Current Genomics最新文献

Background: Also known as Simple Sequence Repetitions (SSRs), microsatellites are profoundly informative molecular markers and powerful tools in genetics and ecology studies on plants. Objective: This research presents a workflow for developing microsatellite markers using genome skimming. Methods: The pipeline was proposed in several stages that must be performed sequentially: obtaining DNA sequences, identifying microsatellite regions, designing primers, and selecting candidate microsatellite regions to develop the markers. Our pipeline efficiency was analyzed using Illumina sequencing data from the non-model tree species Pterodon emarginatus Vog. Results: The pipeline revealed 4,382 microsatellite regions and drew 7,411 pairs of primers for P. emarginatus. However, a much larger number of microsatellite regions with the potential to develop markers were discovered from our pipeline. We selected 50 microsatellite regions with high potential for developing markers and organized 29 microsatellite regions in sets for multiplex PCR. Conclusion: The proposed pipeline is a powerful tool for fast and efficient development of microsatellite markers on a large scale in several species, especially nonmodel plant species.

Background: Histone acetylations acting as active hallmarks for gene transcription is involved in regulating numerous developmental and stress-responsive gene expression. Methods: The data from chromatin immunoprecipitation sequencing (ChIP-seq) was performed by using histone H3 lysine 9 acetylation (H3K9ac) antibody, and RNA sequencing (RNA-seq) utilizing rice seedlings inoculated by Magnaporthe oryzae (M. oryzae) were integrated. Results: RNA-seq data revealed that 422, 460 and 466 genes were up-regulated at 12h, 24h and 48h after inoculation. ChIP-seq data showed that 60%-80% of blast up-regulated genes at different time points were marked with H3K9ac, which was prone to be enriched in both TSS and gene body region. However, the H3K9ac level at a rather small proportion of the up-regulated genes was elevated after M. oryzae inoculation. We found that seven WRKY genes induced by rice blast fungus harbor H3K9ac. For different WRKY genes, blast fungus induction led to the increase of H3K9ac in distinct regions, including promoter, TSS or gene body, indicating that histone acetylation may play diverse roles in the activation of defense-related genes. By searching DNA-binding motifs of transcription factors in the promoter of genes with increased H3K9ac after M. oryzae infection, we found that ERF family protein-binding motifs were enriched with high -log P-value (>20), including ERF1, DEAR3, DREB2C, RAP2.6, RRTF1_3ARY, all of which contain GCC-box (GCCGCC). Conclusion: In this study, we revealed that the vast majority of genes induced by fungus M. oryzae were marked with H3K9ac preferring both TSS and gene body regions. However, H3K9ac enrichment was increased, responding to M. oryzae inoculation only at a low proportion of these genes, including several WRKY genes. Besides, for different genes, the increment of H3K9ac occurred in different regions. Finally, ERF proteins that have been proved to bind GCC-box might be one of the potential transcription factors for recruiting histone acetyltransferases to deposit histone acetylation at defense-related genes in rice.

Psoriasis is an organ-specific autoimmune disease characterized by the aberrant proliferation and differentiation of keratinocytes, leading to skin lesions. Abnormal immune responses mediated by T cells and dendritic cells and increased production of inflammatory cytokines have been suggested as underlying mechanisms in the pathogenesis of psoriasis. Emerging evidence suggests that there is a heritable basis for psoriatic disorders. Moreover, numerous gene variations have been associated with the disease risk, particularly those in innate and adaptive immune responses and antigen presentation pathways. Herein, this article discusses the genetic implications of psoriatic diseases' etiopathogenesis to develop novel investigative and management options.

Background: Open spina bifida (myelomeningocele) is the result of the failure of spinal cord closing completely and is the second most common and severe birth defect. Open neural tube defects are multifactorial, and the exact molecular mechanism of the pathogenesis is not clear due to disease complexity for which prenatal treatment options remain limited worldwide. Artificial intelligence techniques like machine learning tools have been increasingly used in precision diagnosis. Objective: The primary objective of this study is to identify key genes for open neural tube defects using a machine learning approach that provides additional information about myelomeningocele in order to obtain a more accurate diagnosis. Materials and Methods: Our study reports differential gene expression analysis from multiple datasets (GSE4182 and GSE101141) of amniotic fluid samples with open neural tube defects. The sample outliers in the datasets were detected using principal component analysis (PCA). We report a combination of the differential gene expression analysis with recursive feature elimination (RFE), a machine learning approach to get 4 key genes for open neural tube defects. The features selected were validated using five binary classifiers for diseased and healthy samples: Logistic Regression (LR), Decision tree classifier (DT), Support Vector Machine (SVM), Random Forest classifier (RF), and K-nearest neighbour (KNN) with 5-fold cross-validation. Results: Growth Associated Protein 43 (GAP43), Glial fibrillary acidic protein (GFAP), Repetin (RPTN), and CD44 are the important genes identified in the study. These genes are known to be involved in axon growth, astrocyte differentiation in the central nervous system, post-traumatic brain repair, neuroinflammation, and inflammation-linked neuronal injuries. These key genes represent a promising tool for further studies in the diagnosis and early detection of open neural tube defects. Conclusion: These key biomarkers help in the diagnosis and early detection of open neural tube defects, thus evaluating the progress and seriousness in diseases condition. This study strengthens previous literature sources of confirming these biomarkers linked with open NTD's. Thus, among other prenatal treatment options present until now, these biomarkers help in the early detection of open neural tube defects, which provides success in both treatment and prevention of these defects in the advanced stage.

Background: Individuals with a phenotype of early-onset severe obesity associated with intellectual disability can have molecular diagnoses ranging from monogenic to complex genetic traits. Severe overweight is the major sign of a syndromic physical appearance and predicting the influence of a single gene and/or polygenic risk profile is extremely complicated among the majority of the cases. At present, considering rare monogenic bases as the principal etiology for the majority of obesity cases associated with intellectual disability is scientifically poor. The diversity of the molecular bases responsible for the two entities makes the appliance of the current routinely powerful genomics diagnostic tools essential. Objective: Clinical investigation of these difficult-to-diagnose patients requires pediatricians and neurologists to use optimized descriptions of signs and symptoms to improve genotype correlations. Methods: The use of modern integrated bioinformatics strategies which are conducted by experienced multidisciplinary clinical teams. Evaluation of the phenotype of the patient's family is also of importance. Results: The next step involves discarding the monogenic canonical obesity syndromes and considering infrequent unique molecular cases, and/or then polygenic bases. Adequate management of the application of the new technique and its diagnostic phases is essential for achieving good cost/efficiency balances. Conclusion: With the current clinical management, it is necessary to consider the potential coincidence of risk mutations for obesity in patients with genetic alterations that induce intellectual disability. In this review, we describe an updated algorithm for the molecular characterization and diagnosis of patients with a syndromic obesity phenotype.

Background: The multiple isoforms are often generated from a single gene via Alternative Splicing (AS) in plants, and the functional diversity of the plant genome is significantly increased. Despite well-studied gene functions, the specific functions of isoforms are little known, therefore, the accurate prediction of isoform functions is exceedingly wanted. Methods: Here we perform the first global analysis of AS of Dichocarpum, a medicinal genus of Ranunculales, by utilizing full-length transcriptome datasets of five Chinese endemic Dichocarpum taxa. Multiple software were used to identify AS events, the gene function was annotated based on seven databases, and the protein-coding sequence of each AS isoform was translated into an amino acid sequence. The self-developed software DIFFUSE was used to predict the functions of AS isoforms. Results: Among 8,485 genes with AS events, the genes with two isoforms were the most (6,038), followed by those with three isoforms and four isoforms. Retained intron (RI, 551) was predominant among 1,037 AS events, and alternative 3' splice sites and alternative 5' splice sites were second. The software DIFFUSE was effective in predicting functions of Dichocarpum isoforms, which have not been unearthed. When compared with the sequence alignment-based database annotations, DIFFUSE performed better in differentiating isoform functions. The DIFFUSE predictions on the terms GO:0003677 (DNA binding) and GO: 0010333 (terpene synthase activity) agreed with the biological features of transcript isoforms. Conclusion: Numerous AS events were for the first time identified from full-length transcriptome datasets of five Dichocarpum taxa, and functions of AS isoforms were successfully predicted by the self-developed software DIFFUSE. The global analysis of Dichocarpum AS events and predicting isoform functions can help understand the metabolic regulations of medicinal taxa and their pharmaceutical explorations.

Background: Earthworms are annelids. They play a major role in agriculture and soil fertility. Vermicompost is the best organic manure for plant crops. Eudrilus eugeniae is an earthworm well suited for efficient vermicompost production. The worm is also used to study the cell and molecular biology of regeneration, molecular toxicology, developmental biology, etc., because of its abilities like high growth rate, rapid reproduction, tolerability toward wide temperature range, and less cost of maintenance. Objective: The whole genome has been revealed only for Eisenia andrei and Eisenia fetida. Methods: In the present work, we sequenced the genome of E. eugeniae using the Illumina platform and generated 160,684,383 paired-end reads. Results: The reads were assembled into a draft genome of size 488 Mb with 743,870 contigs and successfully annotated 24,599 genes. Further, 208 stem cell-specific genes and 3,432 non-coding genes were identified. Conclusion: The sequence and annotation details were hosted in a web application available at https://sudhakar-sivasubramaniam-labs.shinyapps.io/eudrilus_genome/.

Background: Binning of metagenomic reads is an active area of research, and many unsupervised machine learning-based techniques have been used for taxonomic independent binning of metagenomic reads. Objective: It is important to find the optimum number of the cluster as well as develop an efficient pipeline for deciphering the complexity of the microbial genome. Methods: Applying unsupervised clustering techniques for binning requires finding the optimal number of clusters beforehand and is observed to be a difficult task. This paper describes a novel method, MetaConClust, using coverage information for grouping of contigs and automatically finding the optimal number of clusters for binning of metagenomics data using a consensus-based clustering approach. The coverage of contigs in a metagenomics sample has been observed to be directly proportional to the abundance of species in the sample and is used for grouping of data in the first phase by MetaConClust. The Partitioning Around Medoid (PAM) method is used for clustering in the second phase for generating bins with the initial number of clusters determined automatically through a consensus-based method. Results: Finally, the quality of the obtained bins is tested using silhouette index, rand Index, recall, precision, and accuracy. Performance of MetaConClust is compared with recent methods and tools using benchmarked low complexity simulated and real metagenomic datasets and is found better for unsupervised and comparable for hybrid methods. Conclusion: This is suggestive of the proposition that the consensus-based clustering approach is a promising method for automatically finding the number of bins for metagenomics data.

Background: Next-generation sequencing (NGS) technologies are being continuously used for high-throughput sequencing data generation that requires easy-to-use GUI-based data analysis software. These kinds of software could be used in-parallel with sequencing for the automatic data analysis. At present, very few software are available for use and most of them are commercial, thus creating a gap between data generation and data analysis. Methods: GAAP is developed on the NodeJS platform that uses HTML, JavaScript as the front-end for communication with users. We have implemented FastQC and trimmomatic tool for quality checking and control. Velvet and Prodigal are integrated for genome assembly and gene prediction. The annotation will be done with the help of remote NCBI Blast and IPR-Scan. In the back- end, we have used PERL and JavaScript for the processing of data. To evaluate the performance of GAAP, we have assembled a viral (SRR11621811), bacterial (SRR17153353) and human genome (SRR16845439). Results: We have used GAAP software to assemble, and annotate a COVID-19 genome on a desktop computer that resulted in a single contig of 27994bp with 99.57% reference genome coverage. This assembly predicted 11 genes, of which 10 were annotated using annotation module of GAAP. We have also assembled a bacterial and human genome 138 and 194281 contigs with N50 value 100399 and 610, respectively. Conclusion: In this study, we have developed freely available, platform-independent genome assembly and annotation (GAAP) software (www.deepaklab.com/gaap). The software itself acts as a complete data analysis package with quality check, quality control, de-novo genome assembly, gene prediction and annotation (Blast, PFAM, GO-Term, pathway and enzyme mapping) modules.

Background: The virulence of phytophagous insects is predominantly determined by their ability to evade or suppress host defense for their survival. The rice gall midge (GM, Orseolia oryzae), a monophagous pest of rice, elicits a host defense similar to the one elicited upon pathogen attack. This could be due to the GM feeding behaviour, wherein the GM endosymbionts are transferred to the host plant via oral secretions, and as a result, the host mounts an appropriate defense response(s) (i.e., up-regulation of the salicylic acid pathway) against these endosymbionts. Methods: The current study aimed to analyze the microbiome present at the feeding site of GM maggots to determine the exchange of bacterial species between GM and its host and to elucidate their role in rice-GM interaction using a next-generation sequencing approach. Results: Our results revealed differential representation of the phylum Proteobacteria in the GM-infested and -uninfested rice tissues. Furthermore, analysis of the species diversity of Pseudomonas and Wolbachia supergroups at the feeding sites indicated the exchange of bacterial species between GM and its host upon infestation. Conclusion: As rice-GM microbial associations remain relatively unstudied, these findings not only add to our current understanding of microbe-assisted insect-plant interactions but also provide valuable insights into how these bacteria drive insect-plant coevolution. Moreover, to the best of our knowledge, this is the first report analyzing the microbiome of a host plant (rice) at the feeding site of its insect pest (GM).