PARP6 inhibition as a strategy to exploit centrosome clustering in cancer cells?

Integrating RNA-seq and scRNA-seq to explore the biological significance of NAD + metabolism-related genes in the initial diagnosis and relapse of childhood B-cell acute lymphoblastic leukemia

BACKGROUND

Nicotinamide Adenine Dinucleotide (NAD) depletion is reported to be a potential treatment for B-cell Acute Lymphoblastic Leukemia (B-ALL), but the mechanism of NAD metabolism-related genes (NMRGs) in B-ALL relapse remains unclear.

METHODS

Transcriptome data (GSE3912), and single-cell sequencing data (GSE130116) of B-ALL patients were downloaded from Gene Expression Omnibus (GEO) database. NMRGs were sourced from Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome databases. Further, the differentially expressed NMRGs (DE-NMRGs) were selected from the analysis between initial diagnosis and relapse B-ALL samples, which further performed functional enrichment analyses. The biomarkers were obtained through random forest (RF) algorithm and repeated cross validation. Additionally, cell type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm was used to evaluate the immune cell differences between the initial diagnosis and relapse samples, and the correlations between biomarkers and gene markers of differential immune cells were analyzed. Furthermore, single cell RNA sequencing was conducted in the GSE130116 dataset to find key cell clusters. In addition, according to biomarkers expressions, cell clusters were categorized into high and low biomarker expression groups, and Gene Set Enrichment Analysis (GSEA) analysis was performed on them. Finally, the cell clusters with the highest expression of biomarkers were selected to explore the roles of biomarkers in different cell clusters and identify transcription factors (TFs) influencing biological markers.

RESULTS

23 DE-NMRGs were screened out, which were mainly enriched in nucleoside phosphate metabolic process, nucleotide metabolic process, and Nicotinate and nicotinamide metabolism. Moreover, 3 biomarkers (NADSYN1, SIRT3, and PARP6) were identified from the machine learning. CIBERSORT results demonstrated that four types of immune cells (B Cells naive, Monocyte, Neutrophils, and T cells CD4 memory Activated) were significantly different between the initial diagnosis and the relapse B-ALL samples, and there were strong correlations between biomarkers and differential immune cells such as positive correlation between NADSYN1 and B Cells naive. The single cell analyses showed that the biomarkers were highly expressed in common myeloid progenitors (CMP), granulocyte-macrophage progenitor (GMP), and megakaryocyte-erythroid progenitor (MEP) cell clusters. Gene set enrichment analysis (GSEA) results indicated that 55 GO terms and 3 KEGG pathways were enriched by the genes in high and low biomarker expression groups. It was found that TF CREB3L2(+) was significantly reduced in the high expression group, which may be the TF affecting biomarkers in the high expression group.

CONCLUSION

This study identified NADSYN1, SIRT3, and PARP6 as the biomarkers of B-ALL, explored biological significance of NMRGs in the initial diagnosis and relapse of B-ALL, and revealed mechanism of biomarkers at the level of the single cell.

Recent developments in PARP14 research

This review aims to reflect upon the major developments in PARP14 research from late 2017 to early 2020. In doing so, this report will focus on the continual elucidation of PARP14's function including an emerging role in viral replication. This is in addition to other functional developments in cancer and inflammation, along with reflecting upon the leads in inhibitor design, including the increased attention toward the macrodomain. This report will also include a brief recap on contemporary poly(ADP-ribose) polymerase inhibitors and reflect upon the development surrounding the other poly(ADP-ribose) polymerases to overall give a succinct update to assist the development of selective PARP14 inhibitors.

The Role of PARPs in Inflammation-and Metabolic-Related Diseases: Molecular Mechanisms and Beyond

Poly(ADP-ribosyl)ation (PARylation) is an essential post-translational modification catalyzed by poly(ADP-ribose) polymerase (PARP) enzymes. Poly(ADP-ribose) polymerase 1 (PARP1) is a well-characterized member of the PARP family. PARP1 plays a crucial role in multiple biological processes and PARP1 activation contributes to the development of various inflammatory and malignant disorders, including lung inflammatory disorders, cardiovascular disease, ovarian cancer, breast cancer, and diabetes. In this review, we will focus on the role and molecular mechanisms of PARPs enzymes in inflammation- and metabolic-related diseases. Specifically, we discuss the molecular mechanisms and signaling pathways that PARP1 is associated with in the regulation of pathogenesis. Recently, increasing evidence suggests that PARP inhibition is a promising strategy for intervention of some diseases. Thus, our in-depth understanding of the mechanism of how PARPs are activated and how their signaling downstream effecters can provide more potential therapeutic targets for the treatment of the related diseases in the future is crucial.