The altered expression of α1 and β3 subunits of the gamma-aminobutyric acid A receptor is related to the hepatitis C virus infection

The modulation of the gamma-aminobutyric acid type A (GABA A) receptors activity was observed in several chronic hepatitis failures, including hepatitis C. The expression of GABA A receptor subunits α1 and β3 was detected in peripheral blood mononuclear cells (PBMCs) originated from healthy donors. The aim of the study was to evaluate if GABA A α1 and β3 expression can also be observed in PBMCs from chronic hepatitis C (CHC) patients and to evaluate a possible association between their expression and the course of hepatitis C virus (HCV) infection. GABA A α1- and β3-specific mRNAs presence and a protein expression in PBMCs from healthy donors and CHC patients were screened by reverse transcription polymerase chain reaction (RT-PCR) and Western blot, respectively. In patients, HCV RNA was determined in sera and PBMCs. It was shown that GABA A α1 and β3 expression was significantly different in PBMCs from CHC patients and healthy donors. In comparison to healthy donors, CHC patients were found to present an increase in the expression of GABA A α1 subunit and a decrease in the expression of β3 subunit in their PBMCs. The modulation of α1 and β3 GABA A receptors subunits expression in PBMCs may be associated with ongoing or past HCV infection

Human cell types important for Hepatitis C Virus replication in vivo and in vitro. Old assertions and current evidence

Hepatitis C Virus (HCV) is a single stranded RNA virus which produces negative strand RNA as a replicative intermediate. We analyzed 75 RT-PCR studies that tested for negative strand HCV RNA in liver and other human tissues. 85% of the studies that investigated extrahepatic replication of HCV found one or more samples positive for replicative RNA. Studies using in situ hybridization, immunofluorescence, immunohistochemistry, and quasispecies analysis also demonstrated the presence of replicating HCV in various extrahepatic human tissues, and provide evidence that HCV replicates in macrophages, B cells, T cells, and other extrahepatic tissues. We also analyzed both short term and long term in vitro systems used to culture HCV. These systems vary in their purposes and methods, but long term culturing of HCV in B cells, T cells, and other cell types has been used to analyze replication. It is therefore now possible to study HIV-HCV co-infections and HCV replication in vitro

The Pediatric Cell Atlas:Defining the Growth Phase of Human Development at Single-Cell Resolution

Single-cell gene expression analyses of mammalian tissues have uncovered profound stage-specific molecular regulatory phenomena that have changed the understanding of unique cell types and signaling pathways critical for lineage determination, morphogenesis, and growth. We discuss here the case for a Pediatric Cell Atlas as part of the Human Cell Atlas consortium to provide single-cell profiles and spatial characterization of gene expression across human tissues and organs. Such data will complement adult and developmentally focused HCA projects to provide a rich cytogenomic framework for understanding not only pediatric health and disease but also environmental and genetic impacts across the human lifespan

Evaluation of methods to assign cell type labels to cell clusters from single-cell RNA-sequencing data.

Background: Identification of cell type subpopulations from complex cell mixtures using single-cell RNA-sequencing (scRNA-seq) data includes automated steps from normalization to cell clustering. However, assigning cell type labels to cell clusters is often conducted manually, resulting in limited documentation, low reproducibility and uncontrolled vocabularies. This is partially due to the scarcity of reference cell type signatures and because some methods support limited cell type signatures. Methods: In this study, we benchmarked five methods representing first-generation enrichment analysis (ORA), second-generation approaches (GSEA and GSVA), machine learning tools (CIBERSORT) and network-based neighbor voting (METANEIGHBOR), for the task of assigning cell type labels to cell clusters from scRNA-seq data. We used five scRNA-seq datasets: human liver, 11 Tabula Muris mouse tissues, two human peripheral blood mononuclear cell datasets, and mouse retinal neurons, for which reference cell type signatures were available. The datasets span Drop-seq, 10X Chromium and Seq-Well technologies and range in size from ~3,700 to ~68,000 cells. Results: Our results show that, in general, all five methods perform well in the task as evaluated by receiver operating characteristic curve analysis (average area under the curve (AUC) = 0.91, sd = 0.06), whereas precision-recall analyses show a wide variation depending on the method and dataset (average AUC = 0.53, sd = 0.24). We observed an influence of the number of genes in cell type signatures on performance, with smaller signatures leading more frequently to incorrect results. Conclusions: GSVA was the overall top performer and was more robust in cell type signature subsampling simulations, although different methods performed well using different datasets. METANEIGHBOR and GSVA were the fastest methods. CIBERSORT and METANEIGHBOR were more influenced than the other methods by analyses including only expected cell types. We provide an extensible framework that can be used to evaluate other methods and datasets at https://github.com/jdime/scRNAseq_cell_cluster_labeling

Evaluation of methods to assign cell type labels to cell clusters from single-cell RNA-sequencing data

Background: Identification of cell type subpopulations from complex cell mixtures using single-cell RNA-sequencing (scRNA-seq) data includes automated computational steps like data normalization, dimensionality reduction and cell clustering. However, assigning cell type labels to cell clusters is still conducted manually by most researchers, resulting in limited documentation, low reproducibility and uncontrolled vocabularies. Two bottlenecks to automating this task are the scarcity of reference cell type gene expression signatures and the fact that some dedicated methods are available only as web servers with limited cell type gene expression signatures. Methods: In this study, we benchmarked four methods (CIBERSORT, GSEA, GSVA, and ORA) for the task of assigning cell type labels to cell clusters from scRNA-seq data. We used scRNA-seq datasets from liver, peripheral blood mononuclear cells and retinal neurons for which reference cell type gene expression signatures were available. Results: Our results show that, in general, all four methods show a high performance in the task as evaluated by receiver operating characteristic curve analysis (average area under the curve (AUC) = 0.94, sd = 0.036), whereas precision-recall curve analyses show a wide variation depending on the method and dataset (average AUC = 0.53, sd = 0.24). Conclusions: CIBERSORT and GSVA were the top two performers. Additionally, GSVA was the fastest of the four methods and was more robust in cell type gene expression signature subsampling simulations. We provide an extensible framework to evaluate other methods and datasets at https://github.com/jdime/scRNAseq_cell_cluster_labeling

Persistence of Hepatitis C Virus during and after Otherwise Clinically Successful Treatment of Chronic Hepatitis C with Standard Pegylated Interferon α-2b and Ribavirin Therapy

Resolution of chronic hepatitis C is considered when serum HCV RNA becomes repeatedly undetectable and liver enzymes normalize. However, long-term persistence of HCV following therapy with pegylated interferon-α/ribavirin (PegIFN/R) was reported when more sensitive assays and testing of serial plasma, lymphoid cells (PBMC) and/or liver biopsies was applied. Our aim was to reassess plasma and PBMCs collected during and after standard PegIFN/R therapy from individuals who became HCV RNA nonreactive by clinical testing. Of particular interest was to determine if HCV genome and its replication remain detectable during ongoing treatment with PegIFN/R when evaluated by more sensitive detection approaches. Plasma acquired before (n = 11), during (n = 25) and up to 12–88 weeks post-treatment (n = 20) from 9 patients and PBMC (n = 23) from 3 of them were reanalyzed for HCV RNA with sensitivity <2 IU/mL. Clone sequencing of the HCV 5′-untranslated region from plasma and PBMCs was done in 2 patients. HCV RNA was detected in 17/25 (68%) plasma and 8/10 (80%) PBMC samples collected from 8 of 9 patients during therapy, although only 5.4% plasma samples were positive by clinical assays. Among post-treatment HCV RNA-negative plasma samples, 9 of 20 (45.3%) were HCV reactive for up to 59 weeks post-treatment. Molecularly evident replication was found in 6/12 (50%) among PBMC reactive for virus RNA positive strand collected during or after treatment. Pre-treatment point mutations persisted in plasma and/or PBMC throughout therapy and follow-up. Therefore, HCV is not completely cleared during ongoing administration of PegIFN/R otherwise capable of ceasing progression of CHC and virus commonly persists at levels not detectable by the current clinical testing. The findings suggest the need for continued evaluation even after patients achieve undetectable HCV RNA post-treatment