Journal articles: 'INTEGRATIVE TRANSCRIPTOME'

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Rhodes, Daniel R., and Arul M. Chinnaiyan. "Integrative analysis of the cancer transcriptome." Nature Genetics 37, S6 (June 2005): S31—S37. http://dx.doi.org/10.1038/ng1570.

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Berger, M. F., J. Z. Levin, K. Vijayendran, A. Sivachenko, X. Adiconis, J. Maguire, L. A. Johnson, et al. "Integrative analysis of the melanoma transcriptome." Genome Research 20, no. 4 (February 23, 2010): 413–27. http://dx.doi.org/10.1101/gr.103697.109.

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Grausa, Kristina, Ivars Mozga, Karlis Pleiko, and Agris Pentjuss. "Integrative Gene Expression and Metabolic Analysis Tool IgemRNA." Biomolecules 12, no. 4 (April 16, 2022): 586. http://dx.doi.org/10.3390/biom12040586.

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Genome-scale metabolic modeling is widely used to study the impact of metabolism on the phenotype of different organisms. While substrate modeling reflects the potential distribution of carbon and other chemical elements within the model, the additional use of omics data, e.g., transcriptome, has implications when researching the genotype–phenotype responses to environmental changes. Several algorithms for transcriptome analysis using genome-scale metabolic modeling have been proposed. Still, they are restricted to specific objectives and conditions and lack flexibility, have software compatibility issues, and require advanced user skills. We classified previously published algorithms, summarized transcriptome pre-processing, integration, and analysis methods, and implemented them in the newly developed transcriptome analysis tool IgemRNA, which (1) has a user-friendly graphical interface, (2) tackles compatibility issues by combining previous data input and pre-processing algorithms in MATLAB, and (3) introduces novel algorithms for the automatic comparison of different transcriptome datasets with or without Cobra Toolbox 3.0 optimization algorithms. We used publicly available transcriptome datasets from Saccharomyces cerevisiae BY4741 and H4-S47D strains for validation. We found that IgemRNA provides a means for transcriptome and environmental data validation on biochemical network topology since the biomass function varies for different phenotypes. Our tool can detect problematic reaction constraints.

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Singh, Amar V., Kenneth B. Knudsen, and Thomas B. Knudsen. "Integrative analysis of the mouse embryonic transcriptome." Bioinformation 1, no. 10 (April 10, 2007): 406–13. http://dx.doi.org/10.6026/97320630001406.

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Sneha, Nela Pragathi, S. Akila Parvathy Dharshini, Y. H. Taguchi, and M. Michael Gromiha. "Integrative Meta-Analysis of Huntington’s Disease Transcriptome Landscape." Genes 13, no. 12 (December 16, 2022): 2385. http://dx.doi.org/10.3390/genes13122385.

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Huntington’s disease (HD) is a neurodegenerative disorder with autosomal dominant inheritance caused by glutamine expansion in the Huntingtin gene (HTT). Striatal projection neurons (SPNs) in HD are more vulnerable to cell death. The executive striatal population is directly connected with the Brodmann Area (BA9), which is mainly involved in motor functions. Analyzing the disease samples from BA9 from the SRA database provides insights related to neuron degeneration, which helps to identify a promising therapeutic strategy. Most gene expression studies examine the changes in expression and associated biological functions. In this study, we elucidate the relationship between variants and their effect on gene/downstream transcript expression. We computed gene and transcript abundance and identified variants from RNA-seq data using various pipelines. We predicted the effect of genome-wide association studies (GWAS)/novel variants on regulatory functions. We found that many variants affect the histone acetylation pattern in HD, thereby perturbing the transcription factor networks. Interestingly, some variants affect miRNA binding as well as their downstream gene expression. Tissue-specific network analysis showed that mitochondrial, neuroinflammation, vasculature, and angiogenesis-related genes are disrupted in HD. From this integrative omics analysis, we propose that abnormal neuroinflammation acts as a two-edged sword that indirectly affects the vasculature and associated energy metabolism. Rehabilitation of blood-brain barrier functionality and energy metabolism may secure the neuron from cell death.

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Morabito, Samuel, Emily Miyoshi, Neethu Michael, and Vivek Swarup. "Integrative genomics approach identifies conserved transcriptomic networks in Alzheimer’s disease." Human Molecular Genetics 29, no. 17 (August 17, 2020): 2899–919. http://dx.doi.org/10.1093/hmg/ddaa182.

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Abstract Alzheimer’s disease (AD) is a devastating neurological disorder characterized by changes in cell-type proportions and consequently marked alterations of the transcriptome. Here we use a data-driven systems biology meta-analytical approach across three human AD cohorts, encompassing six cortical brain regions, and integrate with multi-scale datasets comprising of DNA methylation, histone acetylation, transcriptome- and genome-wide association studies and quantitative trait loci to further characterize the genetic architecture of AD. We perform co-expression network analysis across more than 1200 human brain samples, identifying robust AD-associated dysregulation of the transcriptome, unaltered in normal human aging. We assess the cell-type specificity of AD gene co-expression changes and estimate cell-type proportion changes in human AD by integrating co-expression modules with single-cell transcriptome data generated from 27 321 nuclei from human postmortem prefrontal cortical tissue. We also show that genetic variants of AD are enriched in a microglial AD-associated module and identify key transcription factors regulating co-expressed modules. Additionally, we validate our results in multiple published human AD gene expression datasets, which can be easily accessed using our online resource (https://swaruplab.bio.uci.edu/consensusAD).

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Gan, Jingyi, Hans-Joachim Sonntag, Mei kuen Tang, Dongqing Cai, and Kenneth Ka Ho Lee. "Integrative Analysis of the Developing Postnatal Mouse Heart Transcriptome." PLOS ONE 10, no. 7 (July 22, 2015): e0133288. http://dx.doi.org/10.1371/journal.pone.0133288.

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Zhang, Zhe, Zeyad Hailat, Marni J. Falk, and Xue-wen Chen. "Integrative analysis of independent transcriptome data for rare diseases." Methods 69, no. 3 (October 2014): 315–25. http://dx.doi.org/10.1016/j.ymeth.2014.06.003.

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Gusev, Alexander, Arthur Ko, Huwenbo Shi, Gaurav Bhatia, Wonil Chung, Brenda W. J. H. Penninx, Rick Jansen, et al. "Integrative approaches for large-scale transcriptome-wide association studies." Nature Genetics 48, no. 3 (February 8, 2016): 245–52. http://dx.doi.org/10.1038/ng.3506.

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Li, Shuxin, Jiarui Wang, Jiale Li, Meihong Yue, Chuncheng Liu, Libing Ma, and Ying Liu. "Integrative analysis of transcriptome complexity in pig granulosa cells by long-read isoform sequencing." PeerJ 10 (May 25, 2022): e13446. http://dx.doi.org/10.7717/peerj.13446.

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Background In intensive and large-scale farms, abnormal estradiol levels in sows can cause reproductive disorders. The high incidence rate of reproductive disturbance will induce the elimination of productive sows in large quantities, and the poor management will bring great losses to the pig farms. The change in estradiol level has an important effect on follicular development and estrus of sows. To solve this practical problem and improve the productive capacity of sows, it is significant to further clarify the regulatory mechanism of estradiol synthesis in porcine granulosa cells (GCs). The most important function of granulosa cells is to synthesize estradiol. Thus, the studies about the complex transcriptome in porcine GCs are significant. As for precursor-messenger RNAs (pre-mRNAs), their post-transcriptional modification, such as alternative polyadenylation (APA) and alternative splicing (AS), together with long non-coding RNAs (lncRNAs), may regulate the functions of granulosa cells. However, the above modification events and their function are unclear within pig granulosa cells. Methods Combined PacBio long-read isoform sequencing (Iso-Seq) was conducted in this work for generating porcine granulosa cells’ transcriptomic data. We discovered new transcripts and possible gene loci via comparison against reference genome. Later, combined Iso-Seq data were adopted to uncover those post-transcriptional modifications such as APA or AS, together with lncRNA within porcine granulosa cells. For confirming that the Iso-Seq data were reliable, we chose four AS genes and analyzed them through RT-PCR. Results The present article illustrated that pig GCs had a complex transcriptome, which gave rise to 8,793 APA, 3,465 AS events, 703 candidate new gene loci, as well as 92 lncRNAs. The results of this study revealed the complex transcriptome in pig GCs. It provided a basis for the interpretation of the molecular mechanism in GCs.

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Bell, Joshua SK, Aarti Venkat, Jerod Parsons, Catherine Igartua, Benjamin D. Leibowitz, Robert Tell, and Kevin White. "An integrative molecular framework to predict homologous recombination deficiency." Journal of Clinical Oncology 38, no. 15_suppl (May 20, 2020): e15664-e15664. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.e15664.

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e15664 Background: Homologous recombination deficiency (HRD) is the primary biomarker for sensitivity to PARP inhibitors, but identifying the genetic and transcriptomic characteristics that fully capture all HRD patients has remained difficult. For example, DNA-based approaches are limited to patients with pathogenic mutations and loss of heterozygosity (LOH) events, and can fail to properly classify patients with variants of unknown significance. To capture more dynamic cellular processes that arise immediately upon induction of HRD through silencing or loss of BRCA 1/2, a more integrated approach that includes both RNA and DNA based models is necessary. Methods: Using DNA sequencing we developed a genome-wide LOH score that combines pathogenic mutation status and LOH at the BRCA1/2 loci, and the proportion of bases sequenced in the Tempus xT panel that undergo LOH. We also developed three independent RNA-based models to predict BRCA deficiency: 1) An elastic net transcriptome model to predict DNA-based HRD scores derived from exome and SNP array data for each tumor type represented in TCGA; 2) A logistic model to detect BRCA1 promoter hypermethylation from the transcriptome in TCGA data; 3) A model that leveraged the mSigDB annotated gene sets to conduct single sample gene set enrichment analysis (ssGSEA) on Tempus-sequenced patients, selecting over a hundred gene sets that were predictive of BRCA-deficiency. These 4 features were combined to develop a stacked, linear-regression model to distinguish BRCA-intact from BRCA-deficient patients. Results: We found that the genome-wide LOH score alone is predictive of BRCA deficiency. However, our integrated model was highly accurate at distinguishing between BRCA-intact and BRCA-deficient patients and outperformed any single RNA- or DNA-based model. Using this model, we identified many patients that are likely to respond to PARP inhibitors that would have been overlooked using RNA or DNA-based inferences alone. Conclusions: Our approach highlights the strength of integrating diverse molecular features to refine diagnosis and enable oncologists to deliver the most effective therapies to patients.

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Mirza, Bilal, Wei Wang, Jie Wang, Howard Choi, Neo Christopher Chung, and Peipei Ping. "Machine Learning and Integrative Analysis of Biomedical Big Data." Genes 10, no. 2 (January 28, 2019): 87. http://dx.doi.org/10.3390/genes10020087.

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Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues.

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Kasavi, Ceyda, Serpil Eraslan, Ebru Toksoy Oner, and Betul Kirdar. "An integrative analysis of transcriptomic response of ethanol tolerant strains to ethanol in Saccharomyces cerevisiae." Molecular BioSystems 12, no. 2 (2016): 464–76. http://dx.doi.org/10.1039/c5mb00622h.

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El-Hachem, Nehme, Edward Eid, Georges Nemer, Ghassan Dbaibo, Ossama Abbas, Nelly Rubeiz, Salah Zeineldine, et al. "Integrative Transcriptome Analyses Empower the Anti-COVID-19 Drug Arsenal." iScience 23, no. 11 (November 2020): 101697. http://dx.doi.org/10.1016/j.isci.2020.101697.

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Sauler, Maor, Maxime Lamontagne, Eric Finnemore, Jose D. Herazo-Maya, John Tedrow, Xuchen Zhang, Julia E. Morneau, et al. "The DNA repair transcriptome in severe COPD." European Respiratory Journal 52, no. 4 (September 6, 2018): 1701994. http://dx.doi.org/10.1183/13993003.01994-2017.

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Inadequate DNA repair is implicated in the pathogenesis of chronic obstructive pulmonary disease (COPD). However, the mechanisms that underlie inadequate DNA repair in COPD are poorly understood. We applied an integrative genomic approach to identify DNA repair genes and pathways associated with COPD severity.We measured the transcriptomic changes of 419 genes involved in DNA repair and DNA damage tolerance that occur with severe COPD in three independent cohorts (n=1129). Differentially expressed genes were confirmed with RNA sequencing and used for patient clustering. Clinical and genome-wide transcriptomic differences were assessed following cluster identification. We complemented this analysis by performing gene set enrichment analysis, Z-score and weighted gene correlation network analysis to identify transcriptomic patterns of DNA repair pathways associated with clinical measurements of COPD severity.We found 15 genes involved in DNA repair and DNA damage tolerance to be differentially expressed in severe COPD. K-means clustering of COPD cases based on this 15-gene signature identified three patient clusters with significant differences in clinical characteristics and global transcriptomic profiles. Increasing COPD severity was associated with downregulation of the nucleotide excision repair pathway.Systematic analysis of the lung tissue transcriptome of individuals with severe COPD identified DNA repair responses associated with disease severity that may underlie COPD pathogenesis.

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Chang, Jae-Woong, Hsin-Sung Yeh, Meeyeon Park, Luke Erber, Jiao Sun, Sze Cheng, Alexander M. Bui, et al. "mTOR-regulated U2af1 tandem exon splicing specifies transcriptome features for translational control." Nucleic Acids Research 47, no. 19 (September 3, 2019): 10373–87. http://dx.doi.org/10.1093/nar/gkz761.

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Abstract U2 auxiliary factor 1 (U2AF1) functions in 3′-splice site selection during pre-mRNA processing. Alternative usage of duplicated tandem exons in U2AF1 produces two isoforms, U2AF1a and U2AF1b, but their functional differences are unappreciated due to their homology. Through integrative approaches of genome editing, customized-transcriptome profiling and crosslinking-mediated interactome analyses, we discovered that the expression of U2AF1 isoforms is controlled by mTOR and they exhibit a distinctive molecular profile for the splice site and protein interactomes. Mechanistic dissection of mutually exclusive alternative splicing events revealed that U2AF1 isoforms’ inherent differential preferences of nucleotide sequences and their stoichiometry determine the 3′-splice site. Importantly, U2AF1a-driven transcriptomes feature alternative splicing events in the 5′-untranslated region (5′-UTR) that are favorable for translation. These findings unveil distinct roles of duplicated tandem exon-derived U2AF1 isoforms in the regulation of the transcriptome and suggest U2AF1a-driven 5′-UTR alternative splicing as a molecular mechanism of mTOR-regulated translational control.

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Jeon, Youngsic, Tae Kyeom Kang, Wook-Bin Lee, Sang Hoon Jung, and Young-Joo Kim. "Gene Signatures and Associated Transcription Factors of Allergic Rhinitis: KLF4 Expression Is Associated with Immune Response." BioMed Research International 2023 (May 10, 2023): 1–12. http://dx.doi.org/10.1155/2023/1317998.

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This study is aimed at investigating the potential molecular features of allergic rhinitis (AR) and identifying gene signatures and related transcription factors using transcriptome analysis and in silico datasets. Transcriptome profiles were obtained using three independent cohorts (GSE101720, GSE19190, and GSE46171) comprising healthy controls (HC) and patients with AR. The pooled dataset ( n = 82 ) was used to identify the critical signatures of AR compared with HC. Subsequently, key transcription factors were identified by a combined analysis using transcriptome and in silico datasets. Gene ontology: bioprocess (GO: BP) analysis using differentially expressed genes (DEGs) revealed that immune response-related genes were significantly enriched in AR compared with HC. Among them, IL1RL1, CD274, and CD44 were significantly higher in AR patients. We also identified key transcription factors between HC and AR using the in silico dataset and found that AR samples frequently express KLF transcription factor 4 (KLF4), which regulates immune response-related genes including IL1RL1, CD274, and CD44 in human nasal epithelial cells. Our integrative analysis of transcriptomic regulation provides new insights into AR, which may help in developing precision management for patients with AR.

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Wei, Xiaochun, Rujiao Liao, Xiaowei Zhang, Yanyan Zhao, Zhengqing Xie, Shuangjuan Yang, Henan Su, et al. "Integrative Transcriptome, miRNAs, Degradome, and Phytohormone Analysis of Brassica rapa L. in Response to Plasmodiophora brassicae." International Journal of Molecular Sciences 24, no. 3 (January 26, 2023): 2414. http://dx.doi.org/10.3390/ijms24032414.

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Clubroot is an infectious root disease caused by Plasmodiophora brassicae in Brassica crops, which can cause immeasurable losses. We analyzed integrative transcriptome, small RNAs, degradome, and phytohormone comprehensively to explore the infection mechanism of P. brassicae. In this study, root samples of Brassica rapa resistant line material BrT24 (R-line) and susceptible line material Y510-9 (S-line) were collected at four different time points for cytological, transcriptome, miRNA, and degradome analyses. We found the critical period of disease resistance and infection were at 0–3 DAI (days after inoculation) and 9–20 DAI, respectively. Based on our finding, we further analyzed the data of 9 DAI vs. 20 DAI of S-line and predicted the key genes ARF8, NAC1, NAC4, TCP10, SPL14, REV, and AtHB, which were related to clubroot disease development and regulating disease resistance mechanisms. These genes are mainly related to auxin, cytokinin, jasmonic acid, and ethylene cycles. We proposed a regulatory model of plant hormones under the mRNA–miRNA regulation in the critical period of P. brassicae infection by using the present data of the integrative transcriptome, small RNAs, degradome, and phytohormone with our previously published results. Our integrative analysis provided new insights into the regulation relationship of miRNAs and plant hormones during the process of disease infection with P. brassicae.

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Kim, Hani Jieun, Yingxin Lin, Thomas A. Geddes, Jean Yee Hwa Yang, and Pengyi Yang. "CiteFuse enables multi-modal analysis of CITE-seq data." Bioinformatics 36, no. 14 (April 30, 2020): 4137–43. http://dx.doi.org/10.1093/bioinformatics/btaa282.

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Abstract Motivation Multi-modal profiling of single cells represents one of the latest technological advancements in molecular biology. Among various single-cell multi-modal strategies, cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) allows simultaneous quantification of two distinct species: RNA and cell-surface proteins. Here, we introduce CiteFuse, a streamlined package consisting of a suite of tools for doublet detection, modality integration, clustering, differential RNA and protein expression analysis, antibody-derived tag evaluation, ligand–receptor interaction analysis and interactive web-based visualization of CITE-seq data. Results We demonstrate the capacity of CiteFuse to integrate the two data modalities and its relative advantage against data generated from single-modality profiling using both simulations and real-world CITE-seq data. Furthermore, we illustrate a novel doublet detection method based on a combined index of cell hashing and transcriptome data. Finally, we demonstrate CiteFuse for predicting ligand–receptor interactions by using multi-modal CITE-seq data. Collectively, we demonstrate the utility and effectiveness of CiteFuse for the integrative analysis of transcriptome and epitope profiles from CITE-seq data. Availability and implementation CiteFuse is freely available at http://shiny.maths.usyd.edu.au/CiteFuse/ as an online web service and at https://github.com/SydneyBioX/CiteFuse/ as an R package. Contact pengyi.yang@sydney.edu.au Supplementary information Supplementary data are available at Bioinformatics online.

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Li, Yinghao, Pin Lv, Junzhen Mi, Baoping Zhao, and Jinghui Liu. "Integrative Transcriptome and Metabolome Analyses of the Interaction of Oat–Oat Stem Rust." Agronomy 12, no. 10 (September 29, 2022): 2353. http://dx.doi.org/10.3390/agronomy12102353.

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Stem rust, caused by Puccinia graminis f. sp. avenae (Pga) Eriks. and E. Henn., is a worldwide and harmful disease of oat (Avena sativa L.). Currently, no resistant varieties are used in production as the molecular resistance mechanism of oat to stem rust remains unclear. Here, oat plants were inoculated with Pga pathogens, and the metabolome and transcriptome of leaves were detected to investigate the molecular and physiological changes. Our results showed that Pga inoculation increased the activities of superoxide dismutase (SOD), peroxidase (POD), catalase (CAT), and phenylalnine ammonialyase (PAL), which triggered defense responses. The transcriptomic and metabolomic analyses were performed to detect the key genes and metabolites of oat interacting with Pga. We identified 1814 upregulated and 1955 downregulated genes in Pga infected leaves. These genes were mainly involved in the ‘phenylpropanoid biosynthesis’, ‘flavonoid biosynthesis’, and ‘photosynthesis-antenna proteins’. We also detected 162 differential metabolites between Pga-infected and non-infected leaves, including flavonoids and derivatives, amino acids, organic acids, and carbohydrates. The integrated analysis revealed four pathways, including the ‘citrate cycle’, ‘cysteine and methionine metabolism’, ‘tryptophan metabolism’, and ‘glyoxylate and dicarboxylate metabolism’. The networks for these pathways were subsequently constructed. Overall, the results suggested that oat plants fight against Pga by activating the metabolism of amino acids, organic acids, and flavonoids. This study provides valuable molecular information about the response of oat to Pga infection.

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Domhan, Sophie, Christian Schwager, Quanxiang Wei, Stefan Muschal, Claudia Sommerer, Christian Morath, Wolfgang Wick, et al. "Deciphering the Systems Biology of mTOR Inhibition by Integrative Transcriptome Analysis." Current Pharmaceutical Design 20, no. 1 (January 31, 2014): 88–100. http://dx.doi.org/10.2174/138161282001140113125549.

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Mehan, Michael R., Juan Nunez-Iglesias, Mrinal Kalakrishnan, Michael S. Waterman, and Xianghong Jasmine Zhou. "An Integrative Network Approach to Map the Transcriptome to the Phenome." Journal of Computational Biology 16, no. 8 (August 2009): 1023–34. http://dx.doi.org/10.1089/cmb.2009.0037.

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Hoshida, Yujin, Sebastian M. B. Nijman, Masahiro Kobayashi, Jennifer A. Chan, Jean-Philippe Brunet, Derek Y. Chiang, Augusto Villanueva, et al. "Integrative Transcriptome Analysis Reveals Common Molecular Subclasses of Human Hepatocellular Carcinoma." Cancer Research 69, no. 18 (September 1, 2009): 7385–92. http://dx.doi.org/10.1158/0008-5472.can-09-1089.

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Obermayer, Alyssa, Li Dong, Qianqian Hu, Michael Golden, Jerald D. Noble, Paulo Rodriguez, Timothy J. Robinson, Mingxiang Teng, Aik-Choon Tan, and Timothy I. Shaw. "DRPPM-EASY: A Web-Based Framework for Integrative Analysis of Multi-Omics Cancer Datasets." Biology 11, no. 2 (February 8, 2022): 260. http://dx.doi.org/10.3390/biology11020260.

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High-throughput transcriptomic and proteomic analyses are now routinely applied to study cancer biology. However, complex omics integration remains challenging and often time-consuming. Here, we developed DRPPM-EASY, an R Shiny framework for integrative multi-omics analysis. We applied our application to analyze RNA-seq data generated from a USP7 knockdown in T-cell acute lymphoblastic leukemia (T-ALL) cell line, which identified upregulated expression of a TAL1-associated proliferative signature in T-cell acute lymphoblastic leukemia cell lines. Next, we performed proteomic profiling of the USP7 knockdown samples. Through DRPPM-EASY-Integration, we performed a concurrent analysis of the transcriptome and proteome and identified consistent disruption of the protein degradation machinery and spliceosome in samples with USP7 silencing. To further illustrate the utility of the R Shiny framework, we developed DRPPM-EASY-CCLE, a Shiny extension preloaded with the Cancer Cell Line Encyclopedia (CCLE) data. The DRPPM-EASY-CCLE app facilitates the sample querying and phenotype assignment by incorporating meta information, such as genetic mutation, metastasis status, sex, and collection site. As proof of concept, we verified the expression of TP53 associated DNA damage signature in TP53 mutated ovary cancer cells. Altogether, our open-source application provides an easy-to-use framework for omics exploration and discovery.

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Liu, Chenglin, Yu-Hang Zhang, Qinfang Deng, Yixue Li, Tao Huang, Songwen Zhou, and Yu-Dong Cai. "Cancer-Related Triplets of mRNA-lncRNA-miRNA Revealed by Integrative Network in Uterine Corpus Endometrial Carcinoma." BioMed Research International 2017 (2017): 1–7. http://dx.doi.org/10.1155/2017/3859582.

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The regulation of transcriptome expression level is a complex process involving multiple-level interactions among molecules such as protein coding RNA (mRNA), long noncoding RNA (lncRNA), and microRNA (miRNA), which are essential for the transcriptome stability and maintenance and regulation of body homeostasis. The availability of multilevel expression data enables a comprehensive view of the regulatory network. In this study, we analyzed the coding and noncoding gene expression profiles of 301 patients with uterine corpus endometrial carcinoma (UCEC). A new method was proposed to construct a genome-wide integrative network based on variance inflation factor (VIF) regression method. The cross-regulation relations of mRNA, lncRNA, and miRNA were then selected based on clique-searching algorithm from the network, when any two molecules of the three were shown as interacting according to the integrative network. Such relation, which we call the mRNA-lncRNA-miRNA triplet, demonstrated the complexity in transcriptome regulation process. Finally, six UCEC-related triplets were selected in which the mRNA participates in endometrial carcinoma pathway, such as CDH1 and TP53. The multi-type RNAs are proved to be cross-regulated as to each of the six triplets according to literature. All the triplets demonstrated the association with the initiation and progression of UCEC. Our method provides a comprehensive strategy for the investigation of transcriptome regulation mechanism.

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Liu, David, Bastian Schilling, Derek Liu, Antje Sucker, Elisabeth Livingstone, Livnat Jerby-Arnon, Lisa Zimmer, et al. "Integrative molecular and clinical modeling of clinical outcomes to PD1 blockade in patients with metastatic melanoma." Nature Medicine 25, no. 12 (December 2019): 1916–27. http://dx.doi.org/10.1038/s41591-019-0654-5.

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AbstractImmune-checkpoint blockade (ICB) has demonstrated efficacy in many tumor types, but predictors of responsiveness to anti-PD1 ICB are incompletely characterized. In this study, we analyzed a clinically annotated cohort of patients with melanoma (n = 144) treated with anti-PD1 ICB, with whole-exome and whole-transcriptome sequencing of pre-treatment tumors. We found that tumor mutational burden as a predictor of response was confounded by melanoma subtype, whereas multiple novel genomic and transcriptomic features predicted selective response, including features associated with MHC-I and MHC-II antigen presentation. Furthermore, previous anti-CTLA4 ICB exposure was associated with different predictors of response compared to tumors that were naive to ICB, suggesting selective immune effects of previous exposure to anti-CTLA4 ICB. Finally, we developed parsimonious models integrating clinical, genomic and transcriptomic features to predict intrinsic resistance to anti-PD1 ICB in individual tumors, with validation in smaller independent cohorts limited by the availability of comprehensive data. Broadly, we present a framework to discover predictive features and build models of ICB therapeutic response.

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Liu, Hailong, Xiaoguang Qiu, and Tao Jiang. "TAMI-74. SPATIOTEMPORAL MULTI-OMIC LANDSCAPE OF HUMAN MEDULLOBLASTOMA AT SINGLE CELL RESOLUTION." Neuro-Oncology 23, Supplement_6 (November 2, 2021): vi213—vi214. http://dx.doi.org/10.1093/neuonc/noab196.856.

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Abstract Medulloblastoma is the most common malignant childhood tumor type with distinct molecular subgroups. While advances in the comprehensive treatment have been made, the mortality in the high-risk group is still very high, driven by an incomplete understanding of cellular diversity. Here we use single-nucleus RNA expression, chromatin accessibility and spatial transcriptomic profiling to generate an integrative multi-omic map in 40 human medulloblastomas spanning all molecular subgroups and human postnatal cerebella, which is supplemented by the bulk whole genome and RNA sequences across 300 cases. This approach provides spatially resolved insights into the medulloblastoma and cerebellum transcriptome and epigenome with identification of distinct cell-type in the tumor microenvironment. Medulloblastoma exhibited three tumor subpopulations including the quiescent, the differentiated, and a stem-like (proliferating) population unique to cancer, which localized to an immunosuppressive-vascular niche. We identified and validated mechanisms of stem-like to differentiated process among the malignant cells that drive tumor progression. Integration of single-cell and spatial data mapped ligand-receptor networks to specific cell types, revealing stem-like malignant cells as a hub for intercellular communication. Multiple features of potential immunosuppression and angiogenesis were observed, including Treg cells and endothelial cells co-localization in compartmentalized tumor stroma. Collectively, our study provides an integrative molecular landscape of human medulloblastoma and represents a reference to advance mechanistic and therapeutic studies of pediatric neuro-oncological disease.

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Bhattacharya, Arjun, Yun Li, and Michael I. Love. "MOSTWAS: Multi-Omic Strategies for Transcriptome-Wide Association Studies." PLOS Genetics 17, no. 3 (March 8, 2021): e1009398. http://dx.doi.org/10.1371/journal.pgen.1009398.

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Traditional predictive models for transcriptome-wide association studies (TWAS) consider only single nucleotide polymorphisms (SNPs) local to genes of interest and perform parameter shrinkage with a regularization process. These approaches ignore the effect of distal-SNPs or other molecular effects underlying the SNP-gene association. Here, we outline multi-omics strategies for transcriptome imputation from germline genetics to allow more powerful testing of gene-trait associations by prioritizing distal-SNPs to the gene of interest. In one extension, we identify mediating biomarkers (CpG sites, microRNAs, and transcription factors) highly associated with gene expression and train predictive models for these mediators using their local SNPs. Imputed values for mediators are then incorporated into the final predictive model of gene expression, along with local SNPs. In the second extension, we assess distal-eQTLs (SNPs associated with genes not in a local window around it) for their mediation effect through mediating biomarkers local to these distal-eSNPs. Distal-eSNPs with large indirect mediation effects are then included in the transcriptomic prediction model with the local SNPs around the gene of interest. Using simulations and real data from ROS/MAP brain tissue and TCGA breast tumors, we show considerable gains of percent variance explained (1–2% additive increase) of gene expression and TWAS power to detect gene-trait associations. This integrative approach to transcriptome-wide imputation and association studies aids in identifying the complex interactions underlying genetic regulation within a tissue and important risk genes for various traits and disorders.

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Wang, Jinkai. "Integrative analyses of transcriptome data reveal the mechanisms of post-transcriptional regulation." Briefings in Functional Genomics 20, no. 4 (February 22, 2021): 207–12. http://dx.doi.org/10.1093/bfgp/elab004.

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Abstract Post-transcriptional processing of RNAs plays important roles in a variety of physiological and pathological processes. These processes can be precisely controlled by a series of RNA binding proteins and cotranscriptionally regulated by transcription factors as well as histone modifications. With the rapid development of high-throughput sequencing techniques, multiomics data have been broadly used to study the mechanisms underlying the important biological processes. However, how to use these high-throughput sequencing data to elucidate the fundamental regulatory roles of post-transcriptional processes is still of great challenge. This review summarizes the regulatory mechanisms of post-transcriptional processes and the general principles and approaches to dissect these mechanisms by integrating multiomics data as well as public resources.

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Cinegaglia, Naiara C., Sonia Cristina S. Andrade, Tomas Tokar, Maísa Pinheiro, Fábio E. Severino, Rogério A. Oliveira, Erica N. Hasimoto, et al. "Integrative transcriptome analysis identifies deregulated microRNA-transcription factor networks in lung adenocarcinoma." Oncotarget 7, no. 20 (April 12, 2016): 28920–34. http://dx.doi.org/10.18632/oncotarget.8713.

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Zhang, Xiaoming, Jing Zhuang, Lijuan Liu, Zhengguo He, Cun Liu, Xiaoran Ma, Jie Li, Xia Ding, and Changgang Sun. "Integrative transcriptome data mining for identification of core lncRNAs in breast cancer." PeerJ 7 (October 7, 2019): e7821. http://dx.doi.org/10.7717/peerj.7821.

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Background Cumulative evidence suggests that long non-coding RNAs (lncRNAs) play an important role in tumorigenesis. This study aims to identify lncRNAs that can serve as new biomarkers for breast cancer diagnosis or screening. Methods First, the linear fitting method was used to identify differentially expressed genes from the breast cancer RNA expression profiles in The Cancer Genome Atlas (TCGA). Next, the diagnostic value of all differentially expressed lncRNAs was evaluated using a receiver operating characteristic (ROC) curve. Then, the top ten lncRNAs with the highest diagnostic value were selected as core genes for clinical characteristics and prognosis analysis. Furthermore, core lncRNA-mRNA co-expression networks based on weighted gene co-expression network analysis (WGCNA) were constructed, and functional enrichment analysis was performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID). The differential expression level and diagnostic value of core lncRNAs were further evaluated by using independent data set from Gene Expression Omnibus (GEO). Finally, the expression status and prognostic value of core lncRNAs in various tumors were analyzed based on Gene Expression Profiling Interactive Analysis (GEPIA). Results Seven core lncRNAs (LINC00478, PGM5-AS1, AL035610.1, MIR143HG, RP11-175K6.1, AC005550.4, and MIR497HG) have good single-factor diagnostic value for breast cancer. AC093850.2 has a prognostic value for breast cancer. AC005550.4 and MIR497HG can better distinguish breast cancer patients in early-stage from the advanced-stage. Low expression of MAGI2-AS3, LINC00478, AL035610.1, MIR143HG, and MIR145 may be associated with lymph node metastasis in breast cancer. Conclusion Our study provides candidate biomarkers for the diagnosis and prognosis of breast cancer, as well as a bioinformatics basis for the further elucidation of the molecular pathological mechanism of breast cancer.

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Tran, Nguyen H., Pankaj Vats, Dan R. Robinson, Mark Zalupski, Katherine E. Hersberger, Mishal Mendiratta-Lala, Chandan Kumar-Sinha, et al. "Integrative whole exome, transcriptome, and clinical profiling of biliary tract cancers (BTCs)." Journal of Clinical Oncology 36, no. 4_suppl (February 1, 2018): 279. http://dx.doi.org/10.1200/jco.2018.36.4_suppl.279.

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279 Background: BTC is clinically and genomically heterogeneous and next generation sequencing may identify disease subsets with distinct prognostic and therapeutic implications. Methods: Patients (pts) with BTC underwent whole exome and transcriptome sequencing via the Michigan Oncology Sequencing (MI-ONCOSEQ) platform between 09/2011 and 07/2017. Results: 53 pts (47.2% female) with median age 60 (range 17-72) years had 38 intrahepatic, 6 perihilar, and 4 extrahepatic distal cholangiocarcinoma (CCA) while 3 had gallbladder and 2 mixed CCA/hepatocellular carcinoma. Forty-one pts (77.3%) had advanced BTC at diagnosis and 40 (75.5%) received platinum doublet as first line therapy. The most frequent somatic mutations were TP53 (35.8%), BAP1 (18.9%), KRAS (17.0%), IDH1 (15.1%), PBRM1 (13.2%), ARID1A (11.3%), and SMAD4 (11.3%). Median overall survival (OS) in 8 pts with IDH1 mutation was 16.8 months (none received IDH1 inhibitor). Putative pathogenic germline variants were noted in 6 (11.3%) pts of which 4 were biallelic (MSH2, BRCA1, BRCA2 and MUTYH) and 2 monoallelic (ATM and FH). Germline mutation in FH has not been reported in BTC. Biallelic DNA damage repair pathway mutations were noted in 14 (26.4%; 11 somatic, 3 germline) pts and their median OS was 16.8 months. Of these 9 pts with advanced BTC received 1st line platinum therapy and had a median PFS of 9.3 months (4 PR, 3 SD). Nine (17%) pts had FGFR2 fusion (6 partners); median OS not yet reached (6 alive; 5 received FGFR inhibitor). Potentially targetable molecular alterations (IDH1, MSH2, BRCA1/2, PALB2, ATM, FGFR2, ERBB2) were identified in 27 (50.9%) pts. Copy number profile shows frequent loss of chr1p, chr3p, chr4, chr6q, chr8p, chr9, and gain of chr1q, chr7, and chr8q. Immune profiling & pathway analysis of BTCs with clinical correlation is ongoing. Conclusions: Integrative sequencing of BTCs with clinical profiling will lead to more precise treatment of pts with BTCs.

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Soltani, Zahra, Ali Moghadam, Ahmad Tahmasebi, and Ali Niazi. "Integrative systems biology analysis of barley transcriptome ─ hormonal signaling against biotic stress." PLOS ONE 18, no. 4 (April 27, 2023): e0281470. http://dx.doi.org/10.1371/journal.pone.0281470.

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Biotic stresses are pests and pathogens that cause a variety of crop diseases and damages. In response to these agents, crops trigger specific defense signal transduction pathways in which hormones play a central role. To recognize hormonal signaling, we integrated barley transcriptome datasets related to hormonal treatments and biotic stresses. In the meta-analysis of each dataset, 308 hormonal and 1232 biotic DEGs were identified respectively. According to the results, 24 biotic TFs belonging to 15 conserved families and 6 hormonal TFs belonging to 6 conserved families were identified, with the NF-YC, GNAT, and WHIRLY families being the most prevalent. Additionally, gene enrichment and pathway analyses revealed that over-represented cis-acting elements were recognized in response to pathogens and hormones. Based on the co-expression analysis, 6 biotic and 7 hormonal modules were uncovered. Finally, the hub genes of PKT3, PR1, SSI2, LOX2, OPR3, and AOS were candidates for further study in JA- or SA-mediated plant defense. The qPCR confirmed that the expression of these genes was induced from 3 to 6 h following exposure to 100 μM MeJA, with peak expression occurring between 12 h and 24 h and decreasing after 48 h. Overexpression of PR1 was one of the first steps toward SAR. As well as regulating SAR, NPR1 has also been shown to be involved in the activation of ISR by the SSI2. LOX2 catalyzes the first step of JA biosynthesis, PKT3 plays an important role in wound-activated responses, and OPR3 and AOS are involved in JA biosynthesis. In addition, many unknown genes were introduced that can be used by crop biotechnologists to accelerate barley genetic engineering.

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Quraishi, Mohammed Nabil, Animesh Acharjee, Andrew D. Beggs, Richard Horniblow, Chris Tselepis, Georgios Gkoutos, Subrata Ghosh, et al. "A Pilot Integrative Analysis of Colonic Gene Expression, Gut Microbiota, and Immune Infiltration in Primary Sclerosing Cholangitis-Inflammatory Bowel Disease: Association of Disease With Bile Acid Pathways." Journal of Crohn's and Colitis 14, no. 7 (February 4, 2020): 935–47. http://dx.doi.org/10.1093/ecco-jcc/jjaa021.

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Abstract Background Although a majority of patients with PSC have colitis [PSC-IBD; primary sclerosing cholangitis-inflammatory bowel disease], this is phenotypically different from ulcerative colitis [UC]. We sought to define further the pathophysiological differences between PSC-IBD and UC, by applying a comparative and integrative approach to colonic gene expression, gut microbiota and immune infiltration data. Methods Colonic biopsies were collected from patients with PSC-IBD [n = 10], UC [n = 10], and healthy controls [HC; n = 10]. Shotgun RNA-sequencing for differentially expressed colonic mucosal genes [DEGs], 16S rRNA analysis for microbial profiling, and immunophenotyping were performed followed by multi-omic integration. Results The colonic transcriptome differed significantly between groups [p = 0.01]. Colonic transcriptomes from HC were different from both UC [1343 DEGs] and PSC-IBD [4312 DEGs]. Of these genes, only 939 had shared differential gene expression in both UC and PSC-IBD compared with HC. Imputed pathways were predominantly associated with upregulation of immune response and microbial defense in both disease cohorts compared with HC. There were 1692 DEGs between PSC-IBD and UC. Bile acid signalling pathways were upregulated in PSC-IBD compared with UC [p = 0.02]. Microbiota profiles were different between the three groups [p = 0.01]; with inferred function in PSC-IBD also being consistent with dysregulation of bile acid metabolism. Th17 cells and IL17-producing CD4 cells were increased in both PSC-IBD and UC when compared with HC [p < 0.05]. Multi-omic integration revealed networks involved in bile acid homeostasis and cancer regulation in PSC-IBD. Conclusions Colonic transcriptomic and microbiota analysis in PSC-IBD point toward dysregulation of colonic bile acid homeostasis compared with UC. This highlights important mechanisms and suggests the possibility of novel approaches in treating PSC-IBD.

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Karunanithi, Sivarajan, Vidya Oruganti, Simone Marker, Angela M. Rodriguez-Viana, Franziska Drews, Marcello Pirritano, Karl Nordström, Martin Simon, and Marcel H. Schulz. "Exogenous RNAi mechanisms contribute to transcriptome adaptation by phased siRNA clusters in Paramecium." Nucleic Acids Research 47, no. 15 (June 28, 2019): 8036–49. http://dx.doi.org/10.1093/nar/gkz553.

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Abstract Extensive research has characterized distinct exogenous RNAi pathways interfering in gene expression during vegetative growth of the unicellular model ciliate Paramecium. However, role of RNAi in endogenous transcriptome regulation, and environmental adaptation is unknown. Here, we describe the first genome-wide profiling of endogenous sRNAs in context of different transcriptomic states (serotypes). We developed a pipeline to identify, and characterize 2602 siRNA producing clusters (SRCs). Our data show no evidence that SRCs produce miRNAs, and in contrast to other species, no preference for strand specificity of siRNAs. Interestingly, most SRCs overlap coding genes and a separate group show siRNA phasing along the entire open reading frame, suggesting that the mRNA transcript serves as a source for siRNAs. Integrative analysis of siRNA abundance and gene expression levels revealed surprisingly that mRNA and siRNA show negative as well as positive associations. Two RNA-dependent RNA Polymerase mutants, RDR1 and RDR2, show a drastic loss of siRNAs especially in phased SRCs accompanied with increased mRNA levels. Importantly, most SRCs depend on both RDRs, reminiscent to primary siRNAs in the RNAi against exogenous RNA, indicating mechanistic overlaps between exogenous and endogenous RNAi contributing to flexible transcriptome adaptation.

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Xu, Duo, Shengchen Liu, Xi Wu, Thomas M. Marti, Patrick Dorn, Ralph A. Schmid, Ren-Wang Peng, and Yongqian Shu. "Dissecting the Immunological Profiles in NSD3-Amplified LUSC through Integrative Multi-Scale Analyses." Cancers 14, no. 20 (October 12, 2022): 4997. http://dx.doi.org/10.3390/cancers14204997.

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The histone H3 lysine 36 (H3K36) methyltransferase NSD3, a neighboring gene of FGFR1, has been identified as a critical genetic driver of lung squamous cell carcinoma (LUSC). However, the molecular characteristics, especially the immunological roles of NSD3 in driving carcinogenesis, are poorly understood. In this study, we systematically integrated multi-omics data (e.g., genome, transcriptome, proteome, and TMA array) to dissect the immunological profiles in NSD3-amplified LUSC. Next, pharmaco-transcriptomic correlation analysis was implemented to identify the molecular underpinnings and therapeutic vulnerabilities in LUSC. We revealed that NSD3-amplified LUSC presents a non-inflamed tumor immune microenvironment (TIME) state in multiple independent LUSC patient cohorts. Predictably, elevated NSD3 expression was correlated with a worse immunotherapy outcome. Further molecular characterizations revealed that the high activity of unfolded protein response (UPR) signaling might be a pivotal mediator for the non-immunogenic phenotype of NSD3-amplified LUSC. Concordantly, we showed that NSD3-amplified LUSCs exhibited a more sensitive phenotype to compounds targeting UPR branches than the wild-type group. In brief, our multi-level analyses point to a previously unappreciated immunological role for NSD3 and provide therapeutic rationales for NSD3-amplified squamous lung cancer.

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Liu, Hailong, Tao Jiang, and Xiaoguang Qiu. "Spatiotemporal multiomic landscape of human medulloblastoma at single cell resolution." Journal of Clinical Oncology 40, no. 16_suppl (June 1, 2022): 2069. http://dx.doi.org/10.1200/jco.2022.40.16_suppl.2069.

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2069 Background: Medulloblastoma is the most common malignant childhood tumor type with distinct molecular subgroups. While advances in the comprehensive treatment have been made, the mortality in the high-risk group is still very high, driven by an incomplete understanding of cellular diversity. Methods: We use single-nucleus RNA expression, chromatin accessibility and spatial transcriptomic profiling to generate an integrative multi-omic map in 40 human medulloblastomas spanning all molecular subgroups and human postnatal cerebella, which is supplemented by the bulk whole genome and RNA sequences across 300 cases. Results: This approach provides spatially resolved insights into the medulloblastoma and cerebellum transcriptome and epigenome with identification of distinct cell-type in the tumor microenvironment. Medulloblastoma exhibited three tumor subpopulations including the quiescent, the differentiated, and a stem-like (proliferating) population unique to cancer, which localized to an immunosuppressive-vascular niche. We identified and validated mechanisms of stem-like to differentiated process among the malignant cells that drive tumor progression. Integration of single-cell and spatial data mapped ligand-receptor networks to specific cell types, revealing stem-like malignant cells as a hub for intercellular communication. Multiple features of potential immunosuppression and angiogenesis were observed, including Treg cells and endothelial cells co-localization in compartmentalized tumor stroma. Conclusions: Our study provides an integrative molecular landscape of human medulloblastoma and represents a reference to advance mechanistic and therapeutic studies of pediatric neuro-oncological disease.

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Li, Zhengchun, Luonan Shen, Qiandong Hou, Zijing Zhou, Lina Mei, Hong Zhao, and Xiaopeng Wen. "Identification of Genes and Metabolic Pathways Involved in Resin Yield in Masson Pine by Integrative Analysis of Transcriptome, Proteome and Biochemical Characteristics." International Journal of Molecular Sciences 23, no. 19 (September 28, 2022): 11420. http://dx.doi.org/10.3390/ijms231911420.

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Masson pine (Pinus massoniana L.) is one of the most important resin-producing tree species in southern China. However, the molecular regulatory mechanisms of resin yield are still unclear in masson pine. In this study, an integrated analysis of transcriptome, proteome, and biochemical characteristics from needles of masson pine with the high and common resin yield was investigated. The results showed that chlorophyll a (Chl a), chlorophyll b (Chl b), total chlorophyll (Chl C), carotenoids (Car), glucose (Glu), gibberellin A9 (GA9), gibberellin A15 (GA15), and gibberellin A53 (GA53) were significantly increased, whereas fructose (Fru), jasmonic acid (JA), jasmonoyl-L-isoleucine (JA-ILE), gibberellin A1 (GA1), gibberellin A3 (GA3), gibberellin A19 (GA19), and gibberellin A24 (GA24) were significantly decreased in the high resin yield in comparison with those in the common one. The integrated analysis of transcriptome and proteome showed that chlorophyll synthase (chlG), hexokinase (HXK), sucrose synthase (SUS), phosphoglycerate kinase (PGK), dihydrolipoamide dehydrogenase (PDH), dihydrolipoamide succinyltransferase (DLST), 12-oxophytodienoic acid reductase (OPR), and jasmonate O-methyltransferases (JMT) were consistent at the transcriptomic, proteomic, and biochemical levels. The pathways of carbohydrate metabolism, terpenoid biosynthesis, photosynthesis, and hormone biosynthesis may play crucial roles in the regulation of resin yield, and some key genes involved in these pathways may be candidates that influence the resin yield. These results provide insights into the molecular regulatory mechanisms of resin yield and also provide candidate genes that can be applied for the molecular-assisted selection and breeding of high resin-yielding masson pine.

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Kirby, Tyler J., R. Grace Walton, Brian Finlin, Beibei Zhu, Resat Unal, Neda Rasouli, Charlotte A. Peterson, and Philip A. Kern. "Integrative mRNA-microRNA analyses reveal novel interactions related to insulin sensitivity in human adipose tissue." Physiological Genomics 48, no. 2 (February 2016): 145–53. http://dx.doi.org/10.1152/physiolgenomics.00071.2015.

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Adipose tissue has profound effects on whole-body insulin sensitivity. However, the underlying biological processes are quite complex and likely multifactorial. For instance, the adipose transcriptome is posttranscriptionally modulated by microRNAs, but the relationship between microRNAs and insulin sensitivity in humans remains to be determined. To this end, we utilized an integrative mRNA-microRNA microarray approach to identify putative molecular interactions that regulate the transcriptome in subcutaneous adipose tissue of insulin-sensitive (IS) and insulin-resistant (IR) individuals. Using the NanoString nCounter Human v1 microRNA Expression Assay, we show that 17 microRNAs are differentially expressed in IR vs. IS. Of these, 16 microRNAs (94%) are downregulated in IR vs. IS, including miR-26b, miR-30b, and miR-145. Using Agilent Human Whole Genome arrays, we identified genes that were predicted targets of miR-26b, miR-30b, and miR-145 and were upregulated in IR subjects. This analysis produced ADAM22, MYO5A, LOX, and GM2A as predicted gene targets of these microRNAs. We then validated that miR-145 and miR-30b regulate these mRNAs in differentiated human adipose stem cells. We suggest that use of bioinformatic integration of mRNA and microRNA arrays yields verifiable mRNA-microRNA pairs that are associated with insulin resistance and can be validated in vitro.

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Liu, Ling, Kang Li, Xiujuan Zhou, and Chuanying Fang. "Integrative Analysis of Metabolome and Transcriptome Reveals the Role of Strigolactones in Wounding-Induced Rice Metabolic Re-Programming." Metabolites 12, no. 9 (August 25, 2022): 789. http://dx.doi.org/10.3390/metabo12090789.

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Plants have evolved mechanisms to adapt to wounding, a threat occurring separately or concomitantly with other stresses. During the last decades, many efforts have been made to elucidate the wounding signaling transduction. However, we know little about the metabolic re-programming under wounding, let alone whether and how strigolactones (SLs) participate in this progress. Here, we reported a metabolomic and transcriptomic analysis of SLs synthetic and signal mutants in rice before and after wounding. A series of metabolites differentially responded to wounding in the SLs mutants and wild-type rice, among which flavones were enriched. Besides, the SLs mutants accumulated more jasmonic acid (JA) and jasmonyl isoleucine (JA-lle) than the wild-type rice after wounding, suggesting an interplay of SLs and JAs during responding to wounding. Further transcriptome data showed that cell wall, ethylene, and flavones pathways might be affected by wounding and SLs. In addition, we identified candidate genes regulated by SLs and responding to wounding. In conclusion, our work provides new insights into wounding-induced metabolic re-programming and the SLs’ function.

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Zhou, Xiujuan, Ling Liu, Yufei Li, Kang Li, Xiaoli Liu, Junjie Zhou, Chenkun Yang, Xianqing Liu, Chuanying Fang, and Jie Luo. "Integrative Metabolomic and Transcriptomic Analyses Reveal Metabolic Changes and Its Molecular Basis in Rice Mutants of the Strigolactone Pathway." Metabolites 10, no. 11 (October 26, 2020): 425. http://dx.doi.org/10.3390/metabo10110425.

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Plants have evolved many metabolites to meet the demands of growth and adaptation. Although strigolactones (SLs) play vital roles in controlling plant architecture, their function in regulating plant metabolism remains elusive. Here we report the integrative metabolomic and transcriptomic analyses of two rice SL mutants, d10 (a biosynthesis mutant) and d14 (a perception mutant). Both mutants displayed a series of metabolic and transcriptional alterations, especially in the lipid, flavonoid, and terpenoid pathways. Levels of several diterpenoid phytoalexins were substantially increased in d10 and d14, together with the induction of terpenoid gene cluster and the corresponding upstream transcription factor WRKY45, an established determinant of plant immunity. The fact that WRKY45 is a target of IPA1, which acted as a downstream transcription factor of SL signaling, suggests that SLs contribute to plant defense through WRKY45 and phytoalexins. Moreover, our data indicated that SLs may modulate rice metabolism through a vast number of clustered or tandemly duplicated genes. Our work revealed a central role of SLs in rice metabolism. Meanwhile, integrative analysis of the metabolome and transcriptome also suggested that SLs may contribute to metabolite-associated growth and defense.

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Kuscu, Canan, Manjari Kiran, Akram Mohammed, Cem Kuscu, Sarthak Satpathy, Aaron Wolen, Elissa Bardhi, et al. "Integrative Analyses of Circulating Small RNAs and Kidney Graft Transcriptome in Transplant Glomerulopathy." International Journal of Molecular Sciences 22, no. 12 (June 9, 2021): 6218. http://dx.doi.org/10.3390/ijms22126218.

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Transplant glomerulopathy develops through multiple mechanisms, including donor-specific antibodies, T cells and innate immunity. This study investigates circulating small RNA profiles in serum samples of kidney transplant recipients with biopsy-proven transplant glomerulopathy. Among total small RNA population, miRNAs were the most abundant species in the serum of kidney transplant patients. In addition, fragments arising from mature tRNA and rRNA were detected. Most of the tRNA fragments were generated from 5′ ends of mature tRNA and mainly from two parental tRNAs: tRNA-Gly and tRNA-Glu. Moreover, transplant patients with transplant glomerulopathy displayed a novel tRNA fragments signature. Gene expression analysis from allograft tissues demonstrated changes in canonical pathways related to immune activation such as iCos-iCosL signaling pathway in T helper cells, Th1 and Th2 activation pathway, and dendritic cell maturation. mRNA targets of down-regulated miRNAs such as miR-1224-5p, miR-4508, miR-320, miR-378a from serum were globally upregulated in tissue. Integration of serum miRNA profiles with tissue gene expression showed that changes in serum miRNAs support the role of T-cell mediated mechanisms in ongoing allograft injury.

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Yamashita, R., N. P. Sathira, A. Kanai, K. Tanimoto, T. Arauchi, Y. Tanaka, S. i. Hashimoto, S. Sugano, K. Nakai, and Y. Suzuki. "Genome-wide characterization of transcriptional start sites in humans by integrative transcriptome analysis." Genome Research 21, no. 5 (March 3, 2011): 775–89. http://dx.doi.org/10.1101/gr.110254.110.

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Suzuki, Ayako, Hiroyuki Wakaguri, Riu Yamashita, Shin Kawano, Katsuya Tsuchihara, Sumio Sugano, Yutaka Suzuki, and Kenta Nakai. "DBTSS as an integrative platform for transcriptome, epigenome and genome sequence variation data." Nucleic Acids Research 43, no. D1 (November 5, 2014): D87—D91. http://dx.doi.org/10.1093/nar/gku1080.

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Miyashita, Naoya, Masafumi Horie, Hiroshi I. Suzuki, Masahito Yoshihara, Dijana Djureinovic, Johan Persson, Hans Brunnström, et al. "An Integrative Analysis of Transcriptome and Epigenome Features of ASCL1–Positive Lung Adenocarcinomas." Journal of Thoracic Oncology 13, no. 11 (November 2018): 1676–91. http://dx.doi.org/10.1016/j.jtho.2018.07.096.

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Luo, Qi, Ziliang Chen, Tingting Xu, Dangzheng Huang, Haitao Hou, Chenjie Hong, Fulin Zhan, et al. "Construction of integrative transcriptome to boost systematic exploration of Bougainvillea." Scientific Reports 12, no. 1 (January 18, 2022). http://dx.doi.org/10.1038/s41598-022-04984-8.

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AbstractMembers of the genus Bougainvillea are rich sources of natural dyes, pigments, and traditional medicines. They are also commonly used as ornamentals in roadside landscape construction. However, the horticultural development of Bougainvillea flowers with extended growth periods and coloration is not always feasible. One reason is limited molecular knowledge and no genomic information for Bougainvillea. Here, we compiled an integrative transcriptome of all expressed transcripts for Bougainvillea × buttiana Miss Manila by integrating 20 Illumina-sequencing RNA transcriptomes. The integrative transcriptome consisted of 97,623 distinct transcripts. Of these, 47,006 were protein-coding, 31,109 were non-coding, and 19,508 were unannotated. In addition, we affirmed that the integrative transcriptome could serve as a surrogate reference to the genome in aiding accurate transcriptome assembly. For convenience, we curated the integrative transcriptome database for Bougainvillea, namely InTransBo, which can be freely accessed at http://www.bio-add.org/InTransBo/index.jsp. To the best of our knowledge, the present study is the most comprehensive genomic resource for Bougainvillea up-to-date. The integrative transcriptome helps fill the genomic gap and elucidate the transcriptional nature of Bougainvillea. It may also advance progress in the precise regulation of flowering in horticulture. The same strategy can be readily applied toward the systematic exploration of other plant species lacking complete genomic information.

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Song, Jaeseung, Daeun Kim, Sora Lee, Junghyun Jung, Jong Wha J. Joo, and Wonhee Jang. "Integrative transcriptome-wide analysis of atopic dermatitis for drug repositioning." Communications Biology 5, no. 1 (June 22, 2022). http://dx.doi.org/10.1038/s42003-022-03564-w.

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AbstractAtopic dermatitis (AD) is one of the most common inflammatory skin diseases, which significantly impact the quality of life. Transcriptome-wide association study (TWAS) was conducted to estimate both transcriptomic and genomic features of AD and detected significant associations between 31 expression quantitative loci and 25 genes. Our results replicated well-known genetic markers for AD, as well as 4 novel associated genes. Next, transcriptome meta-analysis was conducted with 5 studies retrieved from public databases and identified 5 additional novel susceptibility genes for AD. Applying the connectivity map to the results from TWAS and meta-analysis, robustly enriched perturbations were identified and their chemical or functional properties were analyzed. Here, we report the first research on integrative approaches for an AD, combining TWAS and transcriptome meta-analysis. Together, our findings could provide a comprehensive understanding of the pathophysiologic mechanisms of AD and suggest potential drug candidates as alternative treatment options.

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Fülöp, Ádám, Gábor Torma, Norbert Moldován, Kálmán Szenthe, Ferenc Bánáti, Islam A. A. Almsarrhad, Zsolt Csabai, Dóra Tombácz, János Minárovits, and Zsolt Boldogkői. "Integrative profiling of Epstein–Barr virus transcriptome using a multiplatform approach." Virology Journal 19, no. 1 (January 6, 2022). http://dx.doi.org/10.1186/s12985-021-01734-6.

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Abstract Background Epstein–Barr virus (EBV) is an important human pathogenic gammaherpesvirus with carcinogenic potential. The EBV transcriptome has previously been analyzed using both Illumina-based short read-sequencing and Pacific Biosciences RS II-based long-read sequencing technologies. Since the various sequencing methods have distinct strengths and limitations, the use of multiplatform approaches have proven to be valuable. The aim of this study is to provide a more complete picture on the transcriptomic architecture of EBV. Methods In this work, we apply the Oxford Nanopore Technologies MinION (long-read sequencing) platform for the generation of novel transcriptomic data, and integrate these with other’s data generated by another LRS approach, Pacific BioSciences RSII sequencing and Illumina CAGE-Seq and Poly(A)-Seq approaches. Both amplified and non-amplified cDNA sequencings were applied for the generation of sequencing reads, including both oligo-d(T) and random oligonucleotide-primed reverse transcription. EBV transcripts are identified and annotated using the LoRTIA software suite developed in our laboratory. Results This study detected novel genes embedded into longer host genes containing 5′-truncated in-frame open reading frames, which potentially encode N-terminally truncated proteins. We also detected a number of novel non-coding RNAs and transcript length isoforms encoded by the same genes but differing in their start and/or end sites. This study also reports the discovery of novel splice isoforms, many of which may represent altered coding potential, and of novel replication-origin-associated transcripts. Additionally, novel mono- and multigenic transcripts were identified. An intricate meshwork of transcriptional overlaps was revealed. Conclusions An integrative approach applying multi-technique sequencing technologies is suitable for reliable identification of complex transcriptomes because each techniques has different advantages and limitations, and the they can be used for the validation of the results obtained by a particular approach.

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Wu, Fei, Yao-Zhong Liu, and Binhua Ling. "MTD: a unique pipeline for host and meta-transcriptome joint and integrative analyses of RNA-seq data." Briefings in Bioinformatics, April 4, 2022. http://dx.doi.org/10.1093/bib/bbac111.

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Abstract Ribonucleic acid (RNA)-seq data contain not only host transcriptomes but also nonhost information that comprises transcripts from active microbiota in the host cells. Therefore, joint and integrative analyses of both host and meta-transcriptome can reveal gene expression of the microbial community in a given sample as well as the correlative and interactive dynamics of the host response to the microbiome. However, there are no convenient tools that can systemically analyze host–microbiota interactions through simultaneously quantifying the host and meta-transcriptome in the same sample at the tissue and the single-cell level. This poses a challenge for interested researchers with limited expertise in bioinformatics. Here, we developed a software pipeline that can comprehensively and synergistically analyze and correlate the host and meta-transcriptome in a single sample using bulk and single-cell RNA-seq data. This pipeline, named meta-transcriptome detector (MTD), can extensively identify and quantify microbiome, including viruses, bacteria, protozoa, fungi, plasmids and vectors, in the host cells and correlate the microbiome with the host transcriptome. MTD is easy to install and run, involving only a few lines of simple commands. It offers researchers with unique genomics insights into host responses to microorganisms.

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Sun, Zhengwang, Mengchen Yin, Yi Ding, Zixu Zhu, Yangbai Sun, Kun Li, and Wangjun Yan. "Integrative analysis of synovial sarcoma transcriptome reveals different types of transcriptomic changes." Frontiers in Genetics 13 (September 2, 2022). http://dx.doi.org/10.3389/fgene.2022.925564.

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Background: Synovial sarcoma (SS) is a rare and aggressive cancer that can come from distinct soft tissue types including muscle and ligaments. However, the transcriptomic landscape of SS is still poorly understood. This study aimed to systematically dissect the changes in SS transcriptome from different perspectives.Methods: We performed deep total RNA sequencing on ten paired Synovial sarcoma and tumor-adjacent tissues to systematically dissect the transcriptomic profile of SS in terms of gene expression, alternative splicing, gene fusion, and circular RNAs.Results: A total of 2,309 upregulated and 1,977 downregulated genes were identified between SS and tumor-adjacent tissues. Those upregulated genes could lead to the upregulation of the cell cycle, ribosome, and DNA replication pathways, while the downregulated genes may result in the downregulation of a set of metabolic biological processes and signaling pathways. Moreover, 2,511 genes (including 21 splicing factors) were differentially alternative spliced, indicating that the deregulation of alternative splicing could be one important factor that contributes to tumorigenesis. Additionally, we identified the known gene fusions of SS18-SSX1/SSX2 as well as 11 potentially novel gene fusions. Interestingly, 49 circular RNAs were differentially expressed and their parental genes could function in muscle contraction and muscle system processes.Conclusions: Collectively, our comprehensive dissection of the transcriptomic changes of SS from both transcriptional and post-transcriptional levels provides novel insights into the biology and underlying molecular mechanism of SS.

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Journal articles on the topic 'INTEGRATIVE TRANSCRIPTOME'

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