94 results on '"Matthew N. McCall"'
Search Results
2. Smad4 restricts injury-provoked biliary proliferation and carcinogenesis
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William B. Alexander, Wenjia Wang, Margaret A. Hill, Michael R. O'Dell, Luis I. Ruffolo, Bing Guo, Katherine M. Jackson, Nicholas Ullman, Scott C. Friedland, Matthew N. McCall, Ankit Patel, Nathania Figueroa-Guilliani, Mary Georger, Brian A. Belt, Christa L. Whitney-Miller, David C. Linehan, Patrick J. Murphy, and Aram F. Hezel
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cholangiocarcinoma ,biliary epithelium ,murine models of liver injury ,tgfβ/smad4 ,methylation ,Medicine ,Pathology ,RB1-214 - Published
- 2024
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3. Developmental Ethanol Exposure Impacts Purkinje Cells but Not Microglia in the Young Adult Cerebellum
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MaKenna Y. Cealie, James C. Douglas, Hannah K. Swan, Erik D. Vonkaenel, Matthew N. McCall, Paul D. Drew, and Ania K. Majewska
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microglia ,cerebellum ,Purkinje cell ,immune system ,ethanol ,fetal alcohol spectrum disorders (FASD) ,Cytology ,QH573-671 - Abstract
Fetal alcohol spectrum disorders (FASD) caused by developmental ethanol exposure lead to cerebellar impairments, including motor problems, decreased cerebellar weight, and cell death. Alterations in the sole output of the cerebellar cortex, Purkinje cells, and central nervous system immune cells, microglia, have been reported in animal models of FASD. To determine how developmental ethanol exposure affects adult cerebellar microglia and Purkinje cells, we used a human third-trimester binge exposure model in which mice received ethanol or saline from postnatal (P) days 4–9. In adolescence, cerebellar cranial windows were implanted and mice were aged to young adulthood for examination of microglia and Purkinje cells in vivo with two-photon imaging or in fixed tissue. Ethanol had no effect on microglia density, morphology, dynamics, or injury response. However, Purkinje cell linear frequency was reduced by ethanol. Microglia–Purkinje cell interactions in the Purkinje Cell Layer were altered in females compared to males. Overall, developmental ethanol exposure had few effects on cerebellar microglia in young adulthood and Purkinje cells appeared to be more susceptible to its effects.
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- 2024
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- View/download PDF
4. Correction: Multiple imputation and direct estimation for qPCR data with non-detects
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Valeriia Sherina, Helene R. McMurray, Winslow Powers, Harmut Land, Tanzy M. T. Love, and Matthew N. McCall
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Computer applications to medicine. Medical informatics ,R858-859.7 ,Biology (General) ,QH301-705.5 - Published
- 2024
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5. Developmental ethanol exposure has minimal impact on cerebellar microglial dynamics, morphology, and interactions with Purkinje cells during adolescence
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MaKenna Y. Cealie, James C. Douglas, Linh H. D. Le, Erik D. Vonkaenel, Matthew N. McCall, Paul D. Drew, and Ania K. Majewska
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microglia ,cerebellum ,Purkinje cell ,immune system ,ethanol ,fetal alcohol spectrum disorders (FASD) ,Neurosciences. Biological psychiatry. Neuropsychiatry ,RC321-571 - Abstract
IntroductionFetal alcohol spectrum disorders (FASD) are the most common cause of non-heritable, preventable mental disability, occurring in almost 5% of births in the United States. FASD lead to physical, behavioral, and cognitive impairments, including deficits related to the cerebellum. There is no known cure for FASD and their mechanisms remain poorly understood. To better understand these mechanisms, we examined the cerebellum on a cellular level by studying microglia, the principal immune cells of the central nervous system, and Purkinje cells, the sole output of the cerebellum. Both cell types have been shown to be affected in models of FASD, with increased cell death, immune activation of microglia, and altered firing in Purkinje cells. While ethanol administered in adulthood can acutely depress the dynamics of the microglial process arbor, it is unknown how developmental ethanol exposure impacts microglia dynamics and their interactions with Purkinje cells in the long term.MethodsTo address this question, we used a mouse model of human 3rd trimester exposure, whereby L7cre/Ai9+/−/Cx3cr1G/+ mice (with fluorescently labeled microglia and Purkinje cells) of both sexes were subcutaneously treated with a binge-level dose of ethanol (5.0 g/kg/day) or saline from postnatal days 4–9. Cranial windows were implanted in adolescent mice above the cerebellum to examine the long-term effects of developmental ethanol exposure on cerebellar microglia and Purkinje cell interactions using in vivo two-photon imaging.ResultsWe found that cerebellar microglia dynamics and morphology were not affected after developmental ethanol exposure. Microglia dynamics were also largely unaltered with respect to how they interact with Purkinje cells, although subtle changes in these interactions were observed in females in the molecular layer of the cerebellum.DiscussionThis work suggests that there are limited in vivo long-term effects of ethanol exposure on microglia morphology, dynamics, and neuronal interactions, so other avenues of research may be important in elucidating the mechanisms of FASD.
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- 2023
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6. Tissue, age, sex, and disease patterns of matrisome expression in GTEx transcriptome data
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Tim O. Nieuwenhuis, Avi Z. Rosenberg, Matthew N. McCall, and Marc K. Halushka
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Medicine ,Science - Abstract
Abstract The extracellular matrix (ECM) has historically been explored through proteomic methods. Whether or not global transcriptomics can yield meaningful information on the human matrisome is unknown. Gene expression data from 17,382 samples across 52 tissues, were obtained from the Genotype-Tissue Expression (GTEx) project. Additional datasets were obtained from The Cancer Genome Atlas (TCGA) program and the Gene Expression Omnibus for comparisons. Gene expression levels generally matched proteome-derived matrisome expression patterns. Further, matrisome gene expression properly clustered tissue types, with some matrisome genes including SERPIN family members having tissue-restricted expression patterns. Deeper analyses revealed 382 gene transcripts varied by age and 315 varied by sex in at least one tissue, with expression correlating with digitally imaged histologic tissue features. A comparison of TCGA tumor, TCGA adjacent normal and GTEx normal tissues demonstrated robustness of the GTEx samples as a generalized matrix control, while also determining a common primary tumor matrisome. Additionally, GTEx tissues served as a useful non-diseased control in a separate study of idiopathic pulmonary fibrosis (IPF) matrix changes, while identifying 22 matrix genes upregulated in IPF. Altogether, these findings indicate that the transcriptome, in general, and GTEx in particular, has value in understanding the state of organ ECM.
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- 2021
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7. Protocol to use TopNet for gene regulatory network modeling using gene expression data from perturbation experiments
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Helene R. McMurray, Harry A. Stern, Aslihan Ambeskovic, Hartmut Land, and Matthew N. McCall
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Bioinformatics ,Cell Biology ,Cell culture ,Cancer ,Genetics ,Molecular Biology ,Science (General) ,Q1-390 - Abstract
Summary: Inference of gene regulatory networks from gene perturbation experiments is the most reliable approach for investigating interdependence between genes. Here, we describe the initial gene perturbations, expression measurements, and preparation steps, followed by network modeling using TopNet. Summarization and visualization of the estimated networks and optional genetic testing of dependencies revealed by the network model are demonstrated. While developed for gene perturbation experiments, TopNet models data in which nodes are both perturbed and measured.For complete details on the use and execution of this protocol, please refer to McMurray et al. (2021). : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.
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- 2022
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8. The effect of air pollution on the transcriptomics of the immune response to respiratory infection
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Daniel P. Croft, David S. Burton, David J. Nagel, Soumyaroop Bhattacharya, Ann R. Falsey, Steve N. Georas, Philip K. Hopke, Carl J. Johnston, R. Matthew Kottmann, Augusto A. Litonjua, Thomas J. Mariani, David Q. Rich, Kelly Thevenet-Morrison, Sally W. Thurston, Mark J. Utell, and Matthew N. McCall
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Medicine ,Science - Abstract
Abstract Combustion related particulate matter air pollution (PM) is associated with an increased risk of respiratory infections in adults. The exact mechanism underlying this association has not been determined. We hypothesized that increased concentrations of combustion related PM would result in dysregulation of the innate immune system. This epidemiological study includes 111 adult patients hospitalized with respiratory infections who underwent transcriptional analysis of their peripheral blood. We examined the association between gene expression at the time of hospitalization and ambient measurements of particulate air pollutants in the 28 days prior to hospitalization. For each pollutant and time lag, gene-specific linear models adjusting for infection type were fit using LIMMA (Linear Models For Microarray Data), and pathway/gene set analyses were performed using the CAMERA (Correlation Adjusted Mean Rank) program. Comparing patients with viral and/or bacterial infection, the expression patterns associated with air pollution exposure differed. Adjusting for the type of infection, increased concentrations of Delta-C (a marker of biomass smoke) and other PM were associated with upregulation of iron homeostasis and protein folding. Increased concentrations of black carbon (BC) were associated with upregulation of viral related gene pathways and downregulation of pathways related to antigen presentation. The pollutant/pathway associations differed by lag time and by type of infection. This study suggests that the effect of air pollution on the pathogenesis of respiratory infection may be pollutant, timing, and infection specific.
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- 2021
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9. Single cell RNA-seq analysis of the flexor digitorum brevis mouse myofibers
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Rohan X. Verma, Suraj Kannan, Brian L. Lin, Katherine M. Fomchenko, Tim O. Nieuwenhuis, Arun H. Patil, Clarisse Lukban, Xiaoping Yang, Karen Fox-Talbot, Matthew N. McCall, Chulan Kwon, David A. Kass, Avi Z. Rosenberg, and Marc K. Halushka
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Single cell RNA-sequencing ,Skeletal muscle ,Twitch ,Fiber ,Diseases of the musculoskeletal system ,RC925-935 - Abstract
Abstract Background Skeletal muscle myofibers can be separated into functionally distinct cell types that differ in gene and protein expression. Current single cell expression data is generally based upon single nucleus RNA, rather than whole myofiber material. We examined if a whole-cell flow sorting approach could be applied to perform single cell RNA-seq (scRNA-seq) in a single muscle type. Methods We performed deep, whole cell, scRNA-seq on intact and fragmented skeletal myofibers from the mouse fast-twitch flexor digitorum brevis muscle utilizing a flow-gated method of large cell isolation. We performed deep sequencing of 763 intact and fragmented myofibers. Results Quality control metrics across the different gates indicated only 171 of these cells were optimal, with a median read count of 239,252 and an average of 12,098 transcripts per cell. scRNA-seq identified three clusters of myofibers (a slow/fast 2A cluster and two fast 2X clusters). Comparison to a public skeletal nuclear RNA-seq dataset demonstrated a diversity in transcript abundance by method. RISH validated multiple genes across fast and slow twitch skeletal muscle types. Conclusion This study introduces and validates a method to isolate intact skeletal muscle myofibers to generate deep expression patterns and expands the known repertoire of fiber-type-specific genes.
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- 2021
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10. Airway gene-expression classifiers for respiratory syncytial virus (RSV) disease severity in infants
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Lu Wang, Chin-Yi Chu, Matthew N. McCall, Christopher Slaunwhite, Jeanne Holden-Wiltse, Anthony Corbett, Ann R. Falsey, David J. Topham, Mary T. Caserta, Thomas J. Mariani, Edward E. Walsh, and Xing Qiu
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Respiratory syncytial virus ,Respiratory severity score ,Gene expression ,RNA-seq ,Classification ,Internal medicine ,RC31-1245 ,Genetics ,QH426-470 - Abstract
Abstract Background A substantial number of infants infected with RSV develop severe symptoms requiring hospitalization. We currently lack accurate biomarkers that are associated with severe illness. Method We defined airway gene expression profiles based on RNA sequencing from nasal brush samples from 106 full-tem previously healthy RSV infected subjects during acute infection (day 1–10 of illness) and convalescence stage (day 28 of illness). All subjects were assigned a clinical illness severity score (GRSS). Using AIC-based model selection, we built a sparse linear correlate of GRSS based on 41 genes (NGSS1). We also built an alternate model based upon 13 genes associated with severe infection acutely but displaying stable expression over time (NGSS2). Results NGSS1 is strongly correlated with the disease severity, demonstrating a naïve correlation (ρ) of ρ = 0.935 and cross-validated correlation of 0.813. As a binary classifier (mild versus severe), NGSS1 correctly classifies disease severity in 89.6% of the subjects following cross-validation. NGSS2 has slightly less, but comparable, accuracy with a cross-validated correlation of 0.741 and classification accuracy of 84.0%. Conclusion Airway gene expression patterns, obtained following a minimally-invasive procedure, have potential utility for development of clinically useful biomarkers that correlate with disease severity in primary RSV infection.
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- 2021
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11. Multiple imputation and direct estimation for qPCR data with non-detects
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Valeriia Sherina, Helene R. McMurray, Winslow Powers, Harmut Land, Tanzy M. T. Love, and Matthew N. McCall
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Gene expression ,Quantitative real-time PCR (qPCR) ,Missing not at random (MNAR) ,Non-detects ,Direct estimation ,Multiple imputation ,Computer applications to medicine. Medical informatics ,R858-859.7 ,Biology (General) ,QH301-705.5 - Abstract
Abstract Background Quantitative real-time PCR (qPCR) is one of the most widely used methods to measure gene expression. An important aspect of qPCR data that has been largely ignored is the presence of non-detects: reactions failing to exceed the quantification threshold and therefore lacking a measurement of expression. While most current software replaces these non-detects with a value representing the limit of detection, this introduces substantial bias in the estimation of both absolute and differential expression. Single imputation procedures, while an improvement on previously used methods, underestimate residual variance, which can lead to anti-conservative inference. Results We propose to treat non-detects as non-random missing data, model the missing data mechanism, and use this model to impute missing values or obtain direct estimates of model parameters. To account for the uncertainty inherent in the imputation, we propose a multiple imputation procedure, which provides a set of plausible values for each non-detect. We assess the proposed methods via simulation studies and demonstrate the applicability of these methods to three experimental data sets. We compare our methods to mean imputation, single imputation, and a penalized EM algorithm incorporating non-random missingness (PEMM). The developed methods are implemented in the R/Bioconductor package nondetects. Conclusions The statistical methods introduced here reduce discrepancies in gene expression values derived from qPCR experiments in the presence of non-detects, providing increased confidence in downstream analyses.
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- 2020
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12. Consistent RNA sequencing contamination in GTEx and other data sets
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Tim O. Nieuwenhuis, Stephanie Y. Yang, Rohan X. Verma, Vamsee Pillalamarri, Dan E. Arking, Avi Z. Rosenberg, Matthew N. McCall, and Marc K. Halushka
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Science - Abstract
Sample contamination has been reported in high throughput RNA sequencing. Here the authors analyze the RNA sequencing data from the Genotype-Tissue Expression project and describe how highly expressed, tissue specific genes contaminate across samples, which is corroborated in other data sets.
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- 2020
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13. Autoregressive modeling and diagnostics for qPCR amplification.
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Benjamin Hsu, Valeriia Sherina, and Matthew N. McCall
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- 2021
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14. The effect of tissue composition on gene co-expression.
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Yun Zhang 0021, Jonavelle Cuerdo, Marc K. Halushka, and Matthew N. McCall
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- 2021
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15. A systems genomics approach uncovers molecular associates of RSV severity.
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Matthew N. McCall, Chin-Yi Chu, Lu Wang, Lauren Benoodt, Juilee Thakar, Anthony Corbett, Jeanne Holden-Wiltse, Christopher Slaunwhite, Alex Grier, Steven R. Gill, Ann R. Falsey, David J. Topham, Mary T. Caserta, Edward E. Walsh, Xing Qiu, and Thomas J. Mariani
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- 2021
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16. Patterns of unwanted biological and technical expression variation across 49 human tissues
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Tim O. Nieuwenhuis, Hunter H. Giles, Matthew N. McCall, and Marc K. Halushka
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All tissue-based gene expression studies are impacted by biological and technical sources of variation. Numerous methods are used to normalize and batch correct these datasets. A more accurate understanding of all causes of variation could further optimize these approaches. We used 17,282 samples from 49 tissues in the Genotype Tissue Expression (GTEx) dataset (v8) to investigate patterns and causes of expression variation. Transcript expression was normalized to Z-scores and only the most variable 2% of transcripts were evaluated and clustered based on co-expression patterns. Clustered gene sets were solved to different biological or technical causes related to metadata elements and histologic images. We identified 522 variable transcript clusters (median 11 per tissue) across the samples. Of these, 64% were confidently explained, 15% were likely explained, 7% were low confidence explanations and 14% had no clear cause. Common causes included sex, sequencing contamination, immunoglobulin diversity, and compositional tissue differences. Less common biological causes included death interval (Hardy score), muscle atrophy, diabetes status, and menopause. Technical causes included brain pH and harvesting differences. Many of the causes of variation in bulk tissue expression were identifiable in the Tabula Sapiens dataset of single cell expression. This is the largest exploration of the underlying sources of tissue expression variation. It uncovered expected and unexpected causes of variable gene expression. These identified sources of variation will inform which metadata to acquire with tissue harvesting and can be used to improve normalization, batch correction, and analysis of both bulk and single cell RNA-seq data.
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- 2023
17. miRcomp-Shiny: Interactive assessment of qPCR-based microRNA quantification and quality control algorithms [version 1; referees: 3 approved with reservations]
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Lauren Kemperman and Matthew N. McCall
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Software Tool Article ,Articles ,Bioinformatics ,Genomics ,microRNA ,miRcomp ,qPCR ,benchmark data - Abstract
The miRcomp-Shiny web application allows interactive performance assessments and comparisons of qPCR-based microRNA expression and quality estimation methods using a benchmark data set. This work is motivated by two distinct use cases: (1) selection of methodology and quality thresholds for use analyzing one's own data, and (2) comparison of novel expression estimation algorithms with currently-available methodology. The miRcomp-Shiny application is implemented in the R/Shiny language and can be installed on any operating system on which R can be installed. It is made freely available as part of the miRcomp package (version 1.3.3 and later) available through the Bioconductor project at: http://bioconductor.org/packages/miRcomp. The web application is hosted at https://laurenkemperman.shinyapps.io/mircomp/. A detailed description of how to use the web application is available at: http://lkemperm.github.io/miRcomp_shiny_app
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- 2017
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18. A Model-Based Hierarchical Bayesian Approach to Sholl Analysis
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Erik Vonkaenel, Alexis Feidler, Rebecca Lowery, Katherine Andersh, Tanzy Love, Ania Majewska, and Matthew N Mccall
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Article - Abstract
SummaryDue to the link between microglial morphology and function, morphological changes in microglia are frequently used to identify pathological immune responses in the central nervous system. In the absence of pathology, microglia are responsible for maintaining homeostasis, and their morphology can be indicative of how the healthy brain behaves in the presence of external stimuli and genetic differences. Despite recent interest in high throughput methods for morphological analysis, Sholl analysis is still the gold standard for quantifying microglia morphology via imaging data. Often, the raw data are naturally hierarchical, minimally including many cells per image and many images per animal. However, existing methods for performing downstream inference on Sholl data rely on truncating this hierarchy so rudimentary statistical testing procedures can be used. To fill this longstanding gap, we introduce a fully parametric model-based approach for analyzing Sholl data. We generalize our model to a hierarchical Bayesian framework so that inference can be performed without aggressive reduction of otherwise very rich data. We apply our model to three real data examples and perform simulation studies comparing the proposed method with a popular alternative.
- Published
- 2023
19. The effect of air pollution on the transcriptomics of the immune response to respiratory infection
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D. Nagel, Kelly Thevenet-Morrison, Thomas J. Mariani, Sally W. Thurston, David Q. Rich, Mark J. Utell, Ann R. Falsey, Philip K. Hopke, R. Matthew Kottmann, David S. Burton, Augusto A. Litonjua, Steve N. Georas, Soumyaroop Bhattacharya, Matthew N. McCall, Carl J. Johnston, and Daniel P. Croft
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Adult ,Male ,Atmospheric chemistry ,Science ,New York ,Article ,Pathogenesis ,Immune system ,Downregulation and upregulation ,Soot ,Smoke ,Medicine ,Humans ,Respiratory system ,Respiratory Tract Infections ,Pollutant ,Multidisciplinary ,Innate immune system ,Microarray analysis techniques ,business.industry ,Immunity ,Respiratory infection ,Environmental Exposure ,Viral infection ,Immunology ,Female ,Particulate Matter ,Bacterial infection ,business ,Influenza virus ,Transcriptome - Abstract
Combustion related particulate matter air pollution (PM) is associated with an increased risk of respiratory infections in adults. The exact mechanism underlying this association has not been determined. We hypothesized that increased concentrations of combustion related PM would result in dysregulation of the innate immune system. This epidemiological study includes 111 adult patients hospitalized with respiratory infections who underwent transcriptional analysis of their peripheral blood. We examined the association between gene expression at the time of hospitalization and ambient measurements of particulate air pollutants in the 28 days prior to hospitalization. For each pollutant and time lag, gene-specific linear models adjusting for infection type were fit using LIMMA (Linear Models For Microarray Data), and pathway/gene set analyses were performed using the CAMERA (Correlation Adjusted Mean Rank) program. Comparing patients with viral and/or bacterial infection, the expression patterns associated with air pollution exposure differed. Adjusting for the type of infection, increased concentrations of Delta-C (a marker of biomass smoke) and other PM were associated with upregulation of iron homeostasis and protein folding. Increased concentrations of black carbon (BC) were associated with upregulation of viral related gene pathways and downregulation of pathways related to antigen presentation. The pollutant/pathway associations differed by lag time and by type of infection. This study suggests that the effect of air pollution on the pathogenesis of respiratory infection may be pollutant, timing, and infection specific.
- Published
- 2021
20. On non-detects in qPCR data.
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Matthew N. McCall, Helene R. McMurray, Hartmut Land, and Anthony Almudevar
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- 2014
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21. The Gene Expression Barcode 3.0: improved data processing and mining tools.
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Matthew N. McCall, Harris A. Jaffee, Susan J. Zelisko, Neeraj Sinha, Guido J. E. K. Hooiveld, Rafael A. Irizarry, and Michael J. Zilliox
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- 2014
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22. Considerations for Deconvolution: A Case Study with GTEx Coronary Artery Tissues
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Zachary P. Brehm, Valeriia Sherina, Avi Z. Rosenberg, Marc K. Halushka, and Matthew N. McCall
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Differential expression analyses are ubiquitous in the realm of statistical genomics, used to estimate functional differences between genomes of groups of subjects. However, differences in tissue composition between groups may contribute to changes in gene expression, potentially obscuring the detection of functionally significant genes of interest. Deconvolution techniques allow researchers to estimate the abundance of each cell type assumed to be in a tissue. While deconvolution is a useful tool to estimate composition, several crucial considerations must be made when setting up and employing such a workflow in an analysis. We perform a deconvolution on GTEx coronary artery data using CIBERSORT and discuss the challenges and limitations in order to highlight future areas of improvement in the deconvolution framework.
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- 2022
23. ChIP-PED enhances the analysis of ChIP-seq and ChIP-chip data.
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George Wu, Jason T. Yustein, Matthew N. McCall, Michael J. Zilliox, Rafael A. Irizarry, Karen Zeller, Chi V. Dang, and Hongkai Ji
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- 2013
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24. Autoregressive modeling and diagnostics for qPCR amplification
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Valeriia Sherina, Benjamin Hsu, and Matthew N. McCall
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Statistics and Probability ,Supplementary data ,Gradual transition ,Autocorrelation ,Real-Time Polymerase Chain Reaction ,Original Papers ,Biochemistry ,Computer Science Applications ,Computational Mathematics ,Computational Theory and Mathematics ,Autoregressive model ,Parametric model ,Biological system ,Molecular Biology ,Software ,Mathematics - Abstract
Motivation Current methods used to analyze real-time quantitative polymerase chain reaction (qPCR) data exhibit systematic deviations from the assumed model over the progression of the reaction. Slight variations in the amount of the initial target molecule or in early amplifications are likely responsible for these deviations. Commonly used 4- and 5-parameter sigmoidal models appear to be particularly susceptible to this issue, often displaying patterns of autocorrelation in the residuals. The presence of this phenomenon, even for technical replicates, suggests that these parametric models may be misspecified. Specifically, they do not account for the sequential dependent nature of the amplification process that underlies qPCR fluorescence measurements. Results We demonstrate that a Smooth Transition Autoregressive (STAR) model addresses this limitation by explicitly modeling the dependence between cycles and the gradual transition between amplification regimes. In summary, application of a STAR model to qPCR amplification data improves model fit and reduces autocorrelation in the residuals. Availability and implementation R scripts to reproduce all the analyses and results described in this manuscript can be found at: https://github.com/bhsu4/GAPDH.SO. Supplementary information Supplementary data are available at Bioinformatics online.
- Published
- 2020
25. A curated human cellular microRNAome based on 196 primary cell types
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Arun H. Patil, Andrea Baran, Zachary P. Brehm, Matthew N. McCall, and Marc K. Halushka
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MicroRNAs ,Genome ,Sequence Analysis, RNA ,Humans ,Health Informatics ,Sequence Alignment ,Software ,Computer Science Applications - Abstract
BackgroundAn incomplete picture of the expression distribution of microRNAs (miRNAs) across human cell types has long hindered our understanding of this important regulatory class of RNA. With the continued increase in available public small RNA sequencing datasets, there is an opportunity to more fully understand the general distribution of miRNAs at the cell level.ResultsFrom the NCBI Sequence Read Archive, we obtained 6,054 human primary cell datasets and processed 4,184 of them through the miRge3.0 small RNA-seq alignment software. This dataset was curated down, through shared miRNA expression patterns, to 2,077 samples from 196 unique cell types derived from 175 separate studies. Of 2,731 putative miRNAs listed in miRBase (v22.1), 2,452 (89.8%) were detected. Among reasonably expressed miRNAs, 108 were designated as cell specific/near specific, 59 as infrequent, 52 as frequent, 54 as near ubiquitous and 50 as ubiquitous. The complexity of cellular microRNA expression estimates recapitulates tissue expression patterns and informs on the miRNA composition of plasma.ConclusionsThis study represents the most complete reference, to date, of miRNA expression patterns by primary cell type. The data is available through the human cellular microRNAome track at the UCSC Genome Browser (https://genome.ucsc.edu/cgi-bin/hgHubConnect) and an R/Bioconductor package (https://bioconductor.org/packages/microRNAome/).
- Published
- 2022
26. Affymetrix GeneChip microarray preprocessing for multivariate analyses.
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Matthew N. McCall and Anthony Almudevar
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- 2012
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27. The Gene Expression Barcode: leveraging public data repositories to begin cataloging the human and murine transcriptomes.
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Matthew N. McCall, Karan Uppal, Harris A. Jaffee, Michael J. Zilliox, and Rafael A. Irizarry
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- 2011
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28. The effect of tissue composition on gene co-expression
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Matthew N. McCall, Marc K. Halushka, Yun Zhang, and Jonavelle Cuerdo
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AcademicSubjects/SCI01060 ,Computer science ,Review Article ,deconvolution ,Correlation ,transcriptomics ,03 medical and health sciences ,0302 clinical medicine ,Animals ,Humans ,Gene Regulatory Networks ,Molecular Biology ,Gene ,030304 developmental biology ,0303 health sciences ,Models, Genetic ,induced covariance ,tissue composition ,Component (thermodynamics) ,Composition (combinatorics) ,Expression (mathematics) ,co-expression ,Gene Expression Regulation, Neoplastic ,cell-types ,Variable (computer science) ,Organ Specificity ,Deconvolution ,Transcriptome ,Tissue composition ,Biological system ,030217 neurology & neurosurgery ,Information Systems - Abstract
Variable cellular composition of tissue samples represents a significant challenge for the interpretation of genomic profiling studies. Substantial effort has been devoted to modeling and adjusting for compositional differences when estimating differential expression between sample types. However, relatively little attention has been given to the effect of tissue composition on co-expression estimates. In this study, we illustrate the effect of variable cell-type composition on correlation-based network estimation and provide a mathematical decomposition of the tissue-level correlation. We show that a class of deconvolution methods developed to separate tumor and stromal signatures can be applied to two component cell-type mixtures. In simulated and real data, we identify conditions in which a deconvolution approach would be beneficial. Our results suggest that uncorrelated cell-type-specific markers are ideally suited to deconvolute both the expression and co-expression patterns of an individual cell type. We provide a Shiny application for users to interactively explore the effect of cell-type composition on correlation-based co-expression estimation for any cell types of interest.
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- 2019
29. Tissue, age, sex, and disease patterns of matrisome expression in GTEx transcriptome data
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Matthew N. McCall, Marc K. Halushka, Avi Z. Rosenberg, and Tim O. Nieuwenhuis
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Proteomics ,Adult ,Male ,Genotype ,Proteome ,Science ,Computational biology ,Biology ,Matrix (biology) ,Serpin ,Article ,Extracellular matrix ,Transcriptome ,Sex Factors ,Neoplasms ,Disease patterns ,Gene expression ,medicine ,Cluster Analysis ,Humans ,Tissue Distribution ,Gene ,Aged ,Oligonucleotide Array Sequence Analysis ,Extracellular Matrix Proteins ,Multidisciplinary ,Genome, Human ,Gene Expression Profiling ,Functional genomics ,Genomics ,Lung Injury ,Middle Aged ,medicine.disease ,Primary tumor ,Idiopathic Pulmonary Fibrosis ,Extracellular Matrix ,Up-Regulation ,Phenotype ,Gene Expression Regulation ,Medicine ,Female ,Adiponectin ,Systems biology - Abstract
The extracellular matrix (ECM) has historically been explored through proteomic methods. Whether or not global transcriptomics can yield meaningful information on the human matrisome is unknown. Gene expression data from 17,382 samples across 52 tissues, were obtained from the Genotype-Tissue Expression (GTEx) project. Additional datasets were obtained from The Cancer Genome Atlas (TCGA) program and the Gene Expression Omnibus for comparisons. Gene expression levels generally matched proteome-derived matrisome expression patterns. Further, matrisome gene expression properly clustered tissue types, with some matrisome genes including SERPIN family members having tissue-restricted expression patterns. Deeper analyses revealed 382 gene transcripts varied by age and 315 varied by sex in at least one tissue, with expression correlating with digitally imaged histologic tissue features. A comparison of TCGA tumor, TCGA adjacent normal and GTEx normal tissues demonstrated robustness of the GTEx samples as a generalized matrix control, while also determining a common primary tumor matrisome. Additionally, GTEx tissues served as a useful non-diseased control in a separate study of idiopathic pulmonary fibrosis (IPF) matrix changes, while identifying 22 matrix genes upregulated in IPF. Altogether, these findings indicate that the transcriptome, in general, and GTEx in particular, has value in understanding the state of organ ECM.
- Published
- 2021
30. Author response: The role of P2Y12 in the kinetics of microglial self-renewal and maturation in the adult visual cortex in vivo
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Antonio Ladron-de-Guevara, Evelyn Matei, Cassandra E. Lamantia, Ania K. Majewska, Linh Le, Jason Atlas, Matthew N. McCall, Monique S Mendes, and Zachary Brehm
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Visual cortex ,medicine.anatomical_structure ,P2Y12 ,In vivo ,Kinetics ,medicine ,Biology ,Self renewal ,Neuroscience - Published
- 2021
31. The role of P2Y12 in the kinetics of microglial self-renewal and maturation in the adult visual cortex in vivo
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Antonio Ladron-de-Guevara, Jason Atlas, Cassandra E. Lamantia, Zachary Brehm, Monique S Mendes, Ania K. Majewska, Matthew N. McCall, Evelyn Matei, and Linh Le
- Subjects
0301 basic medicine ,in vivo two photon imaging ,repopulation ,Mouse ,QH301-705.5 ,Science ,microglia ,Biology ,General Biochemistry, Genetics and Molecular Biology ,Colony stimulating factor 1 receptor ,03 medical and health sciences ,Mice ,0302 clinical medicine ,Immune system ,P2Y12 ,In vivo ,Cell Movement ,medicine ,Animals ,Biology (General) ,Cell Self Renewal ,Receptor ,Visual Cortex ,Mice, Knockout ,General Immunology and Microbiology ,Microglia ,General Neuroscience ,Brain ,General Medicine ,Models, Theoretical ,Receptors, Purinergic P2Y12 ,Mice, Inbred C57BL ,PLX5622 ,Kinetics ,030104 developmental biology ,Visual cortex ,medicine.anatomical_structure ,nervous system ,ontogeny ,Knockout mouse ,Medicine ,Neuroscience ,030217 neurology & neurosurgery ,Research Article ,colony stimulating factor 1 receptor ,Signal Transduction - Abstract
Microglia are the brain’s resident immune cells with a tremendous capacity to autonomously self-renew. Because microglial self-renewal has largely been studied using static tools, its mechanisms and kinetics are not well understood. Using chronic in vivo two-photon imaging in awake mice, we confirm that cortical microglia show limited turnover and migration under basal conditions. Following depletion, however, microglial repopulation is remarkably rapid and is sustained by the dynamic division of remaining microglia, in a manner that is largely independent of signaling through the P2Y12 receptor. Mathematical modeling of microglial division demonstrates that the observed division rates can account for the rapid repopulation observed in vivo. Additionally, newly born microglia resemble mature microglia within days of repopulation, although morphological maturation is different in newly born microglia in P2Y12 knock out mice. Our work suggests that microglia rapidly locally and that newly born microglia do not recapitulate the slow maturation seen in development but instead take on mature roles in the CNS.
- Published
- 2021
32. Cardioprotection by the mitochondrial unfolded protein response requires ATF5
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Matthew N. McCall, Keith Nehrke, Yunki Lim, Yves T. Wang, Cole M. Haynes, Paul S. Brookes, and Kai-Ting Huang
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Male ,endocrine system ,Physiology ,Activating transcription factor ,Myocardial Reperfusion Injury ,Mitochondrion ,environment and public health ,digestive system ,Mitochondria, Heart ,Physiology (medical) ,Mitochondrial unfolded protein response ,Animals ,Myocytes, Cardiac ,Heart metabolism ,Mice, Knockout ,Cardioprotection ,Regulation of gene expression ,Rapid Report ,biology ,Chemistry ,fungi ,Isolated Heart Preparation ,Activating Transcription Factors ,Cell biology ,Mice, Inbred C57BL ,Disease Models, Animal ,Gene Expression Regulation ,Doxycycline ,Chaperone (protein) ,biological sciences ,Unfolded Protein Response ,biology.protein ,Unfolded protein response ,Female ,Oligomycins ,Cardiology and Cardiovascular Medicine - Abstract
The mitochondrial unfolded protein response (UPRmt) is a cytoprotective signaling pathway triggered by mitochondrial dysfunction. UPRmt activation upregulates chaperones, proteases, antioxidants, and glycolysis at the gene level to restore proteostasis and cell energetics. Activating transcription factor 5 (ATF5) is a proposed mediator of the mammalian UPRmt. Herein, we hypothesized pharmacological UPRmt activation may protect against cardiac ischemia-reperfusion (I/R) injury in an ATF5-dependent manner. Accordingly, in vivo administration of the UPRmt inducers oligomycin or doxycycline 6 h before ex vivo I/R injury (perfused heart) was cardioprotective in wild-type but not global Atf5−/− mice. Acute ex vivo UPRmt activation was not cardioprotective, and loss of ATF5 did not impact baseline I/R injury without UPRmt induction. In vivo UPRmt induction significantly upregulated many known UPRmt-linked genes (cardiac quantitative PCR and Western blot analysis), and RNA-Seq revealed an UPRmt-induced ATF5-dependent gene set, which may contribute to cardioprotection. This is the first in vivo proof of a role for ATF5 in the mammalian UPRmt and the first demonstration that UPRmt is a cardioprotective drug target. NEW & NOTEWORTHY Cardioprotection can be induced by drugs that activate the mitochondrial unfolded protein response (UPRmt). UPRmt protection is dependent on activating transcription factor 5 (ATF5). This is the first in vivo evidence for a role of ATF5 in the mammalian UPRmt.
- Published
- 2019
33. PCRedux: A Data Mining and Machine Learning Toolkit for qPCR Experiments
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Michał Burdukiewicz, Peter Schierack, Rafacz D, Konstantin A. Blagodatskikh, Matthew N. McCall, Spiess A, Stefan Rödiger, and Jim F. Huggett
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Web server ,business.industry ,Computer science ,Open source software ,computer.software_genre ,Machine learning ,Regression ,Rule of thumb ,Black box ,Artificial intelligence ,Data mining ,Sensitivity (control systems) ,business ,computer ,Working environment - Abstract
MotivationQuantitative Real-time PCR (qPCR) is a widely used -omics method for the precise quantification of nucleic acids, in which the result is associated with the presence/absence or quantity of a specific nucleic acid sequence. As the amount of qPCR data increases worldwide, the manual assessment of results becomes challenging and difficult to reproduce. To overcome this, some automatable characteristics of amplification curves have been described in the literature, often with an appropriate “rule of thumb”.ResultsWe developed PCRedux to analyze and calculate 90 numerical qPCR amplification curve descriptors (‘‘features”) from large datasets of qPCR amplification curves that are aimed for interpretable machine learning and development of decision support systems. In a case study of a diverse dataset with 3181 positive, negative and ambiguous amplification curves, as assessed by three human raters, we demonstrate a sensitivity >99 % and specificity >97 % in detecting positive and negative amplification. PCRedux is unique as it goes beyond traditional qPCR analysis to capture curvature properties that improve the characterization and classification of amplification curves. The calculation of the features is reproducible and objective, since R is used as a controllable working environment. PCRedux is not a black box, but open source software following on the principle of mathematically interpretable features. These can be combined with user-defined labels for automatic multi-category classification and regression in machine learning.Availabilityhttps://cran.r-project.org/package=PCRedux. Web server: http://shtest.evrogen.net/PCRedux-app/. Documentation: https://PCRuniversum.github.io/PCRedux/.
- Published
- 2021
34. Sex, age, tissue, and disease patterns of matrisome expression in GTEx transcriptome data
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Marc K. Halushka, Tim O. Nieuwenhuis, Matthew N. McCall, and Avi Z. Rosenberg
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Extracellular matrix ,Transcriptome ,Cancer genome ,Disease patterns ,Gene expression ,medicine ,Computational biology ,Biology ,Matrix (biology) ,medicine.disease ,Gene ,Primary tumor - Abstract
The extracellular matrix (ECM) has historically been explored through proteomic methods. Whether or not global transcriptomics can yield meaningful information on the human matrisome is unknown. Gene expression data from 17,382 samples across 52 tissues, were obtained from the Genotype-Tissue Expression (GTEx) project. Additional datasets were obtained from The Cancer Genome Atlas (TCGA) program and the Gene Expression Omnibus for comparisons. Gene expression levels generally recapitulated proteome-derived matrisome expression patterns. Further, matrisome gene expression properly clustered tissue types, with some matrisome genes including SERPIN family members having tissue-restricted expression patterns. Deeper analyses revealed 388 genes varied by age and 222 varied by sex in at least one tissue, with expression correlating with digitally imaged histologic tissue features. A comparison of TCGA tumor, TCGA adjacent normal and GTEx normal tissues demonstrated robustness of the GTEx samples as a generalized control, while also determining a common primary tumor matrisome. Additionally, GTEx tissues served as a useful non-diseased control in a separate study of idiopathic pulmonary fibrosis matrix changes. Altogether, these findings indicate that the transcriptome, in general, and GTEx in particular, has value in understanding the state of organ ECM.
- Published
- 2021
35. FNDC-1-mediated mitophagy and ATFS-1 coordinate to protect against hypoxia-reoxygenation
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Brandon J. Berry, Paul S. Brookes, Keith Nehrke, Matthew N. McCall, Stephanie Viteri, Christopher Rongo, Yunki Lim, and Eun Chan Park
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0301 basic medicine ,Biology ,Animals, Genetically Modified ,Mitochondrial Proteins ,03 medical and health sciences ,Loss of Function Mutation ,Mitophagy ,Organelle ,Animals ,Caenorhabditis elegans ,Caenorhabditis elegans Proteins ,Hypoxia ,Molecular Biology ,Genes, Helminth ,030102 biochemistry & molecular biology ,Autophagy ,Membrane Proteins ,Cell Biology ,Cell biology ,Crosstalk (biology) ,030104 developmental biology ,Reperfusion Injury ,Hypoxia reoxygenation ,Transcription Factors ,Research Paper - Abstract
Mitochondrial quality control (MQC) balances organelle adaptation and elimination, and mechanistic crosstalk between the underlying molecular processes affects subsequent stress outcomes. FUNDC1 (FUN14 domain containing 1) is a mammalian mitophagy receptor that responds to hypoxia-reoxygenation (HR) stress. Here, we provide evidence that FNDC-1 is the C. elegans ortholog of FUNDC1, and that its loss protects against injury in a worm model of HR. This protection depends upon ATFS-1, a transcription factor that is central to the mitochondrial unfolded protein response (UPRmt). Global mRNA and metabolite profiling suggest that atfs-1-dependent stress responses and metabolic remodeling occur in response to the loss of fndc-1. These data support a role for FNDC-1 in non-hypoxic MQC, and further suggest that these changes are prophylactic in relation to subsequent HR. Our results highlight functional coordination between mitochondrial adaptation and elimination that organizes stress responses and metabolic rewiring to protect against HR injury. Abbreviations: AL: autolysosome; AP: autophagosome; FUNDC1: FUN14 domain containing 1; HR: hypoxia-reperfusion; IR: ischemia-reperfusion; lof: loss of function; MQC: mitochondrial quality control; PCA: principle component analysis; PPP: pentonse phosphate pathway; proK (proteinase K);UPRmt: mitochondrial unfolded protein response; RNAi: RNA interference.
- Published
- 2021
36. A benchmark for microRNA quantification algorithms using the OpenArray platform.
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Matthew N. McCall, Alexander S. Baras, Alexander Crits-Christoph, Roxann Ingersoll, Melissa A. McAlexander, Kenneth W. Witwer, and Marc K. Halushka
- Published
- 2016
- Full Text
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37. Spatial Proteomic Approach to Characterize Skeletal Muscle Myofibers
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Elise M. Walsh, Xiaoping Yang, Avi Z. Rosenberg, Katherine M. Fomchenko, Tim O. Nieuwenhuis, Karen Fox-Talbot, Rohan X. Verma, Matthew N. McCall, David A. Kass, Brian L. Lin, Marc K. Halushka, and Arun H. Patil
- Subjects
Proteomics ,Tissue microarray ,Fiber type ,Human Protein Atlas ,Skeletal muscle ,General Chemistry ,Computational biology ,Biology ,Biochemistry ,Protein expression ,Article ,medicine.anatomical_structure ,Muscle Fibers, Slow-Twitch ,Human muscle ,Muscular Diseases ,Muscle Fibers, Fast-Twitch ,medicine ,Immunohistochemistry ,Humans ,Muscle, Skeletal - Abstract
Skeletal muscle myofibers have differential protein expression resulting in functionally distinct slow- and fast-twitch types. While certain protein classes are well-characterized, the depth of all proteins involved in this process is unknown. We utilized the Human Protein Atlas (HPA) and the HPASubC tool to classify mosaic expression patterns of staining across 49,600 unique tissue microarray (TMA) images using a visual proteomic approach. We identified 2164 proteins with potential mosaic expression, of which 1605 were categorized as "likely" or "real." This list included both well-known fiber-type-specific and novel proteins. A comparison of the 1605 mosaic proteins with a mass spectrometry (MS)-derived proteomic dataset of single human muscle fibers led to the assignment of 111 proteins to fiber types. We additionally used a multiplexed immunohistochemistry approach, a multiplexed RNA-ISH approach, and STRING v11 to further assign or suggest fiber types of newly characterized mosaic proteins. This visual proteomic analysis of mature skeletal muscle myofibers greatly expands the known repertoire of twitch-type-specific proteins.
- Published
- 2020
38. Multiple imputation and direct estimation for qPCR data with non-detects
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Hartmut Land, Tanzy Love, Winslow Powers, Helene R. McMurray, Valeriia Sherina, and Matthew N. McCall
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Computer science ,Inference ,Real-Time Polymerase Chain Reaction ,lcsh:Computer applications to medicine. Medical informatics ,computer.software_genre ,Residual ,Direct estimation ,Biochemistry ,Missing not at random (MNAR) ,03 medical and health sciences ,0302 clinical medicine ,Structural Biology ,Expectation–maximization algorithm ,Humans ,Computer Simulation ,Imputation (statistics) ,lcsh:QH301-705.5 ,Molecular Biology ,030304 developmental biology ,0303 health sciences ,Models, Statistical ,Methodology Article ,Applied Mathematics ,Experimental data ,Missing data ,Computer Science Applications ,Non-detects ,lcsh:Biology (General) ,Sample Size ,030220 oncology & carcinogenesis ,Multiple imputation ,lcsh:R858-859.7 ,Gene expression ,Data mining ,Quantitative real-time PCR (qPCR) ,computer ,Algorithms - Abstract
Background Quantitative real-time PCR (qPCR) is one of the most widely used methods to measure gene expression. An important aspect of qPCR data that has been largely ignored is the presence of non-detects: reactions failing to exceed the quantification threshold and therefore lacking a measurement of expression. While most current software replaces these non-detects with a value representing the limit of detection, this introduces substantial bias in the estimation of both absolute and differential expression. Single imputation procedures, while an improvement on previously used methods, underestimate residual variance, which can lead to anti-conservative inference. Results We propose to treat non-detects as non-random missing data, model the missing data mechanism, and use this model to impute missing values or obtain direct estimates of model parameters. To account for the uncertainty inherent in the imputation, we propose a multiple imputation procedure, which provides a set of plausible values for each non-detect. We assess the proposed methods via simulation studies and demonstrate the applicability of these methods to three experimental data sets. We compare our methods to mean imputation, single imputation, and a penalized EM algorithm incorporating non-random missingness (PEMM). The developed methods are implemented in the R/Bioconductor package . Conclusions The statistical methods introduced here reduce discrepancies in gene expression values derived from qPCR experiments in the presence of non-detects, providing increased confidence in downstream analyses.
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- 2020
39. Gene network modeling via TopNet reveals robust epistatic interactions between functionally diverse tumor critical mediator genes
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Harry A. Stern, Aslihan Ambeskovic, Hartmut Land, Matthew N. McCall, Vijaya Balakrishnan, Helene R. McMurray, Jordan Aldersley, Laurel Newman, and Bradley N. Smith
- Subjects
Mediator ,Gene expression ,Gene regulatory network ,Epistasis ,Robustness (evolution) ,Computational biology ,Biology ,Gene ,Reprogramming ,Phenotype - Abstract
Malignant cell transformation and the underlying genomic scale reprogramming of gene expression require cooperation of multiple oncogenic mutations. Notably, this cooperation is reflected in the synergistic regulation of downstream genes, so-called cooperation response genes (CRGs). CRGs impact diverse hallmark features of cancer cells and are not known to be functionally connected. Yet, they act as critical mediators of the cancer phenotype at an unexpectedly high frequency of >50%, as indicated by genetic perturbations. Here we demonstrate that CRGs function within a network of strong genetic interdependencies that are critical to the robustness of the malignant state. Our approach, termed TopNet, utilizes attractor-based ternary network modeling that takes the novel approach of incorporating uncertainty in the underlying gene perturbation data and is capable of identifying non-linear gene interactions. TopNet reveals topological gene network architecture that effectively predicts previously unknown, functionally relevant epistatic gene interactions, and thus, among a broad range of applications, has utility for identification of non-mutant targets for cancer intervention.
- Published
- 2020
40. Airway Gene Expression Correlates of Respiratory Syncytial Virus Disease Severity and Microbiome Composition in Infants
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Mary T. Caserta, Gloria S. Pryhuber, Lu Wang, David J. Topham, Jeanne Holden-Wiltse, Chin-Yi Chu, Christopher Slaunwhite, Edward E. Walsh, Anthony Corbett, Matthew N. McCall, Alex Grier, Steven R. Gill, Thomas J. Mariani, Ann R. Falsey, and Xing Qiu
- Subjects
Chemokine ,Respiratory System ,macromolecular substances ,Respiratory Syncytial Virus Infections ,Severity of Illness Index ,Virus ,Major Articles and Brief Reports ,Severity of illness ,Immunology and Allergy ,Medicine ,Humans ,Microbiome ,Respiratory system ,biology ,business.industry ,Microbiota ,Respiratory disease ,Infant ,respiratory system ,medicine.disease ,Infectious Diseases ,Respiratory Syncytial Virus, Human ,Immunology ,Cohort ,biology.protein ,business ,Airway ,Transcriptome - Abstract
Background Respiratory syncytial virus (RSV) is the leading cause of severe respiratory disease in infants. The causes and correlates of severe illness in the majority of infants are poorly defined. Methods We recruited a cohort of RSV-infected infants and simultaneously assayed the molecular status of their airways and the presence of airway microbiota. We used rigorous statistical approaches to identify gene expression patterns associated with disease severity and microbiota composition, separately and in combination. Results We measured comprehensive airway gene expression patterns in 106 infants with primary RSV infection. We identified an airway gene expression signature of severe illness dominated by excessive chemokine expression. We also found an association between Haemophilus influenzae, disease severity, and airway lymphocyte accumulation. Exploring the time of onset of clinical symptoms revealed acute activation of interferon signaling following RSV infection in infants with mild or moderate illness, which was absent in subjects with severe illness. Conclusions Our data reveal that airway gene expression patterns distinguish mild/moderate from severe illness. Furthermore, our data identify biomarkers that may be therapeutic targets or useful for measuring efficacy of intervention responses.
- Published
- 2020
41. In vivo imaging of the kinetics of microglial self-renewal and maturation in the adult visual cortex
- Author
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Ania K. Majewska, Zachary Brehm, Antonio Ladron-de-Guevara, Jason Atlas, Matthew N. McCall, and Monique S Mendes
- Subjects
Nervous system ,0303 health sciences ,Microglia ,Developmental maturation ,Biology ,Colony stimulating factor 1 receptor ,03 medical and health sciences ,0302 clinical medicine ,medicine.anatomical_structure ,Visual cortex ,nervous system ,In vivo ,medicine ,Progenitor cell ,Receptor ,Neuroscience ,030217 neurology & neurosurgery ,030304 developmental biology - Abstract
Microglia are the resident immune cells in the brain with the capacity to autonomously self-renew. Under basal conditions, microglial self-renewal appears to be slow and stochastic, although microglia have the ability to proliferate very rapidly following depletion or in response to injury. Because microglial self-renewal has largely been studied using static tools, the mechanisms and kinetics by which microglia renew and acquire mature characteristics in the adult brain are not well understood. Using chronic in vivo two-photon imaging in awake mice and PLX5622 (Colony stimulating factor 1 receptor (CSF1R) inhibitor) to deplete microglia, we set out to understand the dynamic self-organization and maturation of microglia following depletion in the visual cortex. We confirm that under basal conditions, cortical microglia show limited turnover and migration. Following depletion, however, microglial repopulation is remarkably rapid and is sustained by the dynamic division of the remaining microglia in a manner that is largely independent of signaling through the P2Y12 receptor. Mathematical modeling of microglial division demonstrates that the observed division rates can account for the rapid repopulation observed in vivo. Additionally, newly-born microglia resemble mature microglia, in terms of their morphology, dynamics and ability to respond to injury, within days of repopulation. Our work suggests that microglia rapidly self-renew locally, without the involvement of a special progenitor cell, and that newly born microglia do not recapitulate a slow developmental maturation but instead quickly take on mature roles in the nervous system.Graphical Abstract(a) Microglial dynamics during control condition. Cartoon depiction of the heterogenous microglia in the visual cortex equally spaced. (b) During the early stages of repopulation, microglia are irregularly spaced and sparse. (c) During the later stages of repopulation, the number of microglia and the spatial distribution return to baseline. (d-f) We then created and ran a mathematical model that sampled the number of microglia, (d) the persistent doublets, (e) the rapid divisions of microglia and (f) the secondary divisions of microglia during the peak of repopulation day 2-day 3. The mathematical model suggested that residual microglia can account for the rapid repopulation we observed in vivo.
- Published
- 2020
42. Kras and Tp53 Mutations Cause Cholangiocyte- and Hepatocyte-Derived Cholangiocarcinoma
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Aram F. Hezel, Bing Guo, Christa L. Whitney-Miller, Michael R. O'Dell, Matthew N. McCall, William B Alexander, Krushna C. Patra, Nabeel Bardeesy, Yasutaka Kato, and Margaret A Hill
- Subjects
0301 basic medicine ,Cancer Research ,education.field_of_study ,Population ,Context (language use) ,Biology ,medicine.disease_cause ,medicine.disease ,Article ,Cholangiocyte ,03 medical and health sciences ,030104 developmental biology ,Oncology ,Biliary tract ,Hepatocellular carcinoma ,medicine ,Cancer research ,Biliary Intraepithelial Neoplasia ,KRAS ,education ,Carcinogenesis - Abstract
Intrahepatic cholangiocarcinoma (iCCA) is a primary liver cancer epidemiologically linked with liver injury, which has poorly understood incipient stages and lacks early diagnostics and effective therapies. While iCCA is conventionally thought to arise from the biliary tract, studies have suggested that both hepatocytes and biliary cells (cholangiocytes) may give rise to iCCA. Consistent with the plasticity of these cell lineages, primary liver carcinomas exhibit a phenotypic range from hepatocellular carcinoma (HCC) to iCCA, with intermediates along this spectrum. Here, we generated mouse models to examine the consequence of targeting mutant Kras and Tp53, common alterations in human iCCA, to different adult liver cell types. Selective induction of these mutations in the SOX9+ population, predominantly consisting of mature cholangiocytes, resulted in iCCA emerging from premalignant biliary intraepithelial neoplasia (BilIN). In contrast, adult hepatocytes were relatively refractory to these mutations and formed rare HCC. In this context, injury accelerated hepatocyte-derived tumorigenesis and promoted a phenotypic switch to iCCA. BilIN precursor lesions were absent in the hepatocyte-derived iCCA models, pointing toward distinct and direct emergence of a malignant cholangiocytic phenotype from injured, oncogenically primed hepatocytes. Tp53 loss enhanced the reprogramming of hepatocytes to cholangiocytes, which may represent a mechanism facilitating formation of hepatocyte-derived iCCA. Overall, our work shows iCCA driven by Kras and Tp53 may originate from both mature cholangiocytes and hepatocytes, and factors such as chronic liver injury and underlying genetic mutations determine the path of progression and resulting cancer phenotype. Significance: The histopathogenesis of biliary tract cancer, driven by Tp53 and Kras mutations, can be differentially impacted by the cell of origin within the mature liver as well by major epidemiologic risk factors. Cancer Res; 78(16); 4445–51. ©2018 AACR.
- Published
- 2018
43. xMD-miRNA-seq to generate near in vivo miRNA expression estimates in colon epithelial cells
- Author
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Corey M. Porter, Marc K. Halushka, Anandita Rajpurohit, Olga Kovbasnjuk, Carrie Wright, Matthew N. McCall, Avi Z. Rosenberg, Karen Fox-Talbot, Courtney Williams, and Joo Heon Shin
- Subjects
0301 basic medicine ,Small RNA ,Cell type ,Colon ,Cell ,XMD ,lcsh:Medicine ,Cell Separation ,Biology ,Article ,03 medical and health sciences ,0302 clinical medicine ,Gene expression ,microRNA ,medicine ,Humans ,lcsh:Science ,Microdissection ,Cells, Cultured ,030304 developmental biology ,Cell Aggregation ,Regulation of gene expression ,0303 health sciences ,Multidisciplinary ,Sequence Analysis, RNA ,lcsh:R ,Epithelial Cells ,Cell aggregation ,Epithelium ,Cell biology ,MicroRNAs ,030104 developmental biology ,medicine.anatomical_structure ,Gene Expression Regulation ,Cell culture ,030220 oncology & carcinogenesis ,lcsh:Q - Abstract
Accurate, RNA-seq based, microRNA (miRNA) expression estimates from primary cells have recently been described. However, this in vitro data is mainly obtained from cell culture, which is known to alter cell maturity/differentiation status, significantly changing miRNA levels. What is needed is a robust method to obtain in vivo miRNA expression values directly from cells. We introduce expression microdissection miRNA small RNA sequencing (xMD-miRNA-seq), a method to isolate cells directly from formalin fixed paraffin-embedded (FFPE) tissues. xMD-miRNA-seq is a low-cost, high-throughput, immunohistochemistry-based method to capture any cell type of interest. As a proof-of-concept, we isolated colon epithelial cells from two specimens and performed low-input small RNA-seq. We generated up to 600,000 miRNA reads from the samples. Isolated epithelial cells, had abundant epithelial-enriched miRNA expression (miR-192; miR-194; miR-200b; miR-200c; miR-215; miR-375) and overall similar miRNA expression patterns to other epithelial cell populations (colonic enteroids and flow-isolated colon epithelium). xMD-derived epithelial cells were generally not contaminated by other adjacent cells of the colon as noted by t-SNE analysis. xMD-miRNA-seq allows for simple, economical, and efficient identification of cell-specific miRNA expression estimates. Further development will enhance rapid identification of cell-specific miRNA expression estimates in health and disease for nearly any cell type using archival FFPE material.
- Published
- 2018
44. A systems genomics approach uncovers molecular associates of RSV severity
- Author
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Chin-Yi Chu, Matthew N. McCall, Anthony Corbett, Alex Grier, Steven R. Gill, Lauren Benoodt, Xing Qiu, Edward E. Walsh, Jeanne Holden-Wiltse, Lu Wang, Juilee Thakar, Christopher Slaunwhite, Thomas J. Mariani, Ann R. Falsey, Mary T. Caserta, and David J. Topham
- Subjects
Physiology ,viruses ,Gene Expression ,Disease ,Severity of Illness Index ,Epithelium ,Machine Learning ,White Blood Cells ,Mathematical and Statistical Techniques ,Animal Cells ,Medicine and Health Sciences ,Medicine ,Lymphocytes ,RNA-Seq ,Biology (General) ,Principal Component Analysis ,Ecology ,Microbiota ,Statistics ,Acute-phase protein ,Genomics ,respiratory system ,Pathophysiology ,Physiological Parameters ,Computational Theory and Mathematics ,Medical Microbiology ,Modeling and Simulation ,Physical Sciences ,Cellular Types ,Anatomy ,Nasal Cavity ,Research Article ,QH301-705.5 ,Immune Cells ,Immunology ,Microbial Genomics ,Respiratory Syncytial Virus Infections ,Research and Analysis Methods ,Microbiology ,Virus ,Cellular and Molecular Neuroscience ,Immune system ,Gene Types ,Genetics ,Humans ,Gene Regulation ,Statistical Methods ,Molecular Biology ,Ecology, Evolution, Behavior and Systematics ,Innate immune system ,Blood Cells ,business.industry ,Body Weight ,Biology and Life Sciences ,Infant ,Epithelial Cells ,Cell Biology ,Immunity, Innate ,Biological Tissue ,Multivariate Analysis ,Respiratory epithelium ,Regulator Genes ,Microbiome ,business ,Mathematics - Abstract
Respiratory syncytial virus (RSV) infection results in millions of hospitalizations and thousands of deaths each year. Variations in the adaptive and innate immune response appear to be associated with RSV severity. To investigate the host response to RSV infection in infants, we performed a systems-level study of RSV pathophysiology, incorporating high-throughput measurements of the peripheral innate and adaptive immune systems and the airway epithelium and microbiota. We implemented a novel multi-omic data integration method based on multilayered principal component analysis, penalized regression, and feature weight back-propagation, which enabled us to identify cellular pathways associated with RSV severity. In both airway and immune cells, we found an association between RSV severity and activation of pathways controlling Th17 and acute phase response signaling, as well as inhibition of B cell receptor signaling. Dysregulation of both the humoral and mucosal response to RSV may play a critical role in determining illness severity., Author summary This paper presents a novel approach to understanding the localized molecular responses to respiratory syncytial virus (RSV) and the system-level correlates of clinical outcomes. To do this, we developed a novel statistical method able to integrate high dimensional molecular data characterizing the host airway microbota and immune and nasal gene expression. We show that this integrative approach facilitates superior performance in estimating clinical outcome as opposed to any single data type. Using this approach, we identified both cell type-specific and shared biomarkers and regulatory pathways associated with RSV severity. Specifically, we identified an association between RSV severity, activation of pathways controlling Th17, and inhibition of B cell receptor signaling, which were present in both the site of infection airway and in peripheral immune cells. These results can guide future efforts to identify biomarkers for identifying or predicting illness severity following infant RSV infection. They may also be useful as biomarkers to inform the efficacy of future interventions (e.g., therapies) or preventative measures to suppress the rate of severe disease (e.g., vaccines).
- Published
- 2021
45. fRMA ST: frozen robust multiarray analysis for Affymetrix Exon and Gene ST arrays.
- Author
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Matthew N. McCall, Harris A. Jaffee, and Rafael A. Irizarry
- Published
- 2012
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46. A four gene signature predictive of recurrent prostate cancer
- Author
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Matthew N. McCall, James L. Mohler, Wiam Bshara, Hartmut Land, Carl Morrison, Laurel Newman, and Justin Komisarof
- Subjects
Male ,0301 basic medicine ,Biochemical recurrence ,Oncology ,medicine.medical_specialty ,Colorectal cancer ,medicine.medical_treatment ,cooperation response genes ,03 medical and health sciences ,Prostate cancer ,0302 clinical medicine ,Internal medicine ,Biomarkers, Tumor ,biochemical recurrence ,Humans ,Medicine ,algorithmic prediction ,Prostatectomy ,Gynecology ,Roswell Park Cancer Institute ,business.industry ,Gene Expression Profiling ,Prostatic Neoplasms ,Reproducibility of Results ,Cancer ,Gene signature ,Prognosis ,prostate cancer ,medicine.disease ,Survival Analysis ,Primary tumor ,radical prostatectomy ,3. Good health ,030104 developmental biology ,ROC Curve ,030220 oncology & carcinogenesis ,Neoplasm Recurrence, Local ,Transcriptome ,business ,Algorithms ,Research Paper - Abstract
// Justin Komisarof 1 , Matthew McCall 2 , Laurel Newman 1 , Wiam Bshara 3 , James L. Mohler 4 , Carl Morrison 3 , Hartmut Land 1, 5 1 Departments of Biomedical Genetics, University of Rochester Medical Center, Rochester NY, 14642, USA 2 Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester NY, 14642, USA 3 Department of Pathology, Roswell Park Cancer Institute, Buffalo, NY, 14623, USA 4 Department of Urology, Roswell Park Cancer Institute, Buffalo, NY, 14623, USA 5 Wilmot Cancer Institute, University of Rochester Medical Center, Rochester NY, 14642, USA Correspondence to: Hartmut Land, email: Land@urmc.rochester.edu Keywords: prostate cancer, biochemical recurrence, cooperation response genes, radical prostatectomy, algorithmic prediction Received: January 19, 2016 Accepted: November 21, 2016 Published: December 09, 2016 ABSTRACT Prostate cancer is the most common form of non-dermatological cancer among US men, with an increasing incidence due to the aging population. Patients diagnosed with clinically localized disease identified as intermediate or high-risk are often treated by radical prostatectomy. Approximately 33% of these patients will suffer recurrence after surgery. Identifying patients likely to experience recurrence after radical prostatectomy would lead to improved clinical outcomes, as these patients could receive adjuvant radiotherapy. Here, we report a new tool for prediction of prostate cancer recurrence based on the expression pattern of a small set of cooperation response genes (CRGs). CRGs are a group of genes downstream of cooperating oncogenic mutations previously identified in a colon cancer model that are critical to the cancer phenotype. We show that systemic dysregulation of CRGs is also found in prostate cancer, including a 4-gene signature (HBEGF, HOXC13, IGFBP2, and SATB1) capable of differentiating recurrent from non-recurrent prostate cancer. To develop a suitable diagnostic tool to predict disease outcomes in individual patients, multiple algorithms and data handling strategies were evaluated on a training set using leave-one-out cross-validation (LOOCV). The best-performing algorithm, when used in combination with a predictive nomogram based on clinical staging, predicted recurrent and non-recurrent disease outcomes in a blinded validation set with 83% accuracy, outperforming previous methods. Disease-free survival times between the cohort of prostate cancers predicted to recur and predicted not to recur differed significantly (p = 1.38x10 -6 ). Therefore, this test allows us to accurately identify prostate cancer patients likely to experience future recurrent disease immediately following removal of the primary tumor.
- Published
- 2016
47. Big Strides in Cellular MicroRNA Expression
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Bastian Fromm, Marc K. Halushka, Kevin J. Peterson, and Matthew N. McCall
- Subjects
0301 basic medicine ,Cell ,RNA-Seq ,Computational biology ,Biology ,Article ,03 medical and health sciences ,microRNA ,Genetics ,medicine ,Animals ,Humans ,Lack of knowledge ,RNA, Messenger ,Regulation of gene expression ,030102 biochemistry & molecular biology ,Gene Expression Profiling ,High-Throughput Nucleotide Sequencing ,Gene expression profiling ,MicroRNAs ,Eukaryotic Cells ,030104 developmental biology ,medicine.anatomical_structure ,Gene Expression Regulation ,Expression (architecture) ,Organ Specificity - Abstract
A lack of knowledge of the cellular origin of microRNAs (miRNAs) has greatly confounded functional and biomarkers studies. Recently, three studies characterized miRNA expression patterns across >78 human cell types. These combined data expand our knowledge of miRNA expression localization and confirm that many miRNAs show cell-type specific expression patterns.
- Published
- 2018
48. Auto-regressive modeling and diagnostics for qPCR amplification
- Author
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Benjamin Hsu, Valeriia Sherina, and Matthew N. McCall
- Subjects
Autoregressive model ,Gradual transition ,Parametric model ,Autocorrelation ,Biological system ,Mathematics - Abstract
Current methods used to analyze real-time quantitative polymerase chain reaction (qPCR) data exhibit systematic deviations from the assumed model over the progression of the reaction. Slight variations in the amount of the initial target molecule or in early amplifications are likely responsible for these deviations. Commonly-used 4- and 5-parameter sigmoidal models appear to be particularly susceptible to this issue, often displaying patterns of autocorrelation in the residuals. The presence of this phenomenon, even for technical replicates, suggests that these parametric models may be misspecified. Specifically, they do not account for the sequential dependent nature of qPCR fluorescence measurements. We demonstrate that a Smooth Transition Autoregressive (STAR) model addresses this limitation by explicitly modeling the dependence between cycles and the gradual transition between amplification regimes. In summary, application of a STAR model to qPCR amplification data improves model fit and reduces autocorrelation in the residuals.
- Published
- 2019
49. Aims, Study Design, and Enrollment Results From the Assessing Predictors of Infant Respiratory Syncytial Virus Effects and Severity Study
- Author
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Thomas J. Mariani, Jeanne Holden-Wiltse, Steven R. Gill, Anthony Corbett, Matthew N. McCall, David J. Topham, Alex Grier, Ann R. Falsey, Chin-Yi Chu, Xing Qiu, Lauren Benoodt, Mary T. Caserta, Lu Wang, Edward E. Walsh, and Juilee Thakar
- Subjects
medicine.medical_specialty ,020205 medical informatics ,respiratory syncytial virus ,Buccal swab ,immunoglobulins ,02 engineering and technology ,Disease ,Virus ,03 medical and health sciences ,0302 clinical medicine ,Internal medicine ,0202 electrical engineering, electronic engineering, information engineering ,microbiota ,Medicine ,030212 general & internal medicine ,innate immunity ,T-lymphocytes ,Original Paper ,biology ,business.industry ,General Medicine ,Emergency department ,respiratory system ,3. Good health ,Nasal Swab ,Cord blood ,Cohort ,biology.protein ,gene expression ,Antibody ,business ,transcriptome - Abstract
Background: The majority of infants hospitalized with primary respiratory syncytial virus (RSV) infection have no obvious risk factors for severe disease. Objective: The aim of this study (Assessing Predictors of Infant RSV Effects and Severity, AsPIRES) was to identify factors associated with severe disease in full-term healthy infants younger than 10 months with primary RSV infection. Methods: RSV infected infants were enrolled from 3 cohorts during consecutive winters from August 2012 to April 2016 in Rochester, New York. A birth cohort was prospectively enrolled and followed through their first winter for development of RSV infection. An outpatient supplemental cohort was enrolled in the emergency department or pediatric offices, and a hospital cohort was enrolled on admission with RSV infection. RSV was diagnosed by reverse transcriptase-polymerase chain reaction. Demographic and clinical data were recorded and samples collected for assays: buccal swab (cytomegalovirus polymerase chain reaction, PCR), nasal swab (RSV qualitative PCR, complete viral gene sequence, 16S ribosomal ribonucleic acid [RNA] amplicon microbiota analysis), nasal wash (chemokine and cytokine assays), nasal brush (nasal respiratory epithelial cell gene expression using RNA sequencing [RNAseq]), and 2 to 3 ml of heparinized blood (flow cytometry, RNAseq analysis of purified cluster of differentiation [CD]4+, CD8+, B cells and natural killer cells, and RSV-specific antibody). Cord blood (RSV-specific antibody) was also collected for the birth cohort. Univariate and multivariate logistic regression will be used for analysis of data using a continuous Global Respiratory Severity Score (GRSS) as the outcome variable. Novel statistical methods will be developed for integration of the large complex datasets. Results: A total of 453 infants were enrolled into the 3 cohorts; 226 in the birth cohort, 60 in the supplemental cohort, and 78 in the hospital cohort. A total of 126 birth cohort infants remained in the study and were evaluated for 150 respiratory illnesses. Of the 60 RSV positive infants in the supplemental cohort, 42 completed the study, whereas all 78 of the RSV positive hospital cohort infants completed the study. A GRSS was calculated for each RSV-infected infant and is being used to analyze each of the complex datasets by correlation with disease severity in univariate and multivariate methods. Conclusions: The AsPIRES study will provide insights into the complex pathogenesis of RSV infection in healthy full-term infants with primary RSV infection. The analysis will allow assessment of multiple factors potentially influencing the severity of RSV infection including the level of RSV specific antibodies, the innate immune response of nasal epithelial cells, the adaptive response by various lymphocyte subsets, the resident airway microbiota, and viral factors. Results of this study will inform disease interventions such as vaccines and antiviral therapies.
- Published
- 2019
50. Insufficiency in airway interferon activation defines clinical severity to infant RSV infection
- Author
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Chin-Yi Chu, Thomas J. Mariani, Christopher Slaunwhite, Jeanne Holden-Wiltse, Xing Qiu, Edward E. Walsh, Lu Wang, Gloria S. Pryhuber, Alex Grier, Qian Wang, Christopher S. Anderson, Steven R. Gill, Mary T. Caserta, Matthew N. McCall, Anthony Corbett, David J. Topham, and Ann R. Falsey
- Subjects
Chemokine ,biology ,business.industry ,Lymphocyte ,Respiratory disease ,Context (language use) ,respiratory system ,medicine.disease ,Virus ,medicine.anatomical_structure ,Interferon ,Immunology ,medicine ,biology.protein ,Biomarker (medicine) ,business ,Airway ,medicine.drug - Abstract
Respiratory syncytial virus (RSV) is the leading cause of severe respiratory disease in infants. Other than age at the time of infection, the causes and correlates of severe illness in infants lacking known risk factors are poorly defined. We recruited a cohort of confirmed RSV-infected infants and simultaneously assayed the presence of resident airway microbiota and the molecular status of their airways using a novel method. Rigorous statistical analyses identified a molecular airway gene expression signature of severe illness dominated by excessive chemokine expression. Global 16S rRNA sequencing confirmed an association between H. influenzae and clinical severity. Interestingly, adjusting for H. influenzae in our gene expression analysis revealed an association between severity and airway lymphocyte accumulation. Exploring the relationship between airway gene expression and the time of onset of clinical symptoms revealed a robust, acute activation of interferon (IFN) signaling, which was absent in subjects with severe illness. Finally, we explored the relationship between IFN activity, airway gene expression and productive RSV infection using a novel in vitro model of bona fide pediatric human airway epithelial cells. Interestingly, blocking IFN signaling, but not IFN ligand production, in these cells leads to increased viral infection. Our data reveal that acute airway interferon responses are physiologically relevant in the context of infant RSV infection and may be a target for therapeutic intervention. Additionally, the airway gene expression signature we define may be useful as a biomarker for efficacy of intervention responses.
- Published
- 2019
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