Transgelin: a new gene involved in LDL endocytosis identified by a genome-wide CRISPR-Cas9 screen

A significant proportion of patients with elevated LDL and a clinical presentation of familial hypercholesterolemia do not carry known genetic mutations associated with hypercholesterolemia, such as defects in the LDL receptor. To identify new genes involved in the cellular uptake of LDL, we developed a novel whole-genome clustered regularly interspaced short palindromic repeat-Cas9 KO screen in HepG2 cells. We identified transgelin (TAGLN), an actin-binding protein, as a potentially new gene involved in LDL endocytosis. In silico validation demonstrated that genetically predicted differences in expression of TAGLN in human populations were significantly associated with elevated plasma lipids (triglycerides, total cholesterol, and LDL-C) in the Global Lipids Genetics Consortium and lipid-related phenotypes in the UK Biobank. In biochemical studies, TAGLN-KO HepG2 cells showed a reduction in cellular LDL uptake, as measured by flow cytometry. In confocal microscopy imaging, TAGLN-KO cells had disrupted actin filaments as well as an accumulation of LDL receptor on their surface because of decreased receptor internalization. Furthermore, TAGLN-KO cells exhibited a reduction in total and free cholesterol content, activation of SREBP2, and a compensatory increase in cholesterol biosynthesis. TAGLN deficiency also disrupted the uptake of VLDL and transferrin, other known cargoes for receptors that depend upon clathrin-mediated endocytosis. Our data suggest that TAGLN is a novel factor involved in the actin-dependent phase of clathrin-mediated endocytosis of LDL. The identification of novel genes involved in the endocytic uptake of LDL may improve the diagnosis of hypercholesterolemia and provide future therapeutic targets for the prevention of cardiovascular disease.     

Immunopeptidomic Analyses of Colorectal Cancers With and Without Microsatellite Instability

Colorectal cancer is the second leading cause of cancer death worldwide, and the incidence of this disease is expected to increase as global socioeconomic changes occur. Immune checkpoint inhibition therapy is effective in treating a minority of colorectal cancer tumors; however, microsatellite stable tumors do not respond well to this treatment. Emerging cancer immunotherapeutic strategies aim to activate a cytotoxic T cell response against tumor-specific antigens, presented exclusively at the cell surface of cancer cells. These antigens are rare and are most effectively identified with a mass spectrometry–based approach, which allows the direct sampling and sequencing of these peptides. Although the few tumor-specific antigens identified to date are derived from coding regions of the genome, recent findings indicate that a large proportion of tumor-specific antigens originate from allegedly noncoding regions. Here, we employed a novel proteogenomic approach to identify tumor antigens in a collection of colorectal cancer–derived cell lines and biopsy samples consisting of matched tumor and normal adjacent tissue. The generation of personalized cancer databases paired with mass spectrometry analyses permitted the identification of more than 30,000 unique MHC I–associated peptides. We identified 19 tumor-specific antigens in both microsatellite stable and unstable tumors, over two-thirds of which were derived from noncoding regions. Many of these peptides were derived from source genes known to be involved in colorectal cancer progression, suggesting that antigens from these genes could have therapeutic potential in a wide range of tumors. These findings could benefit the development of T cell–based vaccines, in which T cells are primed against these antigens to target and eradicate tumors. Such a vaccine could be used in tandem with existing immune checkpoint inhibition therapies, to bridge the gap in treatment efficacy across subtypes of colorectal cancer with varying prognoses. Data are available via ProteomeXchange with identifier PXD028309.     

SIMSI-Transfer: Software-Assisted Reduction of Missing Values in Phosphoproteomic and Proteomic Isobaric Labeling Data Using Tandem Mass Spectrum Clustering

Isobaric stable isotope labeling techniques such as tandem mass tags (TMTs) have become popular in proteomics because they enable the relative quantification of proteins with high precision from up to 18 samples in a single experiment. While missing values in peptide quantification are rare in a single TMT experiment, they rapidly increase when combining multiple TMT experiments. As the field moves toward analyzing ever higher numbers of samples, tools that reduce missing values also become more important for analyzing TMT datasets. To this end, we developed SIMSI-Transfer (Similarity-based Isobaric Mass Spectra 2 [MS2] Identification Transfer), a software tool that extends our previously developed software MaRaCluster (© Matthew The) by clustering similar tandem MS2 from multiple TMT experiments. SIMSI-Transfer is based on the assumption that similarity-clustered MS2 spectra represent the same peptide. Therefore, peptide identifications made by database searching in one TMT batch can be transferred to another TMT batch in which the same peptide was fragmented but not identified. To assess the validity of this approach, we tested SIMSI-Transfer on masked search engine identification results and recovered >80% of the masked identifications while controlling errors in the transfer procedure to below 1% false discovery rate. Applying SIMSI-Transfer to six published full proteome and phosphoproteome datasets from the Clinical Proteomic Tumor Analysis Consortium led to an increase of 26 to 45% of identified MS2 spectra with TMT quantifications. This significantly decreased the number of missing values across batches and, in turn, increased the number of peptides and proteins identified in all TMT batches by 43 to 56% and 13 to 16%, respectively.     

A Novel Spectral Annotation Strategy Streamlines Reporting of Mono-ADP-ribosylated Peptides Derived from Mouse Liver and Spleen in Response to IFN-γ

Mass-spectrometry-enabled ADP-ribosylation workflows are developing rapidly, providing researchers a variety of ADP-ribosylome enrichment strategies and mass spectrometric acquisition options. Despite the growth spurt in upstream technologies, systematic ADP-ribosyl (ADPr) peptide mass spectral annotation methods are lacking. HCD-dependent ADP-ribosylome studies are common, but the resulting MS2 spectra are complex, owing to a mixture of b/y-ions and the m/p-ion peaks representing one or more dissociation events of the ADPr moiety (m-ion) and peptide (p-ion). In particular, p-ions that dissociate further into one or more fragment ions can dominate HCD spectra but are not recognized by standard spectral annotation workflows. As a result, annotation strategies that are solely reliant upon the b/y-ions result in lower spectral scores that in turn reduce the number of reportable ADPr peptides. To improve the confidence of spectral assignments, we implemented an ADPr peptide annotation and scoring strategy. All MS2 spectra are scored for the ADPr m-ions, but once spectra are assigned as an ADPr peptide, they are further annotated and scored for the p-ions. We implemented this novel workflow to ADPr peptides enriched from the liver and spleen isolated from mice post 4 h exposure to systemic IFN-γ. HCD collision energy experiments were first performed on the Orbitrap Fusion Lumos and the Q Exactive, with notable ADPr peptide dissociation properties verified with CID (Lumos). The m-ion and p-ion series score distributions revealed that ADPr peptide dissociation properties vary markedly between instruments and within instrument collision energy settings, with consequences on ADPr peptide reporting and amino acid localization. Consequentially, we increased the number of reportable ADPr peptides by 25% (liver) and 17% (spleen) by validation and the inclusion of lower confidence ADPr peptide spectra. This systematic annotation strategy will streamline future reporting of ADPr peptides that have been sequenced using any HCD/CID-based method.     

Proteins in Tumor-Derived Plasma Extracellular Vesicles Indicate Tumor Origin

Cancer-derived extracellular vesicles (EVs) promote tumorigenesis, premetastatic niche formation, and metastasis via their protein cargo. However, the proteins packaged by patient tumors into EVs cannot be determined in vivo because of the presence of EVs derived from other tissues. We therefore developed a cross-species proteomic method to quantify the human tumor-derived proteome of plasma EVs produced by patient-derived xenografts of four cancer types. Proteomic profiling revealed individualized packaging of novel protein cargo, and machine learning accurately classified the type of the underlying tumor.     

Genome-wide DNA methylation profiling of stomach cancer in the ethnic population of Mizoram,North East India

Stomach cancer is the fifth most common cancer in terms of prevalence and incidence and the fourth leading cause of mortality in men and women worldwide. It is well-established that aberrant DNA methylation in cells can lead to carcinogenesis. The primary objective of our study was to investigate the aberrant DNA methylation status of genes associated with stomach cancer with a particular reference to the ethnic population of Mizoram, North East India. The site-level analysis identified 2883 CpG sites differentially methylated, representing ～922 genes. Out of which 476 Differentially Methylated Positions (DMPs) were promoter-associated, 452 DMPs were hypermethylated, and 24 were hypomethylated. The region-level analysis identified 462 Differentially Methylated Regions (DMRs) corresponding to ～320 genes, of which ～281 genes were hypermethylated and ～40 genes were hypomethylated. TCGA analysis showed that some of the genes had been previously implicated in other cancers including stomach cancer. Five hypermethylated genes were selected as candidate genes for further investigations and they have shown to be novel and could serve as candidate hypermethylation biomarkers for stomach cancer in this particular ethnic group.     

Proteomic Discovery of Plasma Protein Biomarkers and Development of Models Predicting Prognosis of High-Grade Serous Ovarian Carcinoma

Ovarian cancer is one of the most lethal female cancers. For accurate prognosis prediction, this study aimed to investigate novel, blood-based prognostic biomarkers for high-grade serous ovarian carcinoma (HGSOC) using mass spectrometry–based proteomics methods. We conducted label-free liquid chromatography–tandem mass spectrometry using frozen plasma samples obtained from patients with newly diagnosed HGSOC (n = 20). Based on progression-free survival (PFS), the samples were divided into two groups: good (PFS ≥18 months) and poor prognosis groups (PFS <18 months). Proteomic profiles were compared between the two groups. Referring to proteomics data that we previously obtained using frozen cancer tissues from chemotherapy-naïve patients with HGSOC, overlapping protein biomarkers were selected as candidate biomarkers. Biomarkers were validated using an independent set of HGSOC plasma samples (n = 202) via enzyme-linked immunosorbent assay (ELISA). To construct models predicting the 18-month PFS rate, we performed stepwise selection based on the area under the receiver operating characteristic curve (AUC) with 5-fold cross-validation. Analysis of differentially expressed proteins in plasma samples revealed that 35 and 61 proteins were upregulated in the good and poor prognosis groups, respectively. Through hierarchical clustering and bioinformatic analyses, GSN, VCAN, SND1, SIGLEC14, CD163, and PRMT1 were selected as candidate biomarkers and were subjected to ELISA. In multivariate analysis, plasma GSN was identified as an independent poor prognostic biomarker for PFS (adjusted hazard ratio, 1.556; 95% confidence interval, 1.073–2.256; p = 0.020). By combining clinical factors and ELISA results, we constructed several models to predict the 18-month PFS rate. A model consisting of four predictors (FIGO stage, residual tumor after surgery, and plasma levels of GSN and VCAN) showed the best predictive performance (mean validated AUC, 0.779). The newly developed model was converted to a nomogram for clinical use. Our study results provided insights into protein biomarkers, which might offer clues for developing therapeutic targets.     

Ionizing Radiation Drives Key Regulators of Antigen Presentation and a Global Expansion of the Immunopeptidome

Little is known about the pathways regulating MHC antigen presentation and the identity of treatment-specific T cell antigens induced by ionizing radiation. For this reason, we investigated the radiation-specific changes in the colorectal tumor cell proteome. We found an increase in DDX58 and ZBP1 protein expression, two nucleic acid sensing molecules likely involved in induction of the dominant interferon response signature observed after genotoxic insult. We further observed treatment-induced changes in key regulators and effector proteins of the antigen processing and presentation machinery. Differential regulation of MHC allele expression was further driving the presentation of a significantly broader MHC-associated peptidome postirradiation, defining a radiation-specific peptide repertoire. Interestingly, treatment-induced peptides originated predominantly from proteins involved in catecholamine synthesis and metabolic pathways. A nuanced relationship between protein expression and antigen presentation was observed where radiation-induced changes in proteins do not correlate with increased presentation of associated peptides. Finally, we detected an increase in the presentation of a tumor-specific neoantigen derived from Mtch1. This study provides new insights into how radiation enhances antigen processing and presentation that could be suitable for the development of combinatorial therapies. Data are available via ProteomeXchange with identifier PXD032003.     

An Optimized Proteomics Approach Reveals Novel Alternative Proteins in Mouse Liver Development

Alternative ORFs (AltORFs) are unannotated sequences in genome that encode novel peptides or proteins named alternative proteins (AltProts). Although ribosome profiling and bioinformatics predict a large number of AltProts, mass spectrometry as the only direct way of identification is hampered by the short lengths and relative low abundance of AltProts. There is an urgent need for improvement of mass spectrometry methodologies for AltProt identification. Here, we report an approach based on size-exclusion chromatography for simultaneous enrichment and fractionation of AltProts from complex proteome. This method greatly simplifies the variance of AltProts discovery by enriching small proteins smaller than 40 kDa. In a systematic comparison between 10 methods, the approach we reported enabled the discovery of more AltProts with overall higher intensities, with less cost of time and effort compared to other workflows. We applied this approach to identify 89 novel AltProts from mouse liver, 39 of which were differentially expressed between embryonic and adult mice. During embryonic development, the upregulated AltProts were mainly involved in biological pathways on RNA splicing and processing, whereas the AltProts involved in metabolisms were more active in adult livers. Our study not only provides an effective approach for identifying AltProts but also novel AltProts that are potentially important in developmental biology.     

Comprehensive analysis of gene expression profiles to identify differential prognostic factors of primary and metastatic breast cancer

Breast cancer accounts for nearly half of all cancer-related deaths in women worldwide. However, the molecular mechanisms that lead to tumour development and progression remain poorly understood and there is a need to identify candidate genes associated with primary and metastatic breast cancer progression and prognosis. In this study, candidate genes associated with prognosis of primary and metastatic breast cancer were explored through a novel bioinformatics approach. Primary and metastatic breast cancer tissues and adjacent normal breast tissues were evaluated to identify biomarkers characteristic of primary and metastatic breast cancer. The Cancer Genome Atlas-breast invasive carcinoma (TCGA-BRCA) dataset (ID: HS-01619) was downloaded using the mRNASeq platform. Genevestigator 8.3.2 was used to analyse TCGA-BRCA gene expression profiles between the sample groups and identify the differentially-expressed genes (DEGs) in each group. For each group, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were used to determine the function of DEGs. Networks of protein–protein interactions were constructed to identify the top hub genes with the highest degree of interaction. Additionally, the top hub genes were validated based on overall survival and immunohistochemistry using The Human Protein Atlas. Of the top 20 hub genes identified, four (KRT14, KIT, RAD51, and TTK) were considered as prognostic risk factors based on overall survival. KRT14 and KIT expression levels were upregulated while those of RAD51 and TTK were downregulated in patients with breast cancer. The four proposed candidate hub genes might aid in further understanding the molecular changes that distinguish primary breast tumours from metastatic tumours as well as help in developing novel therapeutics. Furthermore, they may serve as effective prognostic risk markers based on the strong correlation between their expression and patient overall survival.     

Heparanase promotes myeloma stemness and in vivo tumorigenesis

Heparanase is known to enhance the progression of many cancer types and is associated with poor patient prognosis. We recently reported that after patients with multiple myeloma were treated with high dose chemotherapy, the tumor cells that emerged upon relapse expressed a much higher level of heparanase than was present prior to therapy. Because tumor cells having stemness properties are thought to seed tumor relapse, we investigated whether heparanase had a role in promoting myeloma stemness. When plated at low density and grown in serum-free conditions that support survival and expansion of stem-like cells, myeloma cells expressing a low level of heparanase formed tumor spheroids poorly. In contrast, cells expressing a high level of heparanase formed significantly more and larger spheroids than did the heparanase low cells. Importantly, heparanase-low expressing cells exhibited plasticity and were induced to exhibit stemness properties when exposed to recombinant heparanase or to exosomes that contained a high level of heparanase cargo. The spheroid-forming heparanase-high cells had elevated expression of GLI1, SOX2 and ALDH1A1, three genes known to be associated with myeloma stemness. Inhibitors that block the heparan sulfate degrading activity of heparanase significantly diminished spheroid formation and expression of stemness genes implying a direct role of the enzyme in regulating stemness. Blocking the NF-κB pathway inhibited spheroid formation and expression of stemness genes demonstrating a role for NF-κB in heparanase-mediated stemness. Myeloma cells made deficient in heparanase exhibited decreased stemness properties in vitro and when injected into mice they formed tumors poorly compared to the robust tumorigenic capacity of cells expressing higher levels of heparanase. These studies reveal for the first time a role for heparanase in promoting cancer stemness and provide new insight into its function in driving tumor progression and its association with poor prognosis in cancer patients.     

Lysine Succinylation of VBS Contributes to Sclerotia Development and Aflatoxin Biosynthesis in Aspergillus flavus

Aspergillus flavus is a common saprophytic and pathogenic fungus, and its secondary metabolic pathways are one of the most highly characterized owing to its aflatoxin (AF) metabolite affecting global economic crops and human health. Different natural environments can cause significant variations in AF synthesis. Succinylation was recently identified as one of the most critical regulatory post-translational modifications affecting metabolic pathways. It is primarily reported in human cells and bacteria with few studies on fungi. Proteomic quantification of lysine succinylation (Ksuc) exploring its potential involvement in secondary metabolism regulation (including AF production) has not been performed under natural conditions in A. flavus. In this study, a quantification method was performed based on tandem mass tag labeling and antibody-based affinity enrichment of succinylated peptides via high accuracy nano-liquid chromatography with tandem mass spectrometry to explore the succinylation mechanism affecting the pathogenicity of naturally isolated A. flavus strains with varying toxin production. Altogether, 1240 Ksuc sites in 768 proteins were identified with 1103 sites in 685 proteins quantified. Comparing succinylated protein levels between high and low AF-producing A. flavus strains, bioinformatics analysis indicated that most succinylated proteins located in the AF biosynthetic pathway were downregulated, which directly affected AF synthesis. Versicolorin B synthase is a key catalytic enzyme for heterochrome B synthesis during AF synthesis. Site-directed mutagenesis and biochemical studies revealed that versicolorin B synthase succinylation is an important regulatory mechanism affecting sclerotia development and AF biosynthesis in A. flavus. In summary, our quantitative study of the lysine succinylome in high/low AF-producing strains revealed the role of Ksuc in regulating AF biosynthesis. We revealed novel insights into the metabolism of AF biosynthesis using naturally isolated A. flavus strains and identified a rich source of metabolism-related enzymes regulated by succinylation.     

Concerted Antibody and Antigen Discovery by Differential Whole-cell Phage Display Selections and Multi-omic Target Deconvolution

Monoclonal antibody (mAb)-based biologics are well established treatments of cancer. Antibody discovery campaigns are typically directed at a single target of interest, which inherently limits the possibility of uncovering novel antibody specificities or functionalities. Here, we present a target-unbiased approach for antibody discovery that relies on generating mAbs against native target cell surfaces via phage display. This method combines a previously reported method for improved whole-cell phage display selections with next-generation sequencing analysis to efficiently identify mAbs with the desired target cell reactivity. Applying this method to multiple myeloma cells yielded a panel of >50 mAbs with unique sequences and diverse reactivities. To uncover the identities of the cognate antigens recognized by this panel, representative mAbs from each unique reactivity cluster were used in a multi-omic target deconvolution approach. From this, we identified and validated three cell surface antigens: PTPRG, ICAM1, and CADM1. PTPRG and CADM1 remain largely unstudied in the context of multiple myeloma, which could warrant further investigation into their potential as therapeutic targets. These results highlight the utility of optimized whole-cell phage display selection methods and could motivate further interest in target-unbiased antibody discovery workflows.     

RAN proteins in neurodegenerative disease: Repeating themes and unifying therapeutic strategies

Microsatellite-expansion mutations cause >50 neurological diseases but there are no effective treatments. Mechanistic studies have historically focused on protein loss-of-function and protein or RNA gain-of-function effects. It is now clear that many expansion mutations are bidirectionally transcribed producing two toxic expansion RNAs, which can produce up to six mutant proteins by repeat associated non-AUG (RAN) translation. Multiple types of RAN proteins have been shown to be toxic in cell and animal models, to lead to common types of neuropathological changes, and to dysregulate key pathways. How RAN proteins are produced without the canonical AUG or close-cognate AUG-like initiation codons is not yet completely understood but RNA structure, flanking sequences and stress pathways have been shown to be important. Here, we summarize recent progress in understanding the role of RAN proteins, mechanistic insights into their production, and the identification of novel therapeutic strategies that may be applicable across these neurodegenerative disorders.     

Relative Quantification of Proteins in Formalin-Fixed Paraffin-Embedded Breast Cancer Tissue Using Multiplexed Mass Spectrometry Assays

The identification of clinically relevant biomarkers represents an important challenge in oncology. This problem can be addressed with biomarker discovery and verification studies performed directly in tumor samples using formalin-fixed paraffin-embedded (FFPE) tissues. However, reliably measuring proteins in FFPE samples remains challenging. Here, we demonstrate the use of liquid chromatography coupled to multiple reaction monitoring mass spectrometry (LC-MRM/MS) as an effective technique for such applications. An LC-MRM/MS method was developed to simultaneously quantify hundreds of peptides extracted from FFPE samples and was applied to the targeted measurement of 200 proteins in 48 triple-negative, 19 HER2-overexpressing, and 20 luminal A breast tumors. Quantitative information was obtained for 185 proteins, including known markers of breast cancer such as HER2, hormone receptors, Ki-67, or inflammation-related proteins. LC-MRM/MS results for these proteins matched immunohistochemistry or chromogenic in situ hybridization data. In addition, comparison of our results with data from the literature showed that several proteins representing potential biomarkers were identified as differentially expressed in triple-negative breast cancer samples. These results indicate that LC-MRM/MS assays can reliably measure large sets of proteins using the analysis of surrogate peptides extracted from FFPE samples. This approach allows to simultaneously quantify the expression of target proteins from various pathways in tumor samples. LC-MRM/MS is thus a powerful tool for the relative quantification of proteins in FFPE tissues and for biomarker discovery.