Bayesian Markov Random Field Analysis for Protein Function Prediction Based on Network Data

Inference of protein functions is one of the most important aims of modern biology. To fully exploit the large volumes of genomic data typically produced in modern-day genomic experiments, automated computational methods for protein function prediction are urgently needed. Established methods use sequence or structure similarity to infer functions but those types of data do not suffice to determine the biological context in which proteins act. Current high-throughput biological experiments produce large amounts of data on the interactions between proteins. Such data can be used to infer interaction networks and to predict the biological process that the protein is involved in. Here, we develop a probabilistic approach for protein function prediction using network data, such as protein-protein interaction measurements. We take a Bayesian approach to an existing Markov Random Field method by performing simultaneous estimation of the model parameters and prediction of protein functions. We use an adaptive Markov Chain Monte Carlo algorithm that leads to more accurate parameter estimates and consequently to improved prediction performance compared to the standard Markov Random Fields method. We tested our method using a high quality S.cereviciae validation network with 1622 proteins against 90 Gene Ontology terms of different levels of abstraction. Compared to three other protein function prediction methods, our approach shows very good prediction performance. Our method can be directly applied to protein-protein interaction or coexpression networks, but also can be extended to use multiple data sources. We apply our method to physical protein interaction data from S. cerevisiae and provide novel predictions, using 340 Gene Ontology terms, for 1170 unannotated proteins and we evaluate the predictions using the available literature

AT(1)R blockade in adverse milieus: role of SMRT and corepressor complexes

ANG II type 1 receptor blockade (AT(1)R-BLK) is used extensively to slow down the progression of proteinuric kidney diseases. We hypothesized that AT(1)R-BLK provides podocyte protection through regulation of silencing mediator of retinoic acid and thyroid hormone receptor (SMRT) and vitamin D receptor (VDR) expression under adverse milieus such as high glucose and human immunodeficiency virus infection. Both AT(1)R-BLK and VDR agonists (VDAs) stimulated VDR complex formation that differed not only in their composition but also in their functionality. AT(1)R-BLK-induced VDR complexes contained predominantly unliganded VDR, SMRT, and phosphorylated histone deacetylase 3, whereas VDA-VDR complexes were constituted by liganded VDR and CREB-binding protein/p300. AT(1)R-BLK-induced complexes attenuated podocyte acetyl-histone 3 levels as well as cytochrome P-450 family 24A1 expression, thus indicating their deacetylating and repressive properties. On the other hand, VDA-VDR complexes not only increased podocyte acetyl-histone 3 levels but also enhanced cytochrome P-450 family 24A1 expression, thus suggesting their acetylating and gene activation properties. AT(1)R-BLK-induced podocyte SMRT inhibited expression of the proapoptotic gene BAX through downregulation of Wip1 and phosphorylation of checkpoint kinase 2 in high-glucose milieu. Since SMRT-depleted podocytes lacked AT(1)R-BLK-mediated protection against DNA damage, it appears that SMRT is necessary for DNA repairs during AT(1)R-BLK. We conclude that AT(1)R-BLK provides podocyte protection in adverse milieus predominantly through SMRT expression and partly through unliganded VDR expression in 1,25(OH)(2)D-deficient states; on the other hand, AT(1)R-BLK contributes to liganded VDR expression in 1,25(OH)(2)D-sufficient states

Genome-wide remodeling of the epigenetic landscape during myogenic differentiation

We have examined changes in the chromatin landscape during muscle differentiation by mapping the genome-wide location of ten key histone marks and transcription factors in mouse myoblasts and terminally differentiated myotubes, providing an exceptionally rich dataset that has enabled discovery of key epigenetic changes underlying myogenesis. Using this compendium, we focused on a well-known repressive mark, histone H3 lysine 27 trimethylation, and identified novel regulatory elements flanking the myogenin gene that function as a key differentiation-dependent switch during myogenesis. Next, we examined the role of Polycomb-mediated H3K27 methylation in gene repression by systematically ablating components of both PRC1 and PRC2 complexes. Surprisingly, we found mechanistic differences between transient and permanent repression of muscle differentiation and lineage commitment genes and observed that the loss of PRC1 and PRC2 components produced opposing differentiation defects. These phenotypes illustrate striking differences as compared to embryonic stem cell differentiation and suggest that PRC1 and PRC2 do not operate sequentially in muscle cells. Our studies of PRC1 occupancy also suggested a “fail-safe” mechanism, whereby PRC1/Bmi1 concentrates at genes specifying nonmuscle lineages, helping to retain H3K27me3 in the face of declining Ezh2-mediated methyltransferase activity in differentiated cells