Capturing changes in gene expression dynamics by gene set differential coordination analysis

Analyzing gene expression data at the gene set level greatly improves feature extraction and data interpretation. Currently most efforts in gene set analysis are focused on differential expression analysis - finding gene sets whose genes show first-order relationship with the clinical outcome. However the regulation of the biological system is complex, and much of the change in gene expression dynamics do not manifest in the form of differential expression. At the gene set level, capturing the change in expression dynamics is difficult due to the complexity and heterogeneity of the gene sets. Here we report a systematic approach to detect gene sets that show differential coordination patterns with the rest of the transcriptome, as well as pairs of gene sets that are differentially coordinated with each other. We demonstrate that the method can identify biologically relevant gene sets, many of which do not show first-order relationship with the clinical outcome

Improving gene expression data interpretation by finding latent factors that co-regulate gene modules with clinical factors

<p>Abstract</p> <p>Background</p> <p>In the analysis of high-throughput data with a clinical outcome, researchers mostly focus on genes/proteins that show first-order relations with the clinical outcome. While this approach yields biomarkers and biological mechanisms that are easily interpretable, it may miss information that is important to the understanding of disease mechanism and/or treatment response. Here we test the hypothesis that unobserved factors can be mobilized by the living system to coordinate the response to the clinical factors.</p> <p>Results</p> <p>We developed a computational method named Guided Latent Factor Discovery (GLFD) to identify hidden factors that act in combination with the observed clinical factors to control gene modules. In simulation studies, the method recovered masked factors effectively. Using real microarray data, we demonstrate that the method identifies latent factors that are biologically relevant, and extracts more information than analyzing only the first-order response to the clinical outcome.</p> <p>Conclusions</p> <p>Finding latent factors using GLFD brings extra insight into the mechanisms of the disease/drug response. The R code of the method is available at <url>http://userwww.service.emory.edu/~tyu8/GLFD</url>.</p

EgoNet: Identification of human disease ego-network modules

Background: Mining novel biomarkers from gene expression profiles for accurate disease classification is challenging due to small sample size and high noise in gene expression measurements. Several studies have proposed integrated analyses of microarray data and protein-protein interaction (PPI) networks to find diagnostic subnetwork markers. However, the neighborhood relationship among network member genes has not been fully considered by those methods, leaving many potential gene markers unidentified. The main idea of this study is to take full advantage of the biological observation that genes associated with the same or similar diseases commonly reside in the same neighborhood of molecular networks.Results: We present EgoNet, a novel method based on egocentric network-analysis techniques, to exhaustively search and prioritize disease subnetworks and gene markers from a large-scale biological network. When applied to a triple-negative breast cancer (TNBC) microarray dataset, the top selected modules contain both known gene markers in TNBC and novel candidates, such as RAD51 and DOK1, which play a central role in their respective ego-networks by connecting many differentially expressed genes.Conclusions: Our results suggest that EgoNet, which is based on the ego network concept, allows the identification of novel biomarkers and provides a deeper understanding of their roles in complex diseases

Analyzing LC/MS Metabolic Profiling Data in the Context of Existing Metabolic Networks

Metabolic profiling is the unbiased detection and quantification of low molecular-weight metabolites in a living system. It is rapidly developing in biological and translational research, contributing to disease mechanism elucidation, environmental chemical surveillance, biomarker detection, and health outcome prediction. Recent developments in experimental and computational technology allow more and more known metabolites to be detected and quantified from complex samples. As the coverage of the metabolic network improves, it has become feasible to examine metabolic profiling data from a systems perspective, i.e. interpreting the data and performing statistical inference in the context of pathways andgenome-scale metabolic networks. Recently a number of methods have been developed in this area, and much improvement in algorithms and databases are still needed. In this review, we survey some methods for the analysis of metabolic profiling data based on metabolic networks

MicroRNA as novel mechanism in cyclosporine induced kidney fibrosis

Calcineurin inhibitors (CNIs) including cyclosporine A (CsA) and tacrolimus (FK506) have been revolutionary immunosuppressants for the prevention of allograft rejection in solid organ transplantation. While CNIs remain a mainstay of immunosuppressive therapy in organ transplantation, CNI-induced nephrotoxicity affects virtually every patient. Fibrosis is one of the major pathophysiological outcomes of CNI nephrotoxicity and remains a clinical challenge because the underlying mechanisms are incompletely understood and there are no therapeutic options to prevent or reverse renal damage. MicroRNAs (miRs) are small non-coding RNAs that regulate gene expression through modulation of mRNA stability and repression of target mRNA translation. There is increasing evidence that miRs are critical mediators of fibrosis and may be regulated by CNIs. In this study, 12 mice were randomly divided into two groups. 6 mice received CsA treatment for 4 weeks, the other 6 mice received placebo for 4 weeks. The miR expression profiles in the kidney tissues between mice that received CsA and placebo were compared by miR microarray. The miR microarray results strongly indicate that CsA induces significant changes in kidney miR expression profile. Using a combined criteria of False Discovery Rate (≤0.1), fold change (≥2) and median signal strength (≥50), 77 significantly regulated miRs were identified. 27 of the 77 selected miRs regulate genes in the TGFβ pathway, including miRs that have been reported to be involved in kidney fibrosis (such as miR21) and miRs that have not been reported to be related to kidney disease (such as miR28). Histology study of the kidney tissues from the CsA-treated mice showed clear signs of fibrosis. The mRNA expression profiles in the same kidney tissues between mice that received CsA and placebo were also compared with mRNA microarray. The mRNA and miR array data were analyzed in parallel. The results are consistent with regulation of the TGFβ pathway by miRs. In conclusion, CsA treatment can change the expression pattern of both miRs and mRNA in mouse a model. MiRs that regulate mRNAs in TGFβ pathway may play an important role in kidney fibrosis induced by CsA

Talking about Problems and Countermeasures of Small-sized Apartments Design

In order to promote the healthy development for small apartments, the author made a full investigation to Zhengzhou of China, analyzed the existing problems of small-sized houses, and proposed improvement suggestions from traffic space, family-unit design and plan-combination. These measures will help to create a suitable living environment for users

Talking about Problems and Countermeasures of Small-sized Apartments Design

In order to promote the healthy development for small apartments, the author made a full investigation to Zhengzhou of China, analyzed the existing problems of small-sized houses, and proposed improvement suggestions from traffic space, family-unit design and plan-combination. These measures will help to create a suitable living environment for users

Two proteins share immunological epitopes on the tumor-associated antigen 17-1A

The mouse monoclonal antibody (mAb) 17-1A which recognizes the tumor-associated antigen 17-1A (also called EGP-40 or EpCAM) was successfully used in adjuvant therapy for colorectal carcinoma. In the 17-1A antigen analysis, we isolated not only a protein of 33 kDa (P33) which was reported as the tumor associated antigen 17-1A, but also a protein of 65 kDa (P65) using affinity chromatography from cell lysates of HCT, and another protein of 50 kDa (P50) from lysates of human colorectal tumor tissues. The mAbs 17-1A and M79 (mAb M79 recognizes a different epitope on the 17-1A antigen) both could bind P33 and P50, but only M79 bound to P65 in an enzyme-linked immunosorbant assay (ELISA). These results indicate that P33 and P50 share at least two epitopes, and a common immunological epitope exists among P33, P50 and P65, suggesting that the two new proteins (P50 and P65) are related to the tumor-associated antigen 17-1A

Antibodies to HIV-1 gp41 recognize synthetic peptides of human IFN-a and IFN-ß

Based on our finding that a common epitope exists between HIV-1 gp41 and human type I interferons (IFN-a and IFN-ß), and increased levels of antibodies against human IFN-a and IFN-ß were observed in HIV-1-infected individuals, we tried to explain the mechanism of increased levels of antibodies. Mouse antisera recognizing HIV-1 recombinant soluble (rs) gp41 (aa 539-684) interacted with two synthetic peptides sequence-corresponding to the IFN-a/ß receptor binding site on human IFN-a and IFN-ß, while normal mouse serum (pooled normal sera) did not. The anti-rspg41 antisera after adsorption by IFN-ß sepharose column lost the activity of interaction with both synthetic peptides. In another experiment, rsgp41 could bind to sepharose column conjugated with anti-IFN-ß polyclonal antibodies (IgG). These results indicate that the common epitope on gp41 and type I interferons could induce antibodies recognizing the receptor binding site on IFN-a, and IFN-ß, suggesting that increased levels of antibodies against IFN-a and IFN-ß in HIV-1-infected individuals could be induced by gp41