Evaluation of statistical correlation and validation methods for construction of gene co-expression networks

High-throughput technologies such as microarrays have led to the rapid accumulation of large scale genomic data providing opportunities to systematically infer gene function and co-expression networks. Typical steps of co-expression network analysis using microarray data consist of estimation of pair-wise gene co-expression using some similarity measure, construction of co-expression networks, identification of clusters of co-expressed genes and post-cluster analyses such as cluster validation. This dissertation is primarily concerned with development and evaluation of approaches for the first and the last steps – estimation of gene co-expression matrices and validation of network clusters. Since clustering methods are not a focus, only a paraclique clustering algorithm will be used in this evaluation. First, a novel Bayesian approach is presented for combining the Pearson correlation with prior biological information from Gene Ontology, yielding a biologically relevant estimate of gene co-expression. The addition of biological information by the Bayesian approach reduced noise in the paraclique gene clusters as indicated by high silhouette and increased homogeneity of clusters in terms of molecular function. Standard similarity measures including correlation coefficients from Pearson, Spearman, Kendall’s Tau, Shrinkage, Partial, and Mutual information, and Euclidean and Manhattan distance measures were evaluated. Based on quality metrics such as cluster homogeneity and stability with respect to ontological categories, clusters resulting from partial correlation and mutual information were more biologically relevant than those from any other correlation measures. Second, statistical quality of clusters was evaluated using approaches based on permutation tests and Mantel correlation to identify significant and informative clusters that capture most of the covariance in the dataset. Third, the utility of statistical contrasts was studied for classification of temporal patterns of gene expression. Specifically, polynomial and Helmert contrast analyses were shown to provide a means of labeling the co-expressed gene sets because they showed similar temporal profiles

A Microbe Associated with Sleep Revealed by a Novel Systems Genetic Analysis of the Microbiome in Collaborative Cross Mice.

The microbiome influences health and disease through complex networks of host genetics, genomics, microbes, and environment. Identifying the mechanisms of these interactions has remained challenging. Systems genetics in laboratory mice (Mus musculus) enables data-driven discovery of biological network components and mechanisms of host-microbial interactions underlying disease phenotypes. To examine the interplay among the whole host genome, transcriptome, and microbiome, we mapped QTL and correlated the abundance of cecal messenger RNA, luminal microflora, physiology, and behavior in a highly diverse Collaborative Cross breeding population. One such relationship, regulated by a variant on chromosome 7, was the association of Odoribacter (Bacteroidales) abundance and sleep phenotypes. In a test of this association in the BKS.Cg-Dock7m +/+ Leprdb/J mouse model of obesity and diabetes, known to have abnormal sleep and colonization by Odoribacter, treatment with antibiotics altered sleep in a genotype-dependent fashion. The many other relationships extracted from this study can be used to interrogate other diseases, microbes, and mechanisms

Decrease in high on-treatment platelet reactivity (HPR) prevalence on switching from clopidogrel to prasugrel: insights from the switching anti-platelet (SWAP) study

The prevalence of high platelet reactivity (HPR) in patients who have switched from clopidogrel to prasugrel during maintenance phase after an acute coronary syndrome (ACS) event is unknown. Therefore, the effect of switching from clopidogrel to prasugrel on the prevalence of HPR was evaluated. This analysis from the previously reported SWAP (SWitching Anti Platelet) study assessed HPR at baseline, 2 and 24 hours, and seven days after switching from clopidogrel to prasugrel maintenance dose (MD), with or without a prasugrel loading dose (MD) using four definitions: maximum platelet aggregation (MPA) >65% (primary endpoint), MPA >50%, P2Y12 reaction units (PRU) >235, and platelet reactivity index (PRI) >= 50%. A total of 95 patients were available for analysis; 56 patients provided DNA for genetic assessments of cytochrome P450 (CYP) 2C19. There were 26 (27.4%) patients with HPR at the end of the clopidogrel run-in (defined as MPA >65%). The HPR prevalence varied by each definition and ranged from 19% (PRU >235) to 68% (PRI >= 50%). A significantly higher HPR prevalence was observed during clopidogrel versus the combined prasugrel therapy groups at seven days as measured by MPA >65% (21.2% vs. 4.5%, p235 (18.8% vs. 0%, p=0.001), and PRI >= 50% (66.7% vs. 7.9%, p65% (p=0.02) or PRU >235 (p=0.05) than non-carriers with HPR. Switching ACS patients during maintenance clopidogrel therapy to prasugrel with or without an LD is associated with a reduced HPR prevalence and may provide an alternative strategy to treat patients with HPR, independent of CYP2C19 genotype

Transferring from clopidogrel loading dose to prasugrel loading dose in acute coronary syndrome patients High on-treatment platelet reactivity analysis of the TRIPLET trial

High on-treatment platelet reactivity (HPR) has been identified as an independent risk factor for ischaemic events. The randomised, double-blind, TRIPLET trial included a pre-defined comparison of H PR in acute coronary syndrome (ACS) patients undergoing percutaneous coronary intervention (PCI) following a placebo/600-mg clopidogrel loading dose (LD) immediately before a subsequent prasugrel 60-mg or 30-mg LD. Platelet reactivity was assessed using the VerifyNow (R) P2Y12 assay (P2Y12 Reaction Units, PRU) within 24 hours (h) following the placebo/clopidogrel LD (immediately prior to prasugrel LD), and at 2, 6, 24, 72 h following prasugrel LDs. The impact of CYP2C19 predicted metaboliser phenotype (extensive metabolisers [EM] and reduced metabolisers [RM])) on HPR status was also assessed. HPR (PRU >= 240) following the clopidogrel LD (prior to the prasugrel LD) was 58.5% in the combined clopidogrel LD groups. No significant difference was noted when stratified by time between the clopidogrel and prasugrel LDs (56 hs vs >6 h). At 6 h following the 2nd loading dose in the combined prasugrel LD groups, HPR was 7.1%, with 0% HPR by 72 h. There was no significant effect of CYP2C19 genotype on pharmacodynamic (PD) response following either prasugrel LD treatments at any time point, regardless of whether it was preceded by a clopidogrel 600-mg LD. In conclusion, in this study, patients with ACS intended for PCI showed a high prevalence of HPR after clopidogrel 600-mg LD regardless of metaboliser status. When prasugrel LD was added, HPR decreased substantially by 6 h, and was not seen by 72 h

The effect of CYP2C19 gene polymorphisms on the pharmacokinetics and pharmacodynamics of prasugrel 5-mg, prasugrel 10-mg and clopidogrel 75-mg in patients with coronary artery disease

CYP2C19 genotype has been shown to impact response to clopidogrel 75-mg but not prasugrel 10-mg. Here, we assessed effects of CYP2C19 metaboliser status on pharmacokinetics (PK) and pharmacodynamic (PD) responses to prasugrel 5-mg and 10-mg and clopidogrel 75-mg using data from two PK/PD studies in stable coronary artery disease (CAD) patients (GENERATIONS and FEATHER). Active metabolite concentrations (area under the curve, AUC([0-tlast])), maximum platelet aggregation (MPA) measured by light transmission aggregometry, vasodilator-stimulated phosphoprotein platelet reactivity index, and VerifyNow P2Y12-platelet reaction units (VN-PRU) were analysed by CYP2C19-predicted phenotype (extensive metaboliser [EM; N=154], *2-*8 non-carriers, vs reduced metaboliser [RM; N=41],*2-*8 carriers/*17 non-carriers). AUC((0-tlast)) was unaffected by metaboliser status for prasugrel 5-mg and 10-mg (geometric mean EM/RM ratios 1.00, 95% confidence interval [Cl]: 0.86,1.17, pgreater than0.99; and 0.97, 95% CI:0.85,1.12, p=0.71, respectively), but was lower among RMs receiving clopidogrel 75-mg (1.37, 95% CI:1.14,1.65, pless than0.001). Platelet reactivity was not significantly affected by CYP2C19 metaboliser status for prasugrel 5-mg, or for prasugrel 10-mg by MPA and VN-PRU, but for clopidogrel 75-mg was significantly higher in reduced metabolisers (all measures pless than0.01). Prasugrel 10-mg showed greater antiplatelet effects vs clopidogrel 75-mg (all comparisons pless than0.001). Prasugrel 5-mg showed greater antiplatelet effects vs clopidogrel 75-mg in RMs (all pless than0.001), and comparable effects in EMs (all p greater than= 0.37). In contrast to clopidogrel, prasugrel active metabolite PK was not influenced by CYP2C19 genotype. Antiplatelet effect for prasugrel 10-mg was greater irrespective of metaboliser status and for prasugrel 5-mg was greater for RMs and comparable for EMs as compared to clopidogrel 75-mg.Funding Agencies|Daiichi Sankyo Co., Ltd.; Eli Lilly and Company</p

A microbe associated with sleep revealed by a novel systems genetic analysis of the microbiome in collaborative cross mice

The microbiome influences health and disease through complex networks of host genetics, genomics, microbes, and environment. Identifying the mechanisms of these interactions has remained challenging. Systems genetics in laboratory mice (Mus musculus) enables data-driven discovery of biological network components and mechanisms of host-microbial interactions underlying disease phenotypes. To examine the interplay among the whole host genome, transcriptome, and microbiome, we mapped QTL and correlated the abundance of cecal messenger RNA, luminal microflora, physiology, and behavior in a highly diverse Collaborative Cross breeding population. One such relationship, regulated by a variant on chromosome 7, was the association of Odoribacter (Bacteroidales) abundance and sleep phenotypes. In a test of this association in the BKS.Cg-Dock7m +/+ Leprdb/J mouse model of obesity and diabetes, known to have abnormal sleep and colonization by Odoribacter, treatment with antibiotics altered sleep in a genotype-dependent fashion. The many other relationships extracted from this study can be used to interrogate other diseases, microbes, and mechanisms