Construction of the Classification Model Using Key Genes Identified Between Benign and Malignant Thyroid Nodules From Comprehensive Transcriptomic Data

Thyroid nodules are present in upto 50% of the population worldwide, and thyroid malignancy occurs in only 5–15% of nodules. Until now, fine-needle biopsy with cytologic evaluation remains the diagnostic choice to determine the risk of malignancy, yet it fails to discriminate as benign or malignant in one-third of cases. In order to improve the diagnostic accuracy and reliability, molecular testing based on transcriptomic data has developed rapidly. However, gene signatures of thyroid nodules identified in a plenty of transcriptomic studies are highly inconsistent and extremely difficult to be applied in clinical application. Therefore, it is highly necessary to identify consistent signatures to discriminate benign or malignant thyroid nodules. In this study, five independent transcriptomic studies were combined to discover the gene signature between benign and malignant thyroid nodules. This combined dataset comprises 150 malignant and 93 benign thyroid samples. Then, there were 279 differentially expressed genes (DEGs) discovered by the feature selection method (Student’s t test and fold change). And the weighted gene co-expression network analysis (WGCNA) was performed to identify the modules of highly co-expressed genes, and 454 genes in the gray module were discovered as the hub genes. The intersection between DEGs by the feature selection method and hub genes in the WGCNA model was identified as the key genes for thyroid nodules. Finally, four key genes (ST3GAL5, NRCAM, MT1F, and PROS1) participated in the pathogenesis of malignant thyroid nodules were validated using an independent dataset. Moreover, a high-performance classification model for discriminating thyroid nodules was constructed using these key genes. All in all, this study might provide a new insight into the key differentiation of benign and malignant thyroid nodules.

DNA methylation aging and transcriptomic studies in horses

Cytosine methylation patterns have not yet been thoroughly studied in horses. Here, we profile n = 333 samples from 42 horse tissue types at loci that are highly conserved between mammalian species using a custom array (HorvathMammalMethylChip40). Using the blood and liver tissues from horses, we develop five epigenetic aging clocks: a multi-tissue clock, a blood clock, a liver clock and two dual-species clocks that apply to both horses and humans. In addition, using blood methylation data from three additional equid species (plains zebra, Grevy’s zebras and Somali asses), we develop another clock that applies across all equid species. Castration does not significantly impact the epigenetic aging rate of blood or liver samples from horses. Methylation and RNA data from the same tissues define the relationship between methylation and RNA expression across horse tissues. We expect that the multi-tissue atlas will become a valuable resource.

NetREx – Network-based Rice Expression Analysis Server for Abiotic Stress Conditions

Recent focus on transcriptomic studies in food crops like rice, wheat and maize provide new opportunities to address issues related to agriculture and climate change. Re-analysis of such data available in public domain supplemented with annotations across molecular hierarchy can be of immense help to the plant research community, particularly co-expression networks representing transcriptionally coordinated genes that are often part of the same biological process. With this objective we have developed NetREx, a Network based Rice Expression Analysis Server, that hosts ranked co-expression networks of Oryza sativa using publicly available mRNA-seq data across uniform experimental conditions. It provides a range of interactable data viewers and modules for analysing user queried genes across different stress conditions (drought, flood, cold and osmosis) and hormonal treatments (abscisic and jasmonic acid) and tissues (root and shoot). Subnetworks of user-defined genes can be queried in preconstructed tissue-specific networks, allowing users to view the fold-change, module memberships, gene annotations and analysis of their neighborhood genes and associated pathways. The webserver also allows querying of orthologous genes from Arabidopsis, wheat, maize, barley, and sorghum. Here we demonstrate that NetREx can be used to identify novel candidate genes and tissue-specific interactions under stress conditions and can aid in the analysis and understanding of complex phenotypes linked to stress response in rice. Available at: https://bioinf.iiit.ac.in/netrex/index.html

Integration of Diverse Transcriptomics Datasets using Random Forest to Predict Universal Functional Pathways in Tfr Cells

Motivation T follicular regulatory (Tfr) cells are a specialized cell subset that controls humoral immunity. Despite a number of individual transcriptomic studies on these cells, core functional pathways have been difficult to uncover due to the substantial transcriptional overlap of these cells with other effector cell types, as well as transcriptional changes occurring due to disease settings. Developing a core transcriptional module for Tfr cells that integrates multiple cell type comparisons as well as diverse disease settings will allow a more accurate prediction of functional pathways. Researchers studying allergic reactions, immune responses to vaccines, autoimmunity and cancer could use this gene set to better understand the roles of Tfr cells in controlling disease progression. Additional cell types beyond Tfr cells that have similar features of transcriptomic complexity within diverse disease settings may also be studied using similar approaches. High-throughput sequencing technologies allow the generation of large datasets that require specific tools to best interpret the data. The development of a core transcriptional module for Tfr cells will allow investigators to determine if Tfr cells may have functional roles within their biological systems with little knowledge of Tfr biology. With this work, we have addressed the need of core gene modules to define specific subsets of immune cells. Results We introduce an integrated "core Tfr cell gene module" that can be incorporated into GSEA analysis using various input sizes. The integrated core Tfr gene module was built using transcriptomic studies in Tfr cells from several different tissues, disease settings, and cell type comparisons. Random forest was used to integrate the transcriptomic studies to generate the core gene module. A GSEA gene set was formulated from the integrated core Tfr gene module for incorporation into end-user friendly GSEA. The gene sets are presented along with random genes taken from the GTEX data set and are presented as GMT files. The user can upload the gene set to the GSEA website or any gene set tool which takes GMT files. We also present the full results of the model including p-values calculated by random forest. This allows the user to choose a p-value cutoff that is most appropriate for the experimental setting.

MECP2pedia: A Comprehensive Transcriptome Portal for MECP2 Disease Research

Mutations in MeCP2 result in a crippling neurological disease, but we lack a lucid picture of MeCP2s molecular role. Focusing on individual transcriptomic studies yields inconsistent differentially expressed genes. We have aggregated and homogeneously processed modern public MeCP2 transcriptome data, which we present in a web portal. With this big data, we discovered a commonly perturbed core set of genes that transcends the limitations of any individual study. We then found distinct consistently up and downregulated subsets within these genes. We observe enrichment for this mouse core in other species MeCP2 models and see overlap between this core and ASD models. Analysis of signal to noise finds that many studies lack enough biological replicates. By integrating and examining transcriptomic data at scale, we have generated a valuable resource and insight on MeCP2 function.

Malate Transport and Metabolism in Nitrogen-Fixing Legume Nodules

Legumes form a symbiosis with rhizobia, a soil bacterium that allows them to access atmospheric nitrogen and deliver it to the plant for growth. Biological nitrogen fixation occurs in specialized organs, termed nodules, that develop on the legume root system and house nitrogen-fixing rhizobial bacteroids in organelle-like structures termed symbiosomes. The process is highly energetic and there is a large demand for carbon by the bacteroids. This carbon is supplied to the nodule as sucrose, which is broken down in nodule cells to organic acids, principally malate, that can then be assimilated by bacteroids. Sucrose may move through apoplastic and/or symplastic routes to the uninfected cells of the nodule or be directly metabolised at the site of import within the vascular parenchyma cells. Malate must be transported to the infected cells and then across the symbiosome membrane, where it is taken up by bacteroids through a well-characterized dct system. The dicarboxylate transporters on the infected cell and symbiosome membranes have been functionally characterized but remain unidentified. Proteomic and transcriptomic studies have revealed numerous candidates, but more work is required to characterize their function and localise the proteins in planta. GABA, which is present at high concentrations in nodules, may play a regulatory role, but this remains to be explored.

The Role of Long Non-Coding RNAs in Endometriosis

Endometriosis is a chronic gynecological disorder affecting the quality of life and fertility of many women around the world. Heterogeneous and non-specific symptoms may lead to a delay in diagnosis, with treatment options limited to surgery and hormonal therapy. Hence, there is a need to better understand the pathogenesis of the disease to improve diagnosis and treatment. Long non-coding RNAs (lncRNAs) have been increasingly shown to be involved in gene regulation but remain relatively under investigated in endometriosis. Mutational and transcriptomic studies have implicated lncRNAs in the pathogenesis of endometriosis. Single-nucleotide polymorphisms (SNPs) in lncRNAs or their regulatory regions have been associated with endometriosis. Genome-wide transcriptomic studies have identified lncRNAs that show deregulated expression in endometriosis, some of which have been subjected to further experiments, which support a role in endometriosis. Mechanistic studies indicate that lncRNAs may regulate genes involved in endometriosis by acting as a molecular sponge for miRNAs, by directly targeting regulatory elements via interactions with chromatin or transcription factors or by affecting signaling pathways. Future studies should concentrate on determining the role of uncharacterized lncRNAs revealed by endometriosis transcriptome studies and the relevance of lncRNAs implicated in the disease by in vitro and animal model studies.