Unique RNA signature of different lesion types in the brain white matter in progressive multiple sclerosis

APOBEC3C is a novel target for the immune treatment of lower-grade gliomas

BACKGROUND

Apolipoprotein B mRNA editing catalytic polypeptide-like (APOBEC) type 3C (A3C) has been identified as a cancer molecular biomarker in the past decade. However, the practical role of A3C in lower-grade gliomas (LGGs) in improving the clinical outcome remains unclear. This study aims to discuss the function of A3C in immunotherapy in LGGs.

METHODS

The RNA-Sequencing (RNA-seq) and corresponding clinical data were extracted from UCSC Xena and the results were verified in the Chinese Glioma Genome Atlas (CGGA). Weighted gene co-expression network analysis (WGCNA) was used for screening A3C-related genes. Comprehensive bioinformation analyses were performed and multiple levels of expression, survival rate, and biological functions were assessed to explore the functions of A3C.

RESULTS

A3C expression was significantly higher in LGGs than in normal tissues but lower than in glioblastoma (GBM), indicating its role as an independent prognosis predictor for LGGs. Twenty-eight A3C-related genes were found with WGCNA for unsupervised clustering analysis and three modification patterns with different outcomes and immune cell infiltration were identified. A3C and the A3C score were also correlated with immune cell infiltration and the expression of immune checkpoints. In addition, the A3C score was correlated with increased sensitivity to chemotherapy. Single-cell RNA (scRNA) analysis indicated that A3C most probably expresses on immune cells, such as T cells, B cells and macrophage.

CONCLUSIONS

A3C is an immune-related prognostic biomarker in LGGs. Developing drugs to block A3C could enhance the efficiency of immunotherapy and improve disease survival.Abbreviation: A3C: Apolipoprotein B mRNA editing catalytic polypeptide-like (APOBEC) type 3C; LGGs: lower-grade gliomas; CGGA: Chinese Glioma Genome Atlas; WGCNA: Weighted gene co-expression network analysis; scRNA: Single-cell RNA; HGG: higher-grade glioma; OS: overall survival; TME: tumor microenvironment; KM: Kaplan-Meier; PFI: progression-free interval; IDH: isocitrate dehydrogenase; ROC: receiver operating characteristic; GS: gene significance; MM: module membership; TIMER: Tumor IMmune Estimation Resource; GSVA: gene set variation analysis; ssGSEA: single-sample gene-set enrichment analysis; PCA: principal component analysis; AUC: area under ROC curve; HAVCR2: hepatitis A virus cellular receptor 2; PDCD1: programmed cell death 1; PDCD1LG2: PDCD1 ligand 2; PTPRC: protein tyrosine phosphatase receptor type C; ACC: Adrenocortical carcinoma; BLCA: Bladder Urothelial Carcinoma;BRCA: Breast invasive carcinoma; CESC: Cervical squamous cell carcinoma and endocervical adenocarcinoma; CHOLCholangiocarcinoma; COADColon adenocarcinoma; DLBC: Lymphoid Neoplasm Diffuse Large B-cell Lymphoma; ESCA: Esophageal carcinoma; GBM: Glioblastoma multiforme; HNSC: Head and Neck squamous cell carcinoma; KICH: Kidney Chromophobe; KIRC: Kidney renal clear cell carcinoma; KIRP: Kidney renal papillary cell carcinoma; LAML: Acute Myeloid Leukemia; LGG: Brain Lower Grade Glioma; LIHC: Liver hepatocellular carcinoma; LUAD: Lung adenocarcinoma; LUSC: Lung squamous cell carcinoma; MESO: Mesothelioma; OV: Ovarian serous cystadenocarcinoma; PAAD: Pancreatic adenocarcinoma; PCPG: Pheochromocytoma and Paraganglioma; PRAD: Prostate adenocarcinoma; READ: Rectum adenocarcinoma; SARC: Sarcoma; SKCM: Skin Cutaneous Melanoma; STAD: Stomach adenocarcinoma; TGCT: Testicular Germ Cell Tumors; THCA: Thyroid carcinoma; THYM: Thymoma; UCEC: Uterine Corpus Endometrial Carcinoma; UCS: Uterine Carcinosarcoma; UVM: Uveal Melanoma.

It takes two to remyelinate: A bioengineered platform to study astrocyte-oligodendrocyte crosstalk and potential therapeutic targets in remyelination

The loss of the myelin sheath insulating axons is the hallmark of demyelinating diseases. These pathologies often lead to irreversible neurological impairment and patient disability. No effective therapies are currently available to promote remyelination. Several elements contribute to the inadequacy of remyelination, thus understanding the intricacies of the cellular and signaling microenvironment of the remyelination niche might help us to devise better strategies to enhance remyelination. Here, using a new in vitro rapid myelinating artificial axon system based on engineered microfibres, we investigated how reactive astrocytes influence oligodendrocyte (OL) differentiation and myelination ability. This artificial axon culture system enables the effective uncoupling of molecular cues from the biophysical properties of the axons, allowing the detailed study of the astrocyte-OL crosstalk. Oligodendrocyte precursor cells (OPCs) were cultured on poly(trimethylene carbonate-co-ε-caprolactone) copolymer electrospun microfibres that served as surrogate axons. This platform was then combined with a previously established tissue engineered glial scar model of astrocytes embedded in 1 % (w/v) alginate matrices, in which astrocyte reactive phenotype was acquired using meningeal fibroblast conditioned medium. OPCs were shown to adhere to uncoated engineered microfibres and differentiate into myelinating OL. Reactive astrocytes were found to significantly impair OL differentiation ability, after six and eight days in a co-culture system. Differentiation impairment was seen to be correlated with astrocytic miRNA release through exosomes. We found significantly reduction on the expression of pro-myelinating miRNAs (miR-219 and miR-338) and an increase in anti-myelinating miRNA (miR-125a-3p) content between reactive and quiescent astrocytes. Additionally, we show that OPC differentiation inhibition could be reverted by rescuing the activated astrocytic phenotype with ibuprofen, a chemical inhibitor of the small rhoGTPase RhoA. Overall, these findings show that modulating astrocytic function might be an interesting therapeutic avenue for demyelinating diseases. The use of these engineered microfibres as an artificial axon culture system will enable the screening for potential therapeutic agents that promote OL differentiation and myelination while providing valuable insight on the myelination/remyelination processes.

Mapping the cortico-striatal transcriptome in attention deficit hyperactivity disorder

Despite advances in identifying rare and common genetic variants conferring risk for ADHD, the lack of a transcriptomic understanding of cortico-striatal brain circuitry has stymied a molecular mechanistic understanding of this disorder. To address this gap, we mapped the transcriptome of the caudate nucleus and anterior cingulate cortex in post-mortem tissue from 60 individuals with and without ADHD. Significant differential expression of genes was found in the anterior cingulate cortex and, to a lesser extent, the caudate. Significant downregulation emerged of neurotransmitter gene pathways, particularly glutamatergic, in keeping with models that implicate these neurotransmitters in ADHD. Consistent with the genetic overlap between mental disorders, correlations were found between the cortico-striatal transcriptomic changes seen in ADHD and those seen in other neurodevelopmental and mood disorders. This transcriptomic evidence points to cortico-striatal neurotransmitter anomalies in the pathogenesis of ADHD, consistent with current models of the disorder.

Proteomic changes during experimental de- and remyelination in the corpus callosum

BACKGROUND

In the cuprizone model of multiple sclerosis, de- and remyelination can be studied without major interference from the adaptive immune responses. Since previous proteomic studies did not focus on the corpus callosum, where cuprizone causes the most pronounced demyelination, we performed a bottom up proteomic analysis on this brain region.

METHODS

Eight week-old mice treated with 0.2% cuprizone, for 4 weeks and controls (C) were sacrificed after termination of the treatment (4wD), and 2 (2dR) or 14 (2wR) days later. Homogenates of dissected corpus callosum were analysed by quantitative proteomics. For data processing, clustering, gene ontology analysis, and regulatory network prediction, we used Perseus, PANTHER and Ingenuity Pathway Analysis softwares, respectively.

RESULTS

We identified 4886 unmodified, single- or multi phosphorylated and/or gycosylated (PTM) proteins. Out of them, 191 proteins were differentially regulated in at least one experimental group. We found 57 proteins specific for demyelination, 27 for early- and 57 for late remyelinationwhile 36 proteins were affected in two, and 23 proteins in all three groups. Phosphorylation represented 92% of the post translational modifications among differentially regulated modified (PTM) proteins with decreased level, while it was only 30% of the PTM proteins with increased level. Gene ontology analysis could not classify the demyelination specific proteins into any biological process category, while allocated the remyelination specific ones to nervous system development and myelination as the most specific subcategory. We also identified a protein network in experimental remyelination, and the gene orthologues of the network were differentially expressed in remyelinating multiple sclerosis brain lesions consistent with an early remyelination pattern.

CONCLUSION

Proteomic analysis seems more informative for remyelination than demyelination in the cuprizone model.

The Cuprizone Model: Dos and Do Nots

Multiple sclerosis (MS) is a chronic inflammatory demyelinating disease of the central nervous system. Various pre-clinical models with different specific features of the disease are available to study MS pathogenesis and to develop new therapeutic options. During the last decade, the model of toxic demyelination induced by cuprizone has become more and more popular, and it has contributed substantially to our understanding of distinct yet important aspects of the MS pathology. Here, we aim to provide a practical guide on how to use the cuprizone model and which pitfalls should be avoided.

Molecular signature of different lesion types in the brain white matter of patients with progressive multiple sclerosis

To identify pathogenetic markers and potential drivers of different lesion types in the white matter (WM) of patients with progressive multiple sclerosis (PMS), we sequenced RNA from 73 different WM areas. Compared to 25 WM controls, 6713 out of 18,609 genes were significantly differentially expressed in MS tissues (FDR < 0.05). A computational systems medicine analysis was performed to describe the MS lesion endophenotypes. The cellular source of specific molecules was examined by RNAscope, immunohistochemistry, and immunofluorescence. To examine common lesion specific mechanisms, we performed de novo network enrichment based on shared differentially expressed genes (DEGs), and found TGFβ-R2 as a central hub. RNAscope revealed astrocytes as the cellular source of TGFβ-R2 in remyelinating lesions. Since lesion-specific unique DEGs were more common than shared signatures, we examined lesion-specific pathways and de novo networks enriched with unique DEGs. Such network analysis indicated classic inflammatory responses in active lesions; catabolic and heat shock protein responses in inactive lesions; neuronal/axonal specific processes in chronic active lesions. In remyelinating lesions, de novo analyses identified axonal transport responses and adaptive immune markers, which was also supported by the most heterogeneous immunoglobulin gene expression. The signature of the normal-appearing white matter (NAWM) was more similar to control WM than to lesions: only 465 DEGs differentiated NAWM from controls, and 16 were unique. The upregulated marker CD26/DPP4 was expressed by microglia in the NAWM but by mononuclear cells in active lesions, which may indicate a special subset of microglia before the lesion develops, but also emphasizes that omics related to MS lesions should be interpreted in the context of different lesions types. While chronic active lesions were the most distinct from control WM based on the highest number of unique DEGs (n = 2213), remyelinating lesions had the highest gene expression levels, and the most different molecular map from chronic active lesions. This may suggest that these two lesion types represent two ends of the spectrum of lesion evolution in PMS. The profound changes in chronic active lesions, the predominance of synaptic/neural/axonal signatures coupled with minor inflammation may indicate end-stage irreversible molecular events responsible for this less treatable phase.

High Speed Ventral Plane Videography as a Convenient Tool to Quantify Motor Deficits during Pre-Clinical Experimental Autoimmune Encephalomyelitis

Experimental autoimmune encephalomyelitis (EAE) is the most commonly used multiple sclerosis animal model. EAE mice typically develop motor deficits in a caudal-to-rostral pattern when inflammatory lesions have already developed. However, to monitor more subtle behavioral deficits during lesion development (i.e., pre-clinical phase), more sophisticated methods are needed. Here, we investigated whether high speed ventral plane videography can be applied to monitor early motor deficits during ‘pre-clinical’ EAE. For this purpose, EAE was induced in C57BL/6 mice and gait abnormalities were quantified using the DigiGait™ apparatus. Gait deficits were related to histopathological changes. 10 out of 10 control (100%), and 14 out of 18 (77.8%) pre-clinical EAE mice could be evaluated using DigiGait™. EAE severity was not influenced by DigiGait™-related mice handlings. Most gait parameters recorded from day 6 post-immunization until the end of the experiment were found to be stable in control mice. During the pre-clinical phase, when conventional EAE scorings failed to detect any functional impairment, EAE mice showed an increased Swing Time, increased %Swing Stride, decreased %Stance Stride, decreased Stance/Swing, and an increased Absolute Paw Angle. In summary, DigiGait™ is more sensitive than conventional scoring approaches to study motor deficits during the EAE pre-clinical phase.