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Malik S, Ali SA, Mehdi AM, Raza A, Bashir S, Baig DN. A pilot study: Examining cytoskeleton gene expression profiles in Pakistani children with autism spectrum disorder. Int J Dev Neurosci 2024; 84:769-778. [PMID: 39285780 DOI: 10.1002/jdn.10372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 08/14/2024] [Accepted: 08/14/2024] [Indexed: 11/09/2024] Open
Abstract
BACKGROUND Finding effective pharmacological interventions to address the complex array of neurodevelopmental disorders is currently an urgent imperative within the scientific community as these conditions present significant challenges for patients and their families, often impacting cognitive, emotional, and social development. In this study, we aimed to explore non-invasive method to diagnose autism spectrum disorders (ASD) within Pakistan children population and to identify clinical drugs for its treatment. AIMS The current report outlines a comprehensive bidirectional investigation showcasing the successful utilization of saliva samples to quantify the expression patterns of profilins (PFN1, 2, and 3); and ERM (ezrin, radixin, and moesin) proteins; and additionally moesin pseudogene 1 and moesin pseudogene 1 antisense (MSNP1AS). Subsequently, these expression profiles were employed to forecast interactions between drugs and genes in children diagnosed with ASD. METHODS This study sought to delve into the intricate gene expression profiles using qualitative polymerase chain reaction of profilin isoforms (PFN1, 2, and 3) and ERM genes extracted from saliva samples obtained from children diagnosed with ASD. Through this analysis, we aimed to elucidate potential molecular mechanisms underlying ASD pathogenesis, shedding light on novel biomarkers and therapeutic targets for this complex neurological condition. (n = 22). Subsequently, we implemented a diagnostic model utilizing sparse partial least squares discriminant analysis (sPLS-DA) to predict drugs against our genes of interest. Furthermore, connectivity maps were developed to illustrate the predicted associations of 24 drugs with the genes expression. RESULTS Our study results showed varied expression profile of cytoskeleton linked genes. Similarly, sPLS-DA model precisely predicted drug to genes response. Sixteen of the examined drugs had significant positive correlations with the expression of the targeted genes whereas eight of the predicted drugs had shown negative correlations. CONCLUSION Here we report the role of cytoskeleton linked genes (PFN and ERM) in co-relation to ASD. Furthermore, variable yet significant quantitative expression of these genes successfully predicted drug-gene interactions as shown with the help of connectivity maps that can be used to support the clinical use of these drugs to treat individuals with ASD in future studies.
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Affiliation(s)
- Sana Malik
- Kauser Abdullah Malik School of Life Sciences, Forman Christian College (A Chartered University) Lahore, Lahore, Pakistan
| | - Syed Aoun Ali
- Australian Institute of Bioengineering and Nanotechnology, University of Queensland, Brisbane, Queensland, Australia
| | - Ahmed Murtaza Mehdi
- Diamantina Institute, Faculty of Medicine, Translational Research Institute, University of Queensland, Brisbane, Queensland, Australia
| | - Amir Raza
- Department of Biotechnology, Knowledge Unit of Science, University of Management and Technology (Sialkot Campus), Sialkot, Pakistan
| | - Shahid Bashir
- Neuroscience Center, King Fahad Specialist Hospital, Dammam, Saudi Arabia
| | - Deeba Noreen Baig
- Kauser Abdullah Malik School of Life Sciences, Forman Christian College (A Chartered University) Lahore, Lahore, Pakistan
- University of Western Australia, Perth, Western Australia, Australia
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Mănescu M, Mănescu IB, Grama A. A Review of Stage 0 Biomarkers in Type 1 Diabetes: The Holy Grail of Early Detection and Prevention? J Pers Med 2024; 14:878. [PMID: 39202069 PMCID: PMC11355657 DOI: 10.3390/jpm14080878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2024] [Revised: 08/16/2024] [Accepted: 08/19/2024] [Indexed: 09/03/2024] Open
Abstract
Type 1 diabetes mellitus (T1D) is an incurable autoimmune disease characterized by the destruction of pancreatic islet cells, resulting in lifelong dependency on insulin treatment. There is an abundance of review articles addressing the prediction of T1D; however, most focus on the presymptomatic phases, specifically stages 1 and 2. These stages occur after seroconversion, where therapeutic interventions primarily aim to delay the onset of T1D rather than prevent it. This raises a critical question: what happens before stage 1 in individuals who will eventually develop T1D? Is there a "stage 0" of the disease, and if so, how can we detect it to increase our chances of truly preventing T1D? In pursuit of answers to these questions, this narrative review aimed to highlight recent research in the field of early detection and prediction of T1D, specifically focusing on biomarkers that can predict T1D before the onset of islet autoimmunity. Here, we have compiled influential research from the fields of epigenetics, omics, and microbiota. These studies have identified candidate biomarkers capable of predicting seroconversion from very early stages to several months prior, suggesting that the prophylactic window begins at birth. As the therapeutic landscape evolves from treatment to delay, and ideally from delay to prevention, it is crucial to both identify and validate such "stage 0" biomarkers predictive of islet autoimmunity. In the era of precision medicine, this knowledge will enable early intervention with the potential for delaying, modifying, or completely preventing autoimmunity and T1D in at-risk children.
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Affiliation(s)
- Măriuca Mănescu
- Department of Pediatrics, Emergency County Clinical Hospital of Targu Mures, 50 Gheorghe Marinescu, 540136 Targu Mures, Romania;
| | - Ion Bogdan Mănescu
- Department of Laboratory Medicine, Faculty of Medicine, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Targu Mures, 38 Gheorghe Marinescu, 540142 Targu Mures, Romania;
| | - Alina Grama
- Department of Pediatrics, Emergency County Clinical Hospital of Targu Mures, 50 Gheorghe Marinescu, 540136 Targu Mures, Romania;
- Department of Pediatrics, Faculty of Medicine, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Targu Mures, 38 Gheorghe Marinescu, 540142 Targu Mures, Romania
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Oleksiewicz U, Machnik M, Sobocińska J, Molenda S, Olechnowicz A, Florczak A, Smolibowski M, Kaczmarek M. ZNF714 Supports Pro-Oncogenic Features in Lung Cancer Cells. Int J Mol Sci 2023; 24:15530. [PMID: 37958512 PMCID: PMC10649060 DOI: 10.3390/ijms242115530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 10/10/2023] [Accepted: 10/19/2023] [Indexed: 11/15/2023] Open
Abstract
Despite the ongoing progress in diagnosis and treatments, cancer remains a threat to more than one-third of the human population. The emerging data indicate that many Krüppel-associated box zinc finger proteins (KRAB-ZNF) belonging to a large gene family may be involved in carcinogenesis. Our previous study identified Zinc Finger Protein 714 (ZNF714), a KRAB-ZNF gene of unknown function, as being commonly overexpressed in many tumors, pointing to its hypothetical oncogenic role. Here, we harnessed The Cancer Genome Atlas (TCGA)-centered databases and performed functional studies with transcriptomic and methylomic profiling to explore ZNF714 function in cancer. Our pan-cancer analyses confirmed frequent ZNF714 overexpression in multiple tumors, possibly due to regional amplification, promoter hypomethylation, and Nuclear Transcription Factor Y Subunit Beta (NFYB) signaling. We also showed that ZNF714 expression correlates with tumor immunosuppressive features. The in vitro studies indicated that ZNF714 expression positively associates with proliferation, migration, and invasion. The transcriptomic analysis of ZNF714 knocked-down cells demonstrated deregulation of cell adhesion, migration, proliferation, apoptosis, and differentiation. Importantly, we provided evidence that ZNF714 negatively regulates the expression of several known TSGs indirectly via promoter methylation. However, as ZNF714 did not show nuclear localization in our research model, the regulatory mechanisms exerted by ZNF714 require further investigation. In conclusion, our results reveal, for the first time, that ZNF714 may support pro-oncogenic features in lung cancer cells.
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Affiliation(s)
- Urszula Oleksiewicz
- Department of Cancer Immunology, Chair of Medical Biotechnology, Poznan University of Medical Sciences, 8 Rokietnicka Street, 60-806 Poznan, Poland
- Department of Diagnostics and Cancer Immunology, Greater Poland Cancer Center, Garbary 15, 61-866 Poznan, Poland
| | - Marta Machnik
- Department of Cancer Immunology, Chair of Medical Biotechnology, Poznan University of Medical Sciences, 8 Rokietnicka Street, 60-806 Poznan, Poland
- Department of Diagnostics and Cancer Immunology, Greater Poland Cancer Center, Garbary 15, 61-866 Poznan, Poland
| | - Joanna Sobocińska
- Department of Cancer Immunology, Chair of Medical Biotechnology, Poznan University of Medical Sciences, 8 Rokietnicka Street, 60-806 Poznan, Poland
- Department of Diagnostics and Cancer Immunology, Greater Poland Cancer Center, Garbary 15, 61-866 Poznan, Poland
| | - Sara Molenda
- Department of Cancer Immunology, Chair of Medical Biotechnology, Poznan University of Medical Sciences, 8 Rokietnicka Street, 60-806 Poznan, Poland
- Department of Diagnostics and Cancer Immunology, Greater Poland Cancer Center, Garbary 15, 61-866 Poznan, Poland
- Doctoral School, Poznan University of Medical Sciences, 60-812 Poznan, Poland
| | - Anna Olechnowicz
- Department of Cancer Immunology, Chair of Medical Biotechnology, Poznan University of Medical Sciences, 8 Rokietnicka Street, 60-806 Poznan, Poland
- Doctoral School, Poznan University of Medical Sciences, 60-812 Poznan, Poland
- Department of Histology and Embryology, Poznan University of Medical Sciences, Swiecickiego 6 Street, 60-781 Poznan, Poland
| | - Anna Florczak
- Department of Cancer Immunology, Chair of Medical Biotechnology, Poznan University of Medical Sciences, 8 Rokietnicka Street, 60-806 Poznan, Poland
- Department of Diagnostics and Cancer Immunology, Greater Poland Cancer Center, Garbary 15, 61-866 Poznan, Poland
| | - Mikołaj Smolibowski
- Department of Cancer Immunology, Chair of Medical Biotechnology, Poznan University of Medical Sciences, 8 Rokietnicka Street, 60-806 Poznan, Poland
| | - Mariusz Kaczmarek
- Department of Cancer Immunology, Chair of Medical Biotechnology, Poznan University of Medical Sciences, 8 Rokietnicka Street, 60-806 Poznan, Poland
- Department of Diagnostics and Cancer Immunology, Greater Poland Cancer Center, Garbary 15, 61-866 Poznan, Poland
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Khan A, Gharavi AG. Emerging Genetic Insight into ATIN. J Am Soc Nephrol 2023; 34:732-735. [PMID: 37126669 PMCID: PMC10371293 DOI: 10.1681/asn.0000000000000121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/03/2023] Open
Affiliation(s)
- Atlas Khan
- Division of Nephrology, Department of Medicine, Columbia University, New York, New York
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Zhou XJ, Su T, Xie J, Xie QH, Wang LZ, Hu Y, Chen G, Jia Y, Huang JW, Li G, Liu Y, Yu XJ, Nath SK, Tsoi LC, Patrick MT, Berthier CC, Liu G, Wang SX, Xu H, Chen N, Hao CM, Zhang H, Yang L. Genome-Wide Association Study in Acute Tubulointerstitial Nephritis. J Am Soc Nephrol 2023; 34:895-908. [PMID: 36749126 PMCID: PMC10125656 DOI: 10.1681/asn.0000000000000091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 12/28/2022] [Indexed: 02/08/2023] Open
Abstract
SIGNIFICANCE STATEMENT Polymorphisms of HLA genes may confer susceptibility to acute tubulointerstitial nephritis (ATIN), but small sample sizes and candidate gene design have hindered their investigation. The first genome-wide association study of ATIN identified two significant loci, risk haplotype DRB1*14-DQA1*0101-DQB1*0503 (DR14 serotype) and protective haplotype DRB1*1501-DQA1*0102-DQB1*0602 (DR15 serotype), with amino acid position 60 in the peptide-binding groove P10 of HLA-DR β 1 key. Risk alleles were shared among different causes of ATIN and HLA genotypes associated with kidney injury and immune therapy response. HLA alleles showed the strongest association. The findings suggest that a genetically conferred risk of immune dysregulation is part of the pathogenesis of ATIN. BACKGROUND Acute tubulointerstitial nephritis (ATIN) is a rare immune-related disease, accounting for approximately 10% of patients with unexplained AKI. Previous elucidation of the relationship between genetic factors that contribute to its pathogenesis was hampered because of small sample sizes and candidate gene design. METHODS We undertook the first two-stage genome-wide association study and meta-analysis involving 544 kidney biopsy-defined patients with ATIN and 2346 controls of Chinese ancestry. We conducted statistical fine-mapping analysis, provided functional annotations of significant variants, estimated single nucleotide polymorphism (SNP)-based heritability, and checked genotype and subphenotype correlations. RESULTS Two genome-wide significant loci, rs35087390 of HLA-DQA1 ( P =3.01×10 -39 ) on 6p21.32 and rs2417771 of PLEKHA5 on 12p12.3 ( P =2.14×10 -8 ), emerged from the analysis. HLA imputation using two reference panels suggested that HLA-DRB1*14 mainly drives the HLA risk association . HLA-DRB1 residue 60 belonging to pocket P10 was the key amino acid position. The SNP-based heritability estimates with and without the HLA locus were 20.43% and 10.35%, respectively. Different clinical subphenotypes (drug-related or tubulointerstitial nephritis and uveitis syndrome) seemed to share the same risk alleles. However, the HLA risk genotype was associated with disease severity and response rate to immunosuppressive therapy. CONCLUSIONS We identified two candidate genome regions associated with susceptibility to ATIN. The findings suggest that a genetically conferred risk of immune dysregulation is involved in the pathogenesis of ATIN.
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Affiliation(s)
- Xu-Jie Zhou
- Renal Division, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, China
- Key Laboratory of Renal Disease, Ministry of Health of China, Beijing, China
- Key Laboratory of Chronic Kidney Disease Prevention and Treatment (Peking University), Ministry of Education, Beijing, China
| | - Tao Su
- Renal Division, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, China
- Key Laboratory of Renal Disease, Ministry of Health of China, Beijing, China
- Key Laboratory of Chronic Kidney Disease Prevention and Treatment (Peking University), Ministry of Education, Beijing, China
| | - Jingyuan Xie
- Department of Nephrology, Institute of Nephrology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Qiong-Hong Xie
- Division of Nephrology, Huashan Hospital, Fudan University, Shanghai, China
| | - Li-Zhong Wang
- WeGene, Shenzhen Zaozhidao Technology Co., Ltd., Shenzhen, China
- Human Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, China
- Shenzhen WeGene Clinical Laboratory, Shenzhen, China
| | - Yong Hu
- Beijing Institute of Biotechnology, Beijing, China
| | - Gang Chen
- WeGene, Shenzhen Zaozhidao Technology Co., Ltd., Shenzhen, China
- Human Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, China
- Shenzhen WeGene Clinical Laboratory, Shenzhen, China
| | - Yan Jia
- Renal Division, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, China
- Key Laboratory of Renal Disease, Ministry of Health of China, Beijing, China
- Key Laboratory of Chronic Kidney Disease Prevention and Treatment (Peking University), Ministry of Education, Beijing, China
| | - Jun-Wen Huang
- Renal Division, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, China
- Key Laboratory of Renal Disease, Ministry of Health of China, Beijing, China
- Key Laboratory of Chronic Kidney Disease Prevention and Treatment (Peking University), Ministry of Education, Beijing, China
| | - Gui Li
- Renal Division, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, China
- Key Laboratory of Renal Disease, Ministry of Health of China, Beijing, China
- Key Laboratory of Chronic Kidney Disease Prevention and Treatment (Peking University), Ministry of Education, Beijing, China
| | - Yang Liu
- Renal Division, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, China
- Key Laboratory of Renal Disease, Ministry of Health of China, Beijing, China
- Key Laboratory of Chronic Kidney Disease Prevention and Treatment (Peking University), Ministry of Education, Beijing, China
| | - Xiao-Juan Yu
- Renal Division, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, China
- Key Laboratory of Renal Disease, Ministry of Health of China, Beijing, China
- Key Laboratory of Chronic Kidney Disease Prevention and Treatment (Peking University), Ministry of Education, Beijing, China
| | - Swapan K. Nath
- Arthritis and Clinical Immunology Program, Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma
| | - Lam C. Tsoi
- Department of Dermatology, University of Michigan Medical School, Ann Arbor, Michigan
- Center for Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, Michigan
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan
| | - Matthew T. Patrick
- Department of Dermatology, University of Michigan Medical School, Ann Arbor, Michigan
- Center for Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, Michigan
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan
| | - Celine C. Berthier
- Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan
| | - Gang Liu
- Renal Division, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, China
- Key Laboratory of Renal Disease, Ministry of Health of China, Beijing, China
- Key Laboratory of Chronic Kidney Disease Prevention and Treatment (Peking University), Ministry of Education, Beijing, China
| | - Su-Xia Wang
- Renal Division, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, China
- Key Laboratory of Renal Disease, Ministry of Health of China, Beijing, China
- Key Laboratory of Chronic Kidney Disease Prevention and Treatment (Peking University), Ministry of Education, Beijing, China
| | - Huji Xu
- Department of Rheumatology and Immunology, Shanghai Changzheng Hospital, Second Military Medical University, Shanghai, China
- Peking-Tsinghua Center for Life Sciences, Tsinghua University, Beijing, China
- School of Clinical Medicine, Tsinghua University, Beijing, China
| | - Nan Chen
- Department of Nephrology, Institute of Nephrology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Chuan-Ming Hao
- Division of Nephrology, Huashan Hospital, Fudan University, Shanghai, China
| | - Hong Zhang
- Renal Division, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, China
- Key Laboratory of Renal Disease, Ministry of Health of China, Beijing, China
- Key Laboratory of Chronic Kidney Disease Prevention and Treatment (Peking University), Ministry of Education, Beijing, China
| | - Li Yang
- Renal Division, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, China
- Key Laboratory of Renal Disease, Ministry of Health of China, Beijing, China
- Key Laboratory of Chronic Kidney Disease Prevention and Treatment (Peking University), Ministry of Education, Beijing, China
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Brenu EW, Harris M, Hamilton-Williams EE. Circulating biomarkers during progression to type 1 diabetes: A systematic review. Front Endocrinol (Lausanne) 2023; 14:1117076. [PMID: 36817583 PMCID: PMC9935596 DOI: 10.3389/fendo.2023.1117076] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 01/25/2023] [Indexed: 02/05/2023] Open
Abstract
AIM Progression to type 1 diabetes (T1D) is defined in stages and clinical disease is preceded by a period of silent autoimmunity. Improved prediction of the risk and rate of progression to T1D is needed to reduce the prevalence of diabetic ketoacidosis at presentation as well as for staging participants for clinical trials. This systematic review evaluates novel circulating biomarkers associated with future progression to T1D. METHODS PubMed, Ovid, and EBSCO databases were used to identify a comprehensive list of articles. The eligibility criteria included observational studies that evaluated the usefulness of circulating markers in predicting T1D progression in at-risk subjects <20 years old. RESULTS Twenty-six studies were identified, seventeen were cohort studies and ten were case control studies. From the 26 studies, 5 found evidence for protein and lipid dysregulation, 11 identified molecular markers while 12 reported on changes in immune parameters during progression to T1D. An increased risk of T1D progression was associated with the presence of altered gene expression, immune markers including regulatory T cell dysfunction and higher short-lived effector CD8+ T cells in progressors. DISCUSSION Several circulating biomarkers are dysregulated before T1D diagnosis and may be useful in predicting either the risk or rate of progression to T1D. Further studies are required to validate these biomarkers and assess their predictive accuracy before translation into broader use. SYSTEMATIC REVIEW REGISTRATION https://www.crd.york.ac.uk/prospero, identifier (CRD42020166830).
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Affiliation(s)
- Ekua W. Brenu
- School of Medicine, University of Notre Dame, Sydney, NSW, Australia
| | - Mark Harris
- Endocrinology Department, Queensland Children’s Hospital, South Brisbane, QLD, Australia
| | - Emma E. Hamilton-Williams
- Frazer Institute, The University of Queensland, Woolloongabba, QLD, Australia
- *Correspondence: Emma E. Hamilton-Williams,
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Muralidharan S, Ali S, Yang L, Badshah J, Zahir SF, Ali RA, Chandra J, Frazer IH, Thomas R, Mehdi AM. Environmental pathways affecting gene expression (E.PAGE) as an R package to predict gene-environment associations. Sci Rep 2022; 12:18710. [PMID: 36333579 PMCID: PMC9636158 DOI: 10.1038/s41598-022-21988-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 10/07/2022] [Indexed: 11/06/2022] Open
Abstract
The purpose of this study is to manually and semi-automatically curate a database and develop an R package that will act as a comprehensive resource to understand how biological processes are dysregulated due to interactions with environmental factors. The initial database search run on the Gene Expression Omnibus and the Molecular Signature Database retrieved a total of 90,018 articles. After title and abstract screening against pre-set criteria, a total of 237 datasets were selected and 522 gene modules were manually annotated. We then curated a database containing four environmental factors, cigarette smoking, diet, infections and toxic chemicals, along with a total of 25,789 genes that had an association with one or more of gene modules. The database and statistical analysis package was then tested with the differentially expressed genes obtained from the published literature related to type 1 diabetes, rheumatoid arthritis, small cell lung cancer, COVID-19, cobalt exposure and smoking. On testing, we uncovered statistically enriched biological processes, which revealed pathways associated with environmental factors and the genes. The curated database and enrichment tool are available as R packages at https://github.com/AhmedMehdiLab/E.PATH and https://github.com/AhmedMehdiLab/E.PAGE respectively.
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Affiliation(s)
- Sachin Muralidharan
- grid.1003.20000 0000 9320 7537The University of Queensland Diamantina Institute, Translational Research Institute, The University of Queensland, 37 Kent St, Woolloongabba, QLD 4102 Australia
| | - Sarah Ali
- grid.1003.20000 0000 9320 7537Centre for Microscopy and Microanalysis, University of Queensland, St. Lucia, QLD 4072 Australia
| | - Lilin Yang
- grid.1003.20000 0000 9320 7537The University of Queensland Diamantina Institute, Translational Research Institute, The University of Queensland, 37 Kent St, Woolloongabba, QLD 4102 Australia
| | - Joshua Badshah
- grid.1003.20000 0000 9320 7537The University of Queensland Diamantina Institute, Translational Research Institute, The University of Queensland, 37 Kent St, Woolloongabba, QLD 4102 Australia
| | - Syeda Farah Zahir
- QCIF Facility for Advanced Bioinformatics, Queensland Cyber Infrastructure Foundation Ltd, Brisbane, QLD Australia
| | - Rubbiya A. Ali
- grid.1003.20000 0000 9320 7537Centre for Microscopy and Microanalysis, University of Queensland, St. Lucia, QLD 4072 Australia
| | - Janin Chandra
- grid.1003.20000 0000 9320 7537The University of Queensland Diamantina Institute, Translational Research Institute, The University of Queensland, 37 Kent St, Woolloongabba, QLD 4102 Australia
| | - Ian H. Frazer
- grid.1003.20000 0000 9320 7537The University of Queensland Diamantina Institute, Translational Research Institute, The University of Queensland, 37 Kent St, Woolloongabba, QLD 4102 Australia
| | - Ranjeny Thomas
- grid.1003.20000 0000 9320 7537The University of Queensland Diamantina Institute, Translational Research Institute, The University of Queensland, 37 Kent St, Woolloongabba, QLD 4102 Australia
| | - Ahmed M. Mehdi
- grid.1003.20000 0000 9320 7537The University of Queensland Diamantina Institute, Translational Research Institute, The University of Queensland, 37 Kent St, Woolloongabba, QLD 4102 Australia ,QCIF Facility for Advanced Bioinformatics, Queensland Cyber Infrastructure Foundation Ltd, Brisbane, QLD Australia
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Ochsner SA, Pillich RT, Rawool D, Grethe JS, McKenna NJ. Transcriptional regulatory networks of circulating immune cells in type 1 diabetes: A community knowledgebase. iScience 2022; 25:104581. [PMID: 35832893 PMCID: PMC9272393 DOI: 10.1016/j.isci.2022.104581] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 06/01/2022] [Accepted: 06/07/2022] [Indexed: 12/02/2022] Open
Abstract
Investigator-generated transcriptomic datasets interrogating circulating immune cell (CIC) gene expression in clinical type 1 diabetes (T1D) have underappreciated re-use value. Here, we repurposed these datasets to create an open science environment for the generation of hypotheses around CIC signaling pathways whose gain or loss of function contributes to T1D pathogenesis. We firstly computed sets of genes that were preferentially induced or repressed in T1D CICs and validated these against community benchmarks. We then inferred and validated signaling node networks regulating expression of these gene sets, as well as differentially expressed genes in the original underlying T1D case:control datasets. In a set of three use cases, we demonstrated how informed integration of these networks with complementary digital resources supports substantive, actionable hypotheses around signaling pathway dysfunction in T1D CICs. Finally, we developed a federated, cloud-based web resource that exposes the entire data matrix for unrestricted access and re-use by the research community. Re-use of transcriptomic type 1 diabetes (T1D) circulating immune cells (CICs) datasets We generated transcriptional regulatory networks for T1D CICs Use cases generate substantive hypotheses around signaling pathway dysfunction in T1D CICs Networks are freely accessible on the web for re-use by the research community
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Affiliation(s)
- Scott A. Ochsner
- Department of Molecular, Baylor College of Medicine, Houston, TX 77030, USA
- Cellular Biology and Medicine, Baylor College of Medicine, Houston, TX 77030, USA
| | - Rudolf T. Pillich
- Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
| | - Deepali Rawool
- Center for Research in Biological Systems, University of California San Diego, La Jolla, CA 92093, USA
| | - Jeffrey S. Grethe
- Center for Research in Biological Systems, University of California San Diego, La Jolla, CA 92093, USA
| | - Neil J. McKenna
- Department of Molecular, Baylor College of Medicine, Houston, TX 77030, USA
- Cellular Biology and Medicine, Baylor College of Medicine, Houston, TX 77030, USA
- Corresponding author
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Bediaga NG, Garnham AL, Naselli G, Bandala-Sanchez E, Stone NL, Cobb J, Harbison JE, Wentworth JM, Ziegler AG, Couper JJ, Smyth GK, Harrison LC. Cytotoxicity-Related Gene Expression and Chromatin Accessibility Define a Subset of CD4+ T Cells That Mark Progression to Type 1 Diabetes. Diabetes 2022; 71:566-577. [PMID: 35007320 PMCID: PMC8893947 DOI: 10.2337/db21-0612] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Accepted: 12/12/2021] [Indexed: 11/13/2022]
Abstract
Type 1 diabetes in children is heralded by a preclinical phase defined by circulating autoantibodies to pancreatic islet antigens. How islet autoimmunity is initiated and then progresses to clinical diabetes remains poorly understood. Only one study has reported gene expression in specific immune cells of children at risk associated with progression to islet autoimmunity. We analyzed gene expression with RNA sequencing in CD4+ and CD8+ T cells, natural killer (NK) cells, and B cells, and chromatin accessibility by assay for transposase-accessible chromatin sequencing (ATAC-seq) in CD4+ T cells, in five genetically at risk children with islet autoantibodies who progressed to diabetes over a median of 3 years ("progressors") compared with five children matched for sex, age, and HLA-DR who had not progressed ("nonprogressors"). In progressors, differentially expressed genes (DEGs) were largely confined to CD4+ T cells and enriched for cytotoxicity-related genes/pathways. Several top-ranked DEGs were validated in a semi-independent cohort of 13 progressors and 11 nonprogressors. Flow cytometry confirmed that progression was associated with expansion of CD4+ cells with a cytotoxic phenotype. By ATAC-seq, progression was associated with reconfiguration of regulatory chromatin regions in CD4+ cells, some linked to differentially expressed cytotoxicity-related genes. Our findings suggest that cytotoxic CD4+ T cells play a role in promoting progression to type 1 diabetes.
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Affiliation(s)
- Naiara G. Bediaga
- The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia
- Department of Medical Biology, The University of Melbourne, Parkville, Australia
| | - Alexandra L. Garnham
- The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia
- Department of Medical Biology, The University of Melbourne, Parkville, Australia
| | - Gaetano Naselli
- The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia
| | - Esther Bandala-Sanchez
- The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia
- Department of Medical Biology, The University of Melbourne, Parkville, Australia
| | - Natalie L. Stone
- The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia
| | - Joanna Cobb
- Murdoch Children’s Research Institute, Parkville, Australia
| | - Jessica E. Harbison
- Department of Endocrinology and Diabetes, Women’s and Children’s Hospital, North Adelaide, Australia
- Robinson Research Institute, The University of Adelaide, Adelaide, Australia
| | - John M. Wentworth
- The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia
- Department of Medical Biology, The University of Melbourne, Parkville, Australia
- Department of Diabetes and Endocrinology, Royal Melbourne Hospital, Parkville, Australia
| | - Annette-G. Ziegler
- Institute of Diabetes Research, Helmholtz Zentrum München, Neuherberg, Germany
| | - Jennifer J. Couper
- Department of Endocrinology and Diabetes, Women’s and Children’s Hospital, North Adelaide, Australia
- Robinson Research Institute, The University of Adelaide, Adelaide, Australia
| | - Gordon K. Smyth
- The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia
- School of Mathematics and Statistics, The University of Melbourne, Parkville, Australia
| | - Leonard C. Harrison
- The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia
- Department of Medical Biology, The University of Melbourne, Parkville, Australia
- Corresponding author: Leonard C. Harrison,
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Santos AS, Cunha-Neto E, Gonfinetti NV, Bertonha FB, Brochet P, Bergon A, Moreira-Filho CA, Chevillard C, da Silva MER. Prevalence of Inflammatory Pathways Over Immuno-Tolerance in Peripheral Blood Mononuclear Cells of Recent-Onset Type 1 Diabetes. Front Immunol 2022; 12:765264. [PMID: 35058920 PMCID: PMC8764313 DOI: 10.3389/fimmu.2021.765264] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Accepted: 12/10/2021] [Indexed: 12/15/2022] Open
Abstract
Background Changes in innate and adaptive immunity occurring in/around pancreatic islets had been observed in peripheral blood mononuclear cells (PBMC) of Caucasian T1D patients by some, but not all researchers. The aim of our study was to investigate whether gene expression patterns of PBMC of the highly admixed Brazilian population could add knowledge about T1D pathogenic mechanisms. Methods We assessed global gene expression in PBMC from two groups matched for age, sex and BMI: 20 patients with recent-onset T1D (≤ 6 months from diagnosis, in a time when the autoimmune process is still highly active), testing positive for one or more islet autoantibodies and 20 islet autoantibody-negative healthy controls. Results We identified 474 differentially expressed genes between groups. The most expressed genes in T1D group favored host defense, inflammatory and anti-bacterial/antiviral effects (LFT, DEFA4, DEFA1, CTSG, KCNMA1) and cell cycle progression. Several of the downregulated genes in T1D target cellular repair, control of inflammation and immune tolerance. They were related to T helper 2 pathway, induction of FOXP3 expression (AREG) and immune tolerance (SMAD6). SMAD6 expression correlated negatively with islet ZnT8 antibody. The expression of PDE12, that offers resistance to viral pathogens was decreased and negatively related to ZnT8A and GADA levels. The increased expression of long non coding RNAs MALAT1 and NEAT1, related to inflammatory mediators, autoimmune diseases and innate immune response against viral infections reinforced these data. Conclusions Our analysis suggested the activation of cell development, anti-infectious and inflammatory pathways, indicating immune activation, whereas immune-regulatory pathways were downregulated in PBMC from recent-onset T1D patients with a differential genetic profile.
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Affiliation(s)
- Aritania Sousa Santos
- Laboratorio de Carboidratos e Radioimunoensaios LIM 18, Faculdade de Medicina, University of Sao Paulo Hospital of Clinics, São Paulo, Brazil
| | - Edécio Cunha-Neto
- Laboratory of Immunology, Heart Institute, School of Medicine, University of São Paulo, São Paulo, Brazil
| | | | | | - Pauline Brochet
- Aix Marseille Université, Inserm, TAGC Theories and Approaches of Genomic Complexity, INSERM, UMR_1090, Marseille, France
| | - Aurelie Bergon
- Aix Marseille Université, Inserm, TAGC Theories and Approaches of Genomic Complexity, INSERM, UMR_1090, Marseille, France
| | | | - Christophe Chevillard
- Aix Marseille Université, Inserm, TAGC Theories and Approaches of Genomic Complexity, INSERM, UMR_1090, Marseille, France
| | - Maria Elizabeth Rossi da Silva
- Laboratorio de Carboidratos e Radioimunoensaios LIM 18, Faculdade de Medicina, University of Sao Paulo Hospital of Clinics, São Paulo, Brazil
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11
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Hanna SJ, Tatovic D, Thayer TC, Dayan CM. Insights From Single Cell RNA Sequencing Into the Immunology of Type 1 Diabetes- Cell Phenotypes and Antigen Specificity. Front Immunol 2021; 12:751701. [PMID: 34659258 PMCID: PMC8519581 DOI: 10.3389/fimmu.2021.751701] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Accepted: 09/14/2021] [Indexed: 01/10/2023] Open
Abstract
In the past few years, huge advances have been made in techniques to analyse cells at an individual level using RNA sequencing, and many of these have precipitated exciting discoveries in the immunology of type 1 diabetes (T1D). This review will cover the first papers to use scRNAseq to characterise human lymphocyte phenotypes in T1D in the peripheral blood, pancreatic lymph nodes and islets. These have revealed specific genes such as IL-32 that are differentially expressed in islet -specific T cells in T1D. scRNAseq has also revealed wider gene expression patterns that are involved in T1D and can predict its development even predating autoantibody production. Single cell sequencing of TCRs has revealed V genes and CDR3 motifs that are commonly used to target islet autoantigens, although truly public TCRs remain elusive. Little is known about BCR repertoires in T1D, but scRNAseq approaches have revealed that insulin binding BCRs commonly use specific J genes, share motifs between donors and frequently demonstrate poly-reactivity. This review will also summarise new developments in scRNAseq technology, the insights they have given into other diseases and how they could be leveraged to advance research in the type 1 diabetes field to identify novel biomarkers and targets for immunotherapy.
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Affiliation(s)
- Stephanie J. Hanna
- Diabetes Research Group, Division of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - Danijela Tatovic
- Diabetes Research Group, Division of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - Terri C. Thayer
- Diabetes Research Group, Division of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, United Kingdom
- Department of Biological and Chemical Sciences, School of Natural and Social Sciences, Roberts Wesleyan College, Rochester, NY, United States
| | - Colin M. Dayan
- Diabetes Research Group, Division of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, United Kingdom
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
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12
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Xhonneux LP, Knight O, Lernmark Å, Bonifacio E, Hagopian WA, Rewers MJ, She JX, Toppari J, Parikh H, Smith KGC, Ziegler AG, Akolkar B, Krischer JP, McKinney EF. Transcriptional networks in at-risk individuals identify signatures of type 1 diabetes progression. Sci Transl Med 2021; 13:eabd5666. [PMID: 33790023 PMCID: PMC8447843 DOI: 10.1126/scitranslmed.abd5666] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 09/24/2020] [Accepted: 03/12/2021] [Indexed: 12/11/2022]
Abstract
Type 1 diabetes (T1D) is a disease of insulin deficiency that results from autoimmune destruction of pancreatic islet β cells. The exact cause of T1D remains unknown, although asymptomatic islet autoimmunity lasting from weeks to years before diagnosis raises the possibility of intervention before the onset of clinical disease. The number, type, and titer of islet autoantibodies are associated with long-term disease risk but do not cause disease, and robust early predictors of individual progression to T1D onset remain elusive. The Environmental Determinants of Diabetes in the Young (TEDDY) consortium is a prospective cohort study aiming to determine genetic and environmental interactions causing T1D. Here, we analyzed longitudinal blood transcriptomes of 2013 samples from 400 individuals in the TEDDY study before both T1D and islet autoimmunity. We identified and interpreted age-associated gene expression changes in healthy infancy and age-independent changes tracking with progression to both T1D and islet autoimmunity, beginning before other evidence of islet autoimmunity was present. We combined multivariate longitudinal data in a Bayesian joint model to predict individual risk of T1D onset and validated the association of a natural killer cell signature with progression and the model's predictive performance on an additional 356 samples from 56 individuals in the independent Type 1 Diabetes Prediction and Prevention study. Together, our results indicate that T1D is characterized by early and longitudinal changes in gene expression, informing the immunopathology of disease progression and facilitating prediction of its course.
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Affiliation(s)
- Louis-Pascal Xhonneux
- Cambridge Institute of Therapeutic Immunology and Infectious Disease, Jeffrey Cheah Biomedical Centre, Cambridge CB2 0AW, UK
- Department of Medicine, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, UK
| | - Oliver Knight
- Cambridge Institute of Therapeutic Immunology and Infectious Disease, Jeffrey Cheah Biomedical Centre, Cambridge CB2 0AW, UK
- Department of Medicine, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, UK
| | - Åke Lernmark
- Department of Clinical Sciences, Lund University/CRC Skåne University Hospital Malmo, Jan Waldenströms gata 35, Malmö, Sweden
| | - Ezio Bonifacio
- Center for Regenerative Therapies, Technische Universität Dresden, Fetscherstraße 105, 01307, Dresden, Germany
| | - William A Hagopian
- Pacific Northwest Research Institute, 720 Broadway, Seattle, WA 98122, USA
| | - Marian J Rewers
- Barbara Davis Center for Childhood Diabetes, University of Colorado, 1775 Aurora Ct, Aurora, CO 80045, USA
| | - Jin-Xiong She
- Center for Biotechnology and Genomic Medicine, Medical College of Georgia, Augusta University, 1462 Laney Walker Blvd., Augusta, GA 30912, USA
| | - Jorma Toppari
- Department of Pediatrics, Turku University Hospital, Kiinamyllynkatu 4-8, 20521 Turku, Finland
- Institute of Biomedicine, Research Centre for Integrative Physiology and Pharmacology, University of Turku, FI-20014 Turun Lyliopisto, Finland
| | - Hemang Parikh
- Health Informatics Institute, Morsani College of Medicine, University of South Florida, Tampa, FL 33612, USA
| | - Kenneth G C Smith
- Cambridge Institute of Therapeutic Immunology and Infectious Disease, Jeffrey Cheah Biomedical Centre, Cambridge CB2 0AW, UK
- Department of Medicine, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, UK
| | - Anette-G Ziegler
- Institute of Diabetes Research, Helmholtz Zentrum München, and Klinikum rechts der Isar, Technische, Universität München, Forschergruppe Diabetes e.V., Arcisstraße 21, 80333 München, Germany
| | - Beena Akolkar
- National Institute of Diabetes and Digestive and Kidney Diseases, 9000 Rockville Pike Bethesda, MD 20892, USA
| | - Jeffrey P Krischer
- Health Informatics Institute, Morsani College of Medicine, University of South Florida, Tampa, FL 33612, USA
| | - Eoin F McKinney
- Cambridge Institute of Therapeutic Immunology and Infectious Disease, Jeffrey Cheah Biomedical Centre, Cambridge CB2 0AW, UK.
- Department of Medicine, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, UK
- Cambridge Centre for Artificial Intelligence in Medicine, University of Cambridge, Cambridge, UK
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13
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Alcazar O, Hernandez LF, Nakayasu ES, Nicora CD, Ansong C, Muehlbauer MJ, Bain JR, Myer CJ, Bhattacharya SK, Buchwald P, Abdulreda MH. Parallel Multi-Omics in High-Risk Subjects for the Identification of Integrated Biomarker Signatures of Type 1 Diabetes. Biomolecules 2021; 11:383. [PMID: 33806609 PMCID: PMC7999903 DOI: 10.3390/biom11030383] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 02/26/2021] [Accepted: 03/02/2021] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Biomarkers are crucial for detecting early type-1 diabetes (T1D) and preventing significant β-cell loss before the onset of clinical symptoms. Here, we present proof-of-concept studies to demonstrate the potential for identifying integrated biomarker signature(s) of T1D using parallel multi-omics. METHODS Blood from human subjects at high risk for T1D (and healthy controls; n = 4 + 4) was subjected to parallel unlabeled proteomics, metabolomics, lipidomics, and transcriptomics. The integrated dataset was analyzed using Ingenuity Pathway Analysis (IPA) software for disturbances in the at-risk subjects compared to controls. RESULTS The final quadra-omics dataset contained 2292 proteins, 328 miRNAs, 75 metabolites, and 41 lipids that were detected in all samples without exception. Disease/function enrichment analyses consistently indicated increased activation, proliferation, and migration of CD4 T-lymphocytes and macrophages. Integrated molecular network predictions highlighted central involvement and activation of NF-κB, TGF-β, VEGF, arachidonic acid, and arginase, and inhibition of miRNA Let-7a-5p. IPA-predicted candidate biomarkers were used to construct a putative integrated signature containing several miRNAs and metabolite/lipid features in the at-risk subjects. CONCLUSIONS Preliminary parallel quadra-omics provided a comprehensive picture of disturbances in high-risk T1D subjects and highlighted the potential for identifying associated integrated biomarker signatures. With further development and validation in larger cohorts, parallel multi-omics could ultimately facilitate the classification of T1D progressors from non-progressors.
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Affiliation(s)
- Oscar Alcazar
- Diabetes Research Institute, University of Miami Miller School of Medicine, Miami, FL 33136, USA; (O.A.); (L.F.H.)
| | - Luis F. Hernandez
- Diabetes Research Institute, University of Miami Miller School of Medicine, Miami, FL 33136, USA; (O.A.); (L.F.H.)
| | - Ernesto S. Nakayasu
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA; (E.S.N.); (C.D.N.); (C.A.)
| | - Carrie D. Nicora
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA; (E.S.N.); (C.D.N.); (C.A.)
| | - Charles Ansong
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA; (E.S.N.); (C.D.N.); (C.A.)
| | - Michael J. Muehlbauer
- Duke Molecular Physiology Institute, Duke University Medical Center, Durham, NC 27701, USA; (M.J.M.); (J.R.B.)
| | - James R. Bain
- Duke Molecular Physiology Institute, Duke University Medical Center, Durham, NC 27701, USA; (M.J.M.); (J.R.B.)
| | - Ciara J. Myer
- Department of Ophthalmology, University of Miami Miller School of Medicine, Miami, FL 33136, USA; (C.J.M.); (S.K.B.)
- Miami Integrative Metabolomics Research Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Sanjoy K. Bhattacharya
- Department of Ophthalmology, University of Miami Miller School of Medicine, Miami, FL 33136, USA; (C.J.M.); (S.K.B.)
- Miami Integrative Metabolomics Research Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Peter Buchwald
- Diabetes Research Institute, University of Miami Miller School of Medicine, Miami, FL 33136, USA; (O.A.); (L.F.H.)
- Department of Molecular and Cellular Pharmacology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Midhat H. Abdulreda
- Diabetes Research Institute, University of Miami Miller School of Medicine, Miami, FL 33136, USA; (O.A.); (L.F.H.)
- Department of Ophthalmology, University of Miami Miller School of Medicine, Miami, FL 33136, USA; (C.J.M.); (S.K.B.)
- Department of Surgery, University of Miami Miller School of Medicine, Miami, FL 33136, USA
- Department of Microbiology and Immunology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
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14
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Yap YA, Mariño E. Dietary SCFAs Immunotherapy: Reshaping the Gut Microbiota in Diabetes. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2021; 1307:499-519. [PMID: 32193865 DOI: 10.1007/5584_2020_515] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Diet-microbiota related inflammatory conditions such as obesity, autoimmune type 1 diabetes (T1D), type 2 diabetes (T2D), cardiovascular disease (CVD) and gut infections have become a stigma in Western societies and developing nations. This book chapter examines the most relevant pre-clinical and clinical studies about diet-gut microbiota approaches as an alternative therapy for diabetes. We also discuss what we and others have extensively investigated- the power of dietary short-chain fatty acids (SCFAs) technology that naturally targets the gut microbiota as an alternative method to prevent and treat diabetes and its related complications.
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Affiliation(s)
- Yu Anne Yap
- Infection and Immunity Program, Biomedicine Discovery Institute, Department of Biochemistry, Monash University, Melbourne, VIC, Australia
| | - Eliana Mariño
- Infection and Immunity Program, Biomedicine Discovery Institute, Department of Biochemistry, Monash University, Melbourne, VIC, Australia.
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15
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Grant SFA, Wells AD, Rich SS. Next steps in the identification of gene targets for type 1 diabetes. Diabetologia 2020; 63:2260-2269. [PMID: 32797243 PMCID: PMC7527360 DOI: 10.1007/s00125-020-05248-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Accepted: 06/16/2020] [Indexed: 12/17/2022]
Abstract
The purpose of this review is to provide a view of the future of genomics and other omics approaches in defining the genetic contribution to all stages of risk of type 1 diabetes and the functional impact and clinical implementations of the associated variants. From the recognition nearly 50 years ago that genetics (in the form of HLA) distinguishes risk of type 1 diabetes from type 2 diabetes, advances in technology and sample acquisition through collaboration have identified over 60 loci harbouring SNPs associated with type 1 diabetes risk. Coupled with HLA region genes, these variants account for the majority of the genetic risk (~50% of the total risk); however, relatively few variants are located in coding regions of genes exerting a predicted protein change. The vast majority of genetic risk in type 1 diabetes appears to be attributed to regions of the genome involved in gene regulation, but the target effectors of those genetic variants are not readily identifiable. Although past genetic studies clearly implicated immune-relevant cell types involved in risk, the target organ (the beta cell) was left untouched. Through emergent technologies, using combinations of genetics, gene expression, epigenetics, chromosome conformation and gene editing, novel landscapes of how SNPs regulate genes have emerged. Furthermore, both the immune system and the beta cell and their biological pathways have been implicated in a context-specific manner. The use of variants from immune and beta cell studies distinguish type 1 diabetes from type 2 diabetes and, when they are combined in a genetic risk score, open new avenues for prediction and treatment. Graphical abstract.
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Affiliation(s)
- Struan F A Grant
- Center for Spatial and Functional Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Departments of Pediatrics and Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Divisions of Human Genetics and Endocrinology, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Andrew D Wells
- Center for Spatial and Functional Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA.
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, USA.
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Sphingolipids in Type 1 Diabetes: Focus on Beta-Cells. Cells 2020; 9:cells9081835. [PMID: 32759843 PMCID: PMC7465050 DOI: 10.3390/cells9081835] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 08/01/2020] [Accepted: 08/03/2020] [Indexed: 12/28/2022] Open
Abstract
Type 1 diabetes (T1DM) is a chronic autoimmune disease, with a strong genetic background, leading to a gradual loss of pancreatic beta-cells, which secrete insulin and control glucose homeostasis. Patients with T1DM require life-long substitution with insulin and are at high risk for development of severe secondary complications. The incidence of T1DM has been continuously growing in the last decades, indicating an important contribution of environmental factors. Accumulating data indicates that sphingolipids may be crucially involved in T1DM development. The serum lipidome of T1DM patients is characterized by significantly altered sphingolipid composition compared to nondiabetic, healthy probands. Recently, several polymorphisms in the genes encoding the enzymatic machinery for sphingolipid production have been identified in T1DM individuals. Evidence gained from studies in rodent islets and beta-cells exposed to cytokines indicates dysregulation of the sphingolipid biosynthetic pathway and impaired function of several sphingolipids. Moreover, a number of glycosphingolipids have been suggested to act as beta-cell autoantigens. Studies in animal models of autoimmune diabetes, such as the Non Obese Diabetic (NOD) mouse and the LEW.1AR1-iddm (IDDM) rat, indicate a crucial role of sphingolipids in immune cell trafficking, islet infiltration and diabetes development. In this review, the up-to-date status on the findings about sphingolipids in T1DM will be provided, the under-investigated research areas will be identified and perspectives for future studies will be given.
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The chilling of adenylyl cyclase 9 and its translational potential. Cell Signal 2020; 70:109589. [PMID: 32105777 DOI: 10.1016/j.cellsig.2020.109589] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Revised: 02/21/2020] [Accepted: 02/23/2020] [Indexed: 12/26/2022]
Abstract
A recent break-through paper has revealed for the first time the high-resolution, three-dimensional structure of a mammalian trans-membrane adenylyl cyclase (tmAC) obtained by cryo-electronmicroscopy (cryo-EM). Reporting the structure of adenylyl cyclase 9 (AC9) in complex with activated Gsα, the cryo-EM study revealed that AC9 has three functionally interlinked, yet structurally distinct domains. The array of the twelve transmembrane helices is connected to the cytosolic catalytic core by two helical segments that are stabilized through the formation of a parallel coiled-coil. Surprisingly, in the presence of Gsα, the isoform-specific carboxyl-terminal tail of AC9 occludes the forskolin- as well as the active substrate-sites, resulting in marked autoinhibition of the enzyme. As AC9 has the lowest primary sequence homology with the eight further mammalian tmAC paralogues, it appears to be the best candidate for selective pharmacologic targeting. This is now closer to reality as the structural insight provided by the cryo-EM study indicates that all of the three structural domains are potential targets for bioactive agents. The present paper summarizes for molecular physiologists and pharmacologists what is known about the biological role of AC9, considers the potential modes of physiologic regulation, as well as pharmacologic targeting on the basis of the high-resolution cryo-EM structure. The translational potential of AC9 is considered upon highlighting the current state of genome-wide association screens, and the corresponding experimental evidence. Overall, whilst the high- resolution structure presents unique opportunities for the full understanding of the control of AC9, the data on the biological role of the enzyme and its translational potential are far from complete, and require extensive further study.
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Rehaume LM, Matigian N, Mehdi AM, Lachner N, Bowerman KL, Daly J, Lê Cao KA, Hugenholtz P, Thomas R. IL-23 favours outgrowth of spondyloarthritis-associated pathobionts and suppresses host support for homeostatic microbiota. Ann Rheum Dis 2019; 78:494-503. [DOI: 10.1136/annrheumdis-2018-214381] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2018] [Revised: 01/06/2019] [Accepted: 01/07/2019] [Indexed: 12/14/2022]
Abstract
ObjectivesCertain gut bacterial families, including Bacteroidaceae, Porphyromonadaceae and Prevotellaceae, are increased in people suffering from spondyloarthropathy (SpA), a disease group associated with IL23R signalling variants. To understand the relationship between host interleukin (IL)-23 signalling and gut bacterial dysbiosis in SpA, we inhibited IL-23 in dysbiotic ZAP-70-mutant SKG mice that develop IL-23-dependent SpA-like arthritis, psoriasis-like skin inflammation and Crohn’s-like ileitis in response to microbial beta 1,3-glucan (curdlan).MethodsWe treated SKG mice weekly with anti-IL-23 or isotype mAb for 3 weeks, rested them for 3 weeks, then administered curdlan or saline. We collected faecal samples longitudinally, assessed arthritis, spondylitis, psoriasis and ileitis histologically, and analysed the microbiota community profiles using next-generation sequencing. We used multivariate sparse partial least squares discriminant analysis to identify operational taxonomic unit (OTU) signatures best classifying treatment groups and linear regression to develop a predictive model of disease severity.ResultsIL-23p19 inhibition in naïve SKG mice decreased Bacteroidaceae, Porphyromonadaceae and Prevotellaceae. Abundance of Clostridiaceae and Lachnospiraceae families concomitantly increased, and curdlan-mediated SpA development decreased. Abundance of Enterobacteriaceae and Porphyromonadaceae family and reduction in Lachnospiraceae Dorea genus OTUs early in disease course were associated with disease severity in affected tissues.ConclusionsDysbiosis in SKG mice reflects human SpA and is IL-23p19 dependent. In genetically susceptible hosts, IL-23p19 favours outgrowth of SpA-associated pathobionts and reduces support for homeostatic-inducing microbiota. The relative abundance of specific pathobionts is associated with disease severity.
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Daily JW, Liu M, Park S. High genetic risk scores of SLIT3, PLEKHA5 and PPP2R2C variants increased insulin resistance and interacted with coffee and caffeine consumption in middle-aged adults. Nutr Metab Cardiovasc Dis 2019; 29:79-89. [PMID: 30454882 DOI: 10.1016/j.numecd.2018.09.009] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/10/2018] [Revised: 09/19/2018] [Accepted: 09/20/2018] [Indexed: 01/03/2023]
Abstract
BACKGROUNDS AND AIMS Insulin resistance is a common feature of metabolic syndrome that may be influenced by genetic risk factors. We hypothesized that genetic risk scores (GRS) of SNPs that influence insulin resistance and signaling interact with lifestyles to modulate insulin resistance in Korean adults. METHODS AND RESULTS Genome-wide association studies (GWAS) of subjects aged 40-65 years who participated in the Ansung/Ansan cohorts (8842 adults) in Korea revealed 52 genetic variants that influence insulin resistance. The best gene-gene interaction model was explored using the generalized multifactor dimensionality reduction (GMDR) method. GRS from the best model were calculated and the GRS were divided into low, medium and high groups. The best model for representing insulin resistance included SLIT3_rs2974430, PLEKHA5_rs1077044, and PPP2R2C_rs16838853. The odds ratios for insulin resistance were increased by 150% in the High-GRS group compared to the Low-GRS group. However, ORs for insulin secretion capacity, measured by HOMA-B, were not associated with GRS. Coffee and caffeine intake and GRS had an interaction with insulin resistance: In subjects with high coffee (≥10 cups/week) or caffeine intake (≥220 mg caffeine/day), insulin resistance was significantly elevated in the High-GRS group, but not in the Low-GRS. However, alcohol intake, smoking and physical activity did not have an interaction with GRS. Insulin secretion capacity was not significantly influenced by GRS when evaluating the adjusted odds ratios. CONCLUSIONS Subjects with High-GRS may be susceptible to increased insulin resistance by 50% and its risk may be exacerbated by consuming more than 10 cups coffee/week or 220 mg caffeine/day.
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Affiliation(s)
- J W Daily
- Dept. of R&D, Daily Manufacturing Inc., Rockwell, NC, USA
| | - M Liu
- Dept. of Food and Nutrition, Obesity/Diabetes Research Center, Hoseo University, Asan, South Korea
| | - S Park
- Dept. of Food and Nutrition, Obesity/Diabetes Research Center, Hoseo University, Asan, South Korea.
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Barbitoff YA, Serebryakova EA, Nasykhova YA, Predeus AV, Polev DE, Shuvalova AR, Vasiliev EV, Urazov SP, Sarana AM, Scherbak SG, Gladyshev DV, Pokrovskaya MS, Sivakova OV, Meshkov AN, Drapkina OM, Glotov OS, Glotov AS. Identification of Novel Candidate Markers of Type 2 Diabetes and Obesity in Russia by Exome Sequencing with a Limited Sample Size. Genes (Basel) 2018; 9:genes9080415. [PMID: 30126146 PMCID: PMC6115942 DOI: 10.3390/genes9080415] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2018] [Revised: 08/11/2018] [Accepted: 08/13/2018] [Indexed: 12/22/2022] Open
Abstract
Type 2 diabetes (T2D) and obesity are common chronic disorders with multifactorial etiology. In our study, we performed an exome sequencing analysis of 110 patients of Russian ethnicity together with a multi-perspective approach based on biologically meaningful filtering criteria to detect novel candidate variants and loci for T2D and obesity. We have identified several known single nucleotide polymorphisms (SNPs) as markers for obesity (rs11960429), T2D (rs9379084, rs1126930), and body mass index (BMI) (rs11553746, rs1956549 and rs7195386) (p < 0.05). We show that a method based on scoring of case-specific variants together with selection of protein-altering variants can allow for the interrogation of novel and known candidate markers of T2D and obesity in small samples. Using this method, we identified rs328 in LPL (p = 0.023), rs11863726 in HBQ1 (p = 8 × 10−5), rs112984085 in VAV3 (p = 4.8 × 10−4) for T2D and obesity, rs6271 in DBH (p = 0.043), rs62618693 in QSER1 (p = 0.021), rs61758785 in RAD51B (p = 1.7 × 10−4), rs34042554 in PCDHA1 (p = 1 × 10−4), and rs144183813 in PLEKHA5 (p = 1.7 × 10−4) for obesity; and rs9379084 in RREB1 (p = 0.042), rs2233984 in C6orf15 (p = 0.030), rs61737764 in ITGB6 (p = 0.035), rs17801742 in COL2A1 (p = 8.5 × 10−5), and rs685523 in ADAMTS13 (p = 1 × 10−6) for T2D as important susceptibility loci in Russian population. Our results demonstrate the effectiveness of whole exome sequencing (WES) technologies for searching for novel markers of multifactorial diseases in cohorts of limited size in poorly studied populations.
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Affiliation(s)
- Yury A Barbitoff
- Biobank of the Research Park, Saint Petersburg State University, 199034 Saint Petersburg, Russia.
- Bioinformatics Institute, 194100 Saint Petersburg, Russia.
- Department of Genetics and Biotechnology, Saint Petersburg State University, 199034 Saint Petersburg, Russia.
- Institute of Translation Biomedicine, Saint Petersburg State University, 199034 Saint Petersburg, Russia.
| | - Elena A Serebryakova
- Biobank of the Research Park, Saint Petersburg State University, 199034 Saint Petersburg, Russia.
- Laboratory of Prenatal Diagnostics of Hereditary Diseases, FSBSI «The Research Institute of Obstetrics, Gynaecology and Reproductology Named after D.O. Ott», 199034 Saint Petersburg, Russia.
- City Hospital No. 40, Sestroretsk, 197706 Saint Petersburg, Russia.
| | - Yulia A Nasykhova
- Biobank of the Research Park, Saint Petersburg State University, 199034 Saint Petersburg, Russia.
- Laboratory of Prenatal Diagnostics of Hereditary Diseases, FSBSI «The Research Institute of Obstetrics, Gynaecology and Reproductology Named after D.O. Ott», 199034 Saint Petersburg, Russia.
| | | | - Dmitrii E Polev
- Biobank of the Research Park, Saint Petersburg State University, 199034 Saint Petersburg, Russia.
| | - Anna R Shuvalova
- Biobank of the Research Park, Saint Petersburg State University, 199034 Saint Petersburg, Russia.
| | | | | | - Andrey M Sarana
- Institute of Translation Biomedicine, Saint Petersburg State University, 199034 Saint Petersburg, Russia.
- City Hospital No. 40, Sestroretsk, 197706 Saint Petersburg, Russia.
| | - Sergey G Scherbak
- Institute of Translation Biomedicine, Saint Petersburg State University, 199034 Saint Petersburg, Russia.
- City Hospital No. 40, Sestroretsk, 197706 Saint Petersburg, Russia.
| | | | - Maria S Pokrovskaya
- Federal State Institution «National Medical Research Center for Preventive Medicine» of the Ministry of Healthcare of the Russian Federation, 101990 Moscow, Russia.
| | - Oksana V Sivakova
- Federal State Institution «National Medical Research Center for Preventive Medicine» of the Ministry of Healthcare of the Russian Federation, 101990 Moscow, Russia.
| | - Aleksey N Meshkov
- Federal State Institution «National Medical Research Center for Preventive Medicine» of the Ministry of Healthcare of the Russian Federation, 101990 Moscow, Russia.
| | - Oxana M Drapkina
- Federal State Institution «National Medical Research Center for Preventive Medicine» of the Ministry of Healthcare of the Russian Federation, 101990 Moscow, Russia.
| | - Oleg S Glotov
- Laboratory of Prenatal Diagnostics of Hereditary Diseases, FSBSI «The Research Institute of Obstetrics, Gynaecology and Reproductology Named after D.O. Ott», 199034 Saint Petersburg, Russia.
- City Hospital No. 40, Sestroretsk, 197706 Saint Petersburg, Russia.
| | - Andrey S Glotov
- Biobank of the Research Park, Saint Petersburg State University, 199034 Saint Petersburg, Russia.
- Laboratory of Prenatal Diagnostics of Hereditary Diseases, FSBSI «The Research Institute of Obstetrics, Gynaecology and Reproductology Named after D.O. Ott», 199034 Saint Petersburg, Russia.
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