Objective To determine the key inflammatory pathways that are activated in the peripheral and CNS compartments at the mild cognitive impairment (MCI) stage of Alzheimer’s disease (AD). Methods A cross‐sectional study of patients with clinical and biomarker characteristics consistent with MCI‐AD in a discovery cohort, with replication in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort. Inflammatory analytes were measured in the CSF and plasma with the same validated multiplex analyte platform in both cohorts and correlated with AD biomarkers (CSF Aβ42, total tau (t‐tau), phosphorylated tau (p‐tau) to identify key inflammatory pathway activations. The pathways were additionally validated by evaluating genes related to all analytes in coexpression networks of brain tissue transcriptome from an autopsy confirmed AD cohort to interrogate if the same pathway activations were conserved in the brain tissue gene modules. Results Analytes of the tumor necrosis factor (TNF) signaling pathway (KEGG ID:4668) in the CSF and plasma best correlated with CSF t‐tau and p‐tau levels, and analytes of the complement and coagulation pathway (KEGG ID:4610) best correlated with CSF Aβ42 levels. The top inflammatory signaling pathways of significance were conserved in the peripheral and the CNS compartments. They were also confirmed to be enriched in AD brain transcriptome gene clusters. Interpretation A cell‐protective rather than a proinflammatory analyte profile predominates in the CSF in relation to neurodegeneration markers among MCI‐AD patients. Analytes from the TNF signaling and the complement and coagulation pathways are relevant in evaluating disease severity at the MCI stage of AD.

The precise molecular etiology of obstructive sleep apnea (OSA) is unknown; however recent research indicates that several interconnected aberrant pathways and molecular abnormalities are contributors to OSA. Identifying the genes and pathways associated with OSA can help to expand our understanding of the risk factors for the disease as well as provide new avenues for potential treatment. Towards these goals, we have integrated relevant high dimensional data from various sources, such as genome-wide expression data (microarray), protein-protein interaction (PPI) data and results from genome-wide association studies (GWAS) in order to define sub-network elements that connect some of the known pathways related to the disease as well as define novel regulatory modules related to OSA. Two distinct approaches are applied to identify sub-networks significantly associated with OSA. In the first case we used a biased approach based on sixty genes/proteins with known associations with sleep disorders and/or metabolic disease to seed a search using commercial software to discover networks associated with disease followed by information theoretic (mutual information) scoring of the sub-networks. In the second case we used an unbiased approach and generated an interactome constructed from publicly available gene expression profiles and PPI databases, followed by scoring of the network with p-values from GWAS data derived from OSA patients to uncover sub-networks significant for the disease phenotype. A comparison of the approaches reveals a number of proteins that have been previously known to be associated with OSA or sleep. In addition, our results indicate a novel association of Phosphoinositide 3-kinase, the STAT family of proteins and its related pathways with OSA.

We conducted integrated transcriptomics network analyses of miRNA and mRNA interactions in human myometrium to identify novel molecular candidates potentially involved in human parturition. Myometrial biopsies were collected from women undergoing primary Cesarean deliveries in well-characterized clinical scenarios: (1) spontaneous term labor (TL, n = 5); (2) term nonlabor (TNL, n = 5); (3) spontaneous preterm birth (PTB) with histologic chorioamnionitis (PTB-HCA, n = 5); and (4) indicated PTB nonlabor (PTB-NL, n = 5). RNAs were profiled using RNA sequencing, and miRNA-target interaction networks were mined for key discriminatory subnetworks. Forty miRNAs differed between TL and TNL myometrium, while seven miRNAs differed between PTB-HCA vs. PTB-NL specimens; six of these were cross-validated using quantitative PCR. Based on the combined sequencing data, unsupervised clustering revealed two nonoverlapping cohorts that differed primarily by absence or presence of uterine quiescence, rather than gestational age or original clinical cohort. The intersection of differentially expressed miRNAs and their targets predicted 22 subnetworks with enriched representation of miR-146b-5p, miR-223-3p, and miR-150-5p among miRNAs, and of myocyte enhancer factor-2C (MEF2C) among mRNAs. Of four known MEF2 transcription factors, decreased MEF2A and MEF2C expression in women with uterine nonquiescence was observed in the sequencing data, and validated in a second cohort by quantitative PCR. Immunohistochemistry localized MEF2A and MEF2C to myometrial smooth muscle cells and confirmed decreased abundance with labor. Collectively, these results suggest altered MEF2 expression may represent a previously unrecognized process through which miRNAs contribute to the phenotypic switch from quiescence to labor in human myometrium.

We present CoPhosK to predict kinase-substrate associations for phosphopeptide substrates detected by mass spectrometry (MS). The tool utilizes a Naïve Bayes framework with priors of known kinase-substrate associations (KSAs) to generate its predictions. Through the mining of MS data for the collective dynamic signatures of the kinases’ substrates revealed by correlation analysis of phosphopeptide intensity data, the tool infers KSAs in the data for the considerable body of substrates lacking such annotations. We benchmarked the tool against existing approaches for predicting KSAs that rely on static information (e.g. sequences, structures and interactions) using publically available MS data, including breast, colon, and ovarian cancer models. The benchmarking reveals that co-phosphorylation analysis can significantly improve prediction performance when static information is available (about 35% of sites) while providing reliable predictions for the remainder, thus tripling the KSAs available from the experimental MS data providing to a comprehensive and reliable characterization of the landscape of kinase-substrate interactions well beyond current limitations.

BackgroundInteractions among genomic loci (also known as epistasis) have been suggested as one of the potential sources of missing heritability in single locus analysis of genome-wide association studies (GWAS). The computational burden of searching for interactions is compounded by the extremely low threshold for identifying significant p-values due to multiple hypothesis testing corrections. Utilizing prior biological knowledge to restrict the set of candidate SNP pairs to be tested can alleviate this problem, but systematic studies that investigate the relative merits of integrating different biological frameworks and GWAS data have not been conducted.ResultsWe developed four biologically based frameworks to identify pairwise interactions among candidate SNP pairs as follows: (1) for each human protein-coding gene, a set of SNPs associated with that gene was constructed providing a gene-based interaction model, (2) for each known biological pathway, a set of SNPs associated with the genes in the pathway was constructed providing a pathway-based interaction model, (3) a set of SNPs associated with genes in a disease-related subnetwork provides a network-based interaction model, and (4) a framework is based on the function of SNPs. The last approach uses expression SNPs (eSNPs or eQTLs), which are SNPs or loci that have defined effects on the abundance of transcripts of other genes. We constructed pairs of eSNPs and SNPs located in the target genes whose expression is regulated by eSNPs. For all four frameworks the SNP sets were exhaustively tested for pairwise interactions within the sets using a traditional logistic regression model after excluding genes that were previously identified to associate with the trait. Using previously published GWAS data for type 2 diabetes (T2D) and the biologically based pair-wise interaction modeling, we identify twelve genes not seen in the previous single locus analysis.ConclusionWe present four approaches to detect interactions associated with complex diseases. The results show our approaches outperform the traditional single locus approaches in detecting genes that previously did not reach significance; the results also provide novel drug targets and biomarkers relevant to the underlying mechanisms of disease.