Transmembrane protein TMEM230, regulator of metalloproteins and motor proteins in gliomas and gliosis.

Glial cells provide physical and chemical support and protection for neurons and for the extracellular compartments of neural tissue through secretion of soluble factors, insoluble scaffolds, and vesicles. Additionally, glial cells have regenerative capacity by remodeling their physical microenvironment and changing physiological properties of diverse cell types in their proximity. Various types of aberrant glial and macrophage cells are associated with human diseases, disorders, and malignancy. We previously demonstrated that transmembrane protein, TMEM230 has tissue revascularization and regenerating capacity by its ability to secrete pro-angiogenic factors and metalloproteinases, inducing endothelial cell sprouting and channel formation. In healthy normal neural tissue, TMEM230 is predominantly expressed in glial and marcophate cells, suggesting a prominent role in neural tissue homeostasis. TMEM230 regulation of the endomembrane system was supported by co-expression with RNASET2 (lysosome, mitochondria, and vesicles) and STEAP family members (Golgi complex). Intracellular trafficking and extracellular secretion of glial cellular components are associated with endocytosis, exocytosis and phagocytosis mediated by motor proteins. Trafficked components include metalloproteins, metalloproteinases, glycans, and glycoconjugate processing and digesting enzymes that function in phagosomes and vesicles to regulate normal neural tissue microenvironment, homeostasis, stress response, and repair following neural tissue injury or degeneration. Aberrantly high sustained levels TMEM230 promotes metalloprotein expression, trafficking and secretion which contribute to tumor associated infiltration and hypervascularization of high tumor grade gliomas. Following injury of the central nervous or peripheral systems, transcient regulated upregulation of TMEM230 promotes tissue wound healing, remodeling and revascularization by activating glial and macrophage generated microchannels/microtubules (referred to as vascular mimicry) and blood vessel sprouting and branching. Our results support that TMEM230 may act as a master regulator of motor protein mediated trafficking and compartmentalization of a large class of metalloproteins in gliomas and gliosis.

Genetic Contribution to Medium-Term Disease Activity in Multiple Sclerosis.

Multiple sclerosis (MS) is a complex disorder characterized by high heterogeneity in terms of phenotypic expression, prognosis and treatment response. In the present study, we aimed to explore the genetic contribution to MS disease activity at different levels: genes, pathways and tissue-specific networks. Two cohorts of relapsing-remitting MS patients who started a first-line treatment (n = 1294) were enrolled to evaluate the genetic association with disease activity after 4 years of follow-up. The analyses were performed at whole-genome SNP and gene level, followed by the construction of gene-gene interaction networks specific for brain and lymphocytes. The resulting gene modules were evaluated to highlight key players from a topological and functional perspective. We identified 23 variants and 223 genes with suggestive association to 4-years disease activity, highlighting genes like PON2 involved in oxidative stress and in mitochondria functions and other genes, like ILRUN, involved in the modulation of the immune system. Network analyses led to the identification of a brain module composed of 228 genes and a lymphocytes module composed of 287 genes. The network analysis allowed us to prioritize genes relevant for their topological properties; among them, there are MPHOSPH9 (connector hub in both brain and lymphocyte module) and OPA1 (in brain module), two genes already implicated in MS. Modules showed the enrichment of both shared and tissue-specific pathways, mainly implicated in inflammation. In conclusion, our results suggest that the processes underlying disease activity act on shared mechanisms across brain and lymphocyte tissues.

Transmembrane Protein TMEM230, Regulator of Glial Cell Vascular Mimicry and Endothelial Cell Angiogenesis in High-Grade Heterogeneous Infiltrating Gliomas and Glioblastoma.

High-grade gliomas (HGGs) and glioblastoma multiforme (GBM) are characterized by a heterogeneous and aggressive population of tissue-infiltrating cells that promote both destructive tissue remodeling and aberrant vascularization of the brain. The formation of defective and permeable blood vessels and microchannels and destructive tissue remodeling prevent efficient vascular delivery of pharmacological agents to tumor cells and are the significant reason why therapeutic chemotherapy and immunotherapy intervention are primarily ineffective. Vessel-forming endothelial cells and microchannel-forming glial cells that recapitulate vascular mimicry have both infiltration and destructive remodeling tissue capacities. The transmembrane protein TMEM230 (C20orf30) is a master regulator of infiltration, sprouting of endothelial cells, and microchannel formation of glial and phagocytic cells. A high level of TMEM230 expression was identified in patients with HGG, GBM, and U87-MG cells. In this study, we identified candidate genes and molecular pathways that support that aberrantly elevated levels of TMEM230 play an important role in regulating genes associated with the initial stages of cell infiltration and blood vessel and microchannel (also referred to as tumor microtubule) formation in the progression from low-grade to high-grade gliomas. As TMEM230 regulates infiltration, vascularization, and tissue destruction capacities of diverse cell types in the brain, TMEM230 is a promising cancer target for heterogeneous HGG tumors.

scMuffin: an R package to disentangle solid tumor heterogeneity by single-cell gene expression analysis.

INTRODUCTION: Single-cell (SC) gene expression analysis is crucial to dissect the complex cellular heterogeneity of solid tumors, which is one of the main obstacles for the development of effective cancer treatments. Such tumors typically contain a mixture of cells with aberrant genomic and transcriptomic profiles affecting specific sub-populations that might have a pivotal role in cancer progression, whose identification eludes bulk RNA-sequencing approaches. We present scMuffin, an R package that enables the characterization of cell identity in solid tumors on the basis of a various and complementary analyses on SC gene expression data. RESULTS: scMuffin provides a series of functions to calculate qualitative and quantitative scores, such as: expression of marker sets for normal and tumor conditions, pathway activity, cell state trajectories, Copy Number Variations, transcriptional complexity and proliferation state. Thus, scMuffin facilitates the combination of various evidences that can be used to distinguish normal and tumoral cells, define cell identities, cluster cells in different ways, link genomic aberrations to phenotypes and identify subtle differences between cell subtypes or cell states. We analysed public SC expression datasets of human high-grade gliomas as a proof-of-concept to show the value of scMuffin and illustrate its user interface. Nevertheless, these analyses lead to interesting findings, which suggest that some chromosomal amplifications might underlie the invasive tumor phenotype and the presence of cells that possess tumor initiating cells characteristics. CONCLUSIONS: The analyses offered by scMuffin and the results achieved in the case study show that our tool helps addressing the main challenges in the bioinformatics analysis of SC expression data from solid tumors.

A novel gene signature to diagnose MASLD in metabolically unhealthy obese individuals.

Visceral adipose tissue (VAT) contributes to metabolic dysfunction-associated steatotic liver disease (MASLD), releasing lipogenic substrates and cytokines which promote inflammation. Metabolic healthy obese individuals (MHO) may shift towardsunhealthy ones (MUHO) who develop MASLD, although the mechanisms are still unexplained. Therefore, we aimed to identify dysfunctional pathways and transcriptomic signatures shared by liver and VAT and to outline novel obesity-related biomarkers which feature MASLD in MUHO subjects, at higher risk of progressive liver disease and extrahepatic comorbidities. We performed RNA-sequencing in 167 hepatic samples and in a subset of 79 matched VAT, stratified in MHO and MUHO. A validation analysis was performed in hepatic samples and primary adipocytes from 12 bariatric patients, by qRT-PCR and western blot. We identified a transcriptomic signature that discriminate MUHO vs MHO, including 498 deregulated genes in liver and 189 in VAT. According to pathway and network analyses, oxidative phosphorylation resulted the only significantly downregulated pathway in both tissues in MUHO subjects. Next, we highlighted 5 genes commonly deregulated in liver and VAT, encompassing C6, IGF1, OXA1L, NDUFB11 and KLHL5 and we built a tissue-related score by integrating their expressions. Accordingly to RNAseq data, serum levels of C6 and IGF1, which are the only secreted proteins among those included in the gene signature were downregulated in MUHO vs MHO. Finally, the expression pattern of this 5-genes was confirmed in hepatic and VAT samples. We firstly identified the liver and VAT transcriptional phenotype of MUHO and a gene signature associated with the presence of MASLD in these at risk individuals.

SeqDeχ: A Sequence Deconvolution Tool for Genome Separation of Endosymbionts From Mixed Sequencing Samples.

In recent years, the advent of NGS technology has made genome sequencing much cheaper than in the past; the high parallelization capability and the possibility to sequence more than one organism at once have opened the door to processing whole symbiotic consortia. However, this approach needs the development of specific bioinformatics tools able to analyze these data. In this work, we describe SeqDex, a tool that starts from a preliminary assembly obtained from sequencing a mixture of DNA from different organisms, to identify the contigs coming from one organism of interest. SeqDex is a fully automated machine learning-based tool exploiting partial taxonomic affiliations and compositional analysis to predict the taxonomic affiliations of contigs in an assembly. In literature, there are few methods able to deconvolve host-symbiont datasets, and most of them heavily rely on user curation and are therefore time consuming. The problem has strong similarities with metagenomic studies, where mixed samples are sequenced and the bioinformatics challenge is trying to separate contigs on the basis of their source organism; however, in symbiotic systems, additional information can be exploited to improve the output. To assess the ability of SeqDex to deconvolve host-symbiont datasets, we compared it to state-of-the-art methods for metagenomic binning and for host-symbiont deconvolution on three study cases. The results point out the good performances of the presented tool that, in addition to the ease of use and customization potential, make SeqDex a useful tool for rapid identification of endosymbiont sequences.