bims-mascan Biomed News
on Mass spectrometry in cancer research
Issue of 2024‒03‒03
25 papers selected by
Giovanny Rodriguez Blanco, University of Edinburgh



  1. bioRxiv. 2024 Feb 14. pii: 2024.02.13.580048. [Epub ahead of print]
      To standardize metabolomics data analysis and facilitate future computational developments, it is essential is have a set of well-defined templates for common data structures. Here we describe a collection of data structures involved in metabolomics data processing and illustrate how they are utilized in a full-featured Python-centric pipeline. We demonstrate the performance of the pipeline, and the details in annotation and quality control using large-scale LC-MS metabolomics and lipidomics data and LC-MS/MS data. Multiple previously published datasets are also reanalyzed to showcase its utility in biological data analysis. This pipeline allows users to streamline data processing, quality control, annotation, and standardization in an efficient and transparent manner. This work fills a major gap in the Python ecosystem for computational metabolomics.Author Summary: All life processes involve the consumption, creation, and interconversion of metabolites. Metabolomics is the comprehensive study of these small molecules, often using mass spectrometry, to provide critical information of health and disease. Automated processing of such metabolomics data is desired, especially for the bioinformatics community with familiar tools and infrastructures. Despite of Python's popularity in bioinformatics and machine learning, the Python ecosystem in computational metabolomics still misses a complete data pipeline. We have developed an end-to-end computational metabolomics data processing pipeline, based on the raw data preprocessor Asari [1]. Our pipeline takes experimental data in .mzML or .raw format and outputs annotated feature tables for subsequent biological interpretation. We demonstrate the application of this pipeline to multiple metabolomics and lipidomics datasets. Accompanying the pipeline, we have designed a set of reusable data structures, released as the MetDataModel package, which shall promote more consistent terminology and software interoperability in this area.
    DOI:  https://doi.org/10.1101/2024.02.13.580048
  2. Methods Mol Biol. 2024 ;2785 221-260
      Recent research has revealed the potential of lipidomics and metabolomics in identifying new biomarkers and mechanistic insights for neurodegenerative disorders. To contribute to this promising area, we present a detailed protocol for conducting an integrated lipidomic and metabolomic profiling of brain tissue and biofluid samples. In this method, a single-phase methanol extraction is employed for extracting both nonpolar and highly polar lipids and metabolites from each biological sample. The extracted samples are then subjected to liquid chromatography-mass spectrometry-based assays to provide relative or semiquantitative measurements for hundreds of selected lipids and metabolites per sample. This high-throughput approach enables the generation of new hypotheses regarding the mechanistic and functional significance of lipid and metabolite alterations in neurodegenerative disorders while also facilitating the discovery of new biomarkers to support drug development.
    Keywords:  Biomarker; Drug development; Drug discovery; Lipidomics; Lipids; Metabolomics; Neurodegeneration; Omics
    DOI:  https://doi.org/10.1007/978-1-0716-3774-6_14
  3. Chem Sci. 2024 Feb 22. 15(8): 2833-2847
      Drug development is plagued by inefficiency and high costs due to issues such as inadequate drug efficacy and unexpected toxicity. Mass spectrometry (MS)-based proteomics, particularly isobaric quantitative proteomics, offers a solution to unveil resistance mechanisms and unforeseen side effects related to off-targeting pathways. Thermal proteome profiling (TPP) has gained popularity for drug target identification at the proteome scale. However, it involves experiments with multiple temperature points, resulting in numerous samples and considerable variability in large-scale TPP analysis. We propose a high-throughput drug target discovery workflow that integrates single-temperature TPP, a fully automated proteomics sample preparation platform (autoSISPROT), and data independent acquisition (DIA) quantification. The autoSISPROT platform enables the simultaneous processing of 96 samples in less than 2.5 hours, achieving protein digestion, desalting, and optional TMT labeling (requires an additional 1 hour) with 96-channel all-in-tip operations. The results demonstrated excellent sample preparation performance with >94% digestion efficiency, >98% TMT labeling efficiency, and >0.9 intra- and inter-batch Pearson correlation coefficients. By automatically processing 87 samples, we identified both known targets and potential off-targets of 20 kinase inhibitors, affording over a 10-fold improvement in throughput compared to classical TPP. This fully automated workflow offers a high-throughput solution for proteomics sample preparation and drug target/off-target identification.
    DOI:  https://doi.org/10.1039/d3sc05937e
  4. bioRxiv. 2024 Feb 16. pii: 2024.02.15.580399. [Epub ahead of print]
      Single-cell mass spectrometry (MS) opens a proteomic window onto the inner workings of cells. Here, we report the discovery characterization of the subcellular proteome of single, identified embryonic cells in record speed and molecular coverage. We integrated subcellular capillary microsampling, fast capillary electrophoresis (CE), high-efficiency nano-flow electrospray ionization, and orbitrap tandem MS. In proof-of-principle tests, we found shorter separation times to hinder proteome detection using DDA, but not DIA. Within a 15-min effective separation window, CE data-independent acquisition (DIA) was able to identify 1,161 proteins from single HeLa-cell-equivalent (∼200 pg) proteome digests vs. 401 proteins by the reference data-dependent acquisition (DDA) on the same platform. The approach measured 1,242 proteins from subcellular niches in an identified cell in the live Xenopus laevis (frog) embryo, including many canonical components of organelles. CE-MS with DIA enables fast, sensitive, and deep profiling of the (sub)cellular proteome, expanding the bioanalytical toolbox of cell biology.Authorship Contributions: P.N. and B.S. designed the study. L.R.P. collected the X. laevis cell aspirates. B.S. prepared and measured the samples. B.S. and P.N. analyzed the data and interpreted the results. P.N. and B.S. wrote the manuscript. All the authors commented on the manuscript.
    DOI:  https://doi.org/10.1101/2024.02.15.580399
  5. Brief Bioinform. 2024 Jan 22. pii: bbae056. [Epub ahead of print]25(2):
      Accurate metabolite annotation and false discovery rate (FDR) control remain challenging in large-scale metabolomics. Recent progress leveraging proteomics experiences and interdisciplinary inspirations has provided valuable insights. While target-decoy strategies have been introduced, generating reliable decoy libraries is difficult due to metabolite complexity. Moreover, continuous bioinformatics innovation is imperative to improve the utilization of expanding spectral resources while reducing false annotations. Here, we introduce the concept of ion entropy for metabolomics and propose two entropy-based decoy generation approaches. Assessment of public databases validates ion entropy as an effective metric to quantify ion information in massive metabolomics datasets. Our entropy-based decoy strategies outperform current representative methods in metabolomics and achieve superior FDR estimation accuracy. Analysis of 46 public datasets provides instructive recommendations for practical application.
    Keywords:  FDR estimation; entropy; mass spectrometry; metabolite annotation; metabolomics
    DOI:  https://doi.org/10.1093/bib/bbae056
  6. J Proteome Res. 2024 Feb 28.
      Mass spectrometry (MS)-based top-down proteomics (TDP) has revolutionized biological research by measuring intact proteoforms in cells, tissues, and biofluids. Capillary zone electrophoresis-tandem MS (CZE-MS/MS) is a valuable technique for TDP, offering a high peak capacity and sensitivity for proteoform separation and detection. However, the long-term reproducibility of CZE-MS/MS in TDP remains unstudied, which is a crucial aspect for large-scale studies. This work investigated the long-term qualitative and quantitative reproducibility of CZE-MS/MS for TDP for the first time, focusing on a yeast cell lysate. Over 1000 proteoforms were identified per run across 62 runs using one linear polyacrylamide (LPA)-coated separation capillary, highlighting the robustness of the CZE-MS/MS technique. However, substantial decreases in proteoform intensity and identification were observed after some initial runs due to proteoform adsorption onto the capillary inner wall. To address this issue, we developed an efficient capillary cleanup procedure using diluted ammonium hydroxide, achieving high qualitative and quantitative reproducibility for the yeast sample across at least 23 runs. The data underscore the capability of CZE-MS/MS for large-scale quantitative TDP of complex samples, signaling its readiness for deployment in broad biological applications. The MS RAW files were deposited in ProteomeXchange Consortium with the data set identifier of PXD046651.
    Keywords:  capillary zone electrophoresis; label-free quantification; mass spectrometry; proteoform; reproducibility; top-down proteomics; yeast cell lysate
    DOI:  https://doi.org/10.1021/acs.jproteome.3c00872
  7. bioRxiv. 2024 Feb 14. pii: 2024.02.14.580329. [Epub ahead of print]
      Progesterone production by the corpus luteum is fundamental for establishing and maintaining pregnancy. The pituitary gonadotropin luteinizing hormone (LH) is recognized as the primary stimulus for luteal formation and progesterone synthesis, regardless of species. Previous studies demonstrated an elevation in abundance of genes related to glucose and lipid metabolism during the follicular to luteal transition. However, the metabolic phenotype of these highly steroidogenic cells has not been studied. Herein, we determined acute metabolic changes induced by LH in primary luteal cells and defined pathways required for progesterone synthesis. Untargeted metabolomics analysis revealed that LH induces rapid changes in vital metabolic pathways, including glycolysis, tricarboxylic acid (TCA) cycle, pentose phosphate pathway, de novo lipogenesis, and hydrolysis of phospholipids. LH stimulated glucose uptake, enhanced glycolysis, and flux of [U- 13 C 6 ]-labeled glucose-derived carbons into metabolic branches associated with adenosine 5'-triphosphate (ATP) and NADH/NADPH production, synthesis of nucleotides, proteins, and lipids, glycosylation of proteins or lipids, and redox homeostasis. Selective use of small molecule inhibitors targeting the most significantly changed pathways, such as glycolysis, TCA cycle, and lipogenesis, uncovered cellular metabolic routes required for LH-stimulated steroidogenesis. Furthermore, LH via the protein kinase A (PKA) pathway triggered post- translational modification of acetyl-CoA carboxylase alpha (ACACA) and ATP citrate lyase (ACLY), enzymes involved in de novo synthesis of fatty acids. Inhibition of ACLY and fatty acid transport into mitochondria reduced LH-stimulated ATP, cAMP production, PKA activation, and progesterone synthesis. Taken together, these findings reveal novel hormone-sensitive metabolic pathways essential for maintaining LHCGR/PKA signaling and steroidogenesis in ovarian luteal cells.Significance: The establishment and maintenance of pregnancy require a well-developed corpus luteum, an endocrine gland within the ovary that produces progesterone. Although there is increased awareness of intracellular signaling events initiating the massive production of progesterone during the reproductive cycle and pregnancy, there are critical gaps in our knowledge of the metabolic and lipidomic pathways required for initiating and maintaining luteal progesterone synthesis. Here, we describe rapid, hormonally triggered metabolic pathways, and define metabolic targets crucial for progesterone synthesis by ovarian steroidogenic cells. Understanding hormonal control of metabolic pathways may help elucidate approaches for improving ovarian function and successful reproduction or identifying metabolic targets for developing nonhormonal contraceptives.
    DOI:  https://doi.org/10.1101/2024.02.14.580329
  8. Prostate. 2024 Feb 26.
      BACKGROUND: Lipid reprogramming is a known mechanism to increase the energetic demands of proliferating cancer cells to drive and support tumorigenesis and progression. Elevated lipid droplets (LDs) are a well-known alteration of lipid reprogramming in many cancers, including prostate cancer (PCa), and are associated with high tumor aggressiveness as well as therapy resistance. The mechanism of LD accumulation and specific LD functions are still not well understood; however, it has been shown that LDs can form as a protective mechanism against lipotoxicity and lipid peroxidation in the cell.METHODS: This study investigated the significance of LDs in PCa. This was done by staining, imaging, image quantification, and flow cytometry analysis of LDs in PCa cells. Additionally, lipidomics and metabolomics experiments were performed to assess the difference of metabolites and lipids in control and treatment surviving cancer cells. Lastly, to assess clinical significance, multiple publicly available datasets were mined for LD-related data.
    RESULTS: Our study demonstrated that prostate and breast cancer cells that survive 72 h of chemotherapy treatment have elevated LDs. These LDs formed in tandem with elevated reactive oxygen species levels to sequester damaged and excess lipids created by oxidative stress, which promoted cell survival. Additionally, by inhibiting diacylglycerol O-acyltransferase 1 (DGAT1) (which catalyzes triglyceride synthesis into LDs) and treating with chemotherapy simultaneously, we were able to decrease the overall amount of LDs and increase cancer cell death compared to treating with chemotherapy alone.
    CONCLUSIONS: Overall, our study proposes a potential combination therapy of DGAT1 inhibitors and chemotherapy to increase cancer cell death.
    Keywords:  ROS; lipid droplets; lipid metabolism; lipid peroxidation; lipidomics; metabolism; prostate cancer
    DOI:  https://doi.org/10.1002/pros.24680
  9. Methods Mol Biol. 2024 ;2785 75-86
      The integration of complementary analytical platforms is nowadays the most common strategy for comprehensive metabolomics analysis of complex biological systems. In this chapter, we describe methods and tips for the application of a mass spectrometry multi-platform in Alzheimer's disease research, based on the combination of direct mass spectrometry and orthogonal hyphenated approaches, namely, reversed-phase ultrahigh-performance liquid chromatography and gas chromatography. These procedures have been optimized for the analysis of multiple biological samples from human patients and transgenic animal models, including blood serum, various brain regions (e.g., hippocampus, cortex, cerebellum, striatum, olfactory bulbs), and other peripheral organs (e.g., liver, kidney, spleen, thymus).
    Keywords:  Alzheimer’s disease; Direct MS analysis; Gas chromatography; Mass spectrometry; Metabolomics; Multi-platform; Ultrahigh-performance liquid chromatography
    DOI:  https://doi.org/10.1007/978-1-0716-3774-6_6
  10. ACS Meas Sci Au. 2024 Feb 21. 4(1): 104-116
      Although MALDI-ToF platforms for microbial identifications have found great success in clinical microbiology, the sole use of protein fingerprints for the discrimination of closely related species, strain-level identifications, and detection of antimicrobial resistance remains a challenge for the technology. Several alternative mass spectrometry-based methods have been proposed to address the shortcomings of the protein-centric approach, including MALDI-ToF methods for fatty acid/lipid profiling and LC-MS profiling of metabolites. However, the molecular diversity of microbial pathogens suggests that no single "ome" will be sufficient for the accurate and sensitive identification of strain- and susceptibility-level profiling of bacteria. Here, we describe the development of an alternative approach to microorganism profiling that relies upon both metabolites and lipids rather than a single class of biomolecule. Single-phase extractions based on butanol, acetonitrile, and water (the BAW method) were evaluated for the recovery of lipids and metabolites from Gram-positive and -negative microorganisms. We found that BAW extraction solutions containing 45% butanol provided optimal recovery of both molecular classes in a single extraction. The single-phase extraction method was coupled to hydrophilic interaction liquid chromatography (HILIC) and ion mobility-mass spectrometry (IM-MS) to resolve similar-mass metabolites and lipids in three dimensions and provide multiple points of evidence for feature annotation in the absence of tandem mass spectrometry. We demonstrate that the combined use of metabolites and lipids can be used to differentiate microorganisms to the species- and strain-level for four of the ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Acinetobacter baumannii, and Pseudomonas aeruginosa) using data from a single ionization mode. These results present promising, early stage evidence for the use of multiomic signatures for the identification of microorganisms by liquid chromatography, ion mobility, and mass spectrometry that, upon further development, may improve upon the level of identification provided by current methods.
    DOI:  https://doi.org/10.1021/acsmeasuresciau.3c00051
  11. Anal Chim Acta. 2024 Apr 01. pii: S0003-2670(24)00148-X. [Epub ahead of print]1296 342347
      Correct identification and quantification of different sterol biomarkers can be used as a first-line diagnostic approach for inherited metabolic disorders (IMD). The main drawbacks of current methodologies are related to lack of selectivity and sensitivity for some of these compounds. To address this, we developed and validated two sensitive and selective assays for quantification of six cholesterol biosynthesis pathway intermediates (total amount (free and esterified form) of 7-dehydrocholesterol (7-DHC), 8-dehydrocholesterol (8-DHC), desmosterol, lathosterol, lanosterol and cholestanol), two phytosterols (total amount (free and esterified form) of campesterol and sitosterol) and free form of two oxysterols (7-ketocholesterol (7-KC) and 3β,5α,6β-cholestane-triol (C-triol). For quantification of four cholesterol intermediates we based our analytical approach on sterol derivatization with 4-phenyl-1,2,4-triazoline-3,5-dione (PTAD). Quantification of all analytes is performed using UPLC coupled to an Orbitrap high resolution mass spectrometry (HRMS) system, with detection of target ions through full scan acquisition using positive atmospheric pressure chemical ionization (APCI) mode. UPLC and MS parameters were optimized to achieve high sensitivity and selectivity. Analog stable isotope labeled for each compound was used for proper quantification and correction for recovery, matrix effects and process efficiency. Precision (2.4%-12.3% inter-assay variation), lower limit of quantification (0.027 nM-50.5 nM) and linearity (5.5 μM (R2 0.999) - 72.3 μM (R2 0.997)) for phyto- and oxysterols were determined. The diagnostic potential of these two assays in a cohort of patients (n = 31, 50 samples) diagnosed with IMD affecting cholesterol and lysosomal/peroxisomal homeostasis is demonstrated.
    Keywords:  Cholesterol intermediates; Inherited metabolic diseases; Oxysterols; PTAD derivatization; Phytosterols; UPLC-Orbitrap-HRMS
    DOI:  https://doi.org/10.1016/j.aca.2024.342347
  12. Methods Mol Biol. 2024 ;2772 137-148
      Plant ER membranes are the major site of biosynthesis of several lipid families (phospholipids, sphingolipids, neutral lipids such as sterols and triacylglycerols). The structural diversity of lipids presents considerable challenges to comprehensive lipid analysis. This chapter will briefly review the various biosynthetic pathways and will detail several aspects of the lipid analysis: lipid extraction, handling, separation, detection, identification, and data presentation. The different tools/approaches used for lipid analysis will also be discussed in relation to the studies to be carried out on lipid metabolism and function.
    Keywords:  Ceramides; GC-FID; GC-MS; Glucosylceramides; HPTLC; LC-MS; Long-chain bases (LCB); Phospholipids; Phytosterols; Triacylglycerols
    DOI:  https://doi.org/10.1007/978-1-0716-3710-4_10
  13. bioRxiv. 2024 Feb 12. pii: 2024.02.11.579776. [Epub ahead of print]
      Mammalian tissues feed on nutrients in the blood circulation. At the organism-level, mammalian energy metabolism comprises of oxidation, interconverting, storing and releasing of circulating nutrients. Though much is known about the individual processes and nutrients, a holistic and quantitative model describing these processes for all major circulating nutrients is lacking. Here, by integrating isotope tracer infusion, mass spectrometry, and isotope gas analyzer measurement, we developed a framework to systematically quantify fluxes through these processes for 10 major circulating energy nutrients in mice, resulting in an organism-level quantitative flux model of energy metabolism. This model revealed in wildtype mice that circulating nutrients' metabolic cycling fluxes are more dominant than their oxidation fluxes, with distinct partition between cycling and oxidation flux for individual circulating nutrients. Applications of this framework in obese mouse models showed on a per animal basis extensive elevation of metabolic cycling fluxes in ob/ob mice, but not in diet-induced obese mice. Thus, our framework describes quantitatively the functioning of energy metabolism at the organism-level, valuable for revealing new features of energy metabolism in physiological and disease conditions.
    Keywords:  circulating nutrients; energy metabolism; in vivo flux quantification; isotope tracing; obesity
    DOI:  https://doi.org/10.1101/2024.02.11.579776
  14. Adv Exp Med Biol. 2024 ;1443 23-32
      Protein glycosylation is a post-translational modification involving the addition of carbohydrates to proteins and plays a crucial role in protein folding and various biological processes such as cell recognition, differentiation, and immune response. The vast array of natural sugars available allows the generation of plenty of unique glycan structures in proteins, adding complexity to the regulation and biological functions of glycans. The diversity is further increased by enzymatic site preferences and stereochemical conjugation, leading to an immense amount of different glycan structures. Understanding glycosylation heterogeneity is vital for unraveling the impact of glycans on different biological functions. Evaluating site occupancies and structural heterogeneity aids in comprehending glycan-related alterations in biological processes. Several software tools are available for large-scale glycoproteomics studies; however, integrating identification and quantitative data to assess heterogeneity complexity often requires extensive manual data processing. To address this challenge, we present a python script that automates the integration of Byonic and MaxQuant outputs for glycoproteomic data analysis. The script enables the calculation of site occupancy percentages by glycans and facilitates the comparison of glycan structures and site occupancies between two groups. This automated tool offers researchers a means to organize and interpret their high-throughput quantitative glycoproteomic data effectively.
    Keywords:  Computational platform; Glycans; Glycoproteomics; Mass spectrometry; Quantitative analysis
    DOI:  https://doi.org/10.1007/978-3-031-50624-6_2
  15. Annu Rev Anal Chem (Palo Alto Calif). 2024 Feb 29.
      In this review, we discuss the cutting-edge developments in mass spectrometry proteomics and metabolomics that have brought improvements for the identification of new disease-based biomarkers. A special focus is placed on psychiatric disorders, for example, schizophrenia, because they are considered to be not a single disease entity but rather a spectrum of disorders with many overlapping symptoms. This review includes descriptions of various types of commonly used mass spectrometry platforms for biomarker research, as well as complementary techniques to maximize data coverage, reduce sample heterogeneity, and work around potentially confounding factors. Finally, we summarize the different statistical methods that can be used for improving data quality to aid in reliability and interpretation of proteomics findings, as well as to enhance their translatability into clinical use and generalizability to new data sets. Expected final online publication date for the Annual Review of Analytical Chemistry, Volume 17 is May 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
    DOI:  https://doi.org/10.1146/annurev-anchem-061522-041154
  16. Nat Commun. 2024 Feb 29. 15(1): 1879
      Cancer cells integrate multiple biosynthetic demands to drive unrestricted proliferation. How these cellular processes crosstalk to fuel cancer cell growth is still not fully understood. Here, we uncover the mechanisms by which the transcription factor Carbohydrate responsive element binding protein (ChREBP) functions as an oncogene during hepatocellular carcinoma (HCC) development. Mechanistically, ChREBP triggers the expression of the PI3K regulatory subunit p85α, to sustain the activity of the pro-oncogenic PI3K/AKT signaling pathway in HCC. In parallel, increased ChREBP activity reroutes glucose and glutamine metabolic fluxes into fatty acid and nucleic acid synthesis to support PI3K/AKT-mediated HCC growth. Thus, HCC cells have a ChREBP-driven circuitry that ensures balanced coordination between PI3K/AKT signaling and appropriate cell anabolism to support HCC development. Finally, pharmacological inhibition of ChREBP by SBI-993 significantly suppresses in vivo HCC tumor growth. Overall, we show that targeting ChREBP with specific inhibitors provides an attractive therapeutic window for HCC treatment.
    DOI:  https://doi.org/10.1038/s41467-024-45548-w
  17. J Proteome Res. 2024 Feb 26.
      The co-occurrence of multiple chronic metabolic diseases is highly prevalent, posing a huge health threat. Clarifying the metabolic associations between them, as well as identifying metabolites which allow discrimination between diseases, will provide new biological insights into their co-occurrence. Herein, we utilized targeted serum metabolomics and lipidomics covering over 700 metabolites to characterize metabolic alterations and associations related to seven chronic metabolic diseases (obesity, hypertension, hyperuricemia, hyperglycemia, hypercholesterolemia, hypertriglyceridemia, fatty liver) from 1626 participants. We identified 454 metabolites were shared among at least two chronic metabolic diseases, accounting for 73.3% of all 619 significant metabolite-disease associations. We found amino acids, lactic acid, 2-hydroxybutyric acid, triacylglycerols (TGs), and diacylglycerols (DGs) showed connectivity across multiple chronic metabolic diseases. Many carnitines were specifically associated with hyperuricemia. The hypercholesterolemia group showed obvious lipid metabolism disorder. Using logistic regression models, we further identified distinguished metabolites of seven chronic metabolic diseases, which exhibited satisfactory area under curve (AUC) values ranging from 0.848 to 1 in discovery and validation sets. Overall, quantitative metabolome and lipidome data sets revealed widespread and interconnected metabolic disorders among seven chronic metabolic diseases. The distinguished metabolites are useful for diagnosing chronic metabolic diseases and provide a reference value for further clinical intervention and management based on metabolomics strategy.
    Keywords:  chronic metabolic disease; distinguished metabolites; lipidomics; liquid chromatography−mass spectrometry; metabolomics; quantitative analysis
    DOI:  https://doi.org/10.1021/acs.jproteome.3c00760
  18. Biosci Rep. 2024 Feb 28. pii: BSR20232035. [Epub ahead of print]
      Endometrial carcinoma (EC) is a common malignancy which originates from the endometrium and grows in female reproductive system. Surgeries, as current treatment for the cancer, however, cannot meet the fertility needs of young women patients. Thus, progesterone (P4) therapy is indispensable due to its effective temporary preservation of female fertility. Many cancer cells are often accompanied by changes in metabolic phenotypes, and abnormally dependent on the amino acid glutamine. However, whether P4 exerts effect on EC via glutamine metabolism is unknown. In this study, we found that P4 could inhibit glutamine metabolism in EC cells and downregulate the expression of the glutamine transporter ASCT2. This regulation of ASCT2 affects the uptake of glutamine. Furthermore, the in vivo xenograft studies showed that P4 inhibited tumor growth and the expression of key enzymes involved in glutamine metabolism. Our study demonstrated that the direct regulation of glutamine metabolism by P4 and its anticancer effect was mediated through the inhibition of ASCT2. These results provide a mechanism underlying the effects of P4 therapy on EC from the perspective of glutamine metabolism.
    Keywords:  ASCT2; Endometrial carcinoma; Glutamine metabolism; Progesterone
    DOI:  https://doi.org/10.1042/BSR20232035
  19. Sci Rep. 2024 02 28. 14(1): 4841
      We used the Exploris 240 mass spectrometer for non-targeted metabolomics on Saccharomyces cerevisiae strain BY4741 and tested AcquireX software for increasing the number of detectable compounds and Compound Discoverer 3.3 software for identifying compounds by MS2 spectral library matching. AcquireX increased the number of potentially identifiable compounds by 50% through six iterations of MS2 acquisition. On the basis of high-scoring MS2 matches made by Compound Discoverer, there were 483 compounds putatively identified from nearly 8000 candidate spectra. Comparisons to 20 amino acid standards, however, revealed instances whereby compound matches could be incorrect despite strong scores. Situations included the candidate with the top score not being the correct compound, matching the same compound at two different chromatographic peaks, assigning the highest score to a library compound much heavier than the mass for the parent ion, and grouping MS2 isomers to a single parent ion. Because the software does not calculate false positive and false discovery rates at these multiple levels where such errors can propagate, we conclude that manual examination of findings will be required post software analysis. These results will interest scientists who may use this platform for metabolomics research in diverse disciplines including medical science, environmental science, and agriculture.
    DOI:  https://doi.org/10.1038/s41598-024-55356-3
  20. Cell Rep. 2024 Feb 28. pii: S2211-1247(24)00196-7. [Epub ahead of print]43(3): 113868
      Modeling tumor metabolism in vitro remains challenging. Here, we used galactose as an in vitro tool compound to mimic glycolytic limitation. In contrast to the established idea that high glycolytic flux reduces pyruvate kinase isozyme M2 (PKM2) activity to support anabolic processes, we have discovered that glycolytic limitation also affects PKM2 activity. Surprisingly, despite limited carbon availability and energetic stress, cells induce a near-complete block of PKM2 to divert carbons toward serine metabolism. Simultaneously, TCA cycle flux is sustained, and oxygen consumption is increased, supported by glutamine. Glutamine not only supports TCA cycle flux but also serine synthesis via distinct mechanisms that are directed through PKM2 inhibition. Finally, deleting mitochondrial one-carbon (1C) cycle reversed the PKM2 block, suggesting a potential formate-dependent crosstalk that coordinates mitochondrial 1C flux and cytosolic glycolysis to support cell survival and proliferation during nutrient-scarce conditions.
    Keywords:  CP: Cancer; CP: Metabolism
    DOI:  https://doi.org/10.1016/j.celrep.2024.113868
  21. J Proteome Res. 2024 Feb 27.
      Apolipoprotein E (apoE), a polymorphic plasma protein, plays a pivotal role in lipid transportation. The human apoE gene possesses three major alleles (ε2, ε3, and ε4), which differ by single amino acid (cysteine to arginine) substitutions. The ε4 allele represents the primary genetic risk factor for Alzheimer's disease (AD), whereas the ε2 allele protects against the disease. Knowledge of a patient's apoE genotype has high diagnostic value. A recent study has introduced an LC-MRM-MS-based proteomic approach for apoE isoform genotyping using stable isotope-labeled peptide internal standards (SIS). Here, our goal was to develop a simplified LC-MRM-MS assay for identifying apoE genotypes in plasma samples, eliminating the need for the use of SIS peptides. To determine the apoE genotypes, we monitored the chromatographic peak area ratios of isoform-specific peptides relative to a peptide that is common to all apoE isoforms. The assay results correlated well with the standard TaqMan allelic discrimination assay, and we observed a concordance between the two methods for all but three out of 172 samples. DNA sequencing of these three samples has confirmed that the results of the LC-MRM-MS method were correct. Thus, our simplified UPLC-MRM-MS assay is a feasible and reliable method for identifying apoE genotypes without using SIS internal standard peptides. The approach can be seamlessly incorporated into existing quantitative proteomics assays and kits, providing additional valuable apoE genotype information. The principle of using signal ratios of the protein isoform-specific peptides to the peptide common for all of the protein isoforms has the potential to be used for protein isoform determination in general.
    Keywords:  Alzheimer’s disease; LC-MRM-MS; apoE genotype; apolipoprotein E
    DOI:  https://doi.org/10.1021/acs.jproteome.3c00557
  22. Anal Chem. 2024 Mar 01.
      Julia combines the virtues of high-level and low-level programming languages: The code is human-readable, and the performance of the created binaries competes with machine-orientated compilers. Thus, Julia is popular in "Big Data" sciences. Reading mass spectrometry (MS) data with Julia was impossible until now due to missing libraries. Here, we present a Julia library for importing mass spectrometry (MS) data in HUPO standard mzML and imzML formats and demonstrate its function with direct and ambient ionization MS, liquid chromatography-MS, and MS imaging data on standard platforms (Windows, Linux, and Mac OS). The processing speed of Julia for reading imzML MS imaging files was up to 214 times faster than the comparable code in R. Julia can remove bottlenecks for computationally demanding tasks in large-scale MS-Omics and MS imaging data processing workflows and supports their agile development. In addition, time-critical and complex data evaluation tasks become possible, such as following the real-time monitoring of biological processes and pattern recognition in large MS imaging projects. Our mzML/imzML libraries and code examples are available under the terms of the MIT license from https://github.com/CINVESTAV-LABI/julia_mzML_imzML.
    DOI:  https://doi.org/10.1021/acs.analchem.3c05853
  23. Anal Chem. 2024 Mar 01.
      Proteomic analysis by mass spectrometry of small (≤2 mg) solid tissue samples from diverse formats requires high throughput and comprehensive proteome coverage. We developed a nearly universal, rapid, and robust protocol for sample preparation, suitable for high-throughput projects that encompass most cell or tissue types. This end-to-end workflow extends from original sample to loading the mass spectrometer and is centered on a one-tube homogenization and digestion method called Heat 'n Beat (HnB). It is applicable to most tissues, regardless of how they were fixed or embedded. Sample preparation was divided into separate challenges. The initial sample washing and final peptide cleanup steps were adapted to three tissue sources: fresh frozen (FF), optimal cutting temperature (OCT) compound embedded (FF-OCT), and formalin-fixed paraffin embedded (FFPE). Third, for core processing, tissue disruption and lysis were decreased to a 7 min heat and homogenization treatment, and reduction, alkylation, and proteolysis were optimized into a single step. The refinements produced near doubled peptide yield when compared to our earlier method ABLE delivered a consistently high digestion efficiency of 85-90%, reported by ProteinPilot, and required only 38 min for core processing in a single tube, with the total processing time being 53-63 min. The robustness of HnB was demonstrated on six organ types, a cell line, and a cancer biopsy. Its suitability for high-throughput applications was demonstrated on a set of 1171 FF-OCT human cancer biopsies, which were processed for end-to-end completion in 92 h, producing highly consistent peptide yield and quality for over 3513 MS runs.
    DOI:  https://doi.org/10.1021/acs.analchem.3c04708
  24. Adv Exp Med Biol. 2024 ;1443 87-101
      Microbiotas are an adaptable component of ecosystems, including human ecology. Microorganisms influence the chemistry of their specialized niche, such as the human gut, as well as the chemistry of distant surroundings, such as other areas of the body. Metabolomics based on mass spectrometry (MS) is one of the primary methods for detecting and identifying small compounds generated by the human microbiota, as well as understanding the functional significance of these microbial metabolites. This book chapter gives basic knowledge on the kinds of untargeted mass spectrometry as well as the data types that may be generated in the context of microbiome study. While data analysis remains a barrier, the emphasis is on data analysis methodologies and integrative analysis, particularly the integration of microbiome sequencing data. Mass spectrometry (MS)-based techniques have resurrected culture methods for studying the human gut microbiota, filling in the gaps left by high-throughput sequencing methods in terms of culturing minor populations.
    Keywords:  Gut microbiota; MALDI-TOF MS; Mass spectrometry; Metabolomics; Microbiome
    DOI:  https://doi.org/10.1007/978-3-031-50624-6_5
  25. Cancer Res. 2024 Feb 28.
      Clear cell renal cell carcinoma (ccRCC) incidence has risen steadily over the last decade. Elevated lipid uptake and storage is required for ccRCC cell viability. As stored cholesterol is the most abundant component in ccRCC intracellular lipid droplets, it may also play an important role in ccRCC cellular homeostasis. In support of this hypothesis, ccRCC cells acquire exogenous cholesterol through the HDL receptor SCARB1, inhibition or suppression of which induces apoptosis. Here, we showed that elevated expression of 3 beta-hydroxy steroid dehydrogenase type 7 (HSD3B7), which metabolizes cholesterol-derived oxysterols in the bile acid biosynthetic pathway, is also essential for ccRCC cell survival. Development of an HSD3B7 enzymatic assay and screening for small molecule inhibitors uncovered the compound celastrol as a potent HSD3B7 inhibitor with low micromolar activity. Repressing HSD3B7 expression genetically or treating ccRCC cells with celastrol resulted in toxic oxysterol accumulation, impaired proliferation, and increased apoptosis in vitro and in vivo. These data demonstrate that bile acid synthesis regulates cholesterol homeostasis in ccRCC and identifies HSD3B7 as a plausible therapeutic target.
    DOI:  https://doi.org/10.1158/0008-5472.CAN-23-0821