Transcriptional and metabolic effects of aspartate-glutamate carrier isoform 1 (AGC1) downregulation in mouse oligodendrocyte precursor cells (OPCs).

Aspartate-glutamate carrier isoform 1 (AGC1) is a carrier responsible for the export of mitochondrial aspartate in exchange for cytosolic glutamate and is part of the malate-aspartate shuttle, essential for the balance of reducing equivalents in the cells. In the brain, mutations in SLC25A12 gene, encoding for AGC1, cause an ultra-rare genetic disease, reported as a neurodevelopmental encephalopathy, whose symptoms include global hypomyelination, arrested psychomotor development, hypotonia and seizures. Among the biological components most affected by AGC1 deficiency are oligodendrocytes, glial cells responsible for myelination processes, and their precursors [oligodendrocyte progenitor cells (OPCs)]. The AGC1 silencing in an in vitro model of OPCs was documented to cause defects of proliferation and differentiation, mediated by alterations of histone acetylation/deacetylation. Disrupting AGC1 activity could possibly reduce the availability of acetyl groups, leading to perturbation of many biological pathways, such as histone modifications and fatty acids formation for myelin production. Here, we explore the transcriptome of mouse OPCs partially silenced for AGC1, reporting results of canonical analyses (differential expression) and pathway enrichment analyses, which highlight a disruption in fatty acids synthesis from both a regulatory and enzymatic stand. We further investigate the cellular effects of AGC1 deficiency through the identification of most affected transcriptional networks and altered alternative splicing. Transcriptional data were integrated with differential metabolite abundance analysis, showing downregulation of several amino acids, including glutamine and aspartate. Taken together, our results provide a molecular foundation for the effects of AGC1 deficiency in OPCs, highlighting the molecular mechanisms affected and providing a list of actionable targets to mitigate the effects of this pathology.

Targeted quantitative metabolic profiling of brain-derived cell cultures by semi-automated MEPS and LC-MS/MS.

The accurate characterisation of metabolic profiles is an important prerequisite to determine the rate and the efficiency of the metabolic pathways taking place in the cells. Changes in the balance of metabolites involved in vital processes such as glycolysis, tricarboxylic acid (TCA) cycle, oxidative phosphorylation (OXPHOS), as well as in the biochemical pathways related to amino acids, lipids, nucleotides, and their precursors reflect the physiological condition of the cells and may contribute to the development of various human diseases. The feasible and reliable measurement of a wide array of metabolites and biomarkers possesses great potential to elucidate physiological and pathological mechanisms, aid preclinical drug development and highlight potential therapeutic targets. An effective, straightforward, sensitive, and selective liquid chromatography-tandem mass spectrometry (LC-MS/MS) approach was developed for the simultaneous quali-quantitative analysis of 41 compounds in both cell pellet and cell growth medium obtained from brain-derived cell cultures. Sample pretreatment miniaturisation was achieved thanks to the development and optimisation of an original extraction/purification approach based on digitally programmed microextraction by packed sorbent (eVol®-MEPS). MEPS allows satisfactory and reproducible clean-up and preconcentration of both low-volume homogenate cell pellet lysate and cell growth medium with advantages including, but not limited to, minimal sample handling and method sustainability in terms of sample, solvents, and energy consumption. The MEPS-LC-MS/MS method showed good sensitivity, selectivity, linearity, and precision. As a proof of concept, the developed method was successfully applied to the analysis of both cell pellet and cell growth medium obtained from a line of mouse immortalised oligodendrocyte precursor cells (OPCs; Oli-neu cell line), leading to the unambiguous determination of all the considered target analytes. This method is thus expected to be suitable for targeted, quantitative metabolic profiling in most brain cell models, thus allowing accurate investigations on the biochemical pathways that can be altered in central nervous system (CNS) neuropathologies, including e.g., mitochondrial respiration and glycolysis, or use of specific nutrients for growth and proliferation, or lipid, amino acid and nucleotide metabolism.

Prediction of Metabolic Profiles from Transcriptomics Data in Human Cancer Cell Lines.

The Metabolome and Transcriptome are mutually communicating within cancer cells, and this interplay is translated into the existence of quantifiable correlation structures between gene expression and metabolite abundance levels. Studying these correlations could provide a novel venue of understanding cancer and the discovery of novel biomarkers and pharmacological strategies, as well as laying the foundation for the prediction of metabolite quantities by leveraging information from the more widespread transcriptomics data. In the current paper, we investigate the correlation between gene expression and metabolite levels in the Cancer Cell Line Encyclopedia dataset, building a direct correlation network between the two molecular ensembles. We show that a metabolite/transcript correlation network can be used to predict metabolite levels in different samples and datasets, such as the NCI-60 cancer cell line dataset, both on a sample-by-sample basis and in differential contrasts. We also show that metabolite levels can be predicted in principle on any sample and dataset for which transcriptomics data are available, such as the Cancer Genome Atlas (TCGA).

The Transcriptome of SH-SY5Y at Single-Cell Resolution: A CITE-Seq Data Analysis Workflow.

Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) is a recently established multimodal single cell analysis technique combining the immunophenotyping capabilities of antibody labeling and cell sorting with the resolution of single-cell RNA sequencing (scRNA-seq). By simply adding a 12-bp nucleotide barcode to antibodies (cell hashing), CITE-seq can be used to sequence antibody-bound tags alongside the cellular mRNA, thus reducing costs of scRNA-seq by performing it at the same time on multiple barcoded samples in a single run. Here, we illustrate an ideal CITE-seq data analysis workflow by characterizing the transcriptome of SH-SY5Y neuroblastoma cell line, a widely used model to study neuronal function and differentiation. We obtained transcriptomes from a total of 2879 single cells, measuring an average of 1600 genes/cell. Along with standard scRNA-seq data handling procedures, such as quality checks and cell filtering procedures, we performed exploratory analyses to identify most stable genes to be possibly used as reference housekeeping genes in qPCR experiments. We also illustrate how to use some popular R packages to investigate cell heterogeneity in scRNA-seq data, namely Seurat, Monocle, and slalom. Both the CITE-seq dataset and the code used to analyze it are freely shared and fully reusable for future research.

Inhibition of fatty acid synthesis induces differentiation and reduces tumor burden in childhood neuroblastoma.

Many metabolic pathways, including lipid metabolism, are rewired in tumors to support energy and biomass production and to allow adaptation to stressful environments. Neuroblastoma is the second deadliest solid tumor in children. Genetic aberrations, as the amplification of the MYCN-oncogene, correlate strongly with disease progression. Yet, there are only a few molecular targets successfully exploited in the clinic. Here we show that inhibition of fatty acid synthesis led to increased neural differentiation and reduced tumor burden in neuroblastoma xenograft experiments independently of MYCN-status. This was accompanied by reduced levels of the MYCN or c-MYC oncoproteins and activation of ERK signaling. Importantly, the expression levels of genes involved in de novo fatty acid synthesis showed prognostic value for neuroblastoma patients. Our findings demonstrate that inhibition of de novo fatty acid synthesis is a promising pharmacological intervention strategy for the treatment of neuroblastoma independently of MYCN-status.

Single-Cell Gene Network Analysis and Transcriptional Landscape of MYCN-Amplified Neuroblastoma Cell Lines.

Neuroblastoma (NBL) is a pediatric cancer responsible for more than 15% of cancer deaths in children, with 800 new cases each year in the United States alone. Genomic amplification of the MYC oncogene family member MYCN characterizes a subset of high-risk pediatric neuroblastomas. Several cellular models have been implemented to study this disease over the years. Two of these, SK-N-BE-2-C (BE2C) and Kelly, are amongst the most used worldwide as models of MYCN-Amplified human NBL. Here, we provide a transcriptome-wide quantitative measurement of gene expression and transcriptional network activity in BE2C and Kelly cell lines at an unprecedented single-cell resolution. We obtained 1105 Kelly and 962 BE2C unsynchronized cells, with an average number of mapped reads/cell of roughly 38,000. The single-cell data recapitulate gene expression signatures previously generated from bulk RNA-Seq. We highlight low variance for commonly used housekeeping genes between different cells (ACTB, B2M and GAPDH), while showing higher than expected variance for metallothionein transcripts in Kelly cells. The high number of samples, despite the relatively low read coverage of single cells, allowed for robust pathway enrichment analysis and master regulator analysis (MRA), both of which highlight the more mesenchymal nature of BE2C cells as compared to Kelly cells, and the upregulation of TWIST1 and DNAJC1 transcriptional networks. We further defined master regulators at the single cell level and showed that MYCN is not constantly active or expressed within Kelly and BE2C cells, independently of cell cycle phase. The dataset, alongside a detailed and commented programming protocol to analyze it, is fully shared and reusable.