Cathepsin S in tumours, regional lymph nodes and sera of patients with lung cancer: relation to prognosis

Cysteine proteinase cathepsin S (Cat S) is expressed mainly in lymphatic tissues and has been characterised as a key enzyme in major histocompatibility complex class II (MHC-II) mediated antigen presentation. Cat S has been measured in tissue cytosols of lung parenchyma, lung tumours and lymph nodes and in sera of patients with lung tumours and of healthy controls, by specific enzyme-linked immunosorbent assay (ELISA). A difference in Cat S level was found between tumour and adjacent control tissue cytosols of 60 lung cancer patients (median 4.3 vs. 2.8 ng mg−1protein). In lymph nodes obtained from 24 patients of the same group, the level of Cat S was significantly higher than in tumours or lung parenchyma (P< 0.001). Additionally, significantly higher levels were found in non-infiltrated than in infiltrated lymph nodes (median 16.6 vs 7.5 ng mg−1protein). Patients with low levels of Cat S in tumours and lung parenchyma exhibited a significantly higher risk of death than those with high levels of Cat S (P= 0.025 – tumours;P= 0.02 – parenchyma). Immunohistochemical analysis (IHA) of lung parenchyma revealed a staining reaction in alveolar type II cells, macrophages and bronchial epithelial cells. In regional lymph node tissue, strong staining of Cat S was found in lymphocytes and histiocytes. Nevertheless, Cat S was detected also in tumour cells, independently of their origin. Our results provide evidence that Cat S may be involved in malignant progression. Its role, however, differs from that of the related Cats B and L and could be associated with the immune response rather than with remodelling of extracellular matrix. © 2001 Cancer Research Campaign  http://www.bjcancer.co

Tissue-specific transcriptome analysis reveals candidate genes for terpenoid and phenylpropanoid metabolism in the medicinal plant ferula assafoetida

© 2019 ABRF. Methionine oxidation plays a critical role in many processes of biologic and biomedical importance, including cellular redox responses and stability of protein pharmaceuticals. Bottom-up methods for analysis of methionine oxidation can suffer from incomplete sequence coverage, as well as an inability to readily detect correlated oxidation between 2 or more methionines. However, the methodology for quantifying protein oxidation in top-down analyses is lacking. Previous work has shown that electron transfer dissociation (ETD)–based tandem mass spectrometry (MS/MS) fragmentation offers accurate and precise quantification of amino acid oxidation in peptides, even in complex samples. However, the ability of ETD-based MS/MS fragmentation to accurately quantify amino acid oxidation of proteins in a top-down manner has not been reported. Using apomyoglobin and calmodulin as model proteins, we partially converted methionines into methionine sulfoxide by incubation in H2O2. Using top-down ETD-based fragmentation, we quantified the amount of oxidation of various ETD product ions and compared the quantified values with those from traditional bottom-up analysis. We find that overall quantification of methionine oxidation by top-down MS/MS ranges from good agreement with traditional bottom-up methods to vast differences between the 2 techniques, including missing oxidized product ions and large differences in measured oxidation quantities. Care must be taken in transitioning ETD-based quantitation of oxidation from the peptide level to the intact protein level

MetaboAnalyst: a web server for metabolomic data analysis and interpretation

Metabolomics is a newly emerging field of ‘omics’ research that is concerned with characterizing large numbers of metabolites using NMR, chromatography and mass spectrometry. It is frequently used in biomarker identification and the metabolic profiling of cells, tissues or organisms. The data processing challenges in metabolomics are quite unique and often require specialized (or expensive) data analysis software and a detailed knowledge of cheminformatics, bioinformatics and statistics. In an effort to simplify metabolomic data analysis while at the same time improving user accessibility, we have developed a freely accessible, easy-to-use web server for metabolomic data analysis called MetaboAnalyst. Fundamentally, MetaboAnalyst is a web-based metabolomic data processing tool not unlike many of today's web-based microarray analysis packages. It accepts a variety of input data (NMR peak lists, binned spectra, MS peak lists, compound/concentration data) in a wide variety of formats. It also offers a number of options for metabolomic data processing, data normalization, multivariate statistical analysis, graphing, metabolite identification and pathway mapping. In particular, MetaboAnalyst supports such techniques as: fold change analysis, t-tests, PCA, PLS-DA, hierarchical clustering and a number of more sophisticated statistical or machine learning methods. It also employs a large library of reference spectra to facilitate compound identification from most kinds of input spectra. MetaboAnalyst guides users through a step-by-step analysis pipeline using a variety of menus, information hyperlinks and check boxes. Upon completion, the server generates a detailed report describing each method used, embedded with graphical and tabular outputs. MetaboAnalyst is capable of handling most kinds of metabolomic data and was designed to perform most of the common kinds of metabolomic data analyses. MetaboAnalyst is accessible at http://www.metaboanalyst.c

RORγ is a targetable master regulator of cholesterol biosynthesis in a cancer subtype.

Tumor subtype-specific metabolic reprogrammers could serve as targets of therapeutic intervention. Here we show that triple-negative breast cancer (TNBC) exhibits a hyper-activated cholesterol-biosynthesis program that is strongly linked to nuclear receptor RORγ, compared to estrogen receptor-positive breast cancer. Genetic and pharmacological inhibition of RORγ reduces tumor cholesterol content and synthesis rate while preserving host cholesterol homeostasis. We demonstrate that RORγ functions as an essential activator of the entire cholesterol-biosynthesis program, dominating SREBP2 via its binding to cholesterol-biosynthesis genes and its facilitation of the recruitment of SREBP2. RORγ inhibition disrupts its association with SREBP2 and reduces chromatin acetylation at cholesterol-biosynthesis gene loci. RORγ antagonists cause tumor regression in patient-derived xenografts and immune-intact models. Their combination with cholesterol-lowering statins elicits superior anti-tumor synergy selectively in TNBC. Together, our study uncovers a master regulator of the&nbsp;cholesterol-biosynthesis program and an attractive target for TNBC

Mesothelial Cell HIF1 alpha Expression Is Metabolically Downregulated by Metformin to Prevent Oncogenic Tumor-Stromal Crosstalk

The tumor microenvironment (TME) plays a pivotal role in cancer progression, and, in ovarian cancer (OvCa), the primary TME is the omentum. Here, we show that the diabetes drug metformin alters mesothelial cells in the omental microenvironment. Metformin interrupts bidirectional signaling between tumor and mesothelial cells by blocking OvCa cell TGF-beta signaling and mesothelial cell production of CCL2 and IL-8. Inhibition of tumor-stromal crosstalk by metformin is caused by the reduced expression of the tricarboxylic acid (TCA) enzyme succinyl CoA ligase (SUCLG2). Through repressing this TCA enzyme and its metabolite, succinate, metformin activated prolyl hydroxylases (PHDs), resulting in the degradation of hypoxia-inducible factor 1 alpha (HIF1 alpha) in mesothelial cells. Disruption of HIF1 alpha-driven IL-8 signaling in mesothelial cells by metformin results in reduced OvCa invasion in an organotypic 3D model. These findings indicate that tumor-promoting signaling between mesothelial and OvCa cells in the TME can be targeted using metformin

An Introductory Guide to Aligning Networks Using SANA, the Simulated Annealing Network Aligner.

Sequence alignment has had an enormous impact on our understanding of biology, evolution, and disease. The alignment of biological networks holds similar promise. Biological networks generally model interactions between biomolecules such as proteins, genes, metabolites, or mRNAs. There is strong evidence that the network topology-the "structure" of the network-is correlated with the functions performed, so that network topology can be used to help predict or understand function. However, unlike sequence comparison and alignment-which is an essentially solved problem-network comparison and alignment is an NP-complete problem for which heuristic algorithms must be used.Here we introduce SANA, the Simulated Annealing Network Aligner. SANA is one of many algorithms proposed for the arena of biological network alignment. In the context of global network alignment, SANA stands out for its speed, memory efficiency, ease-of-use, and flexibility in the arena of producing alignments between two or more networks. SANA produces better alignments in minutes on a laptop than most other algorithms can produce in hours or days of CPU time on large server-class machines. We walk the user through how to use SANA for several types of biomolecular networks

Pharmacogenetics Meets Metabolomics: Discovery of Tryptophan as a New Endogenous OCT2 Substrate Related to Metformin Disposition

Genetic polymorphisms of the organic cation transporter 2 (OCT2), encoded by SLC22A2, have been investigated in association with metformin disposition. A functional decrease in transport function has been shown to be associated with the OCT2 variants. Using metabolomics, our study aims at a comprehensive monitoring of primary metabolite changes in order to understand biochemical alteration associated with OCT2 polymorphisms and discovery of potential endogenous metabolites related to the genetic variation of OCT2. Using GC-TOF MS based metabolite profiling, clear clustering of samples was observed in Partial Least Square Discriminant Analysis, showing that metabolic profiles were linked to the genetic variants of OCT2. Tryptophan and uridine presented the most significant alteration in SLC22A2-808TT homozygous and the SLC22A2-808G>T heterozygous variants relative to the reference. Particularly tryptophan showed gene-dose effects of transporter activity according to OCT2 genotypes and the greatest linear association with the pharmacokinetic parameters (Clrenal, Clsec, Cl/F/kg, and Vd/F/kg) of metformin. An inhibition assay demonstrated the inhibitory effect of tryptophan on the uptake of 1-methyl-4-phenyl pyrinidium in a concentration dependent manner and subsequent uptake experiment revealed differential tryptophan-uptake rate in the oocytes expressing OCT2 reference and variant (808G>T). Our results collectively indicate tryptophan can serve as one of the endogenous substrate for the OCT2 as well as a biomarker candidate indicating the variability of the transport activity of OCT2

Metabolomic and transcriptomic analysis of the rice response to the bacterial blight pathogen Xanthomonas oryzae pv. oryzae

Bacterial leaf blight (BLB), caused by Xanthomonas oryzae pv. oryzae (Xoo), gives rise to devastating crop losses in rice. Disease resistant rice cultivars are the most economical way to combat the disease. The TP309 cultivar is susceptible to infection by Xoo strain PXO99. A transgenic variety, TP309_Xa21, expresses the pattern recognition receptor Xa21, and is resistant. PXO99△raxST, a strain lacking the raxST gene, is able to overcome Xa21-mediated immunity. We used a single extraction solvent to demonstrate comprehensive metabolomics and transcriptomics profiling under sample limited conditions, and analyze the molecular responses of two rice lines challenged with either PXO99 or PXO99△raxST. LC–TOF raw data file filtering resulted in better within group reproducibility of replicate samples for statistical analyses. Accurate mass match compound identification with molecular formula generation (MFG) ranking of 355 masses was achieved with the METLIN database. GC–TOF analysis yielded an additional 441 compounds after BinBase database processing, of which 154 were structurally identified by retention index/MS library matching. Multivariate statistics revealed that the susceptible and resistant genotypes possess distinct profiles. Although few mRNA and metabolite differences were detected in PXO99 challenged TP309 compared to mock, many differential changes occurred in the Xa21-mediated response to PXO99 and PXO99△raxST. Acetophenone, xanthophylls, fatty acids, alkaloids, glutathione, carbohydrate and lipid biosynthetic pathways were affected. Significant transcriptional induction of several pathogenesis related genes in Xa21 challenged strains, as well as differential changes to GAD, PAL, ICL1 and Glutathione-S-transferase transcripts indicated limited correlation with metabolite changes under single time point global profiling conditions

Quantitative metabolomics based on gas chromatography mass spectrometry: status and perspectives

Metabolomics involves the unbiased quantitative and qualitative analysis of the complete set of metabolites present in cells, body fluids and tissues (the metabolome). By analyzing differences between metabolomes using biostatistics (multivariate data analysis; pattern recognition), metabolites relevant to a specific phenotypic characteristic can be identified. However, the reliability of the analytical data is a prerequisite for correct biological interpretation in metabolomics analysis. In this review the challenges in quantitative metabolomics analysis with regards to analytical as well as data preprocessing steps are discussed. Recommendations are given on how to optimize and validate comprehensive silylation-based methods from sample extraction and derivatization up to data preprocessing and how to perform quality control during metabolomics studies. The current state of method validation and data preprocessing methods used in published literature are discussed and a perspective on the future research necessary to obtain accurate quantitative data from comprehensive GC-MS data is provided