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Discovering the common modules that are co-expressed across various stages can lead to an improved understanding of the underlying molecular mechanisms of cancers. There is a shortage of efficient tools for integrative analysis of gene expression and protein interaction networks for discovering common modules associated with cancer progression. To address this issue, we propose a novel regularized multi-view subspace clustering (rMV-spc) algorithm to obtain a representation matrix for each stage and a joint representation matrix that balances the agreement across various stages. To avoid the heterogeneity of data, the protein interaction network is incorporated into the objective of rMV-spc via regularization. Based on the interior point algorithm, we solve the optimization problem to obtain the common modules. By using artificial networks, we demonstrate that the proposed algorithm outperforms state-of-the-art methods in terms of accuracy. Furthermore, the rMV-spc discovers common modules in breast cancer networks based on the breast data, and these modules serve as biomarkers to predict stages of breast cancer. The proposed model and algorithm effectively integrate heterogeneous data for dynamic modules. [ABSTRACT FROM AUTHOR]

Transcriptomic studies of mental disorders using the human brain tissues have been limited, and gene expression signatures in schizophrenia (SCZ) remain elusive. In this study, we applied three differential co-expression methods to analyze five transcriptomic datasets (three RNA-Seq and two microarray datasets) derived from SCZ and matched normal postmortem brain samples. We aimed to uncover biological pathways where internal correlation structure was rewired or inter-coordination was disrupted in SCZ. In total, we identified 60 rewired pathways, many of which were related to neurotransmitter, synapse, immune, and cell adhesion. We found the hub genes, which were on the center of rewired pathways, were highly mutually consistent among the five datasets. The combinatory list of 92 hub genes was generally multi-functional, suggesting their complex and dynamic roles in SCZ pathophysiology. In our constructed pathway crosstalk network, we found "Clostridium neurotoxicity" and "signaling events mediated by focal adhesion kinase" had the highest interactions. We further identified disconnected gene links underlying the disrupted pathway crosstalk. Among them, four gene pairs (PAK1:SYT1, PAK1:RFC5, DCTN1:STX1A, and GRIA1:MAP2K4) were normally correlated in universal contexts. In summary, we systematically identified rewired pathways, disrupted pathway crosstalk circuits, and critical genes and gene links in schizophrenia transcriptomes. [ABSTRACT FROM AUTHOR]

Xyloglucan endotransglucosylase/hydrolases (XTHs)—a family of xyloglucan modifying enzymes—play an essential role in the construction and restructuring of xyloglucan cross-links. However, no comprehensive study has been performed on this gene family in barley. A total of 24 HvXTH genes (named HvXTH1-24) and an EG16 member were identified using the recently completed genomic database of barley (Hordeum vulgare). Phylogenetic analysis showed that 24 HvXTH genes could be classified into three phylogenetic groups: (I/II, III-A and III-B) and HvXTH15 was in the ancestral group. All HvXTH protein members—except HvXTH15—had a conserved N-glycosylation site. The genomic location of HvXTHs on barley chromosomes showed that the 24 genes are unevenly distributed on the 7 chromosomes, with 10 of them specifically located on chromosome 7H. A structure-based sequence alignment demonstrates that each XTH possesses a highly conserved domain (ExDxE) responsible for catalytic activity. Expression profiles based on the barley genome database showed that HvXTH family members display different expression patterns in different tissues and at different stages. This study is the first systematic genomic analysis of the barley HvXTH gene family. Our results provide valuable information that will help to elucidate the roles of HvXTH genes in the growth and development of barley. [ABSTRACT FROM AUTHOR]