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  1. Article: Connections for Matters of the Heart: Network Medicine in Cardiovascular Diseases.

    Sonawane, Abhijeet Rajendra / Aikawa, Elena / Aikawa, Masanori

    Frontiers in cardiovascular medicine

    2022  Volume 9, Page(s) 873582

    Abstract: Cardiovascular diseases (CVD) are diverse disorders affecting the heart and vasculature in millions of people worldwide. Like other fields, CVD research has benefitted from the deluge of multiomics biomedical data. Current CVD research focuses on disease ...

    Abstract Cardiovascular diseases (CVD) are diverse disorders affecting the heart and vasculature in millions of people worldwide. Like other fields, CVD research has benefitted from the deluge of multiomics biomedical data. Current CVD research focuses on disease etiologies and mechanisms, identifying disease biomarkers, developing appropriate therapies and drugs, and stratifying patients into correct disease endotypes. Systems biology offers an alternative to traditional reductionist approaches and provides impetus for a comprehensive outlook toward diseases. As a focus area, network medicine specifically aids the translational aspect of
    Language English
    Publishing date 2022-05-19
    Publishing country Switzerland
    Document type Journal Article ; Review
    ZDB-ID 2781496-8
    ISSN 2297-055X
    ISSN 2297-055X
    DOI 10.3389/fcvm.2022.873582
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Constructing gene regulatory networks using epigenetic data.

    Sonawane, Abhijeet Rajendra / DeMeo, Dawn L / Quackenbush, John / Glass, Kimberly

    NPJ systems biology and applications

    2021  Volume 7, Issue 1, Page(s) 45

    Abstract: The biological processes that drive cellular function can be represented by a complex network of interactions between regulators (transcription factors) and their targets (genes). A cell's epigenetic state plays an important role in mediating these ... ...

    Abstract The biological processes that drive cellular function can be represented by a complex network of interactions between regulators (transcription factors) and their targets (genes). A cell's epigenetic state plays an important role in mediating these interactions, primarily by influencing chromatin accessibility. However, how to effectively use epigenetic data when constructing a gene regulatory network remains an open question. Almost all existing network reconstruction approaches focus on estimating transcription factor to gene connections using transcriptomic data. In contrast, computational approaches for analyzing epigenetic data generally focus on improving transcription factor binding site predictions rather than deducing regulatory network relationships. We bridged this gap by developing SPIDER, a network reconstruction approach that incorporates epigenetic data into a message-passing framework to estimate gene regulatory networks. We validated SPIDER's predictions using ChIP-seq data from ENCODE and found that SPIDER networks are both highly accurate and include cell-line-specific regulatory interactions. Notably, SPIDER can recover ChIP-seq verified transcription factor binding events in the regulatory regions of genes that do not have a corresponding sequence motif. The networks estimated by SPIDER have the potential to identify novel hypotheses that will allow us to better characterize cell-type and phenotype specific regulatory mechanisms.
    MeSH term(s) Chromatin Immunoprecipitation ; Computational Biology ; Epigenesis, Genetic/genetics ; Gene Regulatory Networks/genetics ; Transcription Factors/genetics ; Transcription Factors/metabolism
    Chemical Substances Transcription Factors
    Language English
    Publishing date 2021-12-09
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ISSN 2056-7189
    ISSN (online) 2056-7189
    DOI 10.1038/s41540-021-00208-3
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Using network science tools to identify novel diet patterns in prodromal dementia.

    Samieri, Cécilia / Sonawane, Abhijeet Rajendra / Lefèvre-Arbogast, Sophie / Helmer, Catherine / Grodstein, Francine / Glass, Kimberly

    Neurology

    2020  Volume 94, Issue 19, Page(s) e2014–e2025

    Abstract: Objective: To use network science to model complex diet relationships a decade before onset of dementia in a large French cohort, the 3-City Bordeaux study.: Methods: We identified cases of dementia incident to the baseline food frequency ... ...

    Abstract Objective: To use network science to model complex diet relationships a decade before onset of dementia in a large French cohort, the 3-City Bordeaux study.
    Methods: We identified cases of dementia incident to the baseline food frequency questionnaire over 12 years of follow-up. For each case, we randomly selected 2 controls among individuals at risk at the age at case diagnosis and matched for age at diet assessment, sex, education, and season of the survey. We inferred food networks in both cases and controls using mutual information, a measure to detect nonlinear associations, and compared food consumption patterns between groups.
    Results: In the nested case-control study, the mean (SD) duration of follow-up and number of visits were 5.0 (2.5) vs 4.9 (2.6) years and 4.1 (1.0) vs 4.4 (0.9) for cases (n = 209) vs controls (n = 418), respectively. While there were few differences in simple, average food intakes, food networks differed substantially between cases and controls. The network in cases was focused and characterized by charcuterie as the main hub, with connections to foods typical of French southwestern diet and snack foods. In contrast, the network of controls included several disconnected subnetworks reflecting diverse and healthier food choices.
    Conclusion: How foods are consumed (and not only the quantity consumed) may be important for dementia prevention. Differences in predementia diet networks, suggesting worse eating habits toward charcuterie and snacking, were evident years before diagnosis in this cohort. Network methods, which are designed to model complex systems, may advance our understanding of risk factors for dementia.
    MeSH term(s) Aged ; Case-Control Studies ; Dementia/psychology ; Feeding Behavior/psychology ; Female ; Humans ; Male ; Nonlinear Dynamics ; Prodromal Symptoms
    Language English
    Publishing date 2020-04-22
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 207147-2
    ISSN 1526-632X ; 0028-3878
    ISSN (online) 1526-632X
    ISSN 0028-3878
    DOI 10.1212/WNL.0000000000009399
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: GRAND: a database of gene regulatory network models across human conditions.

    Ben Guebila, Marouen / Lopes-Ramos, Camila M / Weighill, Deborah / Sonawane, Abhijeet Rajendra / Burkholz, Rebekka / Shamsaei, Behrouz / Platig, John / Glass, Kimberly / Kuijjer, Marieke L / Quackenbush, John

    Nucleic acids research

    2021  Volume 50, Issue D1, Page(s) D610–D621

    Abstract: Gene regulation plays a fundamental role in shaping tissue identity, function, and response to perturbation. Regulatory processes are controlled by complex networks of interacting elements, including transcription factors, miRNAs and their target genes. ... ...

    Abstract Gene regulation plays a fundamental role in shaping tissue identity, function, and response to perturbation. Regulatory processes are controlled by complex networks of interacting elements, including transcription factors, miRNAs and their target genes. The structure of these networks helps to determine phenotypes and can ultimately influence the development of disease or response to therapy. We developed GRAND (https://grand.networkmedicine.org) as a database for computationally-inferred, context-specific gene regulatory network models that can be compared between biological states, or used to predict which drugs produce changes in regulatory network structure. The database includes 12 468 genome-scale networks covering 36 human tissues, 28 cancers, 1378 unperturbed cell lines, as well as 173 013 TF and gene targeting scores for 2858 small molecule-induced cell line perturbation paired with phenotypic information. GRAND allows the networks to be queried using phenotypic information and visualized using a variety of interactive tools. In addition, it includes a web application that matches disease states to potentially therapeutic small molecule drugs using regulatory network properties.
    MeSH term(s) Databases, Genetic ; Databases, Pharmaceutical ; Gene Expression Regulation/genetics ; Gene Regulatory Networks/genetics ; Genome, Human/genetics ; Humans ; MicroRNAs/classification ; MicroRNAs/genetics ; Software ; Transcription Factors/classification ; Transcription Factors/genetics
    Chemical Substances MicroRNAs ; Transcription Factors
    Language English
    Publishing date 2021-09-10
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 186809-3
    ISSN 1362-4962 ; 1362-4954 ; 0301-5610 ; 0305-1048
    ISSN (online) 1362-4962 ; 1362-4954
    ISSN 0301-5610 ; 0305-1048
    DOI 10.1093/nar/gkab778
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 1067: Show issues Location:
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    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
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  5. Article ; Online: Understanding Tissue-Specific Gene Regulation.

    Sonawane, Abhijeet Rajendra / Platig, John / Fagny, Maud / Chen, Cho-Yi / Paulson, Joseph Nathaniel / Lopes-Ramos, Camila Miranda / DeMeo, Dawn Lisa / Quackenbush, John / Glass, Kimberly / Kuijjer, Marieke Lydia

    Cell reports

    2017  Volume 21, Issue 4, Page(s) 1077–1088

    Abstract: Although all human tissues carry out common processes, tissues are distinguished by gene expression patterns, implying that distinct regulatory programs control tissue specificity. In this study, we investigate gene expression and regulation across 38 ... ...

    Abstract Although all human tissues carry out common processes, tissues are distinguished by gene expression patterns, implying that distinct regulatory programs control tissue specificity. In this study, we investigate gene expression and regulation across 38 tissues profiled in the Genotype-Tissue Expression project. We find that network edges (transcription factor to target gene connections) have higher tissue specificity than network nodes (genes) and that regulating nodes (transcription factors) are less likely to be expressed in a tissue-specific manner as compared to their targets (genes). Gene set enrichment analysis of network targeting also indicates that the regulation of tissue-specific function is largely independent of transcription factor expression. In addition, tissue-specific genes are not highly targeted in their corresponding tissue network. However, they do assume bottleneck positions due to variability in transcription factor targeting and the influence of non-canonical regulatory interactions. These results suggest that tissue specificity is driven by context-dependent regulatory paths, providing transcriptional control of tissue-specific processes.
    MeSH term(s) Gene Regulatory Networks ; Genome, Human ; Humans ; Organ Specificity ; Protein Interaction Maps ; Transcription Factors/genetics ; Transcription Factors/metabolism ; Transcriptional Activation ; Transcriptome
    Chemical Substances Transcription Factors
    Language English
    Publishing date 2017-11-13
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2649101-1
    ISSN 2211-1247 ; 2211-1247
    ISSN (online) 2211-1247
    ISSN 2211-1247
    DOI 10.1016/j.celrep.2017.10.001
    Database MEDical Literature Analysis and Retrieval System OnLINE

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