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1. Dimensional reduction of gradient-like stochastic systems with multiplicative noise via Fokker-Planck diffusion maps

Author

Baumgartner, Andrew, Huang, Sui, Hadlock, Jennifer, and Funk, Cory

Subjects
Quantitative Biology - Quantitative Methods, Physics - Biological Physics, Quantitative Biology - Cell Behavior, Quantitative Biology - Genomics
Abstract

Dimensional reduction techniques have long been used to visualize the structure and geometry of high dimensional data. However, most widely used techniques are difficult to interpret due to nonlinearities and opaque optimization processes. Here we present a specific graph based construction for dimensionally reducing continuous stochastic systems with multiplicative noise moving under the influence of a potential. To achieve this, we present a specific graph construction which generates the Fokker-Planck equation of the stochastic system in the continuum limit. The eigenvectors and eigenvalues of the normalized graph Laplacian are used as a basis for the dimensional reduction and yield a low dimensional representation of the dynamics which can be used for downstream analysis such as spectral clustering. We focus on the use case of single cell RNA sequencing data and show how current diffusion map implementations popular in the single cell literature fit into this framework.

Published
2024

2. Circular-SWAT for deep learning based diagnostic classification of Alzheimer's disease: application to metabolome data

Author

Jo, Taeho, Kim, Junpyo, Bice, Paula, Huynh, Kevin, Wang, Tingting, Arnold, Matthias, Meikle, Peter J, Giles, Corey, Kaddurah-Daouk, Rima, Saykin, Andrew J, Nho, Kwangsik, Kueider-Paisley, Alexandra, Doraiswamy, P Murali, Blach, Colette, Moseley, Arthur, Thompson, Will, St John-Williams, Lisa, Mahmoudiandehkhordi, Siamak, Tenenbaum, Jessica, Welsh-Balmer, Kathleen, Plassman, Brenda, Risacher, Shannon L, Kastenmüller, Gabi, Han, Xianlin, Baillie, Rebecca, Knight, Rob, Dorrestein, Pieter, Brewer, James, Mayer, Emeran, Labus, Jennifer, Baldi, Pierre, Gupta, Arpana, Fiehn, Oliver, Barupal, Dinesh, Meikle, Peter, Mazmanian, Sarkis, Rader, Dan, Kling, Mitchel, Shaw, Leslie, Trojanowski, John, van Duijin, Cornelia, Nevado-Holgado, Alejo, Bennett, David, Krishnan, Ranga, Keshavarzian, Ali, Vogt, Robin, Ikram, Arfan, Hankemeier, Thomas, Thiele, Ines, Price, Nathan, Funk, Cory, Baloni, Priyanka, Jia, Wei, Wishart, David, Brinton, Roberta, Chang, Rui, Farrer, Lindsay, Au, Rhoda, Qiu, Wendy, Würtz, Peter, Koal, Therese, Mangravite, Lara, Krumsiek, Jan, Suhre, Karsten, Newman, John, Moreno, Herman, Foroud, Tatania, Sacks, Frank, Jansson, Janet, Weiner, Michael W, Aisen, Paul, Petersen, Ronald, Jack, Clifford R, Jagust, William, Trojanowki, John Q, Toga, Arthur W, Beckett, Laurel, Green, Robert C, Morris, John C, Perrin, Richard J, Shaw, Leslie M, Khachaturian, Zaven, Carrillo, Maria, Potter, William, Barnes, Lisa, Bernard, Marie, Gonzalez, Hector, Ho, Carole, Hsiao, John K, Jackson, Jonathan, Masliah, Eliezer, Masterman, Donna, Okonkwo, Ozioma, Perrin, Richard, and Ryan, Laurie

Subjects
Medical Biochemistry and Metabolomics, Biomedical and Clinical Sciences, Neurosciences, Acquired Cognitive Impairment, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Networking and Information Technology R&D (NITRD), Machine Learning and Artificial Intelligence, Dementia, Aging, Brain Disorders, Alzheimer's Disease, Neurodegenerative, Neurological, Humans, Aged, Magnetic Resonance Imaging, Deep Learning, Alzheimer Disease, Neuroimaging, Metabolome, Lipids, Cognitive Dysfunction, Alzheimer's Disease Metabolomics Consortium, Alzheimer's Disease Neuroimaging Initiative, Alzheimer's disease, Deep learning, Lipidomics, Machine learning, Metabolomics, Clinical Sciences, Public Health and Health Services, Clinical sciences, Epidemiology
Abstract

BackgroundDeep learning has shown potential in various scientific domains but faces challenges when applied to complex, high-dimensional multi-omics data. Alzheimer's Disease (AD) is a neurodegenerative disorder that lacks targeted therapeutic options. This study introduces the Circular-Sliding Window Association Test (c-SWAT) to improve the classification accuracy in predicting AD using serum-based metabolomics data, specifically lipidomics.MethodsThe c-SWAT methodology builds upon the existing Sliding Window Association Test (SWAT) and utilizes a three-step approach: feature correlation analysis, feature selection, and classification. Data from 997 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) served as the basis for model training and validation. Feature correlations were analyzed using Weighted Gene Co-expression Network Analysis (WGCNA), and Convolutional Neural Networks (CNN) were employed for feature selection. Random Forest was used for the final classification.FindingsThe application of c-SWAT resulted in a classification accuracy of up to 80.8% and an AUC of 0.808 for distinguishing AD from cognitively normal older adults. This marks a 9.4% improvement in accuracy and a 0.169 increase in AUC compared to methods without c-SWAT. These results were statistically significant, with a p-value of 1.04 × 10ˆ-4. The approach also identified key lipids associated with AD, such as Cer(d16:1/22:0) and PI(37:6).InterpretationOur results indicate that c-SWAT is effective in improving classification accuracy and in identifying potential lipid biomarkers for AD. These identified lipids offer new avenues for understanding AD and warrant further investigation.FundingThe specific funding of this article is provided in the acknowledgements section.

Published
2023

4. Author Correction: Partial inhibition of mitochondrial complex I ameliorates Alzheimer’s disease pathology and cognition in APP/PS1 female mice

Author

Stojakovic, Andrea, Trushin, Sergey, Sheu, Anthony, Khalili, Layla, Chang, Su-Youne, Li, Xing, Christensen, Trace, Salisbury, Jeffrey L., Geroux, Rachel E., Gateno, Benjamin, Flannery, Padraig J., Dehankar, Mrunal, Funk, Cory C., Wilkins, Jordan, Stepanova, Anna, O’Hagan, Tara, Galkin, Alexander, Nesbitt, Jarred, Zhu, Xiujuan, Tripathi, Utkarsh, Macura, Slobodan, Tchkonia, Tamar, Pirtskhalava, Tamar, Kirkland, James L., Kudgus, Rachel A., Schoon, Renee A., Reid, Joel M., Yamazaki, Yu, Kanekiyo, Takahisa, Zhang, Song, Nemutlu, Emirhan, Dzeja, Petras, Jaspersen, Adam, Kwon, Ye In Christopher, Lee, Michael K., and Trushina, Eugenia

Published
2024
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5. A metabolomic signature of the APOE2 allele

Author

Sebastiani, Paola, Song, Zeyuan, Ellis, Dylan, Tian, Qu, Schwaiger-Haber, Michaela, Stancliffe, Ethan, Lustgarten, Michael S, Funk, Cory C, Baloni, Priyanka, Yao, Cong-Hui, Joshi, Shakchhi, Marron, Megan M, Gurinovich, Anastasia, Li, Mengze, Leshchyk, Anastasia, Xiang, Qingyan, Andersen, Stacy L, Feitosa, Mary F, Ukraintseva, Svetlana, Soerensen, Mette, Fiehn, Oliver, Ordovas, Jose M, Haigis, Marcia, Monti, Stefano, Barzilai, Nir, Milman, Sofiya, Ferrucci, Luigi, Rappaport, Noa, Patti, Gary J, and Perls, Thomas T

Subjects
Medical Biochemistry and Metabolomics, Biomedical and Clinical Sciences, Genetics, Aging, Aged, 80 and over, Humans, Apolipoprotein E2, Polymorphism, Genetic, Alleles, Longitudinal Studies, Apolipoproteins E, Apolipoprotein E, Longevity, Metabolomics, Lipid metabolism, Clinical sciences
Abstract

With the goal of identifying metabolites that significantly correlate with the protective e2 allele of the apolipoprotein E (APOE) gene, we established a consortium of five studies of healthy aging and extreme human longevity with 3545 participants. This consortium includes the New England Centenarian Study, the Baltimore Longitudinal Study of Aging, the Arivale study, the Longevity Genes Project/LonGenity studies, and the Long Life Family Study. We analyzed the association between APOE genotype groups E2 (e2e2 and e2e3 genotypes, N = 544), E3 (e3e3 genotypes, N = 2299), and E4 (e3e4 and e4e4 genotypes, N = 702) with metabolite profiles in the five studies and used fixed effect meta-analysis to aggregate the results. Our meta-analysis identified a signature of 19 metabolites that are significantly associated with the E2 genotype group at FDR

Published
2023

6. Multi-Omic analyses characterize the ceramide/sphingomyelin pathway as a therapeutic target in Alzheimer’s disease

Author

Baloni, Priyanka, Arnold, Matthias, Buitrago, Luna, Nho, Kwangsik, Moreno, Herman, Huynh, Kevin, Brauner, Barbara, Louie, Gregory, Kueider-Paisley, Alexandra, Suhre, Karsten, Saykin, Andrew J, Ekroos, Kim, Meikle, Peter J, Hood, Leroy, Price, Nathan D, Doraiswamy, P Murali, Funk, Cory C, Hernández, A Iván, Kastenmüller, Gabi, Baillie, Rebecca, Han, Xianlin, and Kaddurah-Daouk, Rima

Subjects
Medical Biochemistry and Metabolomics, Biological Sciences, Biomedical and Clinical Sciences, Genetics, Human Genome, Neurosciences, Acquired Cognitive Impairment, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Brain Disorders, Dementia, Neurodegenerative, Pediatric Research Initiative, Alzheimer's Disease, Aging, Aetiology, 2.1 Biological and endogenous factors, Neurological, Alzheimer Disease, Animals, Ceramides, Fingolimod Hydrochloride, Genome-Wide Association Study, Humans, Mice, Sphingolipids, Sphingomyelins, Alzheimer’s Disease Metabolomics Consortium, Biological sciences, Biomedical and clinical sciences
Abstract

Dysregulation of sphingomyelin and ceramide metabolism have been implicated in Alzheimer's disease. Genome-wide and transcriptome-wide association studies have identified various genes and genetic variants in lipid metabolism that are associated with Alzheimer's disease. However, the molecular mechanisms of sphingomyelin and ceramide disruption remain to be determined. We focus on the sphingolipid pathway and carry out multi-omics analyses to identify central and peripheral metabolic changes in Alzheimer's patients, correlating them to imaging features. Our multi-omics approach is based on (a) 2114 human post-mortem brain transcriptomics to identify differentially expressed genes; (b) in silico metabolic flux analysis on context-specific metabolic networks identified differential reaction fluxes; (c) multimodal neuroimaging analysis on 1576 participants to associate genetic variants in sphingomyelin pathway with Alzheimer's disease pathogenesis; (d) plasma metabolomic and lipidomic analysis to identify associations of lipid species with dysregulation in Alzheimer's; and (e) metabolite genome-wide association studies to define receptors within the pathway as a potential drug target. We validate our hypothesis in amyloidogenic APP/PS1 mice and show prolonged exposure to fingolimod alleviated synaptic plasticity and cognitive impairment in mice. Our integrative multi-omics approach identifies potential targets in the sphingomyelin pathway and suggests modulators of S1P metabolism as possible candidates for Alzheimer's disease treatment.

Published
2022

7. Integrative proteomics identifies a conserved Aβ amyloid responsome, novel plaque proteins, and pathology modifiers in Alzheimer’s disease

Author

Levites, Yona, Dammer, Eric B., Ran, Yong, Tsering, Wangchen, Duong, Duc, Abreha, Measho, Gadhavi, Joshna, Lolo, Kiara, Trejo-Lopez, Jorge, Phillips, Jennifer, Iturbe, Andrea, Erquizi, Aya, Moore, Brenda D., Ryu, Danny, Natu, Aditya, Dillon, Kristy, Torrellas, Jose, Moran, Corey, Ladd, Thomas, Afroz, Farhana, Islam, Tariful, Jagirdar, Jaishree, Funk, Cory C., Robinson, Max, Rangaraju, Srikant, Borchelt, David R., Ertekin-Taner, Nilüfer, Kelly, Jeffrey W., Heppner, Frank L., Johnson, Erik C.B., McFarland, Karen, Levey, Allan I., Prokop, Stefan, Seyfried, Nicholas T., and Golde, Todd E.

Published
2024
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8. Manifestations of Alzheimer’s disease genetic risk in the blood are evident in a multiomic analysis in healthy adults aged 18 to 90

Author

Heath, Laura, Earls, John C, Magis, Andrew T, Kornilov, Sergey A, Lovejoy, Jennifer C, Funk, Cory C, Rappaport, Noa, Logsdon, Benjamin A, Mangravite, Lara M, Kunkle, Brian W, Martin, Eden R, Naj, Adam C, Ertekin-Taner, Nilüfer, Golde, Todd E, Hood, Leroy, and Price, Nathan D

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Genetics, Epidemiology, Health Sciences, Medical Biochemistry and Metabolomics, Dementia, Acquired Cognitive Impairment, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Alzheimer's Disease, Aging, Brain Disorders, Neurodegenerative, Human Genome, Clinical Research, Prevention, 2.1 Biological and endogenous factors, Aetiology, Neurological, ATP-Binding Cassette Transporters, Adult, Alzheimer Disease, Apolipoprotein E2, Female, Genetic Predisposition to Disease, Genome-Wide Association Study, Genomics, Humans, Male, Polymorphism, Single Nucleotide, Alzheimer’s Disease Genetics Consortium
Abstract

Genetics play an important role in late-onset Alzheimer's Disease (AD) etiology and dozens of genetic variants have been implicated in AD risk through large-scale GWAS meta-analyses. However, the precise mechanistic effects of most of these variants have yet to be determined. Deeply phenotyped cohort data can reveal physiological changes associated with genetic risk for AD across an age spectrum that may provide clues to the biology of the disease. We utilized over 2000 high-quality quantitative measurements obtained from blood of 2831 cognitively normal adult clients of a consumer-based scientific wellness company, each with CLIA-certified whole-genome sequencing data. Measurements included: clinical laboratory blood tests, targeted chip-based proteomics, and metabolomics. We performed a phenome-wide association study utilizing this diverse blood marker data and 25 known AD genetic variants and an AD-specific polygenic risk score (PGRS), adjusting for sex, age, vendor (for clinical labs), and the first four genetic principal components; sex-SNP interactions were also assessed. We observed statistically significant SNP-analyte associations for five genetic variants after correction for multiple testing (for SNPs in or near NYAP1, ABCA7, INPP5D, and APOE), with effects detectable from early adulthood. The ABCA7 SNP and the APOE2 and APOE4 encoding alleles were associated with lipid variability, as seen in previous studies; in addition, six novel proteins were associated with the e2 allele. The most statistically significant finding was between the NYAP1 variant and PILRA and PILRB protein levels, supporting previous functional genomic studies in the identification of a putative causal variant within the PILRA gene. We did not observe associations between the PGRS and any analyte. Sex modified the effects of four genetic variants, with multiple interrelated immune-modulating effects associated with the PICALM variant. In post-hoc analysis, sex-stratified GWAS results from an independent AD case-control meta-analysis supported sex-specific disease effects of the PICALM variant, highlighting the importance of sex as a biological variable. Known AD genetic variation influenced lipid metabolism and immune response systems in a population of non-AD individuals, with associations observed from early adulthood onward. Further research is needed to determine whether and how these effects are implicated in early-stage biological pathways to AD. These analyses aim to complement ongoing work on the functional interpretation of AD-associated genetic variants.

Published
2022

9. Circular-SWAT for deep learning based diagnostic classification of Alzheimer's disease: application to metabolome data

Author

Kaddurah-Daouk, Rima, Kueider-Paisley, Alexandra, Doraiswamy, P. Murali, Blach, Colette, Moseley, Arthur, Thompson, Will, St John-Williams, Lisa, Mahmoudiandehkhordi, Siamak, Tenenbaum, Jessica, Welsh-Balmer, Kathleen, Plassman, Brenda, Saykin, Andrew J., Nho, Kwangsik, Risacher, Shannon L., Kastenmüller, Gabi, Arnold, Matthias, Han, Xianlin, Baillie, Rebecca, Knight, Rob, Dorrestein, Pieter, Brewer, James, Mayer, Emeran, Labus, Jennifer, Baldi, Pierre, Gupta, Arpana, Fiehn, Oliver, Barupal, Dinesh, Meikle, Peter, Mazmanian, Sarkis, Rader, Dan, Kling, Mitchel, Shaw, Leslie, Trojanowski, John, van Duijin, Cornelia, Nevado-Holgado, Alejo, Bennett, David, Krishnan, Ranga, Keshavarzian, Ali, Vogt, Robin, Ikram, Arfan, Hankemeier, Thomas, Thiele, Ines, Price, Nathan, Funk, Cory, Baloni, Priyanka, Jia, Wei, Wishart, David, Brinton, Roberta, Chang, Rui, Farrer, Lindsay, Au, Rhoda, Qiu, Wendy, Würtz, Peter, Koal, Therese, Mangravite, Lara, Krumsiek, Jan, Suhre, Karsten, Newman, John, Moreno, Herman, Foroud, Tatania, Sacks, Frank, Jansson, Janet, Weiner, Michael W., Aisen, Paul, Petersen, Ronald, Jack, Clifford R., Jr., Jagust, William, Trojanowki, John Q., Toga, Arthur W., Beckett, Laurel, Green, Robert C., Morris, John C., Perrin, Richard J., Shaw, Leslie M., Khachaturian, Zaven, Carrillo, Maria, Potter, William, Barnes, Lisa, Bernard, Marie, Gonzalez, Hector, Ho, Carole, Hsiao, John K., Jackson, Jonathan, Masliah, Eliezer, Masterman, Donna, Okonkwo, Ozioma, Perrin, Richard, Ryan, Laurie, Silverberg, Nina, Fleisher, Adam, Sacrey, Diana Truran, Fockler, Juliet, Conti, Cat, Veitch, Dallas, Neuhaus, John, Jin, Chengshi, Nosheny, Rachel, Ashford, Miriam, Flenniken, Derek, Kormos, Adrienne, Montine, Tom, Rafii, Michael, Raman, Rema, Jimenez, Gustavo, Donohue, Michael, Gessert, Devon, Salazar, Jennifer, Zimmerman, Caileigh, Cabrera, Yuliana, Walter, Sarah, Miller, Garrett, Coker, Godfrey, Clanton, Taylor, Hergesheimer, Lindsey, Smith, Stephanie, Adegoke, Olusegun, Mahboubi, Payam, Moore, Shelley, Pizzola, Jeremy, Shaffer, Elizabeth, Sloan, Brittany, Harvey, Danielle, Forghanian-Arani, Arvin, Borowski, Bret, Ward, Chad, Schwarz, Christopher, Jones, David, Gunter, Jeff, Kantarci, Kejal, Senjem, Matthew, Vemuri, Prashanthi, Reid, Robert, Fox, Nick C., Malone, Ian, Thompson, Paul, Thomopoulos, Sophia I., Nir, Talia M., Jahanshad, Neda, DeCarli, Charles, Knaack, Alexander, Fletcher, Evan, Tosun-Turgut, Duygu, Chen, Stephanie Rossi, Choe, Mark, Crawford, Karen, Yushkevich, Paul A., Das, Sandhitsu, Koeppe, Robert A., Reiman, Eric M., Chen, Kewei, Mathis, Chet, Landau, Susan, Cairns, Nigel J., Householder, Erin, Franklin, Erin, Bernhardt, Haley, Taylor-Reinwald, Lisa, Korecka, Magdalena, Figurski, Michal, Neu, Scott, Apostolova, Liana G., Shen, Li, Foroud, Tatiana M., Nudelman, Kelly, Faber, Kelley, Wilmes, Kristi, Thal, Leon, Silbert, Lisa C., Lind, Betty, Crissey, Rachel, Kaye, Jeffrey A., Carter, Raina, Dolen, Sara, Quinn, Joseph, Schneider, Lon S., Pawluczyk, Sonia, Becerra, Mauricio, Teodoro, Liberty, Dagerman, Karen, Spann, Bryan M., Vanderswag, Helen, Ziolkowski, Jaimie, Heidebrink, Judith L., Zbizek-Nulph, Lisa, Lord, Joanne L., Mason, Sara S., Albers, Colleen S., Knopman, David, Johnson, Kris, Villanueva-Meyer, Javier, Pavlik, Valory, Pacini, Nathaniel, Lamb, Ashley, Kass, Joseph S., Doody, Rachelle S., Shibley, Victoria, Chowdhury, Munir, Rountree, Susan, Dang, Mimi, Stern, Yaakov, Honig, Lawrence S., Mintz, Akiva, Ances, Beau, Winkfield, David, Carroll, Maria, Stobbs-Cucchi, Georgia, Oliver, Angela, Creech, Mary L., Mintun, Mark A., Schneider, Stacy, Geldmacher, David, Love, Marissa Natelson, Griffith, Randall, Clark, David, Brockington, John, Marson, Daniel, Grossman, Hillel, Goldstein, Martin A., Greenberg, Jonathan, Mitsis, Effie, Shah, Raj C., Lamar, Melissa, Samuels, Patricia, Duara, Ranjan, Greig-Custo, Maria T., Rodriguez, Rosemarie, Albert, Marilyn, Onyike, Chiadi, Farrington, Leonie, Rudow, Scott, Brichko, Rottislav, Kielb, Stephanie, Smith, Amanda, Raj, Balebail Ashok, Fargher, Kristin, Sadowski, Martin, Wisniewski, Thomas, Shulman, Melanie, Faustin, Arline, Rao, Julia, Castro, Karen M., Ulysse, Anaztasia, Chen, Shannon, Sheikh, Mohammed O., Singleton-Garvin, Jamika, Petrella, JeffreyR., James, Olga, Wong, Terence Z., Borges-Neto, Salvador, Karlawish, Jason H., Wolk, David A., Vaishnavi, Sanjeev, Clark, Christopher M., Arnold, Steven E., Smith, Charles D., Jicha, Gregory A., Raslau, Flavius D., Lopez, Oscar L., Oakley, MaryAnn, Simpson, Donna M., Porsteinsson, Anton P., Martin, Kim, Kowalski, Nancy, Keltz, Melanie, Goldstein, Bonnie S., Makino, Kelly M., Ismail, M. Saleem, Brand, Connie, Thai, Gaby, Pierce, Aimee, Yanez, Beatriz, Sosa, Elizabeth, Witbracht, Megan, Kelley, Brendan, Nguyen, Trung, Womack, Kyle, Mathews, Dana, Quiceno, Mary, Levey, Allan I., Lah, James J., Hajjar, Ihab, Cellar, Janet S., Burns, Jeffrey M., Swerdlow, Russell H., Brooks, William M., Silverman, Daniel H.S., Kremen, Sarah, Apostolova, Liana, Tingus, Kathleen, Lu, Po H., Bartzokis, George, Woo, Ellen, Teng, Edmond, Graff-Radford, Neill R., Parfitt, Francine, Poki-Walker, Kim, Farlow, Martin R., Hake, Ann Marie, Matthews, Brandy R., Brosch, Jared R., Herring, Scott, van, Christopher H., Mecca, Adam P., Good, Susan P., MacAvoy, Martha G., Carson, Richard E., Varma, Pradeep, Chertkow, Howard, Vaitekunis, Susan, Hosein, Chris, Black, Sandra, Stefanovic, Bojana, Heyn, Chris (Chinthaka), Robin Hsiung, Ging-Yuek, Kim, Ellen, Mudge, Benita, Sossi, Vesna, Feldman, Howard, Assaly, Michele, Finger, Elizabeth, Pasternak, Stephen, Rachinsky, Irina, Kertesz, Andrew, Drost, Dick, Rogers, John, Grant, Ian, Muse, Brittanie, Rogalski, Emily, Robson, Jordan, Mesulam, M.-Marsel, Kerwin, Diana, Wu, Chuang-Kuo, Johnson, Nancy, Lipowski, Kristine, Weintraub, Sandra, Bonakdarpour, Borna, Pomara, Nunzio, Hernando, Raymundo, Sarrael, Antero, Rosen, Howard J., Miller, Bruce L., Perry, David, Turner, Raymond Scott, Johnson, Kathleen, Reynolds, Brigid, MCCann, Kelly, Poe, Jessica, Sperling, Reisa A., Johnson, Keith A., Marshall, Gad A., Yesavage, Jerome, Taylor, Joy L., Chao, Steven, Coleman, Jaila, White, Jessica D., Lane, Barton, Rosen, Allyson, Tinklenberg, Jared, Belden, Christine M., Atri, Alireza, Clark, Kelly A., Zamrini, Edward, Sabbagh, Marwan, Killiany, Ronald, Stern, Robert, Mez, Jesse, Kowall, Neil, Budson, Andrew E., Obisesan, Thomas O., Ntekim, Oyonumo E., Wolday, Saba, Khan, Javed I., Nwulia, Evaristus, Nadarajah, Sheeba, Lerner, Alan, Ogrocki, Paula, Tatsuoka, Curtis, Fatica, Parianne, Maillard, Pauline, Olichney, John, Carmichael, Owen, Bates, Vernice, Capote, Horacio, Rainka, Michelle, Borrie, Michael, Lee, T.-Y., Bartha, Dr Rob, Johnson, Sterling, Asthana, Sanjay, Carlsson, Cynthia M., Perrin, Allison, Burke, Anna, Scharre, Douglas W., Kataki, Maria, Tarawneh, Rawan, Hart, David, Zimmerman, Earl A., Celmins, Dzintra, Miller, Delwyn D., BolesPonto, Laura L., Smith, Karen Ekstam, Koleva, Hristina, Shim, Hyungsub, Nam, Ki Won, Schultz, Susan K., Williamson, Jeff D., Craft, Suzanne, Cleveland, Jo, Yang, Mia, Sink, Kaycee M., Ott, Brian R., Drake, Jonathan, Tremont, Geoffrey, Daiello, Lori A., Drake, Jonathan D., Ritter, Aaron, Bernick, Charles, Munic, Donna, O'Connelll, Abigail, Mintzer, Jacobo, Wiliams, Arthur, Masdeu, Joseph, Shi, Jiong, Garcia, Angelica, Newhouse, Paul, Potkin, Steven, Salloway, Stephen, Malloy, Paul, Correia, Stephen, Kittur, Smita, Pearlson, Godfrey D., Blank, Karen, Anderson, Karen, Flashman, Laura A., Seltzer, Marc, Hynes, Mary L., Santulli, Robert B., Relkin, Norman, Chiang, Gloria, Lee, Athena, Lin, Michael, Ravdin, Lisa, Petersen, Ron, Neylan, Thomas, Grafman, Jordan, Danowski, Sarah, Nguyen-Barrera, Catherine, Hayes, Jacqueline, Finley, Shannon, Bernstein, Matthew, Senjem, Matt, Foster, Norm, Kim, Sungeun, Sood, Ajay, Blanchard, Kimberly S., Fleischman, Debra, Arfanakis, Konstantinos, Varon, Daniel, Greig, Maria T., Petrella, Jeffrey R., Goldstein, Bonnie, Martin, Kimberly S., Reist, Christopher, Sadowsky, Carl, Martinez, Walter, Villena, Teresa, Rosen, Howard, Marshall, Gad, Peskind, Elaine R., Petrie, Eric C., Li, Gail, Mackin, Scott, Jimenez-Maggiora, Gustavo, Drake, Erin, Donohue, Mike, Nelson, Craig, Bickford, David, Butters, Meryl, Zmuda, Michelle, Reyes, Denise, Faber, Kelley M., Nudelman, Kelly N., Au, Yiu Ho, Scherer, Kelly, Catalinotto, Daniel, Stark, Samuel, Ong, Elise, Fernandez, Dariella, Jo, Taeho, Kim, Junpyo, Bice, Paula, Huynh, Kevin, Wang, Tingting, Meikle, Peter J., and Giles, Corey

Published
2023
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10. Metabolic Network Analysis Reveals Altered Bile Acid Synthesis and Metabolism in Alzheimer's Disease.

Author

Baloni, Priyanka, Funk, Cory C, Yan, Jingwen, Yurkovich, James T, Kueider-Paisley, Alexandra, Nho, Kwangsik, Heinken, Almut, Jia, Wei, Mahmoudiandehkordi, Siamak, Louie, Gregory, Saykin, Andrew J, Arnold, Matthias, Kastenmüller, Gabi, Griffiths, William J, Thiele, Ines, Alzheimer’s Disease Metabolomics Consortium, Kaddurah-Daouk, Rima, and Price, Nathan D

Subjects
Alzheimer’s Disease Metabolomics Consortium, Alzheimer's disease, bile acids, cholesterol metabolism, genome-scale metabolic models, metabolomics, transcriptional regulatory networks, transcriptomics
Abstract

Increasing evidence suggests Alzheimer's disease (AD) pathophysiology is influenced by primary and secondary bile acids, the end product of cholesterol metabolism. We analyze 2,114 post-mortem brain transcriptomes and identify genes in the alternative bile acid synthesis pathway to be expressed in the brain. A targeted metabolomic analysis of primary and secondary bile acids measured from post-mortem brain samples of 111 individuals supports these results. Our metabolic network analysis suggests that taurine transport, bile acid synthesis, and cholesterol metabolism differ in AD and cognitively normal individuals. We also identify putative transcription factors regulating metabolic genes and influencing altered metabolism in AD. Intriguingly, some bile acids measured in brain tissue cannot be explained by the presence of enzymes responsible for their synthesis, suggesting that they may originate from the gut microbiome and are transported to the brain. These findings motivate further research into bile acid metabolism in AD to elucidate their possible connection to cognitive decline.

Published
2020

11. Metabolic Network Analysis Reveals Altered Bile Acid Synthesis and Metabolism in Alzheimer’s Disease

Author

Kaddurah-Daouk, Rima, Kueider-Paisley, Alexandra, Louie, Gregory, Doraiswamy, P. Murali, Blach, Colette, Moseley, Arthur, Thompson, J. Will, Mahmoudiandehkhordi, Siamak, Welsh-Balmer, Kathleen, Plassman, Brenda, Saykin, Andrew, Nho, Kwangsik, Kastenmüller, Gabi, Arnold, Matthias, Bhattacharyya, Sudeepa, Han, Xianlin, Baillie, Rebecca, Fiehn, Oliver, Barupal, Dinesh, Meikle, Peter, Mazmanian, Sarkis, Kling, Mitchel, Shaw, Leslie, Trojanowski, John, Toledo, Jon, van Duijin, Cornelia, Hankemier, Thomas, Thiele, Ines, Heinken, Almut, Price, Nathan, Funk, Cory, Baloni, Priyanka, Jia, Wei, Wishart, David, Brinton, Roberta, Chang, Rui, Farrer, Lindsay, Au, Rhoda, Qiu, Wendy, Würtz, Peter, Mangravite, Lara, Krumsiek, Jan, Newman, John, Zhang, Bin, Moreno, Herman, Funk, Cory C., Yan, Jingwen, Yurkovich, James T., Mahmoudiandehkordi, Siamak, Saykin, Andrew J., Griffiths, William J., and Price, Nathan D.

Published
2020
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14. Conserved brain myelination networks are altered in Alzheimer's and other neurodegenerative diseases

Author

Allen, Mariet, Wang, Xue, Burgess, Jeremy D, Watzlawik, Jens, Serie, Daniel J, Younkin, Curtis S, Nguyen, Thuy, Malphrus, Kimberly G, Lincoln, Sarah, Carrasquillo, Minerva M, Ho, Charlotte, Chakrabarty, Paramita, Strickland, Samantha, Murray, Melissa E, Swarup, Vivek, Geschwind, Daniel H, Seyfried, Nicholas T, Dammer, Eric B, Lah, James J, Levey, Allan I, Golde, Todd E, Funk, Cory, Li, Hongdong, Price, Nathan D, Petersen, Ronald C, Graff‐Radford, Neill R, Younkin, Steven G, Dickson, Dennis W, Crook, Julia R, Asmann, Yan W, and Ertekin‐Taner, Nilüfer

Subjects
Biomedical and Clinical Sciences, Biological Psychology, Clinical Sciences, Neurosciences, Psychology, Aging, Acquired Cognitive Impairment, Dementia, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Brain Disorders, Neurodegenerative, Alzheimer's Disease, 2.1 Biological and endogenous factors, Aetiology, 1.1 Normal biological development and functioning, Underpinning research, Neurological, Alzheimer Disease, Brain, Cohort Studies, Computational Biology, Female, Gene Expression Regulation, Genetic Predisposition to Disease, Humans, Male, Myelin Sheath, Neurons, Supranuclear Palsy, Progressive, Transcriptome, Proteinopathies, Alzheimer's disease, Progressive supranuclear palsy, Myelination, Coexpression networks, Temporal cortex, Cerebellum, Geriatrics, Clinical sciences, Biological psychology
Abstract

IntroductionComparative transcriptome analyses in Alzheimer's disease (AD) and other neurodegenerative proteinopathies can uncover both shared and distinct disease pathways.MethodsWe analyzed 940 brain transcriptomes including patients with AD, progressive supranuclear palsy (PSP; a primary tauopathy), and control subjects.ResultsWe identified transcriptional coexpression networks implicated in myelination, which were lower in PSP temporal cortex (TCX) compared with AD. Some of these associations were retained even after adjustments for brain cell population changes. These TCX myelination network structures were preserved in cerebellum but they were not differentially expressed in cerebellum between AD and PSP. Myelination networks were downregulated in both AD and PSP, when compared with control TCX samples.DiscussionDownregulation of myelination networks may underlie both PSP and AD pathophysiology, but may be more pronounced in PSP. These data also highlight conservation of transcriptional networks across brain regions and the influence of cell type changes on these networks.

Published
2018

17. Rare coding variants in PLCG2, ABI3, and TREM2 implicate microglial-mediated innate immunity in Alzheimer's disease.

Author

Sims, Rebecca, van der Lee, Sven J, Naj, Adam C, Bellenguez, Céline, Badarinarayan, Nandini, Jakobsdottir, Johanna, Kunkle, Brian W, Boland, Anne, Raybould, Rachel, Bis, Joshua C, Martin, Eden R, Grenier-Boley, Benjamin, Heilmann-Heimbach, Stefanie, Chouraki, Vincent, Kuzma, Amanda B, Sleegers, Kristel, Vronskaya, Maria, Ruiz, Agustin, Graham, Robert R, Olaso, Robert, Hoffmann, Per, Grove, Megan L, Vardarajan, Badri N, Hiltunen, Mikko, Nöthen, Markus M, White, Charles C, Hamilton-Nelson, Kara L, Epelbaum, Jacques, Maier, Wolfgang, Choi, Seung-Hoan, Beecham, Gary W, Dulary, Cécile, Herms, Stefan, Smith, Albert V, Funk, Cory C, Derbois, Céline, Forstner, Andreas J, Ahmad, Shahzad, Li, Hongdong, Bacq, Delphine, Harold, Denise, Satizabal, Claudia L, Valladares, Otto, Squassina, Alessio, Thomas, Rhodri, Brody, Jennifer A, Qu, Liming, Sánchez-Juan, Pascual, Morgan, Taniesha, Wolters, Frank J, Zhao, Yi, Garcia, Florentino Sanchez, Denning, Nicola, Fornage, Myriam, Malamon, John, Naranjo, Maria Candida Deniz, Majounie, Elisa, Mosley, Thomas H, Dombroski, Beth, Wallon, David, Lupton, Michelle K, Dupuis, Josée, Whitehead, Patrice, Fratiglioni, Laura, Medway, Christopher, Jian, Xueqiu, Mukherjee, Shubhabrata, Keller, Lina, Brown, Kristelle, Lin, Honghuang, Cantwell, Laura B, Panza, Francesco, McGuinness, Bernadette, Moreno-Grau, Sonia, Burgess, Jeremy D, Solfrizzi, Vincenzo, Proitsi, Petra, Adams, Hieab H, Allen, Mariet, Seripa, Davide, Pastor, Pau, Cupples, L Adrienne, Price, Nathan D, Hannequin, Didier, Frank-García, Ana, Levy, Daniel, Chakrabarty, Paramita, Caffarra, Paolo, Giegling, Ina, Beiser, Alexa S, Giedraitis, Vilmantas, Hampel, Harald, Garcia, Melissa E, Wang, Xue, Lannfelt, Lars, Mecocci, Patrizia, Eiriksdottir, Gudny, Crane, Paul K, Pasquier, Florence, and Boccardi, Virginia

Subjects
ARUK Consortium, GERAD/PERADES, CHARGE, ADGC, EADI, Microglia, Humans, Alzheimer Disease, Genetic Predisposition to Disease, Adaptor Proteins, Signal Transducing, Membrane Glycoproteins, Receptors, Immunologic, Odds Ratio, Case-Control Studies, Gene Expression Profiling, Amino Acid Sequence, Sequence Homology, Amino Acid, Gene Frequency, Genotype, Linkage Disequilibrium, Polymorphism, Single Nucleotide, Phospholipase C gamma, Immunity, Innate, Protein Interaction Maps, Exome, Neurodegenerative, Brain Disorders, Neurosciences, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Dementia, Alzheimer's Disease, Aging, Acquired Cognitive Impairment, 2.1 Biological and endogenous factors, Developmental Biology, Biological Sciences, Medical and Health Sciences
Abstract

We identified rare coding variants associated with Alzheimer's disease in a three-stage case-control study of 85,133 subjects. In stage 1, we genotyped 34,174 samples using a whole-exome microarray. In stage 2, we tested associated variants (P < 1 × 10-4) in 35,962 independent samples using de novo genotyping and imputed genotypes. In stage 3, we used an additional 14,997 samples to test the most significant stage 2 associations (P < 5 × 10-8) using imputed genotypes. We observed three new genome-wide significant nonsynonymous variants associated with Alzheimer's disease: a protective variant in PLCG2 (rs72824905: p.Pro522Arg, P = 5.38 × 10-10, odds ratio (OR) = 0.68, minor allele frequency (MAF)cases = 0.0059, MAFcontrols = 0.0093), a risk variant in ABI3 (rs616338: p.Ser209Phe, P = 4.56 × 10-10, OR = 1.43, MAFcases = 0.011, MAFcontrols = 0.008), and a new genome-wide significant variant in TREM2 (rs143332484: p.Arg62His, P = 1.55 × 10-14, OR = 1.67, MAFcases = 0.0143, MAFcontrols = 0.0089), a known susceptibility gene for Alzheimer's disease. These protein-altering changes are in genes highly expressed in microglia and highlight an immune-related protein-protein interaction network enriched for previously identified risk genes in Alzheimer's disease. These genetic findings provide additional evidence that the microglia-mediated innate immune response contributes directly to the development of Alzheimer's disease.

Published
2017

18. Peripheral inflammation is associated with brain atrophy and cognitive decline linked to mild cognitive impairment and Alzheimer’s disease

Author

Liang, Nuanyi, Nho, Kwangsik, Newman, John W., Arnold, Matthias, Huynh, Kevin, Meikle, Peter J., Borkowski, Kamil, Kaddurah-Daouk, Rima, Kueider-Paisley, Alexandra, Doraiswamy, P. Murali, Blach, Colette, Moseley, Arthur, Mahmoudiandehkhordi, Siamak, Welsh-Balmer, Kathleen, Plassman, Brenda, Saykin, Andrew, Risacher, Shannon, Kastenmüller, Gabi, Han, Xianlin, Baillie, Rebecca, Knight, Rob, Dorrestein, Pieter, Brewer, James, Mayer, Emeran, Labus, Jennifer, Baldi, Pierre, Gupta, Arpana, Fiehn, Oliver, Barupal, Dinesh, Meikle, Peter, Mazmanian, Sarkis, Rader, Dan, Shaw, Leslie, van Duijin, Cornelia, Amin, Najaf, Nevado-Holgado, Alejo, Bennett, David, Krishnan, Ranga, Keshavarzian, Ali, Vogt, Robin, Ikram, Arfan, Hankemeier, Thomas, Thiele, Ines, Funk, Cory, Baloni, Priyanka, Jia, Wei, Wishart, David, Brinton, Roberta, Farrer, Lindsay, Au, Rhoda, Liang, Nuanyi, Nho, Kwangsik, Newman, John W., Arnold, Matthias, Huynh, Kevin, Meikle, Peter J., Borkowski, Kamil, Kaddurah-Daouk, Rima, Kueider-Paisley, Alexandra, Doraiswamy, P. Murali, Blach, Colette, Moseley, Arthur, Mahmoudiandehkhordi, Siamak, Welsh-Balmer, Kathleen, Plassman, Brenda, Saykin, Andrew, Risacher, Shannon, Kastenmüller, Gabi, Han, Xianlin, Baillie, Rebecca, Knight, Rob, Dorrestein, Pieter, Brewer, James, Mayer, Emeran, Labus, Jennifer, Baldi, Pierre, Gupta, Arpana, Fiehn, Oliver, Barupal, Dinesh, Meikle, Peter, Mazmanian, Sarkis, Rader, Dan, Shaw, Leslie, van Duijin, Cornelia, Amin, Najaf, Nevado-Holgado, Alejo, Bennett, David, Krishnan, Ranga, Keshavarzian, Ali, Vogt, Robin, Ikram, Arfan, Hankemeier, Thomas, Thiele, Ines, Funk, Cory, Baloni, Priyanka, Jia, Wei, Wishart, David, Brinton, Roberta, Farrer, Lindsay, and Au, Rhoda

Abstract

Inflammation is an important factor in Alzheimer’s disease (AD). An NMR measurement in plasma, glycoprotein acetyls (GlycA), captures the overall level of protein production and glycosylation implicated in systemic inflammation. With its additional advantage of reducing biological variability, GlycA might be useful in monitoring the relationship between peripheral inflammation and brain changes relevant to AD. However, the associations between GlycA and these brain changes have not been fully evaluated. Here, we performed Spearman’s correlation analyses to evaluate these associations cross-sectionally and determined whether GlycA can inform AD-relevant longitudinal measurements among participants in the Alzheimer’s Disease Neuroimaging Initiative (n = 1506), with additional linear models and stratification analyses to evaluate the influences of sex or diagnosis status and confirm findings from Spearman’s correlation analyses. We found that GlycA was elevated in AD patients compared to cognitively normal participants. GlycA correlated negatively with multiple concurrent regional brain volumes in females diagnosed with late mild cognitive impairment (LMCI) or AD. Baseline GlycA level was associated with executive function decline at 3–9 year follow-up in participants diagnosed with LMCI at baseline, with similar but not identical trends observed in the future decline of memory and entorhinal cortex volume. Results here indicated that GlycA is an inflammatory biomarker relevant to AD pathogenesis and that the stage of LMCI might be relevant to inflammation-related intervention.

Published
2024

24. Partial inhibition of mitochondrial complex I ameliorates Alzheimer’s disease pathology and cognition in APP/PS1 female mice

Author

Stojakovic, Andrea, Trushin, Sergey, Sheu, Anthony, Khalili, Layla, Chang, Su-Youne, Li, Xing, Christensen, Trace, Salisbury, Jeffrey L., Geroux, Rachel E., Gateno, Benjamin, Flannery, Padraig J., Dehankar, Mrunal, Funk, Cory C., Wilkins, Jordan, Stepanova, Anna, O’Hagan, Tara, Galkin, Alexander, Nesbitt, Jarred, Zhu, Xiujuan, Tripathi, Utkarsh, Macura, Slobodan, Tchkonia, Tamar, Pirtskhalava, Tamar, Kirkland, James L., Kudgus, Rachel A., Schoon, Renee A., Reid, Joel M., Yamazaki, Yu, Kanekiyo, Takahisa, Zhang, Song, Nemutlu, Emirhan, Dzeja, Petras, Jaspersen, Adam, Kwon, Ye In Christopher, Lee, Michael K., and Trushina, Eugenia

Published
2021
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26. Isoforms of RNA-Editing Enzyme ADAR1 Independently Control Nucleic Acid Sensor MDA5-Driven Autoimmunity and Multi-organ Development

Author

Pestal, Kathleen, Funk, Cory C, Snyder, Jessica M, Price, Nathan D, Treuting, Piper M, and Stetson, Daniel B

Subjects
Biomedical and Clinical Sciences, Immunology, Genetics, Autoimmune Disease, 2.1 Biological and endogenous factors, Aetiology, Adaptor Proteins, Signal Transducing, Adenosine Deaminase, Animals, Autoimmune Diseases of the Nervous System, Autoimmunity, DEAD-box RNA Helicases, HEK293 Cells, Humans, Interferon Type I, Interferon-Induced Helicase, IFIH1, Mice, Nervous System Malformations, Protein Isoforms, RNA, RNA Editing, RNA-Binding Proteins, Signal Transduction
Abstract

Mutations in ADAR, which encodes the ADAR1 RNA-editing enzyme, cause Aicardi-Goutières syndrome (AGS), a severe autoimmune disease associated with an aberrant type I interferon response. How ADAR1 prevents autoimmunity remains incompletely defined. Here, we demonstrate that ADAR1 is a specific and essential negative regulator of the MDA5-MAVS RNA sensing pathway. Moreover, we uncovered a MDA5-MAVS-independent function for ADAR1 in the development of multiple organs. We showed that the p150 isoform of ADAR1 uniquely regulated the MDA5 pathway, whereas both the p150 and p110 isoforms contributed to development. Abrupt deletion of ADAR1 in adult mice revealed that both of these functions were required throughout life. Our findings delineate genetically separable roles for both ADAR1 isoforms in vivo, with implications for the human diseases caused by ADAR mutations.

Published
2015

27. Multi-study Integration of Brain Cancer Transcriptomes Reveals Organ-Level Molecular Signatures

Author

Sung, Jaeyun, Kim, Pan-Jun, Ma, Shuyi, Funk, Cory C., Magis, Andrew T., Wang, Yuliang, Hood, Leroy, Geman, Donald, and Price, Nathan D.

Subjects
Quantitative Biology - Quantitative Methods
Abstract

We utilized abundant transcriptomic data for the primary classes of brain cancers to study the feasibility of separating all of these diseases simultaneously based on molecular data alone. These signatures were based on a new method reported herein that resulted in a brain cancer marker panel of 44 unique genes. Many of these genes have established relevance to the brain cancers examined, with others having known roles in cancer biology. Analyses on large-scale data from multiple sources must deal with significant challenges associated with heterogeneity between different published studies, for it was observed that the variation among individual studies often had a larger effect on the transcriptome than did phenotype differences, as is typical. We found that learning signatures across multiple datasets greatly enhanced reproducibility and accuracy in predictive performance on truly independent validation sets, even when keeping the size of the training set the same. This was most likely due to the meta-signature encompassing more of the heterogeneity across different sources and conditions, while amplifying signal from the repeated global characteristics of the phenotype. When molecular signatures of brain cancers were constructed from all currently available microarray data, 90 percent phenotype prediction accuracy, or the accuracy of identifying a particular brain cancer from the background of all phenotypes, was found. Looking forward, we discuss our approach in the context of the eventual development of organ-specific molecular signatures from peripheral fluids such as the blood., Comment: 27 pages of main text including 4 figures and 4 tables. 32 pages of supplementary material (Text, Figures, and Tables)

Published
2013
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28. Novel TCF4:TCF12 heterodimer inhibits glioblastoma growth.

Author

Mikheeva, Svetlana A., Funk, Cory C., Horner, Philip J., Rostomily, Robert C., and Mikheev, Andrei M.

Abstract

TWIST1 (TW) is a pro‐oncogenic basic helix–loop–helix (bHLH) transcription factor and promotes the hallmark features of malignancy (e.g., cell invasion, cancer cell stemness, and treatment resistance), which contribute to poor prognoses of glioblastoma (GBM). We previously reported that specific TW dimerization motifs regulate unique cellular phenotypes in GBM. For example, the TW:E12 heterodimer increases periostin (POSTN) expression and promotes cell invasion. TW dimer‐specific transcriptional regulation requires binding to the regulatory E‐box consensus sequences, but alternative bHLH dimers that balance TW dimer activity in regulating pro‐oncogenic TW target genes are unknown. We leveraged the ENCODE DNase I hypersensitivity data to identify E‐box sites and tethered TW:E12 and TW:TW proteins to validate dimer binding to E‐boxes in vitro. Subsequently, TW knockdown revealed a novel TCF4:TCF12 bHLH dimer occupying the same TW E‐box site that, when expressed as a tethered TCF4:TCF12 dimer, markedly repressed POSTN expression and extended animal survival. These observations support TCF4:TCF12 as a novel dimer with tumor‐suppressor activity in GBM that functions in part through displacement of and/or competitive inhibition of pro‐oncogenic TW dimers at E‐box sites. [ABSTRACT FROM AUTHOR]

Published
2024
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29. Correction: Reproducible big data science: A case study in continuous FAIRness

Author

Madduri, Ravi, primary, Chard, Kyle, additional, D’Arcy, Mike, additional, Jung, Segun C., additional, Rodriguez, Alexis, additional, Sulakhe, Dinanath, additional, Deutsch, Eric, additional, Funk, Cory, additional, Heavner, Ben, additional, Richards, Matthew, additional, Shannon, Paul, additional, Glusman, Gustavo, additional, Price, Nathan, additional, Kesselman, Carl, additional, and Foster, Ian, additional

Published
2023
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30. Circular-SWAT for deep learning based diagnostic classification of Alzheimer's disease: application to metabolome data

Author

Jo, Taeho, primary, Kim, Junpyo, additional, Bice, Paula, additional, Huynh, Kevin, additional, Wang, Tingting, additional, Arnold, Matthias, additional, Meikle, Peter J., additional, Giles, Corey, additional, Kaddurah-Daouk, Rima, additional, Saykin, Andrew J., additional, Nho, Kwangsik, additional, Kueider-Paisley, Alexandra, additional, Doraiswamy, P. Murali, additional, Blach, Colette, additional, Moseley, Arthur, additional, Thompson, Will, additional, St John-Williams, Lisa, additional, Mahmoudiandehkhordi, Siamak, additional, Tenenbaum, Jessica, additional, Welsh-Balmer, Kathleen, additional, Plassman, Brenda, additional, Risacher, Shannon L., additional, Kastenmüller, Gabi, additional, Han, Xianlin, additional, Baillie, Rebecca, additional, Knight, Rob, additional, Dorrestein, Pieter, additional, Brewer, James, additional, Mayer, Emeran, additional, Labus, Jennifer, additional, Baldi, Pierre, additional, Gupta, Arpana, additional, Fiehn, Oliver, additional, Barupal, Dinesh, additional, Meikle, Peter, additional, Mazmanian, Sarkis, additional, Rader, Dan, additional, Kling, Mitchel, additional, Shaw, Leslie, additional, Trojanowski, John, additional, van Duijin, Cornelia, additional, Nevado-Holgado, Alejo, additional, Bennett, David, additional, Krishnan, Ranga, additional, Keshavarzian, Ali, additional, Vogt, Robin, additional, Ikram, Arfan, additional, Hankemeier, Thomas, additional, Thiele, Ines, additional, Price, Nathan, additional, Funk, Cory, additional, Baloni, Priyanka, additional, Jia, Wei, additional, Wishart, David, additional, Brinton, Roberta, additional, Chang, Rui, additional, Farrer, Lindsay, additional, Au, Rhoda, additional, Qiu, Wendy, additional, Würtz, Peter, additional, Koal, Therese, additional, Mangravite, Lara, additional, Krumsiek, Jan, additional, Suhre, Karsten, additional, Newman, John, additional, Moreno, Herman, additional, Foroud, Tatania, additional, Sacks, Frank, additional, Jansson, Janet, additional, Weiner, Michael W., additional, Aisen, Paul, additional, Petersen, Ronald, additional, Jack, Clifford R., additional, Jagust, William, additional, Trojanowki, John Q., additional, Toga, Arthur W., additional, Beckett, Laurel, additional, Green, Robert C., additional, Morris, John C., additional, Perrin, Richard J., additional, Shaw, Leslie M., additional, Khachaturian, Zaven, additional, Carrillo, Maria, additional, Potter, William, additional, Barnes, Lisa, additional, Bernard, Marie, additional, Gonzalez, Hector, additional, Ho, Carole, additional, Hsiao, John K., additional, Jackson, Jonathan, additional, Masliah, Eliezer, additional, Masterman, Donna, additional, Okonkwo, Ozioma, additional, Perrin, Richard, additional, Ryan, Laurie, additional, Silverberg, Nina, additional, Fleisher, Adam, additional, Sacrey, Diana Truran, additional, Fockler, Juliet, additional, Conti, Cat, additional, Veitch, Dallas, additional, Neuhaus, John, additional, Jin, Chengshi, additional, Nosheny, Rachel, additional, Ashford, Miriam, additional, Flenniken, Derek, additional, Kormos, Adrienne, additional, Montine, Tom, additional, Rafii, Michael, additional, Raman, Rema, additional, Jimenez, Gustavo, additional, Donohue, Michael, additional, Gessert, Devon, additional, Salazar, Jennifer, additional, Zimmerman, Caileigh, additional, Cabrera, Yuliana, additional, Walter, Sarah, additional, Miller, Garrett, additional, Coker, Godfrey, additional, Clanton, Taylor, additional, Hergesheimer, Lindsey, additional, Smith, Stephanie, additional, Adegoke, Olusegun, additional, Mahboubi, Payam, additional, Moore, Shelley, additional, Pizzola, Jeremy, additional, Shaffer, Elizabeth, additional, Sloan, Brittany, additional, Harvey, Danielle, additional, Forghanian-Arani, Arvin, additional, Borowski, Bret, additional, Ward, Chad, additional, Schwarz, Christopher, additional, Jones, David, additional, Gunter, Jeff, additional, Kantarci, Kejal, additional, Senjem, Matthew, additional, Vemuri, Prashanthi, additional, Reid, Robert, additional, Fox, Nick C., additional, Malone, Ian, additional, Thompson, Paul, additional, Thomopoulos, Sophia I., additional, Nir, Talia M., additional, Jahanshad, Neda, additional, DeCarli, Charles, additional, Knaack, Alexander, additional, Fletcher, Evan, additional, Tosun-Turgut, Duygu, additional, Chen, Stephanie Rossi, additional, Choe, Mark, additional, Crawford, Karen, additional, Yushkevich, Paul A., additional, Das, Sandhitsu, additional, Koeppe, Robert A., additional, Reiman, Eric M., additional, Chen, Kewei, additional, Mathis, Chet, additional, Landau, Susan, additional, Cairns, Nigel J., additional, Householder, Erin, additional, Franklin, Erin, additional, Bernhardt, Haley, additional, Taylor-Reinwald, Lisa, additional, Korecka, Magdalena, additional, Figurski, Michal, additional, Neu, Scott, additional, Apostolova, Liana G., additional, Shen, Li, additional, Foroud, Tatiana M., additional, Nudelman, Kelly, additional, Faber, Kelley, additional, Wilmes, Kristi, additional, Thal, Leon, additional, Silbert, Lisa C., additional, Lind, Betty, additional, Crissey, Rachel, additional, Kaye, Jeffrey A., additional, Carter, Raina, additional, Dolen, Sara, additional, Quinn, Joseph, additional, Schneider, Lon S., additional, Pawluczyk, Sonia, additional, Becerra, Mauricio, additional, Teodoro, Liberty, additional, Dagerman, Karen, additional, Spann, Bryan M., additional, Vanderswag, Helen, additional, Ziolkowski, Jaimie, additional, Heidebrink, Judith L., additional, Zbizek-Nulph, Lisa, additional, Lord, Joanne L., additional, Mason, Sara S., additional, Albers, Colleen S., additional, Knopman, David, additional, Johnson, Kris, additional, Villanueva-Meyer, Javier, additional, Pavlik, Valory, additional, Pacini, Nathaniel, additional, Lamb, Ashley, additional, Kass, Joseph S., additional, Doody, Rachelle S., additional, Shibley, Victoria, additional, Chowdhury, Munir, additional, Rountree, Susan, additional, Dang, Mimi, additional, Stern, Yaakov, additional, Honig, Lawrence S., additional, Mintz, Akiva, additional, Ances, Beau, additional, Winkfield, David, additional, Carroll, Maria, additional, Stobbs-Cucchi, Georgia, additional, Oliver, Angela, additional, Creech, Mary L., additional, Mintun, Mark A., additional, Schneider, Stacy, additional, Geldmacher, David, additional, Love, Marissa Natelson, additional, Griffith, Randall, additional, Clark, David, additional, Brockington, John, additional, Marson, Daniel, additional, Grossman, Hillel, additional, Goldstein, Martin A., additional, Greenberg, Jonathan, additional, Mitsis, Effie, additional, Shah, Raj C., additional, Lamar, Melissa, additional, Samuels, Patricia, additional, Duara, Ranjan, additional, Greig-Custo, Maria T., additional, Rodriguez, Rosemarie, additional, Albert, Marilyn, additional, Onyike, Chiadi, additional, Farrington, Leonie, additional, Rudow, Scott, additional, Brichko, Rottislav, additional, Kielb, Stephanie, additional, Smith, Amanda, additional, Raj, Balebail Ashok, additional, Fargher, Kristin, additional, Sadowski, Martin, additional, Wisniewski, Thomas, additional, Shulman, Melanie, additional, Faustin, Arline, additional, Rao, Julia, additional, Castro, Karen M., additional, Ulysse, Anaztasia, additional, Chen, Shannon, additional, Sheikh, Mohammed O., additional, Singleton-Garvin, Jamika, additional, Petrella, JeffreyR., additional, James, Olga, additional, Wong, Terence Z., additional, Borges-Neto, Salvador, additional, Karlawish, Jason H., additional, Wolk, David A., additional, Vaishnavi, Sanjeev, additional, Clark, Christopher M., additional, Arnold, Steven E., additional, Smith, Charles D., additional, Jicha, Gregory A., additional, Raslau, Flavius D., additional, Lopez, Oscar L., additional, Oakley, MaryAnn, additional, Simpson, Donna M., additional, Porsteinsson, Anton P., additional, Martin, Kim, additional, Kowalski, Nancy, additional, Keltz, Melanie, additional, Goldstein, Bonnie S., additional, Makino, Kelly M., additional, Ismail, M. Saleem, additional, Brand, Connie, additional, Thai, Gaby, additional, Pierce, Aimee, additional, Yanez, Beatriz, additional, Sosa, Elizabeth, additional, Witbracht, Megan, additional, Kelley, Brendan, additional, Nguyen, Trung, additional, Womack, Kyle, additional, Mathews, Dana, additional, Quiceno, Mary, additional, Levey, Allan I., additional, Lah, James J., additional, Hajjar, Ihab, additional, Cellar, Janet S., additional, Burns, Jeffrey M., additional, Swerdlow, Russell H., additional, Brooks, William M., additional, Silverman, Daniel H.S., additional, Kremen, Sarah, additional, Apostolova, Liana, additional, Tingus, Kathleen, additional, Lu, Po H., additional, Bartzokis, George, additional, Woo, Ellen, additional, Teng, Edmond, additional, Graff-Radford, Neill R., additional, Parfitt, Francine, additional, Poki-Walker, Kim, additional, Farlow, Martin R., additional, Hake, Ann Marie, additional, Matthews, Brandy R., additional, Brosch, Jared R., additional, Herring, Scott, additional, van, Christopher H., additional, Mecca, Adam P., additional, Good, Susan P., additional, MacAvoy, Martha G., additional, Carson, Richard E., additional, Varma, Pradeep, additional, Chertkow, Howard, additional, Vaitekunis, Susan, additional, Hosein, Chris, additional, Black, Sandra, additional, Stefanovic, Bojana, additional, Heyn, Chris (Chinthaka), additional, Robin Hsiung, Ging-Yuek, additional, Kim, Ellen, additional, Mudge, Benita, additional, Sossi, Vesna, additional, Feldman, Howard, additional, Assaly, Michele, additional, Finger, Elizabeth, additional, Pasternak, Stephen, additional, Rachinsky, Irina, additional, Kertesz, Andrew, additional, Drost, Dick, additional, Rogers, John, additional, Grant, Ian, additional, Muse, Brittanie, additional, Rogalski, Emily, additional, Robson, Jordan, additional, Mesulam, M.-Marsel, additional, Kerwin, Diana, additional, Wu, Chuang-Kuo, additional, Johnson, Nancy, additional, Lipowski, Kristine, additional, Weintraub, Sandra, additional, Bonakdarpour, Borna, additional, Pomara, Nunzio, additional, Hernando, Raymundo, additional, Sarrael, Antero, additional, Rosen, Howard J., additional, Miller, Bruce L., additional, Perry, David, additional, Turner, Raymond Scott, additional, Johnson, Kathleen, additional, Reynolds, Brigid, additional, MCCann, Kelly, additional, Poe, Jessica, additional, Sperling, Reisa A., additional, Johnson, Keith A., additional, Marshall, Gad A., additional, Yesavage, Jerome, additional, Taylor, Joy L., additional, Chao, Steven, additional, Coleman, Jaila, additional, White, Jessica D., additional, Lane, Barton, additional, Rosen, Allyson, additional, Tinklenberg, Jared, additional, Belden, Christine M., additional, Atri, Alireza, additional, Clark, Kelly A., additional, Zamrini, Edward, additional, Sabbagh, Marwan, additional, Killiany, Ronald, additional, Stern, Robert, additional, Mez, Jesse, additional, Kowall, Neil, additional, Budson, Andrew E., additional, Obisesan, Thomas O., additional, Ntekim, Oyonumo E., additional, Wolday, Saba, additional, Khan, Javed I., additional, Nwulia, Evaristus, additional, Nadarajah, Sheeba, additional, Lerner, Alan, additional, Ogrocki, Paula, additional, Tatsuoka, Curtis, additional, Fatica, Parianne, additional, Maillard, Pauline, additional, Olichney, John, additional, Carmichael, Owen, additional, Bates, Vernice, additional, Capote, Horacio, additional, Rainka, Michelle, additional, Borrie, Michael, additional, Lee, T.-Y., additional, Bartha, Dr Rob, additional, Johnson, Sterling, additional, Asthana, Sanjay, additional, Carlsson, Cynthia M., additional, Perrin, Allison, additional, Burke, Anna, additional, Scharre, Douglas W., additional, Kataki, Maria, additional, Tarawneh, Rawan, additional, Hart, David, additional, Zimmerman, Earl A., additional, Celmins, Dzintra, additional, Miller, Delwyn D., additional, BolesPonto, Laura L., additional, Smith, Karen Ekstam, additional, Koleva, Hristina, additional, Shim, Hyungsub, additional, Nam, Ki Won, additional, Schultz, Susan K., additional, Williamson, Jeff D., additional, Craft, Suzanne, additional, Cleveland, Jo, additional, Yang, Mia, additional, Sink, Kaycee M., additional, Ott, Brian R., additional, Drake, Jonathan, additional, Tremont, Geoffrey, additional, Daiello, Lori A., additional, Drake, Jonathan D., additional, Ritter, Aaron, additional, Bernick, Charles, additional, Munic, Donna, additional, O'Connelll, Abigail, additional, Mintzer, Jacobo, additional, Wiliams, Arthur, additional, Masdeu, Joseph, additional, Shi, Jiong, additional, Garcia, Angelica, additional, Newhouse, Paul, additional, Potkin, Steven, additional, Salloway, Stephen, additional, Malloy, Paul, additional, Correia, Stephen, additional, Kittur, Smita, additional, Pearlson, Godfrey D., additional, Blank, Karen, additional, Anderson, Karen, additional, Flashman, Laura A., additional, Seltzer, Marc, additional, Hynes, Mary L., additional, Santulli, Robert B., additional, Relkin, Norman, additional, Chiang, Gloria, additional, Lee, Athena, additional, Lin, Michael, additional, Ravdin, Lisa, additional, Petersen, Ron, additional, Neylan, Thomas, additional, Grafman, Jordan, additional, Danowski, Sarah, additional, Nguyen-Barrera, Catherine, additional, Hayes, Jacqueline, additional, Finley, Shannon, additional, Bernstein, Matthew, additional, Senjem, Matt, additional, Foster, Norm, additional, Kim, Sungeun, additional, Sood, Ajay, additional, Blanchard, Kimberly S., additional, Fleischman, Debra, additional, Arfanakis, Konstantinos, additional, Varon, Daniel, additional, Greig, Maria T., additional, Petrella, Jeffrey R., additional, Goldstein, Bonnie, additional, Martin, Kimberly S., additional, Reist, Christopher, additional, Sadowsky, Carl, additional, Martinez, Walter, additional, Villena, Teresa, additional, Rosen, Howard, additional, Marshall, Gad, additional, Peskind, Elaine R., additional, Petrie, Eric C., additional, Li, Gail, additional, Mackin, Scott, additional, Jimenez-Maggiora, Gustavo, additional, Drake, Erin, additional, Donohue, Mike, additional, Nelson, Craig, additional, Bickford, David, additional, Butters, Meryl, additional, Zmuda, Michelle, additional, Reyes, Denise, additional, Faber, Kelley M., additional, Nudelman, Kelly N., additional, Au, Yiu Ho, additional, Scherer, Kelly, additional, Catalinotto, Daniel, additional, Stark, Samuel, additional, Ong, Elise, additional, and Fernandez, Dariella, additional

Published
2023
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31. A public resource of single cell transcriptomes and multiscale networks from persons with and without Alzheimer’s disease

Author

Wang, Qi, primary, Antone, Jerry, additional, Alsop, Eric, additional, Reiman, Rebecca, additional, Funk, Cory, additional, Bendl, Jaroslav, additional, Dudley, Joel T, additional, Liang, Winnie S, additional, Karr, Timothy L, additional, Roussos, Panos, additional, Bennett, David A, additional, De Jager, Philip L, additional, Serrano, Geidy E, additional, Beach, Thomas G, additional, Keuren-Jensen, Kendall Van, additional, Mastroeni, Diego, additional, Reiman, Eric M, additional, and Readhead, Benjamin P, additional

Published
2023
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32. The neuronal chromatin landscape in adult schizophrenia brains is linked to early fetal development

Author

Girdhar, Kiran, primary, Bendl, Jaroslav, additional, Baumgartner, Andrew, additional, Therrien, Karen, additional, Venkatesh, Sanan, additional, Mathur, Deepika, additional, Dong, Pengfei, additional, Rahman, Samir, additional, Kleopoulos, Steven P., additional, Misir, Ruth, additional, Reach, Sarah M., additional, Auluck, Pavan K., additional, Marenco, Stefano, additional, Lewis, David A., additional, Haroutunian, Vahram, additional, Funk, Cory, additional, Voloudakis, Georgios, additional, Hoffman, Gabriel E., additional, Fullard, John F., additional, and Roussos, Panos, additional

Published
2023
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33. Alzheimer's disease and progressive supranuclear palsy share similar transcriptomic changes in distinct brain regions

Author

Wang, Xue, Allen, Mariet, Ozkan, Reddy, Joseph S., Tutor-New, Frederick Q., Casey, Monica Castanedes, Carrasquillo, Minerva M., Oatman, Stephanie R., Min, Yuhao, Asmann, Yan W., Funk, Cory, Nguyen, Thuy, Ho, Charlotte C.G., Malphrus, Kimberly G., Seyfried, Nicholas T., Levey, Allan I., Younkin, Steven G., Murray, Melissa E., Dickson, Dennis W., Price, Nathan D., Golde, Todd E., and Ertekin-Taner, Nilufer

Subjects
Brain research, Alzheimer's disease -- Development and progression -- Genetic aspects, Genetic transcription -- Research, Progressive supranuclear palsy -- Development and progression -- Genetic aspects, Health care industry
Abstract

Vast numbers of differentially expressed genes and perturbed networks have been identified in Alzheimer's disease (AD), however, neither disease nor brain region specificity of these transcriptome alterations has been explored. Using RNA- Seq data from 231 temporal cortex and 224 cerebellum samples from patients with AD and progressive supranuclear palsy (PSP), a tauopathy, we identified a striking correlation in the directionality and magnitude of gene expression changes between these 2 neurodegenerative proteinopathies. Further, the transcriptomic changes in AD and PSP brains ware highly conserved between the temporal and cerebellar cortices, indicating that highly similar transcriptional changes occur in pathologically affected and grossly less affected, albeit functionally connected, areas of the brain. Shared up- or downregulated genes in AD and PSP are enriched in biological pathways. Many of these genes also have concordant protein changes and evidence of epigenetic control. These conserved transcriptomic alterations of 2 distinct proteinopathies in brain regions with and without significant gross neuropathology have broad implications. AD and other neurodegenerative diseases are likely characterized by common disease or compensatory pathways with widespread perturbations in the whole brain. These findings can be leveraged to develop multifaceted therapies and biomarkers that address these common, complex, and ubiquitous molecular alterations in neurodegenerative diseases., Introduction Neurodegenerative proteinopathies such as Alzheimer's disease (AD) and progressive supranuclear palsy (PSP) are characterized by the aggregation and accumulation of self-proteins within insoluble aggregates (1). AD is a complex [...]

Published
2022
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37. Divergent brain gene expression patterns associate with distinct cell-specific tau neuropathology traits in progressive supranuclear palsy

Author

Allen, Mariet, Wang, Xue, Serie, Daniel J., Strickland, Samantha L., Burgess, Jeremy D., Koga, Shunsuke, Younkin, Curtis S., Nguyen, Thuy T., Malphrus, Kimberly G., Lincoln, Sarah J., Alamprese, Melissa, Zhu, Kuixi, Chang, Rui, Carrasquillo, Minerva M., Kouri, Naomi, Murray, Melissa E., Reddy, Joseph S., Funk, Cory, Price, Nathan D., Golde, Todd E., Younkin, Steven G., Asmann, Yan W., Crook, Julia E., Dickson, Dennis W., and Ertekin-Taner, Nilüfer

Published
2018
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38. A Remotely Coached Multimodal Lifestyle Intervention for Alzheimer's Disease Ameliorates Functional and Cognitive Outcomes.

Author

Roach, Jared C., Rapozo, Molly K., Hara, Junko, Glusman, Gwênlyn, Lovejoy, Jennifer, Shankle, William R., Hood, Leroy, Atthara, Mouna, Baloni, Priyanka, Dill, Lauren, Edens, Lance E., Fischer, Dan, Fortier, Dennis, Fridman, Deborah, Funk, Cory C., Jade, Kathleen, Lovejoy, Jennifer C., Magis, Andrew, Markewych, Daria R., and Price, Nathan

Subjects
ALZHEIMER'S disease, COVID-19 pandemic, PERSONAL coaching, COGNITIVE training, RANDOMIZED controlled trials
Abstract

Background: Comprehensive treatment of Alzheimer's disease and related dementias (ADRD) requires not only pharmacologic treatment but also management of existing medical conditions and lifestyle modifications including diet, cognitive training, and exercise. Personalized, multimodal therapies are needed to best prevent and treat Alzheimer's disease (AD). Objective: The Coaching for Cognition in Alzheimer's (COCOA) trial was a prospective randomized controlled trial to test the hypothesis that a remotely coached multimodal lifestyle intervention would improve early-stage AD. Methods: Participants with early-stage AD were randomized into two arms. Arm 1 (N = 24) received standard of care. Arm 2 (N = 31) additionally received telephonic personalized coaching for multiple lifestyle interventions. The primary outcome was a test of the hypothesis that the Memory Performance Index (MPI) change over time would be better in the intervention arm than in the control arm. The Functional Assessment Staging Test was assessed for a secondary outcome. COCOA collected psychometric, clinical, lifestyle, genomic, proteomic, metabolomic, and microbiome data at multiple timepoints (dynamic dense data) across two years for each participant. Results: The intervention arm ameliorated 2.1 [1.0] MPI points (mean [SD], p = 0.016) compared to the control over the two-year intervention. No important adverse events or side effects were observed. Conclusion: Multimodal lifestyle interventions are effective for ameliorating cognitive decline and have a larger effect size than pharmacological interventions. Dietary changes and exercise are likely to be beneficial components of multimodal interventions in many individuals. Remote coaching is an effective intervention for early stage ADRD. Remote interventions were effective during the COVID pandemic. [ABSTRACT FROM AUTHOR]

Published
2023
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39. Frequent Cognitive Tests Increase Power for Alzheimer’s Disease Clinical Trials

Author

Roach, Jared C., primary, Hara, Junko, additional, Edens, Lance, additional, Rajbhandari, Sophiya, additional, Dill, Lauren, additional, Romansik, Rachel, additional, Rapozo, Molly K., additional, Funk, Cory, additional, Jade, Kathleen, additional, Merrill, David A., additional, Bramen, Jennifer E., additional, Panos, Stella E., additional, Porter, Verna R., additional, Dye, Richelin V., additional, Ganapathi, Aarthi S., additional, Hodes, John F., additional, Slyapich, Colby B., additional, Glatt, Ryan M., additional, Wong, Claudia L., additional, Fortier, Dennis, additional, and Shankle, William R, additional

Published
2022
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40. A multimodal intervention for Alzheimer’s disease results in multifaceted systemic effects reflected in blood and ameliorates functional and cognitive outcomes

Author

Roach, Jared C., primary, Edens, Lance, additional, Markewych, Daria R., additional, Rapozo, Molly K., additional, Hara, Junko, additional, Glusman, Gustavo, additional, Funk, Cory, additional, Bramen, Jennifer, additional, Baloni, Priyanka, additional, Shankle, William R, additional, and Hood, Leroy, additional

Published
2022
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41. A metabolomic signature of the APOE2 allele

Author

Sebastiani, Paola, primary, Song, Zeyuan, additional, Ellis, Dylan, additional, Tian, Qu, additional, Schwaiger-Haber, Michaela, additional, Stancliffe, Ethan, additional, Lustgarten, Michael S., additional, Funk, Cory C., additional, Baloni, Priyanka, additional, Yao, Cong-Hui, additional, Joshi, Shakchhi, additional, Marron, Megan M., additional, Gurinovich, Anastasia, additional, Li, Mengze, additional, Leshchyk, Anastasia, additional, Xiang, Qingyan, additional, Andersen, Stacy L., additional, Feitosa, Mary F., additional, Ukraintseva, Svetlana, additional, Soerensen, Mette, additional, Fiehn, Oliver, additional, Ordovas, Jose M., additional, Haigis, Marcia, additional, Monti, Stefano, additional, Barzilai, Nir, additional, Milman, Sofiya, additional, Ferrucci, Luigi, additional, Rappaport, Noa, additional, Patti, Gary J., additional, and Perls, Thomas T., additional

Published
2022
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42. Meta-Analysis of the Alzheimer’s Disease Human Brain Transcriptome and Functional Dissection in Mouse Models

Author

Wan, Ying-Wooi, Al-Ouran, Rami, Mangleburg, Carl G., Perumal, Thanneer M., Lee, Tom V., Allison, Katherine, Swarup, Vivek, Funk, Cory C., Gaiteri, Chris, Allen, Mariet, Wang, Minghui, Neuner, Sarah M., Kaczorowski, Catherine C., Philip, Vivek M., Howell, Gareth R., Martini-Stoica, Heidi, Zheng, Hui, Mei, Hongkang, Zhong, Xiaoyan, Kim, Jungwoo Wren, Dawson, Valina L., Dawson, Ted M., Pao, Ping-Chieh, Tsai, Li-Huei, Haure-Mirande, Jean-Vianney, Ehrlich, Michelle E., Chakrabarty, Paramita, Levites, Yona, Wang, Xue, Dammer, Eric B., Srivastava, Gyan, Mukherjee, Sumit, Sieberts, Solveig K., Omberg, Larsson, Dang, Kristen D., Eddy, James A., Snyder, Phil, Chae, Yooree, Amberkar, Sandeep, Wei, Wenbin, Hide, Winston, Preuss, Christoph, Ergun, Ayla, Ebert, Phillip J., Airey, David C., Mostafavi, Sara, Yu, Lei, Klein, Hans-Ulrich, Carter, Gregory W., Collier, David A., Golde, Todd E., Levey, Allan I., Bennett, David A., Estrada, Karol, Townsend, T. Matthew, Zhang, Bin, Schadt, Eric, De Jager, Philip L., Price, Nathan D., Ertekin-Taner, Nilüfer, Liu, Zhandong, Shulman, Joshua M., Mangravite, Lara M., Logsdon, Benjamin A., Wan, Ying-Wooi, Al-Ouran, Rami, Mangleburg, Carl G., Perumal, Thanneer M., Lee, Tom V., Allison, Katherine, Swarup, Vivek, Funk, Cory C., Gaiteri, Chris, Allen, Mariet, Wang, Minghui, Neuner, Sarah M., Kaczorowski, Catherine C., Philip, Vivek M., Howell, Gareth R., Martini-Stoica, Heidi, Zheng, Hui, Mei, Hongkang, Zhong, Xiaoyan, Kim, Jungwoo Wren, Dawson, Valina L., Dawson, Ted M., Pao, Ping-Chieh, Tsai, Li-Huei, Haure-Mirande, Jean-Vianney, Ehrlich, Michelle E., Chakrabarty, Paramita, Levites, Yona, Wang, Xue, Dammer, Eric B., Srivastava, Gyan, Mukherjee, Sumit, Sieberts, Solveig K., Omberg, Larsson, Dang, Kristen D., Eddy, James A., Snyder, Phil, Chae, Yooree, Amberkar, Sandeep, Wei, Wenbin, Hide, Winston, Preuss, Christoph, Ergun, Ayla, Ebert, Phillip J., Airey, David C., Mostafavi, Sara, Yu, Lei, Klein, Hans-Ulrich, Carter, Gregory W., Collier, David A., Golde, Todd E., Levey, Allan I., Bennett, David A., Estrada, Karol, Townsend, T. Matthew, Zhang, Bin, Schadt, Eric, De Jager, Philip L., Price, Nathan D., Ertekin-Taner, Nilüfer, Liu, Zhandong, Shulman, Joshua M., Mangravite, Lara M., and Logsdon, Benjamin A.

Abstract

© 2020 The Authors We present a consensus atlas of the human brain transcriptome in Alzheimer's disease (AD), based on meta-analysis of differential gene expression in 2,114 postmortem samples. We discover 30 brain coexpression modules from seven regions as the major source of AD transcriptional perturbations. We next examine overlap with 251 brain differentially expressed gene sets from mouse models of AD and other neurodegenerative disorders. Human-mouse overlaps highlight responses to amyloid versus tau pathology and reveal age- and sex-dependent expression signatures for disease progression. Human coexpression modules enriched for neuronal and/or microglial genes broadly overlap with mouse models of AD, Huntington's disease, amyotrophic lateral sclerosis, and aging. Other human coexpression modules, including those implicated in proteostasis, are not activated in AD models but rather following other, unexpected genetic manipulations. Our results comprise a cross-species resource, highlighting transcriptional networks altered by human brain pathophysiology and identifying correspondences with mouse models for AD preclinical studies.

Published
2022

43. Large eQTL meta-analysis reveals differing patterns between cerebral cortical and cerebellar brain regions

Author

Sieberts, Solveig K., Perumal, Thanneer M., Carrasquillo, Minerva M., Allen, Mariet, Reddy, Joseph S., Hoffman, Gabriel E., Dang, Kristen K., Calley, John, Ebert, Philip J., Eddy, James, Wang, Xue, Greenwood, Anna K., Mostafavi, Sara, Akbarian, Schahram, Bendl, Jaroslav, Breen, Michael S., Brennand, Kristen, Brown, Leanne, Browne, Andrew, Buxbaum, Joseph D., Charney, Alexander, Chess, Andrew, Couto, Lizette, Crawford, Greg, Devillers, Olivia, Devlin, Bernie, Dobbyn, Amanda, Domenici, Enrico, Filosi, Michele, Flatow, Elie, Francoeur, Nancy, Fullard, John, Gil, Sergio Espeso, Girdhar, Kiran, Gulyás-Kovács, Attila, Gur, Raquel, Hahn, Chang-Gyu, Haroutunian, Vahram, Hauberg, Mads Engel, Huckins, Laura, Jacobov, Rivky, Jiang, Yan, Johnson, Jessica S., Kassim, Bibi, Kim, Yungil, Klei, Lambertus, Kramer, Robin, Lauria, Mario, Lehner, Thomas, Lewis, David A., Lipska, Barbara K., Montgomery, Kelsey, Park, Royce, Rosenbluh, Chaggai, Roussos, Panagiotis, Ruderfer, Douglas M., Senthil, Geetha, Shah, Hardik R., Sloofman, Laura, Song, Lingyun, Stahl, Eli, Sullivan, Patrick, Visintainer, Roberto, Wang, Jiebiao, Wang, Ying-Chih, Wiseman, Jennifer, Xia, Eva, Zhang, Wen, Zharovsky, Elizabeth, Addis, Laura, Addo, Sadiya N., Airey, David Charles, Arnold, Matthias, Bennett, David A., Bi, Yingtao, Biber, Knut, Blach, Colette, Bradhsaw, Elizabeth, Brennan, Paul, Canet-Aviles, Rosa, Cao, Sherry, Cavalla, Anna, Chae, Yooree, Chen, William W., Cheng, Jie, Collier, David Andrew, Dage, Jeffrey L., Dammer, Eric B., Davis, Justin Wade, Davis, John, Drake, Derek, Duong, Duc, Eastwood, Brian J., Ehrlich, Michelle, Ellingson, Benjamin, Engelmann, Brett W., Esmaeelinieh, Sahar, Felsky, Daniel, Funk, Cory, Gaiteri, Chris, Gandy, Samuel, Gao, Fan, Gileadi, Opher, Golde, Todd, Grosskurth, Shaun E., Gupta, Rishi R., Gutteridge, Alex X., Hooli, Basavaraj, Humphryes-Kirilov, Neil, Iijima, Koichi, James, Corey, Jung, Paul M., Kaddurah-Daouk, Rima, Kastenmuller, Gabi, Klein, Hans-Ulrich, Kummer, Markus, Lacor, Pascale N., Lah, James, Laing, Emma, Levey, Allan, Li, Yupeng, Lipsky, Samantha, Liu, Yushi, Liu, Jimmy, Liu, Zhandong, Louie, Gregory, Lu, Tao, Ma, Yiyi, Matsuoka, Yasuji Y., Menon, Vilas, Miller, Bradley, Misko, Thomas P., Mollon, Jennifer E., Mukherjee, Sumit, Noggle, Scott, Pao, Ping-Chieh, Pearce, Tracy Young, Pearson, Neil, Penny, Michelle, Petyuk, Vladislav A., Price, Nathan, Quarless, Danjuma X., Ravikumar, Brinda, Ried, Janina S., Ruble, Cara Lee Ann, Runz, Heiko, Saykin, Andrew J., Schadt, Eric, Scherschel, James E., Seyfried, Nicholas, Shulman, Joshua M., Snyder, Phil, Soares, Holly, Srivastava, Gyan P., Stockmann, Henning, Taga, Mariko, Tasaki, Shinya, Tenenbaum, Jessie, Tsai, Li-Huei, Vasanthakumar, Aparna, Wachter, Astrid, Wang, Yaming, Wang, Hong, Wang, Minghui, Whelan, Christopher D., White, Charles, Woo, Kara H., Wren, Paul, Wu, Jessica W., Xi, Hualin S., Yankner, Bruce A., Younkin, Steven G., Yu, Lei, Zavodszky, Maria, Zhang, Wenling, Zhang, Guoqiang, Zhang, Bin, Zhu, Jun, Omberg, Larsson, Peters, Mette A., Logsdon, Benjamin A., De Jager, Philip L., Ertekin-Taner, Nilüfer, Mangravite, Lara M., Sieberts, Solveig K., Perumal, Thanneer M., Carrasquillo, Minerva M., Allen, Mariet, Reddy, Joseph S., Hoffman, Gabriel E., Dang, Kristen K., Calley, John, Ebert, Philip J., Eddy, James, Wang, Xue, Greenwood, Anna K., Mostafavi, Sara, Akbarian, Schahram, Bendl, Jaroslav, Breen, Michael S., Brennand, Kristen, Brown, Leanne, Browne, Andrew, Buxbaum, Joseph D., Charney, Alexander, Chess, Andrew, Couto, Lizette, Crawford, Greg, Devillers, Olivia, Devlin, Bernie, Dobbyn, Amanda, Domenici, Enrico, Filosi, Michele, Flatow, Elie, Francoeur, Nancy, Fullard, John, Gil, Sergio Espeso, Girdhar, Kiran, Gulyás-Kovács, Attila, Gur, Raquel, Hahn, Chang-Gyu, Haroutunian, Vahram, Hauberg, Mads Engel, Huckins, Laura, Jacobov, Rivky, Jiang, Yan, Johnson, Jessica S., Kassim, Bibi, Kim, Yungil, Klei, Lambertus, Kramer, Robin, Lauria, Mario, Lehner, Thomas, Lewis, David A., Lipska, Barbara K., Montgomery, Kelsey, Park, Royce, Rosenbluh, Chaggai, Roussos, Panagiotis, Ruderfer, Douglas M., Senthil, Geetha, Shah, Hardik R., Sloofman, Laura, Song, Lingyun, Stahl, Eli, Sullivan, Patrick, Visintainer, Roberto, Wang, Jiebiao, Wang, Ying-Chih, Wiseman, Jennifer, Xia, Eva, Zhang, Wen, Zharovsky, Elizabeth, Addis, Laura, Addo, Sadiya N., Airey, David Charles, Arnold, Matthias, Bennett, David A., Bi, Yingtao, Biber, Knut, Blach, Colette, Bradhsaw, Elizabeth, Brennan, Paul, Canet-Aviles, Rosa, Cao, Sherry, Cavalla, Anna, Chae, Yooree, Chen, William W., Cheng, Jie, Collier, David Andrew, Dage, Jeffrey L., Dammer, Eric B., Davis, Justin Wade, Davis, John, Drake, Derek, Duong, Duc, Eastwood, Brian J., Ehrlich, Michelle, Ellingson, Benjamin, Engelmann, Brett W., Esmaeelinieh, Sahar, Felsky, Daniel, Funk, Cory, Gaiteri, Chris, Gandy, Samuel, Gao, Fan, Gileadi, Opher, Golde, Todd, Grosskurth, Shaun E., Gupta, Rishi R., Gutteridge, Alex X., Hooli, Basavaraj, Humphryes-Kirilov, Neil, Iijima, Koichi, James, Corey, Jung, Paul M., Kaddurah-Daouk, Rima, Kastenmuller, Gabi, Klein, Hans-Ulrich, Kummer, Markus, Lacor, Pascale N., Lah, James, Laing, Emma, Levey, Allan, Li, Yupeng, Lipsky, Samantha, Liu, Yushi, Liu, Jimmy, Liu, Zhandong, Louie, Gregory, Lu, Tao, Ma, Yiyi, Matsuoka, Yasuji Y., Menon, Vilas, Miller, Bradley, Misko, Thomas P., Mollon, Jennifer E., Mukherjee, Sumit, Noggle, Scott, Pao, Ping-Chieh, Pearce, Tracy Young, Pearson, Neil, Penny, Michelle, Petyuk, Vladislav A., Price, Nathan, Quarless, Danjuma X., Ravikumar, Brinda, Ried, Janina S., Ruble, Cara Lee Ann, Runz, Heiko, Saykin, Andrew J., Schadt, Eric, Scherschel, James E., Seyfried, Nicholas, Shulman, Joshua M., Snyder, Phil, Soares, Holly, Srivastava, Gyan P., Stockmann, Henning, Taga, Mariko, Tasaki, Shinya, Tenenbaum, Jessie, Tsai, Li-Huei, Vasanthakumar, Aparna, Wachter, Astrid, Wang, Yaming, Wang, Hong, Wang, Minghui, Whelan, Christopher D., White, Charles, Woo, Kara H., Wren, Paul, Wu, Jessica W., Xi, Hualin S., Yankner, Bruce A., Younkin, Steven G., Yu, Lei, Zavodszky, Maria, Zhang, Wenling, Zhang, Guoqiang, Zhang, Bin, Zhu, Jun, Omberg, Larsson, Peters, Mette A., Logsdon, Benjamin A., De Jager, Philip L., Ertekin-Taner, Nilüfer, and Mangravite, Lara M.

Abstract

© 2020, The Author(s). The availability of high-quality RNA-sequencing and genotyping data of post-mortem brain collections from consortia such as CommonMind Consortium (CMC) and the Accelerating Medicines Partnership for Alzheimer’s Disease (AMP-AD) Consortium enable the generation of a large-scale brain cis-eQTL meta-analysis. Here we generate cerebral cortical eQTL from 1433 samples available from four cohorts (identifying >4.1 million significant eQTL for >18,000 genes), as well as cerebellar eQTL from 261 samples (identifying 874,836 significant eQTL for >10,000 genes). We find substantially improved power in the meta-analysis over individual cohort analyses, particularly in comparison to the Genotype-Tissue Expression (GTEx) Project eQTL. Additionally, we observed differences in eQTL patterns between cerebral and cerebellar brain regions. We provide these brain eQTL as a resource for use by the research community. As a proof of principle for their utility, we apply a colocalization analysis to identify genes underlying the GWAS association peaks for schizophrenia and identify a potentially novel gene colocalization with lncRNA RP11-677M14.2 (posterior probability of colocalization 0.975).

Published
2022

44. Additional file 5 of Deletion of Abi3/Gngt2 influences age-progressive amyloid β and tau pathologies in distinctive ways

Author

Ibanez, Kristen R., McFarland, Karen N., Phillips, Jennifer, Allen, Mariet, Lessard, Christian B., Zobel, Lillian, De La Cruz, Elsa Gonzalez, Shah, Shivani, Vo, Quan, Wang, Xue, Quicksall, Zachary, Ryu, Daniel, Funk, Cory, Ertekin-Taner, Nilüfer, Prokop, Stefan, Golde, Todd E., and Chakrabarty, Paramita

Abstract

Additional file 5: Fig. S2. Genomic organization at the Abi3 locus of Mus musculus. a. RNAseq based confirmation of Abi3, Gngt2 and APP levels from three Abi3 genotypes in APP transgenic (APP, Tg) mice or nontransgenic (APP, nTg) mice. Abi3, WT: Abi3+/+; Abi3, het: Abi3+/−; Abi3, hom: Abi3−/−. x axis denotes the genotypes and y axis denotes FPKM values of the corresponding RNA indicated on top of the graph. N=4 mice/genotype. b. The Abi3 knockout mice (Abi3tm1.1(KOMP)Vlcg) was generated by cre-mediated excision of the parental Abi3tm1(KOMP)Vlcg allele resulting in the removal of the neomycin selection cassette, leaving behind the inserted lacZ reporter sequence. The sequence that was excised out on chromosome 11 was located between 95842143 and 95832627 (indicated by black bar). This fragment encompasses the Abi3 coding region and 5’ region of a Gngt2 isoform.

Published
2022
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45. Additional file 7 of Deletion of Abi3/Gngt2 influences age-progressive amyloid β and tau pathologies in distinctive ways

Author

Ibanez, Kristen R., McFarland, Karen N., Phillips, Jennifer, Allen, Mariet, Lessard, Christian B., Zobel, Lillian, De La Cruz, Elsa Gonzalez, Shah, Shivani, Vo, Quan, Wang, Xue, Quicksall, Zachary, Ryu, Daniel, Funk, Cory, Ertekin-Taner, Nilüfer, Prokop, Stefan, Golde, Todd E., and Chakrabarty, Paramita

Abstract

Additional file 7: Fig. S3. Hub genes from WGCNA modules identified in 3 month old Abi3-Gngt2−/− mice. a. Volcano plot and table of altered genes representing DEG in 3 month old heterozygous Abi3-Gngt2+/−vs WT Abi3-Gngt2+/+mice. FC, fold change; padj, adjusted pvalue. b-d. WGCNA module membership is plotted against gene connectivity (kWithin) for genes identified within modules significantly correlated with Abi3-Gngt2 genotype and glial burden traits in Abi3-Gngt2−/−mice. Accompanying table shows top hub genes (as ranked by kWithin values) identified in each co-expression WGCNA modules of 3 month old Abi3-Gngt2−/−mice relative to Abi3-Gngt2+/+ mice. The module members of antiquewhite2, coral2 and mediumpurple2 modules are shown. Module statistics are denoted by: kWithin, extent of gene connectivity within the module; MM, module membership value (of gene to module); GS, gene significance value to specific experimental trait. e. The overlap of genes within WGCNA modules with genes previously identified in AD-associated microglial and astrocytic subtypes is expressed as odds ratio value. The different cell signatures are: neurotoxic A1 and neurotrophic A2 astrocyte [38]; PIG network [21]; DAM [25]; MGnD and homeostatic microglia [24]. Higher odds ratio (warmer color) denotes higher correlation. All p values are adjusted for multiple testing. Grey boxes indicate non-significant odds ratio values. f. Genes within WGCNA modules associated with known KEGG pathways based on over-representation of enriched genes in Abi3-Gngt2−/−mice (relative to Abi3-Gngt2+/+) is depicted in a bubble plot. Pathways with an over-represented p-value ≤ 0.05, the number of module genes within the pathway >5 and an enrichment score >1.5 are depicted. The bubble plot is colored by p-value (blue color; higher p value is indicated by deeper blue hues) and sized by the enrichment score (circle diameter). N=4 mice (2 male, 2 female) per cohort.

Published
2022
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46. Additional file 2 of Deletion of Abi3/Gngt2 influences age-progressive amyloid β and tau pathologies in distinctive ways

Author

Ibanez, Kristen R., McFarland, Karen N., Phillips, Jennifer, Allen, Mariet, Lessard, Christian B., Zobel, Lillian, De La Cruz, Elsa Gonzalez, Shah, Shivani, Vo, Quan, Wang, Xue, Quicksall, Zachary, Ryu, Daniel, Funk, Cory, Ertekin-Taner, Nilüfer, Prokop, Stefan, Golde, Todd E., and Chakrabarty, Paramita

Abstract

Additional file 2: Table S2. Normalized RNA levels (FKPM values) of Abi3 and Gngt2 from TgCRND8 mice at various ages.

Published
2022
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47. Additional file 6 of Deletion of Abi3/Gngt2 influences age-progressive amyloid β and tau pathologies in distinctive ways

Author

Ibanez, Kristen R., McFarland, Karen N., Phillips, Jennifer, Allen, Mariet, Lessard, Christian B., Zobel, Lillian, De La Cruz, Elsa Gonzalez, Shah, Shivani, Vo, Quan, Wang, Xue, Quicksall, Zachary, Ryu, Daniel, Funk, Cory, Ertekin-Taner, Nilüfer, Prokop, Stefan, Golde, Todd E., and Chakrabarty, Paramita

Abstract

Additional file 6: Table S4. Analysis of Abi3 and Gngt2 expression changes in mouse studies (transcriptomic data originally reported by Wan et al. [33] and Al Ouran et al [34] and available at http://mmad.nrihub.org ).

Published
2022
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48. Additional file 3 of Deletion of Abi3/Gngt2 influences age-progressive amyloid β and tau pathologies in distinctive ways

Author

Ibanez, Kristen R., McFarland, Karen N., Phillips, Jennifer, Allen, Mariet, Lessard, Christian B., Zobel, Lillian, De La Cruz, Elsa Gonzalez, Shah, Shivani, Vo, Quan, Wang, Xue, Quicksall, Zachary, Ryu, Daniel, Funk, Cory, Ertekin-Taner, Nilüfer, Prokop, Stefan, Golde, Todd E., and Chakrabarty, Paramita

Abstract

Additional file 3: Table S3. Normalized RNA levels (FKPM values) of Abi3 and Gngt2 from rTg4510 mice at various ages.

Published
2022
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49. Additional file 10 of Deletion of Abi3/Gngt2 influences age-progressive amyloid β and tau pathologies in distinctive ways

Author

Ibanez, Kristen R., McFarland, Karen N., Phillips, Jennifer, Allen, Mariet, Lessard, Christian B., Zobel, Lillian, De La Cruz, Elsa Gonzalez, Shah, Shivani, Vo, Quan, Wang, Xue, Quicksall, Zachary, Ryu, Daniel, Funk, Cory, Ertekin-Taner, Nilüfer, Prokop, Stefan, Golde, Todd E., and Chakrabarty, Paramita

Abstract

Additional file 10: Fig. S6. Hub genes from WGCNA modules identified in 3 month old TG-Abi3-Gngt2-/- mice. a. Genes within WGCNA modules associated with known KEGG pathways based on over-representation of enriched genes in TG-Abi3-Gngt2-/-mice (relative to TG-Abi3-Gngt2+/+) is depicted in a bubble plot. Pathways with an over-represented p-value ≤ 0.05, the number of module genes within the pathway >5 and an enrichment score >1.5 are depicted. The bubble plot is colored by p-value (blue color; higher p value is indicated by deeper blue hues) and sized by the enrichment score (circle diameter). b-e. WGCNA module membership is plotted against gene connectivity (kWithin) for genes identified within modules significantly correlated with Abi3-Gngt2 genotype and related neuropathology traits in TG-Abi3-Gngt2-/-mice. The top hub genes (as ranked by kWithin values) identified in each co-expression WGCNA modules of 3 month old TG-Abi3-Gngt2-/- mice relative to TG-Abi3-Gngt2+/+ mice. The module members of honeydew1, plum3, sienna3 and plum4 modules are shown, with top hub genes tabulated. Module statistics are denoted by: kWithin, extent of gene connectivity within the module; MM, module membership value (of gene to module); GS, gene significance value to specific experimental trait. f. The overlap of genes within WGCNA modules with genes previously identified in AD-associated microglial and astrocytic sub-types signatures is expressed as odds ratio value. Higher odds ratio is indicated by warmer color. The different cell signatures are: neurotoxic A1 and neurotrophic A2 astrocyte [38]; Plaque-induced gene (PIG) network [21]; Disease-associated microglia (DAM), microglial neurodegenerative phenotype (MGnD) and homeostatic microglia [2425]. All p values are adjusted for multiple testing (padj). Higher odds ratio denotes higher correlation. Grey boxes indicate non-significant odds ratio values. 4 mice (2 male, 2 female) per cohort.

Published
2022
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50. Motivational, proteostatic and transcriptional deficits precede synapse loss, gliosis and neurodegeneration in the B6.HttQ111/+ model of Huntington’s disease

Author

Bragg, Robert M., Coffey, Sydney R., Weston, Rory M., Ament, Seth A., Cantle, Jeffrey P., Minnig, Shawn, Funk, Cory C., Shuttleworth, Dominic D., Woods, Emily L., Sullivan, Bonnie R., Jones, Lindsey, Glickenhaus, Anne, Anderson, John S., Anderson, Michael D., Dunnett, Stephen B., Wheeler, Vanessa C., MacDonald, Marcy E., Brooks, Simon P., Price, Nathan D., and Carroll, Jeffrey B.

Published
2017
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