Individuals affected with different neuropsychiatric disorders such as autism (AUT), schizophrenia (SCZ) and bipolar disorder (BPD), may share similar clinical manifestations, suggesting shared genetic influences and common biological mechanisms underlying these disorders. Using brain transcriptome data gathered from postmortem donors affected with AUT, SCZ and BPD, it is now possible to identify shared dysregulated gene sets, i.e., those abnormally expressed in brains of neuropsychiatric patients, compared to non-psychiatric controls. Here, we apply a novel aberrant gene expression analysis method, coupled with consensus co-expression network analysis, to identify gene sets with shared dysregulated expression in cortical brains of individuals affected with AUT, SCZ and BPD. We identify eight gene sets with dysregulated expression shared by AUT, SCZ and BPD, 23 by AUT and SCZ, four by AUT and BPD, and two by SCZ and BPD. The identified genes are enriched with functions relevant to amino acid transport, synapse, neurotransmitter release, oxidative stress, nitric oxide synthase biosynthesis, immune response, protein folding, lysophosphatidic acid-mediated signaling and glycolysis. Our method has been proven to be effective in discovering and revealing multigene sets with dysregulated expression shared by different neuropsychiatric disorders. Our findings provide new insights into the common molecular mechanisms underlying the pathogenesis and progression of AUT, SCZ and BPD, contributing to the study of etiological overlap between these neuropsychiatric disorders.

Metastatic prostate cancer remains a major clinical challenge and metastatic lesions are highly heterogeneous and difficult to biopsy. Liquid biopsy provides opportunities to gain insights into the underlying biology. Here, using the highly sensitive enrichment-based sequencing technology, we provide analysis of 60 and 175 plasma DNA methylomes from patients with localized and metastatic prostate cancer, respectively. We show that the cell-free DNA methylome can capture variations beyond the tumor. A global hypermethylation in metastatic samples is observed, coupled with hypomethylation in the pericentromeric regions. Hypermethylation at the promoter of a glucocorticoid receptor geneNR3C1is associated with a decreased immune signature. The cell-free DNA methylome is reflective of clinical outcomes and can distinguish different disease types with 0.989 prediction accuracy. Finally, we show the ability of predicting copy number alterations from the data, providing opportunities for joint genetic and epigenetic analysis on limited biological samples.

Miniature inverted repeat transposable elements (MITEs) are prevalent in eukaryotic genomes, including plants and animals. Classified as a type of non-autonomous DNA transposable elements, they play important roles in genome organization and evolution. Comprehensive and accurate genome-wide detection of MITEs in various eukaryotic genomes can improve our understanding of their origins, transposition processes, regulatory mechanisms and biological relevance with regard to gene structures, expression and regulation. In this paper, we present a new MATLAB-based program called detectMITE that employs a novel numeric calculation algorithm to replace conventional string matching algorithms in MITE detection, adopts the Lempel-Ziv complexity algorithm to filter out MITE candidates with low complexity and utilizes the powerful clustering program CD-HIT to cluster similar MITEs into MITE families. Using the rice genome as test data, we found that detectMITE can more accurately, comprehensively and efficiently detect MITEs on a genome-wide scale than other popular MITE detection tools. Through comparison with the potential MITEs annotated in Repbase, the widely used eukaryotic repeat database, detectMITE has been shown to find known and novel MITEs with a complete structure and full-length copies in the genome. detectMITE is an open source tool (https://sourceforge.net/projects/detectmite).

Background: One of the essential processing events during pre-mRNA maturation is the post-transcriptional addition of a polyadenine [poly(A)] tail. The 3'-end poly(A) track protects mRNA from unregulated degradation, and indicates the integrity of mRNA through recognition by mRNA export and translation machinery. The position of a poly(A) site is predetermined by signals in the pre-mRNA sequence that are recognized by a complex of polyadenylation factors. These signals are generally tri-part sequence patterns around the cleavage site that serves as the future poly(A) site. In plants, there is little sequence conservation among these signal elements, which makes it difficult to develop an accurate algorithm to predict the poly(A) site of a given gene. We attempted to solve this problem. Results: Based on our current working model and the profile of nucleotide sequence distribution of the poly(A) signals and around poly(A) sites in Arabidopsis, we have devised a Generalized Hidden Markov Model based algorithm to predict potential poly(A) sites. The high specificity and sensitivity of the algorithm were demonstrated by testing several datasets, and at the best combinations, both reach 97%. The accuracy of the program, called p oly(A) s ite s leuth or PASS, has been demonstrated by the prediction of many validated poly(A) sites. PASS also predicted the changes of poly(A) site efficiency in poly(A) signal mutants that were constructed and characterized by traditional genetic experiments. The efficacy of PASS was demonstrated by predicting poly(A) sites within long genomic sequences. Conclusion: Based on the features of plant poly(A) signals, a computational model was built to effectively predict the poly(A) sites in Arabidopsis genes. The algorithm will be useful in gene annotation because a poly(A) site signifies the end of the transcript. This algorithm can also be used to predict alternative poly(A) sites in known genes, and will be useful in the design of transgenes for crop genetic engineering by predicting and eliminating undesirable poly(A) sites.

Human aging is associated with cognitive decline and an increased risk of neurodegenerative disease. Our objective for this study was to evaluate potential relationships between age and variation in gene expression across different regions of the brain. We analyzed the Genotype-Tissue Expression (GTEx) data from 54 and 101 tissue samples across 13 brain regions in post-mortem donors of European descent aged between 20 and 70 years at death. After accounting for the effects of covariates and hidden confounding factors, we identified 1,446 protein-coding genes whose expression in one or more brain regions is correlated with chronological age at a false discovery rate of 5%. These genes are involved in various biological processes including apoptosis, mRNA splicing, amino acid biosynthesis, and neurotransmitter transport. The distribution of these genes among brain regions is uneven, suggesting variable regional responses to aging. We also found that the aging response of many genes, e.g., TP37 and C1QA, depends on individuals’ genotypic backgrounds. Finally, using dispersion-specific analysis, we identified genes such as IL7R, MS4A4E, and TERF1/TERF2 whose expressions are differentially dispersed by aging, i.e., variances differ between age groups. Our results demonstrate that age-related gene expression is brain region-specific, genotype-dependent, and associated with both mean and dispersion changes. Our findings provide a foundation for more sophisticated gene expression modeling in the studies of age-related neurodegenerative diseases.