Revealing the History and Mystery of RNA-Seq

Advances in RNA-sequencing technologies have led to the development of intriguing experimental setups, a massive accumulation of data, and high demand for tools to analyze it. To answer this demand, computational scientists have developed a myriad of data analysis pipelines, but it is less often considered what the most appropriate one is. The RNA-sequencing data analysis pipeline can be divided into three major parts: data pre-processing, followed by the main and downstream analyses. Here, we present an overview of the tools used in both the bulk RNA-seq and at the single-cell level, with a particular focus on alternative splicing and active RNA synthesis analysis. A crucial part of data pre-processing is quality control, which defines the necessity of the next steps; adapter removal, trimming, and filtering. After pre-processing, the data are finally analyzed using a variety of tools: differential gene expression, alternative splicing, and assessment of active synthesis, the latter requiring dedicated sample preparation. In brief, we describe the commonly used tools in the sample preparation and analysis of RNA-seq data

O-GlcNAc transferase couples MRE11 to transcriptionally active chromatin to suppress DNA damage

Background: Transcription, metabolism and DNA damage response are tightly regulated to preserve the genomic integrity, and O-GlcNAc transferase (OGT) is positioned to connect the three. Prostate cancer is the most common cancer in men, and androgen-ablation therapy halts disease progression. However, a significant number of prostate cancer patients develop resistance against anti-androgens, and this incurable disease is termed castration-resistant prostate cancer (CRPC). We have shown that combined inhibition of OGT and the transcription elongation kinase CDK9 induce CRPC-selective anti-proliferative effects. Here, we explain the functional basis for these combinatorial effects. Methods: We used comprehensive mass spectrometry profiling of short-term CDK9 inhibitor effects on O-GlcNAcylated proteins in an isogenic cell line system that models transition from PC to CRPC. In addition, we used both ChIP-seq and RNA-seq profiling, and pulldown experiments in multiple CRPC models. Finally, we validated our findings in prostate cancer patient samples. Results: Inhibition of CDK9 results in an OGT-dependent remodeling of the proteome in prostate cancer cells. More specifically, the activity of the DNA damage repair protein MRE11 is regulated in response to CDK9 inhibition in an OGT-dependent manner. MRE11 is enriched at the O-GlcNAc-marked loci. CDK9 inhibition does not decrease the expression of mRNAs whose genes are bound by both O-GlcNAc and MRE11. Combined inhibition of CDK9 and OGT or MRE11 further decreases RNA polymerase II activity, induces DNA damage signaling, and blocks the survival of prostate cancer cells. These effects are seen in CRPC cells but not in normal prostate cells. Mechanistically, OGT activity is required for MRE11 chromatin-loading in cells treated with CDK9 inhibitor. Finally, we show that MRE11 and O-GlcNAc are enriched at the prostate cancer-specific small nucleotide polymorphic sites, and the loss of MRE11 activity results in a hyper-mutator phenotype in patient tumors. Conclusions: Both OGT and MRE11 are essential for the repair of CDK9 inhibitor-induced DNA damage. Our study raises the possibility of targeting CDK9 to elicit DNA damage in CRPC setting as an adjuvant to other treatments.Peer reviewe

CDK9 inhibition activates innate immune response through viral mimicry

Cancer cells frequently exhibit hyperactivation of transcription, which can lead to increased sensitivity to compounds targeting the transcriptional kinases, in particular CDK9. However, mechanistic details of CDK9 inhibition‐induced cancer cell‐selective anti‐proliferative effects remain largely unknown. Here, we discover that CDK9 inhibition activates the innate immune response through viral mimicry in cancer cells. In MYC over‐expressing prostate cancer cells, CDK9 inhibition leads to the gross accumulation of mis‐spliced RNA. Double‐stranded RNA (dsRNA)‐activated kinase can recognize these mis‐spliced RNAs, and we show that the activity of this kinase is required for the CDK9 inhibitor‐induced anti‐proliferative effects. Using time‐resolved transcriptional profiling (SLAM‐seq), targeted proteomics, and ChIP‐seq, we show that, similar to viral infection, CDK9 inhibition significantly suppresses transcription of most genes but allows selective transcription and translation of cytokines related to the innate immune response. In particular, CDK9 inhibition activates NFκB‐driven cytokine signaling at the transcriptional and secretome levels. The transcriptional signature induced by CDK9 inhibition identifies prostate cancers with a high level of genome instability. We propose that it is possible to induce similar effects in patients using CDK9 inhibition, which, we show, causes DNA damage in vitro. In the future, it is important to establish whether CDK9 inhibitors can potentiate the effects of immunotherapy against late‐stage prostate cancer, a currently lethal disease

O-GlcNAc transferase maintains metabolic homeostasis in response to CDK9 inhibition

Co-targeting of O-GlcNAc transferase (OGT) and the transcriptional kinase cyclin-dependent kinase 9 (CDK9) is toxic to prostate cancer cells. As OGT is an essential glycosyltransferase, identifying an alternative target showing similar effects is of great interest. Here, we used a multiomics approach (transcriptomics, metabolomics, and proteomics) to better understand the mechanistic basis of the combinatorial lethality between OGT and CDK9 inhibition. CDK9 inhibition preferentially affected transcription. In contrast, depletion of OGT activity predominantly remodeled the metabolome. Using an unbiased systems biology approach (weighted gene correlation network analysis), we discovered that CDK9 inhibition alters mitochondrial activity/flux, and high OGT activity is essential to maintain mitochondrial respiration when CDK9 activity is depleted. Our metabolite profiling data revealed that pantothenic acid (vitamin B5) is the metabolite that is most robustly induced by both OGT and OGT+CDK9 inhibitor treatments but not by CDK9 inhibition alone. Finally, supplementing prostate cancer cell lines with vitamin B5 in the presence of CDK9 inhibitor mimics the effects of co-targeting OGT and CDK9.Peer reviewe

Inhibition of CDK9 activity compromises global splicing in prostate cancer cells

Cyclin-dependent kinase 9 (CDK9) phosphorylates RNA polymerase II to promote productive transcription elongation. Here we show that short-term CDK9 inhibition affects the splicing of thousands of mRNAs. CDK9 inhibition impairs global splicing and there is no evidence for a coordinated response between the alternative splicing and the overall transcriptome. Alternative splicing is a feature of aggressive prostate cancer (CRPC) and enables the generation of the anti-androgen resistant version of the ligand-independent androgen receptor, AR-v7. We show that CDK9 inhibition results in the loss of AR and AR-v7 expression due to the defects in splicing, which sensitizes CRPC cells to androgen deprivation. Finally, we demonstrate that CDK9 expression increases as PC cells develop CRPC-phenotype both in vitro and also in patient samples. To conclude, here we show that CDK9 inhibition compromises splicing in PC cells, which can be capitalized on by targeting the PC-specific addiction androgen receptor.Peer reviewe

Revealing the History and Mystery of RNA-Seq

Advances in RNA-sequencing technologies have led to the development of intriguing experimental setups, a massive accumulation of data, and high demand for tools to analyze it. To answer this demand, computational scientists have developed a myriad of data analysis pipelines, but it is less often considered what the most appropriate one is. The RNA-sequencing data analysis pipeline can be divided into three major parts: data pre-processing, followed by the main and downstream analyses. Here, we present an overview of the tools used in both the bulk RNA-seq and at the single-cell level, with a particular focus on alternative splicing and active RNA synthesis analysis. A crucial part of data pre-processing is quality control, which defines the necessity of the next steps; adapter removal, trimming, and filtering. After pre-processing, the data are finally analyzed using a variety of tools: differential gene expression, alternative splicing, and assessment of active synthesis, the latter requiring dedicated sample preparation. In brief, we describe the commonly used tools in the sample preparation and analysis of RNA-seq data

Castration-resistant prostate cancer cells are dependent on the high activity of CDK7

Purpose Prostate cancer (PC) is successfully treated with anti-androgens; however, a significant proportion of patients develop resistance against this therapy. Anti-androgen-resistant disease (castration-resistant prostate cancer; CRPC) is currently incurable. Cyclin-dependent kinase 7 (CDK7) is positioned to positively regulate both cell cycle and transcription, the two features critical for the rapid proliferation of the CRPC cells. Here, we assess if CDK7 is a viable target to halt the proliferation of CRPC cells. Methods We use recently developed clinically relevant compounds targeting CDK7 and multiple cell proliferation assays to probe the importance of this kinase for the proliferation of normal, androgen-dependent, and CRPC cells. PC patient data were used to evaluate expression of CDK7 at different disease-stages. Finally, comprehensive glycoproteome-profiling was performed to evaluate CDK7 inhibitor effects on androgen-dependent and CRPC cells. Results We show that CDK7 is overexpressed in PC patients with poor prognosis, and that CRPC cells are highly sensitive to compounds targeting CDK7. Inhibition of O-GlcNAc transferase sensitizes the CRPC, but not androgen-dependent PC cells, to CDK7 inhibitors. Glycoproteome-profiling revealed that CDK7 inhibition induces hyper-O-GlcNAcylation of the positive transcription elongation complex (pTEFB: CDK9 and CCNT1) in the CRPC cells. Accordingly, co-targeting of CDK7 and CDK9 synergistically blocks the proliferation of the CRPC cells but does not have anti-proliferative effects in the normal prostate cells. Conclusion We show that CRPC cells, but not normal prostate cells, are addicted on the high activity of the key transcriptional kinases, CDK7 and CDK9.Peer reviewe