Evaluation of potential role of R-loop and G-quadruplex DNA in the fragility of c-MYC during chromosomal translocation associated with Burkitt's lymphoma.

t(8;14) translocation is the hallmark of Burkitt's lymphoma and results in c-MYC deregulation. During the translocation, c-MYC gene on chromosome 8 gets juxtaposed to the Ig switch regions on chromosome 14. Although the promoter of c-MYC has been investigated for its mechanism of fragility, little is known about other c-MYC breakpoint regions. We have analyzed the translocation break points at the exon 1/intron 1 of c-MYC locus from patients with Burkitt's lymphoma. Results showed that the breakpoint region, when present on a plasmid, could fold into an R-loop confirmation in a transcription-dependent manner. Sodium bisulfite modification assay revealed significant single-strandedness on chromosomal DNA of Burkitt's lymphoma cell line, Raji, and normal lymphocytes, revealing distinct R-loops covering up to 100 bp region. Besides, ChIP-DRIP analysis reveals that the R-loop antibody can bind to the breakpoint region. Further, we show the formation of stable parallel intramolecular G-quadruplex on non-template strand of the genome. Finally, incubation of purified AID in vitro or overexpression of AID within the cells led to enhanced mutation frequency at the c-MYC breakpoint region. Interestingly, anti-γH2AX can bind to DSBs generated at the c-MYC breakpoint region within the cells. The formation of R-loop and G-quadruplex was found to be mutually exclusive. Therefore, our results suggest that AID can bind to the single-stranded region of the R-loop and G4 DNA, leading to the deamination of cytosines to uracil and induction of DNA breaks in one of the DNA strands, leading to double-strand break, which could culminate in t(8;14) chromosomal translocation.

Whole transcriptome sequencing reveals HOXD11-AGAP3, a novel fusion transcript in the Indian acute leukemia cohort.

Introduction: Acute leukemia is a heterogeneous disease with distinct genotypes and complex karyotypes leading to abnormal proliferation of hematopoietic cells. According to GLOBOCAN reports, Asia accounts for 48.6% of leukemia cases, and India reports ~10.2% of all leukemia cases worldwide. Previous studies have shown that the genetic landscape of AML in India is significantly different from that in the western population by WES. Methods: We have sequenced and analyzed 9 acute myeloid leukemia (AML) transcriptome samples in the present study. We performed fusion detection in all the samples and categorized the patients based on cytogenetic abnormalities, followed by a differential expression analysis and WGCNA analysis. Finally, Immune profiles were obtained using CIBERSORTx. Results: We found a novel fusion HOXD11-AGAP3 in 3 patients, BCR-ABL1 in 4, and KMT2A-MLLT3 in one patient. Categorizing the patients based on their cytogenetic abnormalities and performing a differential expression analysis, followed by WGCNA analysis, we observed that in the HOXD11-AGAP3 group, correlated co-expression modules were enriched with genes from pathways like Neutrophil degranulation, Innate Immune system, ECM degradation, and GTP hydrolysis. Additionally, we obtained HOXD11-AGAP3-specific overexpression of chemokines CCL28 and DOCK2. Immune profiling using CIBRSORTx revealed differences in the immune profiles across all the samples. We also observed HOXD11-AGAP3-specific elevated expression of lincRNA HOTAIRM1 and its interacting partner HOXA2. Discussion: The findings highlight population-specific HOXD11-AGAP3, a novel cytogenetic abnormality in AML. The fusion led to alterations in immune system represented by CCL28 and DOCK2 over-expression. Interestingly, in AML, CCL28 is known prognostic marker. Additionally, non-coding signatures (HOTAIRM1) were observed specific to the HOXD11-AGAP3 fusion transcript which are known to be implicated in AML.

MiRAGDB: A Knowledgebase of RAG Regulators.

RAG1 and RAG2 genes generate diversity in immunoglobulin and TCR genes by initiating the process of V-D-J recombination. RAGs recognize specific sequences (heptamer-nonamer) to generate a diversity of immunoglobulins. RAG expression is limited to early B and T cell developmental stages. Aberrant expression of RAG can lead to double strand breaks and translocations as observed in leukemia and lymphoma. The expression of RAG is tightly regulated at the transcriptional and posttranscriptional levels. MicroRNAs (miRNAs) are small non-coding RNAs that are involved in the post-transcriptional regulation of gene expression. This study aimed to identify and catalog RAG regulation by miRNA during normal development and cancer. NGS data from normal B-cell and T-cell developmental stages and blood cancer samples have been analyzed for the expression of miRNAs against RAG1 (1,173 against human RAG1 and 749 against mouse RAG1). The analyzed data has been organized to retrieve the miRNA and mRNA expression of various RAG regulators (10 transcription factors and interacting partners) in normal and diseased states. The database allows users to navigate through the human and mouse RAG regulators, visualize and plot expression. miRAGDB is freely available and can be accessed at http://52.4.112.252/shiny/miragdb/.

Multidimensional Mutational Profiling of the Indian HNSCC Sub-Population Provides IRAK1, a Novel Driver Gene and Potential Druggable Target.

Head and neck squamous cell carcinomas (HNSCC) include heterogeneous group of tumors, classified according to their anatomical site. It is the sixth most prevalent cancer globally. Among South Asian countries, India accounts for 40% of HNC malignancies with significant morbidity and mortality. In the present study, we have performed exome sequencing and analysis of 51 Head and Neck squamous cell carcinoma samples. Besides known mutations in the oncogenes and tumour suppressors, we have identified novel gene signatures differentiating buccal, alveolar, and tongue cancers. Around 50% of the patients showed mutation in tumour suppressor genes TP53 and TP63. Apart from the known mutations, we report novel mutations in the genes AKT1, SPECC1, and LRP1B, which are linked with tumour progression and patient survival. A highly curated process was developed to identify survival signatures. 36 survival-related genes were identified based on the correlation of functional impact of variants identified using exome-seq with gene expression from transcriptome data (GEPIA database) and survival. An independent LASSO regression analysis was also performed. Survival signatures common to both the methods led to identification of 4 dead and 3 alive gene signatures, the accuracy of which was confirmed by performing a ROC analysis (AUC=0.79 and 0.91, respectively). Also, machine learning-based driver gene prediction tool resulted in the identification of IRAK1 as the driver (p-value = 9.7 e-08) and also as an actionable mutation. Modelling of the IRAK1 mutation showed a decrease in its binding to known IRAK1 inhibitors.