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Phongpreecha T, Mathi K, Cholerton B, Fox EJ, Sigal N, Espinosa C, Reincke M, Chung P, Hwang LJ, Gajera CR, Berson E, Perna A, Xie F, Shu CH, Hazra D, Channappa D, Dunn JE, Kipp LB, Poston KL, Montine KS, Maecker HT, Aghaeepour N, Montine TJ. Single-cell peripheral immunoprofiling of lewy body and Parkinson's disease in a multi-site cohort. Mol Neurodegener 2024; 19:59. [PMID: 39090623 PMCID: PMC11295553 DOI: 10.1186/s13024-024-00748-2] [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: 02/06/2024] [Accepted: 07/22/2024] [Indexed: 08/04/2024] Open
Abstract
BACKGROUND Multiple lines of evidence support peripheral organs in the initiation or progression of Lewy body disease (LBD), a spectrum of neurodegenerative diagnoses that include Parkinson's Disease (PD) without or with dementia (PDD) and dementia with Lewy bodies (DLB). However, the potential contribution of the peripheral immune response to LBD remains unclear. This study aims to characterize peripheral immune responses unique to participants with LBD at single-cell resolution to highlight potential biomarkers and increase mechanistic understanding of LBD pathogenesis in humans. METHODS In a case-control study, peripheral mononuclear cell (PBMC) samples from research participants were randomly sampled from multiple sites across the United States. The diagnosis groups comprise healthy controls (HC, n = 159), LBD (n = 110), Alzheimer's disease dementia (ADD, n = 97), other neurodegenerative disease controls (NDC, n = 19), and immune disease controls (IDC, n = 14). PBMCs were activated with three stimulants (LPS, IL-6, and IFNa) or remained at basal state, stained by 13 surface markers and 7 intracellular signal markers, and analyzed by flow cytometry, which generated 1,184 immune features after gating. RESULTS The model classified LBD from HC with an AUROC of 0.87 ± 0.06 and AUPRC of 0.80 ± 0.06. Without retraining, the same model was able to distinguish LBD from ADD, NDC, and IDC. Model predictions were driven by pPLCγ2, p38, and pSTAT5 signals from specific cell populations under specific activation. The immune responses characteristic for LBD were not associated with other common medical conditions related to the risk of LBD or dementia, such as sleep disorders, hypertension, or diabetes. CONCLUSIONS AND RELEVANCE Quantification of PBMC immune response from multisite research participants yielded a unique pattern for LBD compared to HC, multiple related neurodegenerative diseases, and autoimmune diseases thereby highlighting potential biomarkers and mechanisms of disease.
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Affiliation(s)
- Thanaphong Phongpreecha
- Department of Pathology, Stanford University, Stanford, CA, USA
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Kavita Mathi
- Institute for Immunity, Transplantation and Infection, Stanford University, Stanford, CA, USA
| | | | - Eddie J Fox
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Natalia Sigal
- Institute for Immunity, Transplantation and Infection, Stanford University, Stanford, CA, USA
| | - Camilo Espinosa
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Department of Pediatrics, Stanford University, Stanford, CA, USA
| | - Momsen Reincke
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Department of Pediatrics, Stanford University, Stanford, CA, USA
| | - Philip Chung
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA, USA
| | - Ling-Jen Hwang
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | | | - Eloise Berson
- Department of Pathology, Stanford University, Stanford, CA, USA
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Amalia Perna
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Feng Xie
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Department of Pediatrics, Stanford University, Stanford, CA, USA
| | - Chi-Hung Shu
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Department of Pediatrics, Stanford University, Stanford, CA, USA
| | - Debapriya Hazra
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Department of Pediatrics, Stanford University, Stanford, CA, USA
| | - Divya Channappa
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA, USA
| | - Jeffrey E Dunn
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA, USA
| | - Lucas B Kipp
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA, USA
| | - Kathleen L Poston
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA, USA
| | | | - Holden T Maecker
- Institute for Immunity, Transplantation and Infection, Stanford University, Stanford, CA, USA
| | - Nima Aghaeepour
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Department of Pediatrics, Stanford University, Stanford, CA, USA
| | - Thomas J Montine
- Department of Pathology, Stanford University, Stanford, CA, USA.
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2
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Bi X, Wang J, Xue B, He C, Liu F, Chen H, Lin LL, Dong B, Li B, Jin C, Pan J, Xue W, Ye J. SERSomes for metabolic phenotyping and prostate cancer diagnosis. Cell Rep Med 2024; 5:101579. [PMID: 38776910 PMCID: PMC11228451 DOI: 10.1016/j.xcrm.2024.101579] [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: 09/27/2023] [Revised: 03/08/2024] [Accepted: 04/29/2024] [Indexed: 05/25/2024]
Abstract
Molecular phenotypic variations in metabolites offer the promise of rapid profiling of physiological and pathological states for diagnosis, monitoring, and prognosis. Since present methods are expensive, time-consuming, and still not sensitive enough, there is an urgent need for approaches that can interrogate complex biological fluids at a system-wide level. Here, we introduce hyperspectral surface-enhanced Raman spectroscopy (SERS) to profile microliters of biofluidic metabolite extraction in 15 min with a spectral set, SERSome, that can be used to describe the structures and functions of various molecules produced in the biofluid at a specific time via SERS characteristics. The metabolite differences of various biofluids, including cell culture medium and human serum, are successfully profiled, showing a diagnosis accuracy of 80.8% on the internal test set and 73% on the external validation set for prostate cancer, discovering potential biomarkers, and predicting the tissue-level pathological aggressiveness. SERSomes offer a promising methodology for metabolic phenotyping.
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Affiliation(s)
- Xinyuan Bi
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, P.R. China
| | - Jiayi Wang
- Department of Urology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, P.R. China
| | - Bingsen Xue
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, P.R. China; Shanghai Artificial Intelligence Laboratory, Shanghai, China
| | - Chang He
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, P.R. China
| | - Fugang Liu
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, P.R. China
| | - Haoran Chen
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, P.R. China
| | - Linley Li Lin
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, P.R. China
| | - Baijun Dong
- Department of Urology, Jiading District Central Hospital Affiliated Shanghai University of Medicine & Health Science, Shanghai, P.R. China
| | - Butang Li
- Department of Urology, Ningbo Hangzhou Bay Hospital, Ningbo, Zhejiang, P.R. China
| | - Cheng Jin
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, P.R. China; Shanghai Artificial Intelligence Laboratory, Shanghai, China; Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, P.R. China.
| | - Jiahua Pan
- Department of Urology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, P.R. China.
| | - Wei Xue
- Department of Urology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, P.R. China.
| | - Jian Ye
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, P.R. China; Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, P.R. China; Shanghai Key Laboratory of Gynecologic Oncology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, P.R. China.
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3
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Mitchell JM, Chi Y, Thapa M, Pang Z, Xia J, Li S. Common data models to streamline metabolomics processing and annotation, and implementation in a Python pipeline. PLoS Comput Biol 2024; 20:e1011912. [PMID: 38843301 PMCID: PMC11185459 DOI: 10.1371/journal.pcbi.1011912] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Revised: 06/18/2024] [Accepted: 05/20/2024] [Indexed: 06/18/2024] Open
Abstract
To standardize metabolomics data analysis and facilitate future computational developments, it is essential to have a set of well-defined templates for common data structures. Here we describe a collection of data structures involved in metabolomics data processing and illustrate how they are utilized in a full-featured Python-centric pipeline. We demonstrate the performance of the pipeline, and the details in annotation and quality control using large-scale LC-MS metabolomics and lipidomics data and LC-MS/MS data. Multiple previously published datasets are also reanalyzed to showcase its utility in biological data analysis. This pipeline allows users to streamline data processing, quality control, annotation, and standardization in an efficient and transparent manner. This work fills a major gap in the Python ecosystem for computational metabolomics.
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Affiliation(s)
- Joshua M. Mitchell
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, United States of America
| | - Yuanye Chi
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, United States of America
| | - Maheshwor Thapa
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, United States of America
| | - Zhiqiang Pang
- Institute of Parasitology, McGill University, Montreal, Quebec, Canada
| | - Jianguo Xia
- Institute of Parasitology, McGill University, Montreal, Quebec, Canada
| | - Shuzhao Li
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, United States of America
- University of Connecticut School of Medicine, Farmington, Connecticut, United States of America
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4
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Jung S, Wang S, Lee D. CancerGATE: Prediction of cancer-driver genes using graph attention autoencoders. Comput Biol Med 2024; 176:108568. [PMID: 38744009 DOI: 10.1016/j.compbiomed.2024.108568] [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: 11/07/2023] [Revised: 04/13/2024] [Accepted: 05/05/2024] [Indexed: 05/16/2024]
Abstract
Discovery of the cancer type specific-driver genes is important for understanding the molecular mechanisms of each cancer type and for providing proper treatment. Recently, graph deep learning methods became widely used in finding cancer-driver genes. However, previous methods had limited performance in individual cancer types due to a small number of cancer-driver genes used in training and biases toward the cancer-driver genes used in training the models. Here, we introduce a novel pipeline, CancerGATE that predicts the cancer-driver genes using graph attention autoencoder (GATE) to learn in a self-supervised manner and can be applied to each of the cancer types. CancerGATE utilizes biological network topology and multi-omics data from 15 types of cancer of 20,079 samples from the cancer genome atlas (TCGA). Attention coefficients calculated in the model are used to prioritize cancer-driver genes by comparing coefficients of cancer and normal contexts. CancerGATE shows a higher AUPRC with a difference ranging from 1.5 % to 36.5 % compared to the previous graph deep learning models in each cancer type. We also show that CancerGATE is free from the bias toward cancer-driver genes used in training, revealing mechanisms of the cancer-driver genes in specific cancer types. Finally, we propose novel cancer-driver gene candidates that could be therapeutic targets for specific cancer types.
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Affiliation(s)
- Seunghwan Jung
- Department of Bio and Brain Engineering, KAIST, Daejeon 34141, Republic of Korea.
| | - Seunghyun Wang
- Department of Bio and Brain Engineering, KAIST, Daejeon 34141, Republic of Korea.
| | - Doheon Lee
- Department of Bio and Brain Engineering, KAIST, Daejeon 34141, Republic of Korea.
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5
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Pratt SJP, Plunkett CM, Kuzu G, Trinh T, Barbara J, Choconta P, Quackenbush D, Huynh T, Smith A, Barnes SW, New J, Pierce J, Walker JR, Mainquist J, King FJ, Elliott J, Hammack S, Decker RS. A high throughput cell stretch device for investigating mechanobiology in vitro. APL Bioeng 2024; 8:026129. [PMID: 38938688 PMCID: PMC11210978 DOI: 10.1063/5.0206852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Accepted: 05/31/2024] [Indexed: 06/29/2024] Open
Abstract
Mechanobiology is a rapidly advancing field, with growing evidence that mechanical signaling plays key roles in health and disease. To accelerate mechanobiology-based drug discovery, novel in vitro systems are needed that enable mechanical perturbation of cells in a format amenable to high throughput screening. Here, both a mechanical stretch device and 192-well silicone flexible linear stretch plate were designed and fabricated to meet high throughput technology needs for cell stretch-based applications. To demonstrate the utility of the stretch plate in automation and screening, cell dispensing, liquid handling, high content imaging, and high throughput sequencing platforms were employed. Using this system, an assay was developed as a biological validation and proof-of-concept readout for screening. A mechano-transcriptional stretch response was characterized using focused gene expression profiling measured by RNA-mediated oligonucleotide Annealing, Selection, and Ligation with Next-Gen sequencing. Using articular chondrocytes, a gene expression signature containing stretch responsive genes relevant to cartilage homeostasis and disease was identified. The possibility for integration of other stretch sensitive cell types (e.g., cardiovascular, airway, bladder, gut, and musculoskeletal), in combination with alternative phenotypic readouts (e.g., protein expression, proliferation, or spatial alignment), broadens the scope of high throughput stretch and allows for wider adoption by the research community. This high throughput mechanical stress device fills an unmet need in phenotypic screening technology to support drug discovery in mechanobiology-based disease areas.
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Affiliation(s)
- Stephen J. P. Pratt
- Novartis, Biomedical Research 10675 John Jay Hopkins Dr, San Diego, California 92121, USA
| | | | - Guray Kuzu
- Novartis, Biomedical Research 10675 John Jay Hopkins Dr, San Diego, California 92121, USA
| | - Ton Trinh
- Novartis, Biomedical Research 10675 John Jay Hopkins Dr, San Diego, California 92121, USA
| | - Joshua Barbara
- Novartis, Biomedical Research 10675 John Jay Hopkins Dr, San Diego, California 92121, USA
| | - Paula Choconta
- Novartis, Biomedical Research 10675 John Jay Hopkins Dr, San Diego, California 92121, USA
| | - Doug Quackenbush
- Novartis, Biomedical Research 10675 John Jay Hopkins Dr, San Diego, California 92121, USA
| | - Truc Huynh
- Novartis, Biomedical Research 10675 John Jay Hopkins Dr, San Diego, California 92121, USA
| | - Anders Smith
- Novartis, Biomedical Research 10675 John Jay Hopkins Dr, San Diego, California 92121, USA
| | - S. Whitney Barnes
- Novartis, Biomedical Research 10675 John Jay Hopkins Dr, San Diego, California 92121, USA
| | - Joel New
- Novartis, Biomedical Research 10675 John Jay Hopkins Dr, San Diego, California 92121, USA
| | - James Pierce
- Novartis, Biomedical Research 10675 John Jay Hopkins Dr, San Diego, California 92121, USA
| | - John R. Walker
- Novartis, Biomedical Research 10675 John Jay Hopkins Dr, San Diego, California 92121, USA
| | - James Mainquist
- Novartis, Biomedical Research 10675 John Jay Hopkins Dr, San Diego, California 92121, USA
| | - Frederick J. King
- Novartis, Biomedical Research 10675 John Jay Hopkins Dr, San Diego, California 92121, USA
| | - Jimmy Elliott
- Novartis, Biomedical Research 10675 John Jay Hopkins Dr, San Diego, California 92121, USA
| | - Scott Hammack
- Novartis, Biomedical Research 10675 John Jay Hopkins Dr, San Diego, California 92121, USA
| | - Rebekah S. Decker
- Novartis, Biomedical Research 10675 John Jay Hopkins Dr, San Diego, California 92121, USA
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6
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Winnicki MJ, Brown CA, Porter HL, Giles CB, Wren JD. BioVDB: biological vector database for high-throughput gene expression meta-analysis. Front Artif Intell 2024; 7:1366273. [PMID: 38525301 PMCID: PMC10957786 DOI: 10.3389/frai.2024.1366273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2024] [Accepted: 02/26/2024] [Indexed: 03/26/2024] Open
Abstract
High-throughput sequencing has created an exponential increase in the amount of gene expression data, much of which is freely, publicly available in repositories such as NCBI's Gene Expression Omnibus (GEO). Querying this data for patterns such as similarity and distance, however, becomes increasingly challenging as the total amount of data increases. Furthermore, vectorization of the data is commonly required in Artificial Intelligence and Machine Learning (AI/ML) approaches. We present BioVDB, a vector database for storage and analysis of gene expression data, which enhances the potential for integrating biological studies with AI/ML tools. We used a previously developed approach called Automatic Label Extraction (ALE) to extract sample labels from metadata, including age, sex, and tissue/cell-line. BioVDB stores 438,562 samples from eight microarray GEO platforms. We show that it allows for efficient querying of data using similarity search, which can also be useful for identifying and inferring missing labels of samples, and for rapid similarity analysis.
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Affiliation(s)
- Michał J. Winnicki
- Genes and Human Disease Research Program, Oklahoma Medical Research Foundation, Oklahoma City, OK, United States
| | - Chase A. Brown
- Genes and Human Disease Research Program, Oklahoma Medical Research Foundation, Oklahoma City, OK, United States
- Oklahoma Center for Neuroscience, University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States
| | - Hunter L. Porter
- Genes and Human Disease Research Program, Oklahoma Medical Research Foundation, Oklahoma City, OK, United States
| | - Cory B. Giles
- Genes and Human Disease Research Program, Oklahoma Medical Research Foundation, Oklahoma City, OK, United States
| | - Jonathan D. Wren
- Genes and Human Disease Research Program, Oklahoma Medical Research Foundation, Oklahoma City, OK, United States
- Oklahoma Center for Neuroscience, University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States
- Department of Biochemistry and Molecular Biology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States
- Oklahoma Nathan Shock Center, Oklahoma City, OK, United States
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7
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Mitchell JM, Chi Y, Thapa M, Pang Z, Xia J, Li S. Common data models to streamline metabolomics processing and annotation, and implementation in a Python pipeline. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.13.580048. [PMID: 38405981 PMCID: PMC10888883 DOI: 10.1101/2024.02.13.580048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
To standardize metabolomics data analysis and facilitate future computational developments, it is essential is have a set of well-defined templates for common data structures. Here we describe a collection of data structures involved in metabolomics data processing and illustrate how they are utilized in a full-featured Python-centric pipeline. We demonstrate the performance of the pipeline, and the details in annotation and quality control using large-scale LC-MS metabolomics and lipidomics data and LC-MS/MS data. Multiple previously published datasets are also reanalyzed to showcase its utility in biological data analysis. This pipeline allows users to streamline data processing, quality control, annotation, and standardization in an efficient and transparent manner. This work fills a major gap in the Python ecosystem for computational metabolomics.
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Affiliation(s)
- Joshua M. Mitchell
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT 06032, USA
| | - Yuanye Chi
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT 06032, USA
| | - Maheshwor Thapa
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT 06032, USA
| | - Zhiqiang Pang
- Institute of Parasitology, McGill University, Montreal, Quebec, Canada
| | - Jianguo Xia
- Institute of Parasitology, McGill University, Montreal, Quebec, Canada
| | - Shuzhao Li
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT 06032, USA
- University of Connecticut School of Medicine, Farmington, CT 06032, USA
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8
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Flanagan T, Foster TP, Galbato TE, Lum PY, Louie B, Song G, Halberstadt AL, Billac GB, Nichols CD. Serotonin-2 Receptor Agonists Produce Anti-inflammatory Effects through Functionally Selective Mechanisms That Involve the Suppression of Disease-Induced Arginase 1 Expression. ACS Pharmacol Transl Sci 2024; 7:478-492. [PMID: 38357283 PMCID: PMC10863441 DOI: 10.1021/acsptsci.3c00297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 01/11/2024] [Accepted: 01/12/2024] [Indexed: 02/16/2024]
Abstract
Functional selectivity in the context of serotonin 2A (5-HT2A) receptor agonists is often described as differences psychedelic compounds have in the activation of Gq vs β-arrestin signaling in the brain and how that may relate to inducing psychoactive and hallucinatory properties with respect to each other. However, the presence of 5-HT2A receptors throughout the body in several cell types, including endothelial, endocrine, and immune-related tissues, suggests that functional selectivity may exist in the periphery as well. Here, we examine functional selectivity between two 5-HT2A receptor agonists of the phenylalkylamine class: (R)-2,5-dimethoxy-4-iodoamphetamine [(R)-DOI] and (R)-2,5-dimethoxy-4-trifluoromethylamphetamine [(R)-DOTFM]. Despite comparable in vitro activity at the 5-HT2A receptor as well as similar behavioral potency, (R)-DOTFM does not exhibit an ability to prevent inflammation or elevated airway hyperresponsiveness (AHR) in an acute murine ovalbumin-induced asthma model as does (R)-DOI. Furthermore, there are distinct differences between protein expression and inflammatory-related gene expression in pulmonary tissues between the two compounds. Using (R)-DOI and (R)-DOTFM as tools, we further elucidated the anti-inflammatory mechanisms underlying the powerful anti-inflammatory effects of certain psychedelics and identified key mechanistic components of the anti-inflammatory effects of psychedelics, including suppression of arginase 1 expression.
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Affiliation(s)
- Thomas
W. Flanagan
- Department
of Pharmacology and Experimental TherapeuticsLouisiana State University Health Sciences CenterNew Orleans, Louisiana70112, United States
| | - Timothy P. Foster
- Department
of Microbiology, Immunology, and ParasitologyLouisiana State University Health Sciences CenterNew Orleans, Louisiana70112, United States
| | - Thomas E. Galbato
- Department
of Microbiology, Immunology, and ParasitologyLouisiana State University Health Sciences CenterNew Orleans, Louisiana70112, United States
| | - Pek Yee Lum
- Auransa
Inc.Palo Alto, California94301, United States
| | - Brent Louie
- Auransa
Inc.Palo Alto, California94301, United States
| | - Gavin Song
- Auransa
Inc.Palo Alto, California94301, United States
| | - Adam L. Halberstadt
- Department
of PsychiatryUniversity of San Diego, California, San Diego, California92093, United States
| | - Gerald B. Billac
- Department
of Pharmacology and Experimental TherapeuticsLouisiana State University Health Sciences CenterNew Orleans, Louisiana70112, United States
| | - Charles D. Nichols
- Department
of Pharmacology and Experimental TherapeuticsLouisiana State University Health Sciences CenterNew Orleans, Louisiana70112, United States
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VanBuren R, Nguyen A, Marks RA, Mercado C, Pardo A, Pardo J, Schuster J, Aubin BS, Wilson ML, Rhee SY. Variability in drought gene expression datasets highlight the need for community standardization. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.04.578814. [PMID: 38370805 PMCID: PMC10871248 DOI: 10.1101/2024.02.04.578814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Physiologically relevant drought stress is difficult to apply consistently, and the heterogeneity in experimental design, growth conditions, and sampling schemes make it challenging to compare water deficit studies in plants. Here, we re-analyzed hundreds of drought gene expression experiments across diverse model and crop species and quantified the variability across studies. We found that drought studies are surprisingly uncomparable, even when accounting for differences in genotype, environment, drought severity, and method of drying. Many studies, including most Arabidopsis work, lack high-quality phenotypic and physiological datasets to accompany gene expression, making it impossible to assess the severity or in some cases the occurrence of water deficit stress events. From these datasets, we developed supervised learning classifiers that can accurately predict if RNA-seq samples have experienced a physiologically relevant drought stress, and suggest this can be used as a quality control for future studies. Together, our analyses highlight the need for more community standardization, and the importance of paired physiology data to quantify stress severity for reproducibility and future data analyses.
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10
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Salimi Y, Domingo-Fernández D, Hofmann-Apitius M, Birkenbihl C. Data-Driven Thresholding Statistically Biases ATN Profiling across Cohort Datasets. J Prev Alzheimers Dis 2024; 11:185-195. [PMID: 38230732 PMCID: PMC10995057 DOI: 10.14283/jpad.2023.100] [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: 05/19/2023] [Accepted: 07/02/2023] [Indexed: 01/18/2024]
Abstract
BACKGROUND While the amyloid/tau/neurodegeneration (ATN) framework has found wide application in Alzheimer's disease research, it is unclear if thresholds obtained using distinct thresholding methods are concordant within the same dataset and interchangeable across cohorts. OBJECTIVES To investigate the robustness of data-driven thresholding methods and ATN profiling across cohort datasets. DESIGN AND SETTING We evaluated the impact of thresholding methods on ATN profiles by applying five commonly-used methodologies across cohort datasets. We assessed the generalizability of disease patterns discovered within ATN profiles by clustering individuals from different cohorts who were assigned to the same ATN profile. PARTICIPANTS AND MEASUREMENTS Participants with available CSF amyloid-β 1-42, phosphorylated tau, and total tau measurements were included from eleven AD cohort studies. RESULTS We observed high variability among obtained ATN thresholds, both across methods and datasets that impacted the resulting profile assignments of participants significantly. Clustering participants from different cohorts within the same ATN category indicated that identified disease patterns were comparable across most cohorts and biases introduced through distinct thresholding and data representations remained insignificant in most ATN profiles. CONLUSION Thresholding method selection is a decision of statistical relevance that will inevitably bias the resulting profiling and affect its sensitivity and specificity. Thresholds are likely not directly interchangeable between independent cohorts. To apply the ATN framework as an actionable and robust profiling scheme, a comprehensive understanding of the impact of used thresholding methods, their statistical implications, and a validation of results is crucial.
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Affiliation(s)
- Y. Salimi
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, 53757 Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, 53115 Germany
- Schloß Birlinghoven, Sankt Augustin, 53757 Germany
| | - D. Domingo-Fernández
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, 53757 Germany
| | - M. Hofmann-Apitius
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, 53757 Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, 53115 Germany
| | - C. Birkenbihl
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, 53757 Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, 53115 Germany
- Schloß Birlinghoven, Sankt Augustin, 53757 Germany
| | - the Alzheimer’s Disease Neuroimaging Initiative
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, 53757 Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, 53115 Germany
- Schloß Birlinghoven, Sankt Augustin, 53757 Germany
| | - the Japanese Alzheimer’s Disease Neuroimaging Initiative
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, 53757 Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, 53115 Germany
- Schloß Birlinghoven, Sankt Augustin, 53757 Germany
| | - the Alzheimer’s Disease Repository Without Borders Investigators
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, 53757 Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, 53115 Germany
- Schloß Birlinghoven, Sankt Augustin, 53757 Germany
| | - the European Prevention of Alzheimer’s Disease (EPAD) Consortium
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, 53757 Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, 53115 Germany
- Schloß Birlinghoven, Sankt Augustin, 53757 Germany
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11
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Zille M, Oses-Prieto JA, Savage SR, Karuppagounder SS, Chen Y, Kumar A, Morris JH, Scheidt KA, Burlingame AL, Ratan RR. Hemin-Induced Death Models Hemorrhagic Stroke and Is a Variant of Classical Neuronal Ferroptosis. J Neurosci 2022; 42:2065-2079. [PMID: 34987108 PMCID: PMC8916756 DOI: 10.1523/jneurosci.0923-20.2021] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2020] [Revised: 11/12/2021] [Accepted: 12/02/2021] [Indexed: 11/21/2022] Open
Abstract
Ferroptosis is a caspase-independent, iron-dependent form of regulated necrosis extant in traumatic brain injury, Huntington disease, and hemorrhagic stroke. It can be activated by cystine deprivation leading to glutathione depletion, the insufficiency of the antioxidant glutathione peroxidase-4, and the hemolysis products hemoglobin and hemin. A cardinal feature of ferroptosis is extracellular signal-regulated kinase (ERK)1/2 activation culminating in its translocation to the nucleus. We have previously confirmed that the mitogen-activated protein (MAP) kinase kinase (MEK) inhibitor U0126 inhibits persistent ERK1/2 phosphorylation and ferroptosis. Here, we show that hemin exposure, a model of secondary injury in brain hemorrhage and ferroptosis, activated ERK1/2 in mouse neurons. Accordingly, MEK inhibitor U0126 protected against hemin-induced ferroptosis. Unexpectedly, U0126 prevented hemin-induced ferroptosis independent of its ability to inhibit ERK1/2 signaling. In contrast to classical ferroptosis in neurons or cancer cells, chemically diverse inhibitors of MEK did not block hemin-induced ferroptosis, nor did the forced expression of the ERK-selective MAP kinase phosphatase (MKP)3. We conclude that hemin or hemoglobin-induced ferroptosis, unlike glutathione depletion, is ERK1/2-independent. Together with recent studies, our findings suggest the existence of a novel subtype of neuronal ferroptosis relevant to bleeding in the brain that is 5-lipoxygenase-dependent, ERK-independent, and transcription-independent. Remarkably, our unbiased phosphoproteome analysis revealed dramatic differences in phosphorylation induced by two ferroptosis subtypes. As U0126 also reduced cell death and improved functional recovery after hemorrhagic stroke in male mice, our analysis also provides a template on which to build a search for U0126's effects in a variant of neuronal ferroptosis.SIGNIFICANCE STATEMENT Ferroptosis is an iron-dependent mechanism of regulated necrosis that has been linked to hemorrhagic stroke. Common features of ferroptotic death induced by diverse stimuli are the depletion of the antioxidant glutathione, production of lipoxygenase-dependent reactive lipids, sensitivity to iron chelation, and persistent activation of extracellular signal-regulated kinase (ERK) signaling. Unlike classical ferroptosis induced in neurons or cancer cells, here we show that ferroptosis induced by hemin is ERK-independent. Paradoxically, the canonical MAP kinase kinase (MEK) inhibitor U0126 blocks brain hemorrhage-induced death. Altogether, these data suggest that a variant of ferroptosis is unleashed in hemorrhagic stroke. We present the first, unbiased phosphoproteomic analysis of ferroptosis as a template on which to understand distinct paths to cell death that meet the definition of ferroptosis.
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Affiliation(s)
- Marietta Zille
- Burke Neurological Institute, White Plains, New York 10605
- Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, New York 10065
- Department of Pharmaceutical Sciences, Division of Pharmacology and Toxicology, University of Vienna, Vienna 1090, Austria
| | - Juan A Oses-Prieto
- Department of Pharmaceutical Chemistry, University of California, San Francisco, California 94158
| | - Sara R Savage
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, Texas 77030
| | - Saravanan S Karuppagounder
- Burke Neurological Institute, White Plains, New York 10605
- Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, New York 10065
| | - Yingxin Chen
- Burke Neurological Institute, White Plains, New York 10605
- Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, New York 10065
| | - Amit Kumar
- Burke Neurological Institute, White Plains, New York 10605
- Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, New York 10065
| | - John H Morris
- Resource on Biocomputing, Visualization, and Informatics, University of California, San Francisco, California 94158
| | - Karl A Scheidt
- Department of Chemistry, Center for Molecular Innovation and Drug Discovery, Northwestern University, Evanston, Illinois 60208
| | - Alma L Burlingame
- Department of Pharmaceutical Chemistry, University of California, San Francisco, California 94158
| | - Rajiv R Ratan
- Burke Neurological Institute, White Plains, New York 10605
- Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, New York 10065
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