De novo mutation hotspots in homologous protein domains identify function-altering mutations in neurodevelopmental disorders.

Variant interpretation remains a major challenge in medical genetics. We developed Meta-Domain HotSpot (MDHS) to identify mutational hotspots across homologous protein domains. We applied MDHS to a dataset of 45,221 de novo mutations (DNMs) from 31,058 individuals with neurodevelopmental disorders (NDDs) and identified three significantly enriched missense DNM hotspots in the ion transport protein domain family (PF00520). The 37 unique missense DNMs that drive enrichment affect 25 genes, 19 of which were previously associated with NDDs. 3D protein structure modeling supports the hypothesis of function-altering effects of these mutations. Hotspot genes have a unique expression pattern in tissue, and we used this pattern alongside in silico predictors and population constraint information to identify candidate NDD-associated genes. We also propose a lenient version of our method, which identifies 32 hotspot positions across 16 different protein domains. These positions are enriched for likely pathogenic variation in clinical databases and DNMs in other genetic disorders.

Multiview representation learning for identification of novel cancer genes and their causative biological mechanisms.

Tumorigenesis arises from the dysfunction of cancer genes, leading to uncontrolled cell proliferation through various mechanisms. Establishing a complete cancer gene catalogue will make precision oncology possible. Although existing methods based on graph neural networks (GNN) are effective in identifying cancer genes, they fall short in effectively integrating data from multiple views and interpreting predictive outcomes. To address these shortcomings, an interpretable representation learning framework IMVRL-GCN is proposed to capture both shared and specific representations from multiview data, offering significant insights into the identification of cancer genes. Experimental results demonstrate that IMVRL-GCN outperforms state-of-the-art cancer gene identification methods and several baselines. Furthermore, IMVRL-GCN is employed to identify a total of 74 high-confidence novel cancer genes, and multiview data analysis highlights the pivotal roles of shared, mutation-specific, and structure-specific representations in discriminating distinctive cancer genes. Exploration of the mechanisms behind their discriminative capabilities suggests that shared representations are strongly associated with gene functions, while mutation-specific and structure-specific representations are linked to mutagenic propensity and functional synergy, respectively. Finally, our in-depth analyses of these candidates suggest potential insights for individualized treatments: afatinib could counteract many mutation-driven risks, and targeting interactions with cancer gene SRC is a reasonable strategy to mitigate interaction-induced risks for NR3C1, RXRA, HNF4A, and SP1.

Occlusion enhanced pan-cancer classification via deep learning.

Quantitative measurement of RNA expression levels through RNA-Seq is an ideal replacement for conventional cancer diagnosis via microscope examination. Currently, cancer-related RNA-Seq studies focus on two aspects: classifying the status and tissue of origin of a sample and discovering marker genes. Existing studies typically identify marker genes by statistically comparing healthy and cancer samples. However, this approach overlooks marker genes with low expression level differences and may be influenced by experimental results. This paper introduces "GENESO," a novel framework for pan-cancer classification and marker gene discovery using the occlusion method in conjunction with deep learning. we first trained a baseline deep LSTM neural network capable of distinguishing the origins and statuses of samples utilizing RNA-Seq data. Then, we propose a novel marker gene discovery method called "Symmetrical Occlusion (SO)". It collaborates with the baseline LSTM network, mimicking the "gain of function" and "loss of function" of genes to evaluate their importance in pan-cancer classification quantitatively. By identifying the genes of utmost importance, we then isolate them to train new neural networks, resulting in higher-performance LSTM models that utilize only a reduced set of highly relevant genes. The baseline neural network achieves an impressive validation accuracy of 96.59% in pan-cancer classification. With the help of SO, the accuracy of the second network reaches 98.30%, while using 67% fewer genes. Notably, our method excels in identifying marker genes that are not differentially expressed. Moreover, we assessed the feasibility of our method using single-cell RNA-Seq data, employing known marker genes as a validation test.

Unmasking crucial residues in adipose triglyceride lipase for coactivation with comparative gene identification-58.

Lipolysis is an essential metabolic process that releases unesterified fatty acids from neutral lipid stores to maintain energy homeostasis in living organisms. Adipose triglyceride lipase (ATGL) plays a key role in intracellular lipolysis and can be coactivated upon interaction with the protein comparative gene identification-58 (CGI-58). The underlying molecular mechanism of ATGL stimulation by CGI-58 is incompletely understood. Based on analysis of evolutionary conservation, we used site directed mutagenesis to study a C-terminally truncated variant and full-length mouse ATGL providing insights in the protein coactivation on a per-residue level. We identified the region from residues N209-N215 in ATGL as essential for coactivation by CGI-58. ATGL variants with amino acids exchanges in this region were still able to hydrolyze triacylglycerol at the basal level and to interact with CGI-58, yet could not be activated by CGI-58. Our studies also demonstrate that full-length mouse ATGL showed higher tolerance to specific single amino acid exchanges in the N209-N215 region upon CGI-58 coactivation compared to C-terminally truncated ATGL variants. The region is either directly involved in protein-protein interaction or essential for conformational changes required in the coactivation process. Three-dimensional models of the ATGL/CGI-58 complex with the artificial intelligence software AlphaFold demonstrated that a large surface area is involved in the protein-protein interaction. Mapping important amino acids for coactivation of both proteins, ATGL and CGI-58, onto the 3D model of the complex locates these essential amino acids at the predicted ATGL/CGI-58 interface thus strongly corroborating the significance of these residues in CGI-58-mediated coactivation of ATGL.

Genome-wide characterization, functional analysis, and expression profiling of the Aux/IAA gene family in spinach.

BACKGROUND: The auxin/indole-3-acetic acid (Aux/IAA) gene family is a crucial element of the auxin signaling pathway, significantly influencing plant growth and development. Hence, we conducted a comprehensive investigation of Aux/IAAs gene family using the Sp75 and Monoe-Viroflay genomes in spinach. RESULTS: A total of 24 definitive Aux/IAA genes were identified, exhibiting diverse attributes in terms of amino acid length, molecular weight, and isoelectric points. This diversity underscores potential specific roles within the family, such as growth regulation and stress response. Structural analysis revealed significant variations in gene length and molecular weight. These variations indicate distinct roles within the Aux/IAA gene family. Chromosomal distribution analysis exhibited a dispersed pattern, with chromosomes 4 and 1 hosting the highest and lowest numbers of Aux/IAA genes, respectively. Phylogenetic analysis grouped the identified genes into distinct clades, revealing potential evolutionary relationships. Notably, the phylogenetic tree highlighted specific gene clusters suggesting shared genetic ancestry and potential functional synergies within spinach. Expression analysis under NAA treatment unveiled gene-specific and time-dependent responses, with certain genes exhibiting distinct temporal expression patterns. Specifically, SpoIAA5 displayed a substantial increase at 2 h post-NAA treatment, while SpoIAA7 and SpoIAA9 demonstrated continuous rises, peaking at the 4-hour time point. CONCLUSIONS: These observations indicate a complex interplay of gene-specific and temporal regulation in response to auxin. Moreover, the comparison with other plant species emphasized both shared characteristics and unique features in Aux/IAA gene numbers, providing insights into the evolutionary dynamics of this gene family. This comprehensive characterization of Aux/IAA genes in spinach not only establishes the foundation for understanding their specific functions in spinach development but also provides a valuable resource for experimental validation and further exploration of their roles in the intricate network of auxin signaling pathways.

Genomic and transcriptome insight into the structure and immunity role of TRIM proteins in Chinese soft-shelled turtles (Pelodiscus sinensis) after Aeromonas hydrophila infection.

BACKGROUND: TRIM proteins, recognized as a class of E3 ubiquitin ligases, are increasingly acknowledged for their antipathogen immune functions in mammals and fish. In the Chinese soft-shelled turtle (Pelodiscus sinensis), a secondary aquatic reptile that occupies a unique evolutionary position, the TRIM gene has rarely been reported. METHODS AND RESULTS: In the present study, 48 PsTRIM proteins were identified from the genome of Pelodiscus sinensis via Hidden Markov Model (HMM) searches and Signal Transduction ATPases with Numerous Domains (SMART) analysis. These PsTRIMs were found across 43 distinct scaffolds, and phylogenetic analyses classified them into three principal clades. The PsTRIMs feature a conserved assembly of either RING-B-box-coiled-coil (RBCC) or B-box-coiled-coil (BBC) domains at the N-terminus, in addition to eight unique domains at the C-terminus, including the B30.2 domain, 19 of which were identified. Expression profiling revealed ubiquitous expression of the 48 PsTRIMs across various P. sinensis tissues. Notably, seven PsTRIMs exhibited significant differential expression in liver transcriptomes following infection with Aeromonas hydrophila. Weighted gene coexpression network analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis implicated PsTRIM14 and PsTRIM28 as key players in host defense against bacterial invasion. Real-time quantitative PCR results indicated that PsTRIM1, PsTRIM2, PsTRIM14, and PsTRIM28 experienced marked upregulation in P. sinensis livers at 12 h post-infection with A. hydrophila. CONCLUSIONS: Our study is the first to comprehensively identify and analyze the functions of TRIM genes in P. sinensis, unveiling their considerable diversity and potential roles in modulating immune responses.

Screening and identification of photoresponse factors in kiwifruit (Actinidia arguta) development.

BACKGROUND: Light is essential for kiwifruit development, in which photoresponse factors contributes greatly to the quality formation. 'Light sensitive hypocotyls, also known as light-dependent short hypocotyls' (LSH) gene family can participate in fruit development as photoresponse factor. However, the key LSH gene that determine kiwifruit development remains unclear. This study aim to screen and identify the key gene AaLSH9 in A. arguta. MATERIALS AND METHODS: Genome-wide identification of the LSH gene family was used to analyse LSH genes in kiwifruit. Homologous cloning was used to confirm the sequence of candidate LSH genes. qRT-PCR and cluster analysis of expression pattern were used to screen the key AaLSH9 gene. Subcellular localization of AaLSH9 in tobacco leaves and overexpression of AaLSH9 in Arabidopsis thaliana hy5 mutant plants were used to define the acting place in cell and identify molecular function, respectively. RESULTS: We identified 15 LSH genes, which were divided into two sub-families namely A and B. Domain analysis of A and B showed that they contained different domain organizations, which possibly played key roles in the evolution process. Three LSH genes, AaLSH2, AaLSH9, and AaLSH11, were successfully isolated from Actinidia arguta. The expression pattern and cluster analysis of these three AaLSH genes suggested AaLSH9 might be a key photoresponse gene participating in fruit development in A. arguta. Subcellular localization showed AaLSH9 protein was located in the nucleus. The overexpression of AaLSH9 gene in Arabidopsis thaliana hy5 mutant plants partially complemented the long hypocotyls of hy5 mutant, implying AaLSH9 played a key role as photoresponse factor in cells. In addition, the seed coat color of A. thaliana over-expressing AaLSH9 became lighter than the wide type A.thaliana. Finally, AaCOP1 was confirmed as photoresponse factor to participate in developmental process by stable transgenic A. thaliana. CONCLUSIONS: AaLSH9 can be involved in kiwifruit (A. arguta) development as key photoresponse factor. Our results not only identified the photoresponse factors AaLSH9 and AaCOP1 but also provided insights into their key role in fruit quality improvement in the process of light response.

Identification and characterization analysis of the HDM gene family in melon (Cucumis melo L.).

Histone demethylase (HDM) play crucial roles in regulating plant growth and environmental adaptation. In this study, the HDM gene family in melon was identified by bioinformatics methods and the expression patterns of the CmHDM family members in different melon tissues were analyzed using transcriptome data. The results showed that 20 CmHDM genes were identified in the melon genome, which were unevenly distributed across each chromosome. These members fall into two major categories: LSD1 and JmjC. The JmjC group could be further divided into five subgroups with different numbers. The results of collinearity analysis of intraspecific and interspecific relationships showed that there were only one pair of segmental duplication in melon HDM genes, and more collinearity in genetic relationship of HDM genes between melon and tomato. The numbers of conserved domains, exons and introns in each member vary and various cis-acting elements responding to hormones and environmental signals existed in the respective promoter regions. Expression analysis showed that the respective gene members were expressed at different levels in male flowers, female flowers, roots, stems, leaves, ovary, and mature fruits of melon. These results will contribute to the understanding on the potential functions of the HDM genes and their potential functions in regulating melon growth and environmental adaptation.

Accurate Identification of Transcription Regulatory Sequences and Genes in Coronaviruses.

Transcription regulatory sequences (TRSs), which occur upstream of structural and accessory genes as well as the 5' end of a coronavirus genome, play a critical role in discontinuous transcription in coronaviruses. We introduce two problems collectively aimed at identifying these regulatory sequences as well as their associated genes. First, we formulate the TRS Identification problem of identifying TRS sites in a coronavirus genome sequence with prescribed gene locations. We introduce CORSID-A, an algorithm that solves this problem to optimality in polynomial time. We demonstrate that CORSID-A outperforms existing motif-based methods in identifying TRS sites in coronaviruses. Second, we demonstrate for the first time how TRS sites can be leveraged to identify gene locations in the coronavirus genome. To that end, we formulate the TRS and Gene Identification problem of simultaneously identifying TRS sites and gene locations in unannotated coronavirus genomes. We introduce CORSID to solve this problem, which includes a web-based visualization tool to explore the space of near-optimal solutions. We show that CORSID outperforms state-of-the-art gene finding methods in coronavirus genomes. Furthermore, we demonstrate that CORSID enables de novo identification of TRS sites and genes in previously unannotated coronavirus genomes. CORSID is the first method to perform accurate and simultaneous identification of TRS sites and genes in coronavirus genomes without the use of any prior information.

In Silico Integrated Analysis of Genomic, Transcriptomic, and Proteomic Data Reveals QTL-Specific Genes for Bacterial Canker Resistance in Tomato (Solanum lycopersicum L.).

Bacterial canker of tomato, caused by Clavibacter michiganensis subsp. michiganensis (Cmm), is a devasting disease that leads to significant yield losses. Although QTLs originating from three wild species (Solanum arcanum, S. habrochaites, and S. pimpinellifolium) were identified, none of the QTLs was annotated for candidate gene identification. In the present study, a QTL-based physical map was constructed to reveal the meta-QTLs for Cmm resistance. As a result, seven major QTLs were mapped. Functional annotation of QTLs revealed 48 candidate genes. Additionally, experimentally validated Cmm resistance-related genes based on transcriptomic and proteomic studies were mapped in the genome and 25 genes were found to be located in the QTL regions. The present study is the first report to construct a physical map for Cmm resistance QTLs and identify QTL-specific candidate genes. The candidate genes identified in the present study are valuable targets for fine mapping and developing markers for marker-assisted selection in tomatoes for Cmm resistance breeding.

Integration of machine learning to identify diagnostic genes in leukocytes for acute myocardial infarction patients.

BACKGROUND: Acute myocardial infarction (AMI) has two clinical characteristics: high missed diagnosis and dysfunction of leukocytes. Transcriptional RNA on leukocytes is closely related to the course evolution of AMI patients. We hypothesized that transcriptional RNA in leukocytes might provide potential diagnostic value for AMI. Integration machine learning (IML) was first used to explore AMI discrimination genes. The following clinical study was performed to validate the results. METHODS: A total of four AMI microarrays (derived from the Gene Expression Omnibus) were included in bioanalysis (220 sample size). Then, the clinical validation was finished with 20 AMI and 20 stable coronary artery disease patients (SCAD). At a ratio of 5:2, GSE59867 was included in the training set, while GSE60993, GSE62646, and GSE48060 were included in the testing set. IML was explicitly proposed in this research, which is composed of six machine learning algorithms, including support vector machine (SVM), neural network (NN), random forest (RF), gradient boosting machine (GBM), decision trees (DT), and least absolute shrinkage and selection operator (LASSO). IML had two functions in this research: filtered optimized variables and predicted the categorized value. Finally, The RNA of the recruited patients was analyzed to verify the results of IML. RESULTS: Thirty-nine differentially expressed genes (DEGs) were identified between controls and AMI individuals from the training sets. Among the thirty-nine DEGs, IML was used to process the predicted classification model and identify potential candidate genes with overall normalized weights > 1. Finally, two genes (AQP9 and SOCS3) show their diagnosis value with the area under the curve (AUC) > 0.9 in both the training and testing sets. The clinical study verified the significance of AQP9 and SOCS3. Notably, more stenotic coronary arteries or severe Killip classification indicated higher levels of these two genes, especially SOCS3. These two genes correlated with two immune cell types, monocytes and neutrophils. CONCLUSION: AQP9 and SOCS3 in leukocytes may be conducive to identifying AMI patients with SCAD patients. AQP9 and SOCS3 are closely associated with monocytes and neutrophils, which might contribute to advancing AMI diagnosis and shed light on novel genetic markers. Multiple clinical characteristics, multicenter, and large-sample relevant trials are still needed to confirm its clinical value.

Mapping of QTLs associated with yield and related traits under reproductive stage drought stress in rice using SNP linkage map.

BACKGROUND: Drought stress is a major constraint for rice production worldwide. Reproductive stage drought stress (RSDS) leads to heavy yield losses in rice. The prospecting of new donor cultivars for identification and introgression of QTLs of major effect (Quantitative trait locus) for drought tolerance is crucial for the development of drought-resilient rice varieties. METHODS AND RESULTS: Our study aimed to map QTLs associated with yield and its related traits under RSDS conditions. A saturated linkage map was constructed using 3417 GBS (Genotyping by sequencing) derived SNP (Single nucleotide polymorphism) markers spanning 1924.136 cM map length with an average marker density of 0.56 cM, in the F3 mapping population raised via cross made between the traditional ahu rice cultivar, Koniahu (drought tolerant) and a high-yielding variety, Disang (drought susceptible). Using the Inclusive composite interval mapping approach, 35 genomic regions governing yield and related traits were identified in pooled data from 198 F3 and F4 segregating lines evaluated for two consecutive seasons under both RSDS and irrigated control conditions. Of the 35 QTLs, 23 QTLs were identified under RSDS with LOD (Logarithm of odds) values ranging between 2.50 and 7.83 and PVE (phenotypic variance explained) values of 2.95-12.42%. Two major QTLs were found to be linked to plant height (qPH1.29) and number of filled grains per panicle (qNOG5.12) under RSDS. Five putative QTLs for grain yield namely, qGY2.00, qGY5.05, qGY6.16, qGY9.19, and qGY10.20 were identified within drought conditions. Fourteen QTL regions having ≤ 10 Mb QTL interval size were further analysed for candidate gene identification and a total of 4146 genes were detected out of these 2263 (54.63%) genes were annotated to at least one gene ontology (GO) term. CONCLUSION: Several QTLs associated with grain yield and yield components and putative candidate genes were identified. The putative QTLs and candidate genes identified could be employed to augment drought resilience in rice after further validation through MAS strategies.

Mapping Locus RSC11K and predicting candidate gene resistant to Soybean mosaic virus strain SC11 through linkage analysis combined with genome resequencing of the parents in soybean.

Soybean mosaic virus (SMV) strain SC11 was prevalent in middle China. Its resistance was controlled by a Mendelian single dominant gene RSC11K in soybean Kefeng-1. This study aimed at mapping RSC11K and identifying its candidate gene. RSC11K locus was mapped ~217 kb interval between two SNP-linkage-disequilibrium-blocks (Gm02_BLOCK_11273955_11464884 and Gm02_BLOCK_11486875_11491354) in W82.a1.v1 genome using recombinant inbred lines population derived from Kefeng-1 (Resistant) × NN1138-2 (Susceptible), but inserted with a ~245 kb segment in W82.a2.v1 genome. In the entire 462 kb RSC11K region, 429 SNPs, 142 InDels and 34 putative genes were identified with more SNPs/InDels distributed in non-functional regions. Thereinto, ten genes contained SNP/InDel variants with high and moderate functional impacts on proteins, among which Glyma.02G119700 encoded a typical innate immune receptor-like kinase involving in virus disease process and responded to SMV inoculation, therefore was recognized as RSC11K's candidate gene. The novel RSC11K locus and candidate genes may help developing SMV resistance germplasm.

A novel candidate disease gene prioritization method using deep graph convolutional networks and semi-supervised learning.

BACKGROUND: Selecting and prioritizing candidate disease genes is necessary before conducting laboratory studies as identifying disease genes from a large number of candidate genes using laboratory methods, is a very costly and time-consuming task. There are many machine learning-based gene prioritization methods. These methods differ in various aspects including the feature vectors of genes, the used datasets with different structures, and the learning model. Creating a suitable feature vector for genes and an appropriate learning model on a variety of data with different and non-Euclidean structures, including graphs, as well as the lack of negative data are very important challenges of these methods. The use of graph neural networks has recently emerged in machine learning and other related fields, and they have demonstrated superior performance for a broad range of problems. METHODS: In this study, a new semi-supervised learning method based on graph convolutional networks is presented using the novel constructing feature vector for each gene. In the proposed method, first, we construct three feature vectors for each gene using terms from the Gene Ontology (GO) database. Then, we train a graph convolution network on these vectors using protein-protein interaction (PPI) network data to identify disease candidate genes. Our model discovers hidden layer representations encoding in both local graph structure as well as features of nodes. This method is characterized by the simultaneous consideration of topological information of the biological network (e.g., PPI) and other sources of evidence. Finally, a validation has been done to demonstrate the efficiency of our method. RESULTS: Several experiments are performed on 16 diseases to evaluate the proposed method's performance. The experiments demonstrate that our proposed method achieves the best results, in terms of precision, the area under the ROC curve (AUCs), and F1-score values, when compared with eight state-of-the-art network and machine learning-based disease gene prioritization methods. CONCLUSION: This study shows that the proposed semi-supervised learning method appropriately classifies and ranks candidate disease genes using a graph convolutional network and an innovative method to create three feature vectors for genes based on the molecular function, cellular component, and biological process terms from GO data.

Lipid droplet-mitochondria coupling via perilipin 5 augments respiratory capacity but is dispensable for FA oxidation.

Disturbances in lipid homeostasis can cause mitochondrial dysfunction and lipotoxicity. Perilipin 5 (PLIN5) decorates intracellular lipid droplets (LDs) in oxidative tissues and controls triacylglycerol (TG) turnover via its interactions with adipose triglyceride lipase and the adipose triglyceride lipase coactivator, comparative gene identification-58. Furthermore, PLIN5 anchors mitochondria to the LD membrane via the outermost part of the carboxyl terminus. However, the role of this LD-mitochondria coupling (LDMC) in cellular energy catabolism is less established. In this study, we investigated the impact of PLIN5-mediated LDMC in comparison to disrupted LDMC on cellular TG homeostasis, FA oxidation, mitochondrial respiration, and protein interaction. To do so, we established PLIN5 mutants deficient in LDMC whilst maintaining normal interactions with key lipolytic players. Radiotracer studies with cell lines stably overexpressing wild-type or truncated PLIN5 revealed that LDMC has no significant impact on FA esterification upon lipid loading or TG catabolism during stimulated lipolysis. Moreover, we demonstrated that LDMC exerts a minor if any role in mitochondrial FA oxidation. In contrast, LDMC significantly improved the mitochondrial respiratory capacity and metabolic flexibility of lipid-challenged cardiomyocytes, which was corroborated by LDMC-dependent interactions of PLIN5 with mitochondrial proteins involved in mitochondrial respiration, dynamics, and cristae organization. Taken together, this study suggests that PLIN5 preserves mitochondrial function by adjusting FA supply via the regulation of TG hydrolysis and that LDMC is a vital part of mitochondrial integrity.

Chromosome-scale genome assembly and population genomics provide insights into the adaptation, domestication, and flavonoid metabolism of Chinese plum.

Globally, commercialized plum cultivars are mostly diploid Chinese plums (Prunus salicina Lindl.), also known as Japanese plums, and are one of the most abundant and variable fruit tree species. To advance Prunus genomic research, we present a chromosome-scale P. salicina genome assembly, constructed using an integrated strategy that combines Illumina, Oxford Nanopore, and high-throughput chromosome conformation capture (Hi-C) sequencing. The high-quality genome assembly consists of a 318.6-Mb sequence (contig N50 length of 2.3 Mb) with eight pseudo-chromosomes. The expansion of the P. salicina genome is led by recent segmental duplications and a long terminal repeat burst of approximately 0.2 Mya. This resulted in a significant expansion of gene families associated with flavonoid metabolism and plant resistance, which impacted fruit flavor and increased species adaptability. Population structure and domestication history suggest that Chinese plum may have originated from South China and provides a domestication route with accompanying genomic variations. Selection sweep and genetic diversity analysis enabled the identification of several critical genes associated with flowering time, stress tolerance, and flavonoid metabolism, demonstrating the essential roles of related pathways during domestication. Furthermore, we reconstructed and exploited flavonoid-anthocyanin metabolism using multi-omics analysis in Chinese plum and proposed a complete metabolic pathway. Collectively, our results will facilitate further candidate gene discovery for important agronomic traits in Chinese plum and provide insights into future functional genomic studies and DNA-informed breeding.

ARNSHL gene identification: past, present and future.

Autosomal recessive non-syndromic hearing loss (ARNSHL) is the most common hereditary deafness. It is genetically highly heterogeneous and about 89 gene loci and 76 gene's mutations have been implicated in the etiology of ARNSHL. Molecular basis of ARNSHL remains unresolved in 60% of cases and gene mutations are unknown for 23 of 89 reported loci. Techniques used to identify reported ARNSHL gene mutations can be divided into position-dependent and position-independent approaches. The localization of the loci has been facilitated by homozygosity mapping or linkage studies using STR or SNP genotyping in large consanguineous families. First few genes identified for hearing loss exhibited such wide diversity of function and expression patterns that candidate gene approach was not a viable option. The mapping of the disorder to a chromosomal location has been followed by Sanger sequencing of all genes in the target region or confining of the massively parallel sequencing data analyses to the linkage region. Sometimes genes located in the linkage interval were prioritized because there was a reported orthologs with mutations causing hearing loss in mouse or when mutations in the gene caused a related disorder. Position-independent approaches involving use of mouse subtractive cochlear libraries, forward genetic screening, and position-independent analyses of massively parallel sequencing data have helped identify 17 of 68 reported ARNSHL gene mutations. A thorough study of the strategies used in the identification of reported ARNSHL genes and of their relative success can help increase the success rate of future studies.

Gene identification and tissue expression analysis inform the floral organization and color in the basal angiosperm Magnolia polytepala (Magnoliaceae).

MAIN CONCLUSION: In Magnolia polytepala, the formation of floral organization and color was attributed to tissue-dependent differential expression levels of MADS-box genes and anthocyanin biosynthetic genes. In angiosperms, the diversity of floral morphology and organization suggests its value in exploring plant evolution. Magnolia polytepala, an endemic basal angiosperm species in China, possesses three green sepal-like tepals in the outermost whorl and pink petal-like tepals in the inner three whorls, forming unique floral morphology and organization. However, we know little about its underlying molecular regulatory mechanism. Here, we first reported the full-length transcriptome of M. polytepala using PacBio sequencing. A total of 16 MADS-box transcripts were obtained from the transcriptome data, including floral homeotic genes (e.g., MpAPETALA3) and other non-floral homeotic genes (MpAGL6, etc.). Phylogenetic analysis and spatial expression pattern reflected their putative biological function as their homologues in Arabidopsis. In addition, nine structural genes involved in anthocyanin biosynthesis pathway had been screened out, and tepal color difference was significantly associated with their tissue-dependent differential expression levels. This study provides a relatively comprehensive investigation of the MADS-box family and anthocyanin biosynthetic genes in M. polytepala, and will facilitate our understanding of the regulatory mechanism underlying floral organization and color in basal angiosperms.

Transcriptome analysis and identification of sex pheromone biosynthesis and transport related genes in Atrijuglans hetaohei (Lepidoptera: Gelechioidea).

Atrijuglans hetaohei Yang (Lepidoptera: Gelechioidea) is one of the major pests that can seriously damage the walnut tree, leading to harvest loss. Sex pheromones regulate mating communication and reproduction in insects and provide targets for developing a novel pest control strategy. In this study, by transcriptomic sequencing and analysis of the female pheromone gland (PG) and male genitalia of A. hetaohei, we identified 92 putative genes, of which 7 desaturases (Dess), 8 fatty acyl reductases (FARs), 4 fatty acid synthetases (FASs), 2 aldehyde oxidases (AOXs), 4 acetyltransferases (ACTs), 1 chemosensory protein (CSP), and 2 odorant-binding proteins (OBPs) were predominantly expressed in the female PG, while 5 Dess, 11 FARs, 7 FASs, 6 AOXs, 1 ACT, and 1 CSP showed more robust expression in the male genitalia. Moreover, phylogenetic analysis revealed that 7 Dess and 1 FAR were grouped with genes involved in pheromone synthesis in other Lepidoptera species. Thus, we proposed that these candidate genes are possibly involved in the sex pheromone biosynthetic pathway in A. hetaohei. Our findings will provide a solid genetic basis for further exploring the function of the tissue-biased genes and may be useful to screen potential targets for interfering chemical communication in A. hetaohei.

Genome-wide identification and immune response analysis of serine protease inhibitor genes in the blood clam Tegillarca granosa.

Serine protease inhibitors (SPIs) are the main regulators of serine protease activities. In this study, we present a genome-wide identification of SPI genes in T. granosa（TgSPI genes）and their expression characteristics in respond to Vibrio stress. A total of 102 TgSPI genes belonging to eight families, including Serpin, TIL (trypsin inhibitor like cysteine rich domain), Kunitz, Kazal, I84, Pacifastin, WAP (whey acidic protein) and A2M (Alpha-2-macroglobulin) were identified, while no genes belonging to Bowman-Birk, amfpi and Antistasin families were identified. The Kazal family has the most TgSPI genes with 38, and 11 TgSPI genes belong to the mollusc-specific I84 family. The TgSPI genes were found to be randomly distributed on 17 chromosomes with 12 tandem duplicate gene pairs. Expression profiles showed that most TgSPI genes were mainly expressed in immune-related tissues such as hepatopancreas, gill and mantle. In the hepatopancreas, most of TgSPI genes were sensitive to Vibrio stress, 28 and 29 TgSPI genes were up-regulated and down-regulated, respectively. Some up-regulated genes with signal peptides, such as the TgSPIs of I84 family, may act as a mechanism to directly prevent Vibrio from invasion. Six Kazal-type TgSPIs (TgSPI29, 45, 49, 50, 51 and 52) were intracellular proteins and their expression was down-regulated in hemocytes after Vibrio stress. This may have boosted protease activity in hemocytes to the point that more hemoglobin derived peptides were produced and secreted into the hemolymph to exert their anti-Vibrio effects. These findings may provide valuable information for further clarifying the roles of SPIs in the immune defense and will benefit future exploration of the immune function of SPIs in molluscs.