Individual RNA-seq Research Articles

Identification of Hsa-mir-92a-3p as a novel biomarker and PIK3R1 as a key regulatory gene to combat with the diagnostic patterns of autism spectrum disorders

In this study, two individual RNA-Seq datasets viz. GSE64018 and GSE62098 were selected from the Gene Expression Omnibus (GEO) database having samples of ASD and healthy controls. A preliminary meta-analysis was performed by using the Limma method for the identification of differentially expressed genes (DEGs). We further find out the association of DEGs with their responsible miRNAs with the help of mirTarbase software. Target gene prediction was carried out by using the tool mirDB. Hence, the findings reveal that hsa-mir-92a-3p was identified as the potential miRNA which has targeted the maximum no. of genes viz. ITGAV, PIK3R1, EIF5B. Here, PIK3R1 gene was found to be a key regulatory gene as it was commonly targeted by five miRNAs viz. hsa-miR-92a-5p, hsa-miR-93-5p, hsa-miR-17-5p, hsa-miR-106b-5p, hsa-miR-20a-5p, identified for ASD.

Fused inverse-normal method for integrated differential expression analysis of RNA-seq data

BackgroundUse of next-generation sequencing technologies to transcriptomics (RNA-seq) for gene expression profiling has found widespread application in studying different biological conditions including cancers. However, RNA-seq experiments are still small sample size experiments due to the cost. Recently, an increased focus has been on meta-analysis methods for integrated differential expression analysis for exploration of potential biomarkers. In this study, we propose a p-value combination method for meta-analysis of multiple independent but related RNA-seq studies that accounts for sample size of a study and direction of expression of genes in individual studies.ResultsThe proposed method generalizes the inverse-normal method without an increase in statistical or computational complexity and does not pre- or post-hoc filter genes that have conflicting direction of expression in different studies. Thus, the proposed method, as compared to the inverse-normal, has better potential for the discovery of differentially expressed genes (DEGs) with potentially conflicting differential signals from multiple studies related to disease. We demonstrated the use of the proposed method in detection of biologically relevant DEGs in glioblastoma (GBM), the most aggressive brain cancer. Our approach notably enabled the identification of over-expressed tumour suppressor gene RAD51 in GBM compared to healthy controls, which has recently been shown to be a target for inhibition to enhance radiosensitivity of GBM cells during treatment. Pathway analysis identified multiple aberrant GBM related pathways as well as novel regulators such as TCF7L2 and MAPT as important upstream regulators in GBM.ConclusionsThe proposed meta-analysis method generalizes the existing inverse-normal method by providing a way to establish differential expression status for genes with conflicting direction of expression in individual RNA-seq studies. Hence, leading to further exploration of them as potential biomarkers for the disease.

Meta-Analysis of EGF-Stimulated Normal and Cancer Cell Lines to Discover EGF-Associated Oncogenic Signaling Pathways and Prognostic Biomarkers.

Although epidermal growth factor (EGF) controls many crucial processes in the human body, it can increase the risk of developing cancer when overexpresses. This study focused on detecting cancer-associated genes that are dysregulated by EGF overexpression. To identify differentially expressed genes (DEGs), two independent meta-analyses with normal and cancer RNA-Seq samples treated by EGF were conducted. The new DEGs detected only via two meta-analyses were used in all downstream analyses. To reach count data, the tools of FastQC, Trimmomatic, HISAT2, SAMtools, and HTSeq-count were employed. DEGs in each individual RNA-Seq study and the meta-analysis of RNA-Seq studies were identified using DESeq2 and metaSeq R package, respectively. MCODE detected densely interconnected top clusters in the protein-protein interaction (PPI) network of DEGs obtained from normal and cancer datasets. The DEGs were then introduced to Enrichr and ClueGO/CluePedia, and terms, pathways, and hub genes enriched in Gene Ontology (GO) and KEGG and Reactome were detected. The meta-analysis of normal and cancer datasets revealed 990 and 541 new DEGs, all upregulated. A number of DEGs were enriched in protein K48-linked deubiquitination, ncRNA processing, ribosomal large subunit binding, and protein processing in endoplasmic reticulum. Hub genes overexpression (DHX33, INTS8, NMD3, OTUD4, P4HB, RPS3A, SEC13, SKP1, USP34, USP9X, and YOD1) in tumor samples were validated by TCGA and GTEx databases. Overall survival and disease-free survival analysis also confirmed worse survival in patients with hub genes overexpression. The detected hub genes could be used as cancer biomarkers when EGF overexpresses.

Hypoxia classifier for transcriptome datasets

Molecular gene signatures are useful tools to characterize the physiological state of cell populations, but most have developed under a narrow range of conditions and cell types and are often restricted to a set of gene identities. Focusing on the transcriptional response to hypoxia, we aimed to generate widely applicable classifiers sourced from the results of a meta-analysis of 69 differential expression datasets which included 425 individual RNA-seq experiments from 33 different human cell types exposed to different degrees of hypoxia (0.1-5\%O\textsubscript{2}) for 2-48h. The resulting decision trees include both gene identities and quantitative boundaries, allowing for easy classification of individual samples without control or normoxic reference. Each tree is composed by 3-5 genes mostly drawn from a small set of just 8 genes (EGLN1, MIR210HG, NDRG1, ANKRD37, TCAF2, PFKFB3, BHLHE40, and MAFF). In spite of their simplicity, these classifiers achieve over 95\% accuracy in cross validation and over 80\% accuracy when applied to additional challenging datasets. Our results indicate that the classifiers are able to identify hypoxic tumor samples from bulk RNAseq and hypoxic regions within tumor from spatially resolved transcriptomics datasets. Moreover, application of the classifiers to histological sections from normal tissues suggest the presence of a hypoxic gene expression pattern in the kidney cortex not observed in other normoxic organs. Finally, tree classifiers described herein outperform traditional hypoxic gene signatures when compared against a wide range of datasets. This work describes a set of hypoxic gene signatures, structured as simple decision tress, that identify hypoxic samples and regions with high accuracy and can be applied to a broad variety of gene expression datasets and formats.

Hypoxia classifier for transcriptome datasets

Molecular gene signatures are useful tools to characterize the physiological state of cell populations, but most have developed under a narrow range of conditions and cell types and are often restricted to a set of gene identities. Focusing on the transcriptional response to hypoxia, we aimed to generate widely applicable classifiers sourced from the results of a meta-analysis of 69 differential expression datasets which included 425 individual RNA-seq experiments from 33 different human cell types exposed to different degrees of hypoxia (0.1–5%hbox {O}_{2}) for 2–48 h. The resulting decision trees include both gene identities and quantitative boundaries, allowing for easy classification of individual samples without control or normoxic reference. Each tree is composed of 3–5 genes mostly drawn from a small set of just 8 genes (EGLN1, MIR210HG, NDRG1, ANKRD37, TCAF2, PFKFB3, BHLHE40, and MAFF). In spite of their simplicity, these classifiers achieve over 95% accuracy in cross validation and over 80% accuracy when applied to additional challenging datasets. Our results indicate that the classifiers are able to identify hypoxic tumor samples from bulk RNAseq and hypoxic regions within tumor from spatially resolved transcriptomics datasets. Moreover, application of the classifiers to histological sections from normal tissues suggest the presence of a hypoxic gene expression pattern in the kidney cortex not observed in other normoxic organs. Finally, tree classifiers described herein outperform traditional hypoxic gene signatures when compared against a wide range of datasets. This work describes a set of hypoxic gene signatures, structured as simple decision tress, that identify hypoxic samples and regions with high accuracy and can be applied to a broad variety of gene expression datasets and formats.

GEMmaker: process massive RNA-seq datasets on heterogeneous computational infrastructure

BackgroundQuantification of gene expression from RNA-seq data is a prerequisite for transcriptome analysis such as differential gene expression analysis and gene co-expression network construction. Individual RNA-seq experiments are larger and combining multiple experiments from sequence repositories can result in datasets with thousands of samples. Processing hundreds to thousands of RNA-seq data can result in challenges related to data management, access to sufficient computational resources, navigation of high-performance computing (HPC) systems, installation of required software dependencies, and reproducibility. Processing of larger and deeper RNA-seq experiments will become more common as sequencing technology matures.ResultsGEMmaker, is a nf-core compliant, Nextflow workflow, that quantifies gene expression from small to massive RNA-seq datasets. GEMmaker ensures results are highly reproducible through the use of versioned containerized software that can be executed on a single workstation, institutional compute cluster, Kubernetes platform or the cloud. GEMmaker supports popular alignment and quantification tools providing results in raw and normalized formats. GEMmaker is unique in that it can scale to process thousands of local or remote stored samples without exceeding available data storage.ConclusionsWorkflows that quantify gene expression are not new, and many already address issues of portability, reusability, and scale in terms of access to CPUs. GEMmaker provides these benefits and adds the ability to scale despite low data storage infrastructure. This allows users to process hundreds to thousands of RNA-seq samples even when data storage resources are limited. GEMmaker is freely available and fully documented with step-by-step setup and execution instructions.

Intracellular and Intercellular Gene Regulatory Network Inference From Time-Course Individual RNA-Seq.

Gene regulatory network (GRN) inference is an effective approach to understand the molecular mechanisms underlying biological events. Generally, GRN inference mainly targets intracellular regulatory relationships such as transcription factors and their associated targets. In multicellular organisms, there are both intracellular and intercellular regulatory mechanisms. Thus, we hypothesize that GRNs inferred from time-course individual (whole embryo) RNA-Seq during development can reveal intercellular regulatory relationships (signaling pathways) underlying the development. Here, we conducted time-course bulk RNA-Seq of individual mouse embryos during early development, followed by pseudo-time analysis and GRN inference. The results demonstrated that GRN inference from RNA-Seq with pseudo-time can be applied for individual bulk RNA-Seq similar to scRNA-Seq. Validation using an experimental-source-based database showed that our approach could significantly infer GRN for all transcription factors in the database. Furthermore, the inferred ligand-related and receptor-related downstream genes were significantly overlapped. Thus, the inferred GRN based on whole organism could include intercellular regulatory relationships, which cannot be inferred from scRNA-Seq based only on gene expression data. Overall, inferring GRN from time-course bulk RNA-Seq is an effective approach to understand the regulatory relationships underlying biological events in multicellular organisms.

Abstract LB045: Identification of plasma-derived, EV-based biomarkers for glioblastoma

Abstract Background: Glioblastoma (GBM) is the most malignant and aggressive primary brain cancer in adults, with an incidence of 3.2 per 100,000 people. Currently, diagnosis is only performed via histopathological investigation of a tissue sample from a GBM lesion, complemented with molecular diagnostics for identification of select biomarkers. MRI is the standard of care for follow-up and monitoring of treatment response. Therefore, development of a “liquid biopsy” to obtain disease-relevant information from body fluids is highly desirable.Methods: We present the results from a clinical study in which extracellular vesicle (EV)-derived mRNAs were profiled from the plasma of GBM patients and control individuals for a total of 32 individual RNAseq libraries. Initial biomarker discovery was performed on plasma from 16 patients (8 GBM patients at the time of initial diagnosis and 8 age and sex matched controls). Hybrid capture and RNA-seq were performed via our proprietary pipeline on EV-associated mRNA isolated from 1-2mL plasma. Sequencing data was analyzed for differential gene expression. Validation was done with plasma samples from a new cohort of 8 patients and 8 control individuals.Results: We observed 98 mRNAs as differentially detected between GBM and control samples, with 83 mRNAs enriched in GBM samples and 15 mRNAs decreased (p < 0.05). Correlation based on differentially detected mRNAs separated GBM and control samples into two unique populations. We were able to use the differentially expressed gene signature from the first cohort to separate GBM patients and control individuals in the second cohort. We are currently following up these results with a larger patient population. These data, while preliminary, provide a potential basis for the further development of a GBM gene panel test.Conclusions: We have identified a novel gene signature for GBM in plasma-derived EVs, which differs from previously identified biomarkers isolated from tissue. Further work will refine this signature to enable detection, characterization, and patient monitoring for GBM with less invasive and intensive methods. Citation Format: Yevgenia L. Khodor, Sudipto K. Chakrabortty, Christian Fischer, Martina Rauscher, Leonora Balaj, Bob S. Carter, Seth Yu, Johan Skog. Identification of plasma-derived, EV-based biomarkers for glioblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr LB045.

Comprehensive Analysis of RNA-Seq Gene Expression Profiling of Brain Transcriptomes Reveals Novel Genes, Regulators, and Pathways in Autism Spectrum Disorder.

Background: Autism spectrum disorder (ASD) is a neurodevelopmental disorder with deficits in social communication ability and repetitive behavior. The pathophysiological events involved in the brain of this complex disease are still unclear. Methods: In this study, we aimed to profile the gene expression signatures of brain cortex of ASD patients, by using two publicly available RNA-seq studies, in order to discover new ASD-related genes. Results: We detected 1567 differentially expressed genes (DEGs) by meta-analysis, where 1194 were upregulated and 373 were downregulated genes. Several ASD-related genes previously reported were also identified. Our meta-analysis identified 235 new DEGs that were not detected using the individual RNA-seq studies used. Some of those genes, including seven DEGs (PAK1, DNAH17, DOCK8, DAPP1, PCDHAC2, and ERBIN, SLC7A7), have been confirmed in previous reports to be associated with ASD. Gene Ontology (GO) and pathways analysis showed several molecular pathways enriched by the DEGs, namely, osteoclast differentiation, TNF signaling pathway, complement and coagulation cascade. Topological analysis of protein–protein interaction of the ASD brain cortex revealed proteomics hub gene signatures: MYC, TP53, HDAC1, CDK2, BAG3, CDKN1A, GABARAPL1, EZH2, VIM, and TRAF1. We also identified the transcriptional factors (TFs) regulating DEGs, namely, FOXC1, GATA2, YY1, FOXL1, USF2, NFIC, NFKB1, E2F1, TFAP2A, HINFP. Conclusion: Novel core genes and molecular signatures involved with ASD were identified by our meta-analysis.

Meta-Analysis of Dilated Cardiomyopathy Using Cardiac RNA-Seq Transcriptomic Datasets.

Dilated cardiomyopathy (DCM) is one of the most common causes of heart failure. Several studies have used RNA-sequencing (RNA-seq) to profile differentially expressed genes (DEGs) associated with DCM. In this study, we aimed to profile gene expression signatures and identify novel genes associated with DCM through a quantitative meta-analysis of three publicly available RNA-seq studies using human left ventricle tissues from 41 DCM cases and 21 control samples. Our meta-analysis identified 789 DEGs including 581 downregulated and 208 upregulated genes. Several DCM-related genes previously reported, including MYH6, CKM, NKX2–5 and ATP2A2, were among the top 50 DEGs. Our meta-analysis also identified 39 new DEGs that were not detected using those individual RNA-seq datasets. Some of those genes, including PTH1R, ADAM15 and S100A4, confirmed previous reports of associations with cardiovascular functions. Using DEGs from this meta-analysis, the Ingenuity Pathway Analysis (IPA) identified five activated toxicity pathways, including failure of heart as the most significant pathway. Among the upstream regulators, SMARCA4 was downregulated and prioritized by IPA as the top affected upstream regulator for several DCM-related genes. To our knowledge, this study is the first to perform a transcriptomic meta-analysis for clinical DCM using RNA-seq datasets. Overall, our meta-analysis successfully identified a core set of genes associated with DCM.

Effects of oxidative stress on sex-specific gene expression in the copepod Tigriopus californicus revealed by single individual RNA-seq

Oxidative stress reflects the imbalance of pro-oxidants and antioxidants. Prolonged oxidative stress can induce cellular damage, diseases and aging, and the effects may be sex-specific. Tigriopus californicus has recently been proposed as an alternative model system for sex-specific studies due to the absence of sex chromosomes. In this study, we used comparative transcriptomic analyses to assess sex-specific transcriptional responses to oxidative stress. Male and female individuals were maintained separately in one of three treatments: 1) control conditions with an algae diet, 2) pro-oxidant (H2O2) conditions with an algae diet or 3) decreased antioxidant conditions (reduced carotenoids due to a yeast diet). Single individual RNA-seq was then conducted for twenty-four libraries using Ligation Mediated RNA sequencing (LM-Seq). Variance in gene expression was partitioned into 62.3% between sexes, 26.85% among individuals and 10.85% among treatments. Within each of the three treatments, expression was biased toward females. However, compared to the control treatment, males in both pro-oxidant and decreased antioxidant treatments differentially expressed more genes while females differentially expressed fewer genes but with a greater magnitude of fold change. As the first study of copepods to apply single individual RNA-seq, the findings will contribute to a better understanding of transcriptomic variation among individuals as well as sex-specific response mechanisms to oxidative stress in the absence of sex chromosomes.

Effect of consuming endophyte-infected fescue seed on transcript abundance in the mammary gland of lactating and dry cows, as assessed by RNA sequencing

Ergot alkaloids in endophyte-infected grasses inhibit prolactin secretion and reduce milk production in lactating cows. However, we previously showed that prepartum consumption of infected seed throughout the dry period did not inhibit subsequent milk production and prior exposure to bromocriptine (ergot peptide) actually increased production in the next lactation. To identify changes in the transcriptome and molecular pathways mediating the mammary gland's response to ergot alkaloids in the diet, RNA sequencing (RNA-seq) was performed on mammary tissues obtained from 22 multiparous Holstein cows exposed to 1 of 3 treatments. Starting at 90 ± 4 d prepartum, cows were fed endophyte-free fescue seed (control; CON), endophyte-free fescue seed plus 3×/wk subcutaneous injections of bromocriptine (BROMO; 0.1 mg/kg of BW), or endophyte-infected fescue seed (INF) as 10% of the diet. Cows were dried off 60 ± 2 d prepartum. Mammary biopsies from 4 (BROMO, INF) or 5 (CON) cows/treatment at each of the 3 phases were obtained: 7 d before dry off during the initial lactation (L1), mid-dry period (D), and 10 d postpartum (L2). Although tissue from the same cow was preferentially used at 3 phases (L1, D, L2), tissue from additional cows were used to as necessary to provide RNA of sufficient quality. Individual samples were used to generate individual RNA-seq libraries. Normalized reads of the RNA-seq data were organized into technical and biological replicates before processing with the RSEM software package. Each lactation phase was processed separately and genes that differed between any of 3 treatments were identified. A large proportion of genes differentially expressed in at least 1 treatment (n = 866) were found to be similarly expressed in BROMO and INF treatments, but differentially expressed from CON (n = 575, total for 3 phases). Of genes differentially expressed compared with CON, 104 genes were common to the L1 and L2 phases. Consistent with the production findings, networks most affected by treatments in L1 and L2 included lipid metabolism, small molecule biochemistry, and molecular transport, whereas networks related more to developmental and cellular functions and maintenance were evident during D phase. Similar patterns of expression in BROMO and INF during late and early lactation suggest involvement of similar cell signaling pathways or mechanisms of action for BROMO and INF and the importance of prolactin messaging pathways.

Ggsashimi: Sashimi plot revised for browser- and annotation-independent splicing visualization.

We present ggsashimi, a command-line tool for the visualization of splicing events across multiple samples. Given a specified genomic region, ggsashimi creates sashimi plots for individual RNA-seq experiments as well as aggregated plots for groups of experiments, a feature unique to this software. Compared to the existing versions of programs generating sashimi plots, it uses popular bioinformatics file formats, it is annotation-independent, and allows the visualization of splicing events even for large genomic regions by scaling down the genomic segments between splice sites. ggsashimi is freely available at https://github.com/guigolab/ggsashimi. It is implemented in python, and internally generates R code for plotting.

SINC-seq: correlation of transient gene expressions between nucleus and cytoplasm reflects single-cell physiology

We report a microfluidic system that physically separates nuclear RNA (nucRNA) and cytoplasmic RNA (cytRNA) from a single cell and enables single-cell integrated nucRNA and cytRNA-sequencing (SINC-seq). SINC-seq constructs two individual RNA-seq libraries, nucRNA and cytRNA, per cell, quantifies gene expression in the subcellular compartments, and combines them to create novel single-cell RNA-seq data. Leveraging SINC-seq, we discover distinct natures of correlation among cytRNA and nucRNA that reflect the transient physiological state of single cells. These data provide unique insights into the regulatory network of messenger RNA from the nucleus toward the cytoplasm at the single-cell level.

Predicting survival times for neuroblastoma patients using RNA-seq expression profiles

BackgroundNeuroblastoma is the most common tumor of early childhood and is notorious for its high variability in clinical presentation. Accurate prognosis has remained a challenge for many patients. In this study, expression profiles from RNA-sequencing are used to predict survival times directly. Several models are investigated using various annotation levels of expression profiles (genes, transcripts, and introns), and an ensemble predictor is proposed as a heuristic for combining these different profiles.ResultsThe use of RNA-seq data is shown to improve accuracy in comparison to using clinical data alone for predicting overall survival times. Furthermore, clinically high-risk patients can be subclassified based on their predicted overall survival times. In this effort, the best performing model was the elastic net using both transcripts and introns together. This model separated patients into two groups with 2-year overall survival rates of 0.40±0.11 (n=22) versus 0.80±0.05 (n=68). The ensemble approach gave similar results, with groups 0.42±0.10 (n=25) versus 0.82±0.05 (n=65). This suggests that the ensemble is able to effectively combine the individual RNA-seq datasets.ConclusionsUsing predicted survival times based on RNA-seq data can provide improved prognosis by subclassifying clinically high-risk neuroblastoma patients.ReviewersThis article was reviewed by Subharup Guha and Isabel Nepomuceno.

Proteogenomic Study beyond Chromosome 9: New Insight into Expressed Variant Proteome and Transcriptome in Human Lung Adenocarcinoma Tissues.

This is a report of a human proteome project (HPP) related to chromosome 9 (Chr 9). To reveal missing proteins and undiscovered features in proteogenomes, both LC-MS/MS analysis and next-generation RNA sequencing (RNA-seq)-based identification and characterization were conducted on five pairs of lung adenocarcinoma tumors and adjacent nontumor tissues. Before our previous Chromosome-Centric Human Proteome Project (C-HPP) special issue, there were 170 remaining missing proteins on Chr 9 (neXtProt 2013.09.26 rel.); 133 remain at present (neXtProt 2015.04.28 rel.). In the proteomics study, we found two missing protein candidates that require follow-up work and one unrevealed protein across all chromosomes. RNA-seq analysis detected RNA expression for four nonsynonymous (NS) single nucleotide polymorphisms (SNPs) (in CDH17, HIST1H1T, SAPCD2, and ZNF695) and three synonymous SNPs (in CDH17, CST1, and HNF1A) in all five tumor tissues but not in any of the adjacent normal tissues. By constructing a cancer patient sample-specific protein database based on individual RNA-seq data and by searching the proteomics data from the same sample, we identified four missense mutations in four genes (LTF, HDLBP, TF, and HBD). Two of these mutations were found in tumor samples but not in paired normal tissues. In summary, our proteogenomic study of human primary lung tumor tissues detected additional and revealed novel missense mutations and synonymous SNP signatures, some of which are specific to lung cancers. Data from mass spectrometry have been deposited in the ProteomeXchange with the identifier PXD002523.

Root precursors of microRNAs in wild emmer and modern wheats show major differences in response to drought stress.

MicroRNAs, small regulatory molecules with significant impacts on the transcriptional network of all living organisms, have been the focus of several studies conducted mostly on modern wheat cultivars. In this study, we investigated miRNA repertoires of modern durum wheat and its wild relatives, with differing degrees of drought tolerance, to identify miRNA candidates and their targets involved in drought stress response. Root transcriptomes of Triticum turgidum ssp. durum variety Kızıltan and two Triticum turgidum ssp. dicoccoides genotypes TR39477 and TTD-22 under control and drought conditions were assembled from individual RNA-Seq reads and used for in silico identification of miRNAs. A total of 66 miRNAs were identified from all species, across all conditions, of which 46 and 38 of the miRNAs identified from modern durum wheat and wild genotypes, respectively, had not been previously reported. Genotype- and/or stress-specific miRNAs provide insights into our understanding of the complex drought response. Particularly, miR1435, miR5024, and miR7714, identified only from drought-stress roots of drought-tolerant genotype TR39477, can be candidates for future studies to explore and exploit the drought response to develop tolerant varieties.