scholarly journals Tracking HIV-1-Infected Cell Clones Using Integration Site-Specific qPCR

Viruses ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1235
Author(s):  
Leah D. Brandt ◽  
Shuang Guo ◽  
Kevin W. Joseph ◽  
Jana L. Jacobs ◽  
Asma Naqvi ◽  
...  
Keyword(s):  
Integration Site ◽  
Cell Types ◽  
Sequencing Data ◽  
Site Specific ◽  
Cell Clones ◽  
Droplet Pcr ◽  
Relative Rarity ◽  
Deep Integration ◽  
Multiple Samples ◽  
Hiv 1

Efforts to cure HIV-1 infection require better quantification of the HIV-1 reservoir, particularly the clones of cells harboring replication-competent (intact) proviruses, termed repliclones. The digital droplet PCR assays commonly used to quantify intact proviruses do not differentiate among specific repliclones, thus the dynamics of repliclones are not well defined. The major challenge in tracking repliclones is the relative rarity of the cells carrying specific intact proviruses. To date, detection and accurate quantification of repliclones requires in-depth integration site sequencing. Here, we describe a simplified workflow using integration site-specific qPCR (IS-qPCR) to determine the frequencies of the proviruses integrated in individual repliclones. We designed IS-qPCR to determine the frequencies of repliclones and clones of cells that carry defective proviruses in samples from three donors. Comparing the results of IS-qPCR with deep integration site sequencing data showed that the two methods yielded concordant estimates of clone frequencies (r = 0.838). IS-qPCR is a potentially valuable tool that can be applied to multiple samples and cell types over time to measure the dynamics of individual repliclones and the efficacy of treatments designed to eliminate them.

scholarly journals Selective clonal persistence of human retroviruses in vivo: radial chromatin organization, integration site and host transcription

2021 ◽  
Author(s):  
Anat Melamed ◽  
Tomas W Fitzgerald ◽  
Yuchuan Wang ◽  
Jian Ma ◽  
Ewan Birney ◽  
...  
Keyword(s):  
Ex Vivo ◽  
Integration Site ◽  
Cell Clones ◽  
Latently Infected ◽  
Integration Sites ◽  
Genome Transcription ◽  
Selection For ◽  
Hiv 1

The human retroviruses HTLV-1 and HIV-1 persist in vivo, despite the host immune response and antiretroviral therapy, as a reservoir of latently infected T-cell clones. It is poorly understood what determines which clones survive in the reservoir and which are lost. We compared >160,000 HTLV-1 integration sites from T-cells isolated ex vivo from naturally-infected subjects with >230,000 integration sites from in vitro infection, to identify the genomic features that determine selective clonal survival. Three factors explained >40% of the observed variance in clone survival of HTLV-1 in vivo: the radial intranuclear position of the provirus, its absolute genomic distance from the centromere, and the intensity of host genome transcription flanking the provirus. The radial intranuclear position of the provirus and its distance from the centromere also explained ~7% of clonal persistence of HIV-1 in vivo. Selection for transcriptionally repressive nuclear compartments favours clonal persistence of human retroviruses in vivo.

scholarly journals Discovery and Interrogation of Functional Protein Modifications by Hotspot Thermal Profiling

2019 ◽  
Author(s):  
Jun X. Huang ◽  
Gihoon Lee ◽  
Raymond E. Moellering
Keyword(s):  
Posttranslational Modifications ◽  
Protein Function ◽  
Cell Types ◽  
Specific Protein ◽  
Live Cells ◽  
Functional Protein ◽  
Site Specific ◽  
Native Proteins ◽  
Thermal Profiling ◽  
Multiple Samples

Abstract Hundreds of protein posttranslational modification types have been reported across diverse organisms, however we still lack methods to systematically predict, or even prioritize, which modification sites may perturb protein function under specific cellular contexts. This protocol describes a method to detect the effects of site-specific protein phosphorylation on the thermal stability of thousands of native proteins in live cells. This mass spectrometry-based protocol measures shifts in overall protein stability in response to site-specific phosphorylation sites. The resulting dataset can enable discovery of intrinsic changes to protein structure as well as extrinsic changes to protein-protein, and protein-metabolite interactions, and can help prioritize site-specific study in a high-throughput and unbiased fashion. This approach takes several days complete, can be performed with multiple samples in parallel and is applicable to diverse organisms, cell types and posttranslational modifications.

scholarly journals In-depth characterization of HIV-1 reservoirs reveals links to viral rebound during treatment interruption

2021 ◽  
Author(s):  
Basiel Cole ◽  
Laurens Lambrechts ◽  
Zoe Boyer ◽  
Ytse Noppe ◽  
Marie-Angélique De Scheerder ◽  
...  
Keyword(s):  
Infected Cell ◽  
Reservoir Characterization ◽  
Integration Site ◽  
Multiple Displacement Amplification ◽  
Treatment Interruption ◽  
Analytical Treatment ◽  
Cell Clones ◽  
Integration Site Analysis ◽  
Infected Cells ◽  
Hiv 1

AbstractThe HIV-1 reservoir is composed of cells harboring latent proviruses that are capable of refuelling viremia upon antiretroviral treatment interruption. This reservoir is in part maintained by clonal expansion of infected cells. However, the contribution of large, infected cell clones to rebound remains underexplored. Here, we performed an in-depth study on four chronically treated HIV-1 infected individuals that underwent an analytical treatment interruption (ATI). A combination of single-genome sequencing, integration site analysis, near-full length proviral sequencing and multiple displacement amplification was used to identify infected cell clones and link these to plasma viruses before and during an ATI. A total of six proviruses could be linked to plasma sequences recovered during ATI. Interestingly, only two of six proviruses were genome intact, one of which is integrated in the ZNF141 gene. To our knowledge, this is the first instance of an intact provirus with its matched IS being matched to plasma virus during an ATI.These findings demonstrate that with in-depth reservoir characterization, clones of infected cells harboring genome-intact proviruses can be linked to rebound viremia, confirming the previously proposed notion that infected clonal cell populations play an important role in the long-term maintenance of the replication-competent HIV-1 reservoir.

scholarly journals OCHROdb: a comprehensive, quality checked database of open chromatin regions from sequencing data

2018 ◽  
Author(s):  
Parisa Shooshtari ◽  
Samantha Feng ◽  
Viswateja Nelakuditi ◽  
Justin Foong ◽  
Michael Brudno ◽  
...  
Keyword(s):  
Gene Regulation ◽  
Large Scale ◽  
Target Genes ◽  
Cell Types ◽  
Chromatin Accessibility ◽  
Open Chromatin ◽  
Sequencing Data ◽  
Genomic Regions ◽  
Multiple Samples ◽  
Regulatory Sites

ABSTRACTInternational consortia, including ENCODE, Roadmap Epigenomics, Genomics of Gene Regulation and Blueprint Epigenome have made large-scale datasets of open chromatin regions publicly available. While these datasets are extremely useful for studying mechanisms of gene regulation in disease and cell development, they only identify open chromatin regions in individual samples. A uniform comparison of accessibility of the same regulatory sites across multiple samples is necessary to correlate open chromatin accessibility and expression of target genes across matched cell types. Additionally, although replicate samples are available for majority of cell types, a comprehensive replication-based quality checking of individual regulatory sites is still lacking. We have integrated 828 DNase-I hypersensitive sequencing samples, which we have uniformly processed and then clustered their regulatory regions across all samples. We checked the quality of open-chromatin regions using our replication test. This has resulted in a comprehensive, quality-checked database of Open CHROmatin (OCHROdb) regions for 194 unique human cell types and cell lines which can serve as a reference for gene regulatory studies involving open chromatin. We have made this resource publicly available: users can download the whole database, or query it for their genomic regions of interest and visualize the results in an interactive genome browser.

scholarly journals In-depth transcriptomic analysis of human retina reveals molecular mechanisms underlying diabetic retinopathy

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kolja Becker ◽  
Holger Klein ◽  
Eric Simon ◽  
Coralie Viollet ◽  
Christian Haslinger ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Rna Sequencing ◽  
Molecular Mechanisms ◽  
Vision Loss ◽  
Ganglion Cells ◽  
Expression Profiles ◽  
Cell Types ◽  
Sequencing Data ◽  
Disease Stages ◽  
Post Transcriptional Regulation

AbstractDiabetic Retinopathy (DR) is among the major global causes for vision loss. With the rise in diabetes prevalence, an increase in DR incidence is expected. Current understanding of both the molecular etiology and pathways involved in the initiation and progression of DR is limited. Via RNA-Sequencing, we analyzed mRNA and miRNA expression profiles of 80 human post-mortem retinal samples from 43 patients diagnosed with various stages of DR. We found differentially expressed transcripts to be predominantly associated with late stage DR and pathways such as hippo and gap junction signaling. A multivariate regression model identified transcripts with progressive changes throughout disease stages, which in turn displayed significant overlap with sphingolipid and cGMP–PKG signaling. Combined analysis of miRNA and mRNA expression further uncovered disease-relevant miRNA/mRNA associations as potential mechanisms of post-transcriptional regulation. Finally, integrating human retinal single cell RNA-Sequencing data revealed a continuous loss of retinal ganglion cells, and Müller cell mediated changes in histidine and β-alanine signaling. While previously considered primarily a vascular disease, attention in DR has shifted to additional mechanisms and cell-types. Our findings offer an unprecedented and unbiased insight into molecular pathways and cell-specific changes in the development of DR, and provide potential avenues for future therapeutic intervention.

scholarly journals scSorter: assigning cells to known cell types according to marker genes

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Hongyu Guo ◽  
Jun Li
Keyword(s):  
Real Data ◽  
Cell Types ◽  
Exact Expression ◽  
Marker Genes ◽  
Specific Marker ◽  
Sequencing Data ◽  
Reference Dataset ◽  
Over Expression ◽  
Higher Power ◽  
Cell Type Specific

AbstractOn single-cell RNA-sequencing data, we consider the problem of assigning cells to known cell types, assuming that the identities of cell-type-specific marker genes are given but their exact expression levels are unavailable, that is, without using a reference dataset. Based on an observation that the expected over-expression of marker genes is often absent in a nonnegligible proportion of cells, we develop a method called scSorter. scSorter allows marker genes to express at a low level and borrows information from the expression of non-marker genes. On both simulated and real data, scSorter shows much higher power compared to existing methods.

scholarly journals Development of a User-Friendly Pipeline for Mutational Analyses of HIV Using Ultra-Accurate Maximum-Depth Sequencing

Viruses ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1338
Author(s):  
Morgan E. Meissner ◽  
Emily J. Julik ◽  
Jonathan P. Badalamenti ◽  
William G. Arndt ◽  
Lauren J. Mills ◽  
...  
Keyword(s):  
Error Rates ◽  
Maximum Depth ◽  
Sequencing Data ◽  
Background Error ◽  
High Background ◽  
Immunodeficiency Virus ◽  
User Friendly ◽  
Viral Mutagenesis ◽  
Hiv 1

Human immunodeficiency virus type 2 (HIV-2) accumulates fewer mutations during replication than HIV type 1 (HIV-1). Advanced studies of HIV-2 mutagenesis, however, have historically been confounded by high background error rates in traditional next-generation sequencing techniques. In this study, we describe the adaptation of the previously described maximum-depth sequencing (MDS) technique to studies of both HIV-1 and HIV-2 for the ultra-accurate characterization of viral mutagenesis. We also present the development of a user-friendly Galaxy workflow for the bioinformatic analyses of sequencing data generated using the MDS technique, designed to improve replicability and accessibility to molecular virologists. This adapted MDS technique and analysis pipeline were validated by comparisons with previously published analyses of the frequency and spectra of mutations in HIV-1 and HIV-2 and is readily expandable to studies of viral mutation across the genomes of both viruses. Using this novel sequencing pipeline, we observed that the background error rate was reduced 100-fold over standard Illumina error rates, and 10-fold over traditional unique molecular identifier (UMI)-based sequencing. This technical advancement will allow for the exploration of novel and previously unrecognized sources of viral mutagenesis in both HIV-1 and HIV-2, which will expand our understanding of retroviral diversity and evolution.

scholarly journals Single-cell transcriptomic analysis of mIHC images via antigen mapping

2021 ◽  
Vol 7 (10) ◽  
pp. eabc5464
Author(s):  
Kiya W. Govek ◽  
Emma C. Troisi ◽  
Zhen Miao ◽  
Rachael G. Aubin ◽  
Steven Woodhouse ◽  
...  
Keyword(s):  
Single Cell ◽  
Spatial Patterns ◽  
Cell Types ◽  
Level Of Detail ◽  
Cell Populations ◽  
Sequencing Data ◽  
Spatially Resolved ◽  
Murine Spleen ◽  
Single Cell Rna Sequencing ◽  
Antibody Panel

Highly multiplexed immunohistochemistry (mIHC) enables the staining and quantification of dozens of antigens in a tissue section with single-cell resolution. However, annotating cell populations that differ little in the profiled antigens or for which the antibody panel does not include specific markers is challenging. To overcome this obstacle, we have developed an approach for enriching mIHC images with single-cell RNA sequencing data, building upon recent experimental procedures for augmenting single-cell transcriptomes with concurrent antigen measurements. Spatially-resolved Transcriptomics via Epitope Anchoring (STvEA) performs transcriptome-guided annotation of highly multiplexed cytometry datasets. It increases the level of detail in histological analyses by enabling the systematic annotation of nuanced cell populations, spatial patterns of transcription, and interactions between cell types. We demonstrate the utility of STvEA by uncovering the architecture of poorly characterized cell types in the murine spleen using published cytometry and mIHC data of this organ.

scholarly journals Rare genetic variants affecting urine metabolite levels link population variation to inborn errors of metabolism

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Yurong Cheng ◽  
◽  
Pascal Schlosser ◽  
Johannes Hertel ◽  
Peggy Sekula ◽  
...  
Keyword(s):  
Genetic Variants ◽  
Inborn Errors Of Metabolism ◽  
Cell Types ◽  
Population Variation ◽  
Whole Body ◽  
Sequencing Data ◽  
Inborn Errors ◽  
Data Links ◽  
Liver And Kidney ◽  
Constraint Based Modeling

AbstractMetabolite levels in urine may provide insights into genetic mechanisms shaping their related pathways. We therefore investigate the cumulative contribution of rare, exonic genetic variants on urine levels of 1487 metabolites and 53,714 metabolite ratios among 4864 GCKD study participants. Here we report the detection of 128 significant associations involving 30 unique genes, 16 of which are known to underlie inborn errors of metabolism. The 30 genes are strongly enriched for shared expression in liver and kidney (odds ratio = 65, p-FDR = 3e−7), with hepatocytes and proximal tubule cells as driving cell types. Use of UK Biobank whole-exome sequencing data links genes to diseases connected to the identified metabolites. In silico constraint-based modeling of gene knockouts in a virtual whole-body, organ-resolved metabolic human correctly predicts the observed direction of metabolite changes, highlighting the potential of linking population genetics to modeling. Our study implicates candidate variants and genes for inborn errors of metabolism.

scholarly journals Single-cell data clustering based on sparse optimization and low-rank matrix factorization

2021 ◽  
Author(s):  
Yinlei Hu ◽  
Bin Li ◽  
Falai Chen ◽  
Kun Qu
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Matrix Factorization ◽  
Data Clustering ◽  
Cell Types ◽  
Low Rank ◽  
Sequencing Data ◽  
Rank Matrix ◽  
Single Cell Rna Sequencing ◽  
Low Rank Matrix

Abstract Unsupervised clustering is a fundamental step of single-cell RNA sequencing data analysis. This issue has inspired several clustering methods to classify cells in single-cell RNA sequencing data. However, accurate prediction of the cell clusters remains a substantial challenge. In this study, we propose a new algorithm for single-cell RNA sequencing data clustering based on Sparse Optimization and low-rank matrix factorization (scSO). We applied our scSO algorithm to analyze multiple benchmark datasets and showed that the cluster number predicted by scSO was close to the number of reference cell types and that most cells were correctly classified. Our scSO algorithm is available at https://github.com/QuKunLab/scSO. Overall, this study demonstrates a potent cell clustering approach that can help researchers distinguish cell types in single-cell RNA sequencing data.

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