Transcriptional network inference and master regulator analysis of the response to ribosome-inactivating proteins in leukemia cells

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.

Detection of pan-cancer surface protein biomarkers via a network-based approach on transcriptomics data

Cell surface proteins have been used as diagnostic and prognostic markers in cancer research and as targets for the development of anticancer agents. Many of these proteins lie at the top of signaling cascades regulating cell responses and gene expression, therefore acting as ‘signaling hubs’. It has been previously demonstrated that the integrated network analysis on transcriptomic data is able to infer cell surface protein activity in breast cancer. Such an approach has been implemented in a publicly available method called ‘SURFACER’. SURFACER implements a network-based analysis of transcriptomic data focusing on the overall activity of curated surface proteins, with the final aim to identify those proteins driving major phenotypic changes at a network level, named surface signaling hubs. Here, we show the ability of SURFACER to discover relevant knowledge within and across cancer datasets. We also show how different cancers can be stratified in surface-activity-specific groups. Our strategy may identify cancer-wide markers to design targeted therapies and biomarker-based diagnostic approaches.

Detection of subtype-specific breast cancer surface protein biomarkers via a novel transcriptomics approach

BACKGROUND

Cell-surface proteins have been widely used as diagnostic and prognostic markers in cancer research and as targets for the development of anticancer agents. So far, very few attempts have been made to characterize the surfaceome of patients with breast cancer, particularly in relation with the current molecular breast cancer (BRCA) classification. In this view, we developed a new computational method to infer cell-surface protein activities from transcriptomics data, termed 'SURFACER'.

METHODS

Gene expression data from GTEx were used to build a normal breast network model as input to infer differential cell-surface proteins activity in BRCA tissue samples retrieved from TCGA versus normal samples. Data were stratified according to the PAM50 transcriptional subtypes (Luminal A, Luminal B, HER2 and Basal), while unsupervised clustering techniques were applied to define BRCA subtypes according to cell-surface proteins activity.

RESULTS

Our approach led to the identification of 213 PAM50 subtypes-specific deregulated surface genes and the definition of five BRCA subtypes, whose prognostic value was assessed by survival analysis, identifying a cell-surface activity configuration at increased risk. The value of the SURFACER method in BRCA genotyping was tested by evaluating the performance of 11 different machine learning classification algorithms.

CONCLUSIONS

BRCA patients can be stratified into five surface activity-specific groups having the potential to identify subtype-specific actionable targets to design tailored targeted therapies or for diagnostic purposes. SURFACER-defined subtypes show also a prognostic value, identifying surface-activity profiles at higher risk.

Therapeutic Uses of Bacterial Subunit Toxins

The B subunit pentamer verotoxin (VT aka Shiga toxin-Stx) binding to its cellular glycosphingolipid (GSL) receptor, globotriaosyl ceramide (Gb₃) mediates internalization and the subsequent receptor mediated retrograde intracellular traffic of the AB5 subunit holotoxin to the endoplasmic reticulum. Subunit separation and cytosolic A subunit transit via the ER retrotranslocon as a misfolded protein mimic, then inhibits protein synthesis to kill cells, which can cause hemolytic uremic syndrome clinically. This represents one of the most studied systems of prokaryotic hijacking of eukaryotic biology. Similarly, the interaction of cholera AB5 toxin with its GSL receptor, GM1 ganglioside, is the key component of the gastrointestinal pathogenesis of cholera and follows the same retrograde transport pathway for A subunit cytosol access. Although both VT and CT are the cause of major pathology worldwide, the toxin–receptor interaction is itself being manipulated to generate new approaches to control, rather than cause, disease. This arena comprises two areas: anti neoplasia, and protein misfolding diseases. CT/CTB subunit immunomodulatory function and anti-cancer toxin immunoconjugates will not be considered here. In the verotoxin case, it is clear that Gb₃ (and VT targeting) is upregulated in many human cancers and that there is a relationship between GSL expression and cancer drug resistance. While both verotoxin and cholera toxin similarly hijack the intracellular ERAD quality control system of nascent protein folding, the more widespread cell expression of GM1 makes cholera the toxin of choice as the means to more widely utilise ERAD targeting to ameliorate genetic diseases of protein misfolding. Gb₃ is primarily expressed in human renal tissue. Glomerular endothelial cells are the primary VT target but Gb₃ is expressed in other endothelial beds, notably brain endothelial cells which can mediate the encephalopathy primarily associated with VT2-producing E. coli infection. The Gb₃ levels can be regulated by cytokines released during EHEC infection, which complicate pathogenesis. Significantly Gb₃ is upregulated in the neovasculature of many tumours, irrespective of tumour Gb₃ status. Gb₃ is markedly increased in pancreatic, ovarian, breast, testicular, renal, astrocytic, gastric, colorectal, cervical, sarcoma and meningeal cancer relative to the normal tissue. VT has been shown to be effective in mouse xenograft models of renal, astrocytoma, ovarian, colorectal, meningioma, and breast cancer. These studies are herein reviewed. Both CT and VT (and several other bacterial toxins) access the cell cytosol via cell surface ->ER transport. Once in the ER they interface with the protein folding homeostatic quality control pathway of the cell -ERAD, (ER associated degradation), which ensures that only correctly folded nascent proteins are allowed to progress to their cellular destinations. Misfolded proteins are translocated through the ER membrane and degraded by cytosolic proteosome. VT and CT A subunits have a C terminal misfolded protein mimic sequence to hijack this transporter to enter the cytosol. This interface between exogenous toxin and genetically encoded endogenous mutant misfolded proteins, provides a new therapeutic basis for the treatment of such genetic diseases, e.g., Cystic fibrosis, Gaucher disease, Krabbe disease, Fabry disease, Tay-Sachs disease and many more. Studies showing the efficacy of this approach in animal models of such diseases are presented.

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.