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Project description:Acute myeloid leukemia (AML) is a devastating cancer affecting the hematopoietic system. Previous research has relied on RNA sequencing and microarray techniques to study the downstream effects of genomic alterations. While these studies have proven efficacious, they fail to capture the changes that occur at the proteomic level. To interrogate the effect of protein expression alterations in AML, we performed a quantitative mass spectrometry in parallel with RNAseq analysis using AML mouse models. These combined results identified 34 proteins whose expression was upregulated in AML tumors, but strikingly, were unaltered at the transcriptional level. Here we focus on mitochondrial electron transfer proteins ETFA and ETFB. Silencing of ETFA and ETFB led to increased mitochondrial activity, mitochondrial stress, and apoptosis in AML cells, but had little to no effect on normal human CD34+ cells. These studies identify a set of proteins that have not previously been associated with leukemia and may ultimately serve as potential targets for therapeutic manipulation to hinder AML progression and help contribute to our understanding of the disease.
| S-EPMC9061297 | biostudies-literature
Project description:Multi-omics reveals mitochondrial metabolism proteins susceptible for drug discovery in AML
| PRJNA795977 | ENA
Project description:Acute myeloid leukemia (AML) is a devastating cancer affecting the hematopoietic system. Although this disease represents only 35% of diagnosed leukemias, it accounts for nearly 50% of leukemia-related deaths, making it the leading cause of leukemia-related mortality. Although many AML initiating mutations have been identified, the downstream effects leading to disease progression are still largely unknown. Previous research has relied on RNA sequencing and microarray techniques to study the downstream effects, providing data at the transcriptional level. While these studies have proven efficacious, they fail to capture the changes that occur at the proteomic level. To interrogate the effect of protein expression alterations in AML, we performed a quantitative mass spectrometry analysis using mouse models to compare three tumor types to untransformed tumor-initiating population. In parallel, we performed RNA sequencing for the same populations. With these combined results, we identified 34 proteins whose expression was upregulated in AML tumors, but strikingly, were unaltered at the transcriptional level. These proteins are shown to be associated with mitochondrial function as well as RNA processing. These studies identify a set of proteins not previously associated with leukemia, and may ultimately serve as potential targets for therapeutic manipulation.
2022-05-20 | PXD025628 | Pride
Project description:Relapse remains a determinant of treatment failure and contributes significantly to mortality in acute myeloid leukemia (AML) patients. Despite efforts to understand AML progression and relapse mechanisms, findings on acquired gene mutations in relapse vary, suggesting inherent genetic heterogeneity and emphasizing the role of epigenetic modifications. Herein, we characterized genetic and epigenetic changes in AML progression using multi-omics approaches to elucidate the underlying mechanisms of relapse. Differential interaction analysis showed significant 3D chromatin landscape reorganization between relapse and diagnosis samples. Comparing global open chromatin profiles revealed that relapse samples had significantly fewer accessible chromatin regions than diagnosis samples. In addition, we discovered that relapse-related upregulation was achieved either by forming new active enhancer contacts or by losing interactions with poised enhancers/potential silencers. Altogether, our study highlights the impact of genetic and epigenetic changes on AML progression, underlining the importance of multi-omics approaches in understanding disease relapse mechanisms and guiding potential therapeutic interventions.
2024-07-31 | GSE267375 | GEO
Project description:Relapse remains a determinant of treatment failure and contributes significantly to mortality in acute myeloid leukemia (AML) patients. Despite efforts to understand AML progression and relapse mechanisms, findings on acquired gene mutations in relapse vary, suggesting inherent genetic heterogeneity and emphasizing the role of epigenetic modifications. Herein, we characterized genetic and epigenetic changes in AML progression using multi-omics approaches to elucidate the underlying mechanisms of relapse. Differential interaction analysis showed significant 3D chromatin landscape reorganization between relapse and diagnosis samples. Comparing global open chromatin profiles revealed that relapse samples had significantly fewer accessible chromatin regions than diagnosis samples. In addition, we discovered that relapse-related upregulation was achieved either by forming new active enhancer contacts or by losing interactions with poised enhancers/potential silencers. Altogether, our study highlights the impact of genetic and epigenetic changes on AML progression, underlining the importance of multi-omics approaches in understanding disease relapse mechanisms and guiding potential therapeutic interventions.
2024-07-31 | GSE267376 | GEO
Project description:BackgroundRecent DNA/RNA sequencing and other multi-omics technologies have advanced the understanding of the biology and pathophysiology of AD, yet there is still a lack of disease-modifying treatments for AD. A new approach to integration of the genome, transcriptome, proteome, and human interactome in the drug discovery and development process is essential for this endeavor.MethodsIn this study, we developed AlzGPS (Genome-wide Positioning Systems platform for Alzheimer's Drug Discovery, https://alzgps.lerner.ccf.org ), a comprehensive systems biology tool to enable searching, visualizing, and analyzing multi-omics, various types of heterogeneous biological networks, and clinical databases for target identification and development of effective prevention and treatment for AD.ResultsVia AlzGPS: (1) we curated more than 100 AD multi-omics data sets capturing DNA, RNA, protein, and small molecule profiles underlying AD pathogenesis (e.g., early vs. late stage and tau or amyloid endophenotype); (2) we constructed endophenotype disease modules by incorporating multi-omics findings and human protein-protein interactome networks; (3) we provided possible treatment information from ~ 3000 FDA approved/investigational drugs for AD using state-of-the-art network proximity analyses; (4) we curated nearly 300 literature references for high-confidence drug candidates; (5) we included information from over 1000 AD clinical trials noting drug's mechanisms-of-action and primary drug targets, and linking them to our integrated multi-omics view for targets and network analysis results for the drugs; (6) we implemented a highly interactive web interface for database browsing and network visualization.ConclusionsNetwork visualization enabled by AlzGPS includes brain-specific neighborhood networks for genes-of-interest, endophenotype disease module networks for omics-of-interest, and mechanism-of-action networks for drugs targeting disease modules. By virtue of combining systems pharmacology and network-based integrative analysis of multi-omics data, AlzGPS offers actionable systems biology tools for accelerating therapeutic development in AD.
| S-EPMC7804907 | biostudies-literature
Project description:The paper describes a model of acute myeloid leukaemia.
Created by COPASI 4.26 (Build 213)
This model is described in the article:
Optimal control of acute myeloid leukaemia
Jesse A. Sharp, Alexander P Browning, Tarunendu Mapder, Kevin Burrage, Matthew J Simpson
Journal of Theoretical Biology 470 (2019) 30–42
Abstract:
Acute myeloid leukaemia (AML) is a blood cancer affecting haematopoietic stem cells. AML is routinely treated with chemotherapy, and so it is of great interest to develop optimal chemotherapy treatment strategies. In this work, we incorporate an immune response into a stem cell model of AML, since we find that previous models lacking an immune response are inappropriate for deriving optimal control strategies. Using optimal control theory, we produce continuous controls and bang-bang controls, corre- sponding to a range of objectives and parameter choices. Through example calculations, we provide a practical approach to applying optimal control using Pontryagin’s Maximum Principle. In particular, we describe and explore factors that have a profound influence on numerical convergence. We find that the convergence behaviour is sensitive to the method of control updating, the nature of the control, and to the relative weighting of terms in the objective function. All codes we use to implement optimal control are made available.
To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide.
Please refer to CC0 Public Domain Dedication for more information.
2024-09-02 | BIOMD0000000798 | BioModels
Project description:Myelodysplastic syndromes (MDS) and acute myeloid leukemia (AML) are diseases of abnormal hematopoietic differentiation with aberrant epigenetic alterations. Azacitidine (AZA) is a DNA methyltransferase inhibitor (DNMTi) widely used to treat MDS and AML, yet the impact of AZA on the cell surface proteome has not been defined. To identify potential therapeutic targets for use in combination with AZA in AML patients, we investigated the effects of AZA treatment on four AML cell lines representing different stages of differentiation. The effect of AZA treatment on these cell lines was characterized at three levels: the DNA methylome, the transcriptome, and the cell surface proteome. Untreated AML cell lines showed substantial overlap at all three omics level; however, while AZA treatment globally reduced DNA methylation in all cell lines, changes in the transcriptome and surface proteome were subtle and differed among the cell lines. Transcriptome analysis identified five commonly up-regulated coding genes upon AZA treatment in all four cell lines, TRPM4 being the only gene encoding a surface protein, and surface proteomics analysis found no commonly regulated proteins. Gene Set Enrichment Analysis (GSEA) of differentially-regulated RNA and surface proteins showed a decrease in metabolism pathways and an increase in immune defense response pathways. As such, AZA treatment led to diverse effects at the individual gene and protein level but converged to common responses at the pathway level. Given the heterogeneous responses in the four cell lines, we discuss potential therapeutic strategies for AML in combinations with AZA.
2018-10-07 | MSV000083006 | MassIVE
Project description:Purpose of review'Omics studies provide a comprehensive characterisation of a biological entity, such as the genome, epigenome, transcriptome, proteome, metabolome, or microbiome. This review covers the unique properties of these types of 'omics and their roles as causal mediators in cardiovascular disease. Moreover, applications and challenges of integrating multiple types of 'omics data to increase predictive power, improve causal inference, and elucidate biological mechanisms are discussed.Recent findingsMulti-omics approaches are growing in adoption as they provide orthogonal evidence and overcome the limitations of individual types of 'omics data. Studies with multiple types of 'omics data have improved the diagnosis and prediction of disease states and afforded a deeper understanding of underlying pathophysiological mechanisms, beyond any single type of 'omics data. For instance, disease-associated loci in the genome can be supplemented with other 'omics to prioritise causal genes and understand the function of non-coding variants. Alternatively, techniques, such as Mendelian randomisation, can leverage genetics to provide evidence supporting a causal role for disease-associated molecules, and elucidate their role in disease pathogenesis. As technologies improve, costs for 'omics studies will continue to fall and datasets will become increasingly accessible to researchers. The intrinsically unbiased nature of 'omics data is well-suited to exploratory analyses that discover causal mediators of disease, and multi-omics is an emerging discipline that leverages the strengths of each type of 'omics data to provide insights greater than the sum of its parts.
| S-EPMC9807989 | biostudies-literature
Project description:Disease progression prediction and therapeutic drug target discovery for Coronavirus disease 2019 (COVID-19) are particularly important, as there is still no effective strategy for severe COVID-19 patient treatment. Herein, we performed multi-platform omics analysis of serial plasma and urine samples collected from patients during the course of COVID-19. Integrative analyses of these omics data revealed several potential therapeutic targets, such as ANXA1 and CLEC3B. Molecular changes in plasma indicated dysregulation of macrophage and suppression of T cell functions in severe patients compared to those in non-severe patients. Further, we chose 25 important molecular signatures as potential biomarkers for the prediction of disease severity. The prediction power was validated using corresponding urine samples and plasma samples from new COVID-19 patient cohort, with AUC reached to 0.904 and 0.988, respectively. In conclusion, our omics data proposed not only potential therapeutic targets, but also biomarkers for understanding the pathogenesis of severe COVID-19.
| S-EPMC8047575 | biostudies-literature