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Graphical Abstract Highlights d A naturally occurring PPARg isoform is generated by SRSF1mediated splicing d PPARgD5 acts as a dominant-negative modifying PPARgdependent transcriptional network d High PPARgD5 levels impair the differentiation ability of adipocyte precursor cells d PPARgD5 positively correlates with BMI in overweight or obese and diabetic patients

Hybridization- and tag-based technologies have been successfully used in Down syndrome to identify genes involved in various aspects of the pathogenesis. However, these technologies suffer from several limits and drawbacks and, to date, information about rare, even though relevant, RNA species such as long and small non-coding RNAs, is completely missing. Indeed, none of published works has still described the whole transcriptional landscape of Down syndrome. Although the recent advances in high-throughput RNA sequencing have revealed the complexity of transcriptomes, most of them rely on polyA enrichment protocols, able to detect only a small fraction of total RNA content. On the opposite end, massive-scale RNA sequencing on rRNA-depleted samples allows the survey of the complete set of coding and non-coding RNA species, now emerging as novel contributors to pathogenic mechanisms. Hence, in this work we analysed for the first time the complete transcriptome of human trisomic endothelial progenitor cells to an unprecedented level of resolution and sensitivity by RNA-sequencing. Our analysis allowed us to detect differential expression of even low expressed genes crucial for the pathogenesis, to disclose novel regions of active transcription outside yet annotated loci, and to investigate a plethora of non-polyadenilated long as well as short non coding RNAs. Novel splice isoforms for a large subset of crucial genes, and novel extended untranslated regions for known genes—possibly novel miRNA targets or regulatory sites for gene transcription—were also identified in this study. Coupling the rRNA depletion of samples, followed by high-throughput RNA-sequencing, to the easy availability of these cells renders this approach very feasible for transcriptome studies, offering the possibility of investigating in-depth blood-related pathological features of Down syndrome, as well as other genetic disorders.

The nuclear receptor PPARγ is a key regulator of adipogenesis, and alterations of its function are associated with different pathological processes related to metabolic syndrome. We recently identified two PPARG transcripts encoding dominant negative PPARγ isoforms. The existence of different PPARG variants suggests that alternative splicing is crucial to modulate PPARγ function, underlying some underestimated aspects of its regulation. Here we investigate PPARG expression in different tissues and cells affected in metabolic syndrome and, in particular, during adipocyte differentiation of human mesenchymal stem cells. We defined the transcript-specific expression pattern of PPARG variants encoding both canonical and dominant negative isoforms and identified a novel PPARG transcript, γ1ORF4. Our analysis indicated that, during adipogenesis, the transcription of alternative PPARG variants is regulated in a time-specific manner through differential usage of distinct promoters. In addition, our analysis describes—for the first time—the differential contribution of three ORF4 variants to this process, suggesting a still unexplored role for these dominant negative isoforms during adipogenesis. Therefore, our results highlight crucial aspects of PPARG regulation, suggesting the need of further investigation to rule out the differential impact of all PPARG transcripts in both physiologic and pathologic conditions, such as metabolism-related disorders.

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.

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.

Sequencing-based transcriptomics has significantly redefined the concept of genome complexity, leading to the identification of thousands of lncRNA genes identification of thousands of lncRNA genes whose products possess transcriptional and/or post-transcriptional regulatory functions that help to shape cell functionality and fate. Indeed, it is well-established now that lncRNAs play a key role in the regulation of gene expression through epigenetic and posttranscriptional mechanims. The rapid increase of studies reporting lncRNAs alteration in cancers has also highlighted their relevance for tumorigenesis. Herein we describe the most prominent examples of well-established lncRNAs having oncogenic and/or tumor suppressive activity. We also discuss how technical advances have provided new therapeutic strategies based on their targeting, and also report the challenges towards their use in the clinical settings.

BackgroundA novel prediction algorithm is needed for the identification of effective tumor associated mutated neoantigens. Only those with no homology to self wild type antigens are true predicted neoantigens (TPNAs) and can elicit an antitumor T cell response, not attenuated by central tolerance. To this aim, the mutational landscape was evaluated in HCV-associated hepatocellular carcinoma.MethodsLiver tumor biopsies and adjacent non-tumor liver tissues were obtained from 9 HCV-chronically infected subjects and subjected to RNA-Seq analysis. Mutant peptides were derived from single nucleotide variations and TPNAs were predicted using two prediction servers (e.g. NetTepi and NetMHCstabpan) by comparison with corresponding wild-type sequences, non-related self and pathogen-related antigens. Immunological confirmation was obtained in preclinical as well as clinical setting.ResultsThe development of such an improved algorithm resulted in a handful of TPNAs despite the large number of predicted neoantigens. Furthermore, TPNAs may share homology to pathogen’s antigens and be targeted by a pre-existing T cell immunity. Cross-reactivity between such antigens was confirmed in an experimental pre-clinical setting. Finally, TPNAs homologous to pathogen’s antigens were found in the only HCC long-term survival patient, suggesting a correlation between the pre-existing T cell immunity specific for these TPNAs and the favourable clinical outcome.ConclusionsThe new algorithm allowed the identification of the very few TPNAs in cancer cells, and those targeted by a pre-existing immunity strongly correlated with long-term survival. Only such TPNAs represent the optimal candidates for immunotherapy strategies.Electronic supplementary materialThe online version of this article (10.1186/s12967-018-1662-9) contains supplementary material, which is available to authorized users.