DIMet: an open-source tool for differential analysis of targeted isotope-labeled metabolomics data.

MOTIVATION: Many diseases, such as cancer, are characterized by an alteration of cellular metabolism allowing cells to adapt to changes in the microenvironment. Stable isotope-resolved metabolomics (SIRM) and downstream data analyses are widely used techniques for unraveling cells' metabolic activity to understand the altered functioning of metabolic pathways in the diseased state. While a number of bioinformatic solutions exist for the differential analysis of SIRM data, there is currently no available resource providing a comprehensive toolbox. RESULTS: In this work, we present DIMet, a one-stop comprehensive tool for differential analysis of targeted tracer data. DIMet accepts metabolite total abundances, isotopologue contributions, and isotopic mean enrichment, and supports differential comparison (pairwise and multi-group), time-series analyses, and labeling profile comparison. Moreover, it integrates transcriptomics and targeted metabolomics data through network-based metabolograms. We illustrate the use of DIMet in real SIRM datasets obtained from Glioblastoma P3 cell-line samples. DIMet is open-source, and is readily available for routine downstream analysis of isotope-labeled targeted metabolomics data, as it can be used both in the command line interface or as a complete toolkit in the public Galaxy Europe and Workfow4Metabolomics web platforms. AVAILABILITY AND IMPLEMENTATION: DIMet is freely available at https://github.com/cbib/DIMet, and through https://usegalaxy.eu and https://workflow4metabolomics.usegalaxy.fr. All the datasets are available at Zenodo https://zenodo.org/records/10925786.

Lactate dehydrogenases promote glioblastoma growth and invasion via a metabolic symbiosis.

Lactate is a central metabolite in brain physiology but also contributes to tumor development. Glioblastoma (GB) is the most common and malignant primary brain tumor in adults, recognized by angiogenic and invasive growth, in addition to its altered metabolism. We show herein that lactate fuels GB anaplerosis by replenishing the tricarboxylic acid (TCA) cycle in absence of glucose. Lactate dehydrogenases (LDHA and LDHB), which we found spatially expressed in GB tissues, catalyze the interconversion of pyruvate and lactate. However, ablation of both LDH isoforms, but not only one, led to a reduction in tumor growth and an increase in mouse survival. Comparative transcriptomics and metabolomics revealed metabolic rewiring involving high oxidative phosphorylation (OXPHOS) in the LDHA/B KO group which sensitized tumors to cranial irradiation, thus improving mouse survival. When mice were treated with the antiepileptic drug stiripentol, which targets LDH activity, tumor growth decreased. Our findings unveil the complex metabolic network in which both LDHA and LDHB are integrated and show that the combined inhibition of LDHA and LDHB strongly sensitizes GB to therapy.

Engineering selective competitors for the discrimination of highly conserved protein-protein interaction modules.

Designing highly specific modulators of protein-protein interactions (PPIs) is especially challenging in the context of multiple paralogs and conserved interaction surfaces. In this case, direct generation of selective and competitive inhibitors is hindered by high similarity within the evolutionary-related protein interfaces. We report here a strategy that uses a semi-rational approach to separate the modulator design into two functional parts. We first achieve specificity toward a region outside of the interface by using phage display selection coupled with molecular and cellular validation. Highly selective competition is then generated by appending the more degenerate interaction peptide to contact the target interface. We apply this approach to specifically bind a single PDZ domain within the postsynaptic protein PSD-95 over highly similar PDZ domains in PSD-93, SAP-97 and SAP-102. Our work provides a paralog-selective and domain specific inhibitor of PSD-95, and describes a method to efficiently target other conserved PPI modules.

The invasive proteome of glioblastoma revealed by laser-capture microdissection.

BACKGROUND: Glioblastomas are heterogeneous tumors composed of a necrotic and tumor core and an invasive periphery. METHODS: Here, we performed a proteomics analysis of laser-capture micro-dissected glioblastoma core and invasive areas of patient-derived xenografts. RESULTS: Bioinformatics analysis identified enriched proteins in central and invasive tumor areas. Novel markers of invasion were identified, the genes proteolipid protein 1 (PLP1) and Dynamin-1 (DNM1), which were subsequently validated in tumors and by functional assays. CONCLUSIONS: In summary, our results identify new networks and molecules that may play an important role in glioblastoma development and may constitute potential novel therapeutic targets.

Argininosuccinate synthase 1 (ASS1): A marker of unclassified hepatocellular adenoma and high bleeding risk.

Hepatocellular adenomas (HCAs) are rare benign tumors divided into three main subgroups defined by pathomolecular features, HNF1A (H-HCA), mutated ß-catenin (b-HCA), and inflammatory (IHCA). In the case of unclassified HCAs (UHCAs), which are currently identified by default, a high risk of bleeding remains a clinical issue. The objective of this study was to explore UHCA proteome with the aim to identify specific biomarkers. Following dissection of the tumoral (T) and nontumoral (NT) tissue on formalin-fixed, paraffin-embedded HCA tissue sections using laser capture methodology, we performed mass spectrometry analysis to compare T and NT protein expression levels in H-HCA, IHCA, b-HCA, UHCA, and focal nodular hyperplasia. Using this methodology, we searched for proteins which are specifically deregulated in UHCA. We demonstrate that proteomic profiles allow for discriminating known HCA subtypes through identification of classical biomarkers in each HCA subgroup. We observed specific up-regulation of the arginine synthesis pathway associated with overexpression of argininosuccinate synthase (ASS1) and arginosuccinate lyase in UHCA. ASS1 immunohistochemistry identified all the UHCA, of which 64.7% presented clinical bleeding manifestations. Interestingly, we demonstrated that the significance of ASS1 was not restricted to UHCA, but also encompassed certain hemorrhagic cases in other HCA subtypes, particularly IHCA. CONCLUSION: ASS1 + HCA combined with a typical hematoxylin and eosin stain aspect defined a new HCA subgroup at a high risk of bleeding. (Hepatology 2017;66:2016-2028).

Novel roles for KLF1 in erythropoiesis revealed by mRNA-seq.

KLF1 (formerly known as EKLF) regulates the development of erythroid cells from bi-potent progenitor cells via the transcriptional activation of a diverse set of genes. Mice lacking Klf1 die in utero prior to E15 from severe anemia due to the inadequate expression of genes controlling hemoglobin production, cell membrane and cytoskeletal integrity, and the cell cycle. We have recently described the full repertoire of KLF1 binding sites in vivo by performing KLF1 ChIP-seq in primary erythroid tissue (E14.5 fetal liver). Here we describe the KLF1-dependent erythroid transcriptome by comparing mRNA-seq from Klf1(+/+) and Klf1(-/-) erythroid tissue. This has revealed novel target genes not previously obtainable by traditional microarray technology, and provided novel insights into the function of KLF1 as a transcriptional activator. We define a cis-regulatory module bound by KLF1, GATA1, TAL1, and EP300 that coordinates a core set of erythroid genes. We also describe a novel set of erythroid-specific promoters that drive high-level expression of otherwise ubiquitously expressed genes in erythroid cells. Our study has identified two novel lncRNAs that are dynamically expressed during erythroid differentiation, and discovered a role for KLF1 in directing apoptotic gene expression to drive the terminal stages of erythroid maturation.