Kevin J. Williams

3.8k total citations · 4 hit papers
33 papers, 1.7k citations indexed

About

Kevin J. Williams is a scholar working on Molecular Biology, Immunology and Surgery. According to data from OpenAlex, Kevin J. Williams has authored 33 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Molecular Biology, 9 papers in Immunology and 7 papers in Surgery. Recurrent topics in Kevin J. Williams's work include Cholesterol and Lipid Metabolism (6 papers), Immune Response and Inflammation (6 papers) and Metabolomics and Mass Spectrometry Studies (5 papers). Kevin J. Williams is often cited by papers focused on Cholesterol and Lipid Metabolism (6 papers), Immune Response and Inflammation (6 papers) and Metabolomics and Mass Spectrometry Studies (5 papers). Kevin J. Williams collaborates with scholars based in United States, China and United Kingdom. Kevin J. Williams's co-authors include Steven J. Bensinger, Stephen T. Smale, Thomas G. Graeber, Baolong Su, Beth N. Marbois, Joseph P. Argus, Laurent Vergnes, Karen Reue, Evangelia Komisopoulou and Timothy F. Osborne and has published in prestigious journals such as Nature, Nature Communications and Nature Immunology.

In The Last Decade

Kevin J. Williams

33 papers receiving 1.7k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Sterol regulatory element–binding proteins are essential for the metabolic programming of effector T cells and adaptive immunity

2013 419 citations Yoko Kidani, Heidi Elsaesser et al. Nature Immunology profile →
IL-10 constrains sphingolipid metabolism to limit inflammation

2024 113 citations Autumn G. York, Mathias Skadow et al. Nature profile →
Dynamic lipidome alterations associated with human health, disease and ageing

2023 111 citations Daniel Hornburg, Si Wu et al. Nature Metabolism profile →
CDKN2A deletion remodels lipid metabolism to prime glioblastoma for ferroptosis

2023 102 citations Danielle Morrow, Nicholas Bayley et al. Cancer Cell profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Kevin J. Williams United States	18	867	609	421	195	190	33	1.7k
Masataka Kohno Japan	22	1.2k 1.4×	542 0.9×	376 0.9×	109 0.6×	146 0.8×	79	2.3k
Lan Wang China	25	905 1.0×	367 0.6×	338 0.8×	110 0.6×	148 0.8×	74	1.8k
Luis Gil‐de‐Gómez Spain	14	851 1.0×	719 1.2×	396 0.9×	146 0.7×	86 0.5×	19	1.7k
Feng Xie China	23	682 0.8×	403 0.7×	469 1.1×	178 0.9×	159 0.8×	86	1.6k
Till Adhikary Germany	20	1.1k 1.3×	536 0.9×	384 0.9×	113 0.6×	92 0.5×	31	1.9k
Huan Deng China	23	692 0.8×	254 0.4×	240 0.6×	180 0.9×	200 1.1×	84	1.7k
Jinshun Pan China	18	708 0.8×	355 0.6×	444 1.1×	92 0.5×	94 0.5×	30	1.4k
Meng Xia China	26	1.3k 1.5×	314 0.5×	507 1.2×	178 0.9×	172 0.9×	93	2.3k
Sara Martínez‐Martínez Spain	25	1.3k 1.5×	518 0.9×	303 0.7×	146 0.7×	91 0.5×	36	2.2k
Nicola Martelli Italy	22	520 0.6×	496 0.8×	227 0.5×	210 1.1×	157 0.8×	33	1.7k

Countries citing papers authored by Kevin J. Williams

Since Specialization

Citations

This map shows the geographic impact of Kevin J. Williams's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Kevin J. Williams with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kevin J. Williams more than expected).

Fields of papers citing papers by Kevin J. Williams

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kevin J. Williams. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Kevin J. Williams. The network helps show where Kevin J. Williams may publish in the future.

Co-authorship network of co-authors of Kevin J. Williams

This figure shows the co-authorship network connecting the top 25 collaborators of Kevin J. Williams. A scholar is included among the top collaborators of Kevin J. Williams based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Kevin J. Williams. Kevin J. Williams is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Chan, Alvin P., Kelsey E. Jarrett, Angela Cheng, et al.. (2025). Bile acids regulate lipid metabolism through selective actions on fatty acid absorption. Cell Metabolism. 38(2). 263–280.e10. 1 indexed citations

Chan, Hung, Fengwu Li, Tatsuya Dokoshi, et al.. (2025). Psychological stress increases skin infection through the action of TGFβ to suppress immune-acting fibroblasts. Science Immunology. 10(106). eads0519–eads0519. 1 indexed citations

Lee, Elinor, Kevin J. Williams, Cormac McCarthy, et al.. (2024). Alveolar macrophage lipid burden correlates with clinical improvement in patients with pulmonary alveolar proteinosis. Journal of Lipid Research. 65(2). 100496–100496. 5 indexed citations

York, Autumn G., Mathias Skadow, Joonseok Oh, et al.. (2024). IL-10 constrains sphingolipid metabolism to limit inflammation. Nature. 627(8004). 628–635. 113 indexed citations breakdown →

Hornburg, Daniel, Si Wu, Mahdi Moqri, et al.. (2023). Dynamic lipidome alterations associated with human health, disease and ageing. Nature Metabolism. 5(9). 1578–1594. 111 indexed citations breakdown →

Williams, Kevin J., Corey J. White, Andrew P. Hunt, & Mark E. Meyerhoff. (2023). Differential pulsed voltammetry of Δ9‐Tetrahydrocannabinol (THC) on disposable screen‐printed carbon electrodes: A potential in‐field method to detect Δ⁹‐THC in saliva. Electroanalysis. 35(8). 2 indexed citations

Sammarco, Alessandro, Litia Carvalho, Daniel F. Tardiff, et al.. (2023). Targeting de novo lipid synthesis induces lipotoxicity and impairs DNA damage repair in glioblastoma mouse models. Science Translational Medicine. 15(679). eabq6288–eabq6288. 29 indexed citations

Cau, Laura, Kellen Cavagnero, Alan M. O’Neill, et al.. (2023). Commensal Cutibacterium acnes induce epidermal lipid synthesis important for skin barrier function. Science Advances. 9(33). eadg6262–eadg6262. 73 indexed citations

Morrow, Danielle, Nicholas Bayley, Elízabeth Fernández, et al.. (2023). CDKN2A deletion remodels lipid metabolism to prime glioblastoma for ferroptosis. Cancer Cell. 41(6). 1048–1060.e9. 102 indexed citations breakdown →

10.

Magtanong, Leslie, Kevin J. Williams, Maximilian Billmann, et al.. (2022). Context-dependent regulation of ferroptosis sensitivity. Cell chemical biology. 29(9). 1409–1418.e6. 79 indexed citations

11.

Zhang, Zhengyi, An‐Chieh Feng, David Salisbury, et al.. (2020). Collaborative interactions of heterogenous ribonucleoproteins contribute to transcriptional regulation of sterol metabolism in mice. Nature Communications. 11(1). 984–984. 11 indexed citations

12.

Williams, Kevin J., et al.. (2020). Profiling of mouse macrophage lipidome using direct infusion shotgun mass spectrometry. STAR Protocols. 2(1). 100235–100235. 30 indexed citations

13.

Zhou, Quan, Xun Chi, Wei Yuan Hsieh, et al.. (2020). Interferon-mediated reprogramming of membrane cholesterol to evade bacterial toxins. Nature Immunology. 21(7). 746–755. 61 indexed citations

14.

Davis, Brett A., Mariam Okhovat, Xiangming Ding, et al.. (2020). Dynamic changes in chromatin accessibility, altered adipogenic gene expression, and total versus de novo fatty acid synthesis in subcutaneous adipose stem cells of normal-weight polycystic ovary syndrome (PCOS) women during adipogenesis: evidence of cellular programming. Clinical Epigenetics. 12(1). 181–181. 23 indexed citations

15.

Hough, Greg, A. Chattopadhyay, Mohamad Navab, et al.. (2017). Transgenic tomatoes expressing the 6F peptide and ezetimibe prevent diet-induced increases of IFN-β and cholesterol 25-hydroxylase in jejunum. Journal of Lipid Research. 58(8). 1636–1647. 12 indexed citations

16.

York, Autumn G., Kevin J. Williams, Xin Liu, et al.. (2016). Rapid transcriptional reprogramming of the lipid biosynthesis genes in response to type I interferon. The Journal of Immunology. 196(1_Supplement). 202.14–202.14. 2 indexed citations

17.

Meriwether, David, Dawoud Sulaiman, Alan C. Wagner, et al.. (2016). Transintestinal transport of the anti-inflammatory drug 4F and the modulation of transintestinal cholesterol efflux. Journal of Lipid Research. 57(7). 1175–1193. 20 indexed citations

18.

Xie, Letian, Kevin J. Williams, Cuiwen He, et al.. (2015). Resveratrol and para-coumarate serve as ring precursors for coenzyme Q biosynthesis. Journal of Lipid Research. 56(4). 909–919. 35 indexed citations

19.

Kidani, Yoko, Heidi Elsaesser, M. Benjamin Hock, et al.. (2013). Sterol regulatory element–binding proteins are essential for the metabolic programming of effector T cells and adaptive immunity. Nature Immunology. 14(5). 489–499. 419 indexed citations breakdown →

20.

Mukhopadhyay, Subhankar, Annette Plüddemann, Kevin J. Williams, et al.. (2010). Immune Inhibitory Ligand CD200 Induction by TLRs and NLRs Limits Macrophage Activation to Protect the Host from Meningococcal Septicemia. Cell Host & Microbe. 8(3). 236–247. 69 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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