Kaiyu Jiang1, Kerry E Poppenberg2, Laiping Wong3, Yanmin Chen4, Drucy Borowitz5, Danielle Goetz6, Daniel Sheehan7, Carla Frederick8, Vincent M Tutino9, Hui Meng10, James N Jarvis11. 1. Department of Pediatrics, Pediatric Rheumatology Research, University at Buffalo, Jacobs School of Medicine and Biomedical Sciences, Clinical and Translational Research Center, 875 Ellicott St, Buffalo, NY 14203, USA. Electronic address: kaiyujia@buffalo.edu. 2. Department of Biomedical Engineering, University at Buffalo, Jacobs School of Medicine and Biomedical Sciences, Clinical and Translational Research Center, 875 Ellicott St, Buffalo, NY 14203, USA. Electronic address: kerrypop@buffalo.edu. 3. Department of Pediatrics, Pediatric Rheumatology Research, University at Buffalo, Jacobs School of Medicine and Biomedical Sciences, Clinical and Translational Research Center, 875 Ellicott St, Buffalo, NY 14203, USA. Electronic address: laipingw@buffalo.edu. 4. Department of Pediatrics, Pediatric Rheumatology Research, University at Buffalo, Jacobs School of Medicine and Biomedical Sciences, Clinical and Translational Research Center, 875 Ellicott St, Buffalo, NY 14203, USA. Electronic address: yanminch@buffalo.edu. 5. Department of Pediatrics, Pulmonology Section, University at Buffalo, Women and Children's Hospital of Buffalo, 239 Bryant St, Buffalo, NY 14203, USA. Electronic address: borowitz@buffalo.edu. 6. Department of Pediatrics, Pulmonology Section, University at Buffalo, Women and Children's Hospital of Buffalo, 239 Bryant St, Buffalo, NY 14203, USA. Electronic address: dmd22@buffalo.edu. 7. Department of Pediatrics, Pulmonology Section, University at Buffalo, Women and Children's Hospital of Buffalo, 239 Bryant St, Buffalo, NY 14203, USA. Electronic address: dws9@buffalo.edu. 8. Department of Medicine, Section on Pulmonary, Critical Care, and Sleep Medicine, Buffalo General Medical Center Heart and Lung Center, 219 Bryant St./100 High St. B-8, Buffalo, NY 14222, USA. Electronic address: cfrederick@upa.chob.edu. 9. Department of Biomedical Engineering, University at Buffalo, Jacobs School of Medicine and Biomedical Sciences, Clinical and Translational Research Center, 875 Ellicott St, Buffalo, NY 14203, USA. Electronic address: vincentt@buffalo.edu. 10. Department of Biomedical Engineering, University at Buffalo, Jacobs School of Medicine and Biomedical Sciences, Clinical and Translational Research Center, 875 Ellicott St, Buffalo, NY 14203, USA. Electronic address: huimeng@buffalo.edu. 11. Department of Pediatrics, Pediatric Rheumatology Research, University at Buffalo, Jacobs School of Medicine and Biomedical Sciences, Clinical and Translational Research Center, 875 Ellicott St, Buffalo, NY 14203, USA. Electronic address: jamesjar@buffalo.edu.
Abstract
BACKGROUND: There is no effective way to predict cystic fibrosis (CF) pulmonary exacerbations (CFPE) before they become symptomatic or to assess satisfactory treatment responses. METHODS: RNA sequencing of peripheral blood neutrophils from CF patients before and after therapy for CFPE was used to create transcriptome profiles. Transcripts with an average transcripts per million (TPM) level > 1.0 and a false discovery rate (FDR) < 0.05 were used in a cosine K-nearest neighbor (KNN) model. Real time PCR was used to corroborate RNA sequencing expression differences in both neutrophils and whole blood samples from an independent cohort of CF patients. Furthermore, sandwich ELISA was conducted to assess plasma levels of MRP8/14 complexes in CF patients before and after therapy. RESULTS: We found differential expression of 136 transcripts and 83 isoforms when we compared neutrophils from CF patients before and after therapy (>1.5 fold change, FDR-adjusted P < 0.05). The model was able to successfully separate CF flare samples from those taken from the same patients in convalescence with an accuracy of 0.75 in both the training and testing cohorts. Six differently expressed genes were confirmed by real time PCR using both isolated neutrophils and whole blood from an independent cohort of CF patients before and after therapy, even though levels of myeloid related protein MRP8/14 dimers in plasma of CF patients were essentially unchanged by therapy. CONCLUSIONS: Our findings demonstrate the potential of machine learning approaches for classifying disease states and thus developing sensitive biomarkers that can be used to monitor pulmonary disease activity in CF.
BACKGROUND: There is no effective way to predict cystic fibrosis (CF) pulmonary exacerbations (CFPE) before they become symptomatic or to assess satisfactory treatment responses. METHODS: RNA sequencing of peripheral blood neutrophils from CFpatients before and after therapy for CFPE was used to create transcriptome profiles. Transcripts with an average transcripts per million (TPM) level > 1.0 and a false discovery rate (FDR) < 0.05 were used in a cosine K-nearest neighbor (KNN) model. Real time PCR was used to corroborate RNA sequencing expression differences in both neutrophils and whole blood samples from an independent cohort of CFpatients. Furthermore, sandwich ELISA was conducted to assess plasma levels of MRP8/14 complexes in CFpatients before and after therapy. RESULTS: We found differential expression of 136 transcripts and 83 isoforms when we compared neutrophils from CFpatients before and after therapy (>1.5 fold change, FDR-adjusted P < 0.05). The model was able to successfully separate CF flare samples from those taken from the same patients in convalescence with an accuracy of 0.75 in both the training and testing cohorts. Six differently expressed genes were confirmed by real time PCR using both isolated neutrophils and whole blood from an independent cohort of CFpatients before and after therapy, even though levels of myeloid related protein MRP8/14 dimers in plasma of CFpatients were essentially unchanged by therapy. CONCLUSIONS: Our findings demonstrate the potential of machine learning approaches for classifying disease states and thus developing sensitive biomarkers that can be used to monitor pulmonary disease activity in CF.
Authors: Benjamin T Kopp; James Fitch; Lisa Jaramillo; Chandra L Shrestha; Frank Robledo-Avila; Shuzhong Zhang; Sabrina Palacios; Fred Woodley; Don Hayes; Santiago Partida-Sanchez; Octavio Ramilo; Peter White; Asuncion Mejias Journal: J Cyst Fibros Date: 2019-08-29 Impact factor: 5.482
Authors: Frederick W Woodley; Emrah Gecili; Rhonda D Szczesniak; Chandra L Shrestha; Christopher J Nemastil; Benjamin T Kopp; Don Hayes Journal: Respir Med Date: 2021-11-23 Impact factor: 3.415
Authors: Joice de Faria Poloni; Thaiane Rispoli; Maria Lucia Rossetti; Cristiano Trindade; José Eduardo Vargas Journal: Biomed Res Int Date: 2021-12-02 Impact factor: 3.411
Authors: Xi Zhang; Camille M Moore; Laura D Harmacek; Joanne Domenico; Vittobai Rashika Rangaraj; Justin E Ideozu; Jennifer R Knapp; Katherine J Woods; Stephanie Jump; Shuang Jia; Jeremy W Prokop; Russell Bowler; Martin J Hessner; Erwin W Gelfand; Hara Levy Journal: JCI Insight Date: 2022-03-22