Evaluating Cancer Subtypes and Enhancer Dynamics During the Acquisition of Drug Resistance by Nascent RNA-Sequencing
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA850075 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR19716138_1.fastq.gz | Fastqsanger.gz | |||
| SRR19716138_2.fastq.gz | Fastqsanger.gz | |||
| SRR19716139_1.fastq.gz | Fastqsanger.gz | |||
| SRR19716139_2.fastq.gz | Fastqsanger.gz | |||
| SRR19716140_1.fastq.gz | Fastqsanger.gz |
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Project description:BackgroundSubtype C is the most prevalent HIV-1 subtype in the world, mainly in countries with the highest HIV prevalence. However, few studies have evaluated the impact of antiretroviral therapy on this subtype. In southern Brazil, the first developing country to offer free and universal treatment, subtypes B and C co-circulate with equal prevalence, allowing for an extensive evaluation of this issue.Methods and findingsViral RNA of 160 HIV-1+ patients was extracted, and the protease and reverse transcriptase genes were sequenced, subtyped and analyzed for ARV mutations. Sequences were grouped by subtype, and matched to type (PI, NRTI and NNRTI) and time of ARV exposure. Statistical analyses were performed to compare differences in the frequency of ARV-associated mutations. There were no significant differences in time of treatment between subtypes B and C groups, although they showed distinct proportions of resistant strains at different intervals for two of three ARV classes. For PI, 26% of subtype B strains were resistant, compared to only 8% in subtype C (p = 0.0288, Fisher's exact test). For NRTI, 54% of subtype B strains were resistant versus 23% of subtype C (p = 0.0012). Differences were significant from 4 years of exposure, and remained so until the last time point analyzed. The differences observed between both subtypes were independent of time under rebound viremia in cases of virologic failure and of the number of HAART regimens used by treated patients.ConclusionsOur results pointed out to a lower rate of accumulation of mutations conferring resistance to ARV in subtype C than in subtype B. These findings are of crucial importance for current initiatives of ARV therapy roll-out in developing countries, where subtype is C prevalent.
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Project description:Over the past decade, genome-wide assays have underscored the broad sweep of circadian gene expression. A substantial fraction of the transcriptome undergoes oscillations in many organisms and tissues, which governs the many biochemical, physiological and behavioral functions under circadian control. Based predominantly on the transcription feedback loops important for core circadian timekeeping, it is commonly assumed that this widespread mRNA cycling reflects circadian transcriptional cycling. To address this issue, we directly measured dynamic changes in mouse liver transcription using Nascent-Seq. Many genes are rhythmically transcribed over the 24h day, which include precursors of several non-coding RNAs as well as the expected set of core clock genes. Surprisingly however, nascent RNA rhythms overlap poorly with mRNA abundance rhythms assayed by RNA-seq. This is because most mouse liver genes with rhythmic mRNA expression manifest poor transcriptional rhythms, indicating a prominent role of post-transcriptional regulation in setting mRNA cycling amplitude. To gain further insight into circadian transcriptional regulation, we also characterized the rhythmic transcription of liver genes targeted by the transcription factors CLOCK and BMAL1; they directly target other core clock genes and sit at the top of the molecular circadian clock hierarchy in mammals. CLK:BMAL1 rhythmically bind at the same discrete phase of the circadian cycle to all target genes, which not surprisingly have a much higher percentage of rhythmic transcription than the genome as a whole. However, there is a surprisingly heterogeneous set of cycling transcription phases of direct target genes, which even include core clock genes. This indicates a disconnect between rhythmic DNA binding and the peak of transcription, which is likely due to other transcription factors that collaborate with CLK:BMAL1. In summary, the application of Nascent-Seq to a mammalian tissue provides surprising insights into the rhythmic control of gene expression and should have broad applications beyond the analysis of circadian rhythms. Mouse liver nascent RNA profile over 6 time points of the 24h light:dark cycle, in duplicate, sequenced using Ilumina GAII (Nascent-Seq); Mouse liver mRNA profile over 6 time points of the 24h light:dark cycle, in duplicate, sequenced using Ilumina HiSeq2000 (RNA-Seq); CLK and BMAL1 DNA binding profile in the mouse liver at ZT8, sequenced along an Input sample using GAII (ChIP-Seq); Mouse liver strand-specific nascent RNA profile over 6 time points of the 24h light:dark cycle, in duplicate, sequenced using Ilumina HiSeq2000 (Strand-specific Nascent-Seq); Supplementary file NascentSeq_Mouse_Liver_NormalizedGeneSignal.txt represents Nascent RNA abundance (reads per base pair) for each sample.
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