Functionally distinct BMP1 isoforms show an opposite pattern of abundance in plasma from non-small cell lung cancer subjects and controls

Advancements in deep plasma proteomics are enabling high-resolution measurement of plasma proteoforms, which may reveal a rich source of novel biomarkers previously concealed by aggregated protein methods. Here, we analyze 188 plasma proteomes from non-small cell lung cancer subjects (NSCLC) and controls to identify NSCLC-associated protein isoforms by examining differentially abundant peptides as a proxy for isoform-specific exon usage. We find four proteins comprised of peptides with opposite patterns of abundance between cancer and control subjects. One of these proteins, BMP1, has known isoforms that can explain this differential pattern, for which the abundance of the NSCLC-associated isoform increases with stage of NSCLC progression. The presence of cancer and control-associated isoforms suggests differential regulation of BMP1 isoforms. The identified BMP1 isoforms have known functional differences, which may reveal insights into mechanisms impacting NSCLC disease progression.

Abstract 2537: Application of the proteograph to the identification of differential protein isoform plasma abundance in early lung cancer vs. healthy controls

Introduction: Our ~20,000 genes encode over ~1 million protein variants because of alternative splice forms, allelic variation and protein modification. Large-scale genomics studies over the last decade have markedly increased our understanding of cancer biology through analysis of both tissue and biofluids. However, similarly scaled deep and unbiased proteomics studies has been impractical due to the complex workflows required in biofluids. We have previously described Proteograph, a novel platform that leverages the nano-bio interactions of nanoparticles for deep and unbiased proteomic sampling at scale. As proof-of-concept we demonstrated the utility of Proteograph to deeply interrogate the plasma of 141 subjects, 80 healthy controls and 61 early-stage non-small cell lung cancer (NSCLC) subjects, to create a plasma biomarker classifier for the detection of NSCLC versus healthy control with AUC of 0.91 1. Here, we present a further analysis of this data to dissect differences between patients and controls in plasma abundance of protein isoforms arising from alternative gene splicing.

Methods and Results: In the prior study1, we identified 1,664 proteins per individual with a median detection of 7 peptides per protein. Here, we searched for peptides that had differential abundance between controls and cancer (p < 0.05; Benjamini-Hochberg corrected). Next, we filtered for proteins comprising sets of peptides where at least one peptide had significantly higher and another significantly lower plasma abundance in controls vs. cancer, resulting in a set of 16 proteins.

For 3 of these 16 proteins, the differential abundance of the peptides can be explained by differential abundance of the underlying protein isoforms. For example, one of the proteins, BMP1 comprises four protein coding isoforms. Two of these isoforms are substantially longer (~400-800 residues) than the other two isoforms covering additional exons. Peptides mapping to exons that cover all four protein isoforms have higher abundance in cancer relative to controls, whereas peptides mapping to exons that cover only the two longer isoforms have higher abundance in healthy controls. BMP1 is known to play a dual role in cancer, acting as both suppressor and activator2 and this differential pattern of isoform abundance may shed further light into BMP1's role in cancer.

Discussion: We demonstrate that peptide level measurement of the plasma proteome enables quantification of differential isoform abundance patterns, which are inaccessible to prior methods of lesser scale, depth or coverage compared to the Proteograph platform. By extending our approach to include additional features such as protein amino acid variants and PTMs, we anticipate deepening this connection enabling proteogenomics.

1Blume et al. Nat Comm (2020)

2Bach et al Mol Ther Oncolytics (2018).

Citation Format: Asim Siddiqui, John E. Blume, Margaret K. Donovan, Marwin Ko, Ryan W. Benz, Theodore L. Platt, Juan C. Cuevas, Serafim Batzoglou, Omid C. Farokhzad. Application of the proteograph to the identification of differential protein isoform plasma abundance in early lung cancer vs. healthy controls [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2537.

Obesity, diabetes, and leptin resistance promote tau pathology in a mouse model of disease

Obesity and type 2 diabetes mellitus (T2DM) convey an increased risk for developing dementia. The microtubule-associated protein tau is implicated in neurodegenerative disease by undergoing hyperphosphorylation and aggregation, leading to cytotoxicity and neurodegeneration. Enzymes involved in the regulation of tau phosphorylation, such as GSK3β, are tightly associated with pathways found to be dysregulated in T2DM. We have shown previously that leptin-resistant mice, which develop obesity and a diabetic phenotype, display elevated levels of tau phosphorylation. Here we show cells cultured with leptin, an adipokine shown to have neuroprotective effects, reduces tau phosphorylation. To explore how this mechanism works in vivo we transduced an existing diabetic mouse line (Lepr(db/db)) with a tau mutant (tau(P301L)) via adeno-associated virus (AAV). The resulting phenotype included a striking increase in tau phosphorylation and the number of neurofibrillary tangles (NFTs) found within the hippocampus. We conclude that leptin resistance-induced obesity and diabetes accelerates the development of tau pathology. This model of metabolic dysfunction and tauopathy provides a new system in which to explore the mechanisms underlying the ways in which leptin resistance and diabetes influence development of tau pathology, and may ultimately be related to the development of NFTs.