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]

Heme Mobilization in Animals: A Metallolipid's Journey.

Acc Chem Res. 2016 Jun 2;

Authors: Reddi AR, Hamza I

Abstract
Heme is universally recognized as an essential and ubiquitous prosthetic group that enables proteins to carry out a diverse array of functions. All heme-dependent processes, from protein hemylation to heme signaling, require the dynamic and rapid mobilization of heme to hemoproteins present in virtually every subcellular compartment. The cytotoxicity and hydrophobicity of heme necessitates that heme mobilization is carefully controlled at the cellular and systemic level. However, the molecules and mechanisms that mediate heme homeostasis are poorly understood. In this Account, we provide a heuristic paradigm with which to conceptualize heme trafficking and highlight the most recent developments in the mechanisms underlying heme trafficking. As an iron-containing tetrapyrrole, heme exhibits properties of both transition metals and lipids. Accordingly, we propose its transport and trafficking will reflect principles gleaned from the trafficking of both metals and lipids. Using this conceptual framework, we follow the flow of heme from the final step of heme synthesis in the mitochondria to hemoproteins present in various subcellular organelles. Further, given that many cells and animals that cannot make heme can assimilate it intact from nutritional sources, we propose that intercellular heme trafficking pathways must exist. This necessitates that heme be able to be imported and exported from cells, escorted between cells and organs, and regulated at the organismal level via a coordinated systemic process. In this Account, we highlight recently discovered heme transport and trafficking factors and provide the biochemical foundation for the cell and systems biology of heme. Altogether, we seek to reconceptualize heme from an exchange inert cofactor buried in hemoprotein active sites to an exchange labile and mobile metallonutrient.

PMID: 27254265 [PubMed - as supplied by publisher]

Metabolomic Analysis in Brain Research: Opportunities and Challenges.

Front Physiol. 2016;7:183

Authors: Vasilopoulou CG, Margarity M, Klapa MI

Abstract
Metabolism being a fundamental part of molecular physiology, elucidating the structure and regulation of metabolic pathways is crucial for obtaining a comprehensive perspective of cellular function and understanding the underlying mechanisms of its dysfunction(s). Therefore, quantifying an accurate metabolic network activity map under various physiological conditions is among the major objectives of systems biology in the context of many biological applications. Especially for CNS, metabolic network activity analysis can substantially enhance our knowledge about the complex structure of the mammalian brain and the mechanisms of neurological disorders, leading to the design of effective therapeutic treatments. Metabolomics has emerged as the high-throughput quantitative analysis of the concentration profile of small molecular weight metabolites, which act as reactants and products in metabolic reactions and as regulatory molecules of proteins participating in many biological processes. Thus, the metabolic profile provides a metabolic activity fingerprint, through the simultaneous analysis of tens to hundreds of molecules of pathophysiological and pharmacological interest. The application of metabolomics is at its standardization phase in general, and the challenges for paving a standardized procedure are even more pronounced in brain studies. In this review, we support the value of metabolomics in brain research. Moreover, we demonstrate the challenges of designing and setting up a reliable brain metabolomic study, which, among other parameters, has to take into consideration the sex differentiation and the complexity of brain physiology manifested in its regional variation. We finally propose ways to overcome these challenges and design a study that produces reproducible and consistent results.

PMID: 27252656 [PubMed]

Impact of Pathogen Population Heterogeneity and Stress-Resistant Variants on Food Safety.

Annu Rev Food Sci Technol. 2016;7:439-56

Authors: Abee T, Koomen J, Metselaar KI, Zwietering MH, den Besten HM

Abstract
This review elucidates the state-of-the-art knowledge about pathogen population heterogeneity and describes the genotypic and phenotypic analyses of persister subpopulations and stress-resistant variants. The molecular mechanisms underlying the generation of persister phenotypes and genetic variants are identified. Zooming in on Listeria monocytogenes, a comparative whole-genome sequence analysis of wild types and variants that enabled the identification of mutations in variants obtained after a single exposure to lethal food-relevant stresses is described. Genotypic and phenotypic features are compared to those for persistent strains isolated from food processing environments. Inactivation kinetics, models used for fitting, and the concept of kinetic modeling-based schemes for detection of variants are presented. Furthermore, robustness and fitness parameters of L. monocytogenes wild type and variants are used to model their performance in food chains. Finally, the impact of stress-resistant variants and persistence in food processing environments on food safety is discussed.

PMID: 26772414 [PubMed - indexed for MEDLINE]

Advancing metabolic engineering through systems biology of industrial microorganisms.

Curr Opin Biotechnol. 2015 Dec;36:8-15

Authors: Dai Z, Nielsen J

Abstract
Development of sustainable processes to produce bio-based compounds is necessary due to the severe environmental problems caused by the use of fossil resources. Metabolic engineering can facilitate the development of highly efficient cell factories to produce these compounds from renewable resources. The objective of systems biology is to gain a comprehensive and quantitative understanding of living cells and can hereby enhance our ability to characterize and predict cellular behavior. Systems biology of industrial microorganisms is therefore valuable for metabolic engineering. Here we review the application of systems biology tools for the identification of metabolic engineering targets which may lead to reduced development time for efficient cell factories. Finally, we present some perspectives of systems biology for advancing metabolic engineering further.

PMID: 26318074 [PubMed - indexed for MEDLINE]

A Systems Biology Perspective on the Molecular Mechanisms Underlying the Therapeutic Effects of Buyang Huanwu Decoction on Ischemic Stroke.

Rejuvenation Res. 2015 Aug;18(4):313-25

Authors: Guo Q, Zhong M, Xu H, Mao X, Zhang Y, Lin N

Abstract
Ischemic stroke is the leading cause of adult disability worldwide. The outcome is worse in older patients, especially in terms of disability. Buyang Huanwu decoction (BHD), a famous traditional Chinese medicine formula, has been used extensively in the treatment of ischemic stroke for centuries. However, its pharmacological mechanisms have not been fully elucidated. In this study, 82 putative targets for 411 composite compounds contained in BHD were predicted on the basis of our previously developed target prediction system. On the basis of large-scale molecular docking, more than 80% compound-putative target pairs had medium to strong binding efficiency. The pharmacological networks of BHD were built according to relationships among herbs, putative targets, and known therapeutic targets for ischemic stroke, and 121 major nodes were identified by calculating three topological features-degree, node betweenness, and closeness. Importantly, the pathway enrichment analysis identified several signaling pathways involved with major putative targets of BHD, such as the calcium signaling pathway, vascular smooth muscle contraction, and nucleotide-binding oligomerization domain (NOD)-like receptor signaling pathway, which have not hitherto been reported. These data are expected to help find new therapeutic effects of BHD and optimize clinical use of this formula. Collectively, our study developed a comprehensive systems approach integrating drug target prediction and network and functional analyses to reveal the relationships of the herbs in BHD with their putative targets, and for the first time with ischemic stroke-related pathway systems. This is a pilot study based on bioinformatics analysis; thus, further experimental studies are required to validate our findings.

PMID: 25687091 [PubMed - indexed for MEDLINE]

Autism cornered: network analyses reveal mechanisms of autism spectrum disorders.

Mol Syst Biol. 2014;10:778

Authors: Auffray C

Abstract
Despite a wealth of behavioral, cognitive,biological, and genetic studies, the causes of autism have remained largely unknown.In their recent work, Snyder and colleagues(Li et al, 2014) use a systems biology approach and shed light on the molecular and cellular mechanisms underlying autism, thus opening novel avenues forunderstanding the disease and developing potential treatments.

PMID: 25549969 [PubMed - indexed for MEDLINE]

11 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

"Systems Biology"[Title/Abstract] AND ("2005/01/01"[PDAT] : "3000"[PDAT])

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11 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

"Systems Biology"[Title/Abstract] AND ("2005/01/01"[PDAT] : "3000"[PDAT])

These pubmed results were generated on 2016/06/01

PubMed comprises more than 24 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

Role of frameshift ubiquitin B protein in Alzheimer's disease.

Wiley Interdiscip Rev Syst Biol Med. 2016 May 30;

Authors: Chen X, Petranovic D

Abstract
Alzheimer's disease (AD) is the most common neurodegenerative disease and is characterized by accumulation of misfolded and aggregated proteins. Since the ubiquitin-proteasome system (UPS) is the major intracellular protein quality control (PQC) system in eukaryotic cells, it is likely involved in the etiology of AD. The frameshift form of ubiquitin (Ubb(+1) ) accumulates in the neuritic plaques and neurofibrillary tangles in patients with AD. Ubb(+1) accumulates in an age-dependent manner as a result of RNA-polymerase mediated molecular misreading during transcription, which allows the formation of mutant proteins in the absence of gene mutations. The accumulation of the Ubb(+1) protein may act as an endogenous reporter for proteasome dysfunction and a growing number of studies have shown that Ubb(+1) may play more important pathogenic roles in AD etiology than previously hypothesized. The yeast Saccharomyces cerevisiae shares many conserved biological processes with all eukaryotic cells, including human neurons. This organism has been regarded as a model system for investigating the fundamental intracellular mechanisms, including those underlying neurodegeneration. We propose here that yeast systems biology approaches, combined with cell and molecular biology approaches will increase the relevant knowledge needed for advancement and elucidation of mechanisms and complex traits, which could provide new targets for therapeutic intervention in AD. For further resources related to this article, please visit the WIREs website.

PMID: 27240056 [PubMed - as supplied by publisher]

Clearing and Labeling Techniques for Large-Scale Biological Tissues.

Mol Cells. 2016 May 30;

Authors: Seo J, Choe M, Kim SY

Abstract
Clearing and labeling techniques for large-scale biological tissues enable simultaneous extraction of molecular and structural information with minimal disassembly of the sample, facilitating the integration of molecular, cellular and systems biology across different scales. Recent years have witnessed an explosive increase in the number of such methods and their applications, reflecting heightened interest in organ-wide clearing and labeling across many fields of biology and medicine. In this review, we provide an overview and comparison of existing clearing and labeling techniques and discuss challenges and opportunities in the investigations of large-scale biological systems.

PMID: 27239813 [PubMed - as supplied by publisher]

From big data to smart data in Alzheimer's disease. The brain health modeling initiative to foster actionable knowledge.

Alzheimers Dement. 2016 May 26;

Authors: Geerts H, Dacks PA, Devanarayan V, Haas M, Khatchaturian Z, Gordon MF, Maudsley S, Romero K, Stephenson D, Brain Health Modeling Initiative (BHMI)

Abstract
Massive investment and technological advances in the collection of extensive and longitudinal information on thousands of Alzheimer patients results in large amounts of data. These "big-data" databases can potentially advance CNS research and drug development. However, although necessary, they are not sufficient, and we posit that they must be matched with analytical methods that go beyond retrospective data-driven associations with various clinical phenotypes. Although these empirically derived associations can generate novel and useful hypotheses, they need to be organically integrated in a quantitative understanding of the pathology that can be actionable for drug discovery and development. We argue that mechanism-based modeling and simulation approaches, where existing domain knowledge is formally integrated using complexity science and quantitative systems pharmacology can be combined with data-driven analytics to generate predictive actionable knowledge for drug discovery programs, target validation, and optimization of clinical development.

PMID: 27238630 [PubMed - as supplied by publisher]