The transcriptome of the rumen ciliate Entodinium caudatum reveals some of its metabolic features.

BACKGROUND: Rumen ciliates play important roles in rumen function by digesting and fermenting feed and shaping the rumen microbiome. However, they remain poorly understood due to the lack of definitive direct evidence without influence by prokaryotes (including symbionts) in co-cultures or the rumen. In this study, we used RNA-Seq to characterize the transcriptome of Entodinium caudatum, the most predominant and representative rumen ciliate species. RESULTS: Of a large number of transcripts, > 12,000 were annotated to the curated genes in the NR, UniProt, and GO databases. Numerous CAZymes (including lysozyme and chitinase) and peptidases were represented in the transcriptome. This study revealed the ability of E. caudatum to depolymerize starch, hemicellulose, pectin, and the polysaccharides of the bacterial and fungal cell wall, and to degrade proteins. Many signaling pathways, including the ones that have been shown to function in E. caudatum, were represented by many transcripts. The transcriptome also revealed the expression of the genes involved in symbiosis, detoxification of reactive oxygen species, and the electron-transport chain. Overall, the transcriptomic evidence is consistent with some of the previous premises about E. caudatum. However, the identification of specific genes, such as those encoding lysozyme, peptidases, and other enzymes unique to rumen ciliates might be targeted to develop specific and effective inhibitors to improve nitrogen utilization efficiency by controlling the activity and growth of rumen ciliates. The transcriptomic data will also help the assembly and annotation in future genomic sequencing of E. caudatum. CONCLUSION: As the first transcriptome of a single species of rumen ciliates ever sequenced, it provides direct evidence for the substrate spectrum, fermentation pathways, ability to respond to various biotic and abiotic stimuli, and other physiological and ecological features of E. caudatum. The presence and expression of the genes involved in the lysis and degradation of microbial cells highlight the dependence of E. caudatum on engulfment of other rumen microbes for its survival and growth. These genes may be explored in future research to develop targeted control of Entodinium species in the rumen. The transcriptome can also facilitate future genomic studies of E. caudatum and other related rumen ciliates.

Protein contact map prediction using multi-stage hybrid intelligence inference systems.

Proteins are one of the most important molecules in organisms. Protein function can be inferred from its 3D structure. The gap between the number of discovered protein sequences and the number of structures determined by the experimental methods is increasing. Accurate prediction of protein contact map is an important step toward the reconstruction of the protein's 3D structure. In spite of continuous progress in developing contact map predictors, highly accurate prediction is still unresolved problem. In this paper, we introduce a new predictor, JUSTcon, which consists of multiple parallel stages that are based on adaptive neuro-fuzzy inference System (ANFIS) and K nearest neighbors (KNNs) classifier. A smart filtering operation is performed on the final outputs to ensure normal connectivity behaviors of amino acids pairs. The window size of the filter is selected by a simple expert system. The dataset was divided into testing dataset of 50 proteins and training dataset of 450 proteins. The system produced an average accuracy of 45.2% for the sequence separation of six amino acids. In addition, JUSTcon outperformed SVMcon and PROFcon predictors in the cases of large separation distances. JUSTcon produced an average accuracy of 15% for the sequence separation of 24 amino acids after applying it on CASP9 targets.