Background

A new lipid class named 'fatty acid esters of hydroxyl fatty acids' (FAHFA) was recently discovered in mammalian adipose tissue and in blood plasma and some FAHFAs were found to be associated with type 2 diabetes. To facilitate the automatic annotation of FAHFAs in biological specimens, a tandem mass spectra (MS/MS) library is needed. Due to the limitation of the commercial available standard compounds, we proposed building an in silico MS/MS library to extend the coverage of molecules.

Results

We developed a computer-generated library with 3267 tandem mass spectra (MS/MS) for 1089 FAHFA species. FAHFA spectra were generated based on authentic standards with negative mode electrospray ionization and 10, 20, and 40 V collision induced dissociation at 4 spectra/s as used in in ultra-high performance liquid chromatography-QTOF mass spectrometry studies. However, positional information of the hydroxyl group is only obtained either at lower QTOF spectra acquisition rates of 1 spectrum/s or at the MS(3) level in ion trap instruments. Therefore, an additional set of 4290 fragment-rich MS/MS spectra was created to enable distinguishing positional FAHFA isomers. The library was generated based on ion fragmentations and ion intensities of FAHFA external reference standards, developing a heuristic model for fragmentation rules and extending these rules to large swaths of computer-generated structures of FAHFAs with varying chain lengths, degrees of unsaturation and hydroxyl group positions. Subsequently, we validated the new in silico library by discovering several new FAHFA species in egg yolk, showing that this library enables high-throughput screening of FAHFA lipids in various biological matrices.

Conclusions

The developed library and templates are freely available for commercial or noncommercial use at http://fiehnlab.ucdavis.edu/staff/yanma/fahfa-lipid-library. This in silico MS/MS library allows users to annotate FAHFAs from accurate mass tandem mass spectra in an easy and fast manner with NIST MS Search or PepSearch software. The developing template is provided for advanced users to modify the parameters and export customized libraries according to their instrument features. Graphical abstractExample of experimental and in silico MS/MS spectra for FAHFA lipids.

Untargeted metabolomics by liquid chromatography-mass spectrometry generates data-rich chromatograms in the form of m/z-retention time features. Managing such datasets is a bottleneck. Many popular data processing tools, including XCMS-online and MZmine2, yield numerous false-positive peak detections. Flagging and removing such false peaks manually is a time-consuming task and prone to human error. We present a web application, Mass Spectral Feature List Optimizer (MS-FLO), to improve the quality of feature lists after initial processing to expedite the process of data curation. The tool utilizes retention time alignments, accurate mass tolerances, Pearson's correlation analysis, and peak height similarity to identify ion adducts, duplicate peak reports, and isotopic features of the main monoisotopic metabolites. Removing such erroneous peaks reduces the overall number of metabolites in data reports and improves the quality of subsequent statistical investigations. To demonstrate the effectiveness of MS-FLO, we processed 28 biological studies and uploaded raw and results data to the Metabolomics Workbench website ( www.metabolomicsworkbench.org ), encompassing 1481 chromatograms produced by two different data processing programs used in-house (MZmine2 and later MS-DIAL). Post-processing of datasets with MS-FLO yielded a 7.8% automated reduction of total peak features and flagged an additional 7.9% of features, per dataset, for review by the user. When manually curated, 87% of these additional flagged features were verified false positives. MS-FLO is an open source web application that is freely available for use at http://msflo.fiehnlab.ucdavis.edu .

Lung cancer is the leading cause of cancer mortality in the United States with non-small cell lung cancer (NSCLC) adenocarcinoma being the most common histological type. Early perturbations in cellular metabolism are a hallmark of cancer, but the extent of these changes in early stage lung adenocarcinoma remains largely unknown. In the current study, an integrated metabolomics and proteomics approach was utilized to characterize the biochemical and molecular alterations between malignant and matched control tissue from 27 subjects diagnosed with early stage lung adenocarcinoma. Differential analysis identified 71 metabolites and 1102 proteins that delineated tumor from control tissue. Integrated results indicated four major metabolic changes in early stage adenocarcinoma: (1) increased glycosylation and glutaminolysis; (2) elevated Nrf2 activation; (3) increase in nicotinic and nicotinamide salvaging pathways; and (4) elevated polyamine biosynthesis linked to differential regulation of the SAM/nicotinamide methyl-donor pathway. Genomic data from publicly available databases were included to strengthen proteomic findings. Our findings provide insight into the biochemical and molecular biological reprogramming that may accompanies early stage lung tumorigenesis and highlight potential therapeutic targets.

Background

Recent computed tomography (CT) screening trials showed that it is effective for early detection of lung cancer, but were plagued by high false positive rates. Additional blood biomarker tests designed to complement CT screening and reduce false positive rates are highly desirable.

Objective

Identify blood-based metabolite biomarkers for diagnosing lung cancer.

Mehtods

Serum samples from subjects participating in a CT screening trial were analyzed using untargeted GC-TOFMS and HILIC-qTOFMS-based metabolomics. Samples were acquired prior to diagnosis (pre-diagnostic, n= 17), at-diagnosis (n= 25) and post-diagnosis (n= 19) of lung cancer and from subjects with benign nodules (n= 29).

Results

Univariate analysis identified 40, 102 and 30 features which were significantly different between subjects with malignant (pre-, at- and post-diagnosis) solitary pulmonary nodules (SPNs) and benign SPNs, respectively. Ten metabolites were consistently different between subjects presenting malignant (pre- and at-diagnosis) or benign SPNs. Three of these 10 metabolites were phosphatidylethanolamines (PE) suggesting alterations in lipid metabolism. Accuracies of 77%, 83% and 78% in the pre-diagnosis group and 69%, 71% and 67% in the at-diagnosis group were determined for PE(34:2), PE(36:2) and PE(38:4), respectively.

Conclusions

This study demonstrates evidence of early metabolic alterations that can possibly distinguish malignant from benign SPNs. Further studies in larger pools of samples are warranted.

Background

Untargeted metabolomics was used in case-control studies of adenocarcinoma (ADC) lung cancer to develop and test metabolite classifiers in serum and plasma as potential biomarkers for diagnosing lung cancer.

Methods

Serum and plasma were collected and used in two independent case-control studies (ADC1 and ADC2). Controls were frequency matched for gender, age, and smoking history. There were 52 adenocarcinoma cases and 31 controls in ADC1 and 43 adenocarcinoma cases and 43 controls in ADC2. Metabolomics was conducted using gas chromatography time-of-flight mass spectrometry. Differential analysis was performed on ADC1 and the top candidates (FDR < 0.05) for serum and plasma used to develop individual and multiplex classifiers that were then tested on an independent set of serum and plasma samples (ADC2).

Results

Aspartate provided the best accuracy (81.4%) for an individual metabolite classifier in serum, whereas pyrophosphate had the best accuracy (77.9%) in plasma when independently tested. Multiplex classifiers of either 2 or 4 serum metabolites had an accuracy of 72.7% when independently tested. For plasma, a multimetabolite classifier consisting of 8 metabolites gave an accuracy of 77.3% when independently tested. Comparison of overall diagnostic performance between the two blood matrices yielded similar performances. However, serum is most ideal given higher sensitivity for low-abundant metabolites.

Conclusion

This study shows the potential of metabolite-based diagnostic tests for detection of lung adenocarcinoma. Further validation in a larger pool of samples is warranted.

Impact

These biomarkers could improve early detection and diagnosis of lung cancer.

Background

Lung cancer is the leading cause of cancer mortality in the United States. Non-small cell lung cancer accounts for 85% of all lung cancers for which adenocarcinoma is the most common histological type. Management of lung cancer is hindered by high false-positive rates due to difficulty resolving between benign and malignant tumors. Better molecular analysis comparing malignant and non-malignant tissues will provide important evidence of the underlying biology contributing to tumorigenesis.

Methods

We utilized a proteomics approach to analyze 38 malignant and non-malignant paired tissue samples obtained from current or former smokers with early stage (Stage IA/IB) lung adenocarcinoma. Statistical mixed effects modeling and orthogonal partial least squares discriminant analysis were used to identify key cancer-associated perturbations in the adenocarcinoma proteome. Identified proteins were subsequently assessed against clinicopathological variables.

Results

Top cancer-associated protein alterations were characterized by: (1) elevations in APEX1, HYOU1 and PDIA4, indicative of increased DNA repair machinery and heightened anti-oxidant defense mechanisms; (2) increased LRPPRC, STOML2, COPG1 and EPRS, suggesting altered tumor metabolism and inflammation; (3) reductions in SPTB, SPTA1 and ANK1 implying dysregulation of membrane integrity; and (4) decreased SLCA41 suggesting altered pH regulation. Increased protein levels of HYOU1, EPRS and LASP1 in NSCLC adenocarcinoma was independently validated by tissue microarray immunohistochemistry. Immunohistochemistry for HYOU1 and EPRS indicated AUCs of 0.952 and 0.841, respectively, for classifying tissue as malignant. Increased LASP1 correlated with poor overall survival (HR 3.66 per unit increase; CI 1.37-9.78; p = 0.01).

Conclusion

These results reveal distinct proteomic changes associated with early stage lung adenocarcinoma that may be useful prognostic indicators and therapeutic targets.

Pulmonary hypertension (PH) is a common consequence of bronchopulmonary dysplasia (BPD) and remains a primary contributor to increased morbidity and mortality among preterm infants. Unfortunately, at the present time, there are no reliable early predictive markers for BPD-associated PH. Considering its health consequences, understanding in utero perturbations that lead to the development of BPD and BPD-associated PH and identifying early predictive markers is of utmost importance. As part of the discovery phase, we applied a multiplatform metabolomics approach consisting of untargeted and targeted methodologies to screen for metabolic perturbations in umbilical cord blood (UCB) plasma from preterm infants that did ( n = 21; cases) or did not ( n = 21; controls) develop subsequent PH. A total of 1,656 features were detected, of which 407 were annotated by metabolite structures. PH-associated metabolic perturbations were characterized by reductions in major choline-containing phospholipids, such as phosphatidylcholines and sphingomyelins, indicating altered lipid metabolism. The reduction in UCB abundances of major choline-containing phospholipids was confirmed in an independent validation cohort consisting of UCB plasmas from 10 cases and 10 controls matched for gestational age and BPD status. Subanalyses in the discovery cohort indicated that elevations in the oxylipins PGE1, PGE2, PGF2a, 9- and 13-HOTE, 9- and 13-HODE, and 9- and 13-KODE were positively associated with BPD presence and severity. This expansive evaluation of cord blood plasma identifies compounds reflecting dyslipidemia and suggests altered metabolite provision associated with metabolic immaturity that differentiate subjects, both by BPD severity and PH development.

Background

Metabolic and transcriptomic differences between visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) compartments, particularly in the context of obesity, may play a role in colorectal carcinogenesis. We investigated the differential functions of their metabolic compositions.

Objectives

Biochemical differences between adipose tissues (VAT compared with SAT) in patients with colorectal carcinoma (CRC) were investigated by using mass spectrometry metabolomics and gene expression profiling. Metabolite compositions were compared between VAT, SAT, and serum metabolites. The relation between patients' tumor stage and metabolic profiles was assessed.

Design

Presurgery blood and paired VAT and SAT samples during tumor surgery were obtained from 59 CRC patients (tumor stages I-IV) of the ColoCare cohort. Gas chromatography time-of-flight mass spectrometry and liquid chromatography quadrupole time-of-flight mass spectrometry were used to measure 1065 metabolites in adipose tissue (333 identified compounds) and 1810 metabolites in serum (467 identified compounds). Adipose tissue gene expression was measured by using Illumina's HumanHT-12 Expression BeadChips.

Results

Compared with SAT, VAT displayed elevated markers of inflammatory lipid metabolism, free arachidonic acid, phospholipases (PLA2G10), and prostaglandin synthesis-related enzymes (PTGD/PTGS2S). Plasmalogen concentrations were lower in VAT than in SAT, which was supported by lower gene expression of FAR1, the rate-limiting enzyme for ether-lipid synthesis in VAT. Serum sphingomyelin concentrations were inversely correlated (P = 0.0001) with SAT adipose triglycerides. Logistic regression identified lipids in patients' adipose tissues, which were associated with CRC tumor stage.

Conclusions

As one of the first studies, we comprehensively assessed differences in metabolic, lipidomic, and transcriptomic profiles between paired human VAT and SAT and their association with CRC tumor stage. We identified markers of inflammation in VAT, which supports prior evidence regarding the role of visceral adiposity and cancer.

Adenocarcinoma, a type of non-small cell lung cancer, is the most frequently diagnosed lung cancer and the leading cause of lung cancer mortality in the United States. It is well documented that biochemical changes occur early in the transition from normal to cancer cells, but the extent to which these alterations affect tumorigenesis in adenocarcinoma remains largely unknown. Herein, we describe the application of mass spectrometry and multivariate statistical analysis in one of the largest biomarker research studies to date aimed at distinguishing metabolic differences between malignant and nonmalignant lung tissue. Gas chromatography time-of-flight mass spectrometry was used to measure 462 metabolites in 39 malignant and nonmalignant lung tissue pairs from current or former smokers with early stage (stage IA-IB) adenocarcinoma. Statistical mixed effects models, orthogonal partial least squares discriminant analysis and network integration, were used to identify key cancer-associated metabolic perturbations in adenocarcinoma compared with nonmalignant tissue. Cancer-associated biochemical alterations were characterized by (i) decreased glucose levels, consistent with the Warburg effect, (ii) changes in cellular redox status highlighted by elevations in cysteine and antioxidants, alpha- and gamma-tocopherol, (iii) elevations in nucleotide metabolites 5,6-dihydrouracil and xanthine suggestive of increased dihydropyrimidine dehydrogenase and xanthine oxidoreductase activity, (iv) increased 5'-deoxy-5'-methylthioadenosine levels indicative of reduced purine salvage and increased de novo purine synthesis, and (v) coordinated elevations in glutamate and UDP-N-acetylglucosamine suggesting increased protein glycosylation. The present study revealed distinct metabolic perturbations associated with early stage lung adenocarcinoma, which may provide candidate molecular targets for personalizing therapeutic interventions and treatment efficacy monitoring.

Objective

Intra-tumoral expression of the serine hydrolase carboxylesterase 2 (CES2) contributes to the activation of the pro-drug irinotecan in pancreatic ductal adenocarcinoma (PDAC). Given other potential roles of CES2, we assessed its regulation, downstream effects, and contribution to tumor development in PDAC.

Methods

Association between the mRNA expression of CES2 in pancreatic tumors and overall survival was assessed using The Cancer Genome Atlas. Cell viability, clonogenic, and anchorage-independent growth assays as well as an orthotopic mouse model of PDAC were used to evaluate the biological relevance of CES2 in pancreatic cancer. CES2-driven metabolic changes were determined by untargeted and targeted metabolomic analyses.

Results

Elevated tumoral CES2 mRNA expression was a statistically significant predictor of poor overall survival in PDAC patients. Knockdown of CES2 in PDAC cells reduced cell viability, clonogenic capacity, and anchorage-independent growth in vitro and attenuated tumor growth in an orthotopic mouse model of PDAC. Mechanistically, CES2 was found to promote the catabolism of phospholipids resulting in HNF4α activation through a soluble epoxide hydrolase (sEH)-dependent pathway. Targeting of CES2 via siRNA or small molecule inhibitors attenuated HNF4α protein expression and reduced gene expression of classical/progenitor markers and increased basal-like markers. Targeting of the CES2-sEH-HNF4α axis using small molecule inhibitors of CES2 or sEH reduced cell viability.

Conclusions

We establish a novel regulatory loop between CES2 and HNF4α to sustain the progenitor subtype and promote PDAC progression and highlight the potential utility of CES2 or sEH inhibitors for the treatment of PDAC as part of non-irinotecan-containing regimens.