Publisher Correction: Comprehensive discovery and functional characterization of the noncanonical proteome
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Uncovering protein glycosylation dynamics and heterogeneity using deep quantitative glycoprofiling (DQGlyco)
Protein glycosylation regulates essential cellular processes such as signaling, adhesion and cell–cell interactions; however, dysregulated glycosylation is associated with diseases such as cancer. Here we introduce deep quantitative glycoprofiling (DQGlyco), a robust method that integrates high-throughput sample preparation, highly sensitive detection and precise multiplexed quantification to investigate protein glycosylation dynamics at an unprecedented depth. Using DQGlyco, we profiled the mouse brain glycoproteome, identifying 177,198 unique N-glycopeptides—25 times more than previous studies. We quantified glycopeptide changes in human cells treated with a fucosylation inhibitor and characterized surface-exposed glycoforms. Furthermore, we analyzed tissue-specific glycosylation patterns in mice and demonstrated that a defined gut microbiota substantially remodels the mouse brain glycoproteome, shedding light on the link between the gut microbiome and brain protein functions. Additionally, we developed a novel strategy to evaluate glycoform solubility, offering new insights into their biophysical properties. Overall, the in-depth profiling offered by DQGlyco uncovered extensive complexity in glycosylation regulation.
In-depth and high-throughput spatial proteomics for whole-tissue slice profiling by deep learning-facilitated sparse sampling strategy
Mammalian organs and tissues are composed of heterogeneously distributed cells, which interact with each other and the extracellular matrix surrounding them in a spatially defined way. Therefore, spatially resolved gene expression profiling is crucial for determining the function and phenotypes of these cells. While genome mutations and transcriptome alterations act as drivers of diseases, the proteins that they encode regulate essentially all biological functions and constitute the majority of biomarkers and drug targets for disease diagnostics and treatment. However, unlike transcriptomics, which has a recent explosion in high-throughput spatial technologies with deep coverage, spatial proteomics capable of reaching bulk tissue-level coverage is still rare in the field, due to the non-amplifiable nature of proteins and sensitivity limitation of mass spectrometry (MS). More importantly, due to the limited multiplexing capability of the current proteomics methods, whole-tissue slice mapping with high spatial resolution requires a formidable amount of MS matching time. To achieve spatially resolved, deeply covered proteome mapping for centimeter-sized samples, we developed a sparse sampling strategy for spatial proteomics (S4P) using computationally assisted image reconstruction methods, which is potentially capable of reducing the number of samples by tens to thousands of times depending on the spatial resolution. In this way, we generated the largest spatial proteome to date, mapping more than 9000 proteins in the mouse brain, and discovered potential new regional or cell type markers. Considering its advantage in sensitivity and throughput, we expect that the S4P strategy will be applicable to a wide range of tissues in future studies.
Comprehensive discovery and functional characterization of the noncanonical proteome
The systematic identification and functional characterization of noncanonical translation products, such as novel peptides, will facilitate the understanding of the human genome and provide new insights into cell biology. Here, we constructed a high-coverage peptide sequencing reference library with 11,668,944 open reading frames and employed an ultrafiltration tandem mass spectrometry assay to identify novel peptides. Through these methods, we discovered 8945 previously unannotated peptides from normal gastric tissues, gastric cancer tissues and cell lines, nearly half of which were derived from noncoding RNAs. Moreover, our CRISPR screening revealed that 1161 peptides are involved in tumor cell proliferation. The presence and physiological function of a subset of these peptides, selected based on screening scores, amino acid length, and various indicators, were verified through Flag-knockin and multiple other methods. To further characterize the potential regulatory mechanisms involved, we constructed a framework based on artificial intelligence structure prediction and peptide‒protein interaction network analysis for the top 100 candidates and revealed that these cancer-related peptides have diverse subcellular locations and participate in organelle-specific processes. Further investigation verified the interacting partners of pep1-nc-OLMALINC, pep5-nc-TRHDE-AS1, pep-nc-ZNF436-AS1 and pep2-nc-AC027045.3, and the functions of these peptides in mitochondrial complex assembly, energy metabolism, and cholesterol metabolism, respectively. We showed that pep5-nc-TRHDE-AS1 and pep2-nc-AC027045.3 had substantial impacts on tumor growth in xenograft models. Furthermore, the dysregulation of these four peptides is closely correlated with clinical prognosis. Taken together, our study provides a comprehensive characterization of the noncanonical proteome, and highlights critical roles of these previously unannotated peptides in cancer biology.
Subcellular proteomics and iPSC modeling uncover reversible mechanisms of axonal pathology in Alzheimer’s disease
Dystrophic neurites (also termed axonal spheroids) are found around amyloid deposits in Alzheimer’s disease (AD), where they impair axonal electrical conduction, disrupt neural circuits and correlate with AD severity. Despite their importance, the mechanisms underlying spheroid formation remain incompletely understood. To address this, we developed a proximity labeling approach to uncover the proteome of spheroids in human postmortem and mouse brains. Additionally, we established a human induced pluripotent stem cell (iPSC)-derived AD model enabling mechanistic investigation and optical electrophysiology. These complementary approaches revealed the subcellular molecular architecture of spheroids and identified abnormalities in key biological processes, including protein turnover, cytoskeleton dynamics and lipid transport. Notably, the PI3K/AKT/mTOR pathway, which regulates these processes, was activated in spheroids. Furthermore, phosphorylated mTOR levels in spheroids correlated with AD severity in humans. Notably, mTOR inhibition in iPSC-derived neurons and mice ameliorated spheroid pathology. Altogether, our study provides a multidisciplinary toolkit for investigating mechanisms and therapeutic targets for axonal pathology in neurodegeneration.
PSMB4: a potential biomarker and therapeutic target for depression, perspective from integration analysis of depression GWAS data and human plasma proteome
Depression is a common and severe mental disorder that affects more than 300 million people worldwide. While it is known to have a moderate genetic component, identifying specific genes that contribute to the disorder has been challenging. Previous Genome-wide association studies (GWASs) have identified over 100 genomic loci that are significantly associated with depression. But finding useful therapeutic targets and diagnostic biomarkers from this information has proven difficult. To address this challenge, I conducted a plasma protein proteome-wide association study (PWAS) for depression, using human plasma protein QTL (pQTL) and depression GWAS data. I identified four proteins that were significantly associated with depression: BTN3A3 (P value = 6.41 × 10−06), PSMB4 (P value = 1.42 × 10−05), TIMP4 (P value = 3.77 × 10−05), and ITIH1 (P value = 7.86 × 10−05). Specifically, I found that BTN3A3 and PSMB4 play a causal role in depression, as confirmed by colocalization and Mendelian Randomization (MR) analysis. Interestingly, I also discovered that PSMB4 was significantly associated with depression in both the brain proteome studies and the plasma PWAS results, which suggests that it may be a particularly promising candidate for further study. Overall, this work has identified 4 new risk proteins for depression and highlights the potential of plasma proteome data for uncovering novel therapeutic targets and diagnostic biomarkers.
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