Valerio Costa

Uncovering the Complexity of Transcriptomes with RNA-Seq

In recent years, the introduction of massively parallel sequencing platforms for Next Generation Sequencing (NGS) protocols, able to simultaneously sequence hundred thousand DNA fragments, dramatically changed the landscape of the genetics studies. RNA-Seq for transcriptome studies, Chip-Seq for DNA-proteins interaction, CNV-Seq for large genome nucleotide variations are only some of the intriguing new applications supported by these innovative platforms. Among them RNA-Seq is perhaps the most complex NGS application. Expression levels of specific genes, differential splicing, allele-specific expression of transcripts can be accurately determined by RNA-Seq experiments to address many biological-related issues. All these attributes are not readily achievable from previously widespread hybridization-based or tag sequence-based approaches. However, the unprecedented level of sensitivity and the large amount of available data produced by NGS platforms provide clear advantages as well as new challenges and issues. This technology brings the great power to make several new biological observations and discoveries, it also requires a considerable effort in the development of new bioinformatics tools to deal with these massive data files. The paper aims to give a survey of the RNA-Seq methodology, particularly focusing on the challenges that this application presents both from a biological and a bioinformatics point of view.

RNA-Seq and human complex diseases: recent accomplishments and future perspectives

The availability of the human genome sequence has allowed identification of disease-causing mutations in many Mendelian disorders, and detection of significant associations of nucleotide polymorphisms to complex diseases and traits. Despite these progresses, finding the causative variations for most of the common diseases remains a complex task. Several studies have shown gene expression analyses provide a quite unbiased way to investigate complex traits and common disorders’ pathogenesis. Therefore, whole-transcriptome analysis is increasingly acquiring a key role in the knowledge of mechanisms responsible for complex diseases. Hybridization- and tag-based technologies have elucidated the involvement of multiple genes and pathways in pathological conditions, providing insights into the expression of thousand of coding and noncoding RNAs, such as microRNAs. However, the introduction of Next-Generation Sequencing, particularly of RNA-Seq, has overcome some drawbacks of previously used technologies. Identifying, in a single experiment, potentially novel genes/exons and splice isoforms, RNA editing, fusion transcripts and allele-specific expression are some of its advantages. RNA-Seq has been fruitfully applied to study cancer and host-pathogens interactions, and it is taking first steps for studying neurodegenerative diseases (ND) as well as neuropsychiatric diseases. In addition, it is emerging as a very powerful tool to study quantitative trait loci associated with gene expression in complex diseases. This paper provides an overview on gene expression profiling of complex diseases, with emphasis on RNA-Seq, its advantages over conventional technologies for studying cancer and ND, and for linking nucleotide variations to gene expression changes, also discussing its limitations.

Massive-Scale RNA-Seq Analysis of Non Ribosomal Transcriptome in Human Trisomy 21

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.

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.

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.

Detrimental effects of Bartonella henselae are counteracted by L-arginine and nitric oxide in human endothelial progenitor cells.

The recruitment of circulating endothelial progenitor cells (EPCs) might have a beneficial effect on the clinical course of several diseases. Endothelial damage and detachment of endothelial cells are known to occur in infection, tissue ischemia, and sepsis. These detrimental effects in EPCs are unknown. Here we elucidated whether human EPCs internalize Bartonella henselae constituting a circulating niche of the pathogen. B. henselae invades EPCs as shown by gentamicin protection assays and transmission electron microscopy (TEM). Dil-Ac-LDL/lectin double immunostaining and fluorescence-activated cell sorting (FACS) analysis of EPCs revealed EPC bioactivity after infection with B. henselae. Nitric oxide (NO) and its precursor l-arginine (l-arg) exert a plethora of beneficial effects on vascular function and modulation of immune response. Therefore, we tested also the hypothesis that l-arg (1–30 mM) would affect the infection of B. henselae or tumor necrosis factor (TNF) in EPCs. Our data provide evidence that l-arg counteracts detrimental effects induced by TNF or Bartonella infections via NO (confirmed by DETA-NO and L-NMMA experiments) and by modulation of p38 kinase phosphorylation. Microarray analysis indicated several genes involved in immune response were differentially expressed in Bartonella-infected EPCs, whereas these genes returned in steady state when cells were exposed to sustained doses of l-arg. This mechanism may have broad therapeutic applications in tissue ischemia, angiogenesis, immune response, and sepsis.

Understanding gene regulatory mechanisms by integrating ChIP-seq and RNA-seq data: statistical solutions to biological problems

The availability of omic data produced from international consortia, as well as from worldwide laboratories, is offering the possibility both to answer long-standing questions in biomedicine/molecular biology and to formulate novel hypotheses to test. However, the impact of such data is not fully exploited due to a limited availability of multi-omic data integration tools and methods. In this paper, we discuss the interplay between gene expression and epigenetic markers/transcription factors. We show how integrating ChIP-seq and RNA-seq data can help to elucidate gene regulatory mechanisms. In particular, we discuss the two following questions: (i) Can transcription factor occupancies or histone modification data predict gene expression? (ii) Can ChIP-seq and RNA-seq data be used to infer gene regulatory networks? We propose potential directions for statistical data integration. We discuss the importance of incorporating underestimated aspects (such as alternative splicing and long-range chromatin interactions). We also highlight the lack of data benchmarks and the need to develop tools for data integration from a statistical viewpoint, designed in the spirit of reproducible research.

Nutritional genomics era: opportunities toward a genome-tailored nutritional regimen

There is increasing evidence indicating that nutritional genomics represents a promise to improve public health. This goal will be reached by highlighting the mechanisms through which diet can reduce the risk of monogenic and common polygenic diseases. Indeed, nutrition is a very relevant environmental factor involved in the development and progression of metabolic disorders, as well as other kind of diseases. The revolutionary changes in the field of genomics have led to the development and implementation of new technologies and molecular tools. These technologies have a useful application in the nutritional sciences, since they allow a more precise and accurate analysis of biochemical alterations, in addition to filling fundamental gaps in the knowledge of nutrient-genome interactions in both health and disease. Overall, these advances will open undiscovered ways in genome-customized diets for disease prevention and therapy. This review summarizes the recent knowledge concerning this novel nutritional approach, paying attention to the human genome variations, such as single-nucleotide polymorphisms and copy number variations, gene expression and innovative molecular tools to reveal them.

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.

PPARG in Human Adipogenesis: Differential Contribution of Canonical Transcripts and Dominant Negative Isoforms

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.