A combination of differential expression and network connectivity analyses identifies a common set of RNA splicing and processing genes altered with age across human tissues.

Interconnected Gene Networks Underpin the Clinical Overlap of HNRNPH1-Related and Rubinstein-Taybi Intellectual Disability Syndromes.

e17601 Background: The oral antioxidant pterostilbene (PT) (3,5-dimethoxy-40-hydroxystilbene) has demonstrated synergistic antiproliferative effect with megestrol acetate (MA) in endometrial cancer (EC). We conducted a preoperative (window of opportunity) study enrolling endometrial cancer patients scheduled for hysterectomy, who were randomized to preoperative PT+MA vs MA alone. Methods: This prospective, randomized phase II clinical trial (NCT03671811) enrolled patients who were surgical candidates for hysterectomy. Patients were randomized 1:1 to each arm and were treated for 3 weeks with either PT+MA (arm 1) or MA alone (arm 2). Exploratory objectives included histologic response by GOG 211 criteria, and transcriptional changes using whole transcriptome analysis. For the RNA-seq analysis, differential expression analysis used the “limma” R limma package. The differentially expressed genes were selected based on the cutoff values of p-value <= 0.05 and fold change >= 1.5. The gene set enrichment analysis was performed using GSEA software v. 4.3.2 to identify the affected Hallmark and KEGG pathways, using the pre-ranked gene list ranked by the -log10(p-value) with a sign determined by the fold change direction. Results: This prospective, randomized phase II clinical trial (NCT03671811) enrolled patients who were surgical candidates for hysterectomy. Patients were randomized 1:1 to each arm and were treated for 3 weeks with either PT+MA (arm 1) or MA alone (arm 2). Exploratory objectives included histologic response by GOG 211 criteria, and transcriptional changes using whole transcriptome analysis. For the RNA-seq analysis, differential expression analysis used the “limma” R limma package. The differentially expressed genes were selected based on the cutoff values of p-value <= 0.05 and fold change >= 1.5. The gene set enrichment analysis was performed using GSEA software v. 4.3.2 to identify the affected Hallmark and KEGG pathways, using the pre-ranked gene list ranked by the -log10(p-value) with a sign determined by the fold change direction. Conclusions: In endometrial cancer, the oral antioxidant Pterostilbene increases innate immune system pathway expression, while reducing mTOR pathway expression, potentially serving as a useful adjunct to progestin hormonal therapy in EC. Clinical trial information: NCT03671811 .

Recurrent mutations in the splicing factor SRSF2 are associated with poor clinical outcomes in myelodysplastic syndromes (MDS). Their high frequency suggests these mutations drive oncogenesis, yet the molecular explanation for this process is unclear. SRSF2 mutations could directly affect pre-mRNA splicing of a vital gene product; alternatively, a whole network of gene products could be affected. Here we determine how SRSF2 mutations globally affect RNA binding and splicing in vivo using HITS-CLIP. Remarkably, the majority of differential binding events do not translate into alternative splicing of exons with SRSF2P95H binding sites. Alternative splice alterations appear to be dominated by indirect effects. Importantly, SRSF2P95H targets are enriched in RNA processing and splicing genes, including several members of the hnRNP and SR families of proteins, suggesting a “splicing-cascade” phenotype wherein mutation of a single splicing factor leads to widespread modifications in multiple RNA processing and splicing proteins. We show that splice alteration of HNRNPA2B1, a splicing factor differentially bound and spliced by SRSF2P95H, impairs hematopoietic differentiation in vivo. Our data suggests a model whereby the recurrent mutations in splicing factors set off a cascade of gene regulatory events that together affect hematopoiesis and drive cancer.

Longitudinal Effects of 1-Year Smoking Cessation on Human Bronchial Epithelial Transcriptome

HOW to Wrap Axons with Crooked Neck

m6A mRNA Methylation: A New Circadian Pacesetter

Does cellular composition of the endometrial biopsy affect the gene expression profile of endometrial whole-tissue samples? The differences in epithelial and stromal cell proportions in endometrial biopsies modify the whole-tissue gene expression profiles and affect the results of differential expression analyses. Each cell type has its unique gene expression profile. The proportions of epithelial and stromal cells vary in endometrial tissue during the menstrual cycle, along with individual and technical variation due to the method and tools used to obtain the tissue biopsy. Using cell-population specific transcriptome data and computational deconvolution approach, we estimated the epithelial and stromal cell proportions in whole-tissue biopsies taken during early secretory and mid-secretory phases. The estimated cellular proportions were used as covariates in whole-tissue differential gene expression analysis. Endometrial transcriptomes before and after deconvolution were compared and analysed in biological context. Paired early- and mid-secretory endometrial biopsies were obtained from 35 healthy, regularly cycling, fertile volunteers, aged 23-36 years, and analysed by RNA sequencing. Differential gene expression analysis was performed using two approaches. In one of them, computational deconvolution was applied as an intermediate step to adjust for the proportions of epithelial and stromal cells in the endometrial biopsy. The results were then compared to conventional differential expression analysis. Ten paired endometrial samples were analysed with qPCR to validate the results. The estimated average proportions of stromal and epithelial cells in early secretory phase were 65% and 35%, and during mid-secretory phase, 46% and 54%, respectively, correlating well with the results of histological evaluation (r = 0.88, P = 1.1 × 10-6). Endometrial tissue transcriptomic analysis showed that approximately 26% of transcripts (n = 946) differentially expressed in receptive endometrium in cell-type unadjusted analysis also remain differentially expressed after adjustment for biopsy cellular composition. However, the other 74% (n = 2645) become statistically non-significant after adjustment for biopsy cellular composition, underlining the impact of tissue heterogeneity on differential expression analysis. The results suggest new mechanisms involved in endometrial maturation, involving genes like LINC01320, SLC8A1 and GGTA1P, described for the first time in context of endometrial receptivity. The RNA-seq data presented in this study is deposited in the Gene Expression Omnibus database with accession number GSE98386. Only dominant endometrial cell types were considered in gene expression profile deconvolution; however, other less frequent endometrial cell types also contribute to the whole-tissue gene expression profile. The better understanding of molecular processes during transition from pre-receptive to receptive endometrium serves to improve the effectiveness and personalization of assisted reproduction protocols. Biopsy cellular composition should be taken into account in future endometrial 'omics' studies, where tissue heterogeneity could potentially influence the results. This study was funded by: Estonian Ministry of Education and Research (grant IUT34-16); Enterprise Estonia (EU48695); the EU-FP7 Eurostars program (NOTED, EU41564); the EU-FP7 Marie Curie Industry-Academia Partnerships and Pathways (SARM, EU324509); Horizon 2020 innovation program (WIDENLIFE, EU692065); MSCA-RISE-2015 project MOMENDO (No 691058) and the Miguel Servet Program Type I of Instituto de Salud Carlos III (CP13/00038); Spanish Ministry of Economy, Industry and Competitiveness (MINECO) and European Regional Development Fund (FEDER): grants RYC-2016-21199 and ENDORE SAF2017-87526. Authors confirm no competing interests.

Editor's evaluation: Comparative transcriptomic analysis reveals translationally relevant processes in mouse models of malaria

Sepsis is organ dysfunction due to the host's deleterious response to infection, and the kidneys are one of the organs damaged in common sepsis. Sepsis-associated acute kidney injury (SA-AKI) increases the mortality in patients with sepsis. Although a substantial volume of research has improved the prevention and treatment of the disease, SA-SKI is still a significant clinical concern. Aimed to use weighted gene co-expression network analysis (WGCNA) and immunoinfiltration analysis to study SA-AKI-related diagnostic markers and potential therapeutic targets. Immunoinfiltration analysis was performed on SA-AKI expression datasets from the Gene Expression Synthesis (GEO) database. A weighted gene co-expression network analysis (WGCNA) analysis was performed on immune invasion scores as trait data, and modules associated with immune cells of interest were identified as hub modules. Screening hub geneset in the hub module using protein-protein interaction (PPI) network analysis. The hub gene was identified as a target by intersecting with significantly different genes screened by differential expression analysis and validated using two external datasets. Finally, the correlation between the target gene, SA-AKI, and immune cells was verified experimentally. Green modules associated with monocytes were identified using WGCNA and immune infiltration analysis. Differential expression analysis and PPI network analysis identified two hub genes (AFM and GSTA1). Further validation using additional AKI datasets GSE30718 and GSE44925 showed that AFM was significantly downregulated in AKI samples and correlated with the development of AKI. The correlation analysis of hub genes and immune cells showed that AFM was significantly associated with monocyte infiltration and hence, selected as a critical gene. In addition, Gene single-enrichment analysis (GSEA) and PPI analyses results showed that AFM was significantly related to the occurrence and development of SA-AKI. AFM is inversely correlated with the recruitment of monocytes and the release of various inflammatory factors in the kidneys of AKI. AFM can be a potential biomarker and therapeutic target for monocyte infiltration in sepsis-related AKI.

Background The most frequent benign tumor in newborns is infantile hemangioma. The majority of infantile hemangiomas has a favorable prognosis and generally fades away on their own. Some people, however, do experience major consequences. Transcriptome alterations in infantile hemangiomas are yet unclear. The use of transcriptome analysis to uncover diagnostic markers for infantile hemangioma has clinical implications. Methods The dataset GSE127487 for infantile hemangioma was obtained from the GEO database. The gene set most related with infantile hemangioma was investigated using weighted coexpression network analysis. Differential expression analysis was performed to see whether genes were up or downregulated in infantile hemangiomas. The enrichment of gene sets in pathways or functions is determined via enrichment analysis. Hub genes were discovered via protein-protein interaction network analysis. The relationship between hub genes and immune cells was investigated using immunomicroenvironment analysis. Results Turquoise and Pink modules were revealed to be the most linked with infantile hemangioma in a weighted coexpression network analysis (p < 0.001). The genes in the two modules were mostly concentrated in actin filament organization, embryonic organ development, reproductive structure development, cell substrate adhesion, extracellular matrix organization, and so on, according to GO enrichment analysis (p < 0.05). These gene enrichment pathways comprised the PI3K-Akt signaling pathway, human papillomavirus infection, focal adhesion, and hepatitis C pathways, according to KEGG enrichment analyzes (p < 0.05). Differential expressed gene analysis showed 43 upregulated and 21 downregulated genes in infantile hemangiomas. We found the gene set most associated to infantile hemangioma by intersecting the elevated genes with the genes acquired by WGCNA, with FOXF1 serving as the hub gene. FOXF1 was linked to the degree of monocyte infiltration, according to immunocorrelation analysis (p < 0.05). B cell memory, dendritic cells resting, macrophage M0, neutrophils, and T cells helper are all negatively connected (p < 0.05). In the FOXF1 hyperexpression group, GSEA analysis revealed that cholesterol homeostasis and cell cycle-associated pathways G2M checkpoint were primarily activated (p < 0.05). Conclusion FOXF1 was found to be a reliable biomarker of infantile hemangiomas in our research of transcriptome changes in infantile hemangiomas.

Objective Through integrative bioinformatics analysis of multi-source transcriptomic data, potential biomarkers to asthma epithelial cells were identified. The expression of these candidate target was subsequently validated in lung tissues and epithelial cells from asthma models. Methods The gene expression profile data of epithelial cells from three asthma patient cohorts and corresponding healthy controls were integrated from the Gene Expression Omnibus (GEO) database. Differential expression analysis and gene co-expression network analysis were performed to identify key genes and biological pathways associated with asthma. The key genes were validated in lung tissues and epithelial cells in asthma animal models. Results Differential gene expression analysis revealed 1121 upregulated and 1484 downregulated genes in epithelial cells from asthma patients compared with healthy controls. The biological pathway enrichment analysis revealed that the upregulated genes were mainly involved in glycosylation processes, whereas the downregulated genes were mainly associated with immune cell differentiation process. The gene co-expression network analysis revealed that module 9, enriched in glycosylation-related pathways, was significantly positively correlated with asthma, whereas module 17, associated with insulin and other signaling pathways, showed a significant negative correlation with asthma. We identified the genes of polypeptide N-acetylgalactosaminyltransferase 5 (GALNT5), pyrroline-5-carboxylate reductase 1 (PYCR1), and carcinoembryonic antigen-related cell adhesion molecule 5 (CEACAM5) as key genes within module 9, all of which were significantly upregulated in asthma. Finally, we validated that the expression levels of GALNT5, PYCR1, and CEACAM5 were significantly upregulated in epithelial cells from asthmatic lung tissue. Additionally, using a rat asthma model, we further confirmed that the protein levels of these three genes were significantly upregulated in lung tissues of the model group. Conclusion Through data integration and experimental validation, this study identified key genes and biological pathways closely associated with asthma pathogenesis. These findings provide a novel theoretical basis and potential targets for the diagnosis and treatment of asthma.

BackgroundAlzheimer’s disease (AD) is characterized by robust microgliosis and phenotypic changes that accompany disease pathogenesis. Accumulating evidence from genetic studies suggests the importance of phospholipase C γ 2 (PLCG2) in late-onset AD (LOAD) pathophysiology. However, the role of PLCG2 in AD is still poorly understood.MethodsUsing bulk RNA-Seq (N=1249) data from the Accelerating Medicines Partnership-Alzheimer’s Disease Consortium (AMP-AD), we investigated whether PLCG2 expression increased in the brains of LOAD patients. We also evaluated the relationship between PLCG2 expression levels, amyloid plaque density, and expression levels of microglia specific markers (AIF1 and TMEM119). Finally, we investigated the longitudinal changes of PLCG2 expression in the 5xFAD mouse model of AD. To further understand the role of PLCG2 in different signaling pathways, differential gene expression and co-expression network analyses were performed using bulk RNA-Seq and microglial single-cell RNA-Seq data. To substantiate the human analyses, we performed differential gene expression analysis on wild-type (WT) and inactivated Plcg2 mice and used immunostaining to determine if the differentially expressed genes/pathways were altered by microglial cell coverage or morphology.ResultsWe observed significant upregulation of PLCG2 expression in three brain regions of LOAD patients and significant positive correlation of PLCG2 expression with amyloid plaque density. These findings in the human brain were validated in the 5xFAD amyloid mouse model, which showed disease progression-dependent increases in Plcg2 expression associated with amyloid pathology. Of note, increased Plcg2 expression levels in 5xFAD mice were abolished by reducing microglia. Furthermore, using bulk RNA-Seq data, we performed differential expression analysis by comparing cognitively normal older adults (CN) with 75th percentile (high) and 25th percentile (low) PLCG2 gene expression levels to identify pathways related to inflammation and the inflammatory response. The findings in the human brain were validated by differential expression analyses between WT and plcg2 inactivated mice. PLCG2 co-expression network analysis of microglial single-cell RNA-Seq data identified pathways related to the inflammatory response including regulation of I-kappaB/NF-kappa B signaling and response to lipopolysaccharide.ConclusionsOur results provide further evidence that PLCG2 plays an important role in AD pathophysiology and may be a potential target for microglia-targeted AD therapies.

Introduction: Human bone marrow aging is typified by decreased cellularity, stem cell exhaustion and myeloid lineage bias that may set the stage for development of myeloid malignancies. Secondary AML (sAML) is a malignancy that has been associated with alterations in RNA processing genes and currently has few effective treatment options available. A central goal of future therapeutic strategies is to prevent disease relapse and therapeutic resistance by selectively targeting unique gene products that are essential to LSC but not normal HSC function. Therefore, we established whole gene, long non-coding RNA (lncRNA), splice isoform, and RNA editing signatures of benign versus malignant bone marrow progenitor cell aging, and evaluated the therapeutic efficacy of splicing-targeted agents in pre-clinical humanized in vitro and in vivo model systems. Methods: Whole transcriptome sequencing (RNA-Seq) was performed on FACS-purified hematopoietic stem (CD34+CD38-Lin-) and progenitor cells (CD34+CD38+Lin-) from aged (average age = 65.9 ± 6.8 years old) versus young (average age = 25.8 ± 3.0 years old) adult healthy bone marrow samples, and in leukemia stem cells (LSC) from patients with sAML (average age = 71.4 ± 7.9 years old). Comparative gene set enrichment analyses (GSEA), splice isoform, lncRNA, and RNA editing profiles were identified for normal and malignant progenitor cell aging. Then, we evaluated the spliceosome modulatory agent 17S-FD-895 in splicing reporter activity, PCR, and functional in vitro hematopoietic progenitor and in vivo LSC primagraft assays. Results: Disruption of pre-mRNA splicing activity has recently been implicated as a therapeutic vulnerability in some types of cancer. Comparative whole transcriptome RNA sequencing (RNA-seq) analyses revealed pre-mRNA splicing factor gene expression was significantly disrupted in human AML LSC compared with age-matched normal progenitors. Comparative splice isoform RNA-seq and qRT-PCR validation revealed recurrent intron retention and exon skipping in expressed transcripts, such as PTK2B and several protein phosphatase gene products. Notably, transcription factor profiling of AML LSC demonstrated downregulation of key tumor suppressor genes, such as IRF8 and TP53. We then investigated the LSC inhibitory efficacy of a stable and potent splicing modulatory agent, 17S-FD-895, in humanized stromal co-culture and AML LSC primagraft assays. Pharmacological spliceosome modulation disrupted AML LSC maintenance in vivo by altering splicing of stem cell survival and AML-associated transcripts at doses that spared normal hematopoietic progenitors. Conclusions: Detection and targeted modulation of aberrant RNA processing provides an innovative strategy for AML LSC eradication with implications for treatment of a variety of human malignancies and other age-related disorders. Citation Format: Leslie A. Crews, Larisa Balaian, Heather S. Leu, Nathaniel P. Delos Santos, Angela C. Court, Anil Sadarangani, Maria A. Zipeto, James J. La Clair, Reymundo Villa, Sheldon R. Morris, Rainer Storb, Anna Kulidjian, Edward D. Ball, Michael D. Burkart, Catriona H.M. Jamieson. RNA processing signatures of normal versus malignant progenitor cell aging predict leukemia stem cell sensitivity to RNA splicing modulation. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 915.

RNA-Sequencing (RNA-Seq) is performed to measure gene expression, often to ask the question of what genes are differentially expressed across various biological conditions. Statistical methods have been used to model RNA-Seq quantifications in order to determine differential expression, and have traditionally be divided into gene-level methods and transcript-level methods. There has been little attempt to connect the statistical divide, although transcript expression and gene expression are biologically inextricably linked. In this thesis, we provide a case study of a comparative differential expression analysis, demonstrating that many differential expression events happen on the isoform-level, and that performing an analysis using only summarized gene quantifications would fail to capture these events. Furthermore, we develop statistical methods that unify the transcript-level and gene-level analysis. In bulk RNA-Seq, by using p-value aggregation methods, we are able to translate transcript-level results into gene-level results under a unified framework. For single cell RNA-Seq, we propose using multiple logistic regression, leveraging the high dimensionality of the data in order to determine if the transcript quantifications pertaining to a gene are able to constitute a linear discriminant for cell type. This method combines differential transcript expression analysis and differential gene expression analysis into a unified framework which we call “gene differential expression.” Lastly, we demonstrate that our methods could be used on transcript compatibility counts instead of transcript quantifications in order to bypass ambiguous read assignment and improve accuracy. We show that transcript compatibility counts obtained via transcriptome pseudoalignment are comparable in quantification accuracy to quantifications from genome alignment methods.

Reverse transcription quantitative polymerase chain reaction (RT-qPCR) is considered as the gold standard for accurate, sensitive, and fast measurement of gene expression. Prior to downstream statistical analysis, RT-qPCR fluorescence amplification curves are summarized into one single value, the quantification cycle (Cq). When RT-qPCR does not reach the limit of detection, the Cq is labeled as “undetermined”. Current state of the art qPCR data analysis pipelines acknowledge the importance of normalization for removing non-biological sample to sample variation in the Cq values. However, their strategies for handling undetermined Cq values are very ad hoc. We show that popular methods for handling undetermined values can have a severe impact on the downstream differential expression analysis. They introduce a considerable bias and suffer from a lower precision. We propose a novel method that unites preprocessing and differential expression analysis in a single statistical model that provides a rigorous way for handling undetermined Cq values. We compare our method with existing approaches in a simulation study and on published microRNA and mRNA gene expression datasets. We show that our method outperforms traditional RT-qPCR differential expression analysis pipelines in the presence of undetermined values, both in terms of accuracy and precision.