Current Genomics最新文献

Diabetic retinopathy (DR) is a severe complication of diabetes; however, its mechanism is not fully understood. Evidence has recently revealed that long non-coding RNAs (lncRNAs) are abnormally expressed in DR, and lncRNAs may function as pivotal regulators. LncRNAs are able to modulate gene expression at the epigenetic level by acting as scaffolds of histone modification complexes and sponges of binding with microRNAs (miRNAs). LncRNAs are believed to be important epigenetic regulators, which may become beneficial in the diagnosis and therapy of DR. However, the mechanisms of lncRNAs in DR are still unclear. In this review, we summarize the possible functions and mechanisms of lncRNAs in epigenetic regulation to target genes in the progression of DR.

Cervical cancer is the leading cause of death in women, mainly in developing countries, including India. Recent advancements in technologies could allow for more rapid, cost-effective, and sensitive screening and treatment measures for cervical cancer. To this end, deep learning-based methods have received importance for classifying cervical cancer patients into different risk groups. Furthermore, deep learning models are now available to study the progression and treatment of cancerous cervical conditions. Undoubtedly, deep learning methods can enhance our knowledge toward a better understanding of cervical cancer progression. However, it is essential to thoroughly validate the deep learning-based models before they can be implicated in everyday clinical practice. This work reviews recent development in deep learning approaches employed in cervical cancer diagnosis and prognosis. Further, we provide an overview of recent methods and databases leveraging these new approaches for cervical cancer risk prediction and patient outcomes. Finally, we conclude the state-of-the-art approaches for future research opportunities in this domain.

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.

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.

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.