CAP has good sensitivity and specificity for detecting hepatic steatosis; however, based on a meta-analysis, CAP was limited in their accuracy of steatosis, which precluded widespread use in clinical practice.

Autopolyploidy is widespread in higher plants and important for agricultural yield and quality. However, the effects of genome duplication on the chromatin organization and transcriptional regulation are largely unknown in plants. Using High-throughput Chromosome Conformation Capture (Hi-C), we showed that autotetraploid Arabidopsis presented more inter-chromosomal interactions and fewer short-range chromatin interactions compared with its diploid progenitor. In addition, genome duplication contributed to the switching of some loose and compact structure domains with altered H3K4me3 and H3K27me3 histone modification status. 539 genes were identified with altered transcriptions and chromatin interactions in autotetraploid Arabidopsis. Especially, we found that genome duplication changed chromatin looping and H3K27me3 histone modification in Flowering Locus C. We propose that genome doubling modulates the transcription genome-wide by changed chromatin interactions and at the specific locus by altered chromatin loops and histone modifications.

DNA-binding proteins play a pivotal role in gene regulation. It is vitally important to develop an automated and efficient method for timely identification of novel DNA-binding proteins. In this study, we proposed a method based on alone the primary sequences of proteins to predict the DNA-binding proteins. DNA-binding proteins were encoded by autocross-covariance transform, pseudo-amino acid composition, dipeptide composition, respectively and also the different combinations of the three encoded methods; further, these feature matrices were applied to support vector machine classifiers to predict the DNA-binding proteins. All modules were trained and validated by the jackknife cross-validation test. Through comparing the performance of these substituted modules, the best result was obtained from pseudo-amino acid composition with the overall accuracy of 96.6% and the sensitivity of 90.7%. The results suggest that it can efficiently predict the novel DNA-binding proteins only using the primary sequences.

Background & AimsFarnesoid X receptor (FXR, NR1H4) is a ligand-activated transcription factor, belonging to the nuclear receptor superfamily. FXR is highly expressed in the liver and is essential in regulating bile acid homeostasis. FXR deficiency is implicated in numerous liver diseases and mice with modulation of FXR have been used as animal models to study liver physiology and pathology. We have reported genome-wide binding of FXR in mice by chromatin immunoprecipitation - deep sequencing (ChIP-seq), with results indicating that FXR may be involved in regulating diverse pathways in liver. However, limited information exists for the functions of human FXR and the suitability of using murine models to study human FXR functions.MethodsIn the current study, we performed ChIP-seq in primary human hepatocytes (PHHs) treated with a synthetic FXR agonist, GW4064 or DMSO control. In parallel, RNA deep sequencing (RNA-seq) and RNA microarray were performed for GW4064 or control treated PHHs and wild type mouse livers, respectively.ResultsChIP-seq showed similar profiles of genome-wide FXR binding in humans and mice in terms of motif analysis and pathway prediction. However, RNA-seq and microarray showed more different transcriptome profiles between PHHs and mouse livers upon GW4064 treatment.ConclusionsIn summary, we have established genome-wide human FXR binding and transcriptome profiles. These results will aid in determining the human FXR functions, as well as judging to what level the mouse models could be used to study human FXR functions.

Motivation: Protein residue-residue contact prediction can be useful in predicting protein 3D structures. Current algorithms for such a purpose leave room for improvement. Results: We develop ProC_S3, a set of Random Forest algorithmbased models, for predicting residue-residue contact maps. The models are constructed based on a collection of 1490 nonredundant, high-resolution protein structures using >1280 sequencebased features. A new amino acid residue contact propensity matrix and a new set of seven amino acid groups based on contact preference are developed and used in ProC_S3. ProC_S3 delivers a 3-fold cross-validated accuracy of 26.9% with coverage of 4.7% for top L/5 predictions (L is the number of residues in a protein) of long-range contacts (sequence separation ≥ 24). Further benchmark tests deliver an accuracy of 29.7% and coverage of 5.6% for an independent set of 329 proteins. In the recently completed Ninth Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP9), ProC_S3 is ranked as No. 1, No. 3, and No. 2 accuracies in the top L/5, L/10 and best 5 predictions of long-range contacts, respectively, among 18 automatic prediction servers.