Study of drug-target interaction networks is an important topic for drug development. It is both time-consuming and costly to determine compound-protein interactions or potential drug-target interactions by experiments alone. As a complement, the in silico prediction methods can provide us with very useful information in a timely manner. To realize this, drug compounds are encoded with functional groups and proteins encoded by biological features including biochemical and physicochemical properties. The optimal feature selection procedures are adopted by means of the mRMR (Maximum Relevance Minimum Redundancy) method. Instead of classifying the proteins as a whole family, target proteins are divided into four groups: enzymes, ion channels, G-protein- coupled receptors and nuclear receptors. Thus, four independent predictors are established using the Nearest Neighbor algorithm as their operation engine, with each to predict the interactions between drugs and one of the four protein groups. As a result, the overall success rates by the jackknife cross-validation tests achieved with the four predictors are 85.48%, 80.78%, 78.49%, and 85.66%, respectively. Our results indicate that the network prediction system thus established is quite promising and encouraging.

Despite the uniform mortality in pancreatic adenocarcinoma (PDAC), clinical disease heterogeneity exists with limited genomic differences. A highly aggressive tumor subtype termed ‘basal-like’ was identified to show worse outcomes and higher inflammatory responses. Here, we focus on the microbial effect in PDAC progression and present a comprehensive analysis of the tumor microbiome in different PDAC subtypes with resectable tumors using metagenomic sequencing. We found distinctive microbial communities in basal-like tumors and identified an increasing abundance of Acinetobacter, Pseudomonas and Sphingopyxis to be highly associated with carcinogenesis. Functional characterization of microbial genes suggested the potential to induce pathogen-related inflammation. Host-microbiota interplay analysis provided new insights into the tumorigenic role of specific microbiome compositions and demonstrated the influence of host genetics in shaping the tumor microbiome. Taken together, these findings indicated that the tumor microbiome is closely related to PDAC oncogenesis and the induction of inflammation. Additionally, our data revealed the microbial basis of PDAC heterogeneity and proved the predictive value of the microbiome, which will contribute to the intervention and treatment of disease.

In female mammals most X-linked genes are subject to X-inactivation. However, in humans some X-linked genes escape silencing, these escapees being candidates for the phenotypic aberrations seen in polyX karyotypes. These escape genes have been reported to be under stronger purifying selection than other X-linked genes. Although it is known that escape from X-inactivation is much more common in humans than in mice, systematic assays of escape in humans have to date employed only interspecies somatic cell hybrids. Here we provide the first systematic next-generation sequencing analysis of escape in a human cell line. We analyzed RNA and genotype sequencing data obtained from B lymphocyte cell lines derived from Europeans (CEU) and Yorubans (YRI). By replicated detection of heterozygosis in the transcriptome, we identified 114 escaping genes, including 76 not previously known to be escapees. The newly described escape genes cluster on the X chromosome in the same chromosomal regions as the previously known escapees. There is an excess of escaping genes associated with mental retardation, consistent with this being a common phenotype of polyX phenotypes. We find both differences between populations and between individuals in the propensity to escape. Indeed, we provide the first evidence for there being both hyper- and hypo-escapee females in the human population, consistent with the highly variable phenotypic presentation of polyX karyotypes. Considering also prior data, we reclassify genes as being always, never, and sometimes escape genes. We fail to replicate the prior claim that genes that escape X-inactivation are under stronger purifying selection than others.

The metabolic stability is a very important idiosyncracy of proteins that is related to their global flexibility, intramolecular fluctuations, various internal dynamic processes, as well as many marvelous biological functions. Determination of protein's metabolic stability would provide us with useful information for in-depth understanding of the dynamic action mechanisms of proteins. Although several experimental methods have been developed to measure protein's metabolic stability, they are time-consuming and more expensive. Reported in this paper is a computational method, which is featured by (1) integrating various properties of proteins, such as biochemical and physicochemical properties, subcellular locations, network properties and protein complex property, (2) using the mRMR (Maximum Relevance & Minimum Redundancy) principle and the IFS (Incremental Feature Selection) procedure to optimize the prediction engine, and (3) being able to identify proteins among the four types: “short”, “medium”, “long”, and “extra-long” half-life spans. It was revealed through our analysis that the following seven characters played major roles in determining the stability of proteins: (1) KEGG enrichment scores of the protein and its neighbors in network, (2) subcellular locations, (3) polarity, (4) amino acids composition, (5) hydrophobicity, (6) secondary structure propensity, and (7) the number of protein complexes the protein involved. It was observed that there was an intriguing correlation between the predicted metabolic stability of some proteins and the real half-life of the drugs designed to target them. These findings might provide useful insights for designing protein-stability-relevant drugs. The computational method can also be used as a large-scale tool for annotating the metabolic stability for the avalanche of protein sequences generated in the post-genomic age.

Genome-wide interaction-based association (GWIBA) analysis has the potential to identify novel susceptibility loci. These interaction effects could be missed with the prevailing approaches in genome-wide association studies (GWAS). However, no convincing loci have been discovered exclusively from GWIBA methods, and the intensive computation involved is a major barrier for application. Here, we developed a fast, multi-thread/parallel program named “pair-wise interaction-based association mapping” (PIAM) for exhaustive two-locus searches. With this program, we performed a complete GWIBA analysis on seven diseases with stringent control for false positives, and we validated the results for three of these diseases. We identified one pair-wise interaction between a previously identified locus, C1orf106, and one new locus, TEC, that was specific for Crohn's disease, with a Bonferroni corrected P<0.05 (P = 0.039). This interaction was replicated with a pair of proxy linked loci (P = 0.013) on an independent dataset. Five other interactions had corrected P<0.5. We identified the allelic effect of a locus close to SLC7A13 for coronary artery disease. This was replicated with a linked locus on an independent dataset (P = 1.09×10⁻⁷). Through a local validation analysis that evaluated association signals, rather than locus-based associations, we found that several other regions showed association/interaction signals with nominal P<0.05. In conclusion, this study demonstrated that the GWIBA approach was successful for identifying novel loci, and the results provide new insights into the genetic architecture of common diseases. In addition, our PIAM program was capable of handling very large GWAS datasets that are likely to be produced in the future. Recent studies on the genetic basis of common diseases have identified many loci that confer disease susceptibility. However, much of the heritability of these diseases remains unexplained. Loci involved in gene–gene interactions are considered cryptic, because they confer susceptibility, but may not generate a detectable signal on their own. These interactions may account for the “missing heritability” of common diseases. Theoretically, these interactions can be identified with the genome-wide interaction-based association analysis. But, in reality, very few gene–gene interactions have been identified with that method, and most were based on prior biological knowledge. Here, we applied a parallel computing technique that facilitated the identification of multiple new cryptic susceptibility loci involved in common diseases. We applied stringent control for false positives, and we validated our findings with independent datasets. This study demonstrated that interactions between gene loci could be successfully identified with the genome-wide interaction-based approach. With this approach, we also identified cryptic loci with moderate single-locus effects. The identified loci and interactions merit further investigations for fine mapping and functional analyses. Our results extend the current knowledge of common diseases for future studies in genetic mapping. This approach is applicable to current and future genome-wide association datasets.