Quantitative proteomics by SWATH-MS reveals sophisticated metabolic reprogramming in hepatocellular carcinoma tissues

Non-targeted metabolomics study for discovery of hepatocellular carcinoma serum diagnostic biomarker

Hepatocellular carcinoma (HCC) is one of the most prevalent malignant cancers worldwide. Due to the asymptomatic features of HCC at early stages, patients are often diagnosed at advanced stages and missed effective treatment. Thus, there is an urgent need to identify sensitive and specific biomarkers for HCC early diagnosis. In the present study, an ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) approach was used to profile serum metabolites from HCC patients, liver cirrhosis (LC) patients, and normal controls (NC). Univariate and multivariate statistical analyses were performed to obtain the metabolomic differences of the three groups and select significantly changed metabolites that can be used as diagnostic biomarkers. In total, 757 differential metabolites were quantified among the three groups, and pathway enrichment analysis of these metabolites indicated that glycerophospholipid metabolism, pentose and glucuronate interconversions, phenylalanine, tyrosine and tryptophan biosynthesis, and linoleic acid metabolism were the most altered pathways involved in HCC development. Receiver operating characteristic (ROC) curve analysis was performed to select and evaluate the diagnostic biomarker performance. Seven metabolites were identified as potential biomarkers that can differentiate HCC from LC and NC, and LC from NC with the good diagnostic performance of area under the curve (AUC) from 0.890 to 0.990. In summary, our findings provide highly effective biomarker candidates to differentiate HCC from LC and NC, LC, and NC, which shed insight into HCC pathological mechanisms and will be helpful in better understanding and managing HCC.

Optimizing the PROTREC network-based missing protein prediction algorithm

This article summarizes the PROTREC method and investigates the impact that the different hyper-parameters have on the task of missing protein prediction using PROTREC. We evaluate missing protein recovery rates using different PROTREC score selection approaches (MAX, MIN, MEDIAN, and MEAN), different PROTREC score thresholds, as well as different complex size thresholds. In addition, we included two additional cancer datasets in our analysis and introduced a new validation method to check both the robustness of the PROTREC method as well as the correctness of our analysis. Our analysis showed that the missing protein recovery rate can be improved by adopting PROTREC score selection operations of MIN, MEDIAN, and MEAN instead of the default MAX. However, this may come at a cost of reduced numbers of proteins predicted and validated. The users should therefore choose their hyper-parameters carefully to find a balance in the accuracy-quantity trade-off. We also explored the possibility of combining PROTREC with a p-value-based method (FCS) and demonstrated that PROTREC is able to perform well independently without any help from a p-value-based method. Furthermore, we conducted a downstream enrichment analysis to understand the biological pathways and protein networks within the cancerous tissues using the recovered proteins. Missing protein recovery rate using PROTREC can be improved by selecting a different PROTREC score selection method. Different PROTREC score selection methods and other hyper-parameters such as PROTREC score threshold and complex size threshold introduce accuracy-quantity trade-off. PROTREC is able to perform well independently of any filtering using a p-value-based method. Verification of the PROTREC method on additional cancer datasets. Downstream Enrichment Analysis to understand the biological pathways and protein networks in cancerous tissues.

Advances in data-independent acquisition mass spectrometry towards comprehensive digital proteome landscape

The data-independent acquisition mass spectrometry (DIA-MS) has rapidly evolved as a powerful alternative for highly reproducible proteome profiling with a unique strength of generating permanent digital maps for retrospective analysis of biological systems. Recent advancements in data analysis software tools for the complex DIA-MS/MS spectra coupled to fast MS scanning speed and high mass accuracy have greatly expanded the sensitivity and coverage of DIA-based proteomics profiling. Here, we review the evolution of the DIA-MS techniques, from earlier proof-of-principle of parallel fragmentation of all-ions or ions in selected m/z range, the sequential window acquisition of all theoretical mass spectra (SWATH-MS) to latest innovations, recent development in computation algorithms for data informatics, and auxiliary tools and advanced instrumentation to enhance the performance of DIA-MS. We further summarize recent applications of DIA-MS and experimentally-derived as well as in silico spectra library resources for large-scale profiling to facilitate biomarker discovery and drug development in human diseases with emphasis on the proteomic profiling coverage. Toward next-generation DIA-MS for clinical proteomics, we outline the challenges in processing multi-dimensional DIA data set and large-scale clinical proteomics, and continuing need in higher profiling coverage and sensitivity.

Functional transcriptome reveals hepatopancreatic lipid metabolism during the molting cycle of the Chinese mitten crab Eriocheir sinensis

Crustacean molting is highly related to energy and lipid metabolism. This study was conducted to detect the changes of total lipids (TL), triacylglyceride (TAG), phospholipid (PL) and lipid droplets in hepatopancreas, and then to investigate the gene expression patterns related to hepatopancreatic lipid metabolism during the molting cycle of Chinese mitten crab Eriocheir sinensis. Hepatopancreatic TL and TAG increased significantly from post-molt stage to pre-molt stage, then decreased significantly from pre-molt stage to ecdysis stage, which is consistent to the changes of neutral lipid-rich adipocytes in hepatopancreas. By transcriptomic analysis, 65,325 transcripts were sequenced and assembled, and 28,033 transcripts were annotated. Most genes were related to energy metabolism, and the enriched genes were involved in carbohydrate and lipid metabolism and biosynthesis, especially in de novo synthesis of fatty acids and TAG, and ketone body production. Compared to the inter-molt stages, acetyl-CoA carboxylase, fatty acid synthase and other genes related to the synthesis of fatty acids were upregulated in the pre-molt stage. TAG synthesis related genes, including Glycerol-3-phosphate acyltransferase and 1-acylglycerol-3-phosphate acyltransferases, were upregulated in the post-molt stage compared to the inter-molt stage. The expression of ketone body-related genes had no significant changes during the molting cycle. Compared to the TAG synthetic pathway, ketone body biosynthesis may contribute less/secondarily to fatty acid metabolic processes, which could be involved in the other physiological processes or metabolism. In conclusion, these results showed that TAG is the major lipid deposition during inter- and pre-molt stages, and the most genes are related to the fatty acids and TAG metabolism in the hepatopancreas during the molting cycle of E. sinensis.

PHGDH arginine methylation by PRMT1 promotes serine synthesis and represents a therapeutic vulnerability in hepatocellular carcinoma

Serine synthesis is crucial for tumor growth and survival, but its regulatory mechanism in cancer remains elusive. Here, using integrative metabolomics and transcriptomics analyses, we show a heterogeneity between metabolite and transcript profiles. Specifically, the level of serine in hepatocellular carcinoma (HCC) tissues is increased, whereas the expression of phosphoglycerate dehydrogenase (PHGDH), the first rate-limiting enzyme in serine biosynthesis pathway, is markedly downregulated. Interestingly, the increased serine level is obtained by enhanced PHGDH catalytic activity due to protein arginine methyltransferase 1 (PRMT1)-mediated methylation of PHGDH at arginine 236. PRMT1-mediated PHGDH methylation and activation potentiates serine synthesis, ameliorates oxidative stress, and promotes HCC growth in vitro and in vivo. Furthermore, PRMT1-mediated PHGDH methylation correlates with PHGDH hyperactivation and serine accumulation in human HCC tissues, and is predictive of poor prognosis of HCC patients. Notably, blocking PHGDH methylation with a TAT-tagged nonmethylated peptide inhibits serine synthesis and restrains HCC growth in an HCC patient-derived xenograft (PDX) model and subcutaneous HCC cell-derived xenograft model. Overall, our findings reveal a regulatory mechanism of PHGDH activity and serine synthesis, and suggest PHGDH methylation as a potential therapeutic vulnerability in HCC.

Advancements in Oncoproteomics Technologies: Treading toward Translation into Clinical Practice

Proteomics continues to forge significant strides in the discovery of essential biological processes, uncovering valuable information on the identity, global protein abundance, protein modifications, proteoform levels, and signal transduction pathways. Cancer is a complicated and heterogeneous disease, and the onset and progression involve multiple dysregulated proteoforms and their downstream signaling pathways. These are modulated by various factors such as molecular, genetic, tissue, cellular, ethnic/racial, socioeconomic status, environmental, and demographic differences that vary with time. The knowledge of cancer has improved the treatment and clinical management; however, the survival rates have not increased significantly, and cancer remains a major cause of mortality. Oncoproteomics studies help to develop and validate proteomics technologies for routine application in clinical laboratories for (1) diagnostic and prognostic categorization of cancer, (2) real-time monitoring of treatment, (3) assessing drug efficacy and toxicity, (4) therapeutic modulations based on the changes with prognosis and drug resistance, and (5) personalized medication. Investigation of tumor-specific proteomic profiles in conjunction with healthy controls provides crucial information in mechanistic studies on tumorigenesis, metastasis, and drug resistance. This review provides an overview of proteomics technologies that assist the discovery of novel drug targets, biomarkers for early detection, surveillance, prognosis, drug monitoring, and tailoring therapy to the cancer patient. The information gained from such technologies has drastically improved cancer research. We further provide exemplars from recent oncoproteomics applications in the discovery of biomarkers in various cancers, drug discovery, and clinical treatment. Overall, the future of oncoproteomics holds enormous potential for translating technologies from the bench to the bedside.

Unraveling the Sweet Secrets of HCC: Glucometabolic Rewiring in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the primary form of liver cancer. It causes ∼ 800 000 deaths per year, which is expected to increase due to increasing rates of obesity and metabolic dysfunction associated steatotic liver disease (MASLD). Current therapies include immune checkpoint inhibitors, tyrosine kinase inhibitors, and monoclonal antibodies, but these therapies are not satisfactorily effective and often come with multiple side effects and recurrences. Metabolic reprogramming plays a significant role in HCC progression and is often conserved between tumor types. Thus, targeting rewired metabolic pathways could provide an attractive option for targeting tumor cells alone or in conjunction with existing treatments. Therefore, there is an urgent need to identify novel targets involved in cancer-mediated metabolic reprogramming in HCC. In this review, we provide an overview of molecular rewiring and metabolic reprogramming of glucose metabolism in HCC to understand better the concepts that might widen the therapeutic window against this deadly cancer.

Improving SWATH-MS analysis by deep-learning

Data-independent acquisition (DIA) of tandem mass spectrometry spectra has emerged as a promising technology to improve coverage and quantification of proteins in complex mixtures. The success of DIA experiments is dependent on the quality of spectral libraries used for data base searching. Frequently, these libraries need to be generated by labor and time intensive data dependent acquisition (DDA) experiments. Recently, several algorithms have been published that allow the generation of theoretical libraries by an efficient prediction of retention time and intensity of the fragment ions. Sequential windowed acquisition of all theoretical fragment ion spectra mass spectrometry (SWATH-MS) is a DIA method that can be applied at an unprecedented speed, but the fragmentation spectra suffer from a lower quality than data acquired on Orbitrap instruments. To reliably generate theoretical libraries that can be used in SWATH experiments, we developed deep-learning for SWATH analysis (dpSWATH), to improve the sensitivity and specificity of data generated by Q-TOF mass spectrometers. The theoretical library built by dpSWATH allowed us to increase the identification rate of proteins compared to traditional or library-free methods. Based on our analysis we conclude that dpSWATH is a superior prediction framework for SWATH-MS measurements than other algorithms based on Orbitrap data.

Clinical applications of mass spectrometry-based proteomics in cancer: where are we?

Tumor tissue processing methodologies in combination with data-independent acquisition mass spectrometry (DIA-MS) have emerged that can comprehensively analyze the proteome of multiple tumor samples accurately and reproducibly. Increasing recognition and adoption of these technologies has resulted in a tranche of studies providing novel insights into cancer classification systems, functional tumor biology, cancer biomarkers, treatment response and drug targets. Despite this, with some limited exceptions, MS-based proteomics has not yet been implemented in routine cancer clinical practice. Here, we summarize the use of DIA-MS in studies that may pave the way for future clinical cancer applications, and highlight the role of alternative MS technologies and multi-omic strategies. We discuss limitations and challenges of studies in this field to date and propose steps for integrating proteomic data into the cancer clinic. This article is protected by copyright. All rights reserved.

Creatine riboside is a cancer cell-derived metabolite associated with arginine auxotrophy

The metabolic dependencies of cancer cells have substantial potential to be exploited to improve the diagnosis and treatment of cancer. Creatine riboside (CR) is identified as a urinary metabolite associated with risk and prognosis in lung and liver cancer. However, the source of high CR levels in patients with cancer as well as their implications for the treatment of these aggressive cancers remain unclear. By integrating multiomics data on lung and liver cancer, we have shown that CR is a cancer cell–derived metabolite. Global metabolomics and gene expression analysis of human tumors and matched liquid biopsies, together with functional studies, revealed that dysregulation of the mitochondrial urea cycle and a nucleotide imbalance were associated with high CR levels and indicators of a poor prognosis. This metabolic phenotype was associated with reduced immune infiltration and supported rapid cancer cell proliferation that drove aggressive tumor growth. CR^hi cancer cells were auxotrophic for arginine, revealing a metabolic vulnerability that may be exploited therapeutically. This highlights the potential of CR not only as a poor-prognosis biomarker but also as a companion biomarker to inform the administration of arginine-targeted therapies in precision medicine strategies to improve survival for patients with cancer.

Proteomics and metabolomics approach in adult and pediatric glioma diagnostics

The diagnosis of glioma is mainly based on imaging methods that do not distinguish between stage and subtype prior to histopathological analysis. Patients with gliomas are generally diagnosed in the symptomatic stage of the disease. Additionally, healing scar tissue may be mistakenly identified based on magnetic resonance imaging (MRI) as a false positive tumor recurrence in postoperative patients. Current knowledge of molecular alterations underlying gliomagenesis and identification of tumoral biomarkers allow for their use as discriminators of the state of the organism. Moreover, a multiomics approach provides the greatest spectrum and the ability to track physiological changes and can serve as a minimally invasive method for diagnosing asymptomatic gliomas, preceding surgery and allowing for the initiation of prophylactic treatment. It is important to create a vast biomarker library for adults and pediatric patients due to their metabolic differences. This review focuses on the most promising proteomic, metabolomic and lipidomic glioma biomarkers, their pathways, the interactions, and correlations that can be considered characteristic of tumor grade or specific subtype.

High-throughput proteomics of nanogram-scale samples with Zeno SWATH MS

The possibility to record proteomes in high throughput and at high quality has opened new avenues for biomedical research, drug discovery, systems biology, and clinical translation. However, high-throughput proteomic experiments often require high sample amounts and can be less sensitive compared to conventional proteomic experiments. Here, we introduce and benchmark Zeno SWATH MS, a data-independent acquisition technique that employs a linear ion trap pulsing (Zeno trap pulsing) to increase the sensitivity in high-throughput proteomic experiments. We demonstrate that when combined with fast micro- or analytical flow-rate chromatography, Zeno SWATH MS increases protein identification with low sample amounts. For instance, using 20 min micro-flow-rate chromatography, Zeno SWATH MS identified more than 5000 proteins consistently, and with a coefficient of variation of 6%, from a 62.5 ng load of human cell line tryptic digest. Using 5 min analytical flow-rate chromatography (800 µl/min), Zeno SWATH MS identified 4907 proteins from a triplicate injection of 2 µg of a human cell lysate, or more than 3000 proteins from a 250 ng tryptic digest. Zeno SWATH MS hence facilitates sensitive high-throughput proteomic experiments with low sample amounts, mitigating the current bottlenecks of high-throughput proteomics.

Comprehensive kinomic study via a chemical proteomic approach reveals kinome reprogramming in hepatocellular carcinoma tissues

Hepatocellular carcinoma (HCC) is one of the most common cancers worldwide. Kinases are attractive therapeutic targets since they are commonly altered in cancers. Here, to identify kinases of potential therapeutic interest in HCC, a quantitative kinomic study of tumour and adjacent non-tumour liver tissues was performed using a chemical proteomics approach. In total, 124 kinases were found differentially expressed and they were distributed over all nine kinase groups. Exploration of The Cancer Genome Atlas (TCGA) data showed that the dysregulation of 45 kinases was correlated with poor prognosis in HCC patients. We then tested 11 inhibitors targeting 12 crucial protein kinases alone or in combination for their ability to inhibit cell growth in Hep3B and PLC/PRF/5 cell lines. Six inhibitors significantly reduced viability in both cell lines. Combination inhibition of polo-like kinase 1 (PLK1) and casein kinase 1 epsilon (CSNK1E) significantly induced growth arrest in both cell lines synergistically. In summary, our analysis presents the most complete view of kinome reprogramming in HCC and provides novel insight into crucial kinases in HCC and potential therapeutic targets for HCC treatment. Moreover, the identification of hundreds of differentially expressed kinases forms a rich resource for novel drug targets or diagnostic biomarker discovery. Data are available via ProteomeXchange (identifier PXD023806).

Monitoring one-carbon metabolism by mass spectrometry to assess liver function and disease

Precision medicine promises to overcome the constraints of the traditional “one-for-all” healthcare approach through a clear understanding of the molecular features of a disease, allowing for innovative and tailored treatments. State-of-the-art proteomics has the potential to accurately explore the human proteome to identify, quantify, and characterize proteins associated with disease progression. There is a pressing need for informative biomarkers to diagnose liver disease early in its course to prevent severe disease for which no efficient treatment is yet available. Here, we propose the concept of a cellular pathway as a functional biomarker, whose monitorization may inform normal and pathological status. We have developed a standardized targeted selected-reaction monitoring assay to detect and quantify 13 enzymes of one-carbon metabolism (1CM). The assay is compliant with Clinical Proteomics Tumor Analysis Consortium (CPTAC) guidelines and has been included in the protein quantification assays that can be accessed through the assay portal at the CPTAC web page. To test the feasibility of the assay, we conducted a retrospective, proof-of-concept study on a collection of liver samples from healthy controls and from patients with cirrhosis or hepatocellular carcinoma (HCC). Our results indicate a significant reconfiguration of 1CM upon HCC development resulting from a process that can already be identified in cirrhosis. Our findings indicate that the systematic and integrated quantification of 1CM enzymes is a promising cell function-based biomarker for patient stratification, although further experiments with larger cohorts are needed to confirm these findings.

Integrating SWATH-MS proteomics and transcriptome analysis to preliminarily identify three DEGs as biomarkers for proliferative diabetic retinopathy

PURPOSE

We intended to preliminarily find differentially expressed proteins that play crucial roles in proliferative diabetic retinopathy (PDR), and lay the foundation for subsequent further research on the mechanism.

EXPERIMENTAL DESIGN

Here, we developed a new strategy integrated the sequential windowed acquisition of all theoretical fragment ion (SWATH) mass spectra (MS) with multi-dataset joint analysis to screen for the PDR plasma biomarker. The annotation of the given gene list was performed with ClueGO function analysis. Additionally, the protein-protein interaction relationship was also revealed by the STRING database.

RESULTS

In SWATH-MS assays, we identified 23 upregulated and 13 downregulated proteins in PDR plasma. In the mRNA database analysis, 375 genes were identified as differentially expressed genes in GSE102485. Only three genes (FCGR3A, DPEP2, and ADGRF5) were characterized as upregulated in both the dataset and the SWATH-MS list. The area under the ROC curve (AUC) of FCGR3A, DPEP2, and ADGRF5 in distinguishing PDR from others was 0.739, 0.770, and 0.739.

CONCLUSIONS AND CLINICAL RELEVANCE

We provide a novel strategy for biomarker screening and identified plasma FCGR3A, DPEP2, and ADGRF5 as potential biomarkers for patients with PDR. Identifying the key molecules of the disease is essential for the development of new therapeutic molecules and new uses of existing drugs.

Integrated tissue proteome and metabolome reveal key elements and regulatory pathways in cutaneous squamous cell carcinoma

Cutaneous squamous cell carcinoma (CSCC) is a widespread malignancy but has a very low long-term survival rate for patients at the metastatic stage. Therefore, it is urgent to identify prognostic biomarkers for CSCC and improve our understanding of disease progression. Here we took advantage of a data-independent acquisition (DIA)-based nano liquid chromatography equipped with an orbitrap mass spectrometry (nLC-MS/MS) and ultraperformance LC coupled to a time-of-flight tandem MS (UPLC-TOF-MS/MS) technique to analyze cancer and corresponding noncancerous tissues from 20 CSCC patients for integrated proteomic and metabolomic analyses. Overall, 6241 tissue proteins were detected, while 136 proteins were significantly expressed in CSCC tissues. Further functional analysis revealed that various biological processes were highly enriched and participated in the pathogenesis of CSCC, especially DNA damage responses. Moreover, 641 named metabolites in total were identified, among which 181 were significantly changed in CSCC tissues. A total of 101 pathways were significantly enriched including apoptosis, autophagy, PI3K-Akt and mTOR signaling pathways. Interestingly, two pathways, protein digestion & absorption and platelet activation were both enriched in proteomic and metabolomic studies involving 5 proteins and 11 metabolites. Accordingly, a four-metabolite panel consisting of arachidonate, glutamine, glutamic acid, and proline (all area under the curve (AUC) values more than 0.9) was developed with a high accuracy (0.971) to distinguish the 20 detected cancer tissues from their noncancerous tissues. Collectively, our work highlighted the key elements and regulatory pathways involved in the pathogenesis of CSCC. More importantly, the present study not only provided potential biomarkers for the early diagnosis of CSCC, but also expanded our knowledge of the physiopathology of the disease. SIGNIFICANCE: CSCC is the second most common human cancer but has few treatment options and few sensitive biomarkers for diagnosis. Here we comprehensively revealed its molecular characteristics by performing integrated tissue proteomic and metabolomic analyses. Significantly distinct profiles and certain enriched pathways including DNA damage responses were identified as associated with CSCC. Moreover, protein digestion & absorption and platelet activation were both enriched in the proteome and metabolome. These identified molecular changes probably play significant roles in CSCC development. Finally, we developed a four-metabolite panel to distinguish CSCC with high accuracy. Overall, our data not only provided potential diagnostic biomarkers, but also extended knowledge on the pathogenesis of CSCC.

Metformin Inhibits the Urea Cycle and Reduces Putrescine Generation in Colorectal Cancer Cell Lines

The urea cycle (UC) removes the excess nitrogen and ammonia generated by nitrogen-containing compound composites or protein breakdown in the human body. Research has shown that changes in UC enzymes are not only related to tumorigenesis and tumor development but also associated with poor survival in hepatocellular, breast, and colorectal cancers (CRC), etc. Cytoplasmic ornithine, the intermediate product of the urea cycle, is a specific substrate for ornithine decarboxylase (ODC, also known as ODC1) for the production of putrescine and is required for tumor growth. Polyamines (spermidine, spermine, and their precursor putrescine) play central roles in more than half of the steps of colorectal tumorigenesis. Given the close connection between polyamines and cancer, the regulation of polyamine metabolic pathways has attracted attention regarding the mechanisms of action of chemical drugs used to prevent CRC, as the drug most widely used for treating type 2 diabetes (T2D), metformin (Met) exhibits antitumor activity against a variety of cancer cells, with a vaguely defined mechanism. In addition, the influence of metformin on the UC and putrescine generation in colorectal cancer has remained unclear. In our study, we investigated the effect of metformin on the UC and putrescine generation of CRC in vivo and in vitro and elucidated the underlying mechanisms. In nude mice bearing HCT116 tumor xenografts, the administration of metformin inhibited tumor growth without affecting body weight. In addition, metformin treatment increased the expression of monophosphate (AMP)-activated protein kinase (AMPK) and p53 in both HCT116 xenografts and colorectal cancer cell lines and decreased the expression of the urea cycle enzymes, including carbamoyl phosphate synthase 1 (CPS1), arginase 1 (ARG1), ornithine trans-carbamylase (OTC), and ODC. The putrescine levels in both HCT116 xenografts and HCT116 cells decreased after metformin treatment. These results demonstrate that metformin inhibited CRC cell proliferation via activating AMPK/p53 and that there was an association between metformin, urea cycle inhibition and a reduction in putrescine generation.

Immunohistochemical staining reveals differential expression of ACSL3 and ACSL4 in hepatocellular carcinoma and hepatic gastrointestinal metastases

Long-chain fatty acyl CoA synthetases (ACSLs) activate fatty acids by CoA addition thus facilitating their intracellular metabolism. Dysregulated ACSL expression features in several cancers and can affect processes such as ferroptosis, fatty acid β-oxidation, prostaglandin biosynthesis, steroidogenesis and phospholipid acyl chain remodelling. Here we investigate long chain acyl-CoA synthetase 3 (ACSL3) and long chain acyl-CoA synthetase 4 (ACSL4) expression in liver malignancies. The expression and subcellular localisations of the ACSL3 and ACSL4 isoforms in hepatocellular carcinoma (HCC), cholangiocarcinoma (CCA) and hepatic metastases were assessed by immunohistochemical analyses of multiple tumour tissue arrays and by subcellular fractionation of cultured HepG2 cells. The expression of both enzymes was increased in HCC compared with normal liver. Expression of ACSL3 was similar in HCC and hepatic metastases but lower in healthy tissue. Increased ACSL3 expression distinguished HCC from CCA with a sensitivity of 87.2% and a specificity of 75%. ACSL4 expression was significantly greater in HCC than in all other tumours and distinguished HCC from normal liver tissue with a sensitivity of 93.8% and specificity of 93.6%. Combined ACSL3 and ACSL4 staining scores distinguished HCC from hepatic metastases with 80.1% sensitivity and 77.1% specificity. These enzymes had partially overlapping intracellular distributions, ACSL4 localised to the plasma membrane and both isoforms associated with lipid droplets and the endoplasmic reticulum (ER). In conclusion, analysis of ACSL3 and ACSL4 expression can distinguish different classes of hepatic tumours.

From infection to cancer: how DNA tumour viruses alter host cell central carbon and lipid metabolism

Infections cause 13% of all cancers globally, and DNA tumour viruses account for almost 60% of these cancers. All viruses are obligate intracellular parasites and hijack host cell functions to replicate and complete their life cycles to produce progeny virions. While many aspects of viral manipulation of host cells have been studied, how DNA tumour viruses manipulate host cell metabolism and whether metabolic alterations in the virus life cycle contribute to carcinogenesis are not well understood. In this review, we compare the differences in central carbon and fatty acid metabolism in host cells following infection, oncogenic transformation, and virus-driven cancer of DNA tumour viruses including: Epstein-Barr virus, hepatitis B virus, human papillomavirus, Kaposi's sarcoma-associated herpesvirus and Merkel cell polyomavirus.

Label-free proteomics for discovering biomarker candidates of RAD140 administration to castrated horses

Selective androgen receptor (AR) modulators (SARMs) are potent anabolic agents with a high potential of misuse in horseracing and equestrian sports. In this study, we applied label-free proteomics to discover plasma protein biomarkers in geldings (castrated horses) after administration with a popular SARM named RAD140. Tryptic peptides were prepared from plasma samples and analyzed by nano-flow ultrahigh-performance liquid chromatography-high-resolution tandem mass spectrometry (nano-UHPLC-HRMS/MS) using data-independent acquisition (DIA) method. Orthogonal projection on latent structure-discriminant analysis (OPLS-DA) has led to the development of a predictive model that could discriminate RAD140-administered samples from control samples and could also correctly classify 18 out of 19 in-training horses as control samples. The model comprises 75 proteins with variable importance in projection (VIP) score above 1. Gene Ontology (GO) enrichment analysis and literature review have identified upregulation of AR-regulated clusterin, and proteins associated with inflammation (haptoglobin, cluster of differentiation 14 [CD14], and inter-alpha-trypsin inhibitor heavy chain 4 [ITIH4]) and erythropoiesis (glycosylphosphatidylinositol-specific phospholipase D1 [GPLD1]) after RAD140 administration. Their changes were confirmed by selected reaction monitoring (SRM) experiments. Similar effects have been reported by the use of androgens and other SARMs. This is the first reported study that describes the use of a proteomic biomarker approach to detect horses that have been administered with RAD140 by applying label-free proteomic profiling of plasma samples. These results support the concept of a biomarker-driven approach to enhance the doping control of RAD140 and potentially other SARMs in the future.

Data-independent acquisition mass spectrometry (DIA-MS) for proteomic applications in oncology

Data-independent acquisition mass spectrometry (DIA-MS) is a next generation proteomic methodology that generates permanent digital proteome maps offering highly reproducible retrospective analysis of cellular and tissue specimens. The adoption of this technology has ushered a new wave of oncology studies across a wide range of applications including its use in molecular classification, oncogenic pathway analysis, drug and biomarker discovery and unravelling mechanisms of therapy response and resistance. In this review, we provide an overview of the experimental workflows commonly used in DIA-MS, including its current strengths and limitations versus conventional data-dependent acquisition mass spectrometry (DDA-MS). We further summarise a number of key studies to illustrate the power of this technology when applied to different facets of oncology. Finally we offer a perspective of the latest innovations in DIA-MS technology and machine learning-based algorithms necessary for driving the development of high-throughput, in-depth and reproducible proteomic assays that are compatible with clinical diagnostic workflows, which will ultimately enable the delivery of precision cancer medicine to achieve optimal patient outcomes.

Probing SWATH-MS as a tool for proteome level quantification in a nonmodel fish

Quantitative proteomics via mass spectrometry can provide valuable insight into molecular and phenotypic characteristics of a living system. Recent mass spectrometry developments include data‐independent acquisition (SWATH/DIA‐MS), an accurate, sensitive and reproducible method for analysing the whole proteome. The main requirement for this method is the creation of a comprehensive spectral library. New technologies have emerged producing larger and more accurate species‐specific libraries leading to a progressive collection of proteome references for multiple molecular model species. Here, for the first time, we set out to compare different spectral library constructions using multiple tissues from a coral reef fish to demonstrate its value and feasibility for nonmodel organisms. We created a large spectral library composed of 12,553 protein groups from liver and brain tissues. Via identification of differentially expressed proteins under fish exposure to elevated pCO₂ and temperature, we validated the application and usefulness of these different spectral libraries. Successful identification of significant differentially expressed proteins from different environmental exposures occurred using the library with a combination of data‐independent and data‐dependent acquisition methods as well as both tissue types. Further analysis revealed expected patterns of significantly up‐regulated heat shock proteins in a dual condition of ocean warming and acidification indicating the biological accuracy and relevance of the method. This study provides the first reference spectral library for a nonmodel organism. It represents a useful guide for future building of accurate spectral library references in nonmodel organisms allowing the discovery of ecologically relevant changes in the proteome.

A mouse SWATH-mass spectrometry reference spectral library enables deconvolution of species-specific proteomic alterations in human tumour xenografts

SWATH-mass spectrometry (MS) enables accurate and reproducible proteomic profiling in multiple model organisms including the mouse. Here, we present a comprehensive mouse reference spectral library (MouseRefSWATH) that permits quantification of up to 10,597 proteins (62.2% of the mouse proteome) by SWATH-MS. We exploit MouseRefSWATH to develop an analytical pipeline for species-specific deconvolution of proteomic alterations in human tumour xenografts (XenoSWATH). This method overcomes the challenge of high sequence similarity between mouse and human proteins, facilitating the study of host microenvironment-tumour interactions from 'bulk tumour' measurements. We apply the XenoSWATH pipeline to characterize an intraductal xenograft model of breast ductal carcinoma in situ and uncover complex regulation consistent with stromal reprogramming, where the modulation of cell migration pathways is not restricted to tumour cells but also operates in the mouse stroma upon progression to invasive disease. MouseRefSWATH and XenoSWATH open new opportunities for in-depth and reproducible proteomic assessment to address wide-ranging biological questions involving this important model organism.

Integrating SWATH-MS Proteomics and Transcriptome Analysis Identifies CHI3L1 as a Plasma Biomarker for Early Gastric Cancer

Early diagnosis of gastric cancer (GC) provides patients opportunities for minimally invasive endoscopic resection. Here, we developed a new strategy integrated the state-of-the-art sequential windowed acquisition of all theoretical fragment ion (SWATH) mass spectra (MS) with multi-dataset joint analysis to screen for the stage-I GC plasma biomarker. In SWATH-MS assays, we identified 37 upregulated and 21 downregulated proteins in GC plasma. In the mRNA database analysis, 633 genes were identified as differentially expressed genes in at least 4 out of 5 datasets, but there were only 94 genes identified as upregulated. Only 1 gene, CHI3L1, was characterized as upregulated in both the dataset consensus list and the SWATH-MS list. Then, we detected the CHI3L1 level in the plasma of a large cohort consisting of 200 participants. The area under the ROC curve (AUC) of CHI3L1 in distinguishing GC from others was 0.788. Integrating the plasma CHI3L1 level with clinical factors further boosted the AUC to 0.887. In conclusion, we provide a novel strategy for biomarker screening, combining recent MS techniques with public database analysis, and identified plasma CHI3L1 as a potential biomarker for patients with endoscopically resectable GC.

Comprehensive analysis reveals a metabolic ten-gene signature in hepatocellular carcinoma

Background

Due to the complicated molecular and cellular heterogeneity in hepatocellular carcinoma (HCC), the morbidity and mortality still remains high level in the world. However, the number of novel metabolic biomarkers and prognostic models could be applied to predict the survival of HCC patients is still small. In this study, we constructed a metabolic gene signature by systematically analyzing the data from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and International Cancer Genome Consortium (ICGC).

Methods

Differentially expressed genes (DEGs) between tumors and paired non-tumor samples of 50 patients from TCGA dataset were calculated for subsequent analysis. Univariate cox proportional hazard regression and LASSO analysis were performed to construct a gene signature. The Kaplan–Meier analysis, time-dependent receiver operating characteristic (ROC), Univariate and Multivariate Cox regression analysis, stratification analysis were used to assess the prognostic value of the gene signature. Furthermore, the reliability and validity were validated in four types of testing cohorts. Moreover, the diagnostic capability of the gene signature was investigated to further explore the clinical significance. Finally, Go enrichment analysis and Gene Set Enrichment Analysis (GSEA) have been performed to reveal the different biological processes and signaling pathways which were active in high risk or low risk group.

Results

Ten prognostic genes were identified and a gene signature were constructed to predict overall survival (OS). The gene signature has demonstrated an excellent ability for predicting survival prognosis. Univariate and Multivariate analysis revealed the gene signature was an independent prognostic factor. Furthermore, stratification analysis indicated the model was a clinically and statistically significant for all subgroups. Moreover, the gene signature demonstrated a high diagnostic capability in differentiating normal tissue and HCC. Finally, several significant biological processes and pathways have been identified to provide new insights into the development of HCC.

Conclusion

The study have identified ten metabolic prognostic genes and developed a prognostic gene signature to provide more powerful prognostic information and improve the survival prediction for HCC.

Quantitative SWATH-Based Proteomic Profiling for Identification of Mechanism-Driven Diagnostic Biomarkers Conferring in the Progression of Metastatic Prostate Cancer

Prostate cancer (PCa), the most frequently diagnosed malignancy in men is associated with significant mortality and morbidity. Therefore, demand exists for the identification of potential biomarkers for patient stratification according to prognostic risks and the mechanisms involved in cancer development and progression to avoid over/under treatment of patients and prevent relapse. Quantitative proteomic mass spectrometry profiling and gene enrichment analysis of TGF-β induced-EMT in human Prostate androgen-dependent (LNCaP) and androgen-independent (PC-3) adenocarcinoma cell lines was performed to investigate proteomics involved in Prostate carcinogenesis and their effect onto the survival of PCa patients. Amongst 1,795 proteins, which were analyzed, 474 proteins were significantly deregulated. These proteins contributed to apoptosis, gluconeogenesis, transcriptional regulation, RNA splicing, cell cycle, and MAPK cascade and hence indicating the crucial roles of these proteins in PCa initiation and progression. We have identified a panel of six proteins viz., GOT1, HNRNPA2B1, MAPK1, PAK2, UBE2N, and YWHAB, which contribute to cancer development, and the transition of PCa from androgen dependent to independent stages. The prognostic values of identified proteins were evaluated using UALCAN, GEPIA, and HPA datasets. The results demonstrate the utility of SWATH-LC-MS/MS for understanding the proteomics involved in EMT transition of PCa and identification of clinically relevant proteomic biomarkers.

Mass spectrometry based proteomics and metabolomics in personalized oncology

Precision medicine (PM) means the customization of healthcare with decisions and practices adjusted to the individual patient. It includes personalized diagnostics, patients' sub-classification, individual treatment selection and the monitoring of its effectiveness. Currently, in oncology, PM is based on the molecular and cellular features of a tumor, its microenvironment and the patient's genetics and lifestyle. Surprisingly, the available targeted therapies were found effective only in a subset of patients. An in-depth understanding of tumor biology is crucial to improve their effectiveness and develop new therapeutic targets. Completion of genetic information with proteomics and metabolomics can give broader knowledge about tumor biology which consequently provides novel biomarkers and indicates new therapeutic targets. Recently, metabolomics and proteomics have extensively been applied in the field of oncology. In the context of PM, human studies, with the use of mass spectrometry (MS) which allows the detection of thousands of molecules in a large number of samples, are the most valuable. Such studies, focused on cancer biomarkers discovery or patients' stratification, are presented in this review. Moreover, the technical aspects of MS-based clinical proteomics and metabolomics are described.

Reductive Stress Selectively Disrupts Collagen Homeostasis and Modifies Growth Factor-independent Signaling Through the MAPK/Akt Pathway in Human Dermal Fibroblasts

Redox stress is a well-known contributor to aging and diseases in skin. Reductants such as dithiothreitol (DTT) can trigger a stress response by disrupting disulfide bonds. However, the quantitative response of the cellular proteome to reductants has not been explored, particularly in cells such as fibroblasts that produce extracellular matrix proteins. Here, we have used a robust, unbiased, label-free SWATH-MS proteomic approach to quantitate the response of skin fibroblast cells to DTT in the presence or absence of the growth factor PDGF. Of the 4487 proteins identified, only 42 proteins showed a statistically significant change of 2-fold or more with reductive stress. Our proteomics data show that reductive stress results in the loss of a small subset of reductant-sensitive proteins (including the collagens COL1A1/2 and COL3A1, and the myopathy-associated collagens COL6A1/2/3), and the down-regulation of targets downstream of the MAPK pathway. We show that a reducing environment alters signaling through the PDGF-associated MAPK/Akt pathways, inducing chronic dephosphorylation of ERK1/2 at Thr202/Tyr204 and phosphorylation of Akt at Ser473 in a growth factor-independent manner. Our data highlights collagens as sentinel molecules for redox stress downstream of MAPK/Akt, and identifies intervention points to modulate the redox environment to target skin diseases and conditions associated with erroneous matrix deposition.

Gluconeogenesis in cancer cells - Repurposing of a starvation-induced metabolic pathway?

Cancer cells constantly face a fluctuating nutrient supply and interference with adaptive responses might be an effective therapeutic approach. It has been discovered that in the absence of glucose, cancer cells can synthesize crucial metabolites by expressing phosphoenolpyruvate carboxykinase (PEPCK, PCK1 or PCK2) using abbreviated forms of gluconeogenesis. Gluconeogenesis, which in essence is the reverse pathway of glycolysis, uses lactate or amino acids to feed biosynthetic pathways branching from glycolysis. PCK1 and PCK2 have been shown to be critical for the growth of certain cancers. In contrast, fructose-1,6-bisphosphatase 1 (FBP1), a downstream gluconeogenesis enzyme, inhibits glycolysis and tumor growth, partly by non-enzymatic mechanisms. This review sheds light on the current knowledge of cancer cell gluconeogenesis and its role in metabolic reprogramming, cancer cell plasticity, and tumor growth.

Transcriptomic changes associated with PCK1 overexpression in hepatocellular carcinoma cells detected by RNA-seq

Phosphoenolpyruvate carboxykinase 1 (PCK1), a step limiting enzyme of gluconeogenesis, is downregulated in hepatocellular carcinoma (HCC). Overexpression of PCK1 has been shown to suppress hepatoma cell growth, but the underlying mechanism remains unclear. We used recombinant adenovirus overexpressing PCK1 or GFP in Huh7 cells, and the differentially expressed genes (DEGs) were identified by RNA-Seq. 180 were upregulated by PCK1 overexpression, whereas 316 were downregulated. Pathway analysis illustrated that PCK1 was closely correlated with Wnt signaling pathway and TGF-beta signaling pathway. Hence, Wnt signaling pathway and its downstream component, FZD2, FZD6, FZD7 and β-catenin were confirmed by qRT-PCR and Western blot. In vivo we also observed that PCK1 had restrained tumor growth as a result of decreasing expression of β-catenin. Whole-transcriptomic profile analysis discovered that overexpression of PCK1 downregulates several oncogenic signaling pathways in HCC, providing potential therapeutic targets for improving HCC therapy.

Comparative analysis of proteomic and metabolomic profiles of different species of Paris

An extract prepared from species of Paris is the most widely consumed herbal product in China. The genus Paris includes a variety of genotypes with different medicinal component contents but only two are defined as official sources. Closely related species have different medicinal properties because of differential expression of proteins and metabolites. To better understand the molecular basis of these differences, we examined proteomic and metabolomic changes in rhizomes of P. polyphylla var. chinensis, P. polyphylla var. yunnanensis, and P. fargesii var. fargesii using a technique known as sequential window acquisition of all theoretical mass spectra as well as gas chromatography-time-of-flight mass spectrometry. In total, 419 proteins showed significant abundance changes, and 33 metabolites could be used to discriminate Paris species. A complex analysis of proteomic and metabolomic data revealed a higher efficiency of sucrose utilization and an elevated protein abundance in the sugar metabolic pathway of P. polyphylla var. chinensis. The pyruvate content and efficiency of acetyl-CoA-utilization in saponin biosynthesis were also higher in P. polyphylla var. chinensis than in the other two species. The results expand our understanding of the proteome and metabolome of Paris and offer new insights into the species-specific traits of these herbaceous plants. SIGNIFICANCE: The traditional Chinese medicine Paris is the most widely consumed herbal product for the treatment of joint pain, rheumatoid arthritis and antineoplastic. All Paris species have roughly the same morphological characteristics; however, different members have different medicinal compound contents. Efficient exploitation of genetic diversity is a key factor in the development of rare medicinal plants with improved agronomic traits and malleability to challenging environmental conditions. Nevertheless, only a partial understanding of physiological and molecular mechanisms of different plants of Paris can be achieved without proteomics. To better understand the molecular basis of these differences and facilitate the use of other Paris species, we examine proteomic metabolomic changes in rhizomes of Paris using the technique known as SWATH-MS and GC/TOF-MS. Our research has provided information that can be used in other studies to compare metabolic traits in different Paris species. Our findings can also serve as a theoretical basis for the selection and cultivation of other Paris species with a higher medicinal value.

A Pathogen and a Non-pathogen Spotted Fever Group Rickettsia Trigger Differential Proteome Signatures in Macrophages

We have previously reported that Rickettsia conorii and Rickettsia montanensis have distinct intracellular fates within THP-1 macrophages, suggesting that the ability to proliferate within macrophages may be a distinguishable factor between pathogenic and non-pathogenic Spotted fever group (SFG) members. To start unraveling the molecular mechanisms underlying the capacity (or not) of SFG Rickettsia to establish their replicative niche in macrophages, we have herein used quantitative proteomics by SWATH-MS to profile the alterations resulted by the challenge of THP-1 macrophages with R. conorii and R. montanensis. We show that the pathogenic, R. conorii, and the non-pathogenic, R. montanensis, member of SFG Rickettsia trigger differential proteomic signatures in macrophage-like cells upon infection. R. conorii specifically induced the accumulation of several enzymes of the tricarboxylic acid cycle, oxidative phosphorylation, fatty acid β-oxidation, and glutaminolysis, as well as of several inner and outer membrane mitochondrial transporters. These results suggest a profound metabolic rewriting of macrophages by R. conorii toward a metabolic signature of an M2-like, anti-inflammatory activation program. Moreover, several subunits forming the proteasome and immunoproteasome are found in lower abundance upon infection with both rickettsial species, which may help bacteria to escape immune surveillance. R. conorii-infection specifically induced the accumulation of several host proteins implicated in protein processing and quality control in ER, suggesting that this pathogenic Rickettsia may be able to increase the ER protein folding capacity. This work reveals novel aspects of macrophage-Rickettsia interactions, expanding our knowledge of how pathogenic rickettsiae explore host cells to their advantage.

Differential epithelial and stromal protein profiles in cone and non-cone regions of keratoconus corneas

Keratoconus (KC) is an ectatic corneal disease characterized by progressive thinning and irregular astigmatism, and a leading indication for corneal transplantation. KC-associated changes have been demonstrated for the entire cornea, but the pathological thinning and mechanical weakening is usually localized. We performed quantitative proteomics using Sequential Windowed Acquisition of All Theoretical Fragment Ion Mass Spectrometry (SWATH-MS) to analyze epithelial and stromal changes between the topographically-abnormal cone and topographically-normal non-cone regions of advanced KC corneas, compared to age-matched normal corneas. Expression of 20 epithelial and 14 stromal proteins was significantly altered (≥2 or ≤0.5-fold) between cone and non-cone in all 4 KC samples. Ingenuity pathway analysis illustrated developmental and metabolic disorders for the altered epithelial proteome with mitochondrion as the significant gene ontology (GO) term. The differential stromal proteome was related to cellular assembly, tissue organization and connective tissue disorders with endoplasmic reticulum protein folding as the significant GO term. Validation of selected protein expression was performed on archived KC, non-KC and normal corneal specimens by immunohistochemistry. This is the first time to show that KC-associated proteome changes were not limited to the topographically-thinner and mechanically-weakened cone but also non-cone region with normal topography, indicating a peripheral involvement in KC development.

SWATH mass spectrometry as a tool for quantitative profiling of the matrisome

Proteomic analysis of extracellular matrix (ECM) and ECM-associated proteins, collectively known as the matrisome, is a challenging task due to the inherent complexity and insolubility of these proteins. Here we present sequential window acquisition of all theoretical fragment ion spectra mass spectrometry (SWATH MS) as a tool for the quantitative analysis of matrisomal proteins in both non-enriched and ECM enriched tissue without the need for prior fractionation. Utilising a spectral library containing 201 matrisomal proteins, we compared the performance and reproducibility of SWATH MS over conventional data-dependent analysis mass spectrometry (DDA MS) in unfractionated murine lung and liver. SWATH MS conferred a 15-20% increase in reproducible peptide identification across replicate experiments in both tissue types and identified 54% more matrisomal proteins in the liver versus DDA MS. We further use SWATH MS to evaluate the quantitative changes in matrisome content that accompanies ECM enrichment. Our data shows that ECM enrichment led to a systematic increase in core matrisomal proteins but resulted in significant losses in matrisome-associated proteins including the cathepsins and proteins of the S100 family. Our proof-of-principle study demonstrates the utility of SWATH MS as a versatile tool for in-depth characterisation of the matrisome in unfractionated and non-enriched tissues. SIGNIFICANCE: The matrisome is a complex network of extracellular matrix (ECM) and ECM-associated proteins that provides scaffolding function to tissues and plays important roles in the regulation of fundamental cellular processes. However, due to its inherent complexity and insolubility, proteomic studies of the matrisome typically require the application of enrichment workflows prior to MS analysis. Such enrichment strategies often lead to losses in soluble matrisome-associated components. In this study, we present sequential window acquisition of all theoretical fragment ion spectra mass spectrometry (SWATH MS) as a tool for the quantitative analysis of matrisomal proteins. We show that SWATH MS provides a more reproducible coverage of the matrisome compared to data-dependent analysis (DDA) MS. We also demonstrate that SWATH MS is capable of accurate quantification of matrisomal proteins without prior ECM enrichment and fractionation, which may simplify sample handling workflows and avoid losses in matrisome-associated proteins commonly linked to ECM enrichment.

Rewiring urea cycle metabolism in cancer to support anabolism

Cancer cells reprogramme metabolism to maximize the use of nitrogen and carbon for the anabolic synthesis of macromolecules that are required during tumour proliferation and growth. To achieve this aim, one strategy is to reduce catabolism and nitrogen disposal. The urea cycle (UC) in the liver is the main metabolic pathway to convert excess nitrogen into disposable urea. Outside the liver, UC enzymes are differentially expressed, enabling the use of nitrogen for the synthesis of UC intermediates that are required to accommodate cellular needs. Interestingly, the expression of UC enzymes is altered in cancer, revealing a revolutionary mechanism to maximize nitrogen incorporation into biomass. In this Review, we discuss the metabolic benefits underlying UC deregulation in cancer and the relevance of these alterations for cancer diagnosis and therapy.

Liver cancer-associated changes to the proteome: what deserves clinical focus?

INTRODUCTION

Hepatocellular carcinoma (HCC) is recognized as the fifth most common neoplasm and currently represents the second leading form of cancer-related death worldwide. Despite great progress has been done in the understanding of its pathogenesis, HCC represents a heavy societal and economic burden as most patients are still diagnosed at advanced stages and the 5-year survival rate remain below 20%. Early detection and revolutionary therapies that rely on the discovery of new molecular biomarkers and therapeutic targets are therefore urgently needed to develop precision medicine strategies for a more efficient management of patients. Areas covered: This review intends to comprehensively analyse the proteomics-based research conducted in the last few years to address some of the principal still open riddles in HCC biology, based on the identification of molecular drivers of tumor progression and metastasis. Expert commentary: The technical advances in mass spectrometry experienced in the last decade have significantly improved the analytical capacity of proteome wide studies. Large-scale protein and protein variant (post-translational modifications) identification and quantification have allowed detailed dissections of molecular mechanisms underlying HCC progression and are already paving the way for the identification of clinically relevant proteins and the development of their use on patient care.

Identification of a co-target for enhancing efficacy of sorafenib in HCC through a quantitative modeling approach

Sorafenib (SFB), a multi-kinase inhibitor, is the only approved drug for treating hepatocellular carcinoma (HCC). However, SFB shows low efficacy in many cases. HCC related mortality therefore remains to be high worldwide. SFB, a multi-kinase inhibitor is also known to modulate the redox homeostasis in cancer cells. To understand the effect of SFB on the redox status, a quantitative understanding of the system is necessary. Kinetic modeling of the relevant pathways is a useful approach for obtaining a quantitative understanding of the pathway dynamics and to rank the individual factors based on the extent of influence they wield on the pathway. Here, we report a comprehensive model of the glutathione reaction network (GSH_net ), consisting of four modules and includes SFB-induced redox stress. We compared GSH_net simulations for HCC of six different etiologies with healthy liver, and correctly identified the expected variations in cancer. Next, we studied alterations induced in the system upon SFB treatment and observed differential H₂ O₂ dynamics in all the conditions. Using metabolic control analysis, we identified glutathione S-transferase (GST) as the enzyme with the highest selective control coefficient, making it an attractive co-target for potentiating the action of SFB across all six etiologies. As a proof-of-concept, we selected ethacrynic acid (EA), a known inhibitor of GST, and verified ex vivo that EA synergistically potentiates the cytotoxic effect of SFB. Being an FDA approved drug, EA is a promising candidate for repurposing as a combination therapy with SFB for HCC treatment.

The State of "Omics" Research for Farmed Penaeids: Advances in Research and Impediments to Industry Utilization

Elucidating the underlying genetic drivers of production traits in agricultural and aquaculture species is critical to efforts to maximize farming efficiency. "Omics" based methods (i.e., transcriptomics, genomics, proteomics, and metabolomics) are increasingly being applied to gain unprecedented insight into the biology of many aquaculture species. While the culture of penaeid shrimp has increased markedly, the industry continues to be impeded in many regards by disease, reproductive dysfunction, and a poor understanding of production traits. Extensive effort has been, and continues to be, applied to develop critical genomic resources for many commercially important penaeids. However, the industry application of these genomic resources, and the translation of the knowledge derived from "omics" studies has not yet been completely realized. Integration between the multiple "omics" resources now available (i.e., genome assemblies, transcriptomes, linkage maps, optical maps, and proteomes) will prove critical to unlocking the full utility of these otherwise independently developed and isolated resources. Furthermore, emerging "omics" based techniques are now available to address longstanding issues with completing keystone genome assemblies (e.g., through long-read sequencing), and can provide cost-effective industrial scale genotyping tools (e.g., through low density SNP chips and genotype-by-sequencing) to undertake advanced selective breeding programs (i.e., genomic selection) and powerful genome-wide association studies. In particular, this review highlights the status, utility and suggested path forward for continued development, and improved use of "omics" resources in penaeid aquaculture.

SWATH-MS based quantitative proteomics analysis reveals that curcumin alters the metabolic enzyme profile of CML cells by affecting the activity of miR-22/IPO7/HIF-1α axis

BACKGROUND

Chronic myelogenous leukemia (CML) is a myeloproliferative disorder caused by expression of the chimeric BCR-ABL tyrosine kinase oncogene, resulting from the t(9;22) chromosomal translocation. Imatinib (gleevec, STI-571) is a selective inhibitor of BCR-ABL activity highly effective in the treatment of CML. However, even though almost all CML patients respond to treatment with imatinib or third generation inhibitors, these drugs are not curative and need to be taken indefinitely or until patients become resistant. Therefore, to get a definitive eradication of leukemic cells, it is necessary to find novel therapeutic combinations, for achieving greater efficacy and fewer side effects. Curcumin is an Indian spice with several therapeutic properties: anti-oxidant, analgesic, anti-inflammatory, antiseptic and anti-cancer. In cancer disease, it acts by blocking cell transformation, proliferation, and invasion and by inducing cell apoptosis.

METHODS

In the present study, the effect of a sub-toxic dose of curcumin on K562 cells was evaluated by using the technique of Sequential Window Activation of All Theoretical Mass Spectra (SWATH-MS). Bioinformatic analysis of proteomic data was performed to highlight the pathways mostly affected by the treatment. The involvement of Hypoxia inducible factor 1 α (HIF-1α) was assayed by evaluating its activation status and the modulation of importin 7 (IPO7) and miR-22 was assessed by quantitative PCR and western blot analysis. Finally, K562 cells transfected with miR-22 inhibitor were used to confirm the ability of curcumin to elicit miR-22 expression.

RESULTS

Our findings revealed that the most relevant effect induced by curcumin was a consistent decrease of several proteins involved in glucose metabolism, most of which were HIF-1α targets, concomitant with the up-regulation of functional and structural mitochondrial proteins. The mechanism by which curcumin affects metabolic enzyme profile was associated with the reduction of HIF-1α activity, due to the miR-22-mediated down-regulation of IPO7 expression. Finally, the ability of curcumin to enhance in vitro the efficiency of imatinib was reported.

CONCLUSIONS

In summary, our data indicates that the miR-22/IPO7/HIF-1α axis may be considered as a novel molecular target of curcumin adding new insights to better define therapeutic activity and anticancer properties of this natural compound. The MS proteomic data have been deposited to the ProteomeXchange with identifier <PXD007771>.

Characterization of increasing stages of invasiveness identifies stromal/cancer cell crosstalk in rat models of mesothelioma

Sarcomatoid mesothelioma (SM) is a devastating cancer associated with one of the poorest outcome. Therefore, representative preclinical models reproducing different tumor microenvironments (TME) observed in patients would open up new prospects for the identification of markers and evaluation of innovative therapies. Histological analyses of four original models of rat SM revealed their increasing infiltrative and metastatic potential were associated with differences in Ki67 index, blood-vessel density, and T-lymphocyte and macrophage infiltration. In comparison with the noninvasive tumor M5-T2, proteomic analysis demonstrated the three invasive tumors F4-T2, F5-T1 and M5-T1 shared in common a very significant increase in the abundance of the multifunctional proteins galectin-3, prohibitin and annexin A5, and a decrease in proteins involved in cell adhesion, tumor suppression, or epithelial differentiation. The increased metastatic potential of the F5-T1 tumor, relative to F4-T2, was associated with an increased macrophage vs T-cell infiltrate, changes in the levels of expression of a panel of cytokine genes, an increased content of proteins involved in chromatin organization, ribosome structure, splicing, or presenting anti-adhesive properties, and a decreased content of proteins involved in protection against oxidative stress, normoxia and intracellular trafficking. The most invasive tumor, M5-T1, was characterized by a pattern of specific phenotypic and molecular features affecting the presentation of MHC class I-mediated antigens and immune cell infiltration, or involved in the reorganization of the cytoskeleton and composition of the extracellular matrix. These four preclinical models and data represent a new resource available to the cancer research community to catalyze further investigations on invasiveness.

Tissue and plasma proteomics for early stage cancer detection

The pursuit of novel and effective biomarkers is essential in the struggle against cancer, which is a leading cause of mortality worldwide. Here we discuss the relative advantages and disadvantages of the most frequently used proteomics techniques, concentrating on the latest advances and application of tissue and plasma proteomics for novel cancer biomarker discovery.

Multi-omic network-based interrogation of rat liver metabolism following gastric bypass surgery featuring SWATH proteomics

Morbidly obese patients often elect for Roux-en-Y gastric bypass (RYGB), a form of bariatric surgery that triggers a remarkable 30% reduction in excess body weight and reversal of insulin resistance for those who are type II diabetic. A more complete understanding of the underlying molecular mechanisms that drive the complex metabolic reprogramming post-RYGB could lead to innovative non-invasive therapeutics that mimic the beneficial effects of the surgery, namely weight loss, achievement of glycemic control, or reversal of non-alcoholic steatohepatitis (NASH). To facilitate these discoveries, we hereby demonstrate the first multi-omic interrogation of a rodent RYGB model to reveal tissue-specific pathway modules implicated in the control of body weight regulation and energy homeostasis. In this study, we focus on and evaluate liver metabolism three months following RYGB in rats using both SWATH proteomics, a burgeoning label free approach using high resolution mass spectrometry to quantify protein levels in biological samples, as well as MRM metabolomics. The SWATH analysis enabled the quantification of 1378 proteins in liver tissue extracts, of which we report the significant down-regulation of Thrsp and Acot13 in RYGB as putative targets of lipid metabolism for weight loss. Furthermore, we develop a computational graph-based metabolic network module detection algorithm for the discovery of non-canonical pathways, or sub-networks, enriched with significantly elevated or depleted metabolites and proteins in RYGB-treated rat livers. The analysis revealed a network connection between the depleted protein Baat and the depleted metabolite taurine, corroborating the clinical observation that taurine-conjugated bile acid levels are perturbed post-RYGB.

Mass spectrometry-assisted gel-based proteomics in cancer biomarker discovery: approaches and application

There is a critical need for the discovery of novel biomarkers for early detection and targeted therapy of cancer, a major cause of deaths worldwide. In this respect, proteomic technologies, such as mass spectrometry (MS), enable the identification of pathologically significant proteins in various types of samples. MS is capable of high-throughput profiling of complex biological samples including blood, tissues, urine, milk, and cells. MS-assisted proteomics has contributed to the development of cancer biomarkers that may form the foundation for new clinical tests. It can also aid in elucidating the molecular mechanisms underlying cancer. In this review, we discuss MS principles and instrumentation as well as approaches in MS-based proteomics, which have been employed in the development of potential biomarkers. Furthermore, the challenges in validation of MS biomarkers for their use in clinical practice are also reviewed.