For Publishers
DiscoveryMetadataPeer reviewHostingPublishing
For Researchers
JoinPublishReviewCollect
Register
Blog
About
Search
Advanced search
My ScienceOpen
Search
For Publishers
For Researchers
Blog
About
2
views
40
references
 Top references
cited by
0
    
0 reviews
 Review
0
comments
 Comment
0
recommends
+1 Recommend
0 collections
    0
    shares
      201
      similar
       All similar
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Derived myeloid lineage induced pluripotent stem as a platform to study human C-C chemokine receptor type 5Δ32 homozygotes

      research-article

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Summary

          The C-C chemokine receptor type 5 (CCR5) expressed on immune cells supports inflammatory responses by directing cells to the inflammation site. CCR5 is also a major coreceptor for macrophage tropic human immunodeficiency viruses (R5-HIV-1) and its variants can confer protection from HIV infection, making it an ideal candidate to target for therapy. We developed a stepwise protocol that differentiates induced pluripotent stem cells (iPSCs) from individuals homozygous for the CCR5Δ32 variant and healthy volunteers into myeloid lineage induced monocytes (iMono) and macrophages (iMac). By characterizing iMono and iMac against their primary counterparts, we demonstrated that CCR5Δ32 homozygous cells are endowed with similar pluripotent potential for self-renewal and differentiation as iPSC lines generated from non-variant individuals while also showing resistance to HIV infection. In conclusion, these cells are a platform to investigate CCR5 pathophysiology in HIV-positive and negative individuals and to help develop novel therapies.

          Graphical abstract

          Highlights

          • •

            iPSCs derived from CCR5Δ32 subjects can differentiate into myeloid lineage cells

          • •

            iMono derived from CCR5Δ32 subjects were unable to migrate in response to RANTES

          • •

            iMac derived from CCR5Δ32 subjects were resistant to HIV-1 infection

          • •

            CCR5Δ32 subject iMono/iMac are a new platform to investigate CCR5 pathophysiology

          Abstract

          Molecular biology; Immunology; Stem cells research

          Related collections

          Most cited references40

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          Michael Love, Wolfgang Huber, Simon Anders (2014)
          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
          Article 0 comments Cited 23733 times – based on 0 reviews     Review now
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            STAR: ultrafast universal RNA-seq aligner.

            Alexander Dobin, Carrie A. Davis, Felix Schlesinger … (2013)
            Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
            Article 0 comments Cited 13718 times – based on 0 reviews     Review now
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

              Mark Robinson, Davis J. McCarthy, Gordon K. Smyth (2009)
              Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
              Article 0 comments Cited 10133 times – based on 0 reviews     Review now
                Bookmark
                All references

                Author and article information

                Contributors
                Manfred Boehm
                Journal
                Journal ID (nlm-ta): iScience
                Journal ID (iso-abbrev): iScience
                Title: iScience
                Publisher: Elsevier
                ISSN (Electronic): 2589-0042
                Publication date PMC-release: 28 October 2023
                Publication date Collection: 17 November 2023
                Publication date (Electronic): 28 October 2023
                Volume: 26
                Issue: 11
                Electronic Location Identifier: 108331
                Affiliations
                [1 ]Translational Vascular Medicine Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
                [2 ]Unit of Clinical and Experimental Immunology, IRCCS Humanitas Research Hospital, 20089 Rozzano, Italy
                [3 ]Department of Medical Biotechnologies and Translational Medicine (BioMeTra), University of Milan, 20054 Segrate, Italy
                [4 ]Central Laboratory, Peking University School and Hospital of Stomatology, Beijing, China
                [5 ]Centre for Experimental Medicine and Rheumatology, William Harvey Research Institute, London, England, UK
                [6 ]Department of Rheumatology and Immunology, Xiangya Hospital, Central South University, Changsha, China
                [7 ]Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
                [8 ]The American Genome Center, Uniformed Services University, Bethesda, MD, USA
                Author notes
                [∗ ]Corresponding author boehmm@ 123456nhlbi.nih.gov
                [9]

                Senior author

                [10]

                These authors contributed equally

                [11]

                Lead contact

                Article
                Publisher Item ID: S2589-0042(23)02408-2 Publisher ID: 108331
                DOI: 10.1016/j.isci.2023.108331
                PMC ID: 10663745
                SO-VID: c8ae2e93-a301-4f06-9d98-6f65462dae42
                License:

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                Date received : 12 May 2023
                Date revision received : 29 August 2023
                Date accepted : 22 October 2023
                Categories
                Subject: Article

                Keywords: molecular biology,immunology,stem cells research
                Data availability:
                Keywords: molecular biology, immunology, stem cells research

                Comments

                Comment on this article