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Objective To determine if long interspersed element-1 (L1) retrotransposons convey risk for idiopathic temporal lobe epilepsy (TLE). Methods Surgically resected temporal cortex from individuals with TLE (N = 33) and postmortem temporal cortex from individuals with no known neurological disease (N = 33) were analyzed for L1 content by Restriction Enzyme Based Enriched L1Hs sequencing (REBELseq). Expression of three KCNIP4 splice variants was assessed by droplet digital PCR (ddPCR). Protein ANalysis THrough Evolutionary Relationships (PANTHER) was used to determine ontologies and pathways for lists of genes harboring L1 insertions. Results We identified novel L1 insertions specific to individuals with TLE, and others specific to controls. Although there were no statistically significant differences between cases and controls in the numbers of known and novel L1 insertions, PANTHER analyses of intragenic L1 insertions showed statistically significant enrichments for epilepsy-relevant gene ontologies in both cases and controls. Gene ontologies "neuron projection development" and "calcium ion transmembrane transport" were among those found only in individuals with TLE. We confirmed novel L1 insertions in several genes associated with seizures/epilepsy, including a de novo somatic L1 retrotransposition in KCNIP4 that occurred after neural crest formation in one patient. However, ddPCR results suggest this de novo L1 did not alter KCNIP4 mRNA expression. Significance Given current data from this small cohort, we conclude that L1 elements, either rare heritable germline insertions or de novo somatic retrotranspositions, may contribute only minimally to overall genetic risk for idiopathic TLE. We suggest that further studies in additional patients and additional brain regions are warranted.

Background Machine learning (ML) algorithms and methods offer great tools to analyze large complex genomic datasets. Our goal was to compare the genomic architecture of schizophrenia (SCZ) and autism spectrum disorder (ASD) using ML. Methods In this paper, we used regularized gradient boosted machines to analyze whole-exome sequencing (WES) data from individuals SCZ and ASD in order to identify important distinguishing genetic features. We further demonstrated a method of gene clustering to highlight which subsets of genes identified by the ML algorithm are mutated concurrently in affected individuals and are central to each disease (i.e., ASD vs. SCZ “hub” genes). Results In summary, after correcting for population structure, we found that SCZ and ASD cases could be successfully separated based on genetic information, with 86–88% accuracy on the testing dataset. Through bioinformatic analysis, we explored if combinations of genes concurrently mutated in patients with the same condition (“hub” genes) belong to specific pathways. Several themes were found to be associated with ASD, including calcium ion transmembrane transport, immune system/inflammation, synapse organization, and retinoid metabolic process. Moreover, ion transmembrane transport, neurotransmitter transport, and microtubule/cytoskeleton processes were highlighted for SCZ. Conclusions Our manuscript introduces a novel comparative approach for studying the genetic architecture of genetically related diseases with complex inheritance and highlights genetic similarities and differences between ASD and SCZ.

Background Compelling lines of evidence indicate that DNA methylation of non-coding RNAs (ncRNAs) plays critical roles in various tumour progression. In addition, the differential methylation of ncRNAs can predict prognosis of patients. However, little is known about the clear relationship between DNA methylation profile of ncRNAs and the prognosis of pancreatic adenocarcinoma (PAC) patients. Methods The data of DNA methylation, RNA-seq, miRNA-seq and clinical features of PAC patients were collected from TCGA database. The DNA methylation profile was obtained using the Infinium HumanMethylation450 BeadChip array. LASSO regression was performed to construct two methylation-based classifiers. The risk score of methylation-based classifiers was calculated for each patient, and the accuracy of the classifiers in predicting overall survival (OS) was examined by ROC curve analysis. In addition, Cox regression models were utilized to assess whether clinical variables and the classifiers were independent prognostic factors for OS. The targets of miRNA and the genes co-expressed with lncRNA were identified with DIANA microT-CDS and the Multi-Experiment Matrix (MEM), respectively. Moreover, DAVID Bioinformatics Resources were applied to analyse the functional enrichment of these targets and co-expressed genes. Results A total of 4004 CpG sites of miRNA and 11,259 CpG sites of lncRNA were screened. Among these CpG sites, 8 CpG sites of miRNA and 7 CpG sites of lncRNA were found with regression coefficients. By multiplying the sum of methylation degrees of the selected CpGs with these coefficients, two methylation-based classifiers were constructed. The classifiers have shown good performance in predicting the survival rate of PAC patients at varying follow-up times. Interestingly, both of these two classifiers were predominant and independent factors for OS. Furthermore, functional enrichment analysis demonstrated that aberrantly methylated miRNAs and lncRNAs are related to calcium ion transmembrane transport and MAPK, Ras and calcium signalling pathways. Conclusion In the present study, we identified two methylation-based classifiers of ncRNA associated with OS in PAC patients through a comprehensive analysis of miRNA and lncRNA profiles. We are the first group to demonstrate a relationship between the aberrant DNA methylation of ncRNAs and the prognosis of PAC, and this relationship would contribute to individualized PAC therapy. Electronic supplementary material The online version of this article (10.1186/s12935-019-0828-8) contains supplementary material, which is available to authorized users.

The ploidy levels affect the physiological traits and stress tolerance in plants. In comparison to diploids, tetraploids enable to own advanced physiological traits and increase the size and fruit quality and with resistance to abiotic stresses. However, Ziziphus jujuba Mill. ‘Dongzao’ is one of the most popular and consumer favorite fresh fruit in China, which exhibits cold sensitivity within field observation when it is autotetraploid in compared to diploid. Thus, we investigated the molecular mechanism of differential cold tolerance between autotetraploids and diploids in this study. The morphological and physiological analysis under 4 °C treatment conformed that diploids were cold tolerant than autotetraploids. The comparative transcriptome analysis indicated that the diploid samples expressed 56 and 694 differential expressed genes (DEGs) at 6 h and 24 h after cold exposure, while the number of DEGs increased in autotetraploid samples at 6 h (225) and 24 h (2774). According to Go annotation analysis, DEGs were classified into different functional groups including catalytic activity, binding and transporter activity, metabolic pathways, cellular process and response to stimulus. Furthermore, we performed genes clustering and GO enrichment analysis regarding to 882 DEGs and observed the involvement of cold responsive genes in the xyloglucan metabolite process, calcium ion transmembrane transport, and microtubule-based movement/process. Among these DEGs, three genes including Tub1, CML41, and IQD1 showed a significantly differential expressing pattern between diploids and autotetraploids and could be regulated by microtubules changes. These results indicated that microtubules play a crucial role in the expression of CML41 and IQD1 to regulate the cold resistance of diploids and provide the theoretical foundation for the polyploid breeding of cold resistance in Chinese jujube.

Background: Chromosomal translocations of the mixed-lineage leukemia gene (MLL; tMLL) correlate with resistance to therapy and an extremely poor prognosis for individuals with Acute Myeloid Leukemia (AML). The underlying mechanisms are still unknown. This study aims to identify the key genes and the potential molecular mechanisms involved in MLL rearrangement in AML using a bioinformatics approach. Methods: The gene expression profiles from 15 individuals with partial tandem duplication of the MLL gene (MLL-PTD)-AML and 10 tMLL-AML samples were downloaded from the Gene Expression Omnibus (GEO) database. The Differentially Expressed Genes (DEGs) were selected and functional enrichment analyses were performed. The Protein-Protein Interaction (PPI) network was established and visualized in Cytoscape. The hub genes were identified by CytoHubba and significant modules were screened out by Molecular Complex Detection (MCODE). Results: We categorized a total of 885 DEGs comprising 330 upregulated and 555 downregulated genes. The majority of DEGs were significantly enriched for calcium ion transmembrane transport, embryonic skeletal system morphogenesis and cell proliferation processes. Several pathways were enriched, including those associated with PI3K-Akt signaling and insulin resistance. We identified 32 hub genes and screened out 2 modules. Conclusion: The genes we have identified in this study may represent potential biomarkers for MLL-rearranged AML and contribute to the development of novel therapeutic strategies.