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In the nervous system, synapses are special and pervasive structures between axonal and dendritic terminals, which facilitate electrical and chemical communications among neurons. Extensive studies have been conducted in mice and rats to explore the RNA pool at synapses and investigate RNA transport, local protein synthesis, and synaptic plasticity. However, owing to the experimental difficulties of studying human synaptic transcriptomes, the full pool of human synaptic RNAs remains largely unclear. We developed a new machine learning method, called PredSynRNA, to predict the synaptic localization of human RNAs. Training instances of dendritically localized RNAs were compiled from previous rodent studies, overcoming the shortage of empirical instances of human synaptic RNAs. Using RNA sequence and gene expression data as features, various models with different learning algorithms were constructed and evaluated. Strikingly, the models using the developmental brain gene expression features achieved superior performance for predicting synaptically localized RNAs. We examined the relevant expression features learned by PredSynRNA and used an independent test dataset to further validate the model performance. PredSynRNA models were then applied to the prediction and prioritization of candidate RNAs localized to human synapses, providing valuable targets for experimental investigations into neuronal mechanisms and brain disorders.

The three-dimensional (3D) organization of the human genome is of crucial importance for gene regulation, and the CCCTC-binding factor (CTCF) plays an important role in chromatin interactions. However, it is still unclear what sequence patterns in addition to CTCF motif pairs determine chromatin loop formation. To discover the underlying sequence patterns, we have developed a deep learning model, called DeepCTCFLoop, to predict whether a chromatin loop can be formed between a pair of convergent or tandem CTCF motifs using only the DNA sequences of the motifs and their flanking regions. Our results suggest that DeepCTCFLoop can accurately distinguish the CTCF motif pairs forming chromatin loops from the ones not forming loops. It significantly outperforms CTCF-MP, a machine learning model based on word2vec and boosted trees, when using DNA sequences only. Furthermore, we show that DNA motifs binding to several transcription factors, including ZNF384, ZNF263, ASCL1, SP1, and ZEB1, may constitute the complex sequence patterns for CTCF-mediated chromatin loop formation. DeepCTCFLoop has also been applied to disease-associated sequence variants to identify candidates that may disrupt chromatin loop formation. Therefore, our results provide useful information for understanding the mechanism of 3D genome organization and may also help annotate and prioritize the noncoding sequence variants associated with human diseases.

Calcium channels are an integral component in maintaining cellular function. Alterations may lead to channelopathies, primarily manifested in the central nervous system. This study describes the clinical and genetic features of a unique 12-year-old boy harboring two congenital calcium channelopathies, involving the CACNA1A and CACNA1F genes, and provides an unadulterated view of the natural history of sporadic hemiplegic migraine type 1 (SHM1) due to the patient’s inability to tolerate any preventative medication. The patient presents with episodes of vomiting, hemiplegia, cerebral edema, seizure, fever, transient blindness, and encephalopathy. He is nonverbal, nonambulatory, and forced to have a very limited diet due to abnormal immune responses. The SHM1 manifestations apparent in the subject are consistent with the phenotype described in the 48 patients identified as part of a systematic literature review. The ocular symptoms of CACNA1F align with the family history of the subject. The presence of multiple pathogenic variants make it difficult to identify a clear phenotype–genotype correlation in the present case. Moreover, the detailed case description and natural history along with the comprehensive review of the literature contribute to the understanding of this complex disorder and point to the need for comprehensive clinical assessments of SHM1.

Background 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. Results In this study, we performed co-expression network analysis on the developing brain transcriptome to identify potential lncRNAs associated with ASD and possible annotations for functional role of lncRNAs in brain development. We found co-enrichment of lncRNA genes and ASD risk genes in two distinct groups of modules showing elevated prenatal and postnatal expression patterns, respectively. Further enrichment analysis of the module groups indicated that the early expression modules were comprised mainly of transcriptional regulators while the later expression modules were associated with synapse formation. Finally, lncRNAs were prioritized for their connectivity with the known ASD risk genes through analysis of an adjacency matrix. Collectively, the results imply early developmental repression of synaptic genes through lncRNAs and ASD transcriptional regulators. Conclusion Here we demonstrate the utility of mining the publically available brain gene expression data to further functionally annotate the role of lncRNAs in ASD. Our analysis indicates that lncRNAs potentially have a key role in ASD due to their convergence on shared pathways, and we identify lncRNAs of interest that may lead to further avenues of study. Electronic supplementary material The online version of this article (10.1186/s12918-018-0639-x) contains supplementary material, which is available to authorized users.

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BackgroundAutism spectrum disorders (ASD) refer to a range of neurodevelopmental conditions, which are genetically complex and heterogeneous with most of the genetic risk factors also found in the unaffected general population. Although all the currently known ASD risk genes code for proteins, long non-coding RNAs (lncRNAs) as essential regulators of gene expression have been implicated in ASD. Some lncRNAs show altered expression levels in autistic brains, but their roles in ASD pathogenesis are still unclear.ResultsIn this study, we have developed a new machine learning approach to predict candidate lncRNAs associated with ASD. Particularly, the knowledge learnt from protein-coding ASD risk genes was transferred to the prediction and prioritization of ASD-associated lncRNAs. Both developmental brain gene expression data and transcript sequence were found to contain relevant information for ASD risk gene prediction. During the pre-training phase of model construction, an autoencoder network was implemented for a representation learning of the gene expression data, and a random-forest-based feature selection was applied to the transcript-sequence-derivedk-mers. Our models, including logistic regression, support vector machine and random forest, showed robust performance based on tenfold cross-validations as well as candidate prioritization with hypothetical loci. We then utilized the models to predict and prioritize a list of candidate lncRNAs, including some reported to becis-regulators of known ASD risk genes, for further investigation.ConclusionsOur results suggest that ASD risk genes can be accurately predicted using developmental brain gene expression data and transcript sequence features, and the models may provide useful information for functional characterization of the candidate lncRNAs associated with ASD.

Motivation Essential genes are required for the reproductive success at either cellular or organismal level. The identification of essential genes is important for understanding the core biological processes and identifying effective therapeutic drug targets. However, experimental identification of essential genes is costly, time consuming and labor intensive. Although several machine learning models have been developed to predict essential genes, these models are not readily applicable to lncRNAs. Moreover, the currently available models cannot be used to predict essential genes in a specific cancer type. Results In this study, we have developed a new machine learning approach, XGEP (eXpression-based Gene Essentiality Prediction), to predict essential genes and candidate lncRNAs in cancer cells. The novelty of XGEP lies in the utilization of relevant features derived from the TCGA transcriptome dataset through collaborative embedding. When evaluated on the pan-cancer dataset, XGEP was able to accurately predict human essential genes and achieve significantly higher performance than previous models. Notably, several candidate lncRNAs selected by XGEP are reported to promote cell proliferation and inhibit cell apoptosis. Moreover, XGEP also demonstrated superior performance on cancer-type-specific datasets to identify essential genes. The comprehensive lists of candidate essential genes in specific cancer types may be used to guide experimental characterization and facilitate the discovery of drug targets for cancer therapy. Availability and implementation The source code and datasets used in this study are freely available at https://github.com/BioDataLearning/XGEP. Supplementary information Supplementary data are available at Bioinformatics online.

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.

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.

The three-dimensional organization of the human genome is of crucial importance for gene regulation. Results from high-throughput chromosome conformation capture techniques show that the CCCTC-binding factor (CTCF) plays an important role in chromatin interactions, and CTCF-mediated chromatin loops mostly occur between convergent CTCF-binding sites. However, it is still unclear whether and what sequence patterns in addition to the convergent CTCF motifs contribute to the formation of chromatin loops. To discover the complex sequence patterns for chromatin loop formation, we have developed a deep learning model, called DeepCTCFLoop, to predict whether a chromatin loop can be formed between a pair of convergent CTCF motifs using only the DNA sequences of the motifs and their flanking regions. Our results suggest that DeepCTCFLoop can accurately distinguish the convergent CTCF motif pairs forming chromatin loops from the ones not forming loops. It significantly outperforms CTCF-MP, a machine learning model based on word2vec and boosted trees, when using DNA sequences only. Moreover, we show that DNA motifs binding to ASCL1, SP2 and ZNF384 may facilitate the formation of chromatin loops in addition to convergent CTCF motifs. To our knowledge, this is the first published study of using deep learning techniques to discover the sequence motif patterns underlying CTCF-mediated chromatin loop formation. Our results provide useful information for understanding the mechanism of 3D genome organization. The source code and datasets used in this study for model construction are freely available at https://github.com/BioDataLearning/DeepCTCFLoop.

Protein kinases play fundamental roles in plant development and environmental stress responses. Here, we identified the STRESS INDUCED FACTOR (SIF) gene family, which encodes four leucine-rich repeat receptor-like protein kinases in Arabidopsis (Arabidopsis thaliana). The four genes, SIF1 to SIF4, are clustered in the genome and highly conserved, but they have temporally and spatially distinct expression patterns. We employed Arabidopsis SIF knockout mutants and overexpression transgenics to examine SIF involvement during plant pathogen defense. SIF genes are rapidly induced by biotic or abiotic stresses, and SIF proteins localize to the plasma membrane. Simultaneous knockout of SIF1 and SIF2 led to improved plant salt tolerance, whereas SIF2 overexpression enhanced PAMP-triggered immunity and prompted basal plant defenses, significantly improving pathogen resistance. Furthermore, SIF2 overexpression plants exhibited up-regulated expression of the defense-related genes WRKY53 and flg22-INDUCED RECEPTOR-LIKE KINASE1 as well as enhanced MPK3/MPK6 phosphorylation upon pathogen and elicitor treatments. The expression of the calcium signaling-related gene PHOSPHATE-INDUCED1 also was enhanced in the SIF2-overexpressing lines upon pathogen inoculation but repressed in the sif2 mutants. Bimolecular fluorescence complementation demonstrates that the BRI1-ASSOCIATED RECEPTOR KINASE1 protein is a coreceptor of the SIF2 kinase in the signal transduction pathway during pathogen invasion. These findings characterize a stress-responsive protein kinase family and illustrate how SIF2 modulates signal transduction for effective plant pathogenic defense.

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.

Life may have begun in an RNA world, which is supported by increasing evidence of the vital role that RNAs perform in biological systems. In the human genome, most genes actually do not encode proteins; they are noncoding RNA genes. The largest class of noncoding genes is known as long noncoding RNAs (lncRNAs), which are transcripts greater in length than 200 nucleotides, but with no protein-coding capacity. While some lncRNAs have been demonstrated to be key regulators of gene expression and 3D genome organization, most lncRNAs are still uncharacterized. We thus propose several data mining and machine learning approaches for the functional annotation of human lncRNAs by leveraging the vast amount of data from genetic and genomic studies. Recent results from our studies and those of other groups indicate that genomic data mining can give insights into lncRNA functions and provide valuable information for experimental studies of candidate lncRNAs associated with human disease.

Long noncoding RNAs (lncRNAs) have been shown to act as important cell biological regulators including cell fate decisions but are often ignored in human genetics. Combining differential lncRNA expression during neuronal lineage induction with copy number variation morbidity maps of a cohort of children with autism spectrum disorder/intellectual disability versus healthy controls revealed focal genomic mutations affecting several lncRNA candidate loci. Here we find that a t(5:12) chromosomal translocation in a family manifesting neurodevelopmental symptoms disrupts specifically lnc-NR2F1. We further show that lnc-NR2F1 is an evolutionarily conserved lncRNA functionally enhances induced neuronal cell maturation and directly occupies and regulates transcription of neuronal genes including autism-associated genes. Thus, integrating human genetics and functional testing in neuronal lineage induction is a promising approach for discovering candidate lncRNAs involved in neurodevelopmental diseases.

Salt stress has adverse effects on the growth and production of rice crops. In this study, we isolated and characterized SIDP361, which encodes a DUF1644 family protein. This gene was expressed in various rice tissues and was induced by high salt (200 mM NaCl), dehydration, and abscisic acid (100 µM ABA) treatments. Stable expression of SIDP361-GFP in rice cells suggested that SIDP361 is a cytoplasmic protein. When compared with the untransformed wild-type (WT) control, transgenic plants over-expressing SIDP361 exhibited significantly improved tolerance to salt stress at both the seedling and heading stages. Under salinity conditions, the transgenics also had elevated amounts of free proline. Moreover, transcript levels for genes encoding proline synthetase enzymes were significantly higher in transformants than in the WT. The transgenic lines were also hypersensitive to exogenous ABA. Quantitative real-time PCR analysis showed that transcription of several stressrelated genes was greater in SIDP361-overexpressing plants than in the WT under both normal and salt-stressed conditions. These results demonstrate that SIDP361 has high potential as a tool for genetically improving salt tolerance in rice.

Motivation Circular RNAs (circRNAs) are a new class of endogenous RNAs in animals and plants. During pre-RNA splicing, the 5′ and 3′ termini of exon(s) can be covalently ligated to form circRNAs through back-splicing (head-to-tail splicing). CircRNAs can be conserved across species, show tissue- and developmental stage-specific expression patterns, and may be associated with human disease. However, the mechanism of circRNA formation is still unclear although some sequence features have been shown to affect back-splicing. Results In this study, by applying the state-of-art machine learning techniques, we have developed the first deep learning model, DeepCirCode, to predict back-splicing for human circRNA formation. DeepCirCode utilizes a convolutional neural network (CNN) with nucleotide sequence as the input, and shows superior performance over conventional machine learning algorithms such as support vector machine and random forest. Relevant features learnt by DeepCirCode are represented as sequence motifs, some of which match human known motifs involved in RNA splicing, transcription or translation. Analysis of these motifs shows that their distribution in RNA sequences can be important for back-splicing. Moreover, some of the human motifs appear to be conserved in mouse and fruit fly. The findings provide new insight into the back-splicing code for circRNA formation. Availability and implementation All the datasets and source code for model construction are available at https://github.com/BioDataLearning/DeepCirCode. Supplementary information Supplementary data are available at Bioinformatics online.

Long noncoding RNAs (lncRNAs) have been shown to act as important cell biological regulators including cell fate decisions but are often ignored in human genetics. Combining differential lncRNA expression during neuronal lineage induction with copy number variation morbidity maps of a cohort of children with autism spectrum disorder/intellectual disability versus healthy controls revealed focal genomic mutations affecting several lncRNA candidate loci. Here we find that a t(5:12) chromosomal translocation in a family manifesting neurodevelopmental symptoms disrupts specifically lnc-NR2F1. We further show that lnc-NR2F1 is an evolutionarily conserved lncRNA functionally enhances induced neuronal cell maturation and directly occupies and regulates transcription of neuronal genes including autism-associated genes. Thus, integrating human genetics and functional testing in neuronal lineage induction is a promising approach for discovering candidate lncRNAs involved in neurodevelopmental diseases.

Long noncoding RNAs (lncRNAs) have been shown to act as important cell biological regulators including cell fate decisions but are often ignored in human genetics. Combining differential lncRNA expression during neuronal lineage induction with copy number variation morbidity maps of a cohort of children with autism spectrum disorder/intellectual disability versus healthy controls revealed focal genomic mutations affecting several lncRNA candidate loci. Here we find that a t(5:12) chromosomal translocation in a family manifesting neurodevelopmental symptoms disrupts specifically lnc-NR2F1. We further show that lnc-NR2F1 is an evolutionarily conserved lncRNA functionally enhances induced neuronal cell maturation and directly occupies and regulates transcription of neuronal genes including autism-associated genes. Thus, integrating human genetics and functional testing in neuronal lineage induction is a promising approach for discovering candidate lncRNAs involved in neurodevelopmental diseases.

MICA and MICB are stress-induced molecules recognized by NKG2D, one of major activation receptors of natural killer (NK) cells. Upon binding to NKG2D, NKG2D-mediated cytolytic immune response of immune effector cells will be activated against virally infected and tumor cells expressing MICA. In the early oncogenic development, membrane-bound MICA serves as a key signal to recruit anti-tumor immune effectors. Nevertheless, both MICA polymorphic features and its dysregulated expression in evolving tumors have resulted in tumor evasion in various cancer types. Therefore, in order to reconstitute tumor immunosurveilance, it is of great significance that we understand MICA genetics, polymorphisms, mechanisms of MICA-associated tumor escape and molecular/cellular modulation of MICA. In this review, the MICA-associated co-expression networks involving microRNAs (miRNAs) and novel candidate long non-coding RNAs (lncRNAs) were also discussed. Given the current importance in the study of MICA gene, this review paper focuses on the role of MICA in different cancer types, and strategies that we manipulate MICA regulation against tumor proliferation.

We present and demonstrate a compact polarimetric fiber-optic sensor based on Bi4Ge3O12 for ultra-high 8/20μs surge current sensing, up to 200kA. Experimental results show that the sensor have a linear sensitivity of 0.385°/kA.

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.

Domain of unknown function 1644 (DUF1644) is a highly conserved amino acid sequence motif present only in plants. Analysis of expression data of the family of DUF1644-containing genes indicated that they may regulate responses to abiotic stress in rice. Here we present our discovery of the role of OsSIDP366, a member of the DUF1644 gene family, in response to drought and salinity stresses in rice. Transgenic rice plants overexpressing OsSIDP366 showed enhanced drought and salinity tolerance and reduced water loss as compared to that in the control, whereas plants with downregulated OsSIDP366 expression levels using RNA interference (RNAi) were more sensitive to salinity and drought treatments. The sensitivity to abscisic acid (ABA) treatment was not changed in OsSIDP366-overexpressing plants, and OsSIDP366 expression was not affected in ABA-deficient mutants. Subcellular localization analysis revealed that OsSIDP366 is presented in the cytoplasmic foci that colocalized with protein markers for both processing bodies (PBs) and stress granules (SGs) in rice protoplasts. Digital gene expression (DGE) profile analysis indicated that stress-related genes such as SNAC1, OsHAK5 and PRs were upregulated in OsSIDP366-overexpressing plants. These results suggest that OsSIDP366 may function as a regulator of the PBs/SGs and positively regulate salt and drought resistance in rice.

Circular RNAs (circRNAs) are a novel class of RNAs with mostly unknown function, and their biogenesis is still unclear. CircRNAs often show tissue and developmental stage-specific expression, and have been reported to be associated with human diseases such as cancer. During the pre-RNA processing, the 5' and 3' termini of one or more exons can be joined together to form circRNAs, and this process is called back-splicing. However, not all RNA splicing sites can participate in back-splicing. To understand the mechanism of circRNA formation, we used two different machine learning algorithms, random forest (RF) and support vector machine (SVM), to predict back-splicing sites from sequence compositional features. Our results indicate that both RF and SVM models can accurately predict back-splicing sites. Moreover, informative sequence features have been selected using RF, and the identified sequence motifs provide new insight into the mechanism of circRNA formation. To our knowledge, this work represents the first published study to utilize machine learning techniques to model RNA back-splicing sites, and the novel method can be useful for understanding circRNA biogenesis and regulation.

A recent phylogenetic study on UDG superfamily estimated a new clade of family 3 enzymes (SMUG1-like), which shares a lower homology with canonic SMUG1 enzymes. The enzymatic properties of the newly found putative DNA glycosylase are unknown. To test the potential UDG activity and evaluate phylogenetic classification, we isolated one SMUG1-like glycosylase representative from Listeria innocua (Lin). A biochemical screening of DNA glycosylase activity in vitro indicates that Lin SMUG1-like glycosylase is a single-strand selective uracil DNA glycosylase. The UDG activity on DNA bubble structures provides clue to its physiological significance in vivo. Mutagenesis and molecular modeling analyses reveal that Lin SMUG1-like glycosylase has similar functional motifs with SMUG1 enzymes; however, it contains a distinct catalytic doublet S67-S68 in motif 1 that is not found in any families in the UDG superfamily. Experimental investigation shows that the S67M-S68N double mutant is catalytically more active than either S67M or S68N single mutant. Coupled with mutual information analysis, the results indicate a high degree of correlation in the evolution of SMUG1-like enzymes. This study underscores the functional and catalytic diversity in the evolution of enzymes in UDG superfamily.

Fiber-optic magnetic field sensing is an important method of magnetic field monitoring, which is essential for the safety of civil infrastructures, especially for power plant. We theoretically and experimentally demonstrated an optical fiber magnetic field sensor based on a single-mode-multimode-single-mode (SMS) structure immersed into the magnetic fluid (MF). The length of multimode section fiber is determined based on the self-image effect through the simulation. Due to variation characteristics of the refractive index and absorption coefficient of MF under different magnetic fields, an effective method to improve the sensitivity of SMS fiber structure is realized based on the intensity modulation method. This sensor shows a high sensitivity up to 0.097 dB/Oe and a high modulation depth up to 78% in a relatively linear range, for the no-core fiber (NCF) with the diameter of 125 μm and length of 59.8 mm as the multimode section. This optical fiber sensor possesses advantages of low cost, ease of fabrication, high sensitivity, simple structure, and compact size, with great potential applications in measuring the magnetic field.

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.

Domain of Unknown Function 966 (DUF966) gene family was found in the protein family database, which consisted of seven genes in rice. The proteins encoded by these genes contained one or two highly conserved DUF966 domains. The available data of public microarray databases implied that these genes might play crucial roles in plant response to abiotic stresses. In this study, a member of the DUF966 gene family, DUF966-stress repressive gene 2 in Oryza sativa (OsDSR2, Loc_Os01g62200), was cloned and its role in rice responding to salt and simulated drought stresses was functionally characterized. OsDSR2 was expressed mainly in nodes of stems and leaf blades from rice. Expression profile analysis of adversity showed that OsDSR2 had different transcriptional responses to salt, drought, cold, heat and oxidative (H2O2) stresses, as well as abscisic acid (ABA), methyl jasmonate, salicylic acid, gibberellin acid and auxin treatments. Transient expression demonstrated that OsDSR2 was localized in the membrane and nucleus. Overexpression of OsDSR2 could increase salt and simulated drought (polyethyleneglycol)-stress sensitivities in rice by downregulating the expression of ABA- and stress-responsive genes including OsNCED4, SNAC1, OsbZIP23, P5CS, Oslea3 and rab16C. Furthermore, OsDSR2-overexpressing plants showed reduced ABA sensitivity during the post-germination stage. These results suggested that OsDSR2 negatively regulated rice response to salt and simulated drought stresses as well as ABA signaling, which provided some useful data for understanding the functional roles of DUF966 family genes in abiotic stress responses in plants.

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.