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For transcriptomic analysis, there are numerous microarray-based genomic data, especially those generated for cancer research. The typical analysis measures the difference between a cancer sample-group and a matched control group for each transcript or gene. Association rule mining is used to discover interesting item sets through rule-based methodology. Thus, it has advantages to find causal effect relationships between the transcripts. In this work, we introduce two new rule-based similarity measures—weighted rank-based Jaccard and Cosine measures—and then propose a novel computational framework to detect condensed gene co-expression modules ( C o n G E M s) through the association rule-based learning system and the weighted similarity scores. In practice, the list of evolved condensed markers that consists of both singular and complex markers in nature depends on the corresponding condensed gene sets in either antecedent or consequent of the rules of the resultant modules. In our evaluation, these markers could be supported by literature evidence, KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway and Gene Ontology annotations. Specifically, we preliminarily identified differentially expressed genes using an empirical Bayes test. A recently developed algorithm—RANWAR—was then utilized to determine the association rules from these genes. Based on that, we computed the integrated similarity scores of these rule-based similarity measures between each rule-pair, and the resultant scores were used for clustering to identify the co-expressed rule-modules. We applied our method to a gene expression dataset for lung squamous cell carcinoma and a genome methylation dataset for uterine cervical carcinogenesis. Our proposed module discovery method produced better results than the traditional gene-module discovery measures. In summary, our proposed rule-based method is useful for exploring biomarker modules from transcriptomic data.

The Nef protein of human immunodeficiency virus (HIV) promotes viral replication and progression to AIDS. Besides its well-studied effects on intracellular signaling, Nef also functions through its secretion in exosomes, which are nanovesicles containing proteins, microRNAs, and mRNAs and are important for intercellular communication. Nef expression enhances exosome secretion and these exosomes can enter uninfected CD4 T cells leading to apoptotic death. We have recently reported the first miRNome analysis of exosomes secreted from Nef-expressing U937monocytic cells. Here we show genome-wide transcriptome analysis of Nef-expressing U937 cells and their exosomes. We identified four key mRNAs preferentially retained in Nef-expressing cells; these code for MECP2, HMOX1, AARSD1, and ATF2 and are important for chromatin modification and gene expression. Interestingly, their target miRNAs are exported out in exosomes. We also identified three key mRNAs selectively secreted in exosomes from Nef-expressing U937 cells and their corresponding miRNAs being preferentially retained in cells. These are AATK, SLC27A1, and CDKAL and are important in apoptosis and fatty acid transport. Thus, our study identifies selectively expressed mRNAs in Nef-expressing U937 cells and their exosomes and supports a new mode on intercellular regulation by the HIV-1 Nef protein.

A compensatory mutation (CM) counter balances lethal effects of a deleterious mutation (DM), ensuring the persistence of both through natural selection. However, little is known about the biological aspects of CMs those restore the structural alterations of proteins caused by slightly DMs. Here, by analyzing the evolution of the UDP-glycosyltransferase 73B4 protein among monocot-dicot plants, we investigate the occurrence of CMs around slightly DMs in 3D space. Our results illustrate that CMs exhibit significantly higher tendency to occur within the range of electrostatic interaction around the slightly DMs, compared to occurring randomly in the protein.

Introduction : The HIV Nef protein is a multifunctional virulence factor that perturbs intracellular membranes and signalling and is secreted into exosomes. While Nef-containing exosomes have a distinct proteomic profile, no comprehensive analysis of their miRNA cargo has been carried out. Since Nef functions as a viral suppressor of RNA interference and disturbs the distribution of RNA-induced silencing complex proteins between cells and exosomes, we hypothesized that it might also affect the export of miRNAs into exosomes. Method : Exosomes were purified from human monocytic U937 cells that stably expressed HIV-1 Nef. The RNA from cells and exosomes was profiled for 667 miRNAs using a Taqman Low Density Array. Selected miRNAs and their mRNA targets were validated by quantitative RT-PCR. Bioinformatics analyses were used to identify targets and predict pathways. Results : Nef expression affected a significant fraction of miRNAs in U937 cells. Our analysis showed 47 miRNAs to be selectively secreted into Nef exosomes and 2 miRNAs to be selectively retained in Nef-expressing cells. The exosomal miRNAs were predicted to target several cellular genes in inflammatory cytokine and other pathways important for HIV pathogenesis, and an overwhelming majority had targets within the HIV genome. Conclusions : This is the first study to report miRnome analysis of HIV Nef expressing monocytes and exosomes. Our results demonstrate that Nef causes large-scale dysregulation of cellular miRNAs, including their secretion through exosomes. We suggest this to be a novel viral strategy to affect pathogenesis and to limit the effects of RNA interference on viral replication and persistence. Keywords: exosomes; miRNA; HIV; Nef; inflammatory cytokine (Published: 25 March 2014) Citation: Journal of Extracellular Vesicles 2014, 3 : 23129 - http://dx.doi.org/10.3402/jev.v3.23129 To access the supplementary material to this article, please see Supplementary files under Article Tools online.

Using the available crystal structures of 50S ribosomal subunits from three prokaryotic species: Escherichia coli (mesophilic), Thermus thermophilus (thermophilic), and Haloarcula marismortui (halophilic), we have analyzed different structural features of ribosomal RNAs (rRNAs), proteins, and of their interfaces. We have correlated these structural features with the environmental adaptation strategies of the corresponding species. While dense intra-rRNA packing is observed in thermophilic, loose intra-rRNA packing is observed in halophilic (both compared to mesophilic). Interestingly, protein–rRNA interfaces of both the extremophiles are densely packed compared to that of the mesophilic. The intersubunit bridge regions are almost devoid of cavities, probably ensuring the proper formation of each bridge (by not allowing any loosely packed region nearby). During rRNA binding, the ribosomal proteins experience some structural transitions. Here, we have analyzed the intrinsically disordered and ordered regions of the ribosomal proteins, which are subjected to such transitions. The intrinsically disordered and disorder-to-order transition sites of the thermophilic and mesophilic ribosomal proteins are simultaneously (i) highly conserved and (ii) slowly evolving compared to rest of the protein structure. Although high conservation is observed at such sites of halophilic ribosomal proteins, but slow rate of evolution is absent. Such differences between thermophilic, mesophilic, and halophilic can be explained from their environmental adaptation strategy. Interestingly, a universal biophysical principle evident by a linear relationship between the free energy of interface formation, interface area, and structural changes of r-proteins during assembly is always maintained, irrespective of the environmental conditions.

Here we compare the structural and evolutionary attributes of Thermus thermophilus and Escherichia coli small ribosomal subunits (SSU). Our results indicate that with few exceptions, thermophilic 16S ribosomal RNA (16S rRNA) is densely packed compared to that of mesophilic at most of the analogous spatial regions. In addition, we have located species-specific cavity clusters (SSCCs) in both species. E. coli SSCCs are numerous and larger compared to T. thermophilus SSCCs, which again indicates densely packed thermophilic 16S rRNA. Thermophilic ribosomal proteins (r-proteins) have longer disordered regions than their mesophilic homologs and they experience larger disorder-to-order transitions during SSU-assembly. This is reflected in the predicted higher conformational changes of thermophilic r-proteins compared to their mesophilic homologs during SSU-assembly. This high conformational change of thermophilic r-proteins may help them to associate with the 16S ribosomal RNA with high complementary interfaces, larger interface areas, and denser molecular contacts, compared to those of mesophilic. Thus, thermophilic protein-rRNA interfaces are tightly associated with 16S rRNA than their mesophilic homologs. Densely packed 16S rRNA interior and tight protein-rRNA binding of T. thermophilus (compared to those of E. coli) are likely the signatures of its thermal adaptation. We have found a linear correlation between the free energy of protein-RNA interface formation, interface size, and square of conformational changes, which is followed in both prokaryotic and eukaryotic SSU. Disorder is associated with high protein-RNA interface polarity. We have found an evolutionary tendency to maintain high polarity (thereby disorder) at protein-rRNA interfaces, than that at rest of the protein structures. However, some proteins exhibit exceptions to this general trend.

Statistical analysis and association rule mining are two most efficient techniques, where the first one is used to identify differentially expressed/methylated genes across different types of samples or experimental conditions and the second one is used to determine expression/methylation relationships among them. In this article, we have performed an integrated analysis of statistical methods and association rule mining on mRNA expression and DNA methylation datasets for the prediction of Uterine Leiomyoma. Moreover, we have proposed a novel rule-base classifier. Depending on 16 different rule-interestingness measures, we have applied a Genetic Algorithm based rank aggregation technique on the association rules which are generated from the training data by Apriori association rule mining algorithm. After determining the ranks of the rules, we have conducted a majority voting technique on each test point to determine its class-label (i.e. tumor or normal class-label) through weighted-sum method. We have run this classifier on the combined dataset using k-fold cross-validation and also performed a comparative performance analysis with other popular rule-base classifiers. Finally, we have predicted the status of some important genes (through frequency analysis in association rules for tumor and normal class-labels individually) that have a major role for tumor formation in Uterine Leiomyoma.