Integration Of Expression Data Research Articles

Integrated transcriptomics and miRNAomics provide insights into the complex multi-tiered regulatory networks associated with coleoptile senescence in rice.

Coleoptile is the small conical, short-lived, sheath-like organ that safeguards the first leaf and shoot apex in cereals. It is also the first leaf-like organ to senesce that provides nutrition to the developing shoot and is, therefore, believed to play a crucial role in seedling establishment in rice and other grasses. Though histochemical studies have helped in understanding the pattern of cell death in senescing rice coleoptiles, genome-wide expression changes during coleoptile senescence have not yet been explored. With an aim to investigate the gene regulation underlying the coleoptile senescence (CS), we performed a combinatorial whole genome expression analysis by sequencing transcriptome and miRNAome of senescing coleoptiles. Transcriptome analysis revealed extensive reprogramming of 3439 genes belonging to several categories, the most prominent of which encoded for transporters, transcription factors (TFs), signaling components, cell wall organization enzymes, redox homeostasis, stress response and hormone metabolism. Small RNA sequencing identified 41 known and 21 novel miRNAs that were differentially expressed during CS. Comparison of gene expression and miRNA profiles generated for CS with publicly available leaf senescence (LS) datasets revealed that the two aging programs are remarkably distinct at molecular level in rice. Integration of expression data of transcriptome and miRNAome identified high confidence 140 miRNA-mRNA pairs forming 42 modules, thereby demonstrating multi-tiered regulation of CS. The present study has generated a comprehensive resource of the molecular networks that enrich our understanding of the fundamental pathways regulating coleoptile senescence in rice.

Identifying enhancer-driven subtype-specific prognostic markers in breast cancer based on multi-omics data.

Breast cancer is a cancer of high complexity and heterogeneity, with differences in prognosis and survival among patients of different subtypes. Copy number variations (CNVs) within enhancers are crucial drivers of tumorigenesis by influencing expression of their targets. In this study, we performed an integrative approach to identify CNA-driven enhancers and their effect on expression of target genes in four breast cancer subtypes by integrating expression data, copy number data and H3K27ac data. We identified 672, 555, 531, 361 CNA-driven enhancer-gene pairs and 280, 189, 113 and 98 CNA-driven enhancer-lncRNA pairs in the Basal-like, Her2, LumA and LumB subtypes, respectively. We then reconstructed a CNV-driven enhancer-lncRNA-mRNA regulatory network in each subtype. Functional analysis showed CNA-driven enhancers play an important role in the progression of breast cancer subtypes by influencing P53 signaling pathway, PPAR signaling pathway, systemic lupus erythematosus and MAPK signaling pathway in the Basal-like, Her2, LumA and LumB subtypes, respectively. We characterized the potentially prognostic value of target genes of CNV-driven enhancer and lncRNA-mRNA pairs in the subtype-specific network. We identified MUM1 and AC016876.1 as prognostic biomarkers in LumA and Basal-like subtypes, respectively. Higher expression of MUM1 with an amplified enhancer exhibited poorer prognosis in LumA patients. Lower expression of AC016876.1 with a deleted enhancer exhibited poorer survival outcomes of Basal-like patients. We also identified enhancer-related lncRNA-mRNA pairs as prognostic biomarkers, including AC012313.2-MUM1 in the LumA, AC026471.4-PLK5 in the LumB, AC027307.2-OAZ1 in the Basal-like and AC022431.1-HCN2 in the Her2 subtypes. Finally, our results highlighted target genes of CNA-driven enhancers and enhancer-related lncRNA-mRNA pairs could act as prognostic markers and potential therapeutic targets in breast cancer subtypes.

SciCAN: single-cell chromatin accessibility and gene expression data integration via cycle-consistent adversarial network

The boom in single-cell technologies has brought a surge of high dimensional data that come from different sources and represent cellular systems from different views. With advances in these single-cell technologies, integrating single-cell data across modalities arises as a new computational challenge. Here, we present an adversarial approach, sciCAN, to integrate single-cell chromatin accessibility and gene expression data in an unsupervised manner. We benchmarked sciCAN with 5 existing methods in 5 scATAC-seq/scRNA-seq datasets, and we demonstrated that our method dealt with data integration with consistent performance across datasets and better balance of mutual transferring between modalities than the other 5 existing methods. We further applied sciCAN to 10X Multiome data and confirmed that the integrated representation preserves biological relationships within the hematopoietic hierarchy. Finally, we investigated CRISPR-perturbed single-cell K562 ATAC-seq and RNA-seq data to identify cells with related responses to different perturbations in these different modalities.

CeRNAR: An R package for identification and analysis of ceRNA-miRNA triplets.

Competitive endogenous RNA (ceRNA) represents a novel mechanism of gene regulation that controls several biological and pathological processes. Recently, an increasing number of in silico methods have been developed to accelerate the identification of such regulatory events. However, there is still a need for a tool supporting the hypothesis that ceRNA regulatory events only occur at specific miRNA expression levels. To this end, we present an R package, ceRNAR, which allows identification and analysis of ceRNA-miRNA triplets via integration of miRNA and RNA expression data. The ceRNAR package integrates three main steps: (i) identification of ceRNA pairs based on a rank-based correlation between pairs that considers the impact of miRNA and a running sum correlation statistic, (ii) sample clustering based on gene-gene correlation by circular binary segmentation, and (iii) peak merging to identify the most relevant sample patterns. In addition, ceRNAR also provides downstream analyses of identified ceRNA-miRNA triplets, including network analysis, functional annotation, survival analysis, external validation, and integration of different tools. The performance of our proposed approach was validated through simulation studies of different scenarios. Compared with several published tools, ceRNAR was able to identify true ceRNA triplets with high sensitivity, low false-positive rates, and acceptable running time. In real data applications, the ceRNAs common to two lung cancer datasets were identified in both datasets. The bridging miRNA for one of these, the ceRNA for MAP4K3, was identified by ceRNAR as hsa-let-7c-5p. Since similar cancer subtypes do share some biological patterns, these results demonstrated that our proposed algorithm was able to identify potential ceRNA targets in real patients. In summary, ceRNAR offers a novel algorithm and a comprehensive pipeline to identify and analyze ceRNA regulation. The package is implemented in R and is available on GitHub (https://github.com/ywhsiao/ceRNAR).

Aberrant MYCN expression drives oncogenic hijacking of EZH2 as a transcriptional activator in peripheral T-cell lymphoma

AbstractPeripheral T-cell lymphoma (PTCL) is a heterogeneous group of hematological cancers arising from the malignant transformation of mature T cells. In a cohort of 28 PTCL cases, we identified recurrent overexpression of MYCN, a member of the MYC family of oncogenic transcription factors. Approximately half of all PTCL cases was characterized by a MYC expression signature. Inducible expression of MYCN in lymphoid cells in a mouse model caused T-cell lymphoma that recapitulated human PTCL with an MYC expression signature. Integration of mouse and human expression data identified EZH2 as a key downstream target of MYCN. Remarkably, EZH2 was found to be an essential cofactor for the transcriptional activation of the MYCN-driven gene expression program, which was independent of methyltransferase activity but dependent on phosphorylation by CDK1. MYCN-driven T-cell lymphoma was sensitive to EZH2 degradation or CDK1 inhibition, which displayed synergy with US Food and Drug Administration–approved histone deacetylase (HDAC) inhibitors.

ExpressVis: a biologist-oriented interactive web server for exploring multi-omics data.

In the era of life-omics, huge amounts of multi-omics data have been generated and widely used in biomedical research. It is challenging for biologists with limited programming skills to obtain biological insights from multi-omics data. Thus, a biologist-oriented platform containing visualization functions is needed to make complex omics data digestible. Here, we propose an easy-to-use, interactive web server named ExpressVis. In ExpressVis, users can prepare datasets; perform differential expression analysis, clustering analysis, and survival analysis; and integrate expression data with protein–protein interaction networks and pathway maps. These analyses are organized into six modules. Users can use each module independently or use several modules interactively. ExpressVis displays analysis results in interactive figures and tables, and provides comprehensive interactive operations in each figure and table, between figures or tables in each module, and among different modules. It is freely accessible at https://omicsmining.ncpsb.org.cn/ExpressVis and does not require login. To test the performance of ExpressVis for multi-omics studies of clinical cohorts, we re-analyzed a published hepatocellular carcinoma dataset and reproduced their main findings, suggesting that ExpressVis is convenient enough to analyze multi-omics data. Based on its complete analysis processes and unique interactive operations, ExpressVis provides an easy-to-use solution for exploring multi-omics data.

A Model to Detect Significant Prostate Cancer Integrating Urinary Peptide and Extracellular Vesicle RNA Data.

Simple SummaryProstate cancer is one of the leading causes of cancer-related death in men in the world, but a large proportion of men that are diagnosed with prostate cancer do not have a form of the disease that will cause them long term harm. Therefore, there is a need to accurately predict the aggressiveness of the disease without taking an invasive biopsy. In this study, we develop a test that can predict whether a patient has prostate cancer and how aggressive that cancer is. This test combines clinical measurements, levels of four genes collected from a fraction of the urine, and levels of six peptides found in urine. We found that this test, deemed ‘ExoSpec’, has the potential to improve the pathway for men with a clinical suspicion of prostate cancer and could reduce the requirement for biopsies by 30%.There is a clinical need to improve assessment of biopsy-naïve patients for the presence of clinically significant prostate cancer (PCa). In this study, we investigated whether the robust integration of expression data from urinary extracellular vesicle RNA (EV-RNA) with urine proteomic metabolites can accurately predict PCa biopsy outcome. Urine samples collected within the Movember GAP1 Urine Biomarker study (n = 192) were analysed by both mass spectrometry-based urine-proteomics and NanoString gene-expression analysis (167 gene-probes). Cross-validated LASSO penalised regression and Random Forests identified a combination of clinical and urinary biomarkers for predictive modelling of significant disease (Gleason Score (Gs) ≥ 3 + 4). Four predictive models were developed: ‘MassSpec’ (CE-MS proteomics), ‘EV-RNA’, and ‘SoC’ (standard of care) clinical data models, alongside a fully integrated omics-model, deemed ‘ExoSpec’. ExoSpec (incorporating four gene transcripts, six peptides, and two clinical variables) is the best model for predicting Gs ≥ 3 + 4 at initial biopsy (AUC = 0.83, 95% CI: 0.77–0.88) and is superior to a standard of care (SoC) model utilising clinical data alone (AUC = 0.71, p < 0.001, 1000 resamples). As the ExoSpec Risk Score increases, the likelihood of higher-grade PCa on biopsy is significantly greater (OR = 2.8, 95% CI: 2.1–3.7). The decision curve analyses reveals that ExoSpec provides a net benefit over SoC and could reduce unnecessary biopsies by 30%.

Automation of high-throughput mRNA-seq library preparation: a robust, hands-free and time efficient methodology.

Over the last decade, whole transcriptome profiling, also known as RNA-sequencing (RNA-seq), has quickly gained traction as a reliable method for unbiased assessment of gene expression. Integration of RNA-seq expression data into other omics datasets (e.g., proteomics, metabolomics, or epigenetics) solidifies our understanding of cell-specific regulatory patterns, yielding pathways to investigate the key rules of gene regulation. A limitation to efficient, at-scale utilization of RNA-seq is the time-demanding library preparation workflows, which is a 2-day or longer endeavor per cohort/sample size. To tackle this bottleneck, we designed an automated workflow that increases throughput capacity, while minimizing human error to enhance reproducibility. To this end, we converted the manual protocol of the NEBNext Directional Ultra II RNA Library Prep Kit for Illumina on the Beckman Coulter liquid handler, Biomek i7 Hybrid workstation. A total of 84 RNA samples were isolated from two human cell lines and subjected to comparative manual and automated library preparation methods. Qualitative and quantitative results indicated a high degree of similarity between libraries generated manually or through automation. Yet, there was a significant reduction in both hands-on and assay time from a 2-day manual to a 9-hour automated workflow. Using linear regression analysis, we found the Pearson correlation coefficient between libraries generated manually or by automation to be almost identical to a sample being sequenced twice (R²= 0.985 vs 0.983). This demonstrates that high-throughput automated workflows can be of great benefit to genomic laboratories by enhancing efficiency of library preparation, reducing hands-on time and increasing throughput potential.

TAMI-76. INTEGRATED MULTI-OMICS REVEAL INTRATUMOUR HETEROGENEITY AND NOVEL THERAPEUTIC TARGETS IN PAEDIATRIC EPENDYMOMA

Abstract Ependymoma (EPN) is the second most common malignant paediatric brain tumour with a five-year survival rate of only 25% following relapse. While molecular heterogeneity between EPN tumours is well understood, little is known concerning spatially-distinct intratumour heterogeneity within patients. In this context, we present a multi-omics integration of expression data at transcriptomic and metabolomic levels revealing intratumour heterogeneity and novel therapeutic targets. Surgically resected ependymoma tissue from two epigenetic subgroups, posterior fossa-A (PF-A) and supratentorial RELA, were first homogenised and polar metabolites, lipids and RNA simultaneously extracted from the same cellular population. Using liquid chromatography-mass spectrometry (LC-MS) and RNAseq 115 metabolites and 1580 upregulated genes were identified between the two subgroups, therefore validating previously reported genetic clustering of these two subtypes. Sampling of anatomically distinct regions was performed between eight PF-A EPN patients and multi-omic data was compared across 28 intratumour regions, with at least 3 different regions per patient. Integration of genes and metabolites revealed 124 dysregulated metabolic pathways, encompassing 156 genes and 49 metabolites. A large number of interactions occur in the gluconeogenesis and glycine pathways in 6 out of 8 patients, putatively representing therapeutically relevant ubiquitous metabolic pathways critical for EPN survival. Each anatomical region also presented at least one unique gene-metabolite interaction demonstrating heterogeneity within and across PF-A EPN tumours. A subset of the eight most prevalent genes across patients (GAD1, NT5C, FBP1, FMO3, HK3, TALDO1, NT5E, ALDH3A1) were selected for in vitro metabolic assays using 10 repurposed cytotoxic agents against PF-A EPN cell lines derived from intratumour regions of the same patient. 5/8 genes map within the gluconeogenesis metabolic pathway, further highlighting its significance within PF-A EPN. This is the first instance where multi-omic data integration and intratumour heterogeneity has been investigated for paediatric EPN revealing novel potential targets in the context of gene-metabolite correlations.

Dysregulation of the secretory pathway connects Alzheimer's disease genetics to aggregate formation.

Amyloid disorders such as Alzheimer's disease (AD) involve the aggregation of secreted proteins. However, it is largely unclear how secretory-pathway proteins contribute to amyloid formation. We developed a systems biology framework integrating expression data with protein-protein interaction networks to estimate a tissue's fitness for producing specific secreted proteins and analyzed the fitness of the secretory pathway of various brain regions and cell types for synthesizing the AD-associated amyloid precursor protein (APP). While key amyloidogenic pathway components were not differentially expressed in AD brains, we found Aβ deposition correlates with systemic down- and upregulation of the secretory-pathway components proximal to APP and amyloidogenic secretases, respectively, in AD. Our analyses suggest that perturbations from three AD risk loci cascade through the APP secretory-support network and into the endocytosis pathway, connecting amyloidogenesis to dysregulation of secretory-pathway components supporting APP and suggesting novel therapeutic targets for AD. A record of this paper's transparent peer review process is included in the supplemental information.

Inferring Functional Epigenetic Modules by Integrative Analysis of Multiple Heterogeneous Networks.

Gene expression and methylation are critical biological processes for cells, and how to integrate these heterogeneous data has been extensively investigated, which is the foundation for revealing the underlying patterns of cancers. The vast majority of the current algorithms fuse gene methylation and expression into a network, failing to fully explore the relations and heterogeneity of them. To resolve these problems, in this study we define the epigenetic modules as a gene set whose members are co-methylated and co-expressed. To address the heterogeneity of data, we construct gene co-expression and co-methylation networks, respectively. In this case, the epigenetic module is characterized as a common module in multiple networks. Then, a non-negative matrix factorization-based algorithm that jointly clusters the co-expression and co-methylation networks is proposed for discovering the epigenetic modules (called Ep-jNMF). Ep-jNMF is more accurate than the baselines on the artificial data. Moreover, Ep-jNMF identifies more biologically meaningful modules. And the modules can predict the subtypes of cancers. These results indicate that Ep-jNMF is efficient for the integration of expression and methylation data.

Condition-Specific Molecular Network Analysis Revealed That Flagellar Proteins Are Involved in Electron Transfer Processes of Shewanella piezotolerans WP3.

Because of the ability to metabolize a large number of electron acceptors such as nitrate, nitrite, fumarate, and metal oxides, Shewanella species have attracted much attention in recent years. Generally, the use of these electron acceptors is mainly achieved through electron transfer proteins and their interactions which will dynamically change across different environmental conditions in cells. Therefore, functional analysis of condition-specific molecular networks can reveal biological information on electron transfer processes. By integrating expression data and molecular networks, we constructed condition-specific molecular networks for Shewanella piezotolerans WP3. We then identified condition-specific key genes and studied their potential functions with an emphasis on their roles in electron transfer processes. Functional module analysis showed that different flagellar assembly modules appeared under these conditions and suggested that flagellar proteins are important for these conditions. We also identified the electron transfer modules underlying these various environmental conditions. The present results could help with screening electron transfer genes and understanding electron transfer processes under various environmental conditions for the Shewanella species.

Integrating expression data and genomic sequences to investigate transcriptional regulation in barley in response to abiotic stress.

Abiotic stress responses are regulated critically at the transcriptional level. Clarifying the intricate mechanisms that regulate gene expression in response to abiotic stress is crucial and challenging. For this purpose, the factors that regulate gene expression and their binding sites in DNA should be determined. By using bioinformatics tools, the differentially expressed probe sets were studied. A meta-analysis of transcriptomic responses to several abiotic stresses in barley was performed. Motif enrichments revealed that AP2/ERF (APETALA2/Ethylene-Responsive Factor) has the most frequent binding sites. We found that the bHLH transcription factor family has the highest number of transcription factor members. Moreover, network construction revealed that AP2 has the highest number of connections with other genes, which indicates its critical role in abiotic stress responses. The present research further predicted 49 miRNAs belonging to 23 miRNA families. This study identified the probable conserved and enriched motifs, which might have a role in the regulation of differentially expressed genes under abiotic stresses. In addition to shedding light on gene expression regulation, a toolbox of available promoters for genetic engineering of crop plants under such abiotic stresses was developed.

An integrated approach to identify bimodal genes associated with prognosis in câncer.

Bimodal gene expression (where a gene expression distribution has two maxima) is associated with phenotypic diversity in different biological systems. A critical issue, thus, is the integration of expression and phenotype data to identify genuine associations. Here, we developed tools that allow both: i) the identification of genes with bimodal gene expression and ii) their association with prognosis in cancer patients from The Cancer Genome Atlas (TCGA). Bimodality was observed for 554 genes in expression data from 25 tumor types. Furthermore, 96 of these genes presented different prognosis when patients belonging to the two expression peaks were compared. The software to execute the method and the corresponding documentation are available at the Data access section.

Identification of Potential Biomarkers for CAD Using Integrated Expression and Methylation Data.

DNA methylation plays an essential role in the pathogenesis of coronary artery disease (CAD) through regulating mRNA expressions. This study aimed to identify hub genes regulated by DNA methylation as biomarkers of CAD. Gene expression and methylation datasets of peripheral blood leukocytes (PBLs) of CAD were downloaded from the Gene Expression Omnibus (GEO) database. Subsequently, multiple computational approaches were performed to analyze the regulatory networks and to recognize hub genes. Finally, top hub genes were verified in a case-control study, based on their differential expressions and methylation levels between CAD cases and controls. In total, 535 differentially expressed-methylated genes (DEMGs) were identified and partitioned into 4 subgroups. TSS200 and 5′UTR were confirmed as high enrichment areas of differentially methylated CpGs sites (DMCs). The function of DEMGs is enriched in processes of histone H3-K27 methylation, regulation of post-transcription and DNA-directed RNA polymerase activity. Pathway enrichment showed DEMGs participated in the VEGF signaling pathway, adipocytokine signaling pathway, and PI3K-Akt signaling pathway. Besides, expressions of hub genes fibronectin 1 (FN1), phosphatase (PTEN), and tensin homolog and RNA polymerase III subunit A (POLR3A) were discordantly expressed between CAD patients and controls and related with DNA methylation levels. In conclusion, our study identified the potential biomarkers of PBLs for CAD, in which FN1, PTEN, and POLR3A were confirmed.

Integrative Network Biology Framework Elucidates Molecular Mechanisms of SARS-CoV-2 Pathogenesis.

SummaryCOVID-19 (coronavirus disease 2019) is a respiratory illness caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Although the pathophysiology of this virus is complex and largely unknown, we employed a network-biology-fueled approach and integrated transcriptome data pertaining to lung epithelial cells with human interactome to generate Calu-3-specific human-SARS-CoV-2 interactome (CSI). Topological clustering and pathway enrichment analysis show that SARS-CoV-2 targets central nodes of the host-viral network, which participate in core functional pathways. Network centrality analyses discover 33 high-value SARS-CoV-2 targets, which are possibly involved in viral entry, proliferation, and survival to establish infection and facilitate disease progression. Our probabilistic modeling framework elucidates critical regulatory circuitry and molecular events pertinent to COVID-19, particularly the host-modifying responses and cytokine storm. Overall, our network-centric analyses reveal novel molecular components, uncover structural and functional modules, and provide molecular insights into the pathogenicity of SARS-CoV-2 that may help foster effective therapeutic design.

Integrative Proteomic and MicroRNA Analysis: Insights Into Mechanisms of Eyestalk Ablation-Induced Ovarian Maturation in the Swimming Crab Portunus trituberculatus.

Eyestalk ablation is the most common method to induce ovarian maturation in decapod crustacean aquaculture, but it jeopardizes broodstock survival and larvae production. It is important to understand the molecular basis underlying the maturation triggered by ablation and thereby develop an alternative measure for maturation manipulation. In this study, we investigate alterations of ovarian proteome and miRNA profile after ablation in a commercially important marine crab Portunus trituberculatus. Quantitative proteomic analysis using iTRAQ reveals that 163 proteins are differentially expressed following ablation, and modulation of methyl farnesoate metabolism and activation of calcium signaling may play important roles in the ovarian maturation induced by ablation. miRNA expression profiling identifies 31 miRNAs that show statistically significant changes. Integration of miRNA and proteome expression data with miRNA target prediction algorithms generates a potential regulatory network consisting of 26 miRNAs and 30 proteins linked by 71 possible functional associations. The miRNA-protein network analysis suggests that miRNAs are involved in promoting ovarian maturation by controlling expression of proteins related to methyl farnesoate synthesis, calcium signals, and energy metabolism. Experimental validation and temporal expression analysis indicate multiple miRNAs can act synergistically to regulate expression of Farnesoic acid O-methyltransferase and Calmodulin. Our findings provide new insights for elucidating the mechanisms underlying eyestalk ablation-induced ovarian maturation and could be useful for devising an alternative technique for manipulating reproduction in P. trituberculatus and other decapods.

Whole-genome fingerprint of the DNA methylome during chemically induced differentiation of the human AML cell line HL-60/S4.

ABSTRACTEpigenomic regulation plays a vital role in cell differentiation. The leukemic HL-60/S4 [human myeloid leukemic cell line HL-60/S4 (ATCC CRL-3306)] promyelocytic cell can be easily differentiated from its undifferentiated promyelocyte state into neutrophil- and macrophage-like cell states. In this study, we present the underlying genome and epigenome architecture of HL-60/S4 through its differentiation. We performed whole-genome bisulphite sequencing of HL-60/S4 cells and their differentiated counterparts. With the support of karyotyping, we show that HL-60/S4 maintains a stable genome throughout differentiation. Analysis of differential Cytosine-phosphate-Guanine dinucleotide methylation reveals that most methylation changes occur in the macrophage-like state. Differential methylation of promoters was associated with immune-related terms. Key immune genes, CEBPA, GFI1, MAFB and GATA1 showed differential expression and methylation. However, we observed the strongest enrichment of methylation changes in enhancers and CTCF binding sites, implying that methylation plays a major role in large-scale transcriptional reprogramming and chromatin reorganisation during differentiation. Correlation of differential expression and distal methylation with support from chromatin capture experiments allowed us to identify putative proximal and long-range enhancers for a number of immune cell differentiation genes, including CEBPA and CCNF. Integrating expression data, we present a model of HL-60/S4 differentiation in relation to the wider scope of myeloid differentiation.

Stress-induced changes in the expression of antioxidant system genes for rice (Oryza sativa L.) and bread wheat (Triticum aestivum L.).

BackgroundPlant cell metabolism inevitably forms reactive oxygen species (ROS), which can damage cells or lead to their death. The antioxidant system (AOS) evolved to eliminate a high concentration of ROS. For plants, this system consists of the seven classes of antioxidant enzymes and antioxidant compounds. Each enzymatic class contains a various number of genes which may vary from species to species. In such a multi-copy genetic system, the integration of evolutionary characteristics and expression data makes it possible to effectively predict promising breeding targets for the design of highly-yielding cultivars. In the plant cells, ROS production can increase as a result of abiotic stresses. Accordingly, AOS responds to stress by altering the expression of the genes of its components. Expression profiles of AOS enzymes, including their changes under stress, remains incomplete. A comprehensive study of the system behavior in response to stress for different species gives the key to identify the general mechanisms of AOS regulation. In this article, we studied stress-induced changes in the expression of AOS genes in photosynthetic tissues for rice and bread wheat.MethodsA meta-analysis of genome-wide transcriptome data on stress-induced changes in expression profiles of antioxidant genes using microarray and next generation sequencing (NGS) experiments from the GEO NCBI database for rice and bread wheat was carried out. Experimental study of expression changes in short (6 h) and prolonged (24 h) cold stress responses for selected AOS genes of bread wheat cultivars Saratovskaya29 and Yanetzkis Probat was conducted using qPCR.ResultsThe large-scale meta-transcriptome and complementary experimental analysis revealed a summary of fold changes in the AOS gene expression in response to cold and water deficiency for rice and bread wheat.

Integrated Transcriptomic and Genomic Sequencing Identifies Prognostic Constellations of Driver Mutations in Acute Myeloid Leukemia and Myelodysplastic Syndromes

CG Mullighan and T Haferlach: are co-senior authors Introduction: Recent genomic sequencing studies have advanced our understanding of the pathogenesis of myeloid malignancies, including acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS), and improved classification of specific subgroups. Unfortunately, these studies have mostly analyzed specific subtypes and/or used targeted DNA-sequencing, thus limiting discovery of novel mutational patterns and gene expression clusters. Here, we performed an integrated genome-wide mutational/transcriptomic analysis of a large cohort of adult AML and MDS samples to accurately define subtypes of diagnostic, prognostic and therapeutic relevance. Methods: We performed unbiased whole genome (WGS) and transcriptome sequencing (RNA-seq) of 1,304 adult individuals (598 AML and 706 MDS; Fig. 1A), incorporating analysis of somatic and presumed germline sequence mutations, chimeric fusions and structural complex variations. Transcriptomic gene expression data were processed by a rigorous bootstrap procedure to define gene expression subgroups in an unsupervised manner. Associations between genetic variants, gene expression groups and outcome were examined. Results: Genomic/transcriptome sequencing confirmed diagnosis according to WHO 2016 of AML with recurrent genetic abnormalities in 10.9% of cases. These cases had a distinct gene expression profile (Fig. 1A), good prognosis (Fig. 1B) and a combination of mutations in the following genes: KIT, ZBTB7A, ASXL2, RAD21, CSF3R and DNM2 in RUNX1-RUNXT1 leukemia; FLT3, DDX54, WT1 and CALR in PML-RARA promyelocytic leukemia; KIT and BCORL1 in CBFB-rearranged leukemia. In addition, 9% of cases showed rearrangements of KMT2A, with known (e.g. MLLT3) and non-canonical partners (e.g. ACACA, and NCBP1) and poor outcome. Although common targets of mutations have been previously described for myeloid malignancies, the heterogeneity and complexity of mutational patterns, their expression signature and outcome here described are novel. Gene expression analysis identified groups of AML and/or MDS lacking recurrent cytogenetic abnormalities (87%). The spectrum of the most frequently mutated genes (&gt;10 cases) and associated gene expression subtypes is summarized in Figure 1A. TET2 (more frequent in MDS than AML, p=0.0011) and DNMT3A (more frequent in AML than MDS, p&lt;0.0001) were the most frequently mutated genes. Interestingly, mutations in these genes promoting clonal hematopoiesis were significantly enriched in the subgroup with NPM1 mutations. Overall, NPM1 mutations occurred in 27.4% of AML and 1% of MDS and were characterized by four expression signatures with different combination of cooperating mutations in cohesin and signaling genes and outcome (Fig. 1C, gene expression, GE, groups 2, 3, 7 and 8). Co-occurring NPM1 and FLT3 mutations conferred poorer outcome compared to only NPM1, in contrast co-occurring mutations with cohesin genes had better outcome (Fig. 1D). Additional mutations that significantly co-occurred with NPM1 were in PTPN11, IDH1/2, RAD21 and SMC1A. Three gene expression clusters accounted for additional 9% of cases with mutual exclusive mutations in RUNX1,TP53 and CEBPA and co-occurring with a combination of mutations in DNA methylation, splicing and signaling genes (Fig. 1E, GE groups 4, 5 and 6). Interestingly, RUNX1 mutations were significantly associated with SRSF2 mutations but not with SF3B1, showed high expression of MN1 and poor outcome (Fig. 1F). In contrast to the distinct, mutation-associated patterns of gene expression in AML samples, the gene expression profile of MDS was less variable despite diversity in patterns of mutation. MDS was enriched in mutations of SF3B1 (27.2%), mutually exclusive with SFRS2 (14.4%) and U2AF1 (5.5%); TP53 (13.7%) and RUNX1 (10.5%) and a combination of mutations in epigenetic regulators with outcome dependent on mutational pattern (Fig. 1A, G-H). Moreover, structural variations and/or missense mutations of MECOM accounted for 2% of cases. Conclusions: the integration of mutational and expression data from a large cohort of adult pan myeloid leukemia cases enabled the definition of subtypes and constellations of mutations and have prognostic significance that transcends prior gene panel-based classification schema. Disclosures Meggendorfer: MLL Munich Leukemia Laboratory: Employment. Nadarajah:MLL Munich Leukemia Laboratory: Employment. Baer:MLL Munich Leukemia Laboratory: Employment. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Kern:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Mullighan:Illumina: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: sponsored travel; Pfizer: Honoraria, Other: speaker, sponsored travel, Research Funding; AbbVie: Research Funding; Loxo Oncology: Research Funding; Amgen: Honoraria, Other: speaker, sponsored travel. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership.