bims-mascan 2022-04-17 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2022–04–17
twenty papers selected by
Giovanny Rodríguez Blanco, University of Edinburgh

Targeted Lipidomics for Characterization of PUFAs and Eicosanoids in Extracellular Vesicles.
A Sensitive and Controlled Data-Independent Acquisition Method for Proteomic Analysis of Cell Therapies.
DIA-MS proteome analysis of formalin-fixed paraffin-embedded glioblastoma tissues.
Coupling Stable Isotope Labeling and Liquid Chromatography-Trapped Ion Mobility Spectrometry-Time-of-Flight-Tandem Mass Spectrometry for De Novo Mosquito Ovarian Lipid Studies.
Iron metabolism: State of the art in hypoxic cancer cell biology.
Liquid chromatography-tandem mass spectrometry based quantification of arginine metabolites including polyamines in different sample matrices.
A Rapid LC-MS/MS-PRM Assay for Serologic Quantification of Sialylated O-HPX Glycoforms in Patients with Liver Fibrosis.
Molecular networking and collision cross section prediction for structural isomer and unknown compound identification in plant metabolomics: a case study applied to Zhanthoxylum heitzii extracts.
Ketogenic diet and chemotherapy combine to disrupt pancreatic cancer metabolism and growth.
Validation-based model selection for 13C metabolic flux analysis with uncertain measurement errors.
Unravelling Prostate Cancer Heterogeneity Using Spatial Approaches to Lipidomics and Transcriptomics.
Multi-Omics Approach Reveals Dysregulation of Protein Phosphorylation Correlated with Lipid Metabolism in Mouse Non-Alcoholic Fatty Liver.
Treatment against glucose-dependent cancers through metabolic PFKFB3 targeting of glycolytic flux.
Flazin as a Lipid Droplet Regulator against Lipid Disorders.
Impact of Lipid Metabolism on Antitumor Immune Response.
automRm: An R Package for Fully Automatic LC-QQQ-MS Data Preprocessing Powered by Machine Learning.
Biomarkers in disease diagnosis and the role of LC-MS and NMR: A Review.
Pathological implication of protein post-translational modifications in cancer.
Does chromatin function as a metabolite reservoir?
Goslin 2.0 Implements the Recent Lipid Shorthand Nomenclature for MS-Derived Lipid Structures.

Nutrients. 2022 Mar 22. pii: 1319. [Epub ahead of print]14(7):

Targeted Lipidomics for Characterization of PUFAs and Eicosanoids in Extracellular Vesicles.

Madlen Reinicke, Saikal Shamkeeva, Max Hell, Berend Isermann, Uta Ceglarek, Mitja L Heinemann.

  Lipids are increasingly recognized as bioactive mediators of extracellular vesicle (EV) functions. However, while EV proteins and nucleic acids are well described, EV lipids are insufficiently understood due to lack of adequate quantitative methods. We adapted an established targeted and quantitative mass spectrometry (LC-MS/MS) method originally developed for analysis of 94 eicosanoids and seven polyunsaturated fatty acids (PUFA) in human plasma. Additionally, the influence of freeze-thaw (FT) cycles, injection volume, and extraction solvent were investigated. The modified protocol was applied to lipidomic analysis of differently polarized macrophage-derived EVs. We successfully quantified three PUFAs and eight eicosanoids within EVs. Lipid extraction showed reproducible PUFA and eicosanoid patterns. We found a particularly high impact of FT cycles on EV lipid profiles, with significant reductions of up to 70%. Thus, repeated FT will markedly influence analytical results and may alter EV functions, emphasizing the importance of a standardized sample pretreatment protocol for the analysis of bioactive lipids in EVs. EV lipid profiles differed largely depending on the polarization of the originating macrophages. Particularly, we observed major changes in the arachidonic acid pathway. We emphasize the importance of a standardized sample pretreatment protocol for the analysis of bioactive lipids in EVs.

Keywords:  eicosanoids; extracellular vesicles; pre-analytics; quantitative lipidomics

DOI:  https://doi.org/10.3390/nu14071319
J Proteome Res. 2022 Apr 11.

A Sensitive and Controlled Data-Independent Acquisition Method for Proteomic Analysis of Cell Therapies.

Camille Lombard-Banek, Kerstin I Pohl, Edward J Kwee, John T Elliott, John E Schiel.

  Mass spectrometry (MS)-based proteomic measurements are uniquely poised to impact the development of cell and gene therapies. With the adoption of rigorous instrumental performance qualifications (PQs), large-scale proteomics can move from a research to a manufacturing control tool. Especially suited, data-independent acquisition (DIA) approaches have distinctive qualities to extend multiattribute method (MAM) principles to characterize the proteome of cell therapies. Here, we describe the development of a DIA method for the sensitive identification and quantification of proteins on a Q-TOF instrument. Using the improved acquisition parameters, we defined a control strategy and highlighted some metrics to improve the reproducibility of SWATH acquisition-based proteomic measurements. Finally, we applied the method to analyze the proteome of Jurkat cells that here serves as a model for human T-cells. Raw and processed data were deposited in PRIDE (PXD029780).

Keywords:  SWATH acquisition; biopharmaceutical; bottom-up proteomics; cell therapies; data-independent acquisition; mass spectrometry; performance qualification (PQ); quality control (QC)

DOI:  https://doi.org/10.1021/acs.jproteome.1c00887
Anal Chim Acta. 2022 Apr 29. pii: S0003-2670(22)00266-5. [Epub ahead of print]1204 339695

DIA-MS proteome analysis of formalin-fixed paraffin-embedded glioblastoma tissues.

Kenneth Weke, Sachin Kote, Jakub Faktor, Sofian Al Shboul, Naomi Uwugiaren, Paul M Brennan, David R Goodlett, Ted R Hupp, Irena Dapic.

  Developments in quantitative proteomics and data-independent acquisition (DIA) methodology is enabling quantification of proteins in biological samples. Currently, there are a few reports on DIA mass spectrometry (MS) approaches for proteome analysis of formalin-fixed paraffin-embedded (FFPE) tissues. Therefore, to facilitate detection and quantification of immune- and glioblastoma (GBM)-relevant proteins from FFPE patient materials, we established a simple and precise DIA-MS workflow. We first evaluated different lysis buffers for their efficiency in protein extractions from FFPE GBM tissues. Our results showed that more than 1700 proteins were detected and over 1400 proteins were quantified from GBM FFPE tissue microdissections. GBM-relevant proteins (e.g., GFAP, FN1, VIM, and MBP) were quantified with high precision (median coefficient of variation <12%). In addition, immune-related proteins (e.g., ILF2, MIF, and CD38) were consistently detected and quantified. The strategy holds great potential for routinizing protein quantification in FFPE tissue samples.

Keywords:  Data-independent acquisition; Formalin-fixed paraffin-embedded; Glioblastoma; Mass spectrometry; Quantitative proteomics

DOI:  https://doi.org/10.1016/j.aca.2022.339695
Anal Chem. 2022 Apr 14.

Coupling Stable Isotope Labeling and Liquid Chromatography-Trapped Ion Mobility Spectrometry-Time-of-Flight-Tandem Mass Spectrometry for De Novo Mosquito Ovarian Lipid Studies.

Lilian V Tose, Cesar E Ramirez, Veronika Michalkova, Marcela Nouzova, Fernando G Noriega, Francisco Fernandez-Lima.

There is a need to better understand lipid metabolism during mosquito ovarian development. Lipids are the major source of energy supporting ovarian follicles development in mosquitoes. In this paper, we describe the complementary use of stable isotope labeling (SIL) and high-resolution mass spectrometry-based tools for the investigation of de novo triglycerides (TG) and diglycerides (DG) during the ovarian previtellogenic (PVG) stage (4-6 days posteclosion) of female adult Aedes aegypti. Liquid chromatography coupled to high-resolution trapped ion mobility spectrometry-parallel accumulation sequential fragmentation-time-of-flight tandem mass spectrometry (LC-TIMS-PASEF-TOF MS/MS) allowed the separation and quantification of nonlabeled and 2H/13C-labeled TG and DG species. Three SIL strategies were evaluated (H2O/2H2O with 50:50 and 95:5 mixtures, 13C-sucrose, and 13C-glucose). Results showed wide applicability with no signs of lipid ovarian impairment by SIL induced toxicity. The analytical workflow based on LC-TIMS-TOF MS/MS provided high confidence and high reproducibility for lipid DG and TG identification and SIL incorporation based on their separation by retention time (RT), collision cross section (CCS), and accurate m/z. In addition, the SIL fatty acid chain incorporation was evaluated using PASEF MS/MS. The 2H/13C incorporation into the mosquito diet provided information on how TG lipids are consumed, stored, and recycled during the PVG stage of ovarian development.

DOI: https://doi.org/10.1021/acs.analchem.1c05090
Arch Biochem Biophys. 2022 Apr 07. pii: S0003-9861(22)00084-4. [Epub ahead of print] 109199

Iron metabolism: State of the art in hypoxic cancer cell biology.

Sai Liu, Xiongfeng Cao, Dongqing Wang, Haitao Zhu.

  The tumor microenvironment (TME) promotes the malignant transformation of cancer cells, mainly through metabolic reprogramming. As one of the most prominent features of the TME, hypoxia contributes to cancer cell death resistance, invasion, metastasis, and therapy-resistant phenotypes. As an important cofactor for various enzymes, iron is essential for ATP generation, antioxidant protein function, and DNA-damage repair in hypoxic cancer cells. Iron metabolism, as a promoter of aggressive hypoxic cancer cell biology, has attracted an increasing amount of attention. Iron utilization, storage, and efflux are enhanced in hypoxic cancer cells, which further contributes to cancer cell proliferation, metastasis, ferroptosis resistance, and immune escape. This review describes the relationship between iron metabolism and proliferation, metastasis, and ferroptosis of hypoxic cancer cells, as well as several iron-targeted cancer therapy strategies. Understanding the hypoxia-specific regulatory mechanism of iron metabolism could aid the development of targeted therapy against refractory hypoxic cancer cells.

Keywords:  Ferroptosis; Hypoxia; Iron metabolism; Metabolic reprogramming; The tumor microenvironment

DOI:  https://doi.org/10.1016/j.abb.2022.109199
J Chromatogr A. 2022 Apr 05. pii: S0021-9673(22)00216-3. [Epub ahead of print]1671 463021

Liquid chromatography-tandem mass spectrometry based quantification of arginine metabolites including polyamines in different sample matrices.

Mathias Langner, Ivona Mateska, Nicole Bechmann, Ben Wielockx, Triantafyllos Chavakis, Vasileia Ismini Alexaki, Mirko Peitzsch.

  The conditionally essential amino acid arginine and its metabolic products play an important role in different biological processes, such as metabolic regulation of the immune response, including macrophage activation and polarization and regulation of T cell function. Furthermore, the polyamine spermidine has a role in aging and age-related diseases. Additionally, altered polyamine metabolism may be associated with neurodegenerative diseases, while polyamine levels may present useful biomarkers associated with severity of Parkinson's disease or with progression of non-alcoholic fatty liver disease. In the present study, a simple, derivatization-free hydrophilic interaction liquid chromatography based tandem mass spectrometry (LC-MS/MS) method is described, that allows the accurate quantification of arginine and related amine, polyamine and acetylated polyamine metabolites in different experimental sample matrices, such as cell lysates, cell culture supernatants and tissues. Ten arginine metabolites, including citrulline, agmatine, ornithine, putrescine, spermidine, spermine, N1-acetylspermidine, N1-acetylspermine, N1,N12-diacetylspermine and arginine in conjunction with the metabolic cofactors S-adenosylhomocysteine and S-adenosylmethionine are simultaneously analyzed within a total LC-MS/MS run time of 9.5 min. The assay is suitable to quantify concentration ranges over multiple orders of magnitude for all metabolites with averaged accuracies observed at 103.2% ± 6.8%, 99.0% ± 4.2% and 100.4% ± 4.3% in cell lysates, cell culture supernatant and tissue extracts, respectively. Inter-day coefficients of variation ranged from 5.9 to 14.8% in cell lysates, 6.7 to 14.6% in cell culture supernatants and 5.3 to 12.0% in tissue extracts. The method was successfully applied to cell culture systems of different origin as well as different murine tissues and organs. The herein described LC-MS/MS method provides a simple tool for a fast and simultaneous analysis of arginine metabolites, including polyamines and their respective metabolic cofactors. Assay performance characteristics demonstrate suitability for applications in different experimental and preclinical settings.

Keywords:  Arginine metabolites; LC-MS/MS; Polyamines

DOI:  https://doi.org/10.1016/j.chroma.2022.463021
Molecules. 2022 Mar 29. pii: 2213. [Epub ahead of print]27(7):

A Rapid LC-MS/MS-PRM Assay for Serologic Quantification of Sialylated O-HPX Glycoforms in Patients with Liver Fibrosis.

Aswini Panigrahi, Julius Benicky, Renhuizi Wei, Jaeil Ahn, Radoslav Goldman, Miloslav Sanda.

  Development of high throughput robust methods is a prerequisite for a successful clinical use of LC-MS/MS assays. In earlier studies, we reported that nLC-MS/MS measurement of the O-glycoforms of HPX is an indicator of liver fibrosis. In this study, we show that a microflow LC-MS/MS method using a single column setup for capture of the analytes, desalting, fast gradient elution, and on-line mass spectrometry measurements, is robust, substantially faster, and even more sensitive than our nLC setup. We demonstrate applicability of the workflow on the quantification of the O-HPX glycoforms in unfractionated serum samples of control and liver disease patients. The assay requires microliter volumes of serum samples, and the platform is amenable to one hundred sample injections per day, providing a valuable tool for biomarker validation and screening studies.

Keywords:  biomarker; hemopexin; liver fibrosis; mLC-MS/MS; microflow LC-MS

DOI:  https://doi.org/10.3390/molecules27072213
Anal Bioanal Chem. 2022 Apr 13.

Molecular networking and collision cross section prediction for structural isomer and unknown compound identification in plant metabolomics: a case study applied to Zhanthoxylum heitzii extracts.

Valentina Calabrese, Isabelle Schmitz-Afonso, Candice Prevost, Carlos Afonso, Abdelhakim Elomri.

  Mass spectrometry-based plant metabolomics allow large-scale analysis of a wide range of compounds and the discovery of potential new active metabolites with minimal sample preparation. Despite recent tools for molecular networking, many metabolites remain unknown. Our objective is to show the complementarity of collision cross section (CCS) measurements and calculations for metabolite annotation in a real case study. Thus, a systematic and high-throughput investigation of root, bark, branch, and leaf of the Gabonese plant Zhanthoxylum heitzii was performed through ultra-high performance liquid chromatography high-resolution tandem mass spectrometry (UHPLC-QTOF/MS). A feature-based molecular network (FBMN) was employed to study the distribution of metabolites in the organs of the plants and discover potential new components. In total, 143 metabolites belonging to the family of alkaloids, lignans, polyphenols, fatty acids, and amino acids were detected and a semi-quantitative analysis in the different organs was performed. A large proportion of medical plant phytochemicals is often characterized by isomerism and, in the absence of reference compounds, an additional dimension of gas phase separation can result in improvements to both quantitation and compound annotation. The inclusion of ion mobility in the ultra-high performance liquid chromatography mass spectrometry workflow (UHPLC-IMS-MS) has been used to collect experimental CCS values in nitrogen and helium (CCSN2 and CCSHe) of Zhanthoxylum heitzii features. Due to a lack of reference data, the investigation of predicted collision cross section has enabled comparison with the experimental values, helping in dereplication and isomer identification. Moreover, in combination with mass spectra interpretation, the comparison of experimental and theoretical CCS values allowed annotation of unknown features. The study represents a practical example of the potential of modern mass spectrometry strategies in the identification of medicinal plant phytochemical components.

Keywords:  Collision cross section; Liquid chromatography ion mobility mass spectrometry; Metabolomics; Molecular networks; Zhanthoxylum heitzii

DOI:  https://doi.org/10.1007/s00216-022-04059-7
Med (N Y). 2022 Feb 11. 3(2): 119-136

Ketogenic diet and chemotherapy combine to disrupt pancreatic cancer metabolism and growth.

Lifeng Yang, Tara TeSlaa, Serina Ng, Michel Nofal, Lin Wang, Taijin Lan, Xianfeng Zeng, Alexis Cowan, Matthew McBride, Wenyun Lu, Shawn Davidson, Gaoyang Liang, Tae Gyu Oh, Michael Downes, Ronald Evans, Daniel Von Hoff, Jessie Yanxiang Guo, Haiyong Han, Joshua D Rabinowitz.

Background: Ketogenic diet is a potential means of augmenting cancer therapy. Here, we explore ketone body metabolism and its interplay with chemotherapy in pancreatic cancer.
Methods: Metabolism and therapeutic responses of murine pancreatic cancer were studied using KPC primary tumors and tumor chunk allografts. Mice on standard high-carbohydrate diet or ketogenic diet were treated with cytotoxic chemotherapy (nab-paclitaxel, gemcitabine, cisplatin). Metabolic activity was monitored with metabolomics and isotope tracing, including 2H- and 13C-tracers, liquid chromatography-mass spectrometry, and imaging mass spectrometry.
Findings: Ketone bodies are unidirectionally oxidized to make NADH. This stands in contrast to the carbohydrate-derived carboxylic acids lactate and pyruvate, which rapidly interconvert, buffering NADH/NAD. In murine pancreatic tumors, ketogenic diet decreases glucose's concentration and tricarboxylic acid cycle contribution, enhances 3-hydroxybutyrate's concentration and tricarboxylic acid contribution, and modestly elevates NADH, but does not impact tumor growth. In contrast, the combination of ketogenic diet and cytotoxic chemotherapy substantially raises tumor NADH and synergistically suppresses tumor growth, tripling the survival benefits of chemotherapy alone. Chemotherapy and ketogenic diet also synergize in immune-deficient mice, although long-term growth suppression was only observed in mice with an intact immune system.
Conclusions: Ketogenic diet sensitizes murine pancreatic cancer tumors to cytotoxic chemotherapy. Based on these data, we have initiated a randomized clinical trial of chemotherapy with standard versus ketogenic diet for patients with metastatic pancreatic cancer (NCT04631445).

DOI: https://doi.org/10.1016/j.medj.2021.12.008
PLoS Comput Biol. 2022 Apr 11. 18(4): e1009999

Validation-based model selection for 13C metabolic flux analysis with uncertain measurement errors.

Nicolas Sundqvist, Nina Grankvist, Jeramie Watrous, Jain Mohit, Roland Nilsson, Gunnar Cedersund.

Accurate measurements of metabolic fluxes in living cells are central to metabolism research and metabolic engineering. The gold standard method is model-based metabolic flux analysis (MFA), where fluxes are estimated indirectly from mass isotopomer data with the use of a mathematical model of the metabolic network. A critical step in MFA is model selection: choosing what compartments, metabolites, and reactions to include in the metabolic network model. Model selection is often done informally during the modelling process, based on the same data that is used for model fitting (estimation data). This can lead to either overly complex models (overfitting) or too simple ones (underfitting), in both cases resulting in poor flux estimates. Here, we propose a method for model selection based on independent validation data. We demonstrate in simulation studies that this method consistently chooses the correct model in a way that is independent on errors in measurement uncertainty. This independence is beneficial, since estimating the true magnitude of these errors can be difficult. In contrast, commonly used model selection methods based on the χ2-test choose different model structures depending on the believed measurement uncertainty; this can lead to errors in flux estimates, especially when the magnitude of the error is substantially off. We present a new approach for quantification of prediction uncertainty of mass isotopomer distributions in other labelling experiments, to check for problems with too much or too little novelty in the validation data. Finally, in an isotope tracing study on human mammary epithelial cells, the validation-based model selection method identified pyruvate carboxylase as a key model component. Our results argue that validation-based model selection should be an integral part of MFA model development.

DOI: https://doi.org/10.1371/journal.pcbi.1009999
Cancers (Basel). 2022 Mar 27. pii: 1702. [Epub ahead of print]14(7):

Unravelling Prostate Cancer Heterogeneity Using Spatial Approaches to Lipidomics and Transcriptomics.

Shadrack M Mutuku, Xander Spotbeen, Paul J Trim, Marten F Snel, Lisa M Butler, Johannes V Swinnen.

  Due to advances in the detection and management of prostate cancer over the past 20 years, most cases of localised disease are now potentially curable by surgery or radiotherapy, or amenable to active surveillance without treatment. However, this has given rise to a new dilemma for disease management; the inability to distinguish indolent from lethal, aggressive forms of prostate cancer, leading to substantial overtreatment of some patients and delayed intervention for others. Driving this uncertainty is the critical deficit of novel targets for systemic therapy and of validated biomarkers that can inform treatment decision-making and to select and monitor therapy. In part, this lack of progress reflects the inherent challenge of undertaking target and biomarker discovery in clinical prostate tumours, which are cellularly heterogeneous and multifocal, necessitating the use of spatial analytical approaches. In this review, the principles of mass spectrometry-based lipid imaging and complementary gene-based spatial omics technologies, their application to prostate cancer and recent advancements in these technologies are considered. We put in perspective studies that describe spatially-resolved lipid maps and metabolic genes that are associated with prostate tumours compared to benign tissue and increased risk of disease progression, with the aim of evaluating the future implementation of spatial lipidomics and complementary transcriptomics for prognostication, target identification and treatment decision-making for prostate cancer.

Keywords:  MALDI; biomarkers; lipidomics; lipids; mass spectrometry imaging; metabolomics; prostate cancer

DOI:  https://doi.org/10.3390/cancers14071702
Cells. 2022 Mar 30. pii: 1172. [Epub ahead of print]11(7):

Multi-Omics Approach Reveals Dysregulation of Protein Phosphorylation Correlated with Lipid Metabolism in Mouse Non-Alcoholic Fatty Liver.

Sora Q Kim, Rodrigo Mohallem, Jackeline Franco, Kimberly K Buhman, Kee-Hong Kim, Uma K Aryal.

  Obesity caused by overnutrition is a major risk factor for non-alcoholic fatty liver disease (NAFLD). Several lipid intermediates such as fatty acids, glycerophospholipids and sphingolipids are implicated in NAFLD, but detailed characterization of lipids and their functional links to proteome and phosphoproteome remain to be elucidated. To characterize this complex molecular relationship, we used a multi-omics approach by conducting comparative proteomic, phoshoproteomic and lipidomic analyses of high fat (HFD) and low fat (LFD) diet fed mice livers. We quantified 2447 proteins and 1339 phosphoproteins containing 1650 class I phosphosites, of which 669 phosphosites were significantly different between HFD and LFD mice livers. We detected alterations of proteins associated with cellular metabolic processes such as small molecule catabolic process, monocarboxylic acid, long- and medium-chain fatty acid, and ketone body metabolic processes, and peroxisome organization. We observed a significant downregulation of protein phosphorylation in HFD fed mice liver in general. Untargeted lipidomics identified upregulation of triacylglycerols, glycerolipids and ether glycerophosphocholines and downregulation of glycerophospholipids, such as lysoglycerophospholipids, as well as ceramides and acylcarnitines. Analysis of differentially regulated phosphosites revealed phosphorylation dependent deregulation of insulin signaling as well as lipogenic and lipolytic pathways during HFD induced obesity. Thus, this study reveals a molecular connection between decreased protein phosphorylation and lipolysis, as well as lipid-mediated signaling in diet-induced obesity.

Keywords:  HFD; NAFLD; fatty liver; lipidomics; mass spectrometry; proteomics

DOI:  https://doi.org/10.3390/cells11071172
Cancer Metastasis Rev. 2022 Apr 14.

Treatment against glucose-dependent cancers through metabolic PFKFB3 targeting of glycolytic flux.

Brandon C Jones, Paula R Pohlmann, Robert Clarke, Surojeet Sengupta.

  Reprogrammed metabolism and high energy demand are well-established properties of cancer cells that enable tumor growth. Glycolysis is a primary metabolic pathway that supplies this increased energy demand, leading to a high rate of glycolytic flux and a greater dependence on glucose in tumor cells. Finding safe and effective means to control glycolytic flux and curb cancer cell proliferation has gained increasing interest in recent years. A critical step in glycolysis is controlled by the enzyme 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 3 (PFKFB3), which converts fructose 6-phosphate (F6P) to fructose 2,6-bisphosphate (F2,6BP). F2,6BP allosterically activates the rate-limiting step of glycolysis catalyzed by PFK1 enzyme. PFKFB3 is often overexpressed in many human cancers including pancreatic, colon, prostate, and breast cancer. Hence, PFKFB3 has gained increased interest as a compelling therapeutic target. In this review, we summarize and discuss the current knowledge of PFKFB3 functions, its role in cellular pathways and cancer development, its transcriptional and post-translational activity regulation, and the multiple pharmacologic inhibitors that have been used to block PFKFB3 activity in cancer cells. While much remains to be learned, PFKFB3 continues to hold great promise as an important therapeutic target either as a single agent or in combination with current interventions for breast and other cancers.

Keywords:  Aerobic glycolysis; Cancer; Glucose metabolism; PFKFB3; Phosphofructo-2-kinase/fructose-2,6-biphosphatase

DOI:  https://doi.org/10.1007/s10555-022-10027-5
Nutrients. 2022 Apr 03. pii: 1501. [Epub ahead of print]14(7):

Flazin as a Lipid Droplet Regulator against Lipid Disorders.

Xunzhi Wu, Zhen Chen, Yue Wu, Yifan Chen, Jiaping Jia, Nianqiu Shen, Hitoshi Chiba, Shu-Ping Hui.

  Lipid disorders are closely related to numerous metabolic diseases, and lipid droplets (LDs) have been considered as a new target for regulating lipid metabolism. Dietary intervention and nutraceuticals provide safe and long-term beneficial effects for treating metabolic diseases. Flazin is a diet-derived bioactive constituent mainly existing in fermented foods, of which the lipid metabolism improvement function has not been studied. In this study, the effect of flazin on lipid regulation at both cell level and organelle level was investigated. Lipidomic profiling showed that flazin significantly decreased cellular triglyceride (TG) by 12.0-22.4% compared with modeling groups and improved the TG and free fatty acid profile. LD staining revealed that flazin efficiently reduced both cellular neutral lipid content by 17.4-53.9% and LD size by 10.0-35.3%. Furthermore, nanoelectrospray ionization mass spectrometry analysis proved that flazin exhibited a preferential suppression of LD TG and regulated LD morphology, including a size decrease and surface property improvement. An evaluation of related gene expression suggested the mechanism to be lipolysis promotion and lipogenesis inhibition. These findings indicated that flazin might be an LD regulator for reversing lipid metabolism disturbance. Moreover, the strategy proposed in this study may contribute to developing other nutraceuticals for treating lipid disorder-related metabolic diseases.

Keywords:  diabetic nephropathy; functional foods; lipid metabolism; lipid-storage disorders; lipidomics; mass spectrometry; metabolic diseases; nutraceuticals; triglyceride

DOI:  https://doi.org/10.3390/nu14071501
Cancers (Basel). 2022 Apr 06. pii: 1850. [Epub ahead of print]14(7):

Impact of Lipid Metabolism on Antitumor Immune Response.

Nesrine Mabrouk, Baptiste Lecoeur, Ali Bettaieb, Catherine Paul, Frédérique Végran.

  Over the past decade, metabolic reprogramming has been defined as a hallmark of cancer. More recently, a large number of studies have demonstrated that metabolic reprogramming can modulate the differentiation and functions of immune cells, and thus modify the antitumor response. Increasing evidence suggests that modified energy metabolism could be responsible for the failure of antitumor immunity. Indeed, tumor-infiltrating immune cells play a key role in cancer, and metabolic switching in these cells has been shown to help determine their phenotype: tumor suppressive or immune suppressive. Recent studies in the field of immunometabolism focus on metabolic reprogramming in the tumor microenvironment (TME) by targeting innate and adaptive immune cells and their associated anti- or protumor phenotypes. In this review, we discuss the lipid metabolism of immune cells in the TME as well as the effects of lipids; finally, we expose the link between therapies and lipid metabolism.

Keywords:  cancer therapy; immune cells; immunosuppression; lipid metabolism

DOI:  https://doi.org/10.3390/cancers14071850
Anal Chem. 2022 Apr 12.

automRm: An R Package for Fully Automatic LC-QQQ-MS Data Preprocessing Powered by Machine Learning.

Daniel Eilertz, Michael Mitterer, Joerg M Buescher.

Preprocessing of liquid chromatography-mass spectrometry (LC-MS) raw data facilitates downstream statistical and biological data analyses. In the case of targeted LC-MS data, consistent recognition of chromatographic peaks is a main challenge, in particular, for low abundant signals. Fully automatic preprocessing is faster than manual peak review and does not depend on the individual operator. Here, we present the R package automRm for fully automatic preprocessing of LC-MS data recorded in MRM mode. Using machine learning (ML) for detection of chromatographic peaks and quality control of reported results enables the automatic recognition of complex patterns in raw data. In addition, this approach renders automRm generally applicable to a wide range of analytical methods including hydrophilic interaction liquid chromatography (HILIC), which is known for sample-to-sample variations in peak shape and retention time. We demonstrate the impact of the choice of training data set, of the applied ML algorithm, and of individual peak characteristics on automRm's ability to correctly report chromatographic peaks. Next, we show that automRm can replicate results obtained by manual peak review on published data. Moreover, automRm outperforms alternative software solutions regarding the variation in peak integration among replicate measurements and the number of correctly reported peaks when applied to a HILIC-MS data set. The R package is freely available from gitlab (https://gitlab.gwdg.de/joerg.buescher/automrm).

DOI: https://doi.org/10.1021/acs.analchem.1c05224
Curr Pharm Des. 2022 Apr 08.

Biomarkers in disease diagnosis and the role of LC-MS and NMR: A Review.

Irtiza Ashaq, Aadil Ahmad Sheikh, Nikita Beri, Shruti Mittal, Mohd Javed Naim, Ozair Alam, Sandhya Bawa.

   BACKGROUND: Biomarker discovery is regarded as an essential tool to assess early disease diagnosis, disease progression, drug response, disease prevention, and drug target identification. The identification of biomarkers using different detection techniques and the characterization of these biomarkers is of great clinical importance. The integration of proteomics and metabolomics with LC-MS and NMR has every potential to map the early biochemical changes in diseases, making the identification of biomarkers facile. A vigorous and sentient technique is insisted upon to analyze an advanced biological system. The characteristics like susceptibility, explicitness, correctness, pace, and enhanced productiveness have led to the evolution of LC-MS for being a paramount methodical manifesto for biomarker analysis and discovery. The same technology has been employed to study large molecules such as nucleic acids and proteins. NMR allows nondestructive identification and quantification of an immense aggregate of unconventional metabolite biomarkers in both biofluids and tissues, therefore metabolomics based on NMR has demonstrated a substantial assurance in the diagnosis of a disease as well as biomarker discovery.
OBJECTIVES: This article highlights biomarker identification tools in different diseases and the current role of LC-MS and NMR in biomarker discovery for disease diagnosis.
METHODS: The role of LC-MS and NMR has been studied thoroughly as a disease diagnosis tool.
RESULTS: The advancements in Mass and 1H NMR analysis as well as their amalgamation and multifaceted scrutiny of statistical data commit increased efficiency in premature detection of disease, identification of impaired metabolic reactions, hence drug discovery.
CONCLUSION: The review focuses on the integration of biomarker identification for disease diagnostics using emerging high-throughput technologies and will be necessary to achieve the 'personalization' of treatment and disease prevention.

Keywords:  Biomarker; Disease diagnostics; Drug target Identification; LC-MS; NMR

DOI:  https://doi.org/10.2174/1381612828666220408233542
Mol Aspects Med. 2022 Apr 07. pii: S0098-2997(22)00042-5. [Epub ahead of print] 101097

Pathological implication of protein post-translational modifications in cancer.

Sheng Pan, Ru Chen.

  Protein post-translational modifications (PTMs) profoundly influence protein functions and play crucial roles in essentially all cell biological processes. The diverse realm of PTMs and their crosstalk is linked to many critical signaling events involved in neoplastic transformation, carcinogenesis and metastasis. The pathological roles of various PTMs are implicated in all aspects of cancer hallmark functions, cancer metabolism and regulation of tumor microenvironment. Study of PTMs has become an important area in cancer research to understand cancer biology and discover novel biomarkers and therapeutic targets. With a limited scope, this review attempts to discuss some PTMs of high frequency with recognized importance in cancer biology, including phosphorylation, acetylation, glycosylation, palmitoylation and ubiquitination, as well as their implications in clinical applications. These protein modifications are among the most abundant PTMs and profoundly implicated in carcinogenesis.

Keywords:  Cancer; Pathological role; Post-translational modifications; Proteomics; Signaling pathways; Tumorigenesis

DOI:  https://doi.org/10.1016/j.mam.2022.101097
Trends Biochem Sci. 2022 Apr 11. pii: S0968-0004(22)00075-5. [Epub ahead of print]

Does chromatin function as a metabolite reservoir?

Vinícius D Nirello, Dieggo Rodrigues de Paula, Nathália V P Araújo, Patrick D Varga-Weisz.

  Alternative histone acylations integrate gene expression with cellular metabolic states. Recent measurements of cellular acyl-coenzyme A (acyl-CoA) pools highlight the potential that histone post-translational modifications (PTMs) contribute directly to the regulation of metabolite pools. A metabolite-centric view throws new light onto roles and evolution of histone PTMs.

Keywords:  acyl-CoA; acylations; chromatin; histone modifications; metabolism; quiescence

DOI:  https://doi.org/10.1016/j.tibs.2022.03.016
Anal Chem. 2022 Apr 11.

Goslin 2.0 Implements the Recent Lipid Shorthand Nomenclature for MS-Derived Lipid Structures.

Dominik Kopczynski, Nils Hoffmann, Bing Peng, Gerhard Liebisch, Friedrich Spener, Robert Ahrends.

Goslin is the first grammar-based computational library for the recognition/parsing and normalization of lipid names following the hierarchical lipid shorthand nomenclature. The new version Goslin 2.0 implements the latest nomenclature and adds an additional grammar to recognize systematic IUPAC-IUB fatty acyl names as stored, e.g., in the LIPID MAPS database and is perfectly suited to update lipid names in LIPID MAPS or HMDB databases to the latest nomenclature. Goslin 2.0 is available as a standalone web application with a REST API as well as C++, C#, Java, Python 3, and R libraries. Importantly, it can be easily included in lipidomics tools and scripts providing direct access to translation functions. All implementations are open source.

DOI: https://doi.org/10.1021/acs.analchem.1c05430