phylogenetics, and increasingly for systems biology and disease genetics. However, most studies use only a point estimate of a gene's age, neglecting the substantial uncertainty involved in this estimation. Here, we characterize this uncertainty by investigating the effect of algorithm choice on gene-age inference and calculate consensus gene ages with attendant error distributions for a variety of model eukaryotes. We use thirteen orthology inference algorithms to create gene-age datasets and then characterize the error around each age-call on a per-gene and per-algorithm basis. Systematic error was found to be a large factor in estimating gene age, suggesting that simple consensus algorithms are not enough to give a reliable point estimate. We also found that different sources of error can affect downstream analyses, such as gene ontology enrichment. Our consensus gene-age datasets, with associated error terms, are made fully available at so that researchers can propagate this uncertainty through their analyses ( GENEAGESORG: ).

PMID: 27259914 [PubMed - as supplied by publisher]

Mathematical models of breast and ovarian cancers.

Wiley Interdiscip Rev Syst Biol Med. 2016 Jun 3;

Authors: Botesteanu DA, Lipkowitz S, Lee JM, Levy D

Abstract
Women constitute the majority of the aging United States (US) population, and this has substantial implications on cancer population patterns and management practices. Breast cancer is the most common women's malignancy, while ovarian cancer is the most fatal gynecological malignancy in the US. In this review, we focus on these subsets of women's cancers, seen more commonly in postmenopausal and elderly women. In order to systematically investigate the complexity of cancer progression and response to treatment in breast and ovarian malignancies, we assert that integrated mathematical modeling frameworks viewed from a systems biology perspective are needed. Such integrated frameworks could offer innovative contributions to the clinical women's cancers community, as answers to clinical questions cannot always be reached with contemporary clinical and experimental tools. Here, we recapitulate clinically known data regarding the progression and treatment of the breast and ovarian cancers. We compare and contrast the two malignancies whenever possible in order to emphasize areas where substantial contributions could be made by clinically inspired and validated mathematical modeling. We show how current paradigms in the mathematical oncology community focusing on the two malignancies do not make comprehensive use of, nor substantially reflect existing clinical data, and we highlight the modeling areas in most critical need of clinical data integration. We emphasize that the primary goal of any mathematical study of women's cancers should be to address clinically relevant questions. For further resources related to this article, please visit the WIREs website.

PMID: 27259061 [PubMed - as supplied by publisher]

Chronic Lung Allograft Dysfunction: A Systems Review of Mechanisms.

Transplantation. 2016 Jun 2;

Authors: Royer PJ, Olivera-Botello G, Koutsokera A, Aubert JD, Bernasconi E, Tissot A, Pison C, Nicod L, Boissel JP, Magnan A, SysCLAD consortium

Abstract
Chronic lung allograft dysfunction (CLAD) is the major limitation of long-term survival after lung transplantation. Chronic lung allograft dysfunction manifests as bronchiolitis obliterans syndrome or the recently described restrictive allograft syndrome. Although numerous risk factors have been identified so far, the physiopathological mechanisms of CLAD remain poorly understood. We investigate here the immune mechanisms involved in the development of CLAD after lung transplantation. We explore the innate or adaptive immune reactions induced by the allograft itself or by the environment and how they lead to allograft dysfunction. Because current literature suggests bronchiolitis obliterans syndrome and restrictive allograft syndrome as 2 distinct entities, we focus on the specific factors behind one or the other syndromes. Chronic lung allograft dysfunction is a multifactorial disease that remains irreversible and unpredictable so far. We thus finally discuss the potential of systems-biology approach to predict its occurrence and to better understand its underlying mechanisms.

PMID: 27257997 [PubMed - as supplied by publisher]

Gene Co-Expression Network Analysis for Identifying Modules and Functionally Enriched Pathways in Type 1 Diabetes.

PLoS One. 2016;11(6):e0156006

Authors: Riquelme Medina I, Lubovac-Pilav Z

Abstract
Type 1 diabetes (T1D) is a complex disease, caused by the autoimmune destruction of the insulin producing pancreatic beta cells, resulting in the body's inability to produce insulin. While great efforts have been put into understanding the genetic and environmental factors that contribute to the etiology of the disease, the exact molecular mechanisms are still largely unknown. T1D is a heterogeneous disease, and previous research in this field is mainly focused on the analysis of single genes, or using traditional gene expression profiling, which generally does not reveal the functional context of a gene associated with a complex disorder. However, network-based analysis does take into account the interactions between the diabetes specific genes or proteins and contributes to new knowledge about disease modules, which in turn can be used for identification of potential new biomarkers for T1D. In this study, we analyzed public microarray data of T1D patients and healthy controls by applying a systems biology approach that combines network-based Weighted Gene Co-Expression Network Analysis (WGCNA) with functional enrichment analysis. Novel co-expression gene network modules associated with T1D were elucidated, which in turn provided a basis for the identification of potential pathways and biomarker genes that may be involved in development of T1D.

PMID: 27257970 [PubMed - as supplied by publisher]

A Graph Based Framework to Model Virus Integration Sites.

Comput Struct Biotechnol J. 2016;14:69-77

Authors: Fronza R, Vasciaveo A, Benso A, Schmidt M

Abstract
With next generation sequencing thousands of virus and viral vector integration genome targets are now under investigation to uncover specific integration preferences and to define clusters of integration, termed common integration sites (CIS), that may allow to assess gene therapy safety or to detect disease related genomic features such as oncogenes. Here, we addressed the challenge to: 1) define the notion of CIS on graph models, 2) demonstrate that the structure of CIS enters in the category of scale-free networks and 3) show that our network approach analyzes CIS dynamically in an integrated systems biology framework using the Retroviral Transposon Tagged Cancer Gene Database (RTCGD) as a testing dataset.

PMID: 27257470 [PubMed]

Integrated omics approaches provide strategies for rapid erythromycin yield increase in Saccharopolyspora erythraea.

Microb Cell Fact. 2016;15(1):93

Authors: Karničar K, Drobnak I, Petek M, Magdevska V, Horvat J, Vidmar R, Baebler Š, Rotter A, Jamnik P, Fujs Š, Turk B, Fonovič M, Gruden K, Kosec G, Petković H

Abstract
BACKGROUND: Omics approaches have significantly increased our understanding of biological systems. However, they have had limited success in explaining the dramatically increased productivity of commercially important natural products by industrial high-producing strains, such as the erythromycin-producing actinomycete Saccharopolyspora erythraea. Further yield increase is of great importance but requires a better understanding of the underlying physiological processes.
RESULTS: To reveal the mechanisms related to erythromycin yield increase, we have undertaken an integrated study of the genomic, transcriptomic, and proteomic differences between the wild type strain NRRL2338 (WT) and the industrial high-producing strain ABE1441 (HP) of S. erythraea at multiple time points of a simulated industrial bioprocess. 165 observed mutations lead to differences in gene expression profiles and protein abundance between the two strains, which were most prominent in the initial stages of erythromycin production. Enzymes involved in erythromycin biosynthesis, metabolism of branched chain amino acids and proteolysis were most strongly upregulated in the HP strain. Interestingly, genes related to TCA cycle and DNA-repair were downregulated. Additionally, comprehensive data analysis uncovered significant correlations in expression profiles of the erythromycin-biosynthetic genes, other biosynthetic gene clusters and previously unidentified putative regulatory genes. Based on this information, we demonstrated that overexpression of several genes involved in amino acid metabolism can contribute to increased yield of erythromycin, confirming the validity of our systems biology approach.
CONCLUSIONS: Our comprehensive omics approach, carried out in industrially relevant conditions, enabled the identification of key pathways affecting erythromycin yield and suggests strategies for rapid increase in the production of secondary metabolites in industrial environment.

PMID: 27255285 [PubMed - in process]

Systems Biology and Noninvasive Imaging of Atherosclerosis.

Arterioscler Thromb Vasc Biol. 2016 Feb;36(2):e1-8

Authors: Calcagno C, Mulder WJ, Nahrendorf M, Fayad ZA

PMID: 26819466 [PubMed - indexed for MEDLINE]

A tuberculosis ontology for host systems biology.

Tuberculosis (Edinb). 2015 Sep;95(5):570-4

Authors: Levine DM, Dutta NK, Eckels J, Scanga C, Stein C, Mehra S, Kaushal D, Karakousis PC, Salamon H

Abstract
A major hurdle facing tuberculosis (TB) investigators who want to utilize a rapidly growing body of data from both systems biology approaches and omics technologies is the lack of a standard vocabulary for data annotation and reporting. Lacking a means to readily compare samples from different research groups, a significant quantity of potentially informative data is largely ignored by researchers. To facilitate standardizing data across studies, a simple ontology of TB terms was developed to provide a common vocabulary for annotating data sets. New terminology was developed to address animal models and experimental systems, and existing clinically focused terminology was modified and adapted. This ontology can be used to annotate host TB data in public databases and collaborations, thereby standardizing database searches and allowing researchers to more easily compare results. To demonstrate the utility of a standard TB ontology for host systems biology, a web application was developed to annotate and compare human and animal model gene expression data sets.

PMID: 26190839 [PubMed - indexed for MEDLINE]

Systems metabolic engineering of microorganisms to achieve large-scale production of flavonoid scaffolds.

J Biotechnol. 2014 Oct 20;188:72-80

Authors: Wu J, Du G, Zhou J, Chen J

Abstract
Flavonoids possess pharmaceutical potential due to their health-promoting activities. The complex structures of these products make extraction from plants difficult, and chemical synthesis is limited because of the use of many toxic solvents. Microbial production offers an alternate way to produce these compounds on an industrial scale in a more economical and environment-friendly manner. However, at present microbial production has been achieved only on a laboratory scale and improvements and scale-up of these processes remain challenging. Naringenin and pinocembrin, which are flavonoid scaffolds and precursors for most of the flavonoids, are the model molecules that are key to solving the current issues restricting industrial production of these chemicals. The emergence of systems metabolic engineering, which combines systems biology with synthetic biology and evolutionary engineering at the systems level, offers new perspectives on strain and process optimization. In this review, current challenges in large-scale fermentation processes involving flavonoid scaffolds and the strategies and tools of systems metabolic engineering used to overcome these challenges are summarized. This will offer insights into overcoming the limitations and challenges of large-scale microbial production of these important pharmaceutical compounds.

PMID: 25160917 [PubMed - indexed for MEDLINE]