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Lancaster, Samuel M., Akshay Sanghi, Si Wu, and Michael P. Snyder. "A Customizable Analysis Flow in Integrative Multi-Omics." Biomolecules 10, no. 12 (2020): 1606. http://dx.doi.org/10.3390/biom10121606.
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The number of researchers using multi-omics is growing. Though still expensive, every year it is cheaper to perform multi-omic studies, often exponentially so. In addition to its increasing accessibility, multi-omics reveals a view of systems biology to an unprecedented depth. Thus, multi-omics can be used to answer a broad range of biological questions in finer resolution than previous methods. We used six omic measurements—four nucleic acid (i.e., genomic, epigenomic, transcriptomics, and metagenomic) and two mass spectrometry (proteomics and metabolomics) based—to highlight an analysis workflow on this type of data, which is often vast. This workflow is not exhaustive of all the omic measurements or analysis methods, but it will provide an experienced or even a novice multi-omic researcher with the tools necessary to analyze their data. This review begins with analyzing a single ome and study design, and then synthesizes best practices in data integration techniques that include machine learning. Furthermore, we delineate methods to validate findings from multi-omic integration. Ultimately, multi-omic integration offers a window into the complexity of molecular interactions and a comprehensive view of systems biology.
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Li, Jin, Feng Chen, Hong Liang, and Jingwen Yan. "MoNET: an R package for multi-omic network analysis." Bioinformatics 38, no. 4 (2021): 1165–67. http://dx.doi.org/10.1093/bioinformatics/btab722.
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Abstract Motivation The increasing availability of multi-omic data has enabled the discovery of disease biomarkers in different scales. Understanding the functional interaction between multi-omic biomarkers is becoming increasingly important due to its great potential for providing insights of the underlying molecular mechanism. Results Leveraging multiple biological network databases, we integrated the relationship between single nucleotide polymorphisms (SNPs), genes/proteins and metabolites, and developed an R package Multi-omic Network Explorer Tool (MoNET) for multi-omic network analysis. This new tool enables users to not only track down the interaction of SNPs/genes with metabolome level, but also trace back for the potential risk variants/regulators given altered genes/metabolites. MoNET is expected to advance our understanding of the multi-omic findings by unveiling their transomic interactions and is likely to generate new hypotheses for further validation. Availability and implementation The MoNET package is freely available on https://github.com/JW-Yan/MONET. Supplementary information Supplementary data are available at Bioinformatics online.
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Boekel, Jorrit, John M. Chilton, Ira R. Cooke, et al. "Multi-omic data analysis using Galaxy." Nature Biotechnology 33, no. 2 (2015): 137–39. http://dx.doi.org/10.1038/nbt.3134.
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Morota, Gota. "30 Mutli-omic data integration in quantitative genetics." Journal of Animal Science 97, Supplement_2 (2019): 15. http://dx.doi.org/10.1093/jas/skz122.027.
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Abstract The advent of high-throughput technologies has generated diverse omic data including single-nucleotide polymorphisms, copy-number variation, gene expression, methylation, and metabolites. The next major challenge is how to integrate those multi-omic data for downstream analyses to enhance our biological insights. This emerging approach is known as multi-omic data integration, which is in contrast to studying each omic data type independently. I will discuss challenging issues in developing algorithms and methods for multi-omic data integration. The particular focus will be given to the potential for combining diverse types of FAANG data and the utility of multi-omic data integration in association analysis and phenotypic prediction.
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Sangaralingam, Ajanthah, Abu Z. Dayem Ullah, Jacek Marzec, et al. "‘Multi-omic’ data analysis using O-miner." Briefings in Bioinformatics 20, no. 1 (2017): 130–43. http://dx.doi.org/10.1093/bib/bbx080.
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von der Heyde, Silvia, Margarita Krawczyk, Julia Bischof, et al. "Clinically relevant multi-omic analysis of colorectal cancer." Journal of Clinical Oncology 38, no. 15_suppl (2020): e16063-e16063. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.e16063.
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e16063 Background: Cancer is a highly heterogeneous disease, both intra- and inter-individually consisting of complex phenotypes and systems biology. Although genomic data has contributed greatly towards the identification of cancer-specific mutations and the progress of precision medicine, genomic alterations are only one of several important biological drivers of cancer. Furthermore, single-layer omics represent only a small piece of the cancer biology puzzle and provide only partial clues to connecting genotype with clinically relevant phenotypic data. A more integrated approach is urgently needed to unravel the underpinnings of molecular signatures and the phenotypic manifestation of cancer hallmarks. Methods: Here we characterize a colorectal cancer (CRC) cohort of 500 patients across multiple distinct omic data types. Across this CRC cohort, we defined clinically relevant whole genome sequencing based metrics such as micro-satellite-instability (MSI) status, and furthermore investigate gene expression at the transcript level using RNA-Seq, as well as at the proteomic level using tandem mass spectrometry. We further characterized a subgroup of 100 of these patients through 16s rRNA sequencing to identify associated microbiome profiles. Results: We combined these analyses with comprehensive clinical data to observe the impact of ascertained molecular signatures on the CRC patient cohort. Here, we report how patient survival correlates both with specific molecular events across individual omic data types, as well as with combined multi-omic analyses. Conclusions: This project highlights the utility of integrating multiple distinct data types to obtain a more comprehensive overview of the molecular mechanisms underpinning colo-rectal cancer. Furthermore, through combining identified aberrant molecular mechanisms with clinical reports, multi-omic data can be prioritized through their impact on patient cohort survival.
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Beheshti, Ramin, Steven Hicks, and Patrick Frangos. "Multi-omic Analysis Enhances Prediction Of Infantile Wheezing." Journal of Allergy and Clinical Immunology 151, no. 2 (2023): AB210. http://dx.doi.org/10.1016/j.jaci.2022.12.654.
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Hale, Andrew T., Lisa Bastarache, Diego M. Morales, John C. Wellons, David D. Limbrick, and Eric R. Gamazon. "Multi-omic analysis elucidates the genetic basis of hydrocephalus." Cell Reports 35, no. 5 (2021): 109085. http://dx.doi.org/10.1016/j.celrep.2021.109085.
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Henry, V. J., A. E. Bandrowski, A. S. Pepin, B. J. Gonzalez, and A. Desfeux. "OMICtools: an informative directory for multi-omic data analysis." Database 2014 (July 14, 2014): bau069. http://dx.doi.org/10.1093/database/bau069.
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Beheshti, Ramin, Shane Stone, Desirae Chandran, and Steven D. Hicks. "Multi-Omic Profiles in Infants at Risk for Food Reactions." Genes 13, no. 11 (2022): 2024. http://dx.doi.org/10.3390/genes13112024.
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Food reactions (FR) are multifactorial and impacted by medical, demographic, environmental, and immunologic factors. We hypothesized that multi-omic analyses of host-microbial factors in saliva would enhance our understanding of FR development. This longitudinal cohort study included 164 infants followed from birth through two years. The infants were identified as FR (n = 34) or non-FR (n = 130) using the Infant Feeding Practice II survey and medical record confirmation. Saliva was collected at six months for the multi-omic assessment of cytokines, mRNAs, microRNAs, and the microbiome/virome. The levels of one miRNA (miR-203b-3p, adj. p = 0.043, V = 2913) and one viral phage (Proteus virus PM135, adj. p = 0.027, V = 2955) were lower among infants that developed FRs. The levels of one bacterial phylum (Cyanobacteria, adj. p = 0.048, V = 1515) were higher among infants that developed FR. Logistical regression models revealed that the addition of multi-omic features (miR-203b-3p, Cyanobacteria, and Proteus virus PM135) improved predictiveness for future FRs in infants (p = 0.005, X2 = 12.9), predicting FRs with 72% accuracy (AUC = 0.81, sensitivity = 72%, specificity = 72%). The multi-omic analysis of saliva may enhance the accurate identification of infants at risk of FRs and provide insights into the host/microbiome interactions that predispose certain infants to FRs.
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Miller, David T., Isidro Cortés-Ciriano, Nischalan Pillay, et al. "Genomics of MPNST (GeM) Consortium: Rationale and Study Design for Multi-Omic Characterization of NF1-Associated and Sporadic MPNSTs." Genes 11, no. 4 (2020): 387. http://dx.doi.org/10.3390/genes11040387.
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The Genomics of Malignant Peripheral Nerve Sheath Tumor (GeM) Consortium is an international collaboration focusing on multi-omic analysis of malignant peripheral nerve sheath tumors (MPNSTs), the most aggressive tumor associated with neurofibromatosis type 1 (NF1). Here we present a summary of current knowledge gaps, a description of our consortium and the cohort we have assembled, and an overview of our plans for multi-omic analysis of these tumors. We propose that our analysis will lead to a better understanding of the order and timing of genetic events related to MPNST initiation and progression. Our ten institutions have assembled 96 fresh frozen NF1-related (63%) and sporadic MPNST specimens from 86 subjects with corresponding clinical and pathological data. Clinical data have been collected as part of the International MPNST Registry. We will characterize these tumors with bulk whole genome sequencing, RNAseq, and DNA methylation profiling. In addition, we will perform multiregional analysis and temporal sampling, with the same methodologies, on a subset of nine subjects with NF1-related MPNSTs to assess tumor heterogeneity and cancer evolution. Subsequent multi-omic analyses of additional archival specimens will include deep exome sequencing (500×) and high density copy number arrays for both validation of results based on fresh frozen tumors, and to assess further tumor heterogeneity and evolution. Digital pathology images are being collected in a cloud-based platform for consensus review. The result of these efforts will be the largest MPNST multi-omic dataset with correlated clinical and pathological information ever assembled.
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Ohno, Satoshi, Saori Uematsu, and Shinya Kuroda. "Quantitative metabolic fluxes regulated by trans-omic networks." Biochemical Journal 479, no. 6 (2022): 787–804. http://dx.doi.org/10.1042/bcj20210596.
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Cells change their metabolism in response to internal and external conditions by regulating the trans-omic network, which is a global biochemical network with multiple omic layers. Metabolic flux is a direct measure of the activity of a metabolic reaction that provides valuable information for understanding complex trans-omic networks. Over the past decades, techniques to determine metabolic fluxes, including 13C-metabolic flux analysis (13C-MFA), flux balance analysis (FBA), and kinetic modeling, have been developed. Recent studies that acquire quantitative metabolic flux and multi-omic data have greatly advanced the quantitative understanding and prediction of metabolism-centric trans-omic networks. In this review, we present an overview of 13C-MFA, FBA, and kinetic modeling as the main techniques to determine quantitative metabolic fluxes, and discuss their advantages and disadvantages. We also introduce case studies with the aim of understanding complex metabolism-centric trans-omic networks based on the determination of metabolic fluxes.
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Khalyfa, Abdelnaby, Jose M. Marin, David Sanz-Rubio, Zhen Lyu, Trupti Joshi, and David Gozal. "Multi-Omics Analysis of Circulating Exosomes in Adherent Long-Term Treated OSA Patients." International Journal of Molecular Sciences 24, no. 22 (2023): 16074. http://dx.doi.org/10.3390/ijms242216074.
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Obstructive sleep apnea (OSA) is a highly prevalent chronic disease affecting nearly a billion people globally and increasing the risk of multi-organ morbidity and overall mortality. However, the mechanisms underlying such adverse outcomes remain incompletely delineated. Extracellular vesicles (exosomes) are secreted by most cells, are involved in both proximal and long-distance intercellular communication, and contribute toward homeostasis under physiological conditions. A multi-omics integrative assessment of plasma-derived exosomes from adult OSA patients prior to and after 1-year adherent CPAP treatment is lacking. We conducted multi-omic integrative assessments of plasma-derived exosomes from adult OSA patients prior to and following 1-year adherent CPAP treatment to identify potential specific disease candidates. Fasting morning plasma exosomes isolated from 12 adult patients with polysomnographically-diagnosed OSA were analyzed before and after 12 months of adherent CPAP therapy (mean ≥ 6 h/night) (OSAT). Exosomes were characterized by flow cytometry, transmission electron microscopy, and nanoparticle tracking analysis. Endothelial cell barrier integrity, wound healing, and tube formation were also performed. Multi-omics analysis for exosome cargos was integrated. Exosomes derived from OSAT improved endothelial permeability and dysfunction as well as significant improvement in tube formation compared with OSA. Multi-omic approaches for OSA circulating exosomes included lipidomic, proteomic, and small RNA (miRNAs) assessments. We found 30 differentially expressed proteins (DEPs), 72 lipids (DELs), and 13 miRNAs (DEMs). We found that the cholesterol metabolism (has04979) pathway is associated with lipid classes in OSA patients. Among the 12 subjects of OSA and OSAT, seven subjects had complete comprehensive exosome cargo information including lipids, proteins, and miRNAs. Multi-omic approaches identify potential signature biomarkers in plasma exosomes that are responsive to adherent OSA treatment. These differentially expressed molecules may also play a mechanistic role in OSA-induced morbidities and their reversibility. Our data suggest that a multi-omic integrative approach might be useful in understanding how exosomes function, their origin, and their potential clinical relevance, all of which merit future exploration in the context of relevant phenotypic variance. Developing an integrated molecular classification should lead to improved diagnostic classification, risk stratification, and patient management of OSA by assigning molecular disease-specific therapies.
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Oromendia, Ana, Dorina Ismailgeci, Michele Ciofii, et al. "Error-free, automated data integration of exosome cargo protein data with extensive clinical data in an ongoing, multi-omic translational research study." Journal of Clinical Oncology 38, no. 15_suppl (2020): e16743-e16743. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.e16743.
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e16743 Background: Major advances in understanding the biology of cancer have come from genomic analysis of tumor and normal tissue. Integrating extensive patient-related data with deep analysis of omic data is crucial to informing omic data interpretation. Currently, such integrations are a highly manual, asynchronous, and costly process as well as error-prone and time-consuming. To develop new blood assays that may detect very early stage PDAC, a multi-omic investigation with deep clinical annotation is needed. Using pilot data from an on-going study, we test a new platform allowing automated error-free integration of an extensive clinical database with extensive omic data. Methods: Demographic, clinical, family pedigree and pathology data were collected on the Rave EDC platform. Exosomes were purified from 46 plasma samples from 14 controls and 24 PDAC patients and cargo proteins were quantified via SILAC. The Rave Omics platform was used to ingest and integrate clinical and omic data, run quality checks and generate integrated clinical-omic datasets. Data fidelity was validated by systematically computing differences between corresponding values in the source flies with those present in the extracted data object (integrated data). The root mean squared error (RMSE) was calculated for numeric values in each sample. Additional validation was conducted by manual inspection to ascertain data integrity. Results: We demonstrated automatic integration, without human intervention, of a subset of the clinical data and all available SILAC data into an analysis-ready data object. Data transfer was completely faithful, with 100% concordance between the source and the integrated data without loss of features. All proteins (n = 1515) and clinical variables (n = 64) were imported. Their nomenclature and corresponding sample values (n = 69690) and clinical values (n = 2432) matched exactly between datasets. In all samples, the RMSE was exactly zero, indicating no deviation between data sources. Conclusions: We demonstrated that automatic, efficient, and reliable integration of clinical-omic data is achievable during an in-flight PDAC trial. Automatic exploratory analytics supporting biomarker discovery are currently being used to uncover associations between omic and clinical features. The Rave Omics platform is disease-agnostic and we plan to expand to trials of varying size, indication, and completion status where systematic, automated integration of clinical and (multi)omic data is needed.
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Towle-Miller, Lorin M., Jeffrey C. Miecznikowski, Fan Zhang, and David L. Tritchler. "SuMO-Fil: Supervised multi-omic filtering prior to performing network analysis." PLOS ONE 16, no. 8 (2021): e0255579. http://dx.doi.org/10.1371/journal.pone.0255579.
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Multi-omic analyses that integrate many high-dimensional datasets often present significant deficiencies in statistical power and require time consuming computations to execute the analytical methods. We present SuMO-Fil to remedy against these issues which is a pre-processing method for Supervised Multi-Omic Filtering that removes variables or features considered to be irrelevant noise. SuMO-Fil is intended to be performed prior to downstream analyses that detect supervised gene networks in sparse settings. We accomplish this by implementing variable filters based on low similarity across the datasets in conjunction with low similarity with the outcome. This approach can improve accuracy, as well as reduce run times for a variety of computationally expensive downstream analyses. This method has applications in a setting where the downstream analysis may include sparse canonical correlation analysis. Filtering methods specifically for cluster and network analysis are introduced and compared by simulating modular networks with known statistical properties. The SuMO-Fil method performs favorably by eliminating non-network features while maintaining important biological signal under a variety of different signal settings as compared to popular filtering techniques based on low means or low variances. We show that the speed and accuracy of methods such as supervised sparse canonical correlation are increased after using SuMO-Fil, thus greatly improving the scalability of these approaches.
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He, Yuchen, Edrees H. Rashan, Vanessa Linke, et al. "Multi-Omic Single-Shot Technology for Integrated Proteome and Lipidome Analysis." Analytical Chemistry 93, no. 9 (2021): 4217–22. http://dx.doi.org/10.1021/acs.analchem.0c04764.
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Forny, Patrick, Ximena Bonilla, David Lamparter, et al. "INTEGRATED MULTI-OMIC ANALYSIS OF A RARE INBORN ERROR OF METABOLISM." Molecular Genetics and Metabolism 135, no. 4 (2022): 271–72. http://dx.doi.org/10.1016/j.ymgme.2022.01.039.
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Nuno, Kevin, Armon Azizi, Thomas Koehnke, M. Ryan Corces, and Ravi Majeti. "Multi-Omic Analysis Identifies Epigenetic Evolution in Relapsed Acute Myeloid Leukemia." Blood 136, Supplement 1 (2020): 13–14. http://dx.doi.org/10.1182/blood-2020-143141.
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Introduction: Acute myeloid leukemia (AML) is associated with a poor prognosis even with aggressive treatments including high dose chemotherapy. While most patients enter clinical remission, these remissions are often short-lived leading to chemotherapy-resistant relapsed disease that accounts for the majority of deaths. We undertook a meta-analysis of published datasets consisting of 142 genotyped paired diagnosis-relapse AML samples to understand the genetic evolution of AML between the two disease states. This analysis determined that a plurality of cases exhibited the same mutations at diagnosis and relapse, and that genetically stable clones were associated with an increased probability of relapse. The finding that many cases exhibited no clonal genetic evolution upon relapse, yet exhibited chemotherapy resistance, lead us to hypothesize that epigenetic evolution plays a significant role in AML relapse. Here, our objective was to investigate the epigenetic evolution and cis and trans regulatory elements that correlate with AML relapse. Methods: We identified 27 paired diagnosis and relapse specimens from patients treated at Stanford with high dose chemotherapy regimens. Leukemic blasts, and in some cases leukemia stem cell (LSC)-enriched fractions, were purified by FACS. Cells were then analyzed through a multi-omic platform including genotyping with a myeloid malignancy targeted panel, RNA-seq, and ATAC-seq to obtain a molecular and chromatin accessibility profile of each sample. The resulting data set was analyzed to investigate epigenetic evolution in relapsed AML. Results: Genotyping analysis of banked AML specimens identified a similar pattern of genetic evolution as our meta-analysis, with several samples exhibiting the same mutations at diagnosis and relapse. We used an epigenetic matrix of chromatin accessibility data obtained from purified cell populations within the hematopoietic hierarchy and implemented this with the CIBERSORT algorithm to map the regulatory programs active in diagnosis and relapsed AML blasts. This analysis revealed a general trend of epigenetic states associated with more primitive cells (such as hematopoietic stem and progenitor cells) active at relapse, as opposed to more differentiated myeloid cell programs active at diagnosis. Focusing further on samples with no genetic changes between the two disease states, we observed several samples with substantial epigenetic evolution at relapse, with AML blasts shifting from a more differentiated myeloid cell profile to that of stem and progenitor cells. These changes were associated with a loss of accessibility in PU.1 and CEBPα transcription factor motifs, with a corresponding increase in GATA and RUNX motifs, suggesting epigenetic remodeling contributes to relapse even in the absence of genetic changes. We have additionally identified various categories of relapse samples in our cohort that share similar epigenetic profiles relating to genotype; NPM1 and FLT3 double mutant samples, for example, shared active chromatin accessibility features. Given the key importance of LSCs in AML pathogenesis and their potential role in chemotherapy resistance, we further undertook an analysis of cellular subpopulations enriched for these cells in a subset of our sample cohort. ATAC-seq analysis of CD34+CD38- cell fractions revealed these cells share many epigenetic features between samples, yet also have distinct regulatory programs from those active in leukemia non-stem cells and exhibit similar epigenetic reprogramming between diagnosis and relapse. This analysis further indicates that epigenetic evolution at relapse occurs at the single cell level, rather than reflecting selection of cellular subpopulations at relapse. Ongoing work involves identifying the specific regulatory programs upregulated in relapse samples, and LSCs specifically, to understand how these programs contribute to relapse at the gene regulatory level. Conclusion: Our results indicate a substantial role for epigenetic evolution in AML, with the activation of more primitive stem and progenitor programs upon relapse. We have also identified epigenetic classifications for several relapse samples that correspond to genotype and characterized the regulatory programs associated with relapse. We hope this work will permit a deeper understanding of the evolutionary factors that guide AML relapse. Disclosures Majeti: Zenshine Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees; Coherus BioSciences: Membership on an entity's Board of Directors or advisory committees; CircBio Inc.: Research Funding; BeyondSpring Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees; Stanford University: Patents & Royalties: pending patent application on CD93 CAR ; Forty-Seven Inc.: Divested equity in a private or publicly-traded company in the past 24 months; Kodikaz Therapeutic Solutions Inc.: Membership on an entity's Board of Directors or advisory committees; Gilead Sciences, Inc.: Patents & Royalties: inventor on patents related to CD47 cancer immunotherapy.
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Teclemariam, Esei T., Melissa R. Pergande, and Stephanie M. Cologna. "Considerations for mass spectrometry-based multi-omic analysis of clinical samples." Expert Review of Proteomics 17, no. 2 (2020): 99–107. http://dx.doi.org/10.1080/14789450.2020.1724540.
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Hanson, Casey, Junmei Cairns, Liewei Wang, and Saurabh Sinha. "Principled multi-omic analysis reveals gene regulatory mechanisms of phenotype variation." Genome Research 28, no. 8 (2018): 1207–16. http://dx.doi.org/10.1101/gr.227066.117.
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Sureshbabu, V., A. Mallipatna, N. Guha, et al. "Integrated multi-omic analysis of human retinoblastoma identifies novel regulatory networks." Acta Ophthalmologica 93 (September 23, 2015): n/a. http://dx.doi.org/10.1111/j.1755-3768.2015.0544.
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Song, Yang, Zhe Wang, Guangji Zhang, et al. "Integrative Multi-Omic Analysis for Prognosis Stratification in Acute Myeloid Leukemia." Blood 142, Supplement 1 (2023): 5984. http://dx.doi.org/10.1182/blood-2023-173211.
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Acute myeloid leukemia (AML) is a heterogeneous hematopoietic malignancy with a dismal prognosis. European LeukemiaNet (ELN) is crucial for tailoring AML treatment individually. Various AML models correlated survival and clinical drug response with immune cell differentiation state by deconvoluting transcriptomics. However, few comprehensive molecular subtyping models integrate multi-omic profiles for prognostic and drug response prediction. Thus, this study aimed to integrate DNA methylation, genomics, transcriptomics and ex vivo drug sensitivity screening of AML patients and explore their roles in facilitating AML molecular stratification and redefining prognosis. Firstly，data on 90 patients with AML(non-promyelocytic leukemia) were retrieved from The Cancer Genome Atlas (TCGA) for consensus clustering by 10 advanced consensus clustering algorithms. The optimal three distinct subtypes, UAMOCS1, UAMOCS2, and UAMCOS3, were identified to achieve the best clustering effect. Multi-omics were orchestrated together for UAMOCS shaping. The UAMOCS can significantly distinguish overall survival (median 18 vs. 27 vs. 45 months, P= 0.0048) among the three instinct subtypes with the integrated contribution of transcriptomic profiles and DNA methylation and mutation data. Also, the activity of AML-specific transcription factors and chromatin remodeling makers showed a diverse pattern among the three subtypes, epigenetically contributing to UAMOCS shaping. Next, the nearest template prediction, a model-free method, was applied to further validate this predictive model. Similar subtyping under this model is generated robustly by imitating 50 subtype-specific genes and capable of identifying survival in both the “ihCAMs-AML” (Figure 1) and GSE37642. The main validation cohort was a real-world de novo AML cohort (acute promyelocytic leukemia excluded), “ihCAMs-AML”（N=98）, aged 14-70 years, with a median follow-up of 28 (1-55) months. The samples were simultaneously available for methylome (N=31, 850K Methylation Chip), DNA mutation (N=96, targeted deep sequencing), transcriptome (N=98, bulk RNA-seq), and ex vivo drug screening (N=66, 17 drugs under 100% peak plasma concentration) profiling analysis. Clinical relevance showed that traditional cytogenetic stratification was strongly correlated with our system. UAMOCS1, acting as an “immune activator”, is characterized by enriching of AML myelodysplasia-related (AML-MR) gene mutations (especially RUNX1 mutation, P = 0.00839), unstable chromosome (deletions on 7q), increased abundance of immune cells with immune escaping marker and poor risk. UAMOCS2 was defined as an intermediate immune burden and a “monocytic-like” phenotype with intermediate survival, corresponding to its inclining result toward M4/M5. UAMOCS3 was an “immune desert” group supposed to be the most favorable subset among the three groups. The ex vivo drug screening result implied that UAMOCS also provide values in distinguishing drug response. UAMOCS1 hardly benefited from commonly used chemotherapeutic drugs compared with the other two distinct subtypes. Pharmacogenomics database GDSC (the Genomics of Drug Sensitivity in Cancer) among AML cell lines was used to evaluate potential drug targets for overcoming drug resistance. Receptor tyrosine kinase signaling inhibitors (SB505124 and EphB4) might be sensitive to UAMOCS1. Taken together, UAMOCS model was generated that can classify AML patients independently and help develop precise chemotherapeutic strategy for patients with AML in the future.
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Rappoport, Nimrod, and Ron Shamir. "NEMO: cancer subtyping by integration of partial multi-omic data." Bioinformatics 35, no. 18 (2019): 3348–56. http://dx.doi.org/10.1093/bioinformatics/btz058.
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Abstract Motivation Cancer subtypes were usually defined based on molecular characterization of single omic data. Increasingly, measurements of multiple omic profiles for the same cohort are available. Defining cancer subtypes using multi-omic data may improve our understanding of cancer, and suggest more precise treatment for patients. Results We present NEMO (NEighborhood based Multi-Omics clustering), a novel algorithm for multi-omics clustering. Importantly, NEMO can be applied to partial datasets in which some patients have data for only a subset of the omics, without performing data imputation. In extensive testing on ten cancer datasets spanning 3168 patients, NEMO achieved results comparable to the best of nine state-of-the-art multi-omics clustering algorithms on full data and showed an improvement on partial data. On some of the partial data tests, PVC, a multi-view algorithm, performed better, but it is limited to two omics and to positive partial data. Finally, we demonstrate the advantage of NEMO in detailed analysis of partial data of AML patients. NEMO is fast and much simpler than existing multi-omics clustering algorithms, and avoids iterative optimization. Availability and implementation Code for NEMO and for reproducing all NEMO results in this paper is in github: https://github.com/Shamir-Lab/NEMO. Supplementary information Supplementary data are available at Bioinformatics online.
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Tan, Hexin, Xianghui Chen, Nan Liang, et al. "Transcriptome analysis reveals novel enzymes for apo-carotenoid biosynthesis in saffron and allows construction of a pathway for crocetin synthesis in yeast." Journal of Experimental Botany 70, no. 18 (2019): 4819–34. http://dx.doi.org/10.1093/jxb/erz211.
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Beheshti, Ramin, E. Scott Halstead, Bryan Cusack, and Steven D. Hicks. "Multi-Omic Factors Associated with Frequency of Upper Respiratory Infections in Developing Infants." International Journal of Molecular Sciences 24, no. 2 (2023): 934. http://dx.doi.org/10.3390/ijms24020934.
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Susceptibility to upper respiratory infections (URIs) may be influenced by host, microbial, and environmental factors. We hypothesized that multi-omic analyses of molecular factors in infant saliva would identify complex host-environment interactions associated with URI frequency. A cohort study involving 146 infants was used to assess URI frequency in the first year of life. Saliva was collected at 6 months for high-throughput multi-omic measurement of cytokines, microRNAs, transcripts, and microbial RNA. Regression analysis identified environmental (daycare attendance, atmospheric pollution, breastfeeding duration), microbial (Verrucomicrobia, Streptococcus phage), and host factors (miR-22-5p) associated with URI frequency (p < 0.05). These results provide pathophysiologic clues about molecular factors that influence URI susceptibility. Validation of these findings in a larger cohort could one day yield novel approaches to detecting and managing URI susceptibility in infants.
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Sandri, Brian J., Adam Kaplan, Shane W. Hodgson, et al. "Multi-omic molecular profiling of lung cancer in COPD." European Respiratory Journal 52, no. 1 (2018): 1702665. http://dx.doi.org/10.1183/13993003.02665-2017.
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Chronic obstructive pulmonary disease (COPD) is a known risk factor for developing lung cancer but the underlying mechanisms remain unknown. We hypothesise that the COPD stroma contains molecular mechanisms supporting tumourigenesis.We conducted an unbiased multi-omic analysis to identify gene expression patterns that distinguish COPD stroma in patients with or without lung cancer. We obtained lung tissue from patients with COPD and lung cancer (tumour and adjacent non-malignant tissue) and those with COPD without lung cancer for profiling of proteomic and mRNA (both cytoplasmic and polyribosomal). We used the Joint and Individual Variation Explained (JIVE) method to integrate and analyse across the three datasets.JIVE identified eight latent patterns that robustly distinguished and separated the three groups of tissue samples (tumour, adjacent and control). Predictive variables that associated with the tumour, compared to adjacent stroma, were mainly represented in the transcriptomic data, whereas predictive variables associated with adjacent tissue, compared to controls, were represented at the translatomic level. Pathway analysis revealed extracellular matrix and phosphatidylinositol-4,5-bisphosphate 3-kinase–protein kinase B signalling pathways as important signals in the tumour adjacent stroma.The multi-omic approach distinguishes tumour adjacent stroma in lung cancer and reveals two stromal expression patterns associated with cancer.
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Abdelhamid, Sultan S., Jacob Scioscia, Yoram Vodovotz, et al. "Multi-Omic Admission-Based Prognostic Biomarkers Identified by Machine Learning Algorithms Predict Patient Recovery and 30-Day Survival in Trauma Patients." Metabolites 12, no. 9 (2022): 774. http://dx.doi.org/10.3390/metabo12090774.
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Admission-based circulating biomarkers for the prediction of outcomes in trauma patients could be useful for clinical decision support. It is unknown which molecular classes of biomolecules can contribute biomarkers to predictive modeling. Here, we analyzed a large multi-omic database of over 8500 markers (proteomics, metabolomics, and lipidomics) to identify prognostic biomarkers in the circulating compartment for adverse outcomes, including mortality and slow recovery, in severely injured trauma patients. Admission plasma samples from patients (n = 129) enrolled in the Prehospital Air Medical Plasma (PAMPer) trial were analyzed using mass spectrometry (metabolomics and lipidomics) and aptamer-based (proteomics) assays. Biomarkers were selected via Least Absolute Shrinkage and Selection Operator (LASSO) regression modeling and machine learning analysis. A combination of five proteins from the proteomic layer was best at discriminating resolvers from non-resolvers from critical illness with an Area Under the Receiver Operating Characteristic curve (AUC) of 0.74, while 26 multi-omic features predicted 30-day survival with an AUC of 0.77. Patients with traumatic brain injury as part of their injury complex had a unique subset of features that predicted 30-day survival. Our findings indicate that multi-omic analyses can identify novel admission-based prognostic biomarkers for outcomes in trauma patients. Unique biomarker discovery also has the potential to provide biologic insights.
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Klymyshyn, Dmytro, Vaibhav Jain, Lauren Whaley, et al. "Abstract 5496: Multi-omic spatial analysis of the tumor microenvironment in gliomas." Cancer Research 84, no. 6_Supplement (2024): 5496. http://dx.doi.org/10.1158/1538-7445.am2024-5496.
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Abstract Introduction: The diverse tumor environment of high-grade glioma, which remains refractory to treatment, demands new innovative, multi-omic approaches to characterize tumor heterogeneity and expression profiles associated with progression and therapeutic response. Approach: We have applied an integrated, multi-omic approach using paired spatial in situ sequencing (Xenium) and protein profiling (PhenoCycler® Fusion) of glioma tissue to identify distinct glioma cellular phenotypes, activation states and metabolic pathways at true single cell resolution. Our multi-omic approach used hundreds of phenotypic RNA target probes and &gt;50 antibodies in an unbiased single-cell analysis of primary, recurrent, and IDH1 mutant/wild type tumors with a range of heterogeneity. Summary: We were able to distinguish differentially regulated genes and pathways between aggregated primary and recurrent GBM, including cell cycle pathways being upregulated in primary vs recurrent GBM glial cell clusters and a down regulation of ERBB4 signaling in vascular cell clusters in primary GBMs. Our data also suggests that SOX11, known to be involved in tumorigenesis, is &gt;2-fold upregulated in annotated tumor cells in primary compared to recurrent tumors. We were also able to quantitatively localize the expression of Epidermal Growth Factor Receptor, variant III (EGFRvIII) to specific tumor-cell sub-types, which is important given its association with therapeutic resistance. Further, we have been able to establish the cellular neighborhoods and cell-cell and receptor-ligand relationships within the tumors. Our spatial protein analysis identified distinct tumor and immune phenotypes with varying abundance of myeloid and lymphoid populations in IDH1 wt and mt tumors. Moreover, spatial proximity analyses and cellular neighborhood analyses revealed differences in higher order organizational landscapes that may contribute to the differential outcomes across wt and mt tumor subtypes. Conclusion: Multiomic spatial analysis enables deeper characterization of the glioma cellular and functional landscape to broaden our understanding of the key TME features that contribute to disease pathogenesis and prognoses. Our study provides an analytical framework to combine RNA and protein-based spatial data for a holistic investigation into a variety of glioma subtypes and aid in the identification of novel biomarkers, spatial neighborhoods, and functional states that drive glioma progression. Citation Format: Dmytro Klymyshyn, Vaibhav Jain, Lauren Whaley, Emily Hocke, Bassem Ben Cheikh, Alan Smith, Karen Abramson, Nadine Nelson, Diane Satterfield, Elizabeth Thomas, Giselle Lopez, Seetha Hariharan, Michael Brown, Niyati Jhaveri, Roger McLendon, David Ashley, Matthew Waitkus, Simon Gregory. Multi-omic spatial analysis of the tumor microenvironment in gliomas [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5496.
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Creasy, Heather Huot, Victor Felix, Jain Aluvathingal, et al. "HMPDACC: a Human Microbiome Project Multi-omic data resource." Nucleic Acids Research 49, no. D1 (2020): D734—D742. http://dx.doi.org/10.1093/nar/gkaa996.
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Abstract The Human Microbiome Project (HMP) explored microbial communities of the human body in both healthy and disease states. Two phases of the HMP (HMP and iHMP) together generated &gt;48TB of data (public and controlled access) from multiple, varied omics studies of both the microbiome and associated hosts. The Human Microbiome Project Data Coordination Center (HMPDACC) was established to provide a portal to access data and resources produced by the HMP. The HMPDACC provides a unified data repository, multi-faceted search functionality, analysis pipelines and standardized protocols to facilitate community use of HMP data. Recent efforts have been put toward making HMP data more findable, accessible, interoperable and reusable. HMPDACC resources are freely available at www.hmpdacc.org.
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Zhu, Shuwei, Wenping Wang, Wei Fang, and Meiji Cui. "Autoencoder-assisted latent representation learning for survival prediction and multi-view clustering on multi-omics cancer subtyping." Mathematical Biosciences and Engineering 20, no. 12 (2023): 21098–119. http://dx.doi.org/10.3934/mbe.2023933.
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<abstract><p>Cancer subtyping (or cancer subtypes identification) based on multi-omics data has played an important role in advancing diagnosis, prognosis and treatment, which triggers the development of advanced multi-view clustering algorithms. However, the high-dimension and heterogeneity of multi-omics data make great effects on the performance of these methods. In this paper, we propose to learn the informative latent representation based on autoencoder (AE) to naturally capture nonlinear omic features in lower dimensions, which is helpful for identifying the similarity of patients. Moreover, to take advantage of survival information or clinical information, a multi-omic survival analysis approach is embedded when integrating the similarity graph of heterogeneous data at the multi-omics level. Then, the clustering method is performed on the integrated similarity to generate subtype groups. In the experimental part, the effectiveness of the proposed framework is confirmed by evaluating five different multi-omics datasets, taken from The Cancer Genome Atlas. The results show that AE-assisted multi-omics clustering method can identify clinically significant cancer subtypes.</p></abstract>
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Datta, Shalini, Sarah M. Shin, Jessie Kanacharoen, et al. "Abstract 6092: Three-dimensional multi-omic analysis of early invasion of human pancreatic ductal adenocarcinoma from IPMN." Cancer Research 84, no. 6_Supplement (2024): 6092. http://dx.doi.org/10.1158/1538-7445.am2024-6092.
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Abstract Background: Pancreatic ductal adenocarcinoma (PDAC) is a deadly cancer, with only 11% of patients surviving beyond 5 years. Intraductal papillary mucinous neoplasm (IPMN), a precancerous lesion and the most common type of neoplastic pancreatic cyst, presents a critical opportunity for cancer interception, but the drivers of malignant progression in IPMN are still largely unknown. Although the immunosuppressive microenvironment of PDAC has been well characterized, the timing of the development of these immune alterations in IPMNs is not well studied and could provide a rational foundation for cancer immuno-prevention. Moreover, the inception of invasive PDAC and its spread into the microenvironment from IPMN is complex, multifactorial, and, due to its unique anatomy, challenging to analyze with traditional 2-dimensional visualization. We sought to capture the precise point of transition from IPMN to invasive carcinoma utilizing CODA, a supervised deep learning tool for three-dimensional (3D) reconstruction of serially sectioned human tissue, in order to quantitatively assess the molecular and cellular alterations associated with malignant progression. Method: Formalin-fixed paraffin-embedded (FFPE) tissue blocks with PDAC arising from IPMN were serially sectioned, every third slide was stained with H&E, and digitized at 20x magnification. Following pathologist-guided annotations on a subset of H&E slides, CODA generated 3D models of each tissue block, including automated annotation of 9 pancreatic tissue components. Using these annotations, we identified the transition from IPMN to invasive carcinoma in each sample and selected regions for multi-omic profiling based on quantitative features of the cellular microenvironment. Multi-omic profiling of IPMN, transition zone, and PDAC included laser capture microdissection followed by whole exome sequencing to identify somatic DNA alterations, as well as spatial transcriptomics to identify alterations in gene expression in neoplastic cells and spatial proteomics to identify cellular alterations in the surrounding microenvironment. Results: CODA generated accurate annotation on all the H&E slides from each case and robustly identified regions of interest for multi-omic profiling. Spatial proteomic profiling using imaging mass cytometry revealed a decrease in T cell density as IPMNs transition into PDAC. Sub- clustering of the T cell compartment identified decreases in multiple T cell subsets, including activated cytotoxic T cells and helper T cells, in the transition zone compared to IPMN. Conclusion: In this study, we integrated multi-omic profiling with CODA-generated high-resolution 3D tissue maps to identify molecular and cellular drivers of malignant progression in human PDAC. Citation Format: Shalini Datta, Sarah M. Shin, Jessie Kanacharoen, Michael Johannes Pflüger, Katsuya Hirose, André Forjaz, Sarah Graham, Pei-Hsun Wu, Ralph H. Hruban, Denis Wirtz, Won Jin Ho, Ashley L. Kiemen, Laura D. Wood. Three-dimensional multi-omic analysis of early invasion of human pancreatic ductal adenocarcinoma from IPMN [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6092.
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Stewart, Paul, Ashley Lui, Eric Welsh, et al. "Abstract 6029: Multi-omic landscape of squamous cell lung cancer." Cancer Research 83, no. 7_Supplement (2023): 6029. http://dx.doi.org/10.1158/1538-7445.am2023-6029.
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Abstract Patients with squamous cell lung cancer (SCC) have high unmet medical need. Knowledge of these tumors is limited, and a lack of targetable genomic drivers means patients have few treatment options. To provide a detailed analysis on the influence of genomic alterations to proteome-level changes in SCC, we previously integrated DNA copy number, somatic mutations, RNA-sequencing, and expression proteomics in a cohort of 108 SCC patients. A major finding was identification of three proteomic subtypes, two of which made up the majority (87%) of tumors: the “Inflamed” subtype was enriched for B-cell rich tertiary lymphoid structures (TLS), and the “Redox” subtype was enriched for redox pathways and NFE2L2/KEAP1 alterations but had significantly less immune infiltration. We hypothesized these proteomic subtypes would give rise to distinct metabolic signatures. Therefore, we performed untargeted metabolomics on 87 tumors from the same cohort using chromatographic separation on a HILIC column, followed by analysis on a Q Exactive HF mass spectrometer. This analysis yielded 7,344 features corresponding to 7,072 unannotated metabolites and 272 identified metabolites. Glutathione, a key redox metabolite, was anticorrelated with immune score (R = -0.44, padj = 0.004) calculated from our transcriptomic data with the ESTIMATE algorithm, and glutathione was elevated in the Redox proteomic subtype (0.58 log2 ratio, padj = 9.87E-04). Consensus clustering was next used to identify novel metabolomic subtypes of SCC. Surprisingly, none of the five metabolomic subtypes we identified corresponded to proteomic subtype or NFE2L2/KEAP1 alteration (Fisher’s Exact test p-values &gt; 0.05). The fifth subtype had 332 metabolites (26 identified) differentially expressed (&gt; 1.5 fold-change, padj &lt; 0.05) with ascorbate and aldarate metabolism as the top enriched pathway (padj = 3.36E-04). Interestingly, this fifth metabolomic subtype had significantly higher DNp63-alpha (p = 2.40E-05), a primary transcript of delta-N p63 that is known to promote non-small cell lung cancer. Ongoing integrative analyses across omic types will determine how p53, p63, and p73 transcripts influence these metabolomic subtypes, how these transcripts relate to the poor immune infiltration in some SCC tumors, and if these transcripts relate to novel metabolic vulnerabilities in SCC. Citation Format: Paul Stewart, Ashley Lui, Eric Welsh, Dalia Ercan, Vanessa Rubio, Hayley Ackerman, Guohui Li, Bin Fang, Steven Eschrich, John Koomen, Elsa Flores, Eric Haura, Gina DeNicola. Multi-omic landscape of squamous cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6029.
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Eales, James M., Xiao Jiang, Xiaoguang Xu, et al. "Uncovering genetic mechanisms of hypertension through multi-omic analysis of the kidney." Nature Genetics 53, no. 5 (2021): 630–37. http://dx.doi.org/10.1038/s41588-021-00835-w.
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Revilla, Lluís, Aida Mayorgas, Ana M. Corraliza, et al. "Multi-omic modelling of inflammatory bowel disease with regularized canonical correlation analysis." PLOS ONE 16, no. 2 (2021): e0246367. http://dx.doi.org/10.1371/journal.pone.0246367.
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Background Personalized medicine requires finding relationships between variables that influence a patient’s phenotype and predicting an outcome. Sparse generalized canonical correlation analysis identifies relationships between different groups of variables. This method requires establishing a model of the expected interaction between those variables. Describing these interactions is challenging when the relationship is unknown or when there is no pre-established hypothesis. Thus, our aim was to develop a method to find the relationships between microbiome and host transcriptome data and the relevant clinical variables in a complex disease, such as Crohn’s disease. Results We present here a method to identify interactions based on canonical correlation analysis. We show that the model is the most important factor to identify relationships between blocks using a dataset of Crohn’s disease patients with longitudinal sampling. First the analysis was tested in two previously published datasets: a glioma and a Crohn’s disease and ulcerative colitis dataset where we describe how to select the optimum parameters. Using such parameters, we analyzed our Crohn’s disease data set. We selected the model with the highest inner average variance explained to identify relationships between transcriptome, gut microbiome and clinically relevant variables. Adding the clinically relevant variables improved the average variance explained by the model compared to multiple co-inertia analysis. Conclusions The methodology described herein provides a general framework for identifying interactions between sets of omic data and clinically relevant variables. Following this method, we found genes and microorganisms that were related to each other independently of the model, while others were specific to the model used. Thus, model selection proved crucial to finding the existing relationships in multi-omics datasets.
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Sullivan, Kyle, David Kainer, Matthew Lane, et al. "T121. MULTI-OMIC NETWORK ANALYSIS IDENTIFIES KEY NEUROBIOLOGICAL PATHWAYS IN OPIOID ADDICTION." European Neuropsychopharmacology 63 (October 2022): e234. http://dx.doi.org/10.1016/j.euroneuro.2022.07.417.
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Batra, Richa, William Whalen, Sergio Alvarez-Mulett, et al. "Multi-omic comparative analysis of COVID-19 and bacterial sepsis-induced ARDS." PLOS Pathogens 18, no. 9 (2022): e1010819. http://dx.doi.org/10.1371/journal.ppat.1010819.
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Background Acute respiratory distress syndrome (ARDS), a life-threatening condition characterized by hypoxemia and poor lung compliance, is associated with high mortality. ARDS induced by COVID-19 has similar clinical presentations and pathological manifestations as non-COVID-19 ARDS. However, COVID-19 ARDS is associated with a more protracted inflammatory respiratory failure compared to traditional ARDS. Therefore, a comprehensive molecular comparison of ARDS of different etiologies groups may pave the way for more specific clinical interventions. Methods and findings In this study, we compared COVID-19 ARDS (n = 43) and bacterial sepsis-induced (non-COVID-19) ARDS (n = 24) using multi-omic plasma profiles covering 663 metabolites, 1,051 lipids, and 266 proteins. To address both between- and within- ARDS group variabilities we followed two approaches. First, we identified 706 molecules differently abundant between the two ARDS etiologies, revealing more than 40 biological processes differently regulated between the two groups. From these processes, we assembled a cascade of therapeutically relevant pathways downstream of sphingosine metabolism. The analysis suggests a possible overactivation of arginine metabolism involved in long-term sequelae of ARDS and highlights the potential of JAK inhibitors to improve outcomes in bacterial sepsis-induced ARDS. The second part of our study involved the comparison of the two ARDS groups with respect to clinical manifestations. Using a data-driven multi-omic network, we identified signatures of acute kidney injury (AKI) and thrombocytosis within each ARDS group. The AKI-associated network implicated mitochondrial dysregulation which might lead to post-ARDS renal-sequalae. The thrombocytosis-associated network hinted at a synergy between prothrombotic processes, namely IL-17, MAPK, TNF signaling pathways, and cell adhesion molecules. Thus, we speculate that combination therapy targeting two or more of these processes may ameliorate thrombocytosis-mediated hypercoagulation. Conclusion We present a first comprehensive molecular characterization of differences between two ARDS etiologies–COVID-19 and bacterial sepsis. Further investigation into the identified pathways will lead to a better understanding of the pathophysiological processes, potentially enabling novel therapeutic interventions.
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Bordbar, Aarash, Monica L. Mo, Ernesto S. Nakayasu, et al. "Model‐driven multi‐omic data analysis elucidates metabolic immunomodulators of macrophage activation." Molecular Systems Biology 8, no. 1 (2012): 558. http://dx.doi.org/10.1038/msb.2012.21.
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Aviner, Ranen, Anjana Shenoy, Orna Elroy-Stein, and Tamar Geiger. "Uncovering Hidden Layers of Cell Cycle Regulation through Integrative Multi-omic Analysis." PLOS Genetics 11, no. 10 (2015): e1005554. http://dx.doi.org/10.1371/journal.pgen.1005554.
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Sigaud, Romain, Florian Selt, Thomas Hielscher, et al. "LGG-14. MULTI-OMIC ANALYSIS OF MAPK ACTIVATION IN PEDIATRIC PILOCYTIC ASTROCYTOMA." Neuro-Oncology 22, Supplement_3 (2020): iii368. http://dx.doi.org/10.1093/neuonc/noaa222.396.
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Abstract Pilocytic astrocytomas (PA) are low-grade gliomas (pLGG) and are the most frequent childhood brain tumors. They are characterized by oncogene-induced senescence (OIS) initiated and sustained by senescence-associated secretory phenotype (SASP) factors. OIS and SASP in PA are thought to be driven by aberrations of the mitogen-activated protein kinase (MAPK) pathway (e.g. KIAA1549:BRAF fusion, BRAFV600E mutation, for the most common MAPK alterations occuring in PA), leading to its sustained activation. The MAPK pathway cascade is activated in a sequential manner: 1) ERK activation, which phosphorylates downstream partners in both cytoplasm and nucleus. 2) ERK-mediated induction of immediate early genes encoding transcription factors. 3) Induction of MAPK target genes expression. 4) Activation of downstream pathways. Our aim is to unravel the molecular partners involved at each level of the sustained MAPK pathway activation in pLGG with different genetic backgrounds (KIAA1549:BRAF fusion and BRAFV600E mutation), and leading to the induction of OIS and SASP factors expression. pLGG cell lines DKFZ-BT66 (KIAA1549:BRAF) and BT-40 (BRAFV600E) were treated with the MEK inhibitor trametinib at key time points, and gene expression profile analysis was performed, allowing transcriptome analysis at each step of the MAPK cascade. This will be combined with a whole proteomic and phospho-proteomic analysis. Combination of the transcriptome and proteome data layers will allow the identification of a) downstream targetable partners activated by the MAPK pathway involved in PA senescence, b) new putative targets that might bring benefit in combination with MAPK inhibitors.
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Kerr, Katie, Helen McAneney, and Amy Jayne McKnight. "Protocol for a scoping review of multi-omic analysis for rare diseases." BMJ Open 9, no. 5 (2019): e026278. http://dx.doi.org/10.1136/bmjopen-2018-026278.
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IntroductionThe development of next generation sequencing technology has enabled cost-efficient, large scale, multiple ‘omic’ analysis, including epigenomic, genomic, metabolomic, phenomic, proteomic and transcriptomic research. These integrated approaches hold significant promise for rare disease research, with the potential to aid biomarker discovery, improve our understanding of disease pathogenesis and identify novel therapeutic targets. In this paper we outline a systematic approach for a scoping review designed to evaluate what primary research has been performed to date on multi-omics and rare disease.Methods and analysisThis protocol was designed using the Joanna Briggs Institute methodology for scoping reviews. Databases to be searched will include: MEDLINE, EMBASE, PubMed, Web of Science, Scopus and Google Scholar for primary studies relevant to the key terms ‘multi-omics’ and ‘rare disease’, published prior to 30thDecember 2018. Grey literature databases GreyLit and OpenGrey will also be searched, as well as reverse citation screening of relevant articles and forward citation searching using Web of Science Cited Reference Search Tool. Data extraction will be performed using customised forms and a narrative synthesis of the results will be presented.Ethics and disseminationAs a secondary analysis study with no primary data generated, this scoping review does not require ethical approval. We anticipate this review will highlight a gap in rare disease research and provide direction for novel research. The completed review will be submitted for publication in peer-reviewed journals and presented at relevant conferences discussing rare disease research and/or molecular strategies.
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Nakashima, Takuma, Yusuke Funakoshi, Ryo Yamamoto, et al. "10064-GGE-6 A MULTI-OMIC LANDSCAPE OF GLIOBLASTOMA, IDH-WILD TYPE." Neuro-Oncology Advances 5, Supplement_5 (2023): v8. http://dx.doi.org/10.1093/noajnl/vdad141.031.
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Abstract Glioblastoma (GBM) is the most common and highly resistant malignant brain tumor. Although the previous large-scale genomic analyses identified numerous driver genes, limited progress has been achieved in the development of novel treatments. To obtain further insights into the molecular mechanisms underlying the development of GBM, an integrated analysis including epigenetic and transcriptomic analyses, known to regulate malignant tumor progression, is imperative. We analyzed 289 whole-genome sequencing (WGS) including 159 unpublished deep WGS (≥ ×120 coverage) along with RNA-seq, DNA methylation array, whole-genome bisulfite sequencing, and assay for transposase-accessible chromatin with sequencing (ATAC-seq).Mutational analysis identified known driver alterations exhibiting inter-tumoral heterogeneity. Deep WGS enables us to delineate a fine view of clonal architecture demonstrating distinct mutational signatures between clonal and subclonal mutations, suggesting different mutational processes contribute to GBM pathogenesis depending on the developmental stage. Genetic alterations are strongly associated with gene expression subtypes and DNA methylation patterns. Transcriptional deconvolution analysis reveals the heterogeneous proportion of differentiated and stem-like cell states among cases. Tumors predominantly comprised of differentiated cells display genetic and epigenetic profiles that align with the classical subtype, whereas tumors predominantly composed of stem-like cells exhibit profiles consistent with the proneural subtype. Genome-wide chromatin accessibility patterns are well associated with expression subtypes of GBM. Motif enrichment analysis of open chromatin sites identified specific transcription factor binding sites, such as the SOX10 motif in the proneural subtype, known to regulate cell states, and the CREB motif in the mesenchymal and classical subtypes, which promote cell proliferation and angiogenesis through TGF-beta regulation. These findings support a model in which the difference in chromatin structure also regulates the progression of GBM.Our analysis encompassing multilayer molecular mechanisms reveals that GBM evolves through harboring genetic alterations and epigenetic modifications depending on the tumor initiation stage and cellular differentiation status.
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Chantzichristos, Dimitrios, Per-Arne Svensson, Terence Garner, et al. "MiR-122-5p: A Novel Biomarker of Glucocorticoid Action." Journal of the Endocrine Society 5, Supplement_1 (2021): A89. http://dx.doi.org/10.1210/jendso/bvab048.178.
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Abstract Background: Glucocorticoids are among the most prescribed medications for various indications, and treatment with glucocorticoids is associated with increased morbidity and mortality. A biomarker allowing quantification of glucocorticoid action could improve treatment safety and efficacy. Objective: To identify and validate circulating biomarkers of glucocorticoid action using a clinical experimental study and multi-omic network analysis. Methods: In a randomized, controlled, crossover, single-blind trial, 10 subjects without endogenous glucocorticoid production (Addison’s disease) received intravenous hydrocortisone infusion in a circadian pattern (physiological glucocorticoid exposure) or isotonic saline (glucocorticoid withdrawal) over 22 hours. Food intake and sample collections were standardized during both treatment periods. The transcriptomes of peripheral blood mononuclear cells and adipose tissue, plasma miRNAome and serum metabolome were collected at 7 AM (end of infusion). These multi-omic data were compared between the two interventions, within and between subjects, using network analysis of higher order interactions along with statistical and machine learning approaches. Samples from 120 subjects with varying glucocorticoid exposure from independent studies were used for the replication of the miRNA findings. The study was pre-registered at ClinicalTrials.gov with identifier NCT02152553. Results: We identified a transcriptomic profile derived from both peripheral blood mononuclear cells and adipose tissue, and a multi-omic signature including genes, miRNAs and metabolites that were associated with glucocorticoid exposure. Within the multi-omic signature we identified a single microRNA (miR-122-5p, p=0.009) regulated by glucocorticoid exposure, which we then replicated as a novel biomarker of glucocorticoid action in 120 subjects from independent studies (0.01 ≤ p ≤ 0.05). Conclusions: The discovery of miR-122-5p as a novel circulating biomarker of glucocorticoid action may have a significant impact on clinical practice. Our data also improves the understanding of glucocorticoid action and may have impact on future studies on the mechanistic understanding for the role of glucocorticoids in the etiology of common diseases, such as cardiovascular disease and obesity.
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Narvaez-Bandera, Isis Y., Ashley Lui, Eric Welsh, et al. "Abstract 3495: Multi-omic landscape of squamous cell lung cancer." Cancer Research 84, no. 6_Supplement (2024): 3495. http://dx.doi.org/10.1158/1538-7445.am2024-3495.
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Abstract Patients with lung squamous cell carcinoma (LSCC) require new drug targets and improved biomarkers due to a lack of targetable genomic drivers and low response rates to immune checkpoint blockade. In a previous study, we analyzed a cohort of 108 LSCC patients by integrating DNA copy number variation, somatic mutations, RNA-sequencing, and expression proteomics. The principal discovery was the identification of three proteomic subtypes, with the majority (87%) of tumors comprising two of these subtypes. The "Inflamed" subtype showed enrichment for B-cell-rich tertiary lymphoid structures, while the "Redox" subtype exhibited enrichment for redox pathways and NFE2L2/KEAP1 alterations but had notably lower immune infiltration. We hypothesized that these subtypes would result in distinct metabolic signatures. Using ultra-high-performance liquid chromatographic separation on a HILIC column, followed by analysis on a Q Exactive HF high resolution mass spectrometer, we performed untargeted metabolomics on 87 tumors from the same LSCC proteogenomics cohort. A total of 7,392 features were obtained from this analysis, leading to the identification of 446 metabolites through m/z and retention time matching against an internal reference library. To understand if metabolomics could recapitulate our proteomic subtypes, we applied consensus clustering and non-negative matrix factorization (NMF) and assessed the resulting clusters using a Random Forest (RF) supervised classifier. Area Under the Curve (AUC) values for consensus clustering (5 clusters, AUC = 0.72), NMF (4 clusters, AUC = 0.73), and proteomics subtypes (Stewart et al. Nature communications. 2019:10:3578) (3 clusters, AUC = 0.74) suggest that metabolite abundances do indeed recapitulate the proteomic subtypes. Differential expression between Redox and Inflamed yielded 29 differentially expressed metabolites (p-value &lt; 0.05 and 1.5 fold change). Glutathione, a key redox metabolite, was modestly elevated in the Redox proteomic subtype (0.58 log2 ratio, p = 1.14E-05). Notably, we identified glutathione metabolism (p = 1.29-5) and arginine biosynthesis (p = 5.22-4) as among the most significant pathways among differentially expressed metabolites. Glutathione, a key redox metabolite, was modestly elevated in the Redox proteomic subtype (0.58 log2 ratio, p = 1.14E-05). In conclusion, metabolomics recapitulates the proteomic subtypes, and there are distinct differences between these subtypes at the metabolite level. Ongoing work is developing a novel, network-based analysis framework to integrate these data quantitatively. Citation Format: Isis Y. Narvaez-Bandera, Ashley Lui, Eric Welsh, Dalia Ercan, Vanessa Rubio, Hayley Ackerman, Guohui Li, Lancia Darville, Min Liu, Bin Fang, Steven Eschrich, Brooke Fridley, John Koomen, Eric Haura, Gina M. DeNicola, Elsa Flores, Paul Stewart. Multi-omic landscape of squamous cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3495.
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Downing, Tim, and Nicos Angelopoulos. "A primer on correlation-based dimension reduction methods for multi-omics analysis." Journal of The Royal Society Interface 20, no. 207 (2023). http://dx.doi.org/10.1098/rsif.2023.0344.
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The continuing advances of omic technologies mean that it is now more tangible to measure the numerous features collectively reflecting the molecular properties of a sample. When multiple omic methods are used, statistical and computational approaches can exploit these large, connected profiles. Multi-omics is the integration of different omic data sources from the same biological sample. In this review, we focus on correlation-based dimension reduction approaches for single omic datasets, followed by methods for pairs of omics datasets, before detailing further techniques for three or more omic datasets. We also briefly detail network methods when three or more omic datasets are available and which complement correlation-oriented tools. To aid readers new to this area, these are all linked to relevant R packages that can implement these procedures. Finally, we discuss scenarios of experimental design and present road maps that simplify the selection of appropriate analysis methods. This review will help researchers navigate emerging methods for multi-omics and integrating diverse omic datasets appropriately. This raises the opportunity of implementing population multi-omics with large sample sizes as omics technologies and our understanding improve.
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Bardozzo, Francesco, Pietro Lió, and Roberto Tagliaferri. "Signal metrics analysis of oscillatory patterns in bacterial multi-omic networks." Bioinformatics, November 13, 2020. http://dx.doi.org/10.1093/bioinformatics/btaa966.
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Abstract Motivation One of the branches of Systems Biology is focused on a deep understanding of underlying regulatory networks through the analysis of the biomolecules oscillations and their interplay. Synthetic Biology exploits gene or/and protein regulatory networks towards the design of oscillatory networks for producing useful compounds. Therefore, at different levels of application and for different purposes, the study of biomolecular oscillations can lead to different clues about the mechanisms underlying living cells. It is known that network-level interactions involve more than one type of biomolecule as well as biological processes operating at multiple omic levels. Combining network/pathway-level information with genetic information it is possible to describe well-understood or unknown bacterial mechanisms and organism-specific dynamics. Results Following the methodologies used in signal processing and communication engineering, a methodology is introduced to identify and quantify the extent of multi-omic oscillations. These are due to the process of multi-omic integration and depend on the gene positions on the chromosome. Ad hoc signal metrics are designed to allow further biotechnological explanations and provide important clues about the oscillatory nature of the pathways and their regulatory circuits. Our algorithms designed for the analysis of multi-omic signals are tested and validated on 11 different bacteria for thousands of multi-omic signals perturbed at the network level by different experimental conditions. Information on the order of genes, codon usage, gene expression and protein molecular weight is integrated at three different functional levels. Oscillations show interesting evidence that network-level multi-omic signals present a synchronized response to perturbations and evolutionary relations along taxa. Availability and implementation The algorithms, the code (in language R), the tool, the pipeline and the whole dataset of multi-omic signal metrics are available at: https://github.com/lodeguns/Multi-omicSignals. Supplementary information Supplementary data are available at Bioinformatics online.
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Rau, Andrea, Regina Manansala, Michael J. Flister, et al. "Individualized multi-omic pathway deviation scores using multiple factor analysis." Biostatistics, August 6, 2020. http://dx.doi.org/10.1093/biostatistics/kxaa029.
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Summary Malignant progression of normal tissue is typically driven by complex networks of somatic changes, including genetic mutations, copy number aberrations, epigenetic changes, and transcriptional reprogramming. To delineate aberrant multi-omic tumor features that correlate with clinical outcomes, we present a novel pathway-centric tool based on the multiple factor analysis framework called padma. Using a multi-omic consensus representation, padma quantifies and characterizes individualized pathway-specific multi-omic deviations and their underlying drivers, with respect to the sampled population. We demonstrate the utility of padma to correlate patient outcomes with complex genetic, epigenetic, and transcriptomic perturbations in clinically actionable pathways in breast and lung cancer.
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Hertzano, Ronna, and Anup Mahurkar. "Advancing discovery in hearing research via biologist-friendly access to multi-omic data." Human Genetics, March 2, 2022. http://dx.doi.org/10.1007/s00439-022-02445-w.
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AbstractHigh-throughput cell type-specific multi-omic analyses have advanced our understanding of inner ear biology in an unprecedented way. The full benefit of these data, however, is reached from their re-use. Successful re-use of data requires identifying the natural users and ensuring proper data democratization and federation for their seamless and meaningful access. Here we discuss universal challenges in access and re-use of multi-omic data, possible solutions, and introduce the gEAR (the gene Expression Analysis Resource, umgear.org)—a tool for multi-omic data visualization, sharing and access for the ear field.
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Mendelson, Jenna B., Jacob D. Sternbach, Michelle J. Doyle, et al. "A Multi-omic and Multi-Species Analysis of Right Ventricular Dysfunction." Journal of Heart and Lung Transplantation, October 2023. http://dx.doi.org/10.1016/j.healun.2023.09.020.
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Garmany, Ramin, J. Martijn Bos, David J. Tester, et al. "Multi-Omic Architecture of Obstructive Hypertrophic Cardiomyopathy." Circulation: Genomic and Precision Medicine, February 20, 2023. http://dx.doi.org/10.1161/circgen.122.003756.
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BACKGROUND: Hypertrophic cardiomyopathy (HCM) is characterized by asymmetric left ventricular hypertrophy. Currently, hypertrophy pathways responsible for HCM have not been fully elucidated. Their identification could serve as a nidus for the generation of novel therapeutics aimed at halting disease development or progression. Herein, we performed a comprehensive multi-omic characterization of hypertrophy pathways in HCM. METHODS: Flash-frozen cardiac tissues were collected from genotyped HCM patients (n=97) undergoing surgical myectomy and tissue from 23 controls. RNA sequencing and mass spectrometry–enabled deep proteome and phosphoproteomic assessment were performed. Rigorous differential expression, gene set enrichment, and pathway analyses were performed to characterize HCM-mediated alterations with emphasis on hypertrophy pathways. RESULTS: We identified transcriptional dysregulation with 1246 (8%) differentially expressed genes and elucidated downregulation of 10 hypertrophy pathways. Deep proteomic analysis identified 411 proteins (9%) that differed between HCM and controls with strong dysregulation of metabolic pathways. Seven hypertrophy pathways were upregulated with antagonistic upregulation of 5 of 10 hypertrophy pathways shown to be downregulated in the transcriptome. Most upregulated hypertrophy pathways encompassed the RAS-MAPK signaling cascade. Phosphoproteomic analysis demonstrated hyperphosphorylation of the RAS-MAPK system suggesting activation of this signaling cascade. There was a common transcriptomic and proteomic profile regardless of genotype. CONCLUSIONS: At time of surgical myectomy, the ventricular proteome, independent of genotype, reveals widespread upregulation and activation of hypertrophy pathways, mainly involving the RAS-MAPK signaling cascade. In addition, there is a counterregulatory transcriptional downregulation of the same pathways. RAS-MAPK activation may serve a crucial role in hypertrophy observed in HCM.
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Arehart, Christopher H., John D. Sterrett, Rosanna L. Garris, et al. "Poly-omic risk scores predict inflammatory bowel disease diagnosis." mSystems, December 14, 2023. http://dx.doi.org/10.1128/msystems.00677-23.
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ABSTRACT Inflammatory bowel disease (IBD) is characterized by complex etiology and a disrupted colonic ecosystem. We provide a framework for the analysis of multi-omic data, which we apply to study the gut ecosystem in IBD. Specifically, we train and validate models using data on the metagenome, metatranscriptome, virome, and metabolome from the Human Microbiome Project 2 IBD multi-omic database, with 1,785 repeated samples from 130 individuals (103 cases and 27 controls). After splitting the participants into training and testing groups, we used mixed-effects least absolute shrinkage and selection operator regression to select features for each omic. These features, with demographic covariates, were used to generate separate single-omic prediction scores. All four single-omic scores were then combined into a final regression to assess the relative importance of the individual omics and the predictive benefits when considered together. We identified several species, pathways, and metabolites known to be associated with IBD risk, and we explored the connections between data sets. Individually, metabolomic and viromic scores were more predictive than metagenomics or metatranscriptomics, and when all four scores were combined, we predicted disease diagnosis with a Nagelkerke’s R 2 of 0.46 and an area under the curve of 0.80 (95% confidence interval: 0.63, 0.98). Our work supports that some single-omic models for complex traits are more predictive than others, that incorporating multiple omic data sets may improve prediction, and that each omic data type provides a combination of unique and redundant information. This modeling framework can be extended to other complex traits and multi-omic data sets. IMPORTANCE Complex traits are characterized by many biological and environmental factors, such that multi-omic data sets are well-positioned to help us understand their underlying etiologies. We applied a prediction framework across multiple omics (metagenomics, metatranscriptomics, metabolomics, and viromics) from the gut ecosystem to predict inflammatory bowel disease (IBD) diagnosis. The predicted scores from our models highlighted key features and allowed us to compare the relative utility of each omic data set in single-omic versus multi-omic models. Our results emphasized the importance of metabolomics and viromics over metagenomics and metatranscriptomics for predicting IBD status. The greater predictive capability of metabolomics and viromics is likely because these omics serve as markers of lifestyle factors such as diet. This study provides a modeling framework for multi-omic data, and our results show the utility of combining multiple omic data types to disentangle complex disease etiologies and biological signatures.