A MiR181/Sirtuin1 regulatory circuit modulates drug response in biliary cancers.

Despite recent advances, biliary tract cancer (BTC) remains one of the most lethal tumor worldwide due to late diagnosis, limited therapeutic strategies and resistance to conventional therapies. In recent years, high-throughput technologies have enabled extensive genome, and transcriptome sequencing unveiling, among others, the regulatory potential of microRNAs (miRNAs). Compelling evidence shown that miRNA are attractive therapeutic targets and promising candidates as biomarkers for various therapy-resistant tumors. The analysis of miRNA profile successfully identified miR-181c and -181d as significantly downregulated in BTC patients. Low miR-181c and -181d expression levels were correlated with worse prognosis and poor treatment efficacy. In fact, progression-free survival analysis indicated poor survival rates in miR-181c and -181d low expressing patients. The expression profile of miR-181c and -181d in BTC cell lines revealed that both miRNAs were dysregulated. Functional in vitro experiments in BTC cell lines showed that overexpression of miR-181c and -181d affected cell viability and increased sensitivity to chemotherapy compared to controls. In addition, by using bioinformatic tools we showed that the miR-181c/d functional role is determined by binding to their target SIRT1 (Sirtuin 1). Moreover, BTC patients expressing high levels of miR-181 and low SIRT1 shown an improved survival and treatment response. An integrative network analysis demonstrated that, miR-181/SIRT1 circuit had a regulatory effect on several important metabolic tumor-related processes. Our study demonstrated that miR-181c and -181d act as tumor suppressor miRNA in BTC, suggesting the potential use as therapeutic strategy in resistant cancers and as predictive biomarker in the precision medicine of BTC.

Reaping the benefits of liquid handlers for high-throughput gene expression profiling in a marine model invertebrate.

BACKGROUND: Modern high-throughput technologies enable the processing of a large number of samples simultaneously, while also providing rapid and accurate procedures. In recent years, automated liquid handling workstations have emerged as an established technology for reproducible sample preparation. They offer flexibility, making them suitable for an expanding range of applications. Commonly, such approaches are well-developed for experimental procedures primarily designed for cell-line processing and xenobiotics testing. Conversely, little attention is focused on the application of automated liquid handlers in the analysis of whole organisms, which often involves time-consuming laboratory procedures. RESULTS: Here, we present a fully automated workflow for all steps, from RNA extraction to real-time PCR processing, for gene expression quantification in the ascidian marine model Ciona robusta. For procedure validation, we compared the results obtained with the liquid handler with those of the classical manual procedure. The outcome revealed comparable results, demonstrating a remarkable time saving particularly in the initial steps of sample processing. CONCLUSIONS: This work expands the possible application fields of this technology to whole-body organisms, mitigating issues that can arise from manual procedures. By minimizing errors, avoiding cross-contamination, decreasing hands-on time and streamlining the procedure, it could be employed for large-scale screening investigations.

Transcriptional and proteomic analysis of the innate immune response to microbial stimuli in a model invertebrate chordate.

Inflammatory response triggered by innate immunity can act to protect against microorganisms that behave as pathogens, with the aim to restore the homeostatic state between host and beneficial microbes. As a filter-feeder organism, the ascidian Ciona robusta is continuously exposed to external microbes that may be harmful under some conditions. In this work, we used transcriptional and proteomic approaches to investigate the inflammatory response induced by stimuli of bacterial (lipopolysaccharide -LPS- and diacylated lipopeptide - Pam2CSK4) and fungal (zymosan) origin, in Ciona juveniles at stage 4 of metamorphosis. We focused on receptors, co-interactors, transcription factors and cytokines belonging to the TLR and Dectin-1 pathways and on immune factors identified by homology approach (i.e. immunoglobulin (Ig) or C-type lectin domain containing molecules). While LPS did not induce a significant response in juvenile ascidians, Pam2CSK4 and zymosan exposure triggered the activation of specific inflammatory mechanisms. In particular, Pam2CSK4-induced inflammation was characterized by modulation of TLR and Dectin-1 pathway molecules, including receptors, transcription factors, and cytokines, while immune response to zymosan primarily involved C-type lectin receptors, co-interactors, Ig-containing molecules, and cytokines. A targeted proteomic analysis enabled to confirm transcriptional data, also highlighting a temporal delay between transcriptional induction and protein level changes. Finally, a protein-protein interaction network of Ciona immune molecules was rendered to provide a wide visualization and analysis platform of innate immunity. The in vivo inflammatory model described here reveals interconnections of innate immune pathways in specific responses to selected microbial stimuli. It also represents the starting point for studying ontogeny and regulation of inflammatory disorders in different physiological conditions.

Computational Analysis of Single Nucleotide Polymorphisms Associated with Altered Drug Responsiveness in Type 2 Diabetes.

Type 2 diabetes (T2D) is one of the most frequent mortality causes in western countries, with rapidly increasing prevalence. Anti-diabetic drugs are the first therapeutic approach, although many patients develop drug resistance. Most drug responsiveness variability can be explained by genetic causes. Inter-individual variability is principally due to single nucleotide polymorphisms, and differential drug responsiveness has been correlated to alteration in genes involved in drug metabolism (CYP2C9) or insulin signaling (IRS1, ABCC8, KCNJ11 and PPARG). However, most genome-wide association studies did not provide clues about the contribution of DNA variations to impaired drug responsiveness. Thus, characterizing T2D drug responsiveness variants is needed to guide clinicians toward tailored therapeutic approaches. Here, we extensively investigated polymorphisms associated with altered drug response in T2D, predicting their effects in silico. Combining different computational approaches, we focused on the expression pattern of genes correlated to drug resistance and inferred evolutionary conservation of polymorphic residues, computationally predicting the biochemical properties of polymorphic proteins. Using RNA-Sequencing followed by targeted validation, we identified and experimentally confirmed that two nucleotide variations in the CAPN10 gene-currently annotated as intronic-fall within two new transcripts in this locus. Additionally, we found that a Single Nucleotide Polymorphism (SNP), currently reported as intergenic, maps to the intron of a new transcript, harboring CAPN10 and GPR35 genes, which undergoes non-sense mediated decay. Finally, we analyzed variants that fall into non-coding regulatory regions of yet underestimated functional significance, predicting that some of them can potentially affect gene expression and/or post-transcriptional regulation of mRNAs affecting the splicing.

Pharmacogenomics of Drug Response in Type 2 Diabetes: Toward the Definition of Tailored Therapies?

Type 2 diabetes is one of the major causes of mortality with rapidly increasing prevalence. Pharmacological treatment is the first recommended approach after failure in lifestyle changes. However, a significant number of patients shows-or develops along time and disease progression-drug resistance. In addition, not all type 2 diabetic patients have the same responsiveness to drug treatment. Despite the presence of nongenetic factors (hepatic, renal, and intestinal), most of such variability is due to genetic causes. Pharmacogenomics studies have described association between single nucleotide variations and drug resistance, even though there are still conflicting results. To date, the most reliable approach to investigate allelic variants is Next-Generation Sequencing that allows the simultaneous analysis, on a genome-wide scale, of nucleotide variants and gene expression. Here, we review the relationship between drug responsiveness and polymorphisms in genes involved in drug metabolism (CYP2C9) and insulin signaling (ABCC8, KCNJ11, and PPARG). We also highlight the advancements in sequencing technologies that to date enable researchers to perform comprehensive pharmacogenomics studies. The identification of allelic variants associated with drug resistance will constitute a solid basis to establish tailored therapeutic approaches in the treatment of type 2 diabetes.