Non-coding RNA and pseudogenes in neurodegenerative diseases: “The (un)Usual Suspects”

Neurodegenerative disorders and cancer are severe diseases threatening human health. The glaring differences between neurons and cancer cells mask the processes involved in their pathogenesis. Defects in cell cycle, DNA repair, and cell differentiation can determine unlimited proliferation in cancer, or conversely, compromise neuronal plasticity, leading to cell death and neurodegeneration. Alteration in regulatory networks affecting gene expression contribute to human diseases onset, including neurodegenerative disorders, and deregulation of non-coding RNAs – particularly microRNAs (miRNAs) – is supposed to have a significant impact. Recently, competitive endogenous RNAs (ceRNAs) – acting as sponges – have been identified in cancer, indicating a new and intricate regulatory network. Given that neurodegenerative disorders and cancer share altered genes and pathways, and considering the emerging role of miRNAs in neurogenesis, we hypothesize ceRNAs may be implicated in neurodegenerative diseases. Here we propose, and computationally predict, such regulatory mechanism may be shared between the diseases. It is predictable that similar regulation occurs in other complex diseases, and further investigation is needed

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

The "next-generation" knowledge of papillary thyroid carcinoma

The application of Next-Generation Sequencing for studying the genetics of papillary thyroid carcinomas (PTC) has recently revealed new somatic mutations and gene fusions as potential new tumor-initiating events in patients without any known driver lesion. Gene and miRNA expression analyses defined clinically relevant subclasses correlated to tumor progression. In addition, it has been shown that tumor driver mutations in BRAF, and RET rearrangements - altogether termed "BRAF-like" carcinomas - have a very similar expression pattern and constitute a distinct category. Conversely, "RAS-like" carcinomas have a different genomic, epigenomic, and proteomic profile. These findings justify the need to reconsider PTC classification schemes

PPARG: Gene Expression Regulation and Next-Generation Sequencing for Unsolved Issues

Peroxisome proliferator-activated receptor gamma (PPARγ) is one of the most extensively studied ligand-inducible transcription factors (TFs), able to modulate its transcriptional activity through conformational changes. It is of particular interest because of its pleiotropic functions: it plays a crucial role in the expression of key genes involved in adipogenesis, lipid and glucid metabolism, atherosclerosis, inflammation, and cancer. Its protein isoforms, the wide number of PPARγ target genes, ligands, and coregulators contribute to determine the complexity of its function. In addition, the presence of genetic variants is likely to affect expression levels of target genes although the impact of PPARG gene variations on the expression of target genes is not fully understood. The introduction of massively parallel sequencing platforms—in the Next Generation Sequencing (NGS) era—has revolutionized the way of investigating the genetic causes of inherited diseases. In this context, DNA-Seq for identifying—within both coding and regulatory regions of PPARG gene—novel nucleotide variations and haplotypes associated to human diseases, ChIP-Seq for defining a PPARγ binding map, and RNA-Seq for unraveling the wide and intricate gene pathways regulated by PPARG, represent incredible steps toward the understanding of PPARγ in health and disease

Hepatic Insulin Resistance in Hyperthyroid Rat Liver: Vitamin E Supplementation Highlights a Possible Role of ROS

Thyroid hormones are normally involved in glycaemic control, but their excess can lead to altered glucose metabolism and insulin resistance (IR). Since hyperthyroidism-linked increase in ROS results in tissue oxidative stress that is considered a hallmark of conditions leading to IR, it is conceivable a role of ROS in the onset of IR in hyperthyroidism. To verify this hypothesis, we evaluated the effects of vitamin E on thyroid hormone-induced oxidative damage, insulin resistance, and on gene expression of key molecules involved in IR in the rat liver. The factors involved in oxidative damage, namely the total content of ROS, the mitochondrial production of ROS, the activity of antioxidant enzymes, the in vitro susceptibility to oxidative stress, have been correlated to insulin resistance indices, such as insulin activation of hepatic Akt and plasma level of glucose, insulin and HOMA index. Our results indicate that increased levels of oxidative damage ROS content and production and susceptibility to oxidative damage, parallel increased fasting plasma level of glucose and insulin, reduced activation of Akt and increased activation of JNK. This last result suggests a role for JNK in the insulin resistance induced by hyperthyroidism. Furthermore, the variation of the genes Pparg, Ppara, Cd36 and Slc2a2 could explain, at least in part, the observed metabolic phenotypes. © 2022 by the authors. Licensee MDPI, Basel, Switzerland

In Vitro-Generated Hypertrophic-Like Adipocytes Displaying PPARG Isoforms Unbalance Recapitulate Adipocyte Dysfunctions In Vivo

Reduced neo-adipogenesis and dysfunctional lipid-overloaded adipocytes are hallmarks of hypertrophic obesity linked to insulin resistance. Identifying molecular features of hypertrophic adipocytes requires appropriate in vitro models. We describe the generation of a model of human hypertrophic-like adipocytes directly comparable to normal adipose cells and the pathologic evolution toward hypertrophic state. We generate in vitro hypertrophic cells from mature adipocytes, differentiated from human mesenchymal stem cells. Combining optical, confocal, and transmission electron microscopy with mRNA/protein quantification, we characterize this cellular model, confirming specific alterations also in subcutaneous adipose tissue. Specifically, we report the generation and morphological/molecular characterization of human normal and hypertrophic-like adipocytes. The latter displays altered morphology and unbalance between canonical and dominant negative (PPARGΔ5) transcripts of PPARG, paralleled by reduced expression of PPARγ targets, including GLUT4. Furthermore, the unbalance of PPARγ isoforms associates with GLUT4 down-regulation in subcutaneous adipose tissue of individuals with overweight/obesity or impaired glucose tolerance/type 2 diabetes, but not with normal weight or glucose tolerance. In conclusion, the hypertrophic-like cells described herein are an innovative tool for studying molecular dysfunctions in hypertrophic obesity and the unbalance between PPARγ isoforms associates with down-regulation of GLUT4 and other PPARγ targets, representing a new hallmark of hypertrophic adipocytes

DDX11L: a novel transcript family emerging from human subtelomeric regions

BACKGROUND:The subtelomeric regions of human chromosomes exhibit an extraordinary plasticity. To date, due to the high GC content and to the presence of telomeric repeats, the subtelomeric sequences are underrepresented in the genomic libraries and consequently their sequences are incomplete in the finished human genome sequence, and still much remains to be learned about subtelomere organization, evolution and function. Indeed, only in recent years, several studies have disclosed, within human subtelomeres, novel gene family members. RESULTS:During a project aimed to analyze genes located in the telomeric region of the long arm of the human X chromosome, we have identified a novel transcript family, DDX11L, members of which map to 1pter, 2q13/14.1, 2qter, 3qter, 6pter, 9pter/9qter, 11pter, 12pter, 15qter, 16pter, 17pter, 19pter, 20pter/20qter, Xpter/Xqter and Yqter. Furthermore, we partially sequenced the underrepresented subtelomeres of human chromosomes showing a common evolutionary origin.CONCLUSION:Our data indicate that an ancestral gene, originated as a rearranged portion of the primate DDX11 gene, and propagated along many subtelomeric locations, is emerging within subtelomeres of human chromosomes, defining a novel gene family. These findings support the possibility that the high plasticity of these regions, sites of DNA exchange among different chromosomes, could trigger the emergence of new genes

Heart failure: Pilot transcriptomic analysis of cardiac tissue by RNA-sequencing

Background: Despite left ventricular (LV) dysfunction contributing to mortality in chronic heart failure (HF), the molecular mechanisms of LV failure continues to remain poorly understood and myocardial biomarkers have yet to be identified. The aim of this pilot study was to investigate specific transcriptome changes occurring in cardiac tissues of patients with HF compared to healthy condition patients to improve diagnosis and possible treatment of affected subjects. Methods: Unlike other studies, only dilated cardiomyopathy (DCM) (n = 2) and restrictive cardiomyopathy (RCM) (n = 2) patients who did not report family history of the disease were selected with the aim of obtaining a homogeneous population for the study. The transcriptome of all patients were studied by RNA-sequencing (RNA-Seq) and the read counts were adequately filtered and normalized using a recently developed user-friendly tool for RNA-Seq data analysis, based on a new graphical user interface (RNA-SeqGUI). Results: By using this approach in a pairwise comparison with healthy donors, we were able to identify DCM- and RCM-specific expression signatures for protein-coding genes as well as for long noncoding RNAs (lncRNAs). Differential expression of 5 genes encoding different members of the mediator complex was disclosed in this analysis. Interestingly, a significant alteration was found for genes which had never been associated with HF until now, and 27 lncRNA/mRNA pairs that were significantly altered in HF patients. Conclusions: The present findings revealed specific expression pattern of both protein-coding and lncRNAs in HF patients, confirming that new LV myocardial biomarkers could be reliably identified using Next-Generation Sequencing-based approaches

Integrated Network Pharmacology Approach for Drug Combination Discovery : A Multi-Cancer Case Study

Simple Summary Current treatments for complex diseases, including cancer, are generally characterized by high toxicity due to their low selectivity for target cells. Moreover, patients often develop drug resistance, hence becoming less sensitive to the therapy. For this reason, novel, improved, and more specific pharmacological therapies are needed. The high cost and the time required to develop new drugs poses the attention on the development of computational methods for drug repositioning and combination therapy prediction. In this study, we developed an integrated network pharmacology framework that combines mechanistic and chemocentric approaches in order to predict potential drug combinations for cancer therapy. We applied our paradigm in five cancer types, which we used as case studies. Our strategy can be applied to the study of any complex disease by guiding the prioritization of drug combinations. Despite remarkable efforts of computational and predictive pharmacology to improve therapeutic strategies for complex diseases, only in a few cases have the predictions been eventually employed in the clinics. One of the reasons behind this drawback is that current predictive approaches are based only on the integration of molecular perturbation of a certain disease with drug sensitivity signatures, neglecting intrinsic properties of the drugs. Here we integrate mechanistic and chemocentric approaches to drug repositioning by developing an innovative network pharmacology strategy. We developed a multilayer network-based computational framework integrating perturbational signatures of the disease as well as intrinsic characteristics of the drugs, such as their mechanism of action and chemical structure. We present five case studies carried out on public data from The Cancer Genome Atlas, including invasive breast cancer, colon adenocarcinoma, lung squamous cell carcinoma, hepatocellular carcinoma and prostate adenocarcinoma. Our results highlight paclitaxel as a suitable drug for combination therapy for many of the considered cancer types. In addition, several non-cancer-related genes representing unusual drug targets were identified as potential candidates for pharmacological treatment of cancer.Peer reviewe

Characterization of a Novel Polymorphism in PPARG Regulatory Region Associated with Type 2 Diabetes and Diabetic Retinopathy in Italy

Peroxisome proliferator-activated receptor gamma polymorphisms have been widely associated with type 2 diabetes, although their role in the pathogenesis of vascular complications is not yet demonstrated. In this study, a cohort of 211 type 2 diabetes, 205 obese, and 254 control individuals was genotyped for Pro12Ala, C1431T, C-2821T polymorphisms, and for a newly identified polymorphism (A-2819G). The above-mentioned polymorphisms were analyzed by gene-specific PCR and direct sequencing of all samples. A significant difference was found for -2819G frequency when patients with type 2 diabetes—particularly diabetic women with the proliferative retinopathy—were compared with healthy control individuals. In conclusion, we identified a novel polymorphism, A-2819G, in PPARG gene, and we found it to be associated with type 2 diabetes and proliferative retinopathy in diabetic females. In the analyzed population, this variant represents a genetic risk factor for developing the diabetic retinopathy, whereas Pro12Ala and C1431T do not