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Analysis of microarray data reveals extensive links between Alzheimer’s disease and cardiovascular diseases., Background Because of its polygenic nature, Alzheimer's disease is believed to be caused not by defects in single genes, but rather by variations in a large number of genes and their complex interactions. A systems biology approach, such as the generation of a network of co-expressed genes and the identification of functional modules and cis-regulatory elements, to extract insights and knowledge from microarray data will lead to a better understanding of complex diseases such as Alzheimer's disease. In this study, we perform a series of analyses using co-expression networks, cis-regulatory elements, and functions of co-expressed gene modules to analyze single-cell gene expression data from normal and Alzheimer's disease-affected subjects. Results We identified six co-expressed gene modules, each of which represented a biological process perturbed in Alzheimer's disease. Alzheimer's disease-related genes, such as APOE, A2M, PON2 and MAP4, and cardiovascular disease-associated genes, including COMT, CBS and WNK1, all congregated in a single module. Some of the disease-related genes were hub genes while many of them were directly connected to one or more hub genes. Further investigation of this disease-associated module revealed cis-regulatory elements that match to the binding sites of transcription factors involved in Alzheimer's disease and cardiovascular disease. Conclusion Our results show the extensive links between Alzheimer's disease and cardiovascular disease at the co-expression and co-regulation levels, providing further evidence for the hypothesis that cardiovascular disease and Alzheimer's disease are linked. Our results support the notion that diseases in which the same set of biochemical pathways are affected may tend to co-occur with each other.

We summarize the 2015 International Conference on Intelligent Biology and Medicine (ICIBM 2015) and the editorial report of the supplement to BMC Genomics. The supplement includes 20 research articles selected from the manuscripts submitted to ICIBM 2015. The conference was held on November 13–15, 2015 at Indianapolis, Indiana, USA. It included eight scientific sessions, three tutorials, four keynote presentations, three highlight talks, and a poster session that covered current research in bioinformatics, systems biology, computational biology, biotechnologies, and computational medicine.

MicroRNAs are short, noncoding RNAs that play important roles in post-transcriptional regulation. Although many functions of microRNAs in plants and animals have been revealed in recent years, the transcriptional mechanism of microRNA genes is not well understood. To elucidate the transcriptional regulation of microRNA genes, we study and characterize, in a genome scale, the promoters of intergenic microRNA genes in Caenorhabditis elegans, Homo sapiens and Arabidopsis thaliana. Specifically, we show that the known microRNA genes in these species have the same type of promoters as the protein-coding genes. To further characterize the promoters of miRNA genes, we develop a miRNA core promoter prediction method, called common query voting (CoVote). We applied this new method to identify putative core promoters of most known microRNA genes in the three model species of choice.

Background Geminivirus AC2 is a multifunctional protein that acts as a pathogenicity factor. Transcriptional regulation by AC2 appears to be mediated through interaction with a plant specific DNA binding protein, PEAPOD2 (PPD2), that specifically binds to sequences known to mediate activation of the CP promoter of Cabbage leaf curl virus (CaLCuV) and Tomato golden mosaic virus (TGMV). Suppression of both basal and innate immune responses by AC2 in plants is mediated through inactivation of SnRK1.2, an Arabidopsis SNF1 related protein kinase, and adenosine kinase (ADK). An indirect promoter targeting strategy, via AC2-host dsDNA binding protein interactions, and inactivation of SnRK1.2-mediated defense responses could provide the opportunity for geminiviruses to alter host gene expression and in turn, reprogram the host to support virus infection. The goal of this study was to identify changes in the transcriptome of Arabidopsis induced by the transcription activation function of AC2 and the inactivation of SnRK1.2. Results Using full-length and truncated AC2 proteins, microarray analyses identified 834 genes differentially expressed in response to the transcriptional regulatory function of the AC2 protein at one and two days post treatment. We also identified 499 genes differentially expressed in response to inactivation of SnRK1.2 by the AC2 protein at one and two days post treatment. Network analysis of these two sets of differentially regulated genes identified several networks consisting of between four and eight highly connected genes. Quantitative real-time PCR analysis validated the microarray expression results for 10 out of 11 genes tested. Conclusions It is becoming increasingly apparent that geminiviruses manipulate the host in several ways to facilitate an environment conducive to infection, predominantly through the use of multifunctional proteins. Our approach of identifying networks of highly connected genes that are potentially co-regulated by geminiviruses during infection will allow us to identify novel pathways of co-regulated genes that are stimulated in response to pathogen infection in general, and virus infection in particular. Electronic supplementary material The online version of this article (doi:10.1186/s12870-014-0302-7) contains supplementary material, which is available to authorized users.

Background The osteocyte is a type of cell that appears to be one of the key endocrine regulators of bone metabolism and a key responder to initiate bone formation and remodeling. Identifying the regulatory networks in osteocytes may lead to new therapies for osteoporosis and loss of bone. Results Using microarray, we identified 269 genes over-expressed in osteocyte, many of which have known functions in bone and muscle differentiation and contractility. We determined the evolutionarily conserved and enriched TF binding sites in the 5 kb promoter regions of these genes. Using this data, a transcriptional regulatory network was constructed and subsequently partitioned to identify cis-regulatory modules. Conclusion Our results show that many osteocyte-specific genes, including two well-known osteocyte markers DMP1 and Sost, have highly conserved clustering of muscle-related cis-regulatory modules, thus supporting the concept that a muscle-related gene network is important in osteocyte biology and may play a role in contractility and dynamic movements of the osteocyte.

To understand the function of protein complexes and their association with biological processes, a lot of studies have been done towards analyzing the protein-protein interaction (PPI) networks. However, the advancement in high-throughput technology has resulted in a humongous amount of data for analysis. Moreover, high level of noise, sparseness, and skewness in degree distribution of PPI networks limits the performance of many clustering algorithms and further analysis of their interactions. In addressing and solving these problems we present a novel random walk based algorithm that converts the incomplete and binary PPI network into a protein-protein topological similarity matrix (PP-TS matrix). We believe that if two proteins share some high-order topological similarities they are likely to be interacting with each other. Using the obtained PP-TS matrix, we constructed and used weighted networks to further study and analyze the interaction among proteins. Specifically, we applied a fully automated community structure finding algorithm (Auto-HQcut) on the obtained weighted network to cluster protein complexes. We then analyzed the protein complexes for significance in biological processes. To help visualize and analyze these protein complexes we also developed an interface that displays the resulting complexes as well as the characteristics associated with each complex. Applying our approach to a yeast protein-protein interaction network, we found that the predicted protein-protein interaction pairs with high topological similarities have more significant biological relevance than the original protein-protein interactions pairs. When we compared our PPI network reconstruction algorithm with other existing algorithms using gene ontology and gene co-expression, our algorithm produced the highest similarity scores. Also, our predicted protein complexes showed higher accuracy measure compared to the other protein complex predictions.