Los servicios en los esquemas de integración: algunas consideraciones y opciones para Centroamérica: versión provisional

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Cell type-specific novel long non-coding RNA and circular RNA in the BLUEPRINT hematopoietic transcriptomes atlas.

Transcriptional profiling of hematopoietic cell subpopulations has helped to characterize the developmental stages of the hematopoietic system and the molecular bases of malignant and non-malignant blood diseases. Previously, only the genes targeted by expression microarrays could be profiled genome-wide. High-throughput RNA sequencing, however, encompasses a broader repertoire of RNA molecules, without restriction to previously annotated genes. We analyzed the BLUEPRINT consortium RNA-sequencing data for mature hematopoietic cell types. The data comprised 90 total RNA-sequencing samples, each composed of one of 27 cell types, and 32 small RNA-sequencing samples, each composed of one of 11 cell types. We estimated gene and isoform expression levels for each cell type using existing annotations from Ensembl. We then used guided transcriptome assembly to discover unannotated transcripts. We identified hundreds of novel non-coding RNA genes and showed that the majority have cell type-dependent expression. We also characterized the expression of circular RNA and found that these are also cell type-specific. These analyses refine the active transcriptional landscape of mature hematopoietic cells, highlight abundant genes and transcriptional isoforms for each blood cell type, and provide a valuable resource for researchers of hematologic development and diseases. Finally, we made the data accessible via a web-based interface: https://blueprint.haem.cam.ac.uk/bloodatlas/.The authors would like to acknowledge the participation of National Institute of Health Research (NIHR) Cambridge BioResource volunteers and thank the NIHR Cambridge BioResource staff for their support. The work was funded by a grant from the European Commission 7th Framework Program (FP7/2007–2013, grant 282510, BLUEPRINT) to XE, PF, JHAM, MY, HGS and WHO. WHO is an NIHR senior investigator and receives funding from Bristol-Myers Squibb, the British Heart Foundation, the Medical Research Council and the NIHR. OGI, FJM, AF, JMM, LC and PF are funded by the Wellcome Trust (WT108749/Z/15/Z) with additional funding for specific project components such as GENCODE from the National Human Genome Research Institute of the National Institutes of Health (2U41HG007234), accordingly the content of this manuscript is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. KD is a HSST trainee supported by NHS Health Education England. NF is funded by the NIHR Cambridge Biomedical Research Centre. FP is supported by the Fundação Carlos Chagas Filho de Amparo à Pesquisado Estado do Rio de Janeiro (FAPERJ; E-26/203.229/2016). NANJ is a recipient of a scholarship from the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES; Finance Code 001). DS work has been supported in part by an Isaac Newton fellowship to MF. MF is supported by the British Heart Foundation (FS/18/53/33863)

Chromosome contacts in activated T cells identify autoimmune disease candidate genes

Abstract Background Autoimmune disease-associated variants are preferentially found in regulatory regions in immune cells, particularly CD4+ T cells. Linking such regulatory regions to gene promoters in disease-relevant cell contexts facilitates identification of candidate disease genes. Results Within 4 h, activation of CD4+ T cells invokes changes in histone modifications and enhancer RNA transcription that correspond to altered expression of the interacting genes identified by promoter capture Hi-C. By integrating promoter capture Hi-C data with genetic associations for five autoimmune diseases, we prioritised 245 candidate genes with a median distance from peak signal to prioritised gene of 153 kb. Just under half (108/245) prioritised genes related to activation-sensitive interactions. This included IL2RA, where allele-specific expression analyses were consistent with its interaction-mediated regulation, illustrating the utility of the approach. Conclusions Our systematic experimental framework offers an alternative approach to candidate causal gene identification for variants with cell state-specific functional effects, with achievable sample sizes

Cell type-specific novel long non-coding RNA and circular RNA in the BLUEPRINT hematopoietic transcriptomes atlas.

Transcriptional profiling of hematopoietic cell subpopulations has helped to characterize the developmental stages of the hematopoietic system and the molecular bases of malignant and non-malignant blood diseases. Previously, only the genes targeted by expression microarrays could be profiled genome-wide. High-throughput RNA sequencing, however, encompasses a broader repertoire of RNA molecules, without restriction to previously annotated genes. We analyzed the BLUEPRINT consortium RNA-sequencing data for mature hematopoietic cell types. The data comprised 90 total RNA-sequencing samples, each composed of one of 27 cell types, and 32 small RNA-sequencing samples, each composed of one of 11 cell types. We estimated gene and isoform expression levels for each cell type using existing annotations from Ensembl. We then used guided transcriptome assembly to discover unannotated transcripts. We identified hundreds of novel non-coding RNA genes and showed that the majority have cell type-dependent expression. We also characterized the expression of circular RNA and found that these are also cell type-specific. These analyses refine the active transcriptional landscape of mature hematopoietic cells, highlight abundant genes and transcriptional isoforms for each blood cell type, and provide a valuable resource for researchers of hematologic development and diseases. Finally, we made the data accessible via a web-based interface: https://blueprint.haem.cam.ac.uk/bloodatlas/.The authors would like to acknowledge the participation of National Institute of Health Research (NIHR) Cambridge BioResource volunteers and thank the NIHR Cambridge BioResource staff for their support. The work was funded by a grant from the European Commission 7th Framework Program (FP7/2007–2013, grant 282510, BLUEPRINT) to XE, PF, JHAM, MY, HGS and WHO. WHO is an NIHR senior investigator and receives funding from Bristol-Myers Squibb, the British Heart Foundation, the Medical Research Council and the NIHR. OGI, FJM, AF, JMM, LC and PF are funded by the Wellcome Trust (WT108749/Z/15/Z) with additional funding for specific project components such as GENCODE from the National Human Genome Research Institute of the National Institutes of Health (2U41HG007234), accordingly the content of this manuscript is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. KD is a HSST trainee supported by NHS Health Education England. NF is funded by the NIHR Cambridge Biomedical Research Centre. FP is supported by the Fundação Carlos Chagas Filho de Amparo à Pesquisado Estado do Rio de Janeiro (FAPERJ; E-26/203.229/2016). NANJ is a recipient of a scholarship from the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES; Finance Code 001). DS work has been supported in part by an Isaac Newton fellowship to MF. MF is supported by the British Heart Foundation (FS/18/53/33863)

Additional file 13: Table S8c. of Chromosome contacts in activated T cells identify autoimmune disease candidate genes

Whole-genome segmentation of non-activated and activated CD4 T cells into 15 states obtained from a CHROMHMM analysis using ChIP-seq data for non-activated CD4+ T cells. (GZ 1520 kb

Additional file 7: Table S6a. of Chromosome contacts in activated T cells identify autoimmune disease candidate genes

Results of ImmunoChip fine-mapping by GUESSFM. (GZ 2833 kb

Additional file 2: Figures S1–S14. of Chromosome contacts in activated T cells identify autoimmune disease candidate genes

Figure S1. Comparison of longer and shorter CD4+ T cell activation timecourses. Figure S2. Summary distributions of interacting fragments. Figure S3. Validation of PCHi-C by ChIA-PET. Figure S4. Chromatin state profiles of interacting fragments. Figure S5. Relationship of gene expression to PIR number and mRNA half-life. Figure S6. Definition and quantification of regulatory RNAs. Figure S7. blockshifter calibration. Figure S8. MDN1 is prioritised for RA through ImmunoChip but not GWAS data. Figure S9. Gene prioritisation using COGS. Figure S10. Multiple genes on chromosome 1q32.1 (IL10, IL19, IL20, IL24, FCAMR/PIGR) are prioritised for T1D, CRO and UC. Figure S11. Histograms show the distribution of summed PIR length by gene in non-activated CD4+ T cells (top panel) and TAD length in naive CD4+ T cells. Figure S12. IRF8 and EMC8/COX4I1 on chromosome 16 are prioritised for RA and SLE. Figure S13. AHR on chromosome 7 is prioritised for RA in activated CD4+ T cells. Figure S14. Allelic imbalance in mRNA expression in individuals heterozygous for group A SNPs is confirmed with reporter SNP rs12244380 (IL2RA 3’ UTR). (PDF 4243 kb