Article
Biochemistry & Molecular Biology
Belen Callejon-Leblic, Saida Sanchez Espirilla, Carolina Gotera-Rivera, Rafael Santana, Isabel Diaz-Olivares, Jose M. Marin, Ciro Casanova Macario, Borja Garcia Cosio, Antonia Fuster, Ingrid Solanes Garcia, Juan P. de-Torres, Nuria Feu Collado, Carlos Cabrera Lopez, Carlos Amado Diago, Amparo Romero Plaza, Luis Alejandro Padron Fraysse, Eduardo Marquez Martin, Margarita Marin Royo, Eva Balcells Vilarnau, Antonia Llunell Casanovas, Cristina Martinez Gonzalez, Juan Bautista Galdiz Iturri, Celia Lacarcel Bautista, Jose Luis Gomez-Ariza, Antonio Pereira-Vega, Luis Seijo, Jose Luis Lopez-Campos, German Peces-Barba, Tamara Garcia-Barrera
Summary: This study found that the severity of chronic obstructive pulmonary disease (COPD) and lung cancer (LC) significantly impact the elemental composition of human serum. These findings may pave the way for the potential use of elements as biomarkers for the diagnosis and prognosis of these diseases.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2023)
Article
Computer Science, Interdisciplinary Applications
Xuchun Wang, Hao Ren, Jiahui Ren, Wenzhu Song, Yuchao Qiao, Zeping Ren, Ying Zhao, Liqin Linghu, Yu Cui, Zhiyang Zhao, Limin Chen, Lixia Qiu
Summary: This study constructed a risk prediction model for chronic obstructive pulmonary disease (COPD) using machine learning methods to improve its prediction efficiency. The results showed that frequent coughing before the age of 14 and 9 other variables were important parameters for COPD. In addition, by performing feature selection and balancing data processing, machine learning models could automatically identify patients at risk of COPD, providing a simple and scientific approach for early identification of COPD.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2023)
Article
Biochemistry & Molecular Biology
Thilo Bracht, Daniel Kleefisch, Karin Schork, Kathrin E. Witzke, Weiqiang Chen, Malte Bayer, Jan Hovanec, Georg Johnen, Swetlana Meier, Yon-Dschun Ko, Thomas Behrens, Thomas Bruning, Jana Fassunke, Reinhard Buettner, Julian Uszkoreit, Michael Adamzik, Martin Eisenacher, Barbara Sitek
Summary: Chronic obstructive pulmonary disease (COPD) is a major risk factor for the development of lung adenocarcinoma (AC). In this study, mass-spectrometry-based proteomics analysis and machine learning algorithms were used to identify potential biomarkers for differentiation between AC and COPD. The results showed promising performance for distinguishing AC from COPD and AC with COPD from COPD.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2022)
Article
Biochemical Research Methods
Ryan M. Riley, Sandra E. Spencer Miko, Ryan D. Morin, Gregg B. Morin, Gian Luca Negri
Summary: PeptideRanger is a tool that efficiently detects and quantifies low abundance proteins by identifying peptides with suitable physiochemical properties for mass spectrometry analysis. It is a flexible and extensively annotated R package that can be customized to prioritize and filter peptides based on selected properties.
JOURNAL OF PROTEOME RESEARCH
(2023)
Article
Biochemical Research Methods
Ryan M. Riley, Sandra E. Spencer Miko, Ryan D. Morin, Gregg B. Morin, Gian Luca Negri
Summary: Targeted and semitargeted mass spectrometry-based approaches are reliable methods to consistently detect and quantify low abundance proteins of clinical significance. However, their development is time-consuming and often requires costly libraries of synthetic peptides. To address this, we developed PeptideRanger, an R package that identifies peptides from proteins of interest with physiochemical properties suitable for mass spectrometry analysis.
JOURNAL OF PROTEOME RESEARCH
(2023)
Article
Biochemistry & Molecular Biology
Nicolai Bjodstrup Palstrom, Aleksandra M. Rojek, Hanne E. H. Moller, Charlotte Toftmann Hansen, Rune Matthiesen, Lars Melholt Rasmussen, Niels Abildgaard, Hans Christian Beck
Summary: Amyloidosis is a rare disease caused by the misfolding and aggregation of proteins, and accurate identification of the specific proteins is crucial for treatment choice. Mass spectrometry-based proteomics has become the preferred method for identifying the amyloidogenic protein. However, manual interpretation of the data by an expert can introduce bias. To address this, a statistical model-assisted method was developed to identify amyloid-containing biopsies and classify amyloidosis subtypes. This method successfully identified novel amyloid-associated proteins and demonstrated the unbiased and reliable classification of amyloid deposits and subtype using mass spectrometry-based data.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2022)
Article
Respiratory System
Leeran Talker, Daniel Neville, Laura Wiffen, Ahmed Selim, Matthew Haines, Julian Carter, Henry Broomfield, Rui Hen Lim, Gabriel T. Lambert, Scott Weiss, Gail Hayward, Thomas X. Brown, Anoop Chauhan, Ameera Patel
Summary: This study applied feature engineering and machine learning techniques to build a classifier that could distinguish COPD patients from non-COPD patients using capnography data.
RESPIRATORY RESEARCH
(2023)
Article
Biochemistry & Molecular Biology
Alexey S. Kononikhin, Natalia Zakharova, Savva D. Semenov, Anna E. Bugrova, Alexander G. Brzhozovskiy, Maria I. Indeykina, Yana B. Fedorova, Igor Kolykhalov, Polina A. Strelnikova, Anna Yu Ikonnikova, Dmitry A. Gryadunov, Svetlana Gavrilova, Evgeny N. Nikolaev
Summary: Early recognition of Alzheimer's disease onset is a global challenge. This study used proteomic analysis to confirm a panel of consistent protein markers associated with AD, providing a potential method for reliable screening.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2022)
Article
Chemistry, Multidisciplinary
Mireia Farrus, Joan Codina-Filba, Elisenda Reixach, Erik Andres, Mireia Sans, Noemi Garcia, Josep Vilaseca
Summary: The study investigates how physical effort and medication affect speech features of COPD patients, and how different recording conditions influence the performance of an automatic COPD detection system.
APPLIED SCIENCES-BASEL
(2021)
Article
Multidisciplinary Sciences
Shadi Ferdosi, Behzad Tangeysh, Tristan R. Brown, Patrick A. Everley, Michael Figa, Matthew McLean, Eltaher M. Elgierari, Xiaoyan Zhao, Veder J. Garcia, Tianyu Wang, Matthew E. K. Chang, Kateryna Riedesel, Jessica Chu, Max Mahoney, Hongwei Xia, Evan S. O'Brien, Craig Stolarczyk, Damian Harris, Theodore L. Platt, Philip Ma, Martin Goldberg, Robert Langer, Mark R. Flory, Ryan Benz, Wei Tao, Juan Cruz Cuevas, Serafim Batzoglou, John E. Blume, Asim Siddiqui, Daniel Hornburg, Omid C. Farokhzad
Summary: Exploring plasma proteins on a large scale is challenging, but a multi-nanoparticle workflow proves superior to conventional methods in terms of depth and precision. The physicochemical properties and surface functionalization of nanoparticles are found to affect their selectivity for specific proteins, presenting new possibilities for designing multi-nanoparticle panels in complex biological sample analysis.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2022)
Article
Computer Science, Information Systems
Chunlei Tang, Joseph M. Plasek, Xiao Shi, Meihan Wan, Haohan Zhang, Min-Jeoung Kang, Liqin Wang, Sevan M. Dulgarian, Yun Xiong, Jing Ma, David W. Bates, Li Zhou
Summary: This study predicts mortality risk in patients with chronic obstructive pulmonary disease using clinical notes, optimizing the accuracy of linear regression and support vector machines by determining a tolerance range. The results demonstrate an overall improvement in machine learning approaches after considering the optimal tolerance range.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
(2021)
Article
Health Care Sciences & Services
Siyang Zeng, Mehrdad Arjomandi, Yao Tong, Zachary C. Liao, Gang Luo
Summary: This study developed a more accurate model to predict severe COPD exacerbations. The model showed high accuracy and can help identify high-risk COPD patients for preventive care management, thereby improving outcomes.
JOURNAL OF MEDICAL INTERNET RESEARCH
(2022)
Article
Biochemical Research Methods
Heather Desaire, Milani Wijeweera Patabandige, David Hua
Summary: This study addresses the challenge of signal variability in mass spectrometry data and introduces a new statistical approach, the local-balanced model, to classify samples. By utilizing balanced subsets of training data to classify test samples, this model shows potential for generalizability across multiple mass spectrometry domains and can significantly improve classification accuracy compared to simple normalization methods.
ANALYTICAL AND BIOANALYTICAL CHEMISTRY
(2021)
Article
Biochemical Research Methods
David Hua, Heather Desaire
Summary: Mass spectrometry data sets from omics studies are valuable for identifying disease states and biomarkers, but challenges like missing entries and limited sample sizes make effective data utilization difficult. The Aristotle Classifier has been modified to better leverage omics data for disease identification, outperforming existing tools like SVM and XGBoost on proteomics data tests.
JOURNAL OF PROTEOME RESEARCH
(2021)
Article
Biochemistry & Molecular Biology
Shuang Bai, Rui Ye, Cuihong Wang, Pengbo Sun, Di Wang, Yong Yue, Huiying Wang, Si Wu, Miao Yu, Shuhua Xi, Li Zhao
Summary: This study identified differential protein expression in COPD patients with emphysematous phenotype, indicating that KRT17 and DHRS9 are potentially involved in wound healing and retinol metabolism pathways. GO and KEGG analyses highlighted the importance of these pathways in the molecular mechanism of COPD emphysematous phenotype.
FRONTIERS IN MOLECULAR BIOSCIENCES
(2021)
Article
Respiratory System
Sanghun Lee, Dmitry Prokopenko, Rachel S. Kelly, Sharon Lutz, Jessica Ann Lasky-Su, Michael H. Cho, Cecelia Laurie, Juan C. Celedon, Christoph Lange, Scott T. Weiss, Julian Hecker, Dawn L. DeMeo
Summary: This study used whole-genome sequencing data to analyze the sex differences related to immune responses in childhood asthma. They found a SNP on 10q11.21 that showed a significant gene-by-sex interaction for atopy, with opposite effect directions in females and males. Furthermore, gene expression of zinc finger protein 33B (ZNF33B) was moderately associated with atopy in girls. These findings suggest the presence of sex differences in childhood asthma atopy and highlight the potential role of the SNP on 10q11.21 and ZNF33B in driving these differences.
EUROPEAN RESPIRATORY JOURNAL
(2023)
Article
Respiratory System
Ming Chen, Yiliang Zhang, Taylor Adams, Dingjue Ji, Wei Jiang, Louise Wain, Michael Cho, Naftali Kaminski, Hongyu Zhao
Summary: Our study identified new genes associated with IPF susceptibility through integrative analysis, expanding the understanding of the complex genetic architecture and disease mechanism of IPF.
Editorial Material
Critical Care Medicine
Sean Kalra, Michael H. Cho
AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE
(2023)
Letter
Critical Care Medicine
Akinori Hata, Takuya Hino, Yi Li, Takeshi Johkoh, David C. Christiani, David A. Lynch, Michael H. Cho, Edwin K. Silverman, Gary M. Hunninghake, Hiroto Hatabu
AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE
(2023)
Letter
Critical Care Medicine
Auyon J. Ghosh, Matthew Moll, Brian D. Hobbs, Jonathan Cardwell, Aabida Saferali, Peter J. Castaldi, Michael H. Cho, Edwin K. Silverman, Ivana V. Yang, Craig P. Hersh
AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE
(2023)
Article
Biochemistry & Molecular Biology
Yu-Hang Zhang, Michael H. Cho, Jarrett D. Morrow, Peter J. Castaldi, Craig P. Hersh, Mukul K. Midha, Michael R. Hoopmann, Sharon M. Lutz, Robert L. Moritz, Edwin K. Silverman
Summary: The integration of lung tissue transcriptomic and proteomic data with COPD-associated genetic variants provides insight into the biological mechanisms of COPD. Low correlations were observed between transcriptomics and proteomics, but higher correlations were found for COPD-associated proteins. Regulatory cis-QTLs were identified through the integration of COPD risk SNPs or SNPs near COPD-associated proteins with lung transcripts and proteins. Multiple COPD-associated biomarkers were found to be regulated by significant expression QTLs (eQTLs) and protein QTLs (pQTLs). Colocalization analysis, mediation analysis, and correlation-based network analysis identified key genes and proteins working together to influence COPD pathogenesis.
AMERICAN JOURNAL OF RESPIRATORY CELL AND MOLECULAR BIOLOGY
(2023)
Article
Biochemical Research Methods
Mukul K. Midha, Charu Kapil, Michal Maes, David H. Baxter, Seamus R. Morrone, Timothy J. Prokop, Robert L. Moritz
Summary: The efficient generation of peptide molecular ions by the electrospray source is the major contribution to ion detectability in liquid chromatography-driven mass spectrometry-based proteomics. The newly designed vacuum insulated probe heated electrospray ionization (VIP-HESI) source coupled with a Bruker timsTOF PRO mass spectrometer in microspray mode demonstrates superior performance. VIP-HESI significantly improves chromatography signals compared to electrospray ionization (ESI) and nanospray ionization, providing increased protein detection with higher quantitative precision and enhancing reproducibility of sample injection amounts.
JOURNAL OF PROTEOME RESEARCH
(2023)
Article
Genetics & Heredity
Justin Cosentino, Babak Behsaz, Babak Alipanahi, Zachary R. McCaw, Davin Hill, Tae-Hwi Schwantes-An, Dongbing Lai, Andrew Carroll, Brian D. Hobbs, Michael H. Cho, Cory Y. McLean, Farhad Hormozdiari
Summary: A deep convolutional neural network is utilized to predict COPD case-control status using raw spirograms and noisy medical-record-based labels. The machine-learning-based liability score accurately distinguishes COPD cases and controls, predicts COPD-related hospitalization, and is associated with overall survival and exacerbation events. The genome-wide association study on the liability score replicates known COPD and lung function loci and identifies new loci.
Article
Multidisciplinary Sciences
Daniel K. Carlin, Simon Larsen, Vikram Sirupurapu, Michael Cho, Edwin Silverman, Jan Baumbach, Trey Ideker
Summary: Many disease-causing genetic variants converge on common biological functions and pathways, but how to incorporate pathway knowledge in genetic association studies is still unclear. Previous approaches employ a two-step method, while a concise one-step approach called Hierarchical Genetic Analysis (Higana) directly computes phenotype associations against each function in the large hierarchy of biological functions documented by the Gene Ontology. Using Higana, risk genes and functions for Chronic Obstructive Pulmonary Disease (COPD) were identified, including microtubule transport, muscle adaptation, and nicotine receptor signaling pathways, which provide new insights into COPD.
Article
Biology
Jingxin Ren, Yuhang Zhang, Wei Guo, Kaiyan Feng, Ye Yuan, Tao Huang, Yu-Dong Cai
Summary: COVID-19 can cause impairment of smell and taste, and this study used machine learning to analyze gene expression levels in COVID-19 patient samples to identify important biomarkers associated with this loss of sensory ability. The study suggests potential mechanisms for COVID-19 complications and provides biomarkers for predicting olfactory and gustatory impairment.
Article
Multidisciplinary Sciences
Ulrike Kusebauch, Alan P. R. Lorenzetti, David S. Campbell, Min Pan, David Shteynberg, Charu Kapil, Mukul K. Midha, Adrian Lopez Garcia de Lomana, Nitin S. Baliga, Robert L. Moritz
Summary: Researchers have reported a comprehensive spectral assay library for the extreme halophilic archaeon Halobacterium salinarum NRC-1, which enables the measurement of a large fraction of its proteome. This library provides a valuable resource for confidently measuring and quantifying any protein of this archaeon.
Article
Biology
Kengo Watanabe, Tomasz Wilmanski, Priyanka Baloni, Max Robinson, Gonzalo G. Garcia, Michael R. Hoopmann, Mukul K. Midha, David H. Baxter, Michal Maes, Seamus R. Morrone, Kelly M. Crebs, Charu Kapil, Ulrike Kusebauch, Jack Wiedrick, Jodi Lapidus, Lance Pflieger, Christopher Lausted, Jared C. Roach, Gwenlyn Glusman, Steven R. Cummings, Nicholas J. Schork, Nathan D. Price, Leroy Hood, Richard A. Miller, Robert L. Moritz, Noa Rappaport
Summary: This study investigates the molecular regulation of biological processes in the liver under different lifespan-extending interventions. The results show that these interventions generally tighten the regulation of biological modules, particularly in fatty acid oxidation, immune response, and stress response. Differences between proteins and transcripts suggest a mechanism involving cap-independent translation. Integrated analysis also supports systemic shifts in fatty acid metabolism.
COMMUNICATIONS BIOLOGY
(2023)
Article
Biology
Yong Yang, Yuhang Zhang, Jingxin Ren, Kaiyan Feng, Zhandong Li, Tao Huang, Yudong Cai
Summary: This study analyzed single-cell RNA sequencing data from a normal colon to identify genetic markers of 25 immune cell types and reveal quantitative differences between them. Machine learning-based methods were used to analyze the importance of gene features and classify the most important genetic markers. The results provide a reference for exploring the cell composition of the colon cancer microenvironment and clinical immunotherapy.
Article
Geriatrics & Gerontology
Adam R. Burns, Jack Wiedrick, Alicia Feryn, Michal Maes, Mukul K. Midha, David H. Baxter, Seamus R. Morrone, Timothy J. Prokop, Charu Kapil, Michael R. Hoopmann, Ulrike Kusebauch, Eric W. Deutsch, Noa Rappaport, Kengo Watanabe, Robert L. Moritz, Richard A. Miller, Jodi A. Lapidus, Eric S. Orwoll
Summary: By studying the proteome changes induced by interventions known to increase mouse lifespan, we identified a set of proteins and biological mechanisms related to enhancing longevity. Although each intervention resulted in different protein changes, we found a group of proteins that responded to multiple interventions and were associated with peroxisomal oxidation and fatty acid metabolism.
Article
Biology
Kengo Watanabe, Tomasz Wilmanski, Priyanka Baloni, Max Robinson, Gonzalo G. Garcia, Michael R. Hoopmann, Mukul K. Midha, David H. Baxter, Michal Maes, Seamus R. Morrone, Kelly M. Crebs, Charu Kapil, Ulrike Kusebauch, Jack Wiedrick, Jodi Lapidus, Lance Pflieger, Christopher Lausted, Jared C. Roach, Gwenlyn Glusman, Steven R. Cummings, Nicholas J. Schork, Nathan D. Price, Leroy Hood, Richard A. Miller, Robert L. Moritz, Noa Rappaport
Summary: Aging is characterized by progressive deterioration in homeostasis, which requires a systems-level perspective to investigate the molecular dysregulation of underlying biological processes. This study examines the systemic changes in molecular regulation of biological processes under different lifespan-extending interventions. The findings suggest that these interventions generally tighten the regulation of biological modules, particularly in processes such as fatty acid oxidation, immune response, and stress response.
COMMUNICATIONS BIOLOGY
(2023)