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accession-icon SRP148892
Transcriptomic profiling of mock-infected primary CD4+ T cells and a model of HIV latency treated with suberoylanilide hydroxamic acid (SAHA) and Romidepsin (RMD)
  • organism-icon Homo sapiens
  • sample-icon 24 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 2500

Description

Purpose: The goal of this study is to identify host genes whose expression is perturbed in primary CD4+ T cells by histone deacetylase (HDAC) inhibitors (HDACi) SAHA and RMD, which have different potencies and specificities for various HDACs. The study aims to evaluate the effects of SAHA and RMD that may promote or inhibit reactivation of HIV provirus out of latency. Methods: Peripheral blood mononuclear cells were collected from 4 HIV-seronegative donors. CD4+ T cells were isolated and utilized to generate an in vitro model of latent HIV infection (model developed in the Spina laboratory and previously described in Spina et al., 2013). Mock-infected cells were cultured in parallel to evaluate effects of SAHA and RMD that may be dependent on the exposure of cells to virus. Following generation of the model, cells were treated with SAHA, RMD or their solvent dimethyl sulfoxide (DMSO) for 24 hours. Mock-infected cells were treated in parallel. The experiment had 4 biological replicates, 6 conditions for each, for a total of 24 samples. ERCC spikes (Thermo Fisher Scientific, Inc.) were added to cell lysates based on cell number in each sample (10 ul of 1:800 dilution per million cells). Mix 1 was used for DMSO- and mix 2 for SAHA- and RMD-treated cells. After all samples were collected, RNA was extracted and subjected to deep sequencing by Expression Analysis, Inc. Sequence reads that passed quality filters were mapped using Tophat (human genome) or Bowtie (ERCC spikes and HIV) and counted using HTSeq. ERCC spikes with the same concentration in mixes 1 and 2 were utilized to remove unwanted technical variation. Any human gene which did not achieve at least 1 count per million reads in at least 4 samples or any ERCC that did not achieve at least 5 reads in at least 4 samples was discarded. Differential gene expression analysis was performed using library EdgeR in Bioconductor R. National Center for Biotechnology Information (NCBI) HIV-1 Human Interaction Database was then searched for genes that have been implicated in controlling HIV latency. EdgeR output was used to extract expression information of the genes of interest from the NCBI database to identify genes implicated in HIV latency that were modulated by SAHA and RMD. The resulting lists were manually curated to verify relevance to HIV latency, using the Description column of the NCBI database, as well as available PubMed references. Results: Using a custom built data analysis pipeline, ~100 million reads per sample were mapped to the human genome (build hg38). After applying filtering criteria, 16058 human transcripts, 19 ERCC spikes transcripts, and HIV NL4-3 transcripts were identified with the Tophat/Bowtie and HTSeq workflow. Differential expression analysis was performed between SAHA or RMD-treated and DMSO-treated cells. In addition, differential modulation of gene expression by SAHA and RMD in the model of HIV latency and mock-infected cells was assessed using EdgeR. In mock-infected cells, SAHA upregulated 3,971 genes and downregulated 2,940 genes; RMD upregulated 5,068 genes and downregulated 4,050 genes. In the model of HIV latency, SAHA upregulated 3,498 genes and downregulated 2,904 genes; RMD upregulated 5,116 genes and downregulated 4,053 genes (FDR < 0.05). SAHA modulated 6, and RMD 11 genes differentially between mock-infected cells and the model of HIV latency. Following search of the NCBI HIV-1 Human Interaction Database, 27 genes upregulated and 29 downregulated in common between SAHA and RMD were found to be relevant to regulation of HIV latency; 31 were up- and 32 downregulated by RMD only; and 6 were up- and 2 were downregulated by SAHA only. Conclusions: This study demonstrates that SAHA and RMD, which have different potencies and specificities for HDACs, modulate a set of overlapping genes implicated in regulation of HIV latency. Some of these genes may be explored as additional host targets for improving the outcomes of “shock and kill” strategies. Overall design: Transcriptomic profiling of the in vitro model of HIV latency and mock-infected cells treated with SAHA, RMD or the solvent DMSO (N=4 donors) by deep sequencing at Expression Analysis, Inc.

Publication Title

Long non-coding RNAs and latent HIV - A search for novel targets for latency reversal.

Sample Metadata Fields

Specimen part, Treatment, Subject

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accession-icon SRP012289
The post-apoptotic fate of RNAs identified through high-throughput sequencing of human hair
  • organism-icon Homo sapiens
  • sample-icon 1 Downloadable Sample
  • Technology Badge IconIllumina Genome Analyzer II

Description

The hair of all mammals consists of terminally differentiated cells that undergo a specialized form of apoptosis called cornification. While DNA is destroyed during cornification, the extent to which RNA is lost is unknown. Here we find that multiple types of RNA are incompletely degraded after hair shaft formation in both mouse and human. Notably, mRNAs and short regulatory microRNAs (miRNAs) are stable in the hair as far as 10 cm from the scalp. To better characterize the post-apoptotic RNAs that escape degradation in the hair, we performed sequencing (RNA-seq) on RNA isolated from hair shafts pooled from several individuals. This hair shaft RNA library, which encompasses different hair types, genders, and populations, revealed 7,193 mRNAs, 449 miRNAs and thousands of unannotated transcripts that remain in the post-apoptotic hair. A comparison of the hair shaft RNA library to that of viable keratinocytes revealed surprisingly similar patterns of gene coverage and indicates that degradation of RNA is highly inefficient during apoptosis of hair lineages. The generation of a hair shaft RNA library could be used as months of accumulated transcriptional history useful for retrospective detection of disease, drug response and environmental exposure.

Publication Title

The post-apoptotic fate of RNAs identified through high-throughput sequencing of human hair.

Sample Metadata Fields

No sample metadata fields

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accession-icon GSE440
Epidermal keratinocytes and IFNg, TNFa and IL1 treatment
  • organism-icon Homo sapiens
  • sample-icon 8 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U95 Version 2 Array (hgu95av2)

Description

Cultured epidermal keratinocyte controls used for IFNg, TNFa and IL1 treatment.

Publication Title

Unique keratinocyte-specific effects of interferon-gamma that protect skin from viruses, identified using transcriptional profiling.

Sample Metadata Fields

No sample metadata fields

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accession-icon GSE27127
Exon-array profiling of Heat-shock stress response in HeLa cell line
  • organism-icon Homo sapiens
  • sample-icon 8 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Exon 1.0 ST Array [probe set (exon) version (huex10st)

Description

The heat-shock stress response was studied at the level of exons using Affymetrix Exon-array profiling for both sense and anti-sense transcripts. Sense transcript profiling was done as per the protocol of Affymetrix Exon 1.0 ST array and anti-sense transcript array profiling was done using a modified protocol (Xijin Ge et al., BMC Genomics. 2008 Jan 22;9:27).

Publication Title

Heat shock factor binding in Alu repeats expands its involvement in stress through an antisense mechanism.

Sample Metadata Fields

Sex, Specimen part, Cell line

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accession-icon GSE26776
Transcriptome-wide changes in HeLa cell line investigated in response to Heat-shock stress
  • organism-icon Homo sapiens
  • sample-icon 6 Downloadable Samples
  • Technology Badge IconIllumina human-6 v2.0 expression beadchip

Description

The repertoire of transcripts that are differentially regulated in response to Heat-shock were studied using Illumina WG-6 v2.0 BeadChip.

Publication Title

Heat shock factor binding in Alu repeats expands its involvement in stress through an antisense mechanism.

Sample Metadata Fields

Sex, Specimen part, Cell line

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accession-icon GSE28412
Analyzing the metabolic stress response of recombinant Escherichia coli cultures expressing human interferon beta in high cell density fed batch cultures using time course transcriptomic data
  • organism-icon Escherichia coli
  • sample-icon 12 Downloadable Samples
  • Technology Badge Icon Affymetrix E. coli Genome 2.0 Array (ecoli2)

Description

Over expression of recombinant proteins is known to cause a metabolic burden to the host cells which leads to down regulation of both growth rates and protein expression. Most studies in this regard have been conducted in low density shake flask cultures which does not capture the essential features of an industrial scale bioprocess. In the present work we studied the transcriptomic profiling at different specific growth rates while expressing the recombinant human interferon beta in fed batch cultures with complex media. These conditions mimicked the industrial fermentations for recombinant proteins.

Publication Title

Comparative transcriptomic profile analysis of fed-batch cultures expressing different recombinant proteins in Escherichia coli.

Sample Metadata Fields

No sample metadata fields

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accession-icon GSE29440
Design of an improved host platform for the over expression of recombinant proteins in Escherichia coli
  • organism-icon Escherichia coli
  • sample-icon 12 Downloadable Samples
  • Technology Badge Icon Affymetrix E. coli Genome 2.0 Array (ecoli2)

Description

The objective of this work was to design an improved host platform for recombinant protein expression in E. coli. The approach involves first to create a library of the E. coli genomic DNA in different expression vectors and screen for probable transcripts which may lead to slow growing colonies and also simultaneously over-expression of recombinant proteins. To observe its effect on host performance, these genes were knocked out from the E. coli genome. A CG2 strain has been created by knocking in vhb gene gene downstream of the acetate promoter and knocking down ribB gene in DH5 and transformed with Recombinant GFP cloned in pBAD33.

Publication Title

Comparative transcriptomic profile analysis of fed-batch cultures expressing different recombinant proteins in Escherichia coli.

Sample Metadata Fields

No sample metadata fields

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accession-icon GSE37662
CEACAM6 is a PDEF induced gene in breast cancer
  • organism-icon Homo sapiens
  • sample-icon 4 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133A Array (hgu133a)

Description

Identifying PDEF regulated genes may shed light on the mechanism by which PDEF may induce breast cancer progression. To that purpose, we have used the MCF-7 human breast tumor cell line model to identify PDEF induced genes. Briefly, PDEF expression was down regulated by shRNA in MCF-7 cells and RNA probes from PDEF-down regulated and control MCF-7 cells were used to screen the Affymetrics HG-U133A Gene Chips. This analysis found 62 genes that were induced 2-fold or higher by PDEF. Further analysis of 3 of these genes namely S100A7, CEACAM6 and B7-H4 in primary breast tumors showed CEACAM6 as a frequently elevated and co-exressed gene with PDEF in these tumors.

Publication Title

Prostate derived Ets transcription factor and Carcinoembryonic antigen related cell adhesion molecule 6 constitute a highly active oncogenic axis in breast cancer.

Sample Metadata Fields

Cell line

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accession-icon GSE29439
Global transcriptome response of recombinant protein xylanase producing E. coli in high cell density fed batch culture
  • organism-icon Escherichia coli
  • sample-icon 3 Downloadable Samples
  • Technology Badge Icon Affymetrix E. coli Genome 2.0 Array (ecoli2)

Description

Strong production of recombinant proteins interfere with cellular processes in many ways. The extent of the bacterial stress response is determined by the specific properties of the recombinant protein, and by the rates of transcription and translation. The consideration of bacterial stress and starvation responses is of crucial importance for the successful establishment of an industrial large scale bioprocess. Stress genes can be used as marker genes in order to monitor the fitness of industrial bacterial hosts during fermentation processes. For this purpose, here in our study we have applied transcriptome analysis for the description of general and specific stress and starvation responses of Escherichia coli. Producing recombinant protein (Xylanase) in high cell density fed batch culture.

Publication Title

Comparative transcriptomic profile analysis of fed-batch cultures expressing different recombinant proteins in Escherichia coli.

Sample Metadata Fields

Treatment

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accession-icon SRP057272
Endogenous DAF-16 genome-wide recruitment under low Insulin signalling condition in Caenorhabditis elegans
  • organism-icon Caenorhabditis elegans
  • sample-icon 25 Downloadable Samples
  • Technology Badge IconIllumina Genome Analyzer IIx

Description

In order to understand the complexity of gene regulation downstream of IIS, we did RNA-seq in mixed culture in wild-type, daf-2(e1370), daf-16(mgDf50);daf-2(e1370) and daf-2(e1370);daf-12(m20 and correlated it with ChIP-seq data Overall design: RNA-seq profile of different mutants in mix stage

Publication Title

Genome-wide endogenous DAF-16/FOXO recruitment dynamics during lowered insulin signalling in C. elegans.

Sample Metadata Fields

Subject

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refine.bio is a repository of uniformly processed and normalized, ready-to-use transcriptome data from publicly available sources. refine.bio is a project of the Childhood Cancer Data Lab (CCDL)

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Cite refine.bio

Casey S. Greene, Dongbo Hu, Richard W. W. Jones, Stephanie Liu, David S. Mejia, Rob Patro, Stephen R. Piccolo, Ariel Rodriguez Romero, Hirak Sarkar, Candace L. Savonen, Jaclyn N. Taroni, William E. Vauclain, Deepashree Venkatesh Prasad, Kurt G. Wheeler. refine.bio: a resource of uniformly processed publicly available gene expression datasets.
URL: https://www.refine.bio

Note that the contributor list is in alphabetical order as we prepare a manuscript for submission.

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