👤 M Whitfield

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5
Articles
4
Name variants
Also published as: John B Whitfield, John Whitfield, Michael L Whitfield
articles
Helen C Jarnagin, Rezvan Parvizi, Zhiyun Gong +11 more · 2026 · JCI insight · added 2026-04-24
Uncovering the early interactions and spatial distribution of dermal fibroblasts and immune cells in treatment-naive patients with diffuse cutaneous systemic sclerosis (SSc) is critical to understandi Show more
Uncovering the early interactions and spatial distribution of dermal fibroblasts and immune cells in treatment-naive patients with diffuse cutaneous systemic sclerosis (SSc) is critical to understanding the earliest events of skin fibrosis. We generated an integrated multiomic dataset of early-stage, treatment-naive diffuse cutaneous SSc skin. Skin biopsies were analyzed by single-nuclei multiome sequencing (snRNA-Seq and snATAC-Seq) and two spatial transcriptomic methods to comprehensively determine molecular changes. We identified an immunomodulatory niche within the papillary, hypodermis, and vascular regions enriched for activated myeloid cells and fibroblasts characterized by expression of genes such as CXCL12, APOE, and C7. Pathway analyses showed significant enrichment of PI3K/AKT/mTOR signaling pathway expression in these cellular niches, driven by profibrotic growth factor signaling networks. Macrophage subclustering showed SSc-specific macrophage activation of IL-6/JAK/STAT signaling and enrichment of oxidative phosphorylation pathways. Ligand-receptor analysis revealed that SSc macrophages secrete PDGF and TGF-β to activate SSc-dominant fibroblast subclusters. Spatial transcriptomic analyses showed monocyte-derived MRC1+ macrophages express PDGF near PDGFRhiTHY1hi fibroblasts. Multiomic data integration and spatial transcriptomic neighborhood analysis revealed the colocalization of fibroblasts, macrophages, and T cells around the vasculature. These data suggest that interactions between activated immune cells and immunomodulatory fibroblasts around vascular niches are an early event in scleroderma pathogenesis. Show less
📄 PDF DOI: 10.1172/jci.insight.198954
APOE
M Whitfield, R Guiton, J Rispal +4 more · 2017 · Reproduction (Cambridge, England) · added 2026-04-24
Lipid metabolism disorders (dyslipidemia) are causes of male infertility, but little is known about their impact on male gametes when considering post-testicular maturation events, given that studies Show more
Lipid metabolism disorders (dyslipidemia) are causes of male infertility, but little is known about their impact on male gametes when considering post-testicular maturation events, given that studies concentrate most often on endocrine dysfunctions and testicular consequences. In this study, three-month-old wild-type ( Show less
no PDF DOI: 10.1530/REP-17-0467
NR1H3
Rita P S Middelberg, Andrew C Heath, Pamela A F Madden +3 more · 2012 · PloS one · PLOS · added 2026-04-24
A recent meta-analysis of genome-wide association (GWA) studies identified 95 loci that influence lipid traits in the adult population and found that collectively these explained about 25-30% of herit Show more
A recent meta-analysis of genome-wide association (GWA) studies identified 95 loci that influence lipid traits in the adult population and found that collectively these explained about 25-30% of heritability for each trait. Little is known about how these loci affect lipid levels in early life, but there is evidence that genetic effects on HDL- and LDL-cholesterol (HDL-C, LDL-C) and triglycerides vary with age. We studied Australian adults (N = 10,151) and adolescents (N = 2,363) who participated in twin and family studies and for whom we have lipid phenotypes and genotype information for 91 of the 95 genetic variants. Heterogeneity tests between effect sizes in adult and adolescent cohorts showed an excess of heterogeneity for HDL-C (p(Het)<0.05 at 5 out of 37 loci), but no more than expected by chance for LDL-C (1 out of 14 loci), or trigycerides (0 out 24). There were 2 (out of 5) with opposite direction of effect in adolescents compared to adults for HDL-C, but none for LDL-C. The biggest difference in effect size was for LDL-C at rs6511720 near LDLR, adolescents (0.021 ± 0.033 mmol/L) and adults (0.157 ± 0.023 mmol/L), p(Het) = 0.013; followed by ZNF664 (p(Het) = 0.018) and PABPC4 (p(Het) = 0.034) for HDL-C. Our findings suggest that some of the previously identified variants associate differently with lipid traits in adolescents compared to adults, either because of developmental changes or because of greater interactions with environmental differences in adults. Show less
no PDF DOI: 10.1371/journal.pone.0035605
PABPC4
John C Chambers, Weihua Zhang, Joban Sehmi +140 more · 2011 · Nature genetics · Nature · added 2026-04-24
John C Chambers, Weihua Zhang, Joban Sehmi, Xinzhong Li, Mark N Wass, Pim Van der Harst, Hilma Holm, Serena Sanna, Maryam Kavousi, Sebastian E Baumeister, Lachlan J Coin, Guohong Deng, Christian Gieger, Nancy L Heard-Costa, Jouke-Jan Hottenga, Brigitte Kühnel, Vinod Kumar, Vasiliki Lagou, Liming Liang, Jian'an Luan, Pedro Marques Vidal, Irene Mateo Leach, Paul F O'Reilly, John F Peden, Nilufer Rahmioglu, Pasi Soininen, Elizabeth K Speliotes, Xin Yuan, Gudmar Thorleifsson, Behrooz Z Alizadeh, Larry D Atwood, Ingrid B Borecki, Morris J Brown, Pimphen Charoen, Francesco Cucca, Debashish Das, Eco J C de Geus, Anna L Dixon, Angela Döring, Georg Ehret, Gudmundur I Eyjolfsson, Martin Farrall, Nita G Forouhi, Nele Friedrich, Wolfram Goessling, Daniel F Gudbjartsson, Tamara B Harris, Anna-Liisa Hartikainen, Simon Heath, Gideon M Hirschfield, Albert Hofman, Georg Homuth, Elina Hyppönen, Harry L A Janssen, Toby Johnson, Antti J Kangas, Ido P Kema, Jens P Kühn, Sandra Lai, Mark Lathrop, Markus M Lerch, Yun Li, T Jake Liang, Jing-Ping Lin, Ruth J F Loos, Nicholas G Martin, Miriam F Moffatt, Grant W Montgomery, Patricia B Munroe, Kiran Musunuru, Yusuke Nakamura, Christopher J O'Donnell, Isleifur Olafsson, Brenda W Penninx, Anneli Pouta, Bram P Prins, Inga Prokopenko, Ralf Puls, Aimo Ruokonen, Markku J Savolainen, David Schlessinger, Jeoffrey N L Schouten, Udo Seedorf, Srijita Sen-Chowdhry, Katherine A Siminovitch, Johannes H Smit, Timothy D Spector, Wenting Tan, Tanya M Teslovich, Taru Tukiainen, Andre G Uitterlinden, Melanie M Van der Klauw, Ramachandran S Vasan, Chris Wallace, Henri Wallaschofski, H-Erich Wichmann, Gonneke Willemsen, Peter Würtz, Chun Xu, Laura M Yerges-Armstrong, Alcohol Genome-wide Association (AlcGen) Consortium, Diabetes Genetics Replication and Meta-analyses (DIAGRAM+) Study, Genetic Investigation of ANthropometric Traits (GIANT) Consortium, Global Lipids Genetics Consortium, Genetics of Liver Disease (GOLD) Consortium, International Consortium for Blood Pressure (ICBP-GWAS), Meta-analyses of Glucose and Insulin-Related Traits Consortium (MAGIC), Goncalo R Abecasis, Kourosh R Ahmadi, Dorret I Boomsma, Mark Caulfield, William O Cookson, Cornelia M Van Duijn, Philippe Froguel, Koichi Matsuda, Mark I McCarthy, Christa Meisinger, Vincent Mooser, Kirsi H Pietiläinen, Gunter Schumann, Harold Snieder, Michael J E Sternberg, Ronald P Stolk, Howard C Thomas, Unnur Thorsteinsdottir, Manuela Uda, Gérard Waeber, Nicholas J Wareham, Dawn M Waterworth, Hugh Watkins, John B Whitfield, Jacqueline C M Witteman, Bruce H R Wolffenbuttel, Caroline S Fox, Mika Ala-Korpela, Kari Stefansson, Peter Vollenweider, Henry Völzke, Eric E Schadt, James Scott, Marjo-Riitta Järvelin, Paul Elliott, Jaspal S Kooner Show less
Concentrations of liver enzymes in plasma are widely used as indicators of liver disease. We carried out a genome-wide association study in 61,089 individuals, identifying 42 loci associated with conc Show more
Concentrations of liver enzymes in plasma are widely used as indicators of liver disease. We carried out a genome-wide association study in 61,089 individuals, identifying 42 loci associated with concentrations of liver enzymes in plasma, of which 32 are new associations (P = 10(-8) to P = 10(-190)). We used functional genomic approaches including metabonomic profiling and gene expression analyses to identify probable candidate genes at these regions. We identified 69 candidate genes, including genes involved in biliary transport (ATP8B1 and ABCB11), glucose, carbohydrate and lipid metabolism (FADS1, FADS2, GCKR, JMJD1C, HNF1A, MLXIPL, PNPLA3, PPP1R3B, SLC2A2 and TRIB1), glycoprotein biosynthesis and cell surface glycobiology (ABO, ASGR1, FUT2, GPLD1 and ST3GAL4), inflammation and immunity (CD276, CDH6, GCKR, HNF1A, HPR, ITGA1, RORA and STAT4) and glutathione metabolism (GSTT1, GSTT2 and GGT), as well as several genes of uncertain or unknown function (including ABHD12, EFHD1, EFNA1, EPHA2, MICAL3 and ZNF827). Our results provide new insight into genetic mechanisms and pathways influencing markers of liver function. Show less
📄 PDF DOI: 10.1038/ng.970
FADS1
Yurii S Aulchenko, Samuli Ripatti, Ida Lindqvist +55 more · 2009 · Nature genetics · Nature · added 2026-04-24
Recent genome-wide association (GWA) studies of lipids have been conducted in samples ascertained for other phenotypes, particularly diabetes. Here we report the first GWA analysis of loci affecting t Show more
Recent genome-wide association (GWA) studies of lipids have been conducted in samples ascertained for other phenotypes, particularly diabetes. Here we report the first GWA analysis of loci affecting total cholesterol (TC), low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol and triglycerides sampled randomly from 16 population-based cohorts and genotyped using mainly the Illumina HumanHap300-Duo platform. Our study included a total of 17,797-22,562 persons, aged 18-104 years and from geographic regions spanning from the Nordic countries to Southern Europe. We established 22 loci associated with serum lipid levels at a genome-wide significance level (P < 5 x 10(-8)), including 16 loci that were identified by previous GWA studies. The six newly identified loci in our cohort samples are ABCG5 (TC, P = 1.5 x 10(-11); LDL, P = 2.6 x 10(-10)), TMEM57 (TC, P = 5.4 x 10(-10)), CTCF-PRMT8 region (HDL, P = 8.3 x 10(-16)), DNAH11 (LDL, P = 6.1 x 10(-9)), FADS3-FADS2 (TC, P = 1.5 x 10(-10); LDL, P = 4.4 x 10(-13)) and MADD-FOLH1 region (HDL, P = 6 x 10(-11)). For three loci, effect sizes differed significantly by sex. Genetic risk scores based on lipid loci explain up to 4.8% of variation in lipids and were also associated with increased intima media thickness (P = 0.001) and coronary heart disease incidence (P = 0.04). The genetic risk score improves the screening of high-risk groups of dyslipidemia over classical risk factors. Show less
📄 PDF DOI: 10.1038/ng.269
FADS3