1
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Han JH, Karki R, Malireddi RKS, Mall R, Sarkar R, Sharma BR, Klein J, Berns H, Pisharath H, Pruett-Miller SM, Bae SJ, Kanneganti TD. NINJ1 mediates inflammatory cell death, PANoptosis, and lethality during infection conditions and heat stress. Nat Commun 2024; 15:1739. [PMID: 38409108 PMCID: PMC10897308 DOI: 10.1038/s41467-024-45466-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 01/25/2024] [Indexed: 02/28/2024] Open
Abstract
Innate immunity provides the first line of defense through multiple mechanisms, including pyrogen production and cell death. While elevated body temperature during infection is beneficial to clear pathogens, heat stress (HS) can lead to inflammation and pathology. Links between pathogen exposure, HS, cytokine release, and inflammation have been observed, but fundamental innate immune mechanisms driving pathology during pathogen exposure and HS remain unclear. Here, we use multiple genetic approaches to elucidate innate immune pathways in infection or LPS and HS models. Our results show that bacteria and LPS robustly increase inflammatory cell death during HS that is dependent on caspase-1, caspase-11, caspase-8, and RIPK3 through the PANoptosis pathway. Caspase-7 also contributes to PANoptosis in this context. Furthermore, NINJ1 is an important executioner of this cell death to release inflammatory molecules, independent of other pore-forming executioner proteins, gasdermin D, gasdermin E, and MLKL. In an in vivo HS model, mortality is reduced by deleting NINJ1 and fully rescued by deleting key PANoptosis molecules. Our findings suggest that therapeutic strategies blocking NINJ1 or its upstream regulators to prevent PANoptosis may reduce the release of inflammatory mediators and benefit patients.
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Affiliation(s)
- Joo-Hui Han
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
- College of Pharmacy and Research Institute of Pharmaceutical Sciences, Woosuk University, Wanju, 55338, Republic of Korea
| | - Rajendra Karki
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
- Department of Biological Sciences, Seoul National University, Seoul, 08826, Republic of Korea
| | - R K Subbarao Malireddi
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Raghvendra Mall
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
- Biotechnology Research Center, Technology Innovation Institute, Abu Dhabi, P.O. Box 9639, United Arab Emirates
| | - Roman Sarkar
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Bhesh Raj Sharma
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Jonathon Klein
- Center for Advanced Genome Engineering, St Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Harmut Berns
- Center for Advanced Genome Engineering, St Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Harshan Pisharath
- Animal Resources Center, St Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Shondra M Pruett-Miller
- Center for Advanced Genome Engineering, St Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Sung-Jin Bae
- Department of Molecular Biology and Immunology, College of Medicine, Kosin University, Busan, 49267, Republic of Korea
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2
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Mason M, Lapuente-Santana Ó, Halkola AS, Wang W, Mall R, Xiao X, Kaufman J, Fu J, Pfeil J, Banerjee J, Chung V, Chang H, Chasalow SD, Lin HY, Chai R, Yu T, Finotello F, Mirtti T, Mäyränpää MI, Bao J, Verschuren EW, Ahmed EI, Ceccarelli M, Miller LD, Monaco G, Hendrickx WRL, Sherif S, Yang L, Tang M, Gu SS, Zhang W, Zhang Y, Zeng Z, Das Sahu A, Liu Y, Yang W, Bedognetti D, Tang J, Eduati F, Laajala TD, Geese WJ, Guinney J, Szustakowski JD, Vincent BG, Carbone DP. A community challenge to predict clinical outcomes after immune checkpoint blockade in non-small cell lung cancer. J Transl Med 2024; 22:190. [PMID: 38383458 PMCID: PMC10880244 DOI: 10.1186/s12967-023-04705-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 11/05/2023] [Indexed: 02/23/2024] Open
Abstract
BACKGROUND Predictive biomarkers of immune checkpoint inhibitor (ICI) efficacy are currently lacking for non-small cell lung cancer (NSCLC). Here, we describe the results from the Anti-PD-1 Response Prediction DREAM Challenge, a crowdsourced initiative that enabled the assessment of predictive models by using data from two randomized controlled clinical trials (RCTs) of ICIs in first-line metastatic NSCLC. METHODS Participants developed and trained models using public resources. These were evaluated with data from the CheckMate 026 trial (NCT02041533), according to the model-to-data paradigm to maintain patient confidentiality. The generalizability of the models with the best predictive performance was assessed using data from the CheckMate 227 trial (NCT02477826). Both trials were phase III RCTs with a chemotherapy control arm, which supported the differentiation between predictive and prognostic models. Isolated model containers were evaluated using a bespoke strategy that considered the challenges of handling transcriptome data from clinical trials. RESULTS A total of 59 teams participated, with 417 models submitted. Multiple predictive models, as opposed to a prognostic model, were generated for predicting overall survival, progression-free survival, and progressive disease status with ICIs. Variables within the models submitted by participants included tumor mutational burden (TMB), programmed death ligand 1 (PD-L1) expression, and gene-expression-based signatures. The best-performing models showed improved predictive power over reference variables, including TMB or PD-L1. CONCLUSIONS This DREAM Challenge is the first successful attempt to use protected phase III clinical data for a crowdsourced effort towards generating predictive models for ICI clinical outcomes and could serve as a blueprint for similar efforts in other tumor types and disease states, setting a benchmark for future studies aiming to identify biomarkers predictive of ICI efficacy. TRIAL REGISTRATION CheckMate 026; NCT02041533, registered January 22, 2014. CheckMate 227; NCT02477826, registered June 23, 2015.
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Affiliation(s)
- Mike Mason
- Bristol Myers Squibb, Princeton, NJ, USA
| | - Óscar Lapuente-Santana
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Anni S Halkola
- Department of Mathematics and Statistics, University of Turku, Turku, Finland
| | - Wenyu Wang
- Faculty of Medicine, Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland
| | - Raghvendra Mall
- Qatar Computing Research Institute, Hamad Bin Khalifa University, P.O. Box 34110, Doha, Qatar
- Department of Immunology, St. Jude Children's Research Hospital, P.O. Box 38105, Memphis, TN, USA
- Biotechnology Research Center, Technology Innovation Institute, P.O. Box 9639, Abu Dhabi, United Arab Emirates
| | - Xu Xiao
- School of Informatics, Xiamen University, Xiamen, China
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
| | - Jacob Kaufman
- Department of Medicine, Duke University, Durham, NC, USA
- The Ohio State University Comprehensive Cancer Center, Columbus, OH, USA
| | - Jingxin Fu
- Dana-Farber Cancer Institute, Boston, MA, USA
| | | | | | | | - Han Chang
- Bristol Myers Squibb, Princeton, NJ, USA
| | | | | | | | | | - Francesca Finotello
- Institute of Molecular Biology, University of Innsbruck, Innsbruck, Austria
- Digital Science Center (DiSC), University of Innsbruck, Innsbruck, Austria
| | - Tuomas Mirtti
- Department of Pathology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland
- iCAN-Digital Precision Cancer Medicine Flagship, Helsinki, Finland
- Department of Biomedical Engineering, School of Medicine, Emory University, Atlanta, GA, USA
| | - Mikko I Mäyränpää
- Department of Pathology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Jie Bao
- Faculty of Medicine, Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland
| | - Emmy W Verschuren
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Eiman I Ahmed
- Human Immunology Department, Sidra Medicine, P.O. Box 26999, Doha, Qatar
| | - Michele Ceccarelli
- Department of Electrical Engineering and Information Technology (DIETI), University of Naples "Federico II", 80125, Naples, Italy
- BIOGEM Institute of Molecular Biology and Genetics, Via Camporeale, Ariano Irpino, Italy
| | - Lance D Miller
- Department of Cancer Biology, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Atrium Health Wake Forest Baptist Comprehensive Cancer Center, Winston-Salem, NC, USA
| | - Gianni Monaco
- BIOGEM Institute of Molecular Biology and Genetics, Via Camporeale, Ariano Irpino, Italy
| | - Wouter R L Hendrickx
- Human Immunology Department, Sidra Medicine, P.O. Box 26999, Doha, Qatar
- College of Health and Life Sciences, Hamad Bin Khalifa University, P.O. Box 26999, Doha, Qatar
| | - Shimaa Sherif
- Human Immunology Department, Sidra Medicine, P.O. Box 26999, Doha, Qatar
- College of Health and Life Sciences, Hamad Bin Khalifa University, P.O. Box 26999, Doha, Qatar
| | - Lin Yang
- Dana-Farber Cancer Institute, Boston, MA, USA
| | - Ming Tang
- Dana-Farber Cancer Institute, Boston, MA, USA
| | | | | | - Yi Zhang
- Dana-Farber Cancer Institute, Boston, MA, USA
| | - Zexian Zeng
- Dana-Farber Cancer Institute, Boston, MA, USA
| | | | - Yang Liu
- Dana-Farber Cancer Institute, Boston, MA, USA
| | | | - Davide Bedognetti
- Human Immunology Department, Sidra Medicine, P.O. Box 26999, Doha, Qatar
- College of Health and Life Sciences, Hamad Bin Khalifa University, P.O. Box 26999, Doha, Qatar
- Department of Internal Medicine and Medical Specialties, University of Genoa, Genoa, Italy
| | - Jing Tang
- Faculty of Medicine, Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland
- Department of Biochemistry and Developmental Biology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Federica Eduati
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Institute for Complex Molecular Systems (ICMS), Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Teemu D Laajala
- Department of Mathematics and Statistics, University of Turku, Turku, Finland
- Department of Pathology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland
- iCAN-Digital Precision Cancer Medicine Flagship, Helsinki, Finland
- FICAN West Cancer Centre, University of Turku and Turku University Hospital, Turku, Finland
- Department of Pharmacology, Anschutz Medical Campus, University of Colorado, Denver, CO, USA
| | | | | | | | - Benjamin G Vincent
- Department of Medicine, Division of Hematology, Department of Microbiology and Immunology, Curriculum in Bioinformatics and Computational Biology, Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - David P Carbone
- The Ohio State University Comprehensive Cancer Center, Columbus, OH, USA.
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3
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Mall R, Kanneganti TD. Comparative analysis identifies genetic and molecular factors associated with prognostic clusters of PANoptosis in glioma, kidney and melanoma cancer. Sci Rep 2023; 13:20962. [PMID: 38017056 PMCID: PMC10684528 DOI: 10.1038/s41598-023-48098-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Accepted: 11/22/2023] [Indexed: 11/30/2023] Open
Abstract
The importance of inflammatory cell death, PANoptosis, in cancer is increasingly being recognized. PANoptosis can promote or inhibit tumorigenesis in context-dependent manners, and a computational approach leveraging transcriptomic profiling of genes involved in PANoptosis has shown that patients can be stratified into PANoptosis High and PANoptosis Low clusters that have significant differences in overall survival for low grade glioma (LGG), kidney renal cell carcinoma (KIRC) and skin cutaneous melanoma (SKCM). However, the molecular mechanisms that contribute to differential prognosis between PANoptosis clusters require further elucidation. Therefore, we performed a comprehensive comparison of genetic, genomic, tumor microenvironment, and pathway characteristics between the PANoptosis High and PANoptosis Low clusters to determine the relevance of each component in driving the differential associations with prognosis for LGG, KIRC and SKCM. Across these cancer types, we found that activation of the proliferation pathway was significantly different between PANoptosis High and Low clusters. In LGG and SKCM, we also found that aneuploidy and immune cell densities and activations contributed to differences in PANoptosis clusters. In individual cancers, we identified important roles for barrier gene pathway activation (in SKCM) and the somatic mutation profiles of driver oncogenes as well as hedgehog signaling pathway activation (in LGG). By identifying these genetic and molecular factors, we can possibly improve the prognosis for at risk-stratified patient populations based on the PANoptosis phenotype in LGG, KIRC and SKCM. This not only advances our mechanistic understanding of cancer but will allow for the selection of optimal treatment strategies.
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Affiliation(s)
- Raghvendra Mall
- Department of Immunology, St. Jude Children's Research Hospital, MS #351, 262 Danny Thomas Place, Memphis, TN, 38105-2794, USA
- Biotechnology Research Center, Technology Innovation Institute, P.O. Box 9639, Abu Dhabi, United Arab Emirates
| | - Thirumala-Devi Kanneganti
- Department of Immunology, St. Jude Children's Research Hospital, MS #351, 262 Danny Thomas Place, Memphis, TN, 38105-2794, USA.
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4
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Malireddi RKS, Bynigeri RR, Mall R, Connelly JP, Pruett-Miller SM, Kanneganti TD. Inflammatory cell death, PANoptosis, screen identifies host factors in coronavirus innate immune response as therapeutic targets. Commun Biol 2023; 6:1071. [PMID: 37864059 PMCID: PMC10589293 DOI: 10.1038/s42003-023-05414-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 10/03/2023] [Indexed: 10/22/2023] Open
Abstract
The COVID-19 pandemic, caused by the β-coronavirus (β-CoV) severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), continues to cause significant global morbidity and mortality. While vaccines have reduced the overall number of severe infections, there remains an incomplete understanding of viral entry and innate immune activation, which can drive pathology. Innate immune responses characterized by positive feedback between cell death and cytokine release can amplify the inflammatory cytokine storm during β-CoV-mediated infection to drive pathology. Therefore, there remains an unmet need to understand innate immune processes in response to β-CoV infections to identify therapeutic strategies. To address this gap, here we used an MHV model and developed a whole genome CRISPR-Cas9 screening approach to elucidate host molecules required for β-CoV infection and inflammatory cell death, PANoptosis, in macrophages, a sentinel innate immune cell. Our screen was validated through the identification of the known MHV receptor Ceacam1 as the top hit, and its deletion significantly reduced viral replication due to loss of viral entry, resulting in a downstream reduction in MHV-induced cell death. Moreover, this screen identified several other host factors required for MHV infection-induced macrophage cell death. Overall, these findings demonstrate the feasibility and power of using genome-wide PANoptosis screens in macrophage cell lines to accelerate the discovery of key host factors in innate immune processes and suggest new targets for therapeutic development to prevent β-CoV-induced pathology.
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Affiliation(s)
- R K Subbarao Malireddi
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Ratnakar R Bynigeri
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Raghvendra Mall
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
- Biotechnology Research Center, Technology Innovation Institute, Abu Dhabi, P.O. Box 9639, United Arab Emirates
| | - Jon P Connelly
- Center for Advanced Genome Engineering (CAGE), St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Shondra M Pruett-Miller
- Center for Advanced Genome Engineering (CAGE), St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
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5
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Jethalia M, Jani SP, Ceccarelli M, Mall R. Pancancer network analysis reveals key master regulators for cancer invasiveness. J Transl Med 2023; 21:558. [PMID: 37599366 PMCID: PMC10440887 DOI: 10.1186/s12967-023-04435-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 08/12/2023] [Indexed: 08/22/2023] Open
Abstract
BACKGROUND Tumor invasiveness reflects numerous biological changes, including tumorigenesis, progression, and metastasis. To decipher the role of transcriptional regulators (TR) involved in tumor invasiveness, we performed a systematic network-based pan-cancer assessment of master regulators of cancer invasiveness. MATERIALS AND METHODS We stratified patients in The Cancer Genome Atlas (TCGA) into invasiveness high (INV-H) and low (INV-L) groups using consensus clustering based on an established robust 24-gene signature to determine the prognostic association of invasiveness with overall survival (OS) across 32 different cancers. We devise a network-based protocol to identify TRs as master regulators (MRs) unique to INV-H and INV-L phenotypes. We validated the activity of MRs coherently associated with INV-H phenotype and worse OS across cancers in TCGA on a series of additional datasets in the Prediction of Clinical Outcomes from the Genomic Profiles (PRECOG) repository. RESULTS Based on the 24-gene signature, we defined the invasiveness score for each patient sample and stratified patients into INV-H and INV-L clusters. We observed that invasiveness was associated with worse survival outcomes in almost all cancers and had a significant association with OS in ten out of 32 cancers. Our network-based framework identified common invasiveness-associated MRs specific to INV-H and INV-L groups across the ten prognostic cancers, including COL1A1, which is also part of the 24-gene signature, thus acting as a positive control. Downstream pathway analysis of MRs specific to INV-H phenotype resulted in the identification of several enriched pathways, including Epithelial into Mesenchymal Transition, TGF-β signaling pathway, regulation of Toll-like receptors, cytokines, and inflammatory response, and selective expression of chemokine receptors during T-cell polarization. Most of these pathways have connotations of inflammatory immune response and feasibility for metastasis. CONCLUSION Our pan-cancer study provides a comprehensive master regulator analysis of tumor invasiveness and can suggest more precise therapeutic strategies by targeting the identified MRs and downstream enriched pathways for patients across multiple cancers.
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Affiliation(s)
- Mahesh Jethalia
- Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, India
| | - Siddhi P Jani
- Centre of Brain Research, Indian Institute of Sciences, Bangalore, Karnataka, India
- Institute of Science, Nirma University, Ahmedabad, India
| | - Michele Ceccarelli
- Department of Public Health Sciences, University of Miami, Miami, FL, USA
- Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL, USA
| | - Raghvendra Mall
- St. Jude Children's Hospital, Memphis, TN, USA.
- Biotechnology Research Center, Technology Innovation Institute, P.O. Box 9639, Abu Dhabi, United Arab Emirates.
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6
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Sundaram B, Pandian N, Mall R, Wang Y, Sarkar R, Kim HJ, Malireddi RKS, Karki R, Janke LJ, Vogel P, Kanneganti TD. NLRP12-PANoptosome activates PANoptosis and pathology in response to heme and PAMPs. Cell 2023; 186:2783-2801.e20. [PMID: 37267949 PMCID: PMC10330523 DOI: 10.1016/j.cell.2023.05.005] [Citation(s) in RCA: 38] [Impact Index Per Article: 38.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 03/17/2023] [Accepted: 05/05/2023] [Indexed: 06/04/2023]
Abstract
Cytosolic innate immune sensors are critical for host defense and form complexes, such as inflammasomes and PANoptosomes, that induce inflammatory cell death. The sensor NLRP12 is associated with infectious and inflammatory diseases, but its activating triggers and roles in cell death and inflammation remain unclear. Here, we discovered that NLRP12 drives inflammasome and PANoptosome activation, cell death, and inflammation in response to heme plus PAMPs or TNF. TLR2/4-mediated signaling through IRF1 induced Nlrp12 expression, which led to inflammasome formation to induce maturation of IL-1β and IL-18. The inflammasome also served as an integral component of a larger NLRP12-PANoptosome that drove inflammatory cell death through caspase-8/RIPK3. Deletion of Nlrp12 protected mice from acute kidney injury and lethality in a hemolytic model. Overall, we identified NLRP12 as an essential cytosolic sensor for heme plus PAMPs-mediated PANoptosis, inflammation, and pathology, suggesting that NLRP12 and molecules in this pathway are potential drug targets for hemolytic and inflammatory diseases.
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Affiliation(s)
- Balamurugan Sundaram
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Nagakannan Pandian
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Raghvendra Mall
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Yaqiu Wang
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Roman Sarkar
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Hee Jin Kim
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | | | - Rajendra Karki
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Laura J Janke
- Animal Resources Center and the Veterinary Pathology Core, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Peter Vogel
- Animal Resources Center and the Veterinary Pathology Core, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
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7
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Malireddi RS, Bynigeri RR, Mall R, Nadendla EK, Connelly JP, Pruett-Miller SM, Kanneganti TD. Whole-genome CRISPR screen identifies RAVER1 as a key regulator of RIPK1-mediated inflammatory cell death, PANoptosis. iScience 2023; 26:106938. [PMID: 37324531 PMCID: PMC10265528 DOI: 10.1016/j.isci.2023.106938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 04/24/2023] [Accepted: 05/18/2023] [Indexed: 06/17/2023] Open
Abstract
Transforming growth factor-β-activated kinase 1 (TAK1) is a central regulator of innate immunity, cell death, inflammation, and cellular homeostasis. Therefore, many pathogens carry TAK1 inhibitors (TAK1i). As a host strategy to counteract this, inhibition or deletion of TAK1 induces spontaneous inflammatory cell death, PANoptosis, through the RIPK1-PANoptosome complex, containing the NLRP3 inflammasome and caspase-8/FADD/RIPK3 as integral components; however, PANoptosis also promotes pathological inflammation. Therefore, understanding molecular mechanisms that regulate TAK1i-induced cell death is essential. Here, we report a genome-wide CRISPR screen in macrophages that identified TAK1i-induced cell death regulators, including polypyrimidine tract-binding (PTB) protein 1 (PTBP1), a known regulator of RIPK1, and a previously unknown regulator RAVER1. RAVER1 blocked alternative splicing of Ripk1, and its genetic depletion inhibited TAK1i-induced, RIPK1-mediated inflammasome activation and PANoptosis. Overall, our CRISPR screen identified several positive regulators of PANoptosis. Moreover, our study highlights the utility of genome-wide CRISPR-Cas9 screens in myeloid cells for comprehensive characterization of complex cell death pathways to discover therapeutic targets.
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Affiliation(s)
| | - Ratnakar R. Bynigeri
- Department of Immunology, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA
| | - Raghvendra Mall
- Department of Immunology, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA
| | - Eswar Kumar Nadendla
- Department of Immunology, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA
| | - Jon P. Connelly
- Center for Advanced Genome Engineering (CAGE), St. Jude Children’s Research Hospital, Memphis, TN 38105, USA
| | - Shondra M. Pruett-Miller
- Center for Advanced Genome Engineering (CAGE), St. Jude Children’s Research Hospital, Memphis, TN 38105, USA
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8
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Roelands J, Kuppen PJK, Ahmed EI, Mall R, Masoodi T, Singh P, Monaco G, Raynaud C, de Miranda NFCC, Ferraro L, Carneiro-Lobo TC, Syed N, Rawat A, Awad A, Decock J, Mifsud W, Miller LD, Sherif S, Mohamed MG, Rinchai D, Van den Eynde M, Sayaman RW, Ziv E, Bertucci F, Petkar MA, Lorenz S, Mathew LS, Wang K, Murugesan S, Chaussabel D, Vahrmeijer AL, Wang E, Ceccarelli A, Fakhro KA, Zoppoli G, Ballestrero A, Tollenaar RAEM, Marincola FM, Galon J, Khodor SA, Ceccarelli M, Hendrickx W, Bedognetti D. An integrated tumor, immune and microbiome atlas of colon cancer. Nat Med 2023; 29:1273-1286. [PMID: 37202560 PMCID: PMC10202816 DOI: 10.1038/s41591-023-02324-5] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Accepted: 03/28/2023] [Indexed: 05/20/2023]
Abstract
The lack of multi-omics cancer datasets with extensive follow-up information hinders the identification of accurate biomarkers of clinical outcome. In this cohort study, we performed comprehensive genomic analyses on fresh-frozen samples from 348 patients affected by primary colon cancer, encompassing RNA, whole-exome, deep T cell receptor and 16S bacterial rRNA gene sequencing on tumor and matched healthy colon tissue, complemented with tumor whole-genome sequencing for further microbiome characterization. A type 1 helper T cell, cytotoxic, gene expression signature, called Immunologic Constant of Rejection, captured the presence of clonally expanded, tumor-enriched T cell clones and outperformed conventional prognostic molecular biomarkers, such as the consensus molecular subtype and the microsatellite instability classifications. Quantification of genetic immunoediting, defined as a lower number of neoantigens than expected, further refined its prognostic value. We identified a microbiome signature, driven by Ruminococcus bromii, associated with a favorable outcome. By combining microbiome signature and Immunologic Constant of Rejection, we developed and validated a composite score (mICRoScore), which identifies a group of patients with excellent survival probability. The publicly available multi-omics dataset provides a resource for better understanding colon cancer biology that could facilitate the discovery of personalized therapeutic approaches.
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Affiliation(s)
- Jessica Roelands
- Translational Medicine Division, Research Branch, Sidra Medicine, Doha, Qatar
- Department of Surgery, Leiden University Medical Center, Leiden, The Netherlands
- Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands
| | - Peter J K Kuppen
- Department of Surgery, Leiden University Medical Center, Leiden, The Netherlands
| | - Eiman I Ahmed
- Translational Medicine Division, Research Branch, Sidra Medicine, Doha, Qatar
| | - Raghvendra Mall
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN, USA
- Biotechnology Research Center, Technology Innovation Institute, Abu Dhabi, United Arab Emirates
| | - Tariq Masoodi
- Translational Medicine Division, Research Branch, Sidra Medicine, Doha, Qatar
| | - Parul Singh
- Translational Medicine Division, Research Branch, Sidra Medicine, Doha, Qatar
| | - Gianni Monaco
- Institute for Transfusion Medicine and Gene Therapy, Medical Center-University of Freiburg, Freiburg, Germany
- Neuropathology, Medical Center-University of Freiburg, Freiburg, Germany
- BIOGEM Institute of Molecular Biology and Genetics, Ariano Irpino, Italy
| | - Christophe Raynaud
- Translational Medicine Division, Research Branch, Sidra Medicine, Doha, Qatar
| | | | - Luigi Ferraro
- BIOGEM Institute of Molecular Biology and Genetics, Ariano Irpino, Italy
- Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, Naples, Italy
| | | | - Najeeb Syed
- Integrated Genomics Services, Research Branch, Sidra Medicine, Doha, Qatar
| | - Arun Rawat
- Translational Medicine Division, Research Branch, Sidra Medicine, Doha, Qatar
| | - Amany Awad
- Translational Medicine Division, Research Branch, Sidra Medicine, Doha, Qatar
| | - Julie Decock
- Translational Cancer and Immunity Center, Qatar Biomedical Research Institute (QBRI), Hamad Bin Khalifa University (HBKU), Qatar Foundation, Doha, Qatar
- College of Health and Life Sciences, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
| | - William Mifsud
- Department of Pathology, Sidra Medicine, Doha, Qatar
- Weill-Cornell Medicine Qatar, Doha, Qatar
| | - Lance D Miller
- Department of Cancer Biology, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Shimaa Sherif
- Translational Medicine Division, Research Branch, Sidra Medicine, Doha, Qatar
- College of Health and Life Sciences, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
| | - Mahmoud G Mohamed
- Translational Medicine Division, Research Branch, Sidra Medicine, Doha, Qatar
- Women's Wellness and Research Center, Hamad Medical Corporation, Doha, Qatar
- Department of Internal Medicine and Medical Specialties (DiMI), University of Genoa, Genoa, Italy
| | - Darawan Rinchai
- Translational Medicine Division, Research Branch, Sidra Medicine, Doha, Qatar
- Laboratory of Human Genetics of Infectious Diseases, The Rockefeller University, New York, NY, USA
| | - Marc Van den Eynde
- Institut Roi Albert II, Cliniques Universitaires Saint-Luc, UCLouvain, Brussels, Belgium
| | - Rosalyn W Sayaman
- Department of Laboratory Medicine, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, USA
| | - Elad Ziv
- Department of Medicine, Institute for Human Genetics, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, USA
| | - Francois Bertucci
- Laboratory of Predictive Oncology, Centre de Recherche en Cancérologie de Marseille, Institut Paoli-Calmettes, Aix-Marseille Université, Inserm UMR1068, CNRS UMR725, Marseille, France
- Department of Medical Oncology, Institut Paoli-Calmettes, Marseille, France
| | - Mahir Abdulla Petkar
- Department of Laboratory Medicine and Pathology, Hamad Medical Corporation, Doha, Qatar
| | - Stephan Lorenz
- Integrated Genomics Services, Research Branch, Sidra Medicine, Doha, Qatar
| | - Lisa Sara Mathew
- Integrated Genomics Services, Research Branch, Sidra Medicine, Doha, Qatar
| | - Kun Wang
- Integrated Genomics Services, Research Branch, Sidra Medicine, Doha, Qatar
| | | | - Damien Chaussabel
- Translational Medicine Division, Research Branch, Sidra Medicine, Doha, Qatar
- Computational Sciences Department, The Jackson Laboratory, Farmington, CT, USA
| | | | - Ena Wang
- Translational Medicine Division, Research Branch, Sidra Medicine, Doha, Qatar
- Nurix Therapeutics, San Francisco, CA, USA
| | - Anna Ceccarelli
- Medical Oncology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS- Università Cattolica del Sacro Cuore, Rome, Italy
| | - Khalid A Fakhro
- Translational Medicine Division, Research Branch, Sidra Medicine, Doha, Qatar
- College of Health and Life Sciences, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
- Weill-Cornell Medicine Qatar, Doha, Qatar
| | - Gabriele Zoppoli
- Department of Internal Medicine and Medical Specialties (DiMI), University of Genoa, Genoa, Italy
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Alberto Ballestrero
- Department of Internal Medicine and Medical Specialties (DiMI), University of Genoa, Genoa, Italy
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Rob A E M Tollenaar
- Department of Surgery, Leiden University Medical Center, Leiden, The Netherlands
| | - Francesco M Marincola
- Translational Medicine Division, Research Branch, Sidra Medicine, Doha, Qatar
- Sonata Therapeutics, Watertown, MA, USA
| | - Jérôme Galon
- Inserm, Laboratory of Integrative Cancer Immunology, Equipe Labellisée Ligue Contre Le Cancer, Centre de Recherche de Cordeliers, Université de Paris, Sorbonne Université, Paris, France
| | - Souhaila Al Khodor
- Translational Medicine Division, Research Branch, Sidra Medicine, Doha, Qatar
| | - Michele Ceccarelli
- BIOGEM Institute of Molecular Biology and Genetics, Ariano Irpino, Italy
- Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, Naples, Italy
- Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - Wouter Hendrickx
- Translational Medicine Division, Research Branch, Sidra Medicine, Doha, Qatar.
- College of Health and Life Sciences, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar.
| | - Davide Bedognetti
- Translational Medicine Division, Research Branch, Sidra Medicine, Doha, Qatar.
- College of Health and Life Sciences, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar.
- Department of Internal Medicine and Medical Specialties (DiMI), University of Genoa, Genoa, Italy.
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9
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Mir FA, Mall R, Ullah E, Iskandarani A, Cyprian F, Samra TA, Alkasem M, Abdalhakam I, Farooq F, Taheri S, Abou-Samra AB. An integrated multi-omic approach demonstrates distinct molecular signatures between human obesity with and without metabolic complications: a case-control study. J Transl Med 2023; 21:229. [PMID: 36991398 PMCID: PMC10053148 DOI: 10.1186/s12967-023-04074-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 03/22/2023] [Indexed: 03/31/2023] Open
Abstract
OBJECTIVES To examine the hypothesis that obesity complicated by the metabolic syndrome, compared to uncomplicated obesity, has distinct molecular signatures and metabolic pathways. METHODS We analyzed a cohort of 39 participants with obesity that included 21 with metabolic syndrome, age-matched to 18 without metabolic complications. We measured in whole blood samples 754 human microRNAs (miRNAs), 704 metabolites using unbiased mass spectrometry metabolomics, and 25,682 transcripts, which include both protein coding genes (PCGs) as well as non-coding transcripts. We then identified differentially expressed miRNAs, PCGs, and metabolites and integrated them using databases such as mirDIP (mapping between miRNA-PCG network), Human Metabolome Database (mapping between metabolite-PCG network) and tools like MetaboAnalyst (mapping between metabolite-metabolic pathway network) to determine dysregulated metabolic pathways in obesity with metabolic complications. RESULTS We identified 8 significantly enriched metabolic pathways comprising 8 metabolites, 25 protein coding genes and 9 microRNAs which are each differentially expressed between the subjects with obesity and those with obesity and metabolic syndrome. By performing unsupervised hierarchical clustering on the enrichment matrix of the 8 metabolic pathways, we could approximately segregate the uncomplicated obesity strata from that of obesity with metabolic syndrome. CONCLUSIONS The data suggest that at least 8 metabolic pathways, along with their various dysregulated elements, identified via our integrative bioinformatics pipeline, can potentially differentiate those with obesity from those with obesity and metabolic complications.
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Affiliation(s)
- Fayaz Ahmad Mir
- Qatar Metabolic Institute, Academic Health System, Hamad Medical Corporation, PO Box 3050, Doha, Qatar.
| | - Raghvendra Mall
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, USA.
- Biotechnology Research Center, Technology Innovation Institute, P.O. Box 9639, Abu Dhabi, United Arab Emirates.
| | - Ehsan Ullah
- Qatar Computational Research Institute (QCRI), Hamad Bin Khalifa University, Doha, Qatar.
| | - Ahmad Iskandarani
- Qatar Metabolic Institute, Academic Health System, Hamad Medical Corporation, PO Box 3050, Doha, Qatar
| | - Farhan Cyprian
- College of Medicine, QU Health, Qatar University, Doha, Qatar
| | - Tareq A Samra
- Qatar Metabolic Institute, Academic Health System, Hamad Medical Corporation, PO Box 3050, Doha, Qatar
| | - Meis Alkasem
- Qatar Metabolic Institute, Academic Health System, Hamad Medical Corporation, PO Box 3050, Doha, Qatar
| | - Ibrahem Abdalhakam
- Qatar Metabolic Institute, Academic Health System, Hamad Medical Corporation, PO Box 3050, Doha, Qatar
| | - Faisal Farooq
- Qatar Computational Research Institute (QCRI), Hamad Bin Khalifa University, Doha, Qatar
| | - Shahrad Taheri
- Qatar Metabolic Institute, Academic Health System, Hamad Medical Corporation, PO Box 3050, Doha, Qatar
- National Obesity Treatment Center, Hamad Medical Corporation, Doha, Qatar
- Weil Cornell Medicine - Qatar, Doha, Qatar
| | - Abdul-Badi Abou-Samra
- Qatar Metabolic Institute, Academic Health System, Hamad Medical Corporation, PO Box 3050, Doha, Qatar
- National Obesity Treatment Center, Hamad Medical Corporation, Doha, Qatar
- Weil Cornell Medicine - Qatar, Doha, Qatar
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10
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Patel CN, Mall R, Bensmail H. AI-driven drug repurposing and binding pose meta dynamics identifies novel targets for Monkeypox virus. J Infect Public Health 2023; 16:799-807. [PMID: 36966703 PMCID: PMC10014505 DOI: 10.1016/j.jiph.2023.03.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 02/28/2023] [Accepted: 03/05/2023] [Indexed: 03/17/2023] Open
Abstract
Monkeypox virus (MPXV) was confirmed in May 2022 and designated a global health emergency by WHO in July 2022. MPX virions are big, enclosed, brick-shaped, and contain a linear, double-stranded DNA genome as well as enzymes. MPXV particles bind to the host cell membrane via a variety of viral-host protein interactions. As a result, the wrapped structure is a potential therapeutic target. DeepRepurpose, an artificial intelligence-based compound-viral proteins interaction framework, was used via a transfer learning setting to prioritize a set of FDA approved and investigational drugs which can potentially inhibit MPXV viral proteins. To filter and narrow down the lead compounds from curated collections of pharmaceutical compounds, we used a rigorous computational framework that included homology modeling, molecular docking, dynamic simulations, binding free energy calculations, and binding pose metadynamics. We identified Elvitegravir as a potential inhibitor of MPXV virus using our comprehensive pipeline.
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Affiliation(s)
- Chirag N. Patel
- Department of Botany, Bioinformatics and Climate Change Impacts Management, School of Science, Gujarat University, Ahmedabad-380009, India,Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, National Institute of Health, Frederick, MD-21702, USA
| | - Raghvendra Mall
- Department of Immunology, St. Jude Children’s Research Hospital, 262 Danny Thomas Place, Memphis, Tennessee-38105, USA,Biotechnology Research Center, Technology Innovation Institute, Abu Dhabi-9639, United Arab Emirates,Corresponding author at: Department of Immunology, St. Jude Children’s Research Hospital, 262 Danny Thomas Place, Memphis, Tennessee-38105, USA
| | - Halima Bensmail
- Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha-34110, Qatar,Corresponding author
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11
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Hartung T, Deng J, Mall R, Frangi AF, Emmert-Streib F, Pham TD. Editorial: Insights in AI: Medicine and public health 2022. Front Artif Intell 2023; 6:1166426. [PMID: 37205297 PMCID: PMC10185910 DOI: 10.3389/frai.2023.1166426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 02/22/2023] [Indexed: 05/21/2023] Open
Affiliation(s)
- Thomas Hartung
- Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States
| | - Jun Deng
- Department of Therapeutic Radiology, Yale University, New Haven, CT, United States
| | - Raghvendra Mall
- Biotechnology Research Center, Technology Innovation Institute, Masdar City, United Arab Emirates
| | | | - Frank Emmert-Streib
- Predictive Society and Data Analytics Lab, Tampere University of Technology, Tampere, Finland
| | - Tuan D. Pham
- Center for Artificial Intelligence, Prince Mohammad Bin Fahd University, Khobar, Saudi Arabia
- *Correspondence: Tuan D. Pham
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12
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Mall R, Bynigeri RR, Karki R, Malireddi RKS, Sharma B, Kanneganti TD. Pancancer transcriptomic profiling identifies key PANoptosis markers as therapeutic targets for oncology. NAR Cancer 2022; 4:zcac033. [PMID: 36329783 PMCID: PMC9623737 DOI: 10.1093/narcan/zcac033] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 10/03/2022] [Accepted: 10/28/2022] [Indexed: 11/24/2022] Open
Abstract
Resistance to programmed cell death (PCD) is a hallmark of cancer. While some PCD components are prognostic in cancer, the roles of many molecules can be masked by redundancies and crosstalks between PCD pathways, impeding the development of targeted therapeutics. Recent studies characterizing these redundancies have identified PANoptosis, a unique innate immune-mediated inflammatory PCD pathway that integrates components from other PCD pathways. Here, we designed a systematic computational framework to determine the pancancer clinical significance of PANoptosis and identify targetable biomarkers. We found that high expression of PANoptosis genes was detrimental in low grade glioma (LGG) and kidney renal cell carcinoma (KIRC). ZBP1, ADAR, CASP2, CASP3, CASP4, CASP8 and GSDMD expression consistently had negative effects on prognosis in LGG across multiple survival models, while AIM2, CASP3, CASP4 and TNFRSF10 expression had negative effects for KIRC. Conversely, high expression of PANoptosis genes was beneficial in skin cutaneous melanoma (SKCM), with ZBP1, NLRP1, CASP8 and GSDMD expression consistently having positive prognostic effects. As a therapeutic proof-of-concept, we treated melanoma cells with combination therapy that activates ZBP1 and showed that this treatment induced PANoptosis. Overall, through our systematic framework, we identified and validated key innate immune biomarkers from PANoptosis which can be targeted to improve patient outcomes in cancers.
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Affiliation(s)
- Raghvendra Mall
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Ratnakar R Bynigeri
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Rajendra Karki
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | | | - Bhesh Raj Sharma
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
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13
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Khalifa O, H. Mroue K, Mall R, Ullah E, S. Al-Akl N, Arredouani A. Investigation of the Effect of Exendin-4 on Oleic Acid-Induced Steatosis in HepG2 Cells Using Fourier Transform Infrared Spectroscopy. Biomedicines 2022; 10:biomedicines10102652. [PMID: 36289914 PMCID: PMC9599706 DOI: 10.3390/biomedicines10102652] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 07/10/2022] [Accepted: 07/13/2022] [Indexed: 12/04/2022] Open
Abstract
Non-alcoholic fatty liver disease (NAFLD) is a common liver lesion that is untreatable with medications. Glucagon-like peptide-1 receptor (GLP-1R) agonists have recently emerged as a potential NAFLD pharmacotherapy. However, the molecular mechanisms underlying these drugs’ beneficial effects are not fully understood. Using Fourier transform infrared (FTIR) spectroscopy, we sought to investigate the biochemical changes in a steatosis cell model treated or not with the GLP-1R agonist Exendin-4 (Ex-4). HepG2 cells were made steatotic with 400 µM of oleic acid and then treated with 200 nM Ex-4 in order to reduce lipid accumulation. We quantified steatosis using the Oil Red O staining method. We investigated the biochemical alterations induced by steatosis and Ex-4 treatment using Fourier transform infrared (FTIR) spectroscopy and chemometric analyses. Analysis of the Oil Red O staining showed that Ex-4 significantly reduces steatosis. This reduction was confirmed by FTIR analysis, as the phospholipid band (C=O) at 1740 cm−1 in Ex-4 treated cells is significantly decreased compared to steatotic cells. The principal component analysis score plots for both the lipid and protein regions showed that the untreated and Ex-4-treated samples, while still separated, are clustered close to each other, far from the steatotic cells. The biochemical and structural changes induced by OA-induced lipotoxicity are at least partially reversed upon Ex-4 treatment. FTIR and chemometric analyses revealed that Ex-4 significantly reduces OA-induced lipid accumulation, and Ex-4 also restored the lipid and protein biochemical alterations caused by lipotoxicity-induced oxidative stress. In combination with chemometric analyses, FTIR spectroscopy may offer new approaches for investigating the mechanisms underpinning NAFLD.
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Affiliation(s)
- Olfa Khalifa
- Diabetes Research Center, Qatar Biomedical Research Institute, Hamad Bin Khalifa University, Qatar Foundation, Doha 34110, Qatar
| | - Kamal H. Mroue
- Qatar Environment and Energy Research Institute, Hamad Bin Khalifa University (HBKU), Qatar Foundation, Doha 34110, Qatar
| | - Raghvendra Mall
- Qatar Computing Research Institute, Hamad Bin Khalifa University, Qatar Foundation, Doha 34110, Qatar
- Department of Immunology, St. Jude Children’s Research Hospital, 262 Danny Thomas Place, Memphis, TN 38105-3678, USA
| | - Ehsan Ullah
- Qatar Computing Research Institute, Hamad Bin Khalifa University, Qatar Foundation, Doha 34110, Qatar
| | - Nayla S. Al-Akl
- Diabetes Research Center, Qatar Biomedical Research Institute, Hamad Bin Khalifa University, Qatar Foundation, Doha 34110, Qatar
| | - Abdelilah Arredouani
- Diabetes Research Center, Qatar Biomedical Research Institute, Hamad Bin Khalifa University, Qatar Foundation, Doha 34110, Qatar
- College of Health and Life Sciences, Hamad Bin Khalifa University, Qatar Foundation, Doha 34110, Qatar
- Correspondence:
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14
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Wang Y, Karki R, Mall R, Sharma BR, Kalathur RC, Lee S, Kancharana B, So M, Combs KL, Kanneganti TD. Molecular mechanism of RIPK1 and caspase-8 in homeostatic type I interferon production and regulation. Cell Rep 2022; 41:111434. [PMID: 36198273 PMCID: PMC9630927 DOI: 10.1016/j.celrep.2022.111434] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 07/22/2022] [Accepted: 09/08/2022] [Indexed: 11/25/2022] Open
Abstract
Type I interferons (IFNs) are essential innate immune proteins that maintain tissue homeostasis through tonic expression and can be upregulated to drive antiviral resistance and inflammation upon stimulation. However, the mechanisms that inhibit aberrant IFN upregulation in homeostasis and the impacts of tonic IFN production on health and disease remain enigmatic. Here, we report that caspase-8 negatively regulates type I IFN production by inhibiting the RIPK1-TBK1 axis during homeostasis across multiple cell types and tissues. When caspase-8 is deleted or inhibited, RIPK1 interacts with TBK1 to drive elevated IFN production, leading to heightened resistance to norovirus infection in macrophages but also early onset lymphadenopathy in mice. Combined deletion of caspase-8 and RIPK1 reduces the type I IFN signaling and lymphadenopathy, highlighting the critical role of RIPK1 in this process. Overall, our study identifies a mechanism to constrain tonic type I IFN during homeostasis which could be targeted for infectious and inflammatory diseases. Wang et al. report the mechanistic regulation of homeostatic type I IFN production by caspase-8 through the RIPK1-TBK1 axis. Hyper-activation of this pathway due to loss of caspase-8 has profound physiological impacts on natural resistance to viral infection and the pathogenesis of lymphadenopathy.
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15
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Mir FA, Ullah E, Mall R, Iskandarani A, Samra TA, Cyprian F, Parray A, Alkasem M, Abdalhakam I, Farooq F, Abou-Samra AB. Dysregulated Metabolic Pathways in Subjects with Obesity and Metabolic Syndrome. Int J Mol Sci 2022; 23:ijms23179821. [PMID: 36077214 PMCID: PMC9456113 DOI: 10.3390/ijms23179821] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 08/05/2022] [Accepted: 08/09/2022] [Indexed: 11/24/2022] Open
Abstract
Background: Obesity coexists with variable features of metabolic syndrome, which is associated with dysregulated metabolic pathways. We assessed potential associations between serum metabolites and features of metabolic syndrome in Arabic subjects with obesity. Methods: We analyzed a dataset of 39 subjects with obesity only (OBO, n = 18) age-matched to subjects with obesity and metabolic syndrome (OBM, n = 21). We measured 1069 serum metabolites and correlated them to clinical features. Results: A total of 83 metabolites, mostly lipids, were significantly different (p < 0.05) between the two groups. Among lipids, 22 sphingomyelins were decreased in OBM compared to OBO. Among non-lipids, quinolinate, kynurenine, and tryptophan were also decreased in OBM compared to OBO. Sphingomyelin is negatively correlated with glucose, HbA1C, insulin, and triglycerides but positively correlated with HDL, LDL, and cholesterol. Differentially enriched pathways include lysine degradation, amino sugar and nucleotide sugar metabolism, arginine and proline metabolism, fructose and mannose metabolism, and galactose metabolism. Conclusions: Metabolites and pathways associated with chronic inflammation are differentially expressed in subjects with obesity and metabolic syndrome compared to subjects with obesity but without the clinical features of metabolic syndrome.
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Affiliation(s)
- Fayaz Ahmad Mir
- Qatar Metabolic Institute, Academic Health System, Hamad Medical Corporation, Doha, Qatar
- Correspondence: (F.A.M.); (E.U.)
| | - Ehsan Ullah
- Qatar Computing Research Institute (QCRI), Hamad Bin Khalifa University, Doha, Qatar
- Correspondence: (F.A.M.); (E.U.)
| | - Raghvendra Mall
- Qatar Computing Research Institute (QCRI), Hamad Bin Khalifa University, Doha, Qatar
- Department of Immunology, St. Jude Children’s Research Hospital, Memphis, TN 38104, USA
| | - Ahmad Iskandarani
- Qatar Metabolic Institute, Academic Health System, Hamad Medical Corporation, Doha, Qatar
| | - Tareq A. Samra
- Qatar Metabolic Institute, Academic Health System, Hamad Medical Corporation, Doha, Qatar
| | - Farhan Cyprian
- College of Medicine, QU Health, Qatar University, Doha, Qatar
| | - Aijaz Parray
- Qatar Neuroscience Institute, Academic Health System, Hamad Medical Corporation, Doha, Qatar
| | - Meis Alkasem
- Qatar Metabolic Institute, Academic Health System, Hamad Medical Corporation, Doha, Qatar
| | - Ibrahem Abdalhakam
- Qatar Metabolic Institute, Academic Health System, Hamad Medical Corporation, Doha, Qatar
| | - Faisal Farooq
- Qatar Computing Research Institute (QCRI), Hamad Bin Khalifa University, Doha, Qatar
| | - Abdul-Badi Abou-Samra
- Qatar Metabolic Institute, Academic Health System, Hamad Medical Corporation, Doha, Qatar
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16
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Karki R, Lee S, Mall R, Pandian N, Wang Y, Sharma BR, Malireddi RKS, Yang D, Trifkovic S, Steele JA, Connelly JP, Vishwanath G, Sasikala M, Reddy DN, Vogel P, Pruett-Miller SM, Webby R, Jonsson CB, Kanneganti TD. ZBP1-dependent inflammatory cell death, PANoptosis, and cytokine storm disrupt IFN therapeutic efficacy during coronavirus infection. Sci Immunol 2022; 7:eabo6294. [PMID: 35587515 PMCID: PMC9161373 DOI: 10.1126/sciimmunol.abo6294] [Citation(s) in RCA: 72] [Impact Index Per Article: 36.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 05/16/2022] [Indexed: 12/15/2022]
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for coronavirus disease 2019 (COVID-19), continues to cause substantial morbidity and mortality in the ongoing global pandemic. Understanding the fundamental mechanisms that govern innate immune and inflammatory responses during SARS-CoV-2 infection is critical for developing effective therapeutic strategies. Whereas interferon (IFN)-based therapies are generally expected to be beneficial during viral infection, clinical trials in COVID-19 have shown limited efficacy and potential detrimental effects of IFN treatment during SARS-CoV-2 infection. However, the underlying mechanisms responsible for this failure remain unknown. In this study, we found that IFN induced Z-DNA-binding protein 1 (ZBP1)-mediated inflammatory cell death, PANoptosis, in human and murine macrophages and in the lungs of mice infected with β-coronaviruses, including SARS-CoV-2 and mouse hepatitis virus (MHV). In patients with COVID-19, expression of the innate immune sensor ZBP1 was increased in immune cells from those who succumbed to the disease compared with those who recovered, further suggesting a link between ZBP1 and pathology. In mice, IFN-β treatment after β-coronavirus infection increased lethality, and genetic deletion of Zbp1 or its Zα domain suppressed cell death and protected the mice from IFN-mediated lethality during β-coronavirus infection. Overall, our results identify that ZBP1 induced during coronavirus infection limits the efficacy of IFN therapy by driving inflammatory cell death and lethality. Therefore, inhibiting ZBP1 activity may improve the efficacy of IFN therapy, paving the way for the development of new and critically needed therapeutics for COVID-19 as well as other infections and inflammatory conditions where IFN-mediated cell death and pathology occur.
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Affiliation(s)
- Rajendra Karki
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - SangJoon Lee
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Raghvendra Mall
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Nagakannan Pandian
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Yaqiu Wang
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Bhesh Raj Sharma
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - RK Subbarao Malireddi
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Dong Yang
- UTHSC Regional Biocontainment Laboratory, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Sanja Trifkovic
- Department of Infectious Diseases, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Jacob A. Steele
- Center for Advanced Genome Engineering (CAGE), St. Jude Children’s Research Hospital, Memphis, TN, USA
| | - Jon P. Connelly
- Center for Advanced Genome Engineering (CAGE), St. Jude Children’s Research Hospital, Memphis, TN, USA
| | - Gella Vishwanath
- Institute of Pulmonary Medicine and Sleep Disorders, Continental Hospitals, Asian Institute of Gastroenterology, Hyderabad, India
| | - Mitnala Sasikala
- Department of Basic Science, Asian Healthcare Foundation, Asian Institute of Gastroenterology, Hyderabad, India
| | - Duvvur Nageshwar Reddy
- Department of Medical Gastroenterology, Asian Institute of Gastroenterology, Hyderabad, India
| | - Peter Vogel
- Animal Resources Center and the Veterinary Pathology Core, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Shondra M. Pruett-Miller
- Center for Advanced Genome Engineering (CAGE), St. Jude Children’s Research Hospital, Memphis, TN, USA
| | - Richard Webby
- Department of Infectious Diseases, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Colleen Beth Jonsson
- Department of Microbiology, Immunology, & Biochemistry, University of Tennessee Health Science Center, Memphis, TN, USA
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17
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Orecchioni M, Fusco L, Mall R, Bordoni V, Fuoco C, Rinchai D, Guo S, Sainz R, Zoccheddu M, Gurcan C, Yilmazer A, Zavan B, Ménard-Moyon C, Bianco A, Hendrickx W, Bedognetti D, Delogu LG. Graphene oxide activates B cells with upregulation of granzyme B expression: evidence at the single-cell level for its immune-modulatory properties and anticancer activity. Nanoscale 2022; 14:333-349. [PMID: 34796889 DOI: 10.1039/d1nr04355b] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
We recently found by single-cell mass cytometry that ex vivo human B cells internalize graphene oxide (GO). The functional impact of such uptake on B cells remains unexplored. Here, we disclosed the effects of GO and amino-functionalized GO (GONH2) interacting with human B cells in vitro and ex vivo at the protein and gene expression levels. Moreover, our study considered three different subpopulations of B cells and their functionality in terms of: (i) cytokine production, (ii) activation markers, (iii) killing activity towards cancer cells. Single-cell mass cytometry screening revealed the higher impact of GO on cell viability towards naïve, memory, and plasma B cell subsets. Different cytokines such as granzyme B (GrB) and activation markers, like CD69, CD80, CD138, and CD38, were differently regulated by GONH2 compared to GO, supporting possible diverse B cell activation paths. Moreover, co-culture experiments also suggest the functional ability of both GOs to activate B cells and therefore enhance the toxicity towards HeLa cancer cell line. Complete transcriptomic analysis on a B cell line highlighted the distinctive GO and GONH2 elicited responses, inducing pathways such as B cell receptor and CD40 signaling pathways, key players for GrB secretion. B cells were regularly left behind the scenes in graphene biological studies; our results may open new horizons in the development of GO-based immune-modulatory strategies having B cell as main actors.
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Affiliation(s)
- Marco Orecchioni
- Department of Chemistry and Pharmacy University of Sassari, Sassari, Italy.
| | - Laura Fusco
- Department of Immunology, Cancer Program, Sidra Medicine, Education City, Doha, Qatar.
- Department of Biomedical Sciences, University of Padua, Padua, Italy
- Department of Chemical and Pharmaceutical Sciences, University of Trieste, Trieste, Italy
| | - Raghvendra Mall
- Qatar Computing Research Institute (QCRI) Hamad Bin Khalifa University (HBKU), Doha, Qatar
| | - Valentina Bordoni
- Department of Chemistry and Pharmacy University of Sassari, Sassari, Italy.
| | - Claudia Fuoco
- Department of Biology, University of Rome Tor Vergata, Rome, Italy
| | - Darawan Rinchai
- Department of Immunology, Cancer Program, Sidra Medicine, Education City, Doha, Qatar.
| | - Shi Guo
- CNRS, Immunology, Immunopathology and Therapeutic Chemistry, UPR3572, University of Strasbourg, ISIS, 67000 Strasbourg, France
| | - Raquel Sainz
- CNRS, Immunology, Immunopathology and Therapeutic Chemistry, UPR3572, University of Strasbourg, ISIS, 67000 Strasbourg, France
| | - Martina Zoccheddu
- Department of Chemistry and Pharmacy University of Sassari, Sassari, Italy.
| | - Cansu Gurcan
- Department of Biomedical Engineering, Faculty of Engineering, Ankara University, Ankara, Turkey
| | - Acelya Yilmazer
- Department of Biomedical Engineering, Faculty of Engineering, Ankara University, Ankara, Turkey
| | - Barbara Zavan
- Department of Medical Sciences, University of Ferrara, Ferrara, Italy
| | - Cécilia Ménard-Moyon
- CNRS, Immunology, Immunopathology and Therapeutic Chemistry, UPR3572, University of Strasbourg, ISIS, 67000 Strasbourg, France
| | - Alberto Bianco
- CNRS, Immunology, Immunopathology and Therapeutic Chemistry, UPR3572, University of Strasbourg, ISIS, 67000 Strasbourg, France
| | - Wouter Hendrickx
- Department of Immunology, Cancer Program, Sidra Medicine, Education City, Doha, Qatar.
- College of Health and Life Sciences, Hamad Bin Khalifa University, Doha, Qatar
| | - Davide Bedognetti
- Department of Immunology, Cancer Program, Sidra Medicine, Education City, Doha, Qatar.
- College of Health and Life Sciences, Hamad Bin Khalifa University, Doha, Qatar
- Dipartimento di Medicina Interna e Specialità Mediche, Università degli Studi di Genova, Genova, Italy
| | - Lucia Gemma Delogu
- Department of Chemistry and Pharmacy University of Sassari, Sassari, Italy.
- Department of Biomedical Sciences, University of Padua, Padua, Italy
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18
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Vernieri C, Fucà G, Ligorio F, Huber V, Vingiani A, Iannelli F, Raimondi A, Rinchai D, Frigè G, Belfiore A, Lalli L, Chiodoni C, Cancila V, Zanardi F, Ajazi A, Cortellino S, Vallacchi V, Squarcina P, Cova A, Pesce S, Frati P, Mall R, Corsetto PA, Rizzo AM, Ferraris C, Folli S, Garassino MC, Capri G, Bianchi G, Colombo MP, Minucci S, Foiani M, Longo VD, Apolone G, Torri V, Pruneri G, Bedognetti D, Rivoltini L, de Braud F. Fasting-Mimicking Diet Is Safe and Reshapes Metabolism and Antitumor Immunity in Patients with Cancer. Cancer Discov 2022; 12:90-107. [PMID: 34789537 PMCID: PMC9762338 DOI: 10.1158/2159-8290.cd-21-0030] [Citation(s) in RCA: 104] [Impact Index Per Article: 52.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Revised: 08/04/2021] [Accepted: 10/22/2021] [Indexed: 01/07/2023]
Abstract
In tumor-bearing mice, cyclic fasting or fasting-mimicking diets (FMD) enhance the activity of antineoplastic treatments by modulating systemic metabolism and boosting antitumor immunity. Here we conducted a clinical trial to investigate the safety and biological effects of cyclic, five-day FMD in combination with standard antitumor therapies. In 101 patients, the FMD was safe, feasible, and resulted in a consistent decrease of blood glucose and growth factor concentration, thus recapitulating metabolic changes that mediate fasting/FMD anticancer effects in preclinical experiments. Integrated transcriptomic and deep-phenotyping analyses revealed that FMD profoundly reshapes anticancer immunity by inducing the contraction of peripheral blood immunosuppressive myeloid and regulatory T-cell compartments, paralleled by enhanced intratumor Th1/cytotoxic responses and an enrichment of IFNγ and other immune signatures associated with better clinical outcomes in patients with cancer. Our findings lay the foundations for phase II/III clinical trials aimed at investigating FMD antitumor efficacy in combination with standard antineoplastic treatments. SIGNIFICANCE: Cyclic FMD is well tolerated and causes remarkable systemic metabolic changes in patients with different tumor types and treated with concomitant antitumor therapies. In addition, the FMD reshapes systemic and intratumor immunity, finally activating several antitumor immune programs. Phase II/III clinical trials are needed to investigate FMD antitumor activity/efficacy.This article is highlighted in the In This Issue feature, p. 1.
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Affiliation(s)
- Claudio Vernieri
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.,IFOM, The FIRC Institute of Molecular Oncology, Milan, Italy.,Corresponding Authors: Claudio Vernieri, IFOM, The FIRC Institute of Molecular Oncology and Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy. Phone: 390223903066; E-mail: or ; and Licia Rivoltini,
| | - Giovanni Fucà
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Francesca Ligorio
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Veronica Huber
- Unit of Immunotherapy of Human Tumors, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Andrea Vingiani
- Oncology and Haemato-Oncology Department, University of Milan, Milan, Italy.,Deparment of Pathology and Laboratory Medicine, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Fabio Iannelli
- IFOM, The FIRC Institute of Molecular Oncology, Milan, Italy
| | - Alessandra Raimondi
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Darawan Rinchai
- Immunology Department, Cancer Program, Sidra Medicine, Doha, Qatar
| | - Gianmaria Frigè
- Department of Experimental Oncology, European Institute of Oncology (IEO), IRCCS, Milan, Italy
| | - Antonino Belfiore
- Deparment of Pathology and Laboratory Medicine, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Luca Lalli
- Unit of Immunotherapy of Human Tumors, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Claudia Chiodoni
- Molecular Immunology Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Valeria Cancila
- Tumor Immunology Unit, Department of Health Sciences, University of Palermo, Palermo, Italy
| | | | - Arta Ajazi
- IFOM, The FIRC Institute of Molecular Oncology, Milan, Italy
| | | | - Viviana Vallacchi
- Unit of Immunotherapy of Human Tumors, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Paola Squarcina
- Unit of Immunotherapy of Human Tumors, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Agata Cova
- Unit of Immunotherapy of Human Tumors, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Samantha Pesce
- Unit of Immunotherapy of Human Tumors, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Paola Frati
- Unit of Immunotherapy of Human Tumors, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Raghvendra Mall
- Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar
| | - Paola Antonia Corsetto
- Department of Pharmacological and Biomolecular Sciences, University of Milan, Milan, Italy
| | - Angela Maria Rizzo
- Department of Pharmacological and Biomolecular Sciences, University of Milan, Milan, Italy
| | - Cristina Ferraris
- Breast Unit, Fondazione IRCCS Istituto Nazionale dei Tumori. Milan 20133, Italy
| | - Secondo Folli
- Breast Unit, Fondazione IRCCS Istituto Nazionale dei Tumori. Milan 20133, Italy
| | | | - Giuseppe Capri
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Giulia Bianchi
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Mario Paolo Colombo
- Molecular Immunology Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Saverio Minucci
- Department of Experimental Oncology, European Institute of Oncology (IEO), IRCCS, Milan, Italy.,Department of Biosciences, University of Milan, Milan, Italy
| | - Marco Foiani
- IFOM, The FIRC Institute of Molecular Oncology, Milan, Italy.,Oncology and Haemato-Oncology Department, University of Milan, Milan, Italy
| | - Valter Daniel Longo
- IFOM, The FIRC Institute of Molecular Oncology, Milan, Italy.,Longevity Institute, School of Gerontology, Department of Biological Sciences, University of Southern California, Los Angeles, California
| | - Giovanni Apolone
- Scientific Directorate, Fondazione IRCCS Istituto Nazionale dei Tumori. Milan, Italy
| | - Valter Torri
- Laboratory of Methodology for Biomedical Research, Istituto di Ricerche Farmacologiche “Mario Negri” IRCCS, Milan, Italy
| | - Giancarlo Pruneri
- Oncology and Haemato-Oncology Department, University of Milan, Milan, Italy.,Deparment of Pathology and Laboratory Medicine, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Davide Bedognetti
- Immunology Department, Cancer Program, Sidra Medicine, Doha, Qatar.,Dipartimento di Medicina Interna e Specialità Mediche, Università degli Studi di Genova, Genova, Italy.,College of Health and Life Sciences, Hamad Bin Khalifa University, Doha, Qatar
| | - Licia Rivoltini
- Unit of Immunotherapy of Human Tumors, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.,Corresponding Authors: Claudio Vernieri, IFOM, The FIRC Institute of Molecular Oncology and Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy. Phone: 390223903066; E-mail: or ; and Licia Rivoltini,
| | - Filippo de Braud
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.,Oncology and Haemato-Oncology Department, University of Milan, Milan, Italy
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Mir FA, Mall R, Iskandarani A, Ullah E, Samra TA, Cyprian F, Parray A, Alkasem M, Abdalhakam I, Farooq F, Abou-Samra AB. Characteristic MicroRNAs Linked to Dysregulated Metabolic Pathways in Qatari Adult Subjects With Obesity and Metabolic Syndrome. Front Endocrinol (Lausanne) 2022; 13:937089. [PMID: 35937842 PMCID: PMC9352892 DOI: 10.3389/fendo.2022.937089] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 06/24/2022] [Indexed: 11/21/2022] Open
Abstract
BACKGROUND Obesity-associated dysglycemia is associated with metabolic disorders. MicroRNAs (miRNAs) are known regulators of metabolic homeostasis. We aimed to assess the relationship of circulating miRNAs with clinical features in obese Qatari individuals. METHODS We analyzed a dataset of 39 age-matched patients that includes 18 subjects with obesity only (OBO) and 21 subjects with obesity and metabolic syndrome (OBM). We measured 754 well-characterized human microRNAs (miRNAs) and identified differentially expressed miRNAs along with their significant associations with clinical markers in these patients. RESULTS A total of 64 miRNAs were differentially expressed between metabolically healthy obese (OBO) versus metabolically unhealthy obese (OBM) patients. Thirteen out of 64 miRNAs significantly correlated with at least one clinical trait of the metabolic syndrome. Six out of the thirteen demonstrated significant association with HbA1c levels; miR-331-3p, miR-452-3p, and miR-485-5p were over-expressed, whereas miR-153-3p, miR-182-5p, and miR-433-3p were under-expressed in the OBM patients with elevated HbA1c levels. We also identified, miR-106b-3p, miR-652-3p, and miR-93-5p that showed a significant association with creatinine; miR-130b-5p, miR-363-3p, and miR-636 were significantly associated with cholesterol, whereas miR-130a-3p was significantly associated with LDL. Additionally, miR-652-3p's differential expression correlated significantly with HDL and creatinine. CONCLUSIONS MicroRNAs associated with metabolic syndrome in obese subjects may have a pathophysiologic role and can serve as markers for obese individuals predisposed to various metabolic diseases like diabetes.
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Affiliation(s)
- Fayaz Ahmad Mir
- Qatar Metabolic Institute, Academic Health System, Hamad Medical Corporation, Doha, Qatar
| | - Raghvendra Mall
- Qatar Computing Research Institute (QCRI), Hamad Bin Khalifa University, Doha, Qatar
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN, United States
| | - Ahmad Iskandarani
- Qatar Metabolic Institute, Academic Health System, Hamad Medical Corporation, Doha, Qatar
| | - Ehsan Ullah
- Qatar Computing Research Institute (QCRI), Hamad Bin Khalifa University, Doha, Qatar
| | - Tareq A Samra
- Qatar Metabolic Institute, Academic Health System, Hamad Medical Corporation, Doha, Qatar
| | - Farhan Cyprian
- College of Medicine, Qatar University (QU) Health, Qatar University, Doha, Qatar
| | - Aijaz Parray
- Qatar Neuroscience Institute, Academic Health System, Hamad Medical Corporation, Doha, Qatar
| | - Meis Alkasem
- Qatar Metabolic Institute, Academic Health System, Hamad Medical Corporation, Doha, Qatar
| | - Ibrahem Abdalhakam
- Qatar Metabolic Institute, Academic Health System, Hamad Medical Corporation, Doha, Qatar
| | - Faisal Farooq
- Qatar Computing Research Institute (QCRI), Hamad Bin Khalifa University, Doha, Qatar
| | - Abdul-Badi Abou-Samra
- Qatar Metabolic Institute, Academic Health System, Hamad Medical Corporation, Doha, Qatar
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20
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Abbas M, Mall R, Errafii K, Lattab A, Ullah E, Bensmail H, Arredouani A. Simple risk score to screen for prediabetes: A cross-sectional study from the Qatar Biobank cohort. J Diabetes Investig 2021; 12:988-997. [PMID: 33075216 PMCID: PMC8169357 DOI: 10.1111/jdi.13445] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Revised: 10/01/2020] [Accepted: 10/06/2020] [Indexed: 12/30/2022] Open
Abstract
AIMS/INTRODUCTION The progression from prediabetes to type 2 diabetes is preventable by lifestyle intervention and/or pharmacotherapy in a large fraction of individuals with prediabetes. Our objective was to develop a risk score to screen for prediabetes in the Middle East, where diabetes prevalence is one of the highest in the world. MATERIALS AND METHODS In this cross-sectional, case-control study, we used data of 4,895 controls and 2,373 prediabetic adults obtained from the Qatar Biobank cohort. Significant risk factors were identified by logistic regression and other machine learning methods. The receiver operating characteristic was used to calculate the area under curve, cut-off point, sensitivity, specificity, positive and negative predictive values. The prediabetes risk score was developed from data of Qatari citizens, as well as long-term (≥15 years) residents. RESULTS The significant risk factors for the Prediabetes Risk Score in Qatar were age, sex, body mass index, waist circumference and blood pressure. The risk score ranges from 0 to 45. The area under the curve of the score was 80% (95% confidence interval 78-83%), and the cut-off point of 16 yielded sensitivity and specificity of 86.2% (95% confidence interval 82.7-89.2%) and 57.9% (95% confidence interval 65.5-71.4%), respectively. Prediabetes Risk Score in Qatar performed equally in Qatari nationals and long-term residents. CONCLUSIONS Prediabetes Risk Score in Qatar is the first prediabetes screening score developed in a Middle Eastern population. It only uses risk factors measured non-invasively, is simple, cost-effective, and can be easily understood by the general public and health providers. Prediabetes Risk Score in Qatar is an important tool for early detection of prediabetes, and can help tremendously in curbing the diabetes epidemic in the region.
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Affiliation(s)
- Mostafa Abbas
- Qatar Computing Research InstituteHamad Bin Khalifa UniversityDohaQatar
- Department of Imaging Science and InnovationGeisingerDanvillePennsylvaniaUSA
| | - Raghvendra Mall
- Qatar Computing Research InstituteHamad Bin Khalifa UniversityDohaQatar
| | - Khaoula Errafii
- Qatar Biomedical Research InstituteHamad Bin Khalifa UniversityDohaQatar
- College of Health and Life SciencesHamad Bin Khalifa UniversityDohaQatar
| | - Abdelkader Lattab
- Qatar Computing Research InstituteHamad Bin Khalifa UniversityDohaQatar
| | - Ehsan Ullah
- Qatar Computing Research InstituteHamad Bin Khalifa UniversityDohaQatar
| | - Halima Bensmail
- Qatar Computing Research InstituteHamad Bin Khalifa UniversityDohaQatar
| | - Abdelilah Arredouani
- Qatar Biomedical Research InstituteHamad Bin Khalifa UniversityDohaQatar
- College of Health and Life SciencesHamad Bin Khalifa UniversityDohaQatar
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21
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Mall R, Saad M, Roelands J, Rinchai D, Kunji K, Almeer H, Hendrickx W, M Marincola F, Ceccarelli M, Bedognetti D. Network-based identification of key master regulators associated with an immune-silent cancer phenotype. Brief Bioinform 2021; 22:6274817. [PMID: 33979427 PMCID: PMC8574720 DOI: 10.1093/bib/bbab168] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Revised: 03/24/2021] [Accepted: 04/09/2021] [Indexed: 12/15/2022] Open
Abstract
A cancer immune phenotype characterized by an active T-helper 1 (Th1)/cytotoxic response is associated with responsiveness to immunotherapy and favorable prognosis across different tumors. However, in some cancers, such an intratumoral immune activation does not confer protection from progression or relapse. Defining mechanisms associated with immune evasion is imperative to refine stratification algorithms, to guide treatment decisions and to identify candidates for immune-targeted therapy. Molecular alterations governing mechanisms for immune exclusion are still largely unknown. The availability of large genomic datasets offers an opportunity to ascertain key determinants of differential intratumoral immune response. We follow a network-based protocol to identify transcription regulators (TRs) associated with poor immunologic antitumor activity. We use a consensus of four different pipelines consisting of two state-of-the-art gene regulatory network inference techniques, regularized gradient boosting machines and ARACNE to determine TR regulons, and three separate enrichment techniques, including fast gene set enrichment analysis, gene set variation analysis and virtual inference of protein activity by enriched regulon analysis to identify the most important TRs affecting immunologic antitumor activity. These TRs, referred to as master regulators (MRs), are unique to immune-silent and immune-active tumors, respectively. We validated the MRs coherently associated with the immune-silent phenotype across cancers in The Cancer Genome Atlas and a series of additional datasets in the Prediction of Clinical Outcomes from Genomic Profiles repository. A downstream analysis of MRs specific to the immune-silent phenotype resulted in the identification of several enriched candidate pathways, including NOTCH1, TGF-$\beta $, Interleukin-1 and TNF-$\alpha $ signaling pathways. TGFB1I1 emerged as one of the main negative immune modulators preventing the favorable effects of a Th1/cytotoxic response.
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Affiliation(s)
- Raghvendra Mall
- Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar
| | - Mohamad Saad
- Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar
| | - Jessica Roelands
- Cancer Research Department, Research Branch, Sidra Medicince, Doha, Qatar
| | - Darawan Rinchai
- Cancer Research Department, Research Branch, Sidra Medicince, Doha, Qatar
| | - Khalid Kunji
- Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar
| | - Hossam Almeer
- Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar
| | - Wouter Hendrickx
- Cancer Research Department, Research Branch, Sidra Medicince, Doha, Qatar
| | | | - Michele Ceccarelli
- Department of Electrical Engineering and Information Technology (DIETI), University of Naples "Federico II", Via Claudio 21, 80215 Naples, Italy.,Biogem, Istituto di Biologia e Genetica Molecolare, Via Camporeale, Ariano Irpino (AV)
| | - Davide Bedognetti
- Cancer Research Department, Research Branch, Sidra Medicince, Doha, Qatar.,Department of Internal Medicine and Medical Specialities, University of Genova, Genova, Italy.,College of Health and Life Sciences, Hamad Bin Khalifa University, Doha, Qatar
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22
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Park H, Ali A, Mall R, Bensmail H, Sanvito S, El-Mellouhi F. Data-driven enhancement of cubic phase stability in mixed-cation perovskites. Mach Learn : Sci Technol 2021. [DOI: 10.1088/2632-2153/abdaf9] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Abstract
Abstract
Mixing cations has been a successful strategy in perovskite synthesis by solution-processing, delivering improvements in the thermodynamic stability as well as in the lattice parameter control. Unfortunately, the relation between a given cation mixture and the associated structural deformation is not well-established, a fact that hinders an adequate identification of the optimum chemical compositions. Such difficulty arises since local distortion and microscopic disorder influence structural stability and also determine phase segregation. Hence, the search for an optimum composition is currently based on experimental trial and error, a tedious and high-cost process. Here, we report on a machine-learning-reinforced cubic-phase-perovskite stability predictor that has been constructed over an extensive dataset of first-principles calculations. Such a predictor allows us to determine the cubic phase stability at a given cation mixture regardless of the various cations’ pair and concentration, even assessing very dilute concentrations, a notoriously challenging task for first-principles calculations. In particular, we construct machine learning models, predicting multiple target quantities such as the enthalpy of mixing and various octahedral distortions. It is then the combination of these targets that guide the laboratory synthesis. Our theoretical analysis is also validated by the experimental synthesis and characterization of methylammonium–dimethylammonium-mixed perovskite thin films, demonstrating the ability of the stability predictor to drive the chemical design of this class of materials.
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23
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Mall R, Elbasir A, Almeer H, Islam Z, Kolatkar PR, Chawla S, Ullah E. A Modelling Framework for Embedding-based Predictions for Compound-Viral Protein Activity. Bioinformatics 2021; 37:2544-2555. [PMID: 33638345 PMCID: PMC8163000 DOI: 10.1093/bioinformatics/btab130] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Revised: 02/16/2021] [Accepted: 02/24/2021] [Indexed: 11/14/2022] Open
Abstract
Motivation A global effort is underway to identify compounds for the treatment of COVID-19. Since de novo compound design is an extremely long, time-consuming, and expensive process, efforts are underway to discover existing compounds that can be repurposed for COVID-19 and new viral diseases. Model We propose a machine learning representation framework that uses deep learning induced vector embeddings of compounds and viral proteins as features to predict compound-viral protein activity. The prediction model in-turn uses a consensus framework to rank approved compounds against viral proteins of interest. Results Our consensus framework achieves a highmean Pearson correlation of 0.916, mean R2 of 0.840 and a low mean squared error of 0.313 for the task of compound-viral protein activity prediction on an independent test set. As a use case, we identify a ranked list of 47 compounds common to three main proteins of SARS-COV-2 virus (PL-PRO, 3CL-PRO and Spike protein) as potential targets including 21 antivirals, 15 anticancer, 5 antibiotics and 6 other investigationalhuman compounds.We performadditional molecular docking simulations to demonstrate thatmajority of these compounds have low binding energies and thus high binding affinity with the potential to be effective against the SARS-COV-2 virus. Availability All the source code and data is available at: https://github.com/raghvendra5688/Drug-Repurposing and https://dx.doi.org/10.17632/8rrwnbcgmx.3. We also implemented a web-server at: https://machinelearning-protein.qcri.org/index.html. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Raghvendra Mall
- Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, 34110, Qatar
| | - Abdurrahman Elbasir
- ICT Division, College of Science and Engineering, Hamad Bin Khalifa University, Doha, 34110, Qatar
| | - Hossam Almeer
- Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, 34110, Qatar
| | - Zeyaul Islam
- Qatar Biomedical Research Institute, Hamad Bin Khalifa Univeristy, Doha, 34110, Qatar
| | - Prasanna R Kolatkar
- Qatar Biomedical Research Institute, Hamad Bin Khalifa Univeristy, Doha, 34110, Qatar
| | - Sanjay Chawla
- Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, 34110, Qatar
| | - Ehsan Ullah
- Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, 34110, Qatar
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24
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Roelands J, Mall R, Almeer H, Thomas R, Mohamed MG, Bedri S, Al-Bader SB, Junejo K, Ziv E, Sayaman RW, Kuppen PJK, Bedognetti D, Hendrickx W, Decock J. Ancestry-associated transcriptomic profiles of breast cancer in patients of African, Arab, and European ancestry. NPJ Breast Cancer 2021; 7:10. [PMID: 33558495 PMCID: PMC7870839 DOI: 10.1038/s41523-021-00215-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Accepted: 12/23/2020] [Indexed: 02/06/2023] Open
Abstract
Breast cancer largely dominates the global cancer burden statistics; however, there are striking disparities in mortality rates across countries. While socioeconomic factors contribute to population-based differences in mortality, they do not fully explain disparity among women of African ancestry (AA) and Arab ancestry (ArA) compared to women of European ancestry (EA). In this study, we sought to identify molecular differences that could provide insight into the biology of ancestry-associated disparities in clinical outcomes. We applied a unique approach that combines the use of curated survival data from The Cancer Genome Atlas (TCGA) Pan-Cancer clinical data resource, improved single-nucleotide polymorphism-based inferred ancestry assignment, and a novel breast cancer subtype classification to interrogate the TCGA and a local Arab breast cancer dataset. We observed an enrichment of BasalMyo tumors in AA patients (38 vs 16.5% in EA, p = 1.30E - 10), associated with a significant worse overall (hazard ratio (HR) = 2.39, p = 0.02) and disease-specific survival (HR = 2.57, p = 0.03). Gene set enrichment analysis of BasalMyo AA and EA samples revealed differences in the abundance of T-regulatory and T-helper type 2 cells, and enrichment of cancer-related pathways with prognostic implications (AA: PI3K-Akt-mTOR and ErbB signaling; EA: EGF, estrogen-dependent and DNA repair signaling). Strikingly, AMPK signaling was associated with opposing prognostic connotation (AA: 10-year HR = 2.79, EA: 10-year HR = 0.34). Analysis of ArA patients suggests enrichment of BasalMyo tumors with a trend for differential enrichment of T-regulatory cells and AMPK signaling. Together, our findings suggest that the disparity in the clinical outcome of AA breast cancer patients is likely related to differences in cancer-related and microenvironmental features.
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Affiliation(s)
- Jessica Roelands
- Functional Cancer Omics Lab, Cancer Group, Research Branch, Sidra Medicine, Doha, Qatar
- Department of Surgery, Leiden University Medical Center, Leiden, Netherlands
| | - Raghvendra Mall
- Qatar Computing Research Institute (QCRI), Hamad Bin Khalifa University (HBKU), Qatar Foundation (QF), Doha, Qatar
| | - Hossam Almeer
- Qatar Computing Research Institute (QCRI), Hamad Bin Khalifa University (HBKU), Qatar Foundation (QF), Doha, Qatar
| | - Remy Thomas
- Cancer Research Center, Qatar Biomedical Research Institute (QBRI), Hamad Bin Khalifa University (HBKU), Qatar Foundation (QF), Doha, Qatar
| | - Mahmoud G Mohamed
- Women's Hospital, Hamad Medical Corporation, Doha, Qatar
- Department of Internal Medicine and Medical Specialties (DiMI), University of Genoa, Genoa, Italy
| | | | | | - Kulsoom Junejo
- General Surgery Department, Hamad General Hospital, Doha, Qatar
| | - Elad Ziv
- Department of Medicine, Institute for Human Genetics, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, USA
| | - Rosalyn W Sayaman
- Department of Population Sciences, Beckman Research Institute, City of Hope Comprehensive Cancer Center, Duarte, CA, USA
- Department of Laboratory Medicine, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, USA
| | - Peter J K Kuppen
- Department of Surgery, Leiden University Medical Center, Leiden, Netherlands
| | - Davide Bedognetti
- Department of Internal Medicine and Medical Specialties (DiMI), University of Genoa, Genoa, Italy.
- Cancer Immunogenetics Lab, Cancer Group, Research Branch, Sidra Medicine, Doha, Qatar.
- College of Health and Life Sciences (CHLS), Hamad bin Khalifa University (HBKU), Qatar Foundation (QF), Doha, Qatar.
| | - Wouter Hendrickx
- Functional Cancer Omics Lab, Cancer Group, Research Branch, Sidra Medicine, Doha, Qatar.
- College of Health and Life Sciences (CHLS), Hamad bin Khalifa University (HBKU), Qatar Foundation (QF), Doha, Qatar.
| | - Julie Decock
- Cancer Research Center, Qatar Biomedical Research Institute (QBRI), Hamad Bin Khalifa University (HBKU), Qatar Foundation (QF), Doha, Qatar.
- College of Health and Life Sciences (CHLS), Hamad bin Khalifa University (HBKU), Qatar Foundation (QF), Doha, Qatar.
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Elhadd T, Mall R, Bashir M, Palotti J, Fernandez-Luque L, Farooq F, Mohanadi DA, Dabbous Z, Malik RA, Abou-Samra AB. Artificial Intelligence (AI) based machine learning models predict glucose variability and hypoglycaemia risk in patients with type 2 diabetes on a multiple drug regimen who fast during ramadan (The PROFAST - IT Ramadan study). Diabetes Res Clin Pract 2020; 169:108388. [PMID: 32858096 DOI: 10.1016/j.diabres.2020.108388] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/03/2020] [Accepted: 08/19/2020] [Indexed: 10/23/2022]
Abstract
OBJECTIVE To develop a machine-based algorithm from clinical and demographic data, physical activity and glucose variability to predict hyperglycaemic and hypoglycaemic excursions in patients with type 2 diabetes on multiple glucose lowering therapies who fast during Ramadan. PATIENTS AND METHODS Thirteen patients (10 males and three females) with type 2 diabetes on 3 or more anti-diabetic medications were studied with a Fitbit-2 pedometer device and Freestyle Libre (Abbott Diagnostics) 2 weeks before and 2 weeks during Ramadan. Several machine learning techniques were trained to predict blood glucose levels in a regression framework utilising physical activity and contemporaneous blood glucose levels, comparing Ramadan to non-Ramadan days. RESULTS The median age of participants was 51 years (IQR 49-52); median BMI was 33.2 kg/m2 (IQR 33.0-35.9) and median HbA1c was 7.3% (IQR 6.7-7.8). The optimal model using physical activity achieved an R2 of 0.548 and a mean absolute error (MAE) of 30.30. The addition of electronic health record (ehr) information increased R2 to 0.636 and reduced MAE to 26.89 and the time of the day feature further increased R2 to 0.768 and reduced MAE to 20.55. Combining all the features together resulted in an optimal XGBoost model with an R2 of 0.836 and MAE of 17.47. This model accurately estimated normal glucose levels in 2584/2715 (95.2%) readings and hyperglycaemic events in 852/1031 (82.6%) readings, but fewer hypoglycaemic events (48/172 (27.9%)). The optimal XGBoost model prioritized age, gender, BMI and HbA1c followed by glucose levels and physical activity. Interestingly, the blood glucose level prediction by our model was influenced by use of SGLT2i. CONCLUSION XGBoost, a machine learning AI algorithm achieves high predictive performance for normal and hyperglycaemic excursions, but has limited predictive value for hypoglycaemia in patients on multiple therapies who fast during Ramadan.
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Affiliation(s)
| | | | | | - Joao Palotti
- Qatar Computer Research Institute (QCRI), Doha, Qatar; Hamad Medical Corporation, Doha, Qatar; CSAIL, Massachusetts Institute of Technology, USA
| | | | - Faisal Farooq
- Qatar Computer Research Institute (QCRI), Doha, Qatar
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Elbasir A, Mall R, Kunji K, Rawi R, Islam Z, Chuang GY, Kolatkar PR, Bensmail H. BCrystal: an interpretable sequence-based protein crystallization predictor. Bioinformatics 2020; 36:1429-1438. [PMID: 31603511 DOI: 10.1093/bioinformatics/btz762] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Revised: 09/19/2019] [Accepted: 10/08/2019] [Indexed: 02/01/2023] Open
Abstract
MOTIVATION X-ray crystallography has facilitated the majority of protein structures determined to date. Sequence-based predictors that can accurately estimate protein crystallization propensities would be highly beneficial to overcome the high expenditure, large attrition rate, and to reduce the trial-and-error settings required for crystallization. RESULTS In this study, we present a novel model, BCrystal, which uses an optimized gradient boosting machine (XGBoost) on sequence, structural and physio-chemical features extracted from the proteins of interest. BCrystal also provides explanations, highlighting the most important features for the predicted crystallization propensity of an individual protein using the SHAP algorithm. On three independent test sets, BCrystal outperforms state-of-the-art sequence-based methods by more than 12.5% in accuracy, 18% in recall and 0.253 in Matthew's correlation coefficient, with an average accuracy of 93.7%, recall of 96.63% and Matthew's correlation coefficient of 0.868. For relative solvent accessibility of exposed residues, we observed higher values to associate positively with protein crystallizability and the number of disordered regions, fraction of coils and tripeptide stretches that contain multiple histidines associate negatively with crystallizability. The higher accuracy of BCrystal enables it to accurately screen for sequence variants with enhanced crystallizability. AVAILABILITY AND IMPLEMENTATION Our BCrystal webserver is at https://machinelearning-protein.qcri.org/ and source code is available at https://github.com/raghvendra5688/BCrystal. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Abdurrahman Elbasir
- ICT Division, College of Science and Engineering, Hamad Bin Khalifa University
| | - Raghvendra Mall
- Data Analytics, Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha 34110, Qatar
| | - Khalid Kunji
- Data Analytics, Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha 34110, Qatar
| | - Reda Rawi
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Zeyaul Islam
- Diabetes Research Center, Qatar Biomedical Research Institute, Hamad Bin Khalifa University, Doha 34100, Qatar
| | - Gwo-Yu Chuang
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Prasanna R Kolatkar
- Diabetes Research Center, Qatar Biomedical Research Institute, Hamad Bin Khalifa University, Doha 34100, Qatar
| | - Halima Bensmail
- Data Analytics, Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha 34110, Qatar
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Roelands J, Hendrickx W, Zoppoli G, Mall R, Saad M, Halliwill K, Curigliano G, Rinchai D, Decock J, Delogu LG, Turan T, Samayoa J, Chouchane L, Ballestrero A, Wang E, Finetti P, Bertucci F, Miller LD, Galon J, Marincola FM, Kuppen PJK, Ceccarelli M, Bedognetti D. Oncogenic states dictate the prognostic and predictive connotations of intratumoral immune response. J Immunother Cancer 2020; 8:e000617. [PMID: 32376723 PMCID: PMC7223637 DOI: 10.1136/jitc-2020-000617] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/24/2020] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND An immune active cancer phenotype typified by a T helper 1 (Th-1) immune response has been associated with increased responsiveness to immunotherapy and favorable prognosis in some but not all cancer types. The reason of this differential prognostic connotation remains unknown. METHODS To explore the contextual prognostic value of cancer immune phenotypes, we applied a multimodal pan-cancer analysis among 31 different histologies (9282 patients), encompassing immune and oncogenic transcriptomic analysis, mutational and neoantigen load and copy number variations. RESULTS We demonstrated that the favorable prognostic connotation conferred by the presence of a Th-1 immune response was abolished in tumors displaying specific tumor-cell intrinsic attributes such as high transforming growth factor-beta (TGF-β) signaling and low proliferation capacity. This observation was independent of mutation rate. We validated this observation in the context of immune checkpoint inhibition. WNT-β catenin, barrier molecules, Notch, hedgehog, mismatch repair, telomerase activity and AMPK signaling were the pathways most coherently associated with an immune silent phenotype together with mutations of driver genes including IDH1/2, FOXA2, HDAC3, PSIP1, MAP3K1, KRAS, NRAS, EGFR, FGFR3, WNT5A and IRF7. CONCLUSIONS This is the first systematic study demonstrating that the prognostic and predictive role of a bona fide favorable intratumoral immune response is dependent on the disposition of specific oncogenic pathways. This information could be used to refine stratification algorithms and prioritize hierarchically relevant targets for combination therapies.
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Affiliation(s)
- Jessica Roelands
- Cancer Research Department, Research Branch, Sidra Medicine, Doha, Qatar
- Department of Surgery, Leiden University Medical Center (LUMC), Leiden, The Netherlands
| | - Wouter Hendrickx
- Cancer Research Department, Research Branch, Sidra Medicine, Doha, Qatar
- College of Health and Life Sciences (CHLS), Hamad Bin Khalifa University (HBKU), Doha, Qatar
| | - Gabriele Zoppoli
- IRCCS Ospedale Policlinico San Martino, Genova, Italy
- Department of Internal Medicine (DiMI), University of Genova, Genova, Italy
| | - Raghvendra Mall
- Qatar Computing Research Institute (QCRI), Hamad Bin Khalifa University (HBKU), Doha, Qatar
| | - Mohamad Saad
- Qatar Computing Research Institute (QCRI), Hamad Bin Khalifa University (HBKU), Doha, Qatar
| | - Kyle Halliwill
- Genomics Research Center (GRC), AbbVie Biotherapeutics, Redwood City, California, USA
| | - Giuseppe Curigliano
- Department of Oncology and Hemato-Oncology, University of Milano, Milano, Italy
| | - Darawan Rinchai
- Cancer Research Department, Research Branch, Sidra Medicine, Doha, Qatar
| | | | - Lucia G Delogu
- Istituto di Ricerca Pediatrica, Fondazione Città della Speranza, Padua, Italy
| | - Tolga Turan
- Genomics Research Center (GRC), AbbVie Biotherapeutics, Redwood City, California, USA
| | - Josue Samayoa
- Genomics Research Center (GRC), AbbVie Biotherapeutics, Redwood City, California, USA
| | | | - Alberto Ballestrero
- IRCCS Ospedale Policlinico San Martino, Genova, Italy
- Department of Internal Medicine (DiMI), University of Genova, Genova, Italy
| | | | | | | | | | | | | | - Peter J K Kuppen
- Department of Surgery, Leiden University Medical Center (LUMC), Leiden, The Netherlands
| | - Michele Ceccarelli
- Genomics Research Center (GRC), AbbVie Biotherapeutics, Redwood City, California, USA
- Department of Electrical Engineering and Information Technology (DIETI), University of Naples "Federico II", Naples, Italy
- Istituto di Ricerche Genetiche "G. Salvatore", Biogem s.c.ar.l, 83031, Ariano Irpino, Italy
| | - Davide Bedognetti
- Cancer Research Department, Research Branch, Sidra Medicine, Doha, Qatar
- College of Health and Life Sciences (CHLS), Hamad Bin Khalifa University (HBKU), Doha, Qatar
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Perez-Pozuelo I, Zhai B, Palotti J, Mall R, Aupetit M, Garcia-Gomez JM, Taheri S, Guan Y, Fernandez-Luque L. The future of sleep health: a data-driven revolution in sleep science and medicine. NPJ Digit Med 2020; 3:42. [PMID: 32219183 PMCID: PMC7089984 DOI: 10.1038/s41746-020-0244-4] [Citation(s) in RCA: 88] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Accepted: 02/18/2020] [Indexed: 01/04/2023] Open
Abstract
In recent years, there has been a significant expansion in the development and use of multi-modal sensors and technologies to monitor physical activity, sleep and circadian rhythms. These developments make accurate sleep monitoring at scale a possibility for the first time. Vast amounts of multi-sensor data are being generated with potential applications ranging from large-scale epidemiological research linking sleep patterns to disease, to wellness applications, including the sleep coaching of individuals with chronic conditions. However, in order to realise the full potential of these technologies for individuals, medicine and research, several significant challenges must be overcome. There are important outstanding questions regarding performance evaluation, as well as data storage, curation, processing, integration, modelling and interpretation. Here, we leverage expertise across neuroscience, clinical medicine, bioengineering, electrical engineering, epidemiology, computer science, mHealth and human-computer interaction to discuss the digitisation of sleep from a inter-disciplinary perspective. We introduce the state-of-the-art in sleep-monitoring technologies, and discuss the opportunities and challenges from data acquisition to the eventual application of insights in clinical and consumer settings. Further, we explore the strengths and limitations of current and emerging sensing methods with a particular focus on novel data-driven technologies, such as Artificial Intelligence.
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Affiliation(s)
- Ignacio Perez-Pozuelo
- Department of Medicine, University of Cambridge, Cambridge, UK
- The Alan Turing Institute, London, UK
| | - Bing Zhai
- Open Lab, University of Newcastle, Newcastle, UK
| | - Joao Palotti
- Qatar Computing Research Institute, HBKU, Doha, Qatar
- CSAIL, Massachusetts Institute of Technology, Cambridge, MA USA
| | | | | | - Juan M. Garcia-Gomez
- BDSLab, Instituto Universitario de Tecnologias de la Informacion y Comunicaciones-ITACA, Universitat Politecnica de Valencia, Valencia, Spain
| | - Shahrad Taheri
- Department of Medicine and Clinical Research Core, Weill Cornell Medicine - Qatar, Qatar Foundation, Doha, Qatar
| | - Yu Guan
- Open Lab, University of Newcastle, Newcastle, UK
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Islam Z, Ali MH, Popelka A, Mall R, Ullah E, Ponraj J, Kolatkar PR. Probing the fibrillation of lysozyme by nanoscale-infrared spectroscopy. J Biomol Struct Dyn 2020; 39:1481-1490. [PMID: 32131712 DOI: 10.1080/07391102.2020.1734091] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Amyloid fibrillation is the root cause of several neuro as well as non-neurological disorders. Understanding the molecular basis of amyloid aggregate formation is crucial for deciphering various neurodegenerative diseases. In our study, we have examined the lysozyme fibrillation process using nano-infrared spectroscopy (nanoIR). NanoIR enabled us to investigate both structural and chemical characteristics of lysozyme fibrillar species concurrently. The spectroscopic results indicate that lysozyme transformed into a fibrillar structure having mainly parallel β-sheets, with almost no antiparallel β-sheets. Features such as protein stiffness have a good correlation with obtained secondary structural information showing the state of the protein within the fibrillation state. The structural and chemical details were compared with transmission electron microscopy (TEM) and circular dichroism (CD). We have utilized nanoIR and measured infrared spectra to characterize lysozyme amyloid fibril structures in terms of morphology, molecular structure, secondary structure content, stability, and size of the cross-β core. We have shown that the use of nanoIR can complement other biophysical studies to analyze the aggregation process and is particularly useful for studying proteins involved in aggregation to help in designing molecules against amyloid aggregation. Specifically, the nanoIR spectra afford higher resolution information and a characteristic fingerprint for determining states of aggregation.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Zeyaul Islam
- Qatar Biomedical Research Institute (QBRI), Hamad Bin Khalifa University (HBKU), Doha, Qatar
| | - Mohamed H Ali
- Qatar Biomedical Research Institute (QBRI), Hamad Bin Khalifa University (HBKU), Doha, Qatar
| | - Anton Popelka
- Center for Advanced Materials (CAM), Qatar University, Doha, Qatar
| | - Raghvendra Mall
- Qatar Computing Research Institute (QCRI), Hamad Bin Khalifa University, Doha, Qatar
| | - Ehsan Ullah
- Qatar Computing Research Institute (QCRI), Hamad Bin Khalifa University, Doha, Qatar
| | - Janarthanan Ponraj
- Qatar Environment and Energy Research Institute (QEERI), Hamad Bin Khalifa University, Doha, Qatar
| | - Prasanna R Kolatkar
- Qatar Biomedical Research Institute (QBRI), Hamad Bin Khalifa University (HBKU), Doha, Qatar
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Ali MHM, Toor SM, Rakib F, Mall R, Ullah E, Mroue K, Kolatkar PR, Al-Saad K, Elkord E. Investigation of the Effect of PD-L1 Blockade on Triple Negative Breast Cancer Cells Using Fourier Transform Infrared Spectroscopy. Vaccines (Basel) 2019; 7:vaccines7030109. [PMID: 31505846 PMCID: PMC6789440 DOI: 10.3390/vaccines7030109] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2019] [Revised: 08/21/2019] [Accepted: 09/03/2019] [Indexed: 12/24/2022] Open
Abstract
Interactions between programmed death-1 (PD-1) with its ligand PD-L1 on tumor cells can antagonize T cell responses. Inhibiting these interactions using immune checkpoint inhibitors has shown promise in cancer immunotherapy. MDA-MB-231 is a triple negative breast cancer cell line that expresses PD-L1. In this study, we investigated the biochemical changes in MDA-MB-231 cells following treatment with atezolizumab, a specific PD-L1 blocker. Our readouts were Fourier Transform Infrared (FTIR) spectroscopy and flow cytometric analyses. Chemometrical analysis, such as principal component analysis (PCA), was applied to delineate the spectral differences. We were able to identify the chemical alterations in both protein and lipid structure of the treated cells. We found that there was a shift from random coil and α-helical structure to β-sheet conformation of PD-L1 on tumor cells due to atezolizumab treatment, which could hinder binding with its receptors on immune cells, ensuring sustained T cell activation for potent immune responses. This work provides novel information about the effects of atezolizumab at molecular and cellular levels. FTIR bio-spectroscopy, in combination with chemometric analyses, may expedite research and offer new approaches for cancer immunology.
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Affiliation(s)
- Mohamed H M Ali
- Diabetes Research Center, Qatar Biomedical Research Institute (QBRI), Hamad Bin Khalifa University (HBKU), Qatar Foundation (QF), P.O. Box 34110 Doha, Qatar.
| | - Salman M Toor
- Cancer Research Center, Qatar Biomedical Research Institute (QBRI), Hamad Bin Khalifa University (HBKU), Qatar Foundation (QF), P.O. Box 34110 Doha, Qatar
| | - Fazle Rakib
- Department of Chemistry and Earth Sciences, Qatar University (QU), P.O. Box 2713 Doha, Qatar
| | - Raghvendra Mall
- Qatar Computing Research Institute, Hamad Bin Khalifa University (HBKU), Qatar Foundation (QF), P.O. Box 34110 Doha, Qatar
| | - Ehsan Ullah
- Qatar Computing Research Institute, Hamad Bin Khalifa University (HBKU), Qatar Foundation (QF), P.O. Box 34110 Doha, Qatar
| | - Kamal Mroue
- Qatar Environment & Energy Research Institute (QEERI), Hamad Bin Khalifa University (HBKU), Qatar Foundation (QF), P.O. Box 34110 Doha, Qatar
| | - Prasanna R Kolatkar
- Diabetes Research Center, Qatar Biomedical Research Institute (QBRI), Hamad Bin Khalifa University (HBKU), Qatar Foundation (QF), P.O. Box 34110 Doha, Qatar
| | - Khalid Al-Saad
- Department of Chemistry and Earth Sciences, Qatar University (QU), P.O. Box 2713 Doha, Qatar
| | - Eyad Elkord
- Cancer Research Center, Qatar Biomedical Research Institute (QBRI), Hamad Bin Khalifa University (HBKU), Qatar Foundation (QF), P.O. Box 34110 Doha, Qatar.
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Khurana S, Rawi R, Kunji K, Chuang GY, Bensmail H, Mall R. DeepSol: a deep learning framework for sequence-based protein solubility prediction. Bioinformatics 2019; 34:2605-2613. [PMID: 29554211 DOI: 10.1093/bioinformatics/bty166] [Citation(s) in RCA: 93] [Impact Index Per Article: 18.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2017] [Accepted: 03/13/2018] [Indexed: 01/09/2023] Open
Abstract
Motivation Protein solubility plays a vital role in pharmaceutical research and production yield. For a given protein, the extent of its solubility can represent the quality of its function, and is ultimately defined by its sequence. Thus, it is imperative to develop novel, highly accurate in silico sequence-based protein solubility predictors. In this work we propose, DeepSol, a novel Deep Learning-based protein solubility predictor. The backbone of our framework is a convolutional neural network that exploits k-mer structure and additional sequence and structural features extracted from the protein sequence. Results DeepSol outperformed all known sequence-based state-of-the-art solubility prediction methods and attained an accuracy of 0.77 and Matthew's correlation coefficient of 0.55. The superior prediction accuracy of DeepSol allows to screen for sequences with enhanced production capacity and can more reliably predict solubility of novel proteins. Availability and implementation DeepSol's best performing models and results are publicly deposited at https://doi.org/10.5281/zenodo.1162886 (Khurana and Mall, 2018). Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Sameer Khurana
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Reda Rawi
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institute of Health, Bethesda, MD, USA
| | - Khalid Kunji
- Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar
| | - Gwo-Yu Chuang
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institute of Health, Bethesda, MD, USA
| | - Halima Bensmail
- Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar
| | - Raghvendra Mall
- Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar
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Affiliation(s)
- Heesoo Park
- Qatar Environment and Energy Research Institute, Hamad Bin Khalifa University, P.O. Box 34110, Doha, Qatar
| | - Raghvendra Mall
- Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar
| | - Fahhad H. Alharbi
- Qatar Environment and Energy Research Institute, Hamad Bin Khalifa University, P.O. Box 34110, Doha, Qatar
| | - Stefano Sanvito
- School of Physics, AMBER and CRANN Institute, Trinity College, Dublin 2, Ireland
| | - Nouar Tabet
- Qatar Environment and Energy Research Institute, Hamad Bin Khalifa University, P.O. Box 34110, Doha, Qatar
| | - Halima Bensmail
- Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar
| | - Fedwa El-Mellouhi
- Qatar Environment and Energy Research Institute, Hamad Bin Khalifa University, P.O. Box 34110, Doha, Qatar
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Palotti J, Mall R, Aupetit M, Rueschman M, Singh M, Sathyanarayana A, Taheri S, Fernandez-Luque L. Benchmark on a large cohort for sleep-wake classification with machine learning techniques. NPJ Digit Med 2019; 2:50. [PMID: 31304396 PMCID: PMC6555808 DOI: 10.1038/s41746-019-0126-9] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2019] [Accepted: 05/06/2019] [Indexed: 11/17/2022] Open
Abstract
Accurately measuring sleep and its quality with polysomnography (PSG) is an expensive task. Actigraphy, an alternative, has been proven cheap and relatively accurate. However, the largest experiments conducted to date, have had only hundreds of participants. In this work, we processed the data of the recently published Multi-Ethnic Study of Atherosclerosis (MESA) Sleep study to have both PSG and actigraphy data synchronized. We propose the adoption of this publicly available large dataset, which is at least one order of magnitude larger than any other dataset, to systematically compare existing methods for the detection of sleep-wake stages, thus fostering the creation of new algorithms. We also implemented and compared state-of-the-art methods to score sleep-wake stages, which range from the widely used traditional algorithms to recent machine learning approaches. We identified among the traditional algorithms, two approaches that perform better than the algorithm implemented by the actigraphy device used in the MESA Sleep experiments. The performance, in regards to accuracy and F 1 score of the machine learning algorithms, was also superior to the device's native algorithm and comparable to human annotation. Future research in developing new sleep-wake scoring algorithms, in particular, machine learning approaches, will be highly facilitated by the cohort used here. We exemplify this potential by showing that two particular deep-learning architectures, CNN and LSTM, among the many recently created, can achieve accuracy scores significantly higher than other methods for the same tasks.
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Affiliation(s)
- Joao Palotti
- Qatar Computing Research Institute, HBKU, Doha, Qatar
| | | | | | - Michael Rueschman
- Brigham and Women’s Hospital, Boston, MA USA
- Harvard University, Boston, MA USA
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Park H, Mall R, Alharbi FH, Sanvito S, Tabet N, Bensmail H, El-Mellouhi F. Correction: Exploring new approaches towards the formability of mixed-ion perovskites by DFT and machine learning. Phys Chem Chem Phys 2019; 21:2821. [PMID: 30657154 DOI: 10.1039/c9cp90013f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Correction for 'Exploring new approaches towards the formability of mixed-ion perovskites by DFT and machine learning' by Heesoo Park et al., Phys. Chem. Chem. Phys., 2019, DOI: 10.1039/c8cp06528d.
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Affiliation(s)
- Heesoo Park
- Qatar Environment and Energy Research Institute, Hamad Bin Khalifa University, PO Box 34110, Doha, Qatar.
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Rawi R, Mall R, Kunji K, Shen CH, Kwong PD, Chuang GY. PaRSnIP: sequence-based protein solubility prediction using gradient boosting machine. Bioinformatics 2019; 34:1092-1098. [PMID: 29069295 DOI: 10.1093/bioinformatics/btx662] [Citation(s) in RCA: 58] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2017] [Accepted: 10/17/2017] [Indexed: 11/13/2022] Open
Abstract
Motivation Protein solubility can be a decisive factor in both research and production efficiency, and in silico sequence-based predictors that can accurately estimate solubility outcomes are highly sought. Results In this study, we present a novel approach termed PRotein SolubIlity Predictor (PaRSnIP), which uses a gradient boosting machine algorithm as well as an approximation of sequence and structural features of the protein of interest. Based on an independent test set, PaRSnIP outperformed other state-of-the-art sequence-based methods by more than 9% in accuracy and 0.17 in Matthew's correlation coefficient, with an overall accuracy of 74% and Matthew's correlation coefficient of 0.48. Additionally, PaRSnIP provides importance scores for all features used in training. We observed higher fractions of exposed residues to associate positively with protein solubility and tripeptide stretches with multiple histidines to associate negatively with solubility. The improved prediction accuracy of PaRSnIP should enable it to predict protein solubility with greater reliability and to screen for sequence variants with enhanced manufacturability. Availability and implementation PaRSnIP software is available for download under GitHub (https://github.com/RedaRawi/PaRSnIP). Contact gwo-yu.chuang@nih.gov. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Reda Rawi
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Raghvendra Mall
- Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, 34110, Qatar
| | - Khalid Kunji
- Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, 34110, Qatar
| | - Chen-Hsiang Shen
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Peter D Kwong
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Gwo-Yu Chuang
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
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Park H, Mall R, Alharbi FH, Sanvito S, Tabet N, Bensmail H, El-Mellouhi F. Exploring new approaches towards the formability of mixed-ion perovskites by DFT and machine learning. Phys Chem Chem Phys 2019; 21:1078-1088. [DOI: 10.1039/c8cp06528d] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Recent years have witnessed a growing effort in engineering and tuning the properties of hybrid halide perovskites as light absorbers.
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Affiliation(s)
- Heesoo Park
- Qatar Environment and Energy Research Institute
- Hamad Bin Khalifa University
- Doha
- Qatar
| | - Raghvendra Mall
- Qatar Computing Research Institute
- Hamad Bin Khalifa University
- Doha
- Qatar
| | - Fahhad H. Alharbi
- Qatar Environment and Energy Research Institute
- Hamad Bin Khalifa University
- Doha
- Qatar
- Qatar Environment and Energy Research Institute
| | - Stefano Sanvito
- School of Physics
- AMBER and CRANN Institute
- Trinity College
- Dublin 2
- Ireland
| | - Nouar Tabet
- Qatar Environment and Energy Research Institute
- Hamad Bin Khalifa University
- Doha
- Qatar
- Qatar Environment and Energy Research Institute
| | - Halima Bensmail
- Qatar Computing Research Institute
- Hamad Bin Khalifa University
- Doha
- Qatar
| | - Fedwa El-Mellouhi
- Qatar Environment and Energy Research Institute
- Hamad Bin Khalifa University
- Doha
- Qatar
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Ullah E, Mall R, Abbas MM, Kunji K, Nato AQ, Bensmail H, Wijsman EM, Saad M. Comparison and assessment of family- and population-based genotype imputation methods in large pedigrees. Genome Res 2018; 29:125-134. [PMID: 30514702 PMCID: PMC6314157 DOI: 10.1101/gr.236315.118] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2018] [Accepted: 11/30/2018] [Indexed: 01/19/2023]
Abstract
Genotype imputation is widely used in genome-wide association studies to boost variant density, allowing increased power in association testing. Many studies currently include pedigree data due to increasing interest in rare variants coupled with the availability of appropriate analysis tools. The performance of population-based (subjects are unrelated) imputation methods is well established. However, the performance of family- and population-based imputation methods on family data has been subject to much less scrutiny. Here, we extensively compare several family- and population-based imputation methods on family data of large pedigrees with both European and African ancestry. Our comparison includes many widely used family- and population-based tools and another method, Ped_Pop, which combines family- and population-based imputation results. We also compare four subject selection strategies for full sequencing to serve as the reference panel for imputation: GIGI-Pick, ExomePicks, PRIMUS, and random selection. Moreover, we compare two imputation accuracy metrics: the Imputation Quality Score and Pearson's correlation R 2 for predicting power of association analysis using imputation results. Our results show that (1) GIGI outperforms Merlin; (2) family-based imputation outperforms population-based imputation for rare variants but not for common ones; (3) combining family- and population-based imputation outperforms all imputation approaches for all minor allele frequencies; (4) GIGI-Pick gives the best selection strategy based on the R 2 criterion; and (5) R 2 is the best measure of imputation accuracy. Our study is the first to extensively evaluate the imputation performance of many available family- and population-based tools on the same family data and provides guidelines for future studies.
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Affiliation(s)
- Ehsan Ullah
- Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar
| | - Raghvendra Mall
- Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar
| | - Mostafa M Abbas
- Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar
| | - Khalid Kunji
- Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar
| | - Alejandro Q Nato
- Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, Washington 98195-9460, USA.,Department of Biomedical Sciences, Joan C. Edwards School of Medicine, Marshall University, Huntington, West Virginia 25755, USA
| | - Halima Bensmail
- Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar
| | - Ellen M Wijsman
- Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, Washington 98195-9460, USA.,Department of Biostatistics, University of Washington, Seattle, Washington 98195-9460, USA
| | - Mohamad Saad
- Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar
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Elbasir A, Moovarkumudalvan B, Kunji K, Kolatkar PR, Mall R, Bensmail H. DeepCrystal: a deep learning framework for sequence-based protein crystallization prediction. Bioinformatics 2018; 35:2216-2225. [DOI: 10.1093/bioinformatics/bty953] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2018] [Revised: 10/31/2018] [Accepted: 11/17/2018] [Indexed: 12/11/2022] Open
Abstract
Abstract
Motivation
Protein structure determination has primarily been performed using X-ray crystallography. To overcome the expensive cost, high attrition rate and series of trial-and-error settings, many in-silico methods have been developed to predict crystallization propensities of proteins based on their sequences. However, the majority of these methods build their predictors by extracting features from protein sequences, which is computationally expensive and can explode the feature space. We propose DeepCrystal, a deep learning framework for sequence-based protein crystallization prediction. It uses deep learning to identify proteins which can produce diffraction-quality crystals without the need to manually engineer additional biochemical and structural features from sequence. Our model is based on convolutional neural networks, which can exploit frequently occurring k-mers and sets of k-mers from the protein sequences to distinguish proteins that will result in diffraction-quality crystals from those that will not.
Results
Our model surpasses previous sequence-based protein crystallization predictors in terms of recall, F-score, accuracy and Matthew’s correlation coefficient (MCC) on three independent test sets. DeepCrystal achieves an average improvement of 1.4, 12.1% in recall, when compared to its closest competitors, Crysalis II and Crysf, respectively. In addition, DeepCrystal attains an average improvement of 2.1, 6.0% for F-score, 1.9, 3.9% for accuracy and 3.8, 7.0% for MCC w.r.t. Crysalis II and Crysf on independent test sets.
Availability and implementation
The standalone source code and models are available at https://github.com/elbasir/DeepCrystal and a web-server is also available at https://deeplearning-protein.qcri.org.
Supplementary information
Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Abdurrahman Elbasir
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | | | - Khalid Kunji
- Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar
| | - Prasanna R Kolatkar
- Qatar Biomedical Research Institute and Hamad Bin Khalifa University, Doha, Qatar
| | - Raghvendra Mall
- Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar
| | - Halima Bensmail
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
- Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar
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Ullah E, Mall R, Rawi R, Moustaid-Moussa N, Butt AA, Bensmail H. Correction to: Harnessing Qatar Biobank to understand type 2 diabetes and obesity in adult Qataris from the First Qatar Biobank Project. J Transl Med 2018; 16:283. [PMID: 30322395 PMCID: PMC6190666 DOI: 10.1186/s12967-018-1648-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2018] [Accepted: 09/27/2018] [Indexed: 11/24/2022] Open
Affiliation(s)
- Ehsan Ullah
- Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar
| | - Raghvendra Mall
- Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar
| | - Reda Rawi
- Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar.,Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, 20814, USA
| | - Naima Moustaid-Moussa
- Obesity Research Cluster (ORC), Nutrigenomics, Infammation and Obesity Research (NIOR) Laboratory, Texas Tech University, 1301 Akron Street, Lubbock, TX, 79409-1270, USA
| | - Adeel A Butt
- Department of Medicine, Weill Cornell Medical College, Doha, Qatar.,Department of Healthcare Policy and Research, Weil Cornell Medical College, Doha, Qatar.,Department of Medicine, Clinical Epidemiology Research Unit, Hamad Medical Corporation, Doha, Qatar
| | - Halima Bensmail
- Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar.
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Ali MHM, Rakib F, Abdelalim EM, Limbeck A, Mall R, Ullah E, Mesaeli N, McNaughton D, Ahmed T, Al-Saad K. Fourier-Transform Infrared Imaging Spectroscopy and Laser Ablation -ICPMS New Vistas for Biochemical Analyses of Ischemic Stroke in Rat Brain. Front Neurosci 2018; 12:647. [PMID: 30283295 PMCID: PMC6157330 DOI: 10.3389/fnins.2018.00647] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2018] [Accepted: 08/30/2018] [Indexed: 12/13/2022] Open
Abstract
Objective: Stroke is the main cause of adult disability in the world, leaving more than half of the patients dependent on daily assistance. Understanding the post-stroke biochemical and molecular changes are critical for patient survival and stroke management. The aim of this work was to investigate the photo-thrombotic ischemic stroke in male rats with particular focus on biochemical and elemental changes in the primary stroke lesion in the somatosensory cortex and surrounding areas, including the corpus callosum. Materials and Methods: FT-IR imaging spectroscopy and LA-ICPMS techniques examined stroke brain samples, which were compared with standard immunohistochemistry studies. Results: The FTIR results revealed that in the lesioned gray matter the relative distribution of lipid, lipid acyl and protein contents decreased significantly. Also at this locus, there was a significant increase in aggregated protein as detected by high-levels Aβ1-42. Areas close to the stroke focus experienced decrease in the lipid and lipid acyl contents associated with an increase in lipid ester, olefin, and methyl bio-contents with a novel finding of Aβ1-42 in the PL-GM and L-WM. Elemental analyses realized major changes in the different brain structures that may underscore functionality. Conclusion: In conclusion, FTIR bio-spectroscopy is a non-destructive, rapid, and a refined technique to characterize oxidative stress markers associated with lipid degradation and protein denaturation not characterized by routine approaches. This technique may expedite research into stroke and offer new approaches for neurodegenerative disorders. The results suggest that a good therapeutic strategy should include a mechanism that provides protective effect from brain swelling (edema) and neurotoxicity by scavenging the lipid peroxidation end products.
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Affiliation(s)
- Mohamed H M Ali
- Neurological Disorders Research Center, Qatar Biomedical Research Institute, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
| | - Fazle Rakib
- Department of Chemistry and Earth Sciences, Qatar University, Doha, Qatar
| | - Essam M Abdelalim
- Diabetes Research Center, Qatar Biomedical Research Institute, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar.,Department of Cytology and Histology, Faculty of Veterinary Medicine, Suez Canal University, Ismailia, Egypt
| | - Andreas Limbeck
- Institute of Chemical Technologies and Analytics, Vienna University of Technology, Vienna, Austria
| | - Raghvendra Mall
- Qatar Computing Research Institute, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
| | - Ehsan Ullah
- Qatar Computing Research Institute, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
| | - Nasrin Mesaeli
- Weill Cornell Medicine-Qatar, Qatar Foundation, Doha, Qatar
| | - Donald McNaughton
- Centre for Biospectroscopy, School of Chemistry, Monash University, Clayton, VIC, Australia
| | - Tariq Ahmed
- Neurological Disorders Research Center, Qatar Biomedical Research Institute, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
| | - Khalid Al-Saad
- Department of Chemistry and Earth Sciences, Qatar University, Doha, Qatar
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Mall R, Cerulo L, Garofano L, Frattini V, Kunji K, Bensmail H, Sabedot TS, Noushmehr H, Lasorella A, Iavarone A, Ceccarelli M. RGBM: regularized gradient boosting machines for identification of the transcriptional regulators of discrete glioma subtypes. Nucleic Acids Res 2018; 46:e39. [PMID: 29361062 PMCID: PMC6283452 DOI: 10.1093/nar/gky015] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2017] [Accepted: 01/06/2018] [Indexed: 01/05/2023] Open
Abstract
We propose a generic framework for gene regulatory network (GRN) inference approached as a feature selection problem. GRNs obtained using Machine Learning techniques are often dense, whereas real GRNs are rather sparse. We use a Tikonov regularization inspired optimal L-curve criterion that utilizes the edge weight distribution for a given target gene to determine the optimal set of TFs associated with it. Our proposed framework allows to incorporate a mechanistic active biding network based on cis-regulatory motif analysis. We evaluate our regularization framework in conjunction with two non-linear ML techniques, namely gradient boosting machines (GBM) and random-forests (GENIE), resulting in a regularized feature selection based method specifically called RGBM and RGENIE respectively. RGBM has been used to identify the main transcription factors that are causally involved as master regulators of the gene expression signature activated in the FGFR3-TACC3-positive glioblastoma. Here, we illustrate that RGBM identifies the main regulators of the molecular subtypes of brain tumors. Our analysis reveals the identity and corresponding biological activities of the master regulators characterizing the difference between G-CIMP-high and G-CIMP-low subtypes and between PA-like and LGm6-GBM, thus providing a clue to the yet undetermined nature of the transcriptional events among these subtypes.
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Affiliation(s)
- Raghvendra Mall
- Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar
| | - Luigi Cerulo
- Department of Science and Technology, University of Sannio, Benevento, Italy
- BIOGEM Istituto di Ricerche Genetiche “G. Salvatore”, Ariano Irpino, Italy
| | - Luciano Garofano
- Department of Science and Technology, University of Sannio, Benevento, Italy
- BIOGEM Istituto di Ricerche Genetiche “G. Salvatore”, Ariano Irpino, Italy
| | - Veronique Frattini
- Institute for Cancer Genetics, Columbia University Medical Center, New York, NY 10032, USA
| | - Khalid Kunji
- Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar
| | - Halima Bensmail
- Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar
| | - Thais S Sabedot
- Department of Neurosurgery, Brain Tumor Center, Henry Ford Health System, Detroit, MI, USA
- Department of Genetics (CISBi/NAP), Department of Surgery and Anatomy, Ribeirão Preto Medical School, University of Sao Paulo, Monte Alegre, Ribeirao Preto, Brazil
| | - Houtan Noushmehr
- Department of Neurosurgery, Brain Tumor Center, Henry Ford Health System, Detroit, MI, USA
- Department of Genetics (CISBi/NAP), Department of Surgery and Anatomy, Ribeirão Preto Medical School, University of Sao Paulo, Monte Alegre, Ribeirao Preto, Brazil
| | - Anna Lasorella
- Institute for Cancer Genetics, Columbia University Medical Center, New York, NY 10032, USA
- Department of Pathology and Cell Biology, Columbia University Medical Center, New York, New York 10032, USA
- Department of Pediatrics, Columbia University Medical Center, New York, New York 10032, USA
| | - Antonio Iavarone
- Institute for Cancer Genetics, Columbia University Medical Center, New York, NY 10032, USA
- Department of Pathology and Cell Biology, Columbia University Medical Center, New York, New York 10032, USA
- Department of Neurology, Columbia University Medical Center, New York, New York 10032, USA
| | - Michele Ceccarelli
- Department of Science and Technology, University of Sannio, Benevento, Italy
- BIOGEM Istituto di Ricerche Genetiche “G. Salvatore”, Ariano Irpino, Italy
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Ullah E, Mall R, Rawi R, Moustaid-Moussa N, Butt AA, Bensmail H. Harnessing Qatar Biobank to understand type 2 diabetes and obesity in adult Qataris from the First Qatar Biobank Project. J Transl Med 2018; 16:99. [PMID: 29650030 PMCID: PMC5898076 DOI: 10.1186/s12967-018-1472-0] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2018] [Accepted: 04/04/2018] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Human tissues are invaluable resources for researchers worldwide. Biobanks are repositories of such human tissues and can have a strategic importance for genetic research, clinical care, and future discoveries and treatments. One of the aims of Qatar Biobank is to improve the understanding and treatment of common diseases afflicting Qatari population such as obesity and diabetes. METHODS In this study we apply a panorama of state-of-the-art statistical methods and machine learning algorithms to investigate associations and risk factors for diabetes and obesity on a sample of 1000 Qatari population. RESULTS Regarding diabetes, we identified pronounced associations and risk factors in Qatari population including magnesium, chloride, c-peptide of insulin, insulin, and uric acid. Similarly, for obesity, significant associations and risk factors include insulin, c-peptide of insulin, albumin, and uric acid. Moreover, our study has revealed interactions of hypomagnesemia with HDL-C, triglycerides, and free thyroxine. CONCLUSIONS Our study strongly confirms known associations and risk factors associated with diabetes and obesity in Qatari population as previously found in other population studies in different parts of the world. Moreover, interactions of hypomagnesemia with other associations and risk factors merit further investigations.
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Affiliation(s)
- Ehsan Ullah
- Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar
| | - Raghvendra Mall
- Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar
| | - Reda Rawi
- Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar.,Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, 20814, USA
| | - Naima Moustaid-Moussa
- Obesity Research Cluster (ORC), Nutrigenomics, Inflammation and Obesity Research (NIOR) Laboratory, Texas Tech University, 1301 Akron Street, Lubbock, TX, 79409-1270, USA
| | - Adeel A Butt
- Department of Medicine, Weill Cornell Medical College, Doha, Qatar.,Department of Healthcare Policy and Research, Weil Cornell Medical College, Doha, Qatar.,Department of Medicine, Clinical Epidemiology Research Unit Hamad Medical Corporation, Doha, Qatar
| | - Halima Bensmail
- Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar.
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Mall R, Cerulo L, Bensmail H, Iavarone A, Ceccarelli M. Detection of statistically significant network changes in complex biological networks. BMC Syst Biol 2017; 11:32. [PMID: 28259158 PMCID: PMC5336651 DOI: 10.1186/s12918-017-0412-6] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/23/2016] [Accepted: 02/22/2017] [Indexed: 01/10/2023]
Abstract
Background Biological networks contribute effectively to unveil the complex structure of molecular interactions and to discover driver genes especially in cancer context. It can happen that due to gene mutations, as for example when cancer progresses, the gene expression network undergoes some amount of localized re-wiring. The ability to detect statistical relevant changes in the interaction patterns induced by the progression of the disease can lead to the discovery of novel relevant signatures. Several procedures have been recently proposed to detect sub-network differences in pairwise labeled weighted networks. Methods In this paper, we propose an improvement over the state-of-the-art based on the Generalized Hamming Distance adopted for evaluating the topological difference between two networks and estimating its statistical significance. The proposed procedure exploits a more effective model selection criteria to generate p-values for statistical significance and is more efficient in terms of computational time and prediction accuracy than literature methods. Moreover, the structure of the proposed algorithm allows for a faster parallelized implementation. Results In the case of dense random geometric networks the proposed approach is 10-15x faster and achieves 5-10% higher AUC, Precision/Recall, and Kappa value than the state-of-the-art. We also report the application of the method to dissect the difference between the regulatory networks of IDH-mutant versus IDH-wild-type glioma cancer. In such a case our method is able to identify some recently reported master regulators as well as novel important candidates. Conclusions We show that our network differencing procedure can effectively and efficiently detect statistical significant network re-wirings in different conditions. When applied to detect the main differences between the networks of IDH-mutant and IDH-wild-type glioma tumors, it correctly selects sub-networks centered on important key regulators of these two different subtypes. In addition, its application highlights several novel candidates that cannot be detected by standard single network-based approaches. Electronic supplementary material The online version of this article (doi:10.1186/s12918-017-0412-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Raghvendra Mall
- QCRI - Qatar Computing Research Institute, HBKU, Doha, Qatar.
| | - Luigi Cerulo
- Department of Science and Technology, University of Sannio, Benevento, Italy.,BioGeM, Institute of Genetic Research "Gaetano Salvatore", Ariano Irpino (AV), Italy
| | - Halima Bensmail
- QCRI - Qatar Computing Research Institute, HBKU, Doha, Qatar
| | - Antonio Iavarone
- Department of Neurology, Department of Pathology, Institute for Cancer Genetics, Columbia University Medical Center, New York, USA
| | - Michele Ceccarelli
- QCRI - Qatar Computing Research Institute, HBKU, Doha, Qatar. .,Department of Science and Technology, University of Sannio, Benevento, Italy.
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Rawi R, Mall R, Kunji K, El Anbari M, Aupetit M, Ullah E, Bensmail H. COUSCOus: improved protein contact prediction using an empirical Bayes covariance estimator. BMC Bioinformatics 2016; 17:533. [PMID: 27978812 PMCID: PMC5159955 DOI: 10.1186/s12859-016-1400-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2016] [Accepted: 12/01/2016] [Indexed: 11/13/2022] Open
Abstract
Background The post-genomic era with its wealth of sequences gave rise to a broad range of protein residue-residue contact detecting methods. Although various coevolution methods such as PSICOV, DCA and plmDCA provide correct contact predictions, they do not completely overlap. Hence, new approaches and improvements of existing methods are needed to motivate further development and progress in the field. We present a new contact detecting method, COUSCOus, by combining the best shrinkage approach, the empirical Bayes covariance estimator and GLasso. Results Using the original PSICOV benchmark dataset, COUSCOus achieves mean accuracies of 0.74, 0.62 and 0.55 for the top L/10 predicted long, medium and short range contacts, respectively. In addition, COUSCOus attains mean areas under the precision-recall curves of 0.25, 0.29 and 0.30 for long, medium and short contacts and outperforms PSICOV. We also observed that COUSCOus outperforms PSICOV w.r.t. Matthew’s correlation coefficient criterion on full list of residue contacts. Furthermore, COUSCOus achieves on average 10% more gain in prediction accuracy compared to PSICOV on an independent test set composed of CASP11 protein targets. Finally, we showed that when using a simple random forest meta-classifier, by combining contact detecting techniques and sequence derived features, PSICOV predictions should be replaced by the more accurate COUSCOus predictions. Conclusion We conclude that the consideration of superior covariance shrinkage approaches will boost several research fields that apply the GLasso procedure, amongst the presented one of residue-residue contact prediction as well as fields such as gene network reconstruction. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1400-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Reda Rawi
- Computational Science and Engineering, Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar.
| | - Raghvendra Mall
- Computational Science and Engineering, Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar
| | - Khalid Kunji
- Computational Science and Engineering, Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar
| | - Mohammed El Anbari
- Division of Biomedical Informatics, Sidra Medical and Research Center, Doha, Qatar
| | - Michael Aupetit
- Computational Science and Engineering, Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar
| | - Ehsan Ullah
- Computational Science and Engineering, Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar
| | - Halima Bensmail
- Computational Science and Engineering, Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar
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Abstract
Least squares support vector machines (LSSVMs) have been widely applied for classification and regression with comparable performance with SVMs. The LSSVM model lacks sparsity and is unable to handle large-scale data due to computational and memory constraints. A primal fixed-size LSSVM (PFS-LSSVM) introduce sparsity using Nyström approximation with a set of prototype vectors (PVs). The PFS-LSSVM model solves an overdetermined system of linear equations in the primal. However, this solution is not the sparsest. We investigate the sparsity-error tradeoff by introducing a second level of sparsity. This is done by means of L0 -norm-based reductions by iteratively sparsifying LSSVM and PFS-LSSVM models. The exact choice of the cardinality for the initial PV set is not important then as the final model is highly sparse. The proposed method overcomes the problem of memory constraints and high computational costs resulting in highly sparse reductions to LSSVM models. The approximations of the two models allow to scale the models to large-scale datasets. Experiments on real-world classification and regression data sets from the UCI repository illustrate that these approaches achieve sparse models without a significant tradeoff in errors.
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Huber M, Falkenberg N, Gross E, Mall R, Braselmann H, Schmitt M, Aubele M. The impact of the uPAR system and its interaction partners as potential therapeutic targets in TNBC. Ann Oncol 2015. [DOI: 10.1093/annonc/mdv117.29] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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Mehrkanoon S, Alzate C, Mall R, Langone R, Suykens JAK. Multiclass semisupervised learning based upon kernel spectral clustering. IEEE Trans Neural Netw Learn Syst 2015; 26:720-733. [PMID: 25794378 DOI: 10.1109/tnnls.2014.2322377] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
This paper proposes a multiclass semisupervised learning algorithm by using kernel spectral clustering (KSC) as a core model. A regularized KSC is formulated to estimate the class memberships of data points in a semisupervised setting using the one-versus-all strategy while both labeled and unlabeled data points are present in the learning process. The propagation of the labels to a large amount of unlabeled data points is achieved by adding the regularization terms to the cost function of the KSC formulation. In other words, imposing the regularization term enforces certain desired memberships. The model is then obtained by solving a linear system in the dual. Furthermore, the optimal embedding dimension is designed for semisupervised clustering. This plays a key role when one deals with a large number of clusters.
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Huber M, Falkenberg N, Schmitt M, Braselmann H, Mall R, Jakovac M, Walch A, Höfler H, Aubele M. 673: uPAR and its interaction partners: potential new therapy targets in triple negative breast cancer. Eur J Cancer 2014. [DOI: 10.1016/s0959-8049(14)50593-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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50
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Mall R, Langone R, Suykens JAK. Multilevel hierarchical kernel spectral clustering for real-life large scale complex networks. PLoS One 2014; 9:e99966. [PMID: 24949877 PMCID: PMC4065034 DOI: 10.1371/journal.pone.0099966] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2014] [Accepted: 05/20/2014] [Indexed: 11/19/2022] Open
Abstract
Kernel spectral clustering corresponds to a weighted kernel principal component analysis problem in a constrained optimization framework. The primal formulation leads to an eigen-decomposition of a centered Laplacian matrix at the dual level. The dual formulation allows to build a model on a representative subgraph of the large scale network in the training phase and the model parameters are estimated in the validation stage. The KSC model has a powerful out-of-sample extension property which allows cluster affiliation for the unseen nodes of the big data network. In this paper we exploit the structure of the projections in the eigenspace during the validation stage to automatically determine a set of increasing distance thresholds. We use these distance thresholds in the test phase to obtain multiple levels of hierarchy for the large scale network. The hierarchical structure in the network is determined in a bottom-up fashion. We empirically showcase that real-world networks have multilevel hierarchical organization which cannot be detected efficiently by several state-of-the-art large scale hierarchical community detection techniques like the Louvain, OSLOM and Infomap methods. We show that a major advantage of our proposed approach is the ability to locate good quality clusters at both the finer and coarser levels of hierarchy using internal cluster quality metrics on 7 real-life networks.
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