The advent of next-generation sequencing for genetic diagnoses of complex developmental disorders, such as intellectual disability (ID), has facilitated the identification of hundreds of predisposing genetic variants. However, there still exists a vast gap in our knowledge of causal genetic factors for ID as evidenced by low diagnostic yield of genetic screening, in which identifiable genetic causes are not found for the majority of ID cases. Most methods of genetic screening focus on protein-coding genes; however, noncoding RNAs may outnumber protein-coding genes and play important roles in brain development. Long noncoding RNAs (lncRNAs) specifically have been shown to be enriched in the brain and have diverse roles in gene regulation at the transcriptional and posttranscriptional levels. LncRNAs are a vastly uncharacterized group of noncoding genes, which could function in brain development and harbor ID-predisposing genetic variants. We analyzed lncRNAs for coexpression with known ID genes and affected biological pathways within a weighted gene coexpression network derived from RNA-sequencing data spanning human brain development. Several ID-associated gene modules were found to be enriched for lncRNAs, known ID genes, and affected biological pathways. Utilizing a list of de novo and pathogenic copy number variants detected in ID probands, we identified lncRNAs overlapping these genetic structural variants. By integrating our results, we have made a prioritized list of potential ID-associated lncRNAs based on the developing brain gene coexpression network and genetic structural variants found in ID probands.

A number of studies have suggested that olfaction plays an important role in fish migration. Fish use several distinct families of olfactory receptors to detect environmental odorants, including MORs (main olfactory receptors), V1Rs (vomeronasal type-1 receptors), V2Rs (vomeronasal type-2 receptors), TAARs (trace amine-associated receptors), and FPRs (formyl peptide receptors). The V1Rs have been reported to detect pheromones, and a pheromone hypothesis for the spawning migration of anadromous fish has been proposed. Examining whether Coilia nasus relies on V1R-mediated olfaction for spawning migration is important for understanding the molecular basis of spawning migration behavior. Here, we explored the V1R gene family in anadromous C. nasus. Six V1R genes previously reported in other teleost fish were successfully identified. Interestingly, we detected the largest V1R repertoire in teleost fish from C. nasus and identified a species-specific expansion event of V1R3 gene that has previously been detected as single-copy genes in other teleost fish. The V1R loci were found to be populated with repetitive sequences, especially in the expanded V1R3 genes. Additionally, the divergence of V1R3 genetic structures in different populations of C. nasus indicates the copy number variation (CNV) in V1R3 gene among individuals of C. nasus. Most of the putative C. nasus V1R genes were expressed primarily in the olfactory epithelium, consistent with the role of the gene products as functional olfactory receptors. Significant differences in the expression levels of V1R genes were detected between the anadromous and non-anadromous C. nasus. This study represents a first step in the elucidation of the olfactory communication system of C. nasus at the molecular level. Our results indicate that some V1R genes may be involved in the spawning migration of C. nasus, and the study provides new insights into the spawning migration and genome evolution of C. nasus.

Kidney Renal Clear Cell Carcinoma (KIRC) is one of fatal genitourinary diseases and accounts for most malignant kidney tumours. KIRC has been shown resistance to radiotherapy and chemotherapy. Like many types of cancers, there is no curative treatment for metastatic KIRC. Using advanced sequencing technologies, The Cancer Genome Atlas (TCGA) project of NIH/NCI-NHGRI has produced large-scale sequencing data, which provide unprecedented opportunities to reveal new molecular mechanisms of cancer. We combined differentially expressed genes, pathways and network analyses to gain new insights into the underlying molecular mechanisms of the disease development.

Genetic studies have identified many risk loci for autism spectrum disorder (ASD) although causal factors in the majority of cases are still unknown. Currently, known ASD risk genes are all protein-coding genes; however, the vast majority of transcripts in humans are non-coding RNAs (ncRNAs) which do not encode proteins. Recently, long non-coding RNAs (lncRNAs) were shown to be highly expressed in the human brain and crucial for normal brain development. We have constructed a computational pipeline for the integration of various genomic datasets to identify lncRNAs associated with ASD. This pipeline utilizes differential gene expression patterns in affected tissues in conjunction with gene co-expression networks in tissue-matched non-affected samples. We analyzed RNA-seq data from the cortical brain tissues from ASD cases and controls to identify lncRNAs differentially expressed in ASD. We derived a gene co-expression network from an independent human brain developmental transcriptome and detected a convergence of the differentially expressed lncRNAs and known ASD risk genes into specific co-expression modules. Co-expression network analysis facilitates the discovery of associations between previously uncharacterized lncRNAs with known ASD risk genes, affected molecular pathways and at-risk developmental time points. In addition, we show that some of these lncRNAs have a high degree of overlap with major CNVs detected in ASD genetic studies. By utilizing this integrative approach comprised of differential expression analysis in affected tissues and connectivity metrics from a developmental co-expression network, we have prioritized a set of candidate ASD-associated lncRNAs. The identification of lncRNAs as novel ASD susceptibility genes could help explain the genetic pathogenesis of ASD.

Microarray gene expression data are accumulating in public databases. The expression profiles contain valuable information for understanding human gene expression patterns. However, the effective use of public microarray data requires integrating the expression profiles from heterogeneous sources.

Protein-DNA interactions are involved in many biological processes essential for cellular function. To understand the molecular mechanism of protein-DNA recognition, it is necessary to identify the DNA-binding residues in DNA-binding proteins. However, structural data are available for only a few hundreds of protein-DNA complexes. With the rapid accumulation of sequence data, it becomes an important but challenging task to accurately predict DNA-binding residues directly from amino acid sequence data.

Olfaction is essential for fish to detect odorant elements in the environment and plays a critical role in navigating, locating food and detecting predators. Olfactory function is produced by the olfactory transduction pathway and is activated by olfactory receptors (ORs) through the binding of odorant elements. Recently, four types of olfactory receptors have been identified in vertebrate olfactory epithelium, including main odorant receptors (MORs), vomeronasal type receptors (VRs), trace-amine associated receptors (TAARs) and formyl peptide receptors (FPRs). It has been hypothesized that migratory fish, which have the ability to perform spawning migration, use olfactory cues to return to natal rivers. Therefore, obtaining OR genes from migratory fish will provide a resource for the study of molecular mechanisms that underlie fish spawning migration behaviors. Previous studies of OR genes have mainly focused on genomic data, however little information has been gained at the transcript level. In this study, we identified the OR genes of an economically important commercial fish Coilia nasus through searching for olfactory epithelium transcriptomes. A total of 142 candidate MOR, 52 V2R/OlfC, 32 TAAR and two FPR putative genes were identified. In addition, through genomic analysis we identified several MOR genes containing introns, which is unusual for vertebrate MOR genes. The transcriptome-scale mining strategy proved to be fruitful in identifying large sets of OR genes from species whose genome information is unavailable. Our findings lay the foundation for further research into the possible molecular mechanisms underlying the spawning migration behavior in C. nasus.

Understanding how biomolecules interact is a major task of systems biology. To model protein-nucleic acid interactions, it is important to identify the DNA or RNA-binding residues in proteins. Protein sequence features, including the biochemical property of amino acids and evolutionary information in terms of position-specific scoring matrix (PSSM), have been used for DNA or RNA-binding site prediction. However, PSSM is rather designed for PSI-BLAST searches, and it may not contain all the evolutionary information for modelling DNA or RNA-binding sites in protein sequences.

Smith-Magenis Syndrome (SMS) is a complex genomic disorder mostly caused by the haploinsufficiency of the Retinoic Acid Induced 1 gene (RAI1), located in the chromosomal region 17p11.2. In a subset of SMS patients, heterozygous mutations in RAI1 are found. Here we investigate the molecular properties of these mutated forms and their relationship with the resulting phenotype. We compared the clinical phenotype of SMS patients carrying a mutation in RAI1 coding region either in the N-terminal or the C-terminal half of the protein and no significant differences were found. In order to study the molecular mechanism related to these two groups of RAI1 mutations first we analyzed those mutations that result in the truncated protein corresponding to the N-terminal half of RAI1 finding that they have cytoplasmic localization (in contrast to full length RAI1) and no ability to activate the transcription through an endogenous target: the BDNF enhancer. Similar results were found in lymphoblastoid cells derived from a SMS patient carrying RAI1 c.3103insC, where both mutant and wild type products of RAI1 were detected. The wild type form of RAI1 was found in the chromatin bound and nuclear matrix subcellular fractions while the mutant product was mainly cytoplasmic. In addition, missense mutations at the C-terminal half of RAI1 presented a correct nuclear localization but no activation of the endogenous target. Our results showed for the first time a correlation between RAI1 mutations and abnormal protein function plus they suggest that a reduction of total RAI1 transcription factor activity is at the heart of the SMS clinical presentation.

BeetleBase (http://www.bioinformatics.ksu.edu/BeetleBase/) is an integrated resource for the Tribolium research community. The red flour beetle (Tribolium castaneum) is an important model organism for genetics, developmental biology, toxicology and comparative genomics, the genome of which has recently been sequenced. BeetleBase is constructed to integrate the genomic sequence data with information about genes, mutants, genetic markers, expressed sequence tags and publications. BeetleBase uses the Chado data model and software components developed by the Generic Model Organism Database (GMOD) project. This strategy not only reduces the time required to develop the database query tools but also makes the data structure of BeetleBase compatible with that of other model organism databases. BeetleBase will be useful to the Tribolium research community for genome annotation as well as comparative genomics.

Enzymes in Uracil DNA glycosylase (UDG) superfamily are essential for the removal of uracil. Family 4 UDGa is a robust uracil DNA glycosylase that only acts on double-stranded and single-stranded uracil-containing DNA. Based on mutational, kinetic and modeling analyses, a catalytic mechanism involving leaving group stabilization by H155 in motif 2 and water coordination by N89 in motif 3 is proposed. Mutual Information analysis identifies a complexed correlated mutation network including a strong correlation in the EG doublet in motif 1 of family 4 UDGa and in the QD doublet in motif 1 of family 1 UNG. Conversion of EG doublet in family 4 Thermus thermophilus UDGa to QD doublet increases the catalytic efficiency by over one hundred-fold and seventeen-fold over the E41Q and G42D single mutation, respectively, rectifying the strong correlation in the doublet. Molecular dynamics simulations suggest that the correlated mutations in the doublet in motif 1 position the catalytic H155 in motif 2 to stabilize the leaving uracilate anion. The integrated approach has important implications in studying enzyme evolution and protein structure and function.

The importance of mutations in disease phenotype has been studied, with information available in databases such as OMIM. However, it remains a research challenge for the possibility of clustering amino acid residues based on an underlying interaction, such as co-evolution, to understand how mutations in these related sites can lead to different disease phenotypes.

WRKY proteins are newly identified transcription factors involved in many plant processes including plant responses to biotic and abiotic stresses. To date, genes encoding WRKY proteins have been identified only from plants. Comprehensive search for WRKY genes in non-plant organisms and phylogenetic analysis would provide invaluable information about the origin and expansion of the WRKY family.

Coilia nasus (Japanese grenadier anchovy) undergoes spawning migration from the ocean to fresh water inland. Previous studies have suggested that anadromous fish use olfactory cues to perform successful migration to spawn. However, limited genomic information is available for C. nasus. To understand the molecular mechanisms of spawning migration, it is essential to identify the genes and pathways involved in the migratory behavior of C. nasus.

Autism Spectrum Disorder (ASD) is the umbrella term for a group of neurodevelopmental disorders convergent on behavioral phenotypes. While many genes have been implicated in the disorder, the predominant focus of previous research has been on protein coding genes. This leaves a vast number of long non-coding RNAs (lncRNAs) not characterized for their role in the disorder although lncRNAs have been shown to play important roles in development and are highly represented in the brain. Studies have also shown lncRNAs to be differentially expressed in ASD affected brains. However, there has yet to be an enrichment analysis of the shared ontologies and pathways of known ASD genes and lncRNAs in normal brain development.

Understanding how genes are expressed specifically in particular tissues is a fundamental question in developmental biology. Many tissue-specific genes are involved in the pathogenesis of complex human diseases. However, experimental identification of tissue-specific genes is time consuming and difficult. The accurate predictions of tissue-specific gene targets could provide useful information for biomarker development and drug target identification.

N6-adenosine methylation (m6A) is the most abundant internal RNA modification in eukaryotes, and affects RNA metabolism and non-coding RNA function. Previous studies suggest that m6A modifications in mammals occur on the consensus sequence DRACH (D = A/G/U, R = A/G, H = A/C/U). However, only about 10% of such adenosines can be m6A-methylated, and the underlying sequence determinants are still unclear. Notably, the regulation of m6A modifications can be cell-type-specific. In this study, we have developed a deep learning model, called TDm6A, to predict RNA m6A modifications in human cells. For cell types with limited availability of m6A data, transfer learning may be used to enhance TDm6A model performance. We show that TDm6A can learn common and cell-type-specific motifs, some of which are associated with RNA-binding proteins previously reported to be m6A readers or anti-readers. In addition, we have used TDm6A to predict m6A sites on human long non-coding RNAs (lncRNAs) for selection of candidates with high levels of m6A modifications. The results provide new insights into m6A modifications on human protein-coding and non-coding transcripts.

CCCTC-binding factor (CTCF) is a key regulator of 3D genome organization and gene expression. Recent studies suggest that RNA transcripts, mostly long non-coding RNAs (lncRNAs), can serve as locus-specific factors to bind and recruit CTCF to the chromatin. However, it remains unclear whether specific sequence patterns are shared by the CTCF-binding RNA sites, and no RNA motif has been reported so far for CTCF binding. In this study, we have developed DeepLncCTCF, a new deep learning model based on a convolutional neural network and a bidirectional long short-term memory network, to discover the RNA recognition patterns of CTCF and identify candidate lncRNAs binding to CTCF. When evaluated on two different datasets, human U2OS dataset and mouse ESC dataset, DeepLncCTCF was shown to be able to accurately predict CTCF-binding RNA sites from nucleotide sequence. By examining the sequence features learned by DeepLncCTCF, we discovered a novel RNA motif with the consensus sequence, AGAUNGGA, for potential CTCF binding in humans. Furthermore, the applicability of DeepLncCTCF was demonstrated by identifying nearly 5000 candidate lncRNAs that might bind to CTCF in the nucleus. Our results provide useful information for understanding the molecular mechanisms of CTCF function in 3D genome organization.

Cell-adhesion molecules of the immunoglobulin superfamily play critical roles in brain development, as well as in maintaining synaptic plasticity, the dysfunction of which is known to cause cognitive impairment. Recently dysfunction of KIRREL3, a synaptic molecule of the immunoglobulin superfamily, has been implicated in several neurodevelopmental conditions including intellectual disability, autism spectrum disorder, and in the neurocognitive delay associated with Jacobsen syndrome. However, the molecular mechanisms of its physiological actions remain largely unknown. Using a yeast two-hybrid screen, we found that the KIRREL3 extracellular domain interacts with brain expressed proteins MAP1B and MYO16 and its intracellular domain can potentially interact with ATP1B1, UFC1, and SHMT2. The interactions were confirmed by co-immunoprecipitation and colocalization analyses of proteins expressed in human embryonic kidney cells, mouse neuronal cells, and rat primary neuronal cells. Furthermore, we show KIRREL3 colocalization with the marker for the Golgi apparatus and synaptic vesicles. Previously, we have shown that KIRREL3 interacts with the X-linked intellectual disability associated synaptic scaffolding protein CASK through its cytoplasmic domain. In addition, we found a genomic deletion encompassing MAP1B in one patient with intellectual disability, microcephaly and seizures and deletions encompassing MYO16 in two unrelated patients with intellectual disability, autism and microcephaly. MAP1B has been previously implicated in synaptogenesis and is involved in the development of the actin-based membrane skeleton. MYO16 is expressed in hippocampal neurons and also indirectly affects actin cytoskeleton through its interaction with WAVE1 complex. We speculate KIRREL3 interacting proteins are potential candidates for intellectual disability and autism spectrum disorder. Moreover, our findings provide further insight into understanding the molecular mechanisms underlying the physiological action of KIRREL3 and its role in neurodevelopment.

Scenarios of genes to metabolites in Artemisia annua remain uninvestigated. Here, we report the use of an integrated approach combining metabolomics, transcriptomics, and gene function analyses to characterize gene-to-terpene and terpene pathway scenarios in a self-pollinating variety of this species. Eighty-eight metabolites including 22 sesquiterpenes (e.g., artemisinin), 26 monoterpenes, two triterpenes, one diterpene and 38 other non-polar metabolites were identified from 14 tissues. These metabolites were differentially produced by leaves and flowers at lower to higher positions. Sequences from cDNA libraries of six tissues were assembled into 18 871 contigs and genome-wide gene expression profiles in tissues were strongly associated with developmental stages and spatial specificities. Sequence mining identified 47 genes that mapped to the artemisinin, non-amorphadiene sesquiterpene, monoterpene, triterpene, 2-C-methyl-D-erythritol 4-phosphate and mevalonate pathways. Pearson correlation analysis resulted in network integration that characterized significant correlations of gene-to-gene expression patterns and gene expression-to-metabolite levels in six tissues simultaneously. More importantly, manipulations of amorpha-4,11-diene synthase gene expression not only affected the activity of this pathway toward artemisinin, artemisinic acid, and arteannuin b but also altered non-amorphadiene sesquiterpene and genome-wide volatile profiles. Such gene-to-terpene landscapes associated with different tissues are fundamental to the metabolic engineering of artemisinin.