Dynamic control of gene regulatory logic by seemingly redundant transcription factors.

Elife. 2016 Sep 30;5

Authors: AkhavanAghdam Z, Sinha J, Tabbaa OP, Hao N

Abstract
Many transcription factors co-express with their homologs to regulate identical target genes, however the advantages of such redundancies remain elusive. Using single-cell imaging and microfluidics, we study the yeast general stress response transcription factor Msn2 and its seemingly redundant homolog Msn4. We find that gene regulation by these two factors is analogous to logic gate systems. Target genes with fast activation kinetics can be fully induced by either factor, behaving as an 'OR' gate. In contrast, target genes with slow activation kinetics behave as an 'AND' gate, requiring distinct contributions from both factors, upon transient stimulation. Furthermore, such genes become an 'OR' gate when the input duration is prolonged, suggesting that the logic gate scheme is not static but rather dependent on the input dynamics. Therefore, Msn2 and Msn4 enable a time-based mode of combinatorial gene regulation that might be applicable to homologous transcription factors in other organisms.

PMID: 27690227 [PubMed - as supplied by publisher]

Parameter estimation in tree graph metabolic networks.

PeerJ. 2016;4:e2417

Authors: Astola L, Stigter H, Gomez Roldan MV, van Eeuwijk F, Hall RD, Groenenboom M, Molenaar JJ

Abstract
We study the glycosylation processes that convert initially toxic substrates to nutritionally valuable metabolites in the flavonoid biosynthesis pathway of tomato (Solanum lycopersicum) seedlings. To estimate the reaction rates we use ordinary differential equations (ODEs) to model the enzyme kinetics. A popular choice is to use a system of linear ODEs with constant kinetic rates or to use Michaelis-Menten kinetics. In reality, the catalytic rates, which are affected among other factors by kinetic constants and enzyme concentrations, are changing in time and with the approaches just mentioned, this phenomenon cannot be described. Another problem is that, in general these kinetic coefficients are not always identifiable. A third problem is that, it is not precisely known which enzymes are catalyzing the observed glycosylation processes. With several hundred potential gene candidates, experimental validation using purified target proteins is expensive and time consuming. We aim at reducing this task via mathematical modeling to allow for the pre-selection of most potential gene candidates. In this article we discuss a fast and relatively simple approach to estimate time varying kinetic rates, with three favorable properties: firstly, it allows for identifiable estimation of time dependent parameters in networks with a tree-like structure. Secondly, it is relatively fast compared to usually applied methods that estimate the model derivatives together with the network parameters. Thirdly, by combining the metabolite concentration data with a corresponding microarray data, it can help in detecting the genes related to the enzymatic processes. By comparing the estimated time dynamics of the catalytic rates with time series gene expression data we may assess potential candidate genes behind enzymatic reactions. As an example, we show how to apply this method to select prominent glycosyltransferase genes in tomato seedlings.

PMID: 27688960 [PubMed - in process]

Overview of systems biology and omics technologies.

Curr Med Chem. 2016 Sep 26;

Authors: Karahalil B

Abstract
Traditional technologies using reductionist approach are relatively insufficient to solve problems in a biological system. Rather than a reductionist approach, system biology uses a holistic and integrative approach to better figure out the whole process. Both qualitatively and quantitatively of biological system provide information about diseases, toxicities, therapies etc. Omics technologies, which system biology bring, are valuable tools for comprehensive analyses. Automated DNA sequencers enabled the sequencing of genomes; microarray and mass spectrometry analysis permit global transcriptional profiling and lead to large-scale proteomic and metabolomics analysis. These high-throughput data needs to be interpreted by bioinformatics. So far there has been no the concrete published paper that compile omics technologies according to PubMed database. In the present review, it was aimed to give brief description of system biology and information on the advantages and disadvantages of omics technologies.

PMID: 27686657 [PubMed - as supplied by publisher]

Biotechnology for Chemical Production: Challenges and Opportunities.

Trends Biotechnol. 2016 Mar;34(3):187-90

Authors: Burk MJ, Van Dien S

Abstract
Biotechnology offers a new sustainable approach to manufacturing chemicals, enabling the replacement of petroleum-based raw materials with renewable biobased feedstocks, thereby reducing greenhouse gas (GHG) emissions, toxic byproducts, and the safety risks associated with traditional petrochemical processing. Development of such bioprocesses is enabled by recent advances in genomics, molecular biology, and systems biology, and will continue to accelerate as access to these tools becomes faster and cheaper.

PMID: 26683567 [PubMed - indexed for MEDLINE]

Integrative network analysis reveals molecular mechanisms of blood pressure regulation.

Mol Syst Biol. 2015 Jan;11(1):799

Authors: Huan T, Meng Q, Saleh MA, Norlander AE, Joehanes R, Zhu J, Chen BH, Zhang B, Johnson AD, Ying S, Courchesne P, Raghavachari N, Wang R, Liu P, International Consortium for Blood Pressure GWAS (ICBP), O'Donnell CJ, Vasan R, Munson PJ, Madhur MS, Harrison DG, Yang X, Levy D

Abstract
Genome-wide association studies (GWAS) have identified numerous loci associated with blood pressure (BP). The molecular mechanisms underlying BP regulation, however, remain unclear. We investigated BP-associated molecular mechanisms by integrating BP GWAS with whole blood mRNA expression profiles in 3,679 individuals, using network approaches. BP transcriptomic signatures at the single-gene and the coexpression network module levels were identified. Four coexpression modules were identified as potentially causal based on genetic inference because expression-related SNPs for their corresponding genes demonstrated enrichment for BP GWAS signals. Genes from the four modules were further projected onto predefined molecular interaction networks, revealing key drivers. Gene subnetworks entailing molecular interactions between key drivers and BP-related genes were uncovered. As proof-of-concept, we validated SH2B3, one of the top key drivers, using Sh2b3(-/-) mice. We found that a significant number of genes predicted to be regulated by SH2B3 in gene networks are perturbed in Sh2b3(-/-) mice, which demonstrate an exaggerated pressor response to angiotensin II infusion. Our findings may help to identify novel targets for the prevention or treatment of hypertension.

PMID: 25882670 [PubMed - indexed for MEDLINE]

Systems Pharmacology Links GPCRs with Retinal Degenerative Disorders.

Annu Rev Pharmacol Toxicol. 2016;56:273-98

Authors: Chen Y, Palczewski K

Abstract
In most biological systems, second messengers and their key regulatory and effector proteins form links between multiple cellular signaling pathways. Such signaling nodes can integrate the deleterious effects of genetic aberrations, environmental stressors, or both in complex diseases, leading to cell death by various mechanisms. Here we present a systems (network) pharmacology approach that, together with transcriptomics analyses, was used to identify different G protein-coupled receptors that experimentally protected against cellular stress and death caused by linked signaling mechanisms. We describe the application of this concept to degenerative and diabetic retinopathies in appropriate mouse models as an example. Systems pharmacology also provides an attractive framework for devising strategies to combat complex diseases by using (repurposing) US Food and Drug Administration-approved pharmacological agents.

PMID: 25839098 [PubMed - indexed for MEDLINE]

Quantitative proteomic analysis reveals a simple strategy of global resource allocation in bacteria.

Mol Syst Biol. 2015 Jan;11(1):784

Authors: Hui S, Silverman JM, Chen SS, Erickson DW, Basan M, Wang J, Hwa T, Williamson JR

Abstract
A central aim of cell biology was to understand the strategy of gene expression in response to the environment. Here, we study gene expression response to metabolic challenges in exponentially growing Escherichia coli using mass spectrometry. Despite enormous complexity in the details of the underlying regulatory network, we find that the proteome partitions into several coarse-grained sectors, with each sector's total mass abundance exhibiting positive or negative linear relations with the growth rate. The growth rate-dependent components of the proteome fractions comprise about half of the proteome by mass, and their mutual dependencies can be characterized by a simple flux model involving only two effective parameters. The success and apparent generality of this model arises from tight coordination between proteome partition and metabolism, suggesting a principle for resource allocation in proteome economy of the cell. This strategy of global gene regulation should serve as a basis for future studies on gene expression and constructing synthetic biological circuits. Coarse graining may be an effective approach to derive predictive phenomenological models for other 'omics' studies.

PMID: 25678603 [PubMed - indexed for MEDLINE]

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

("orphan disease" OR "rare disease" OR "orphan diseases" OR "rare diseases")

These pubmed results were generated on 2016/09/30

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

"Cystic Fibrosis"

These pubmed results were generated on 2016/09/30

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.

12 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 2016/09/30

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.

MeSHDD: Literature-based drug-drug similarity for drug repositioning.

J Am Med Inform Assoc. 2016 Sep 27;

Authors: Brown AS, Patel CJ

Abstract
OBJECTIVE: Drug repositioning is a promising methodology for reducing the cost and duration of the drug discovery pipeline. We sought to develop a computational repositioning method leveraging annotations in the literature, such as Medical Subject Heading (MeSH) terms.
METHODS: We developed software to determine significantly co-occurring drug-MeSH term pairs and a method to estimate pair-wise literature-derived distances between drugs.
RESULTS: We found that literature-based drug-drug similarities predicted the number of shared indications across drug-drug pairs. Clustering drugs based on their similarity revealed both known and novel drug indications. We demonstrate the utility of our approach by generating repositioning hypotheses for the commonly used diabetes drug metformin.Conclusion Our study demonstrates that literature-derived similarity is useful for identifying potential repositioning opportunities. We provided open-source code and deployed a free-to-use, interactive application to explore our database of similarity-based drug clusters (available at http://apps.chiragjpgroup.org/MeSHDD/).

PMID: 27678460 [PubMed - as supplied by publisher]

A drug target slim: using gene ontology and gene ontology annotations to navigate protein-ligand target space in ChEMBL.

J Biomed Semantics. 2016;7(1):59

Authors: Mutowo P, Bento AP, Dedman N, Gaulton A, Hersey A, Lomax J, Overington JP

Abstract
BACKGROUND: The process of discovering new drugs is a lengthy, time-consuming and expensive process. Modern day drug discovery relies heavily on the rapid identification of novel 'targets', usually proteins that can be modulated by small molecule drugs to cure or minimise the effects of a disease. Of the 20,000 proteins currently reported as comprising the human proteome, just under a quarter of these can potentially be modulated by known small molecules Storing information in curated, actively maintained drug discovery databases can help researchers access current drug discovery information quickly. However with the increase in the amount of data generated from both experimental and in silico efforts, databases can become very large very quickly and information retrieval from them can become a challenge. The development of database tools that facilitate rapid information retrieval is important to keep up with the growth of databases.
DESCRIPTION: We have developed a Gene Ontology-based navigation tool (Gene Ontology Tree) to help users retrieve biological information to single protein targets in the ChEMBL drug discovery database. 99 % of single protein targets in ChEMBL have at least one GO annotation associated with them. There are 12,500 GO terms associated to 6200 protein targets in the ChEMBL database resulting in a total of 140,000 annotations. The slim we have created, the 'ChEMBL protein target slim' allows broad categorisation of the biology of 90 % of the protein targets using just 300 high level, informative GO terms. We used the GO slim method of assigning fewer higher level GO groupings to numerous very specific lower level terms derived from the GOA to describe a set of GO terms relevant to proteins in ChEMBL. We then used the slim created to provide a web based tool that allows a quick and easy navigation of protein target space. Terms from the GO are used to capture information on protein molecular function, biological process and subcellular localisations. The ChEMBL database also provides compound information for small molecules that have been tested for their effects on these protein targets. The 'ChEMBL protein target slim' provides a means of firstly describing the biology of protein drug targets and secondly allows users to easily establish a connection between biological and chemical information regarding drugs and drug targets in ChEMBL. The 'ChEMBL protein target slim' is available as a browsable 'Gene Ontology Tree' on the ChEMBL site under the browse targets tab ( https://www.ebi.ac.uk/chembl/target/browser ). A ChEMBL protein target slim OBO file containing the GO slim terms pertinent to ChEMBL is available from the GOC website ( http://geneontology.org/page/go-slim-and-subset-guide ).
CONCLUSIONS: We have created a protein target navigation tool based on the 'ChEMBL protein target slim'. The 'ChEMBL protein target slim' provides a way of browsing protein targets in ChEMBL using high level GO terms that describe the molecular functions, processes and subcellular localisations of protein drug targets in drug discovery. The tool also allows user to establish a link between ontological groupings representing protein target biology to relevant compound information in ChEMBL. We have demonstrated by the use of a simple example how the 'ChEMBL protein target slim' can be used to link biological processes with drug information based on the information in the ChEMBL database. The tool has potential to aid in areas of drug discovery such as drug repurposing studies or drug-disease-protein pathways.

PMID: 27678076 [PubMed - as supplied by publisher]

Prediction of new drug indications based on clinical data and network modularity.

Sci Rep. 2016;6:32530

Authors: Yu L, Ma X, Zhang L, Zhang J, Gao L

Abstract
Drug repositioning is commonly done within the drug discovery process in order to adjust or expand the application line of an active molecule. Previous computational methods in this domain mainly focused on shared genes or correlations between genes to construct new drug-disease associations. We propose a method that can not only handle drugs or diseases with or without related genes but consider the network modularity. Our method firstly constructs a drug network and a disease network based on side effects and symptoms respectively. Because similar drugs imply similar diseases, we then cluster the two networks to identify drug and disease modules, and connect all possible drug-disease module pairs. Further, based on known drug-disease associations in CTD and using local connectivity of modules, we predict potential drug-disease associations. Our predictions are validated by testing their overlaps with drug indications reported in published literatures and CTD, and KEGG enrichment analysis are also made on their related genes. The experimental results demonstrate that our approach can complement the current computational approaches and its predictions can provide new clues for the candidate discovery of drug repositioning.

PMID: 27678071 [PubMed - as supplied by publisher]

A-DaGO-Fun: an adaptable Gene Ontology semantic similarity-based functional analysis tool.

Bioinformatics. 2016 Feb 1;32(3):477-9

Authors: Mazandu GK, Chimusa ER, Mbiyavanga M, Mulder NJ

Abstract
SUMMARY: Gene Ontology (GO) semantic similarity measures are being used for biological knowledge discovery based on GO annotations by integrating biological information contained in the GO structure into data analyses. To empower users to quickly compute, manipulate and explore these measures, we introduce A-DaGO-Fun (ADaptable Gene Ontology semantic similarity-based Functional analysis). It is a portable software package integrating all known GO information content-based semantic similarity measures and relevant biological applications associated with these measures. A-DaGO-Fun has the advantage not only of handling datasets from the current high-throughput genome-wide applications, but also allowing users to choose the most relevant semantic similarity approach for their biological applications and to adapt a given module to their needs.
AVAILABILITY AND IMPLEMENTATION: A-DaGO-Fun is freely available to the research community at http://web.cbio.uct.ac.za/ITGOM/adagofun. It is implemented in Linux using Python under free software (GNU General Public Licence).
CONTACT: gmazandu@cbio.uct.ac.za or Nicola.Mulder@uct.ac.za
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID: 26476781 [PubMed - indexed for MEDLINE]

Pharmacogenomics and chemical library screens reveal a novel SCF(SKP2) inhibitor that overcomes bortezomib resistance in multiple myeloma.

Leukemia. 2016 Sep 28;

Authors: Malek E, Abdel-Malek MA, Jagannathan S, Vad N, Karns R, Jegga AG, Broyl A, van Duin M, Sonneveld P, Cottini F, Anderson KC, Driscoll JJ

Abstract
While clinical benefit of the proteasome inhibitor (PI) bortezomib for multiple myeloma (MM) patients remains unchallenged, dose-limiting toxicities and drug resistance limit the long-term utility. The E3 ubiquitin (Ub) ligase Skp1-Cullin-1-Skp2 (SCF(Skp2)) promotes proteasomal degradation of the cell cycle inhibitor p27 to enhance tumor growth. Increased SKP2 expression and reduced p27 levels are frequent in human cancers and are associated with therapeutic resistance. SCF(Skp2) activity is increased by the Cullin-1-binding protein Commd1 and the Skp2-binding protein Cks1B. Here, we observed higher CUL1, COMMD1 and SKP2 mRNA levels in CD138(+) cells isolated from bortezomib-resistant MM patients. Higher CUL1, COMMD1, SKP2 and CKS1B mRNA levels in patient CD138(+) cells correlated with decreased progression-free and overall survival. Genetic knockdown of CUL1, COMMD1 or SKP2 disrupted the SCF(Skp2) complex, stabilized p27 and increased the number of annexin-V positive cells after bortezomib treatment. Chemical library screens identified a novel compound, designated DT204, that reduced Skp2 binding to Cullin-1 and Commd1, and synergistically enhanced bortezomib-induced apoptosis. DT204 co-treatment with bortezomib overcame drug resistance and reduced the in vivo growth of myeloma tumors in murine models with survival benefit. Taken together, the results provide proof-of-concept for rationally-designed drug combinations that incorporate SCF(Skp2) inhibitors to treat bortezomib resistant disease.Leukemia accepted article preview online, 28 September 2016. doi:10.1038/leu.2016.258.

PMID: 27677741 [PubMed - as supplied by publisher]

Impact of a personal CYP2D6 testing workshop on physician assistant student attitudes toward pharmacogenetics.

Pharmacogenomics. 2016 Mar;17(4):341-52

Authors: O'Brien TJ, LeLacheur S, Ward C, Lee NH, Callier S, Harralson AF

Abstract
AIM: We assessed the impact of personal CYP2D6 testing on physician assistant student competency in, and attitudes toward, pharmacogenetics (PGx).
MATERIALS & METHODS: Buccal samples were genotyped for CYP2D6 polymorphisms. Results were discussed during a 3-h PGx workshop. PGx knowledge was assessed by pre- and post-tests. Focus groups assessed the impact of the workshop on attitudes toward the clinical utility of PGx.
RESULTS: Both student knowledge of PGx, and its perceived clinical utility, increased immediately following the workshop. However, exposure to PGx on clinical rotations following the workshop seemed to influence student attitudes toward PGx utility.
CONCLUSION: Personal CYP2D6 testing improves both knowledge and comfort with PGx. Continued exposure to PGx concepts is important for transfer of learning.

PMID: 26907849 [PubMed - indexed for MEDLINE]

Genomic variants in the ASS1 gene, involved in the nitric oxide biosynthesis and signaling pathway, predict hydroxyurea treatment efficacy in compound sickle cell disease/β-thalassemia patients.

Pharmacogenomics. 2016 Mar;17(4):393-403

Authors: Chalikiopoulou C, Tavianatou AG, Sgourou A, Kourakli A, Kelepouri D, Chrysanthakopoulou M, Kanelaki VK, Mourdoukoutas E, Siamoglou S, John A, Symeonidis A, Ali BR, Katsila T, Papachatzopoulou A, Patrinos GP

Abstract
AIM: Hemoglobinopathies exhibit a remarkable phenotypic diversity that restricts any safe association between molecular pathology and clinical outcomes.
PATIENTS & METHODS: Herein, we explored the role of genes involved in the nitric oxide biosynthesis and signaling pathway, implicated in the increase of fetal hemoglobin levels and response to hydroxyurea treatment, in 119 Hellenic patients with β-type hemoglobinopathies.
RESULTS: We show that two ASS1 genomic variants (namely, rs10901080 and rs10793902) can serve as pharmacogenomic biomarkers to predict hydroxyurea treatment efficacy in sickle cell disease/β-thalassemia compound heterozygous patients.
CONCLUSION: These markers may exert their effect by inducing nitric oxide biosynthesis, either via altering splicing and/or miRNA binding, as predicted by in silico analysis, and ultimately, increase γ-globin levels, via guanylyl cyclase targeting.

PMID: 26895070 [PubMed - indexed for MEDLINE]

Fluoropyrimidine and platinum toxicity pharmacogenetics: an umbrella review of systematic reviews and meta-analyses.

Pharmacogenomics. 2016 Mar;17(4):435-51

Authors: Campbell JM, Bateman E, Peters MDj, Bowen JM, Keefe DM, Stephenson MD

Abstract
Fluoropyrimidine (FU) and platinum-based chemotherapies are greatly complicated by their associated toxicities. This umbrella systematic review synthesized all systematic reviews that investigated associations between germline variations and toxicity, with the aim of informing personalized medicine. Systematic reviews are important in pharmacogenetics where false positives are common. Four systematic reviews were identified for FU-induced toxicity and three for platinum. Polymorphisms of DPYD and TYMS, but not MTHFR, were statistically significantly associated with FU-induced toxicity (although only DPYD had clinical significance). For platinum, GSTP1 was found to not be associated with toxicity. This umbrella systematic review has synthesized the best available evidence on the pharmacogenetics of FU and platinum toxicity. It provides a useful reference for clinicians and identifies important research gaps.

PMID: 26894782 [PubMed - indexed for MEDLINE]

Pharmacogenomics of anti-TNF response in psoriasis, where are we?

Pharmacogenomics. 2016 Mar;17(4):323-6

Authors: Julià A, Marsal S

PMID: 26871199 [PubMed - indexed for MEDLINE]

Polymorphisms associated with low bone mass and high risk of atraumatic fracture.

Physiol Res. 2015;64(5):621-31

Authors: Zofkova I, Nemcikova P, Kuklik M

Abstract
Osteoporosis is a serious disease characterized by high morbidity and mortality due to atraumatic fractures. In the pathogenesis of osteoporosis, except environment and internal factors, such as hormonal imbalance and genetic background, are also in play. In this study candidate genes for osteoporosis were classified according to metabolic or hormonal pathways, which regulate bone mineral density and bone quality (estrogen, RANKL/RANK/OPG axis, mevalonate, the canonical circuit and genes regulating the vitamin D system). COL1A1 and/or COL1A2 genes, which encode formation of the procollagen 1 molecule, were also studied. Mutations in these genes are well-known causes of the inborn disease 'osteogenesis imperfecta'. In addition to this, polymorphisms in COL1A1 and/or COL1A2 have been found to be associated with parameters of bone quality in adult subjects. The authors discuss the perspectives for the practical utilization of pharmacogenetics (identification of single candidate genes using PCR) and pharmacogenomics (using genome wide association studies (GWAS) to choose optimal treatment for osteoporosis). Potential predictors of antiresorptive therapy efficacy include the following well established genes: ER, FDPS, Cyp19A1, VDR, Col1A1, and Col1A2, as well as the gene for the canonical (Wnt) pathway. Unfortunately, the positive outcomes seen in most association studies have not been confirmed by other researchers. The controversial results could be explained by the use of different methodological approaches in individual studies (different sample size, homogeneity of investigated groups, ethnic differences, or linkage disequilibrium between genes). The key pitfall of association studies is the low variability (7-10 %) of bone phenotypes associated with the investigated genes. Nevertheless, the identification of new genes and the verification of their association with bone density and/or quality (using both PCR and GWAS), remain a great challenge in the optimal prevention and treatment of osteoporosis.

PMID: 25804099 [PubMed - indexed for MEDLINE]