Search bioRxiv⌕ Search

bioRxiv · 10.1101/2024.02.10.579787

Proteomic profiling of intracranial atherosclerotic plaque in the human brain

Abstract

BackgroundIntracranial atherosclerotic disease (ICAD) is one of the major causes of ischemic stroke and associated with high risk of stroke recurrence. There are no reliable and specific fluid biomarkers for ICAD, and little is known about the proteomic profiling of ICAD. In this study we aimed to explore the feasibility of applying proteomics technology to profile intracranial atherosclerotic plaques extracted from postmortem human brain arteries. MethodsEighteen segments (5-10mm in length) of major arteries from 10 postmortem brains were collected from the Mount Sinai Neuropathology Brain Bank. Among these segments, 5 had no evidence of atherosclerotic disease, and 13 had wall thickening or visible plaques with various degree of stenosis. Proteins were extracted from the vessel segments, quantified, and digested into peptides. Subsequently, the peptides underwent tandem mass tag (TMT) labeling, pooling, and analysis using two-dimensional liquid chromatography-tandem mass spectrometry (LC/LC-MS/MS). Protein identification and quantification were performed using the JUMP software. Differentially expressed proteins (DEPs) were defined as proteins with p.adj < 0.05 and absolute log2 (fold change) > log2 (1.2). ResultsA total of 7,492 unique proteins were detected, and 6,726 quantifiable proteins were retained for further analysis. Among these, 265 DEPs, spanning on 252 unique gene, were found to be associated with ICAD by comparing the arterial segments with vs those without atherosclerotic disease. The top 4 most significant DEPs include LONP1, RPS19, MRPL12 and SNU13. Among the top 50 DEPs, FADD, AIFM1 and PGK1 were associated with atherosclerotic disease or cardiovascular events in previous studies. Moreover, the previously reported proteins associated with atherosclerosis such as APCS, MMP12, CTSD were elevated in arterial segments with atherosclerotic changes. Furthermore, the up-regulation of APOE and LPL, the ICAD GWAS risk genes, was shown to be associated with the plaque severity. Finally, gene set enrichment analysis revealed the DEP signature is enriched for biological pathways such as chromatin structure, plasma lipoprotein, nucleosome, and protein-DNA complex, peroxide catabolic and metabolic processes, critical in ICAD pathology. ConclusionsDirect proteomic profiling of fresh-frozen intracranial artery samples by MS-based proteomic technology is a feasible approach to identify ICAD-associated proteins, which can be potential biomarker candidates for ICAD. Further plaque proteomic study in a larger sample size is warranted to uncover mechanistic insights into ICAD and discover novel biomarkers that may help to improve diagnosis and risk stratification in ICAD.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Hao, Q., Wang, E., Wang, J., Wu, Z., Crary, J. F., Sharma, S., Thorn, E. L., Elahi, F., Zhang, B., Peng, J.. 2024-02-12. Proteomic profiling of intracranial atherosclerotic plaque in the human brain. https://doi.org/10.1101/2024.02.10.579787

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Limit-pushing overexpression reveals constraints on protein abundance

Proteins are often classified as toxic or non-toxic without measuring the abundance reached, leaving constraints on tolerable protein abundance unresolved. We established a limit-pushing approach in Saccharomyces cerevisiae combining strong inducible expression with gTOW-mediated high-copy selection to counteract copy-number compensation while measuring protein abundance and growth. Nearly all of approximately 80 chromosome I proteins severely inhibited growth or reduced viability at sufficiently high abundance. We established IE50, the expression level associated with a 50% reduction in growth rate, to quantify their widely varying overexpression tolerance. IE50 was positively associated with predicted structural order and cytoplasmic localization propensity and negatively associated with sulphur content. Single-cell imaging linked higher tolerance to proteins remaining cytoplasmic without becoming aggregation-positive and revealed abundance-dependent changes in localization and organelle morphology. At extreme abundance, Fun12, Nup60, and Pex22 generated distinct large-scale intracellular states through specific sequence regions. These findings establish overexpression toxicity as a quantitative property linked to protein characteristics and reveal both constraints on tolerable abundance and sequence-dependent capacities for intracellular organization.

systems biology↗

Accessing Enzyme Kinetic Data and Prediction Methods at Scale

Enzyme kinetic parameters inform metabolic models, yet experimental measurements are sparse. A growing body of work predicts them from protein and substrate features, but software fragmentation hinders adoption, so downstream tools lock into the most accessible method. We present OpenKinetics Predictor (at predictor.openkinetics.org), an open-source platform integrating thirteen methods in isolated environments behind one interface. The platform optionally reports similarity between query proteins and each method's training data to contextualise reliability. A common featurisation-prediction abstraction keeps it extensible, and independent parties, including original authors, contributed many methods. We pair it with a data portal (at data.openkinetics.org) that exposes CatLog, a curated kinetic dataset, with precomputed embeddings, predicted binding sites, and standardised splits. Both offer a web interface and an API, and the GECKO modelling toolbox already calls the predictor API. As a case study, we predict across an E. coli model and find inter-predictor agreement varies with metabolic context and data availability.

systems biology↗

A thermoregulatory design principle for transitions into hypometabolism

Mammals entering torpor or hibernation undergo an abrupt transition from normothermia to hypothermia, yet how thermoregulation enables this switch remains poorly understood. Here, we identify dynamical signatures that precede these transitions and a mathematical principle that can generate them. In fasting-induced torpor in mice, body-temperature fluctuations increased before torpor onset, providing an early-warning signal that tracked proximity to the transition better than temperature decline alone. A heat-balance model showed that reducing how strongly the effective heat-loss coefficient depends on body temperature reorganizes thermoregulatory stability, allowing a low-temperature equilibrium to emerge while the normothermic state remains stable. This organization is consistent with a symmetry-broken pitchfork involving a saddle-node. Similar increases in temperature fluctuations preceded hibernation onset in hamsters. These findings link pre-transition temperature dynamics to changes in the underlying thermoregulatory landscape and provide a framework for detecting and understanding transitions from normothermia to hypothermia.

systems biology↗