Pathway-Based Analysis Tools for Complex Diseases: A Review

Jin, Long-Fei; Zuo, Xiaoyu; Su, Weiyang; Zhao, Xiaolei; Yuan, Manqiong; Han, Lizhen; Zhao, Xiang; Chen, Yeda; Rao, Shaoqi

doi:10.1016/j.gpb.2014.10.002

Cited by 130 publications

(86 citation statements)

References 65 publications

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“…The AMPK pathway was mostly defined by ACACA, cyclin D1 (CCDN1), fructose-bisphosphatase 1 (FBP1), glycogen synthase (GYS) 1, GYS2, leptin (LEP), PCK2, protein phosphatase 2 scaffold subunit abeta (PPP2R1B), and SCD genes, with all genes except PCK2 and FBP1 being downregulated. To address known limitations from pathway analyses (34,41,42), we repeated these analyses using different annotation databases (Reactome, Gene Ontology). This confirmed the enrichment in pathways linked with lipid and glucose metabolism (Supplemental Table 3).…”

Section: Pathway and Enrichment Analysesmentioning

confidence: 99%

Transcriptome profiling from adipose tissue during a low-calorie diet reveals predictors of weight and glycemic outcomes in obese, nondiabetic subjects

Armenise¹,

Lefebvre

Carayol

et al. 2017

The American Journal of Clinical Nutrition

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Background: A low-calorie diet (LCD) reduces fat mass excess, improves insulin sensitivity, and alters adipose tissue (AT) gene expression, yet the relation with clinical outcomes remains unclear. Objective: We evaluated AT transcriptome alterations during an LCD and the association with weight and glycemic outcomes both at LCD termination and 6 mo after the LCD. Design: Using RNA sequencing (RNAseq), we analyzed transcriptome changes in AT from 191 obese, nondiabetic patients within a multicenter, controlled dietary intervention. Expression changes were associated with outcomes after an 8-wk LCD (800-1000 kcal/d) and 6 mo after the LCD. Results were validated by using quantitative reverse transcriptase-polymerase chain reaction in 350 subjects from the same cohort. Statistical models were constructed to classify weight maintainers or glycemic improvers. Results: With RNAseq analyses, we identified 1173 genes that were differentially expressed after the LCD, of which 350 and 33 were associated with changes in body mass index (BMI; in kg/m 2 ) and Matsuda index values, respectively, whereas 29 genes were associated with both endpoints. Pathway analyses highlighted enrichment in lipid and glucose metabolism. Classification models were constructed to identify weight maintainers. A model based on clinical baseline variables could not achieve any classification , and Nestlé Institute of Health Sciences. This is a free access article, distributed under terms (http://www.nutrition.org/publications/ guidelines-and-policies/license/) that permit unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Supplemental Figures 1 and 2 and Supplemental Tables 1-11 are available from the "Online Supporting Material" link in the online posting of the article and from the same link in the online table of contents at http://ajcn. nutrition.org.CA and AV contributed equally to this work. Address correspondence to AV (e-mail: armand.valsesia@rd.nestle.com). Abbreviations used: AT, adipose tissue; CAV2, caveolin 2; C7, complement component 7; ELOVL5, ELOVL fatty acid elongase 5; ENSG, Ensembl gene identifier; eQTL, expression quantitative trait loci; FDR, false discovery rate; GSDMB, gasdermin B; LCD, low-calorie diet; LEP, leptin; LOX, lysyl oxidase; ME1, malic enzyme 1; MTCH2, mitochondrial carrier 2; NPY1R, neuropeptide Y receptor Y1; PCK2, phosphoenolpyruvate carboxykinase 2; PPAP2A, phosphatidic acid phosphatase type 2A; RNAseq, RNA sequencing; ROC, receiver operating characteristic; RSD, relative SD; RT-qPCR, reverse transcription quantitative polymerase chain reaction; SNP, single nucleotide polymorphism; SPARC, secreted protein acidic and cysteine rich; SVF, stromal vascular fraction; T2D, type 2 diabetes.

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Section: Pathway and Enrichment Analysesmentioning

confidence: 99%

Transcriptome profiling from adipose tissue during a low-calorie diet reveals predictors of weight and glycemic outcomes in obese, nondiabetic subjects

Armenise¹,

Lefebvre

Carayol

et al. 2017

The American Journal of Clinical Nutrition

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“…Thus, evidence obtained in vitro or animal models only was not included. Results obtained by pathway analyses (i.e., using methods based on the analysis of a molecular pathway as a whole, see [121] for a methodological review) and not simply obtained by the study of individual genes with correlated functions are the focus of this paragraph. An overview of the involved pathways is provided in Table 2.…”

Section: Molecular Pathways Involved In Antidepressant Mechanisms Of mentioning

confidence: 99%

Pharmacogenetics of Major Depressive Disorder: Top Genes and Pathways Toward Clinical Applications

Fabbri

Serretti

2015

Curr Psychiatry Rep

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The pharmacogenetics of antidepressants has been not only a challenging but also frustrating research field since its birth in the 1990s. Indeed, great expectations followed the first evidence of familiar aggregation of antidepressant response. Despite the progress from candidate gene studies to genome-wide association studies (GWAS), results fell out the expectations and they were often inconsistent. Anyway, the cumulative evidence supports the involvement of some genes and molecular pathways in antidepressant efficacy. The best single genes are SLC6A4, HTR2A, BDNF, GNB3, FKBP5, ABCB1, and cytochrome P450 genes (CYP2D6 and CYP2C19). Molecular pathways involved in inflammation and neuroplasticity show the greatest support. The first studies evaluating benefits of genotype-guided antidepressant treatments provided encouraging results and confirmed the relevance of SLC6A4, HTR2A, ABCB1, and cytochrome P450 genes. Further progress in genotyping and data analysis would allow to move forward and complete the understanding of antidepressant pharmacogenetics and its translation into clinical applications.

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“…Early linear models applied to gene expression data were able to extract clear signals from superficially noisy time-series data. Conventional tools and methods for studying temporal dynamics of genomic data (35) and pathways (36) often rely on sophisticated machine learning techniques that provide high accuracy at the cost of clarity. The utility of simpler linear models is that their results can be interpreted in terms of known biology.…”

mentioning

confidence: 99%

Proteomic Screening and Lasso Regression Reveal Differential Signaling in Insulin and Insulin-like Growth Factor I (IGF1) Pathways

Erdem

Nagle

Casà

et al. 2016

Molecular & Cellular Proteomics

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Insulin and insulin-like growth factor I (IGF1) influence cancer risk and progression through poorly understood mechanisms. To better understand the roles of insulin and IGF1 signaling in breast cancer, we combined proteomic screening with computational network inference to uncover differences in IGF1 and insulin induced signaling. Using reverse phase protein array, we measured the levels of 134 proteins in 21 breast cancer cell lines stimulated with IGF1 or insulin for up to 48 h. We then constructed directed protein expression networks using three separate methods: (i) lasso regression, (ii) conventional matrix inversion, and (iii) entropy maximization. These networks, named here as the time translation models, were analyzed and the inferred interactions were ranked by differential magnitude to identify pathway differences. Insulin (Ins)1 and type I insulin-like growth factor (IGF1) receptors (InsR and IGF1R, respectively) are receptor tyrosine kinases that are expressed in almost all types of cells. Signaling through InsR and IGF1R initiates a phosphorylation cascade that drives cell growth and proliferation (1-8). Overexpression of these receptors is correlated with higher breast cancer risk (9 -15) and has been shown to influence tumorigenesis, metastasis, and resistance to existing forms of cancer therapy (10,16,17). IGF receptor blockade can slow tumor growth and metastasis, but the receptor has proven to be difficult to target specifically (18 -20). A confounding factor in developing therapies targeting these receptors is their high sequence (ϳ60%) and structural homology. IGF1R and InsR are able to form functional hybrids, and each can partially compensate for the loss or suppression of the other (1-4, 21-24). Moreover, it has been shown that IGF1R signaling is one mechanism of resistance to conventional hormonal therapy (19,(25)(26)(27)(28)(29). Understanding the relationships between IGF1R and InsR signaling cascades and their combinatorial role in cancer is crucial to developing better diagnostics and personalized treatments for cancer.

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Pathway-Based Analysis Tools for Complex Diseases: A Review

Cited by 130 publications

References 65 publications

Transcriptome profiling from adipose tissue during a low-calorie diet reveals predictors of weight and glycemic outcomes in obese, nondiabetic subjects

Transcriptome profiling from adipose tissue during a low-calorie diet reveals predictors of weight and glycemic outcomes in obese, nondiabetic subjects

Pharmacogenetics of Major Depressive Disorder: Top Genes and Pathways Toward Clinical Applications

Proteomic Screening and Lasso Regression Reveal Differential Signaling in Insulin and Insulin-like Growth Factor I (IGF1) Pathways

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