Hongbo Xie

Unfoldomics of human diseases: linking protein intrinsic disorder with diseases

BackgroundIntrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) lack stable tertiary and/or secondary structure yet fulfills key biological functions. The recent recognition of IDPs and IDRs is leading to an entire field aimed at their systematic structural characterization and at determination of their mechanisms of action. Bioinformatics studies showed that IDPs and IDRs are highly abundant in different proteomes and carry out mostly regulatory functions related to molecular recognition and signal transduction. These activities complement the functions of structured proteins. IDPs and IDRs were shown to participate in both one-to-many and many-to-one signaling. Alternative splicing and posttranslational modifications are frequently used to tune the IDP functionality. Several individual IDPs were shown to be associated with human diseases, such as cancer, cardiovascular disease, amyloidoses, diabetes, neurodegenerative diseases, and others. This raises questions regarding the involvement of IDPs and IDRs in various diseases.ResultsIDPs and IDRs were shown to be highly abundant in proteins associated with various human maladies. As the number of IDPs related to various diseases was found to be very large, the concepts of the disease-related unfoldome and unfoldomics were introduced. Novel bioinformatics tools were proposed to populate and characterize the disease-associated unfoldome. Structural characterization of the members of the disease-related unfoldome requires specialized experimental approaches. IDPs possess a number of unique structural and functional features that determine their broad involvement into the pathogenesis of various diseases.ConclusionProteins associated with various human diseases are enriched in intrinsic disorder. These disease-associated IDPs and IDRs are real, abundant, diversified, vital, and dynamic. These proteins and regions comprise the disease-related unfoldome, which covers a significant part of the human proteome. Profound association between intrinsic disorder and various human diseases is determined by a set of unique structural and functional characteristics of IDPs and IDRs. Unfoldomics of human diseases utilizes unrivaled bioinformatics and experimental techniques, paves the road for better understanding of human diseases, their pathogenesis and molecular mechanisms, and helps develop new strategies for the analysis of disease-related proteins.

High Mobility Group Protein B1 Is an Activator of Apoptotic Response to Antimetabolite Drugs

We explored the role of a chromatin-associated nuclear protein high mobility group protein B1 (HMGB1) in apoptotic response to widely used anticancer drugs. A murine fibroblast model system generated from Hmgb1+/+ and Hmgb1-/- mice was used to assess the role of HMGB1 protein in cellular response to anticancer nucleoside analogs and precursors, which act without destroying the integrity of DNA. Chemosensitivity experiments with 5-fluorouracil, cytosine arabinoside (araC), and mercaptopurine (MP) demonstrated that Hmgb1-/- mouse embryonic fibroblasts (MEFs) were 3 to 10 times more resistant to these drugs compared with Hmgb1+/+ MEFs. Hmgb1-deficient cells showed compromised cell cycle arrest and reduced caspase activation after treatment with MP and araC. Phosphorylation of p53 at Ser12 (corresponding to Ser9 in human p53) and Ser18 (corresponding to Ser15 in human p53), as well as phosphorylation of H2AX after drug treatment, was reduced in Hmgb1-deficient cells. trans-Activation experiments demonstrated diminished activation of proapoptotic promoters Bax, Puma, and Noxa in Hmgb1-deficient cells after treatment with MP or araC, consistent with reduced transcriptional activity of p53. We have demonstrated for the first time that Hmgb1 is an essential activator of cellular response to genotoxic stress caused by chemotherapeutic agents (thiopurines, cytarabine, and 5-fluorouracil), which acts at early steps of antimetabolite-induced stress by stimulating phosphorylation of two DNA damage markers, p53 and H2AX. This finding makes HMGB1 a potential target for modulating activity of chemotherapeutic antimetabolites. Identification of proteins sensitive to DNA lesions that occur without the loss of DNA integrity provides new insights into the determinants of drug sensitivity in cancer cells.

Exploiting unlabeled data for improving accuracy of predictive data mining

Predictive data mining typically relies on labeled data without exploiting a much larger amount of available unlabeled data. We show that using unlabeled data can be beneficial in a range of important prediction problems and therefore should be an integral part of the learning process. Given an unlabeled dataset representative of the underlying distribution and a K-class labeled sample that might be biased, our approach is to learn K contrast classifiers each trained to discriminate a certain class of labeled data from the unlabeled population. We illustrate that contrast classifiers can be useful in one-class classification, outlier detection, density estimation, and learning from biased data. The advantages of the proposed approach are demonstrated by an extensive evaluation on synthetic data followed by real-life bioinformatics applications for (1) ranking PubMed articles by their relevance to protein disorder and (2) cost-effective enlargement of a disordered protein database.

Analysis of multiplex gene expression maps obtained by voxelation.

BackgroundGene expression signatures in the mammalian brain hold the key to understanding neural development and neurological disease. Researchers have previously used voxelation in combination with microarrays for acquisition of genome-wide atlases of expression patterns in the mouse brain. On the other hand, some work has been performed on studying gene functions, without taking into account the location information of a genes expression in a mouse brain. In this paper, we present an approach for identifying the relation between gene expression maps obtained by voxelation and gene functions.ResultsTo analyze the dataset, we chose typical genes as queries and aimed at discovering similar gene groups. Gene similarity was determined by using the wavelet features extracted from the left and right hemispheres averaged gene expression maps, and by the Euclidean distance between each pair of feature vectors. We also performed a multiple clustering approach on the gene expression maps, combined with hierarchical clustering. Among each group of similar genes and clusters, the gene function similarity was measured by calculating the average gene function distances in the gene ontology structure.By applying our methodology to find similar genes to certain target genes we were able to improve our understanding of gene expression patterns and gene functions.By applying the clustering analysis method, we obtained significant clusters, which have both very similar gene expression maps and very similar gene functions respectively to their corresponding gene ontologies. The cellular component ontology resulted in prominent clusters expressed in cortex and corpus callosum. The molecular function ontology gave prominent clusters in cortex, corpus callosum and hypothalamus. The biological process ontology resulted in clusters in cortex, hypothalamus and choroid plexus. Clusters from all three ontologies combined were most prominently expressed in cortex and corpus callosum.ConclusionThe experimental results confirm the hypothesis that genes with similar gene expression maps might have similar gene functions. The voxelation data takes into account the location information of gene expression level in mouse brain, which is novel in related research. The proposed approach can potentially be used to predict gene functions and provide helpful suggestions to biologists.

Identifying gene functions using functional expression profiles obtained by voxelation

Gene expression profiles have been widely used in functional genomic studies. However, not much work in traditional gene expression profiling takes into account the location information of a genes expressions in the brain. Gene expression maps, which contain spatial information regarding the expression of genes in mices brain, are obtained by combining voxelation and microarrays. Based on the idea that genes with similar gene expression maps may have similar gene functions, we propose an approach to identify gene functions. A gene function can potentially be associated with a specific gene expression profile. We name this specific gene expression profile, Functional Expression Profile (FEP). A functional expression profile can be obtained either by directly finding genes with a certain function, or by analyzing clusters of genes that have similar expression maps and similar functions. By taking advantage of the identified FEPs, we can annotate gene functions with high accuracy. Compared to the traditional K-nearest neighbor method, our approach shows higher accuracy in predicting functions. The images of FEPs are in good agreement with anatomical components of mices brain, and provide valuable insight in terms of function prediction to biological scientists.

Analysis of Multiplex Gene Expression Maps Obtained by Voxelation

In this paper we present an approach for identifying the relationships between gene expression maps and gene functions based on the multiplex gene expression maps of mouse brain obtained by voxelation. To analyze the dataset, we choose typical genes as queries and aim at discovering similar gene groups. We use the wavelet transform for extracting features from the left and right hemispheres averaged gene expression maps, and the Euclidean distance between each pair of feature vectors to determine gene similarity. We also perform a multiple clustering approach on the gene expression maps, combined with hierarchical clustering. Among each group of similar genes and clusters, the gene function similarity is measured by calculating the average gene function distances in the gene ontology structure. The experimental results confirm the hypothesis that genes with similar gene expression maps might have similar gene functions. The voxelation data takes into account the location information of gene expression level in mouse brain, which is novel in related research. The proposed approach can potentially be used to predict gene functions and provide helpful suggestions to biologists.