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[Preprint]. 2025 May 15:2025.05.12.653562.
doi: 10.1101/2025.05.12.653562.

Characterization of cytokine treatment on human pancreatic islets by top-down proteomics

Ashley N Ives  1 , Tyler Sagendorf  2 , Lorenz Nierves  2 , Tai-Tu Lin  2 , Ercument Dirice  3 , Rohit N Kulkarni  4   5   6 , Ljiljana Paša-Tolić  1 , Wei-Jun Qian  2 , James M Fulcher  1
Affiliations

Characterization of cytokine treatment on human pancreatic islets by top-down proteomics

Ashley N Ives et al. bioRxiv. .

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Abstract

Type 1 diabetes (T1D) results from autoimmune-mediated destruction of insulin-producing β cells in the pancreatic islet. This process is modulated by pro-inflammatory cytokine signaling, which have been previously shown to alter protein expression in ex vivo islets. Herein, we applied top-down proteomics to globally evaluate proteoforms from human islets treated with proinflammatory cytokines (interferon-γ and interleukin-1β). We measured 1636 unique proteoforms across 6 donors and two time points (control and 24-hours post-treatment) and observed consistent changes in abundance across the glicentin-related pancreatic polypeptide (GRPP) and major proglucagon fragment regions of glucagon, as well as the LF-19/catestatin and vasostatin-1/2 region of chromogranin-A. We also observe several proteoforms that increase after cytokine-treatment or are exclusively observed after cytokine-treatment including forms of beta-2 Microglobulin (B2M), high-mobility group N2 protein (HMGN2), and chemokine (C-X-C motif) ligands (CXCL). Together, our quantitative results provide a baseline proteoform profile for human islets and identify several proteoforms that may serve as interesting candidate markers for T1D progression or therapeutic intervention.

Keywords: glucagon; hormone processing; islet; top-down proteomics.

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Conflict of interest statement

Conflict of interest statement The authors have declared no conflict of interest.

Figures

Figure 1.
Figure 1.
(A) Workflow for processing human islets for top-down proteomic analysis. Created with BioRender.com. (B) Mean number of unique genes (gray) and proteoforms (blue) observed from each human subject (N=6, t=2). Error bars represent +/− sd. (C) Total unique genes (gray) and proteoforms (blue) found in the entire study. (D) Percentile bins containing proteoforms with different degrees of data completeness, e.g. the percentage of acquisitions in which a given proteoform is observed.
Figure 2.
Figure 2.
Summary of top 30 most abundant INS and GCG proteoforms. (A) Median spectral count abundance for INS proteoforms. (B) Plot of INS proteoform truncations and modifications (C) Median spectral count abundance for GCG proteoforms. (D) Plot of GCG proteoform truncations and modifications. Right panels map the first and last amino acid of a given proteoform (x-axis), and color fill denotes identified PTMs. Dashed vertical lines annotate the region of a given gene. Proteoforms are sorted top to bottom by ascending C-terminal amino acid ending position, followed by ascending N-terminal amino acid starting position. Y-axis labels denote the first and last amino acid of a given proteoform, and “*” is used to denote modified proteoforms.
Figure 3.
Figure 3.
Volcano plots comparing proteoform fold changes pre- and post-cytokine treatment. (A) Volcano plot showing all proteoforms quantified. Proteoforms that are also significant after P value adjustment are annotated with gene names. Color fill denotes if a proteoform has a P value < 0.05 and log2 fold-change cutoff of >1 (green) or <−1 (red), else proteoforms are colored black. (B) Volcano plot showing quantified INS proteoforms. Color fill denotes which region of INS a proteoform is derived from. “Other” denotes proteoforms that span multiple regions. (C) Volcano plot showing quantified GCG proteoforms. Color fill denotes which region of GCG a proteoform is derived from. (D) Volcano plot showing quantified CHGA proteoforms. Color fill denotes which region of CHGA a proteoform is derived from. Horizontal dotted line indicates P value cutoff (.05), and vertical dotted lines indicate log2 fold-change cutoff of 1 and −1. Point size is scaled to the number of missing values present (i.e. larger point size indicates fewer missing values for a given proteoform).
Figure 4.
Figure 4.
Proteoforms unique to cytokine treatment condition. (A) Heatmap of proteoforms unique to the cytokine treatment condition. Data is plotted as arbitrary proteoform identifiers (Gene_##) versus treatment_patient identifier. Fill denotes the median normalized log2(Intensity) as determined by label-free quantification. NA values are shown in gray. Asterisks denote proteoforms that are significant (probability < 0.01) using a hypergeometric probability distribution. (B) Summarizes all proteoforms plotted in panel (A) including the first and last amino acid (firstAA, lastAA) based on the listed UniProt Accession.
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References

    1. Gregory G. A., Robinson T. I. G., Linklater S. E., Wang F., Colagiuri S., de Beaufort C., Donaghue K. C., International Diabetes Federation Diabetes Atlas Type 1 Diabetes in Adults Special Interest Group, Magliano D. J., Maniam J., Orchard T. J., Rai P., & Ogle G. D. (2022). Global incidence, prevalence, and mortality of type 1 diabetes in 2021 with projection to 2040: a modelling study. The Lancet. Diabetes & Endocrinology, 10(10), 741–760. 10.1016/S2213-8587(22)00218-2 - DOI - PubMed
    1. Herold K. C., Delong T., Perdigoto A. L., Biru N., Brusko T. M., & Walker L. S. K. (2024). The immunology of type 1 diabetes. Nature Reviews. Immunology, 24(6), 435–451. 10.1038/s41577-023-00985-4 - DOI - PMC - PubMed
    1. Eizirik D. L., Colli M. L., & Ortis F. (2009). The role of inflammation in insulitis and beta-cell loss in type 1 diabetes. Nature Reviews. Endocrinology, 5(4), 219–226. 10.1038/nrendo.2009.21 - DOI - PubMed
    1. Ramos-Rodríguez M., Raurell-Vila H., Colli M. L., Alvelos M. I., Subirana-Granés M., Juan-Mateu J., Norris R., Turatsinze J.-V., Nakayasu E. S., Webb-Robertson B.-J. M., Inshaw J. R. J., Marchetti P., Piemonti L., Esteller M., Todd J. A., Metz T. O., Eizirik D. L., & Pasquali L. (2019). The impact of proinflammatory cytokines on the β-cell regulatory landscape provides insights into the genetics of type 1 diabetes. Nature Genetics, 51(11), 1588–1595. 10.1038/s41588-019-0524-6 - DOI - PMC - PubMed
    1. Nakayasu E. S., Syed F., Tersey S. A., Gritsenko M. A., Mitchell H. D., Chan C. Y., Dirice E., Turatsinze J.-V., Cui Y., Kulkarni R. N., Eizirik D. L., Qian W.-J., Webb-Robertson B.-J. M., Evans-Molina C., Mirmira R. G., & Metz T. O. (2020). Comprehensive Proteomics Analysis of Stressed Human Islets Identifies GDF15 as a Target for Type 1 Diabetes Intervention. Cell Metabolism, 31(2), 363–374.e6. 10.1016/j.cmet.2019.12.005 - DOI - PMC - PubMed

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