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Shook LL, Batorsky RA, De Guzman RM, McCrea LT, Brigida SM, Horng JE, Sheridan SD, Kholod O, Cook AM, Li JZ, Goods BA, Perlis RH, Edlow AG. Maternal SARS-CoV-2 impacts fetal placental macrophage programs and placenta-derived microglial models of neurodevelopment. medRxiv 2023:2023.12.29.23300544. [PMID: 38234776 PMCID: PMC10793528 DOI: 10.1101/2023.12.29.23300544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
The SARS-CoV-2 virus activates maternal and placental immune responses, which in the setting of other infections occurring during pregnancy are known to impact fetal brain development. The effects of maternal immune activation on neurodevelopment are mediated at least in part by fetal brain microglia. However, microglia are inaccessible for direct analysis, and there are no validated non-invasive surrogate models to evaluate in utero microglial priming and function. We have previously demonstrated shared transcriptional programs between microglia and Hofbauer cells (HBCs, or fetal placental macrophages) in mouse models. Here, we assessed the impact of maternal SARS-CoV-2 on HBCs isolated from term placentas using single-cell RNA-sequencing. We demonstrated that HBC subpopulations exhibit distinct cellular programs, with specific subpopulations differentially impacted by SARS-CoV-2. Assessment of differentially expressed genes implied impaired phagocytosis, a key function of both HBCs and microglia, in some subclusters. Leveraging previously validated models of microglial synaptic pruning, we showed that HBCs isolated from placentas of SARS-CoV-2 positive pregnancies can be transdifferentiated into microglia-like cells, with altered morphology and impaired synaptic pruning behavior compared to HBC models from negative controls. These findings suggest that HBCs isolated at birth can be used to create personalized cellular models of offspring microglial programming.
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Kholod O, Basket W, Liu D, Mitchem J, Kaifi J, Dooley L, Shyu CR. Identification of Immuno-Targeted Combination Therapies Using Explanatory Subgroup Discovery for Cancer Patients with EGFR Wild-Type Gene. Cancers (Basel) 2022; 14:cancers14194759. [PMID: 36230688 PMCID: PMC9564073 DOI: 10.3390/cancers14194759] [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: 08/15/2022] [Revised: 09/27/2022] [Accepted: 09/28/2022] [Indexed: 11/16/2022] Open
Abstract
(1) Background: Phenotypic and genotypic heterogeneity are characteristic features of cancer patients. To tackle patients’ heterogeneity, immune checkpoint inhibitors (ICIs) represent some the most promising therapeutic approaches. However, approximately 50% of cancer patients that are eligible for treatment with ICIs do not respond well, especially patients with no targetable mutations. Over the years, multiple patient stratification techniques have been developed to identify homogenous patient subgroups, although matching a patient subgroup to a treatment option that can improve patients’ health outcomes remains a challenging task. (2) Methods: We extended our Subgroup Discovery algorithm to identify patient subpopulations that could potentially benefit from immuno-targeted combination therapies in four cancer types: head and neck squamous carcinoma (HNSC), lung adenocarcinoma (LUAD), lung squamous carcinoma (LUSC), and skin cutaneous melanoma (SKCM). We employed the proportional odds model to identify significant drug targets and the corresponding compounds that increased the likelihood of stable disease versus progressive disease in cancer patients with the EGFR wild-type (WT) gene. (3) Results: Our pipeline identified six significant drug targets and thirteen specific compounds for cancer patients with the EGFR WT gene. Three out of six drug targets—FCGR2B, IGF1R, and KIT—substantially increased the odds of having stable disease versus progressive disease. Progression-free survival (PFS) of more than 6 months was a common feature among the investigated subgroups. (4) Conclusions: Our approach could help to better select responders for immuno-targeted combination therapies and improve health outcomes for cancer patients with no targetable mutations.
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Affiliation(s)
- Olha Kholod
- MU Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65212, USA
| | - William Basket
- MU Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65212, USA
| | - Danlu Liu
- Department of Electrical Engineering & Computer Science, University of Missouri, Columbia, MO 65212, USA
| | - Jonathan Mitchem
- MU Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65212, USA
- Department of Surgery, School of Medicine, University of Missouri, Columbia, MO 65212, USA
- Harry S. Truman Memorial Veterans’ Hospital, Columbia, MO 65201, USA
| | - Jussuf Kaifi
- MU Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65212, USA
- Department of Surgery, School of Medicine, University of Missouri, Columbia, MO 65212, USA
| | - Laura Dooley
- Department of Otolaryngology, School of Medicine, University of Missouri, Columbia, MO 65212, USA
| | - Chi-Ren Shyu
- MU Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65212, USA
- Department of Electrical Engineering & Computer Science, University of Missouri, Columbia, MO 65212, USA
- Correspondence:
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Giannaris PS, Al-Taie Z, Kovalenko M, Thanintorn N, Kholod O, Innokenteva Y, Coberly E, Frazier S, Laziuk K, Popescu M, Shyu CR, Xu D, Hammer RD, Shin D. Artificial Intelligence-Driven Structurization of Diagnostic Information in Free-Text Pathology Reports. J Pathol Inform 2020; 11:4. [PMID: 32166042 PMCID: PMC7045509 DOI: 10.4103/jpi.jpi_30_19] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [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: 05/22/2019] [Accepted: 12/18/2019] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Free-text sections of pathology reports contain the most important information from a diagnostic standpoint. However, this information is largely underutilized for computer-based analytics. The vast majority of NLP-based methods lack a capacity to accurately extract complex diagnostic entities and relationships among them as well as to provide an adequate knowledge representation for downstream data-mining applications. METHODS In this paper, we introduce a novel informatics pipeline that extends open information extraction (openIE) techniques with artificial intelligence (AI) based modeling to extract and transform complex diagnostic entities and relationships among them into Knowledge Graphs (KGs) of relational triples (RTs). RESULTS Evaluation studies have demonstrated that the pipeline's output significantly differs from a random process. The semantic similarity with original reports is high (Mean Weighted Overlap of 0.83). The precision and recall of extracted RTs based on experts' assessment were 0.925 and 0.841 respectively (P <0.0001). Inter-rater agreement was significant at 93.6% and inter-rated reliability was 81.8%. CONCLUSION The results demonstrated important properties of the pipeline such as high accuracy, minimality and adequate knowledge representation. Therefore, we conclude that the pipeline can be used in various downstream data-mining applications to assist diagnostic medicine.
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Affiliation(s)
- Pericles S. Giannaris
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, United States
- Department of Pathology and Anatomical Sciences, School of Medicine, University of Missouri, Columbia, MO 65212, United States
| | - Zainab Al-Taie
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, United States
- Department of Computer Science, College of Science for Women, University of Baghdad, Baghdad, Iraq
| | - Mikhail Kovalenko
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, United States
- Department of Pathology and Anatomical Sciences, School of Medicine, University of Missouri, Columbia, MO 65212, United States
| | - Nattapon Thanintorn
- Department of Pathology and Anatomical Sciences, School of Medicine, University of Missouri, Columbia, MO 65212, United States
| | - Olha Kholod
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, United States
- Department of Pathology and Anatomical Sciences, School of Medicine, University of Missouri, Columbia, MO 65212, United States
| | - Yulia Innokenteva
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, United States
| | - Emily Coberly
- Department of Pathology and Anatomical Sciences, School of Medicine, University of Missouri, Columbia, MO 65212, United States
| | - Shellaine Frazier
- Department of Pathology and Anatomical Sciences, School of Medicine, University of Missouri, Columbia, MO 65212, United States
| | - Katsiarina Laziuk
- Department of Pathology and Anatomical Sciences, School of Medicine, University of Missouri, Columbia, MO 65212, United States
| | - Mihail Popescu
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, United States
- Department of Electrical Engineering and Computer Science, College of Engineering, University of Missouri, Columbia, MO 65211, United States
- Department of Health Management and Informatics, School of Medicine, University of Missouri, Columbia, MO 65212, United States
| | - Chi-Ren Shyu
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, United States
- Department of Electrical Engineering and Computer Science, College of Engineering, University of Missouri, Columbia, MO 65211, United States
| | - Dong Xu
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, United States
- Department of Electrical Engineering and Computer Science, College of Engineering, University of Missouri, Columbia, MO 65211, United States
| | - Richard D. Hammer
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, United States
- Department of Pathology and Anatomical Sciences, School of Medicine, University of Missouri, Columbia, MO 65212, United States
| | - Dmitriy Shin
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, United States
- Department of Pathology and Anatomical Sciences, School of Medicine, University of Missouri, Columbia, MO 65212, United States
- Department of Electrical Engineering and Computer Science, College of Engineering, University of Missouri, Columbia, MO 65211, United States
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Al–Taie Z, Thanintorn N, Ersoy I, Kholod O, Taylor K, Hammer R, Shin D. REDESIGN: RDF-based Differential Signaling Framework for Precision Medicine Analytics. AMIA Jt Summits Transl Sci Proc 2018; 2017:35-44. [PMID: 29888036 PMCID: PMC5961787] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Pathway-based analysis holds promise to be instrumental in precision and personalized medicine analytics. However, the majority of pathway-based analysis methods utilize "fixed" or "rigid" data sets that limit their ability to account for complex biological inter-dependencies. Here, we present REDESIGN: RDF-based Differential Signaling Pathway informatics framework. The distinctive feature of the REDESIGN is that it is designed to run on "flexible" ontology-enabled data sets of curated signal transduction pathway maps to uncover high explanatory differential pathway mechanisms on gene-to-gene level. The experiments on two morphoproteomic cases demonstrated REDESIGN's capability to generate actionable hypotheses in precision/personalized medicine analytics.
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Affiliation(s)
- Zainab Al–Taie
- MU Informatics Institute, University of Missouri, Columbia, MO,Department of Pathology and Anatomical Sciences, University of Missouri, Columbia, MO
| | - Nattapon Thanintorn
- Department of Pathology and Anatomical Sciences, University of Missouri, Columbia, MO
| | - Ilker Ersoy
- MU Informatics Institute, University of Missouri, Columbia, MO,Department of Pathology and Anatomical Sciences, University of Missouri, Columbia, MO
| | - Olha Kholod
- MU Informatics Institute, University of Missouri, Columbia, MO,Department of Pathology and Anatomical Sciences, University of Missouri, Columbia, MO
| | - Kristen Taylor
- Department of Pathology and Anatomical Sciences, University of Missouri, Columbia, MO
| | - Richard Hammer
- MU Informatics Institute, University of Missouri, Columbia, MO,Department of Pathology and Anatomical Sciences, University of Missouri, Columbia, MO
| | - Dmitriy Shin
- MU Informatics Institute, University of Missouri, Columbia, MO,Department of Pathology and Anatomical Sciences, University of Missouri, Columbia, MO
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Almamun M, Kholod O, Stuckel AJ, Levinson BT, Johnson NT, Arthur GL, Davis JW, Taylor KH. Inferring a role for methylation of intergenic DNA in the regulation of genes aberrantly expressed in precursor B-cell acute lymphoblastic leukemia. Leuk Lymphoma 2017; 58:1-12. [PMID: 28094574 DOI: 10.1080/10428194.2016.1272683] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
A complete understanding of the mechanisms involved in the development of pre-B ALL is lacking. In this study, we integrated DNA methylation data and gene expression data to elucidate the impact of aberrant intergenic DNA methylation on gene expression in pre-B ALL. We found a subset of differentially methylated intergenic loci that were associated with altered gene expression in pre-B ALL patients. Notably, 84% of these regions were also bound by transcription factors (TF) known to play roles in differentiation and B-cell development in a lymphoblastoid cell line. Further, an overall downregulation of eRNA transcripts was observed in pre-B ALL patients and these transcripts were associated with the downregulation of putative target genes involved in B-cell migration, proliferation, and apoptosis. The identification of novel putative regulatory regions highlights the significance of intergenic DNA sequences and may contribute to the identification of new therapeutic targets for the treatment of pre-B ALL.
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Affiliation(s)
- Md Almamun
- a Department of Pathology and Anatomical Sciences , University of Missouri-Columbia , Columbia , MO , USA
| | - Olha Kholod
- a Department of Pathology and Anatomical Sciences , University of Missouri-Columbia , Columbia , MO , USA
| | - Alexei J Stuckel
- a Department of Pathology and Anatomical Sciences , University of Missouri-Columbia , Columbia , MO , USA
| | - Benjamin T Levinson
- a Department of Pathology and Anatomical Sciences , University of Missouri-Columbia , Columbia , MO , USA
| | - Nathan T Johnson
- b Bioinformatics and Computational Biology , Worcester Polytechnic Institute , Worcester , MA , USA
| | - Gerald L Arthur
- a Department of Pathology and Anatomical Sciences , University of Missouri-Columbia , Columbia , MO , USA
| | - J Wade Davis
- c Department of Health Management and Informatics , University of Missouri-Columbia , Columbia , MO , USA
| | - Kristen H Taylor
- a Department of Pathology and Anatomical Sciences , University of Missouri-Columbia , Columbia , MO , USA
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