Human Retinoblastoma Binding Protein 9, a Serine Hydrolase Implicated in Pancreatic Cancers

Genome-wide siRNA screens identify RBBP9 function as a potential target in Fanconi anaemia-deficient head-and-neck squamous cell carcinoma

Fanconi anaemia (FA) is a rare chromosomal-instability syndrome caused by mutations of any of the 22 known FA DNA-repair genes. FA individuals have an increased risk of head-and-neck squamous-cell-carcinomas (HNSCC), often fatal. Systemic intolerance to standard cisplatin-based protocols due to somatic-cell hypersensitivity underscores the urgent need to develop novel therapies. Here, we performed unbiased siRNA screens to unveil genetic interactions synthetic-lethal with FA-pathway deficiency in FA-patient HNSCC cell lines. We identified based on differential-lethality scores between FA-deficient and FA-proficient cells, next to common-essential genes such as PSMC1, PSMB2, and LAMTOR2, the otherwise non-essential RBBP9 gene. Accordingly, low dose of the FDA-approved RBBP9-targeting drug Emetine kills FA-HNSCC. Importantly both RBBP9-silencing as well as Emetine spared non-tumour FA cells. This study provides a minable genome-wide analyses of vulnerabilities to address treatment challenges in FA-HNSCC. Our investigation divulges a DNA-cross-link-repair independent lead, RBBP9, for targeted treatment of FA-HNSCCs without systemic toxicity.

Identification of Crucial Hub Genes and Differential T Cell Infiltration in Idiopathic Pulmonary Arterial Hypertension Using Bioinformatics Strategies

Background: Idiopathic pulmonary arterial hypertension (IPAH) is a life-threatening disease. Growing evidence indicated that IPAH is a chronic immune disease. This study explored the molecular mechanisms and T cell infiltration of IPAH using integrated bioinformatics methods.

Methods: Gene expression profiles of dataset GSE113439 were downloaded from the Gene Expression Omnibus and analyzed using R. Protein-protein interaction (PPI) network and gene set enrichment analysis (GSEA) were established by NetworkAnalyst. Gene Ontology enrichment analysis was performed using ClueGO. Transcription factors of differentially expressed genes (DEGs) were estimated using iRegulon. Transcription factors and selected hub genes were verified by real-time polymerase chain reaction (qPCR) in the lung tissues of rats with pulmonary artery hypertension. The least absolute shrinkage and selection operator regression model and the area under the receiver operating characteristic curve (AUC) were applied jointly to identify the crucial hub genes. Moreover, immune infiltration in IPAH was calculated using ImmuCellAI, and the correlation between key hub genes and immune cells was analyzed using R.

Results: A total of 512 DEGs were screened, and ten hub genes and three transcription factors were filtered by the DEG PPI network. The DEGs were mainly enriched in mitotic nuclear division, chromosome organization, and nucleocytoplasmic transport. The ten hub genes and three transcription factors were confirmed by qPCR. Moreover, MAPK6 was identified as the most potent biomarker with an AUC of 100%, and ImmuCellAI immune infiltration analysis showed that a higher proportion of CD4-naive T cells and central memory T cells (Tcm) was apparent in the IPAH group, whereas the proportions of cytotoxic T cells (Tc), exhausted T cells (Tex), type 17 T helper cells, effector memory T cells, natural killer T cells (NKT), natural killer cells, gamma-delta T cells, and CD8 T cells were lower. Finally, MAPK6 was positively correlated with Tex and Tcm, and negatively correlated with Tc and NKT.

Conclusion:MAPK6 was identified as a crucial hub gene to discriminate IPAH from the normal group. Dysregulated immune reactions were identified in the lung tissue of patients with IPAH.

Genetic Regulation of Liver Metabolites and Transcripts Linking to Biochemical-Clinical Parameters

Given the central metabolic role of the liver, hepatic metabolites and transcripts reflect the organismal physiological state. Biochemical-clinical plasma biomarkers, hepatic metabolites, transcripts, and single nucleotide polymorphism (SNP) genotypes of some 300 pigs were integrated by weighted correlation networks and genome-wide association analyses. Network-based approaches of transcriptomic and metabolomics data revealed linked of transcripts and metabolites of the pentose phosphate pathway (PPP). This finding was evidenced by using a NADP/NADPH assay and HDAC4 and G6PD transcript quantification with the latter coding for first limiting enzyme of this pathway and by RNAi knockdown experiments of HDAC4. Other transcripts including ARG2 and SLC22A7 showed link to amino acids and biomarkers. The amino acid metabolites were linked with transcripts of immune or acute phase response signaling, whereas the carbohydrate metabolites were highly enrich in cholesterol biosynthesis transcripts. Genome-wide association analyses revealed 180 metabolic quantitative trait loci (mQTL) (p < 10^-4). Trans-4-hydroxy-L-proline (p = 6 × 10^-9), being strongly correlated with plasma creatinine (CREA), showed strongest association with SNPs on chromosome 6 that had pleiotropic effects on PRODH2 expression as revealed by multivariate analysis. Consideration of shared marker association with biomarkers, metabolites, and transcripts revealed 144 SNPs associated with 44 metabolites and 69 transcripts that are correlated with each other, representing 176 mQTL and expression quantitative trait loci (eQTL). This is the first work to report genetic variants associated with liver metabolite and transcript levels as well as blood biochemical-clinical parameters in a healthy porcine model. The identified associations provide links between variation at the genome, transcriptome, and metabolome level molecules with clinically relevant phenotypes. This approach has the potential to detect novel biomarkers displaying individual variation and promoting predictive biology in medicine and animal breeding.