Gene and lncRNA Profiling of ω3/ω6 Polyunsaturated Fatty Acid-Exposed Human Visceral Adipocytes Uncovers Different Responses in Healthy Lean, Obese and Colorectal Cancer-Affected Individuals.

Colorectal cancer (CRC) is a major life-threatening disease, being the third most common cancer and a leading cause of death worldwide. Enhanced adiposity, particularly visceral fat, is a major risk factor for CRC, and obesity-associated alterations in metabolic, inflammatory and immune profiles in visceral adipose tissue (VAT) strongly contribute to promoting or sustaining intestinal carcinogenesis. The role of diet and nutrition in obesity and CRC has been extensively demonstrated, and AT represents the main place where diet-induced signals are integrated. Among the factors introduced with diet and processed or enriched in AT, ω3/ω6 polyunsaturated fatty acids (PUFAs) are endowed with pro- or anti-inflammatory properties and have been shown to exert either promoting or protective roles in CRC. In this study, we investigated the impact of ex vivo exposure to the ω3 and ω6 PUFAs docosahexaenoic and arachidonic acids on VAT adipocyte whole transcription in healthy lean, obese and CRC-affected individuals. High-throughput sequencing of protein-coding and long non-coding RNAs allowed us to identify specific pathways and regulatory circuits controlled by PUFAs and highlighted an impaired responsiveness of obese and CRC-affected individuals as compared to the strong response observed in healthy lean subjects. This further supports the role of healthy diets and balanced ω3/ω6 PUFA intake in the primary prevention of obesity and cancer.

A comprehensive investigation of histotype-specific microRNA and their variants in Stage I epithelial ovarian cancers.

isomiRs, the sequence-variants of microRNA, are known to be tissue and cell type specific but their physiological role is largely unknown. In our study, we explored for the first time the expression of isomiRs across different Stage I epithelial ovarian cancer (EOC) histological subtypes, in order to shed new light on their biological role in tumor growth and progression. In a multicentric retrospective cohort of tumor biopsies (n = 215) we sequenced small RNAs finding 971 expressed miRNAs, 64% of which are isomiRs. Among them, 42 isomiRs showed a clear histotype specific pattern, confirming our previously identified miRNA markers (miR192/194 and miR30a-3p/5p for mucinous and clear cell subtypes, respectively) and uncovering new biomarkers for all the five subtypes. Using integrative models, we found that the 38% of these miRNA expression alterations is the result of copy number variations while the 17% of differential transcriptional activities. Our work represents the first attempt to characterize isomiRs expression in Stage I EOC within and across subtypes and to contextualize their alterations in the framework of the large genomic heterogeneity of this tumor.

A pan-cancer landscape of pathogenic somatic copy number variations.

OBJECTIVE: Copy number variations (CNVs) play crucial roles in physiological and pathological processes, including cancer. However, the functional implications of somatic CNVs in tumor progression and evolution remain unclear. This study focuses on identifying CNV alterations with high pathogenic potential that drive and sustain tumorigenesis, distinguishing them from passenger alterations that accumulate during tumor growth. Our goal is to explore the variability of CNVs across different tumor types and infer their impact on tumor cell functions. METHODS: Starting from 7352 copy number profiles across 33 different cancer types, we infer the pathogenicity of each CNV and perform both intra- and inter-tumor analyses to predict the functional impact of different genomic patterns. We evaluate the actionability of genes belonging to altered regions and we correlate the presence of pathogenic regions with genome instability patterns and patients' survival. RESULTS: Our analysis uncovered large heterogeneity among different tumors suggesting in many cases distinct genetic drivers of tumorigenesis. Recurrent genomic alterations frequently coincide with dysfunctional homologous recombination pathways and negative regulation of the immune system. In certain tumors, the number of pathogenic CNVs emerged as a prognostic biomarker, highlighting their significance in cancer progression. CONCLUSION: This study contributes to elucidate the functional impact of pathogenic CNVs in tumor progression and sheds light on their potential as prognostic markers in specific cancer types.

MOSClip: multi-omic and survival pathway analysis for the identification of survival associated gene and modules.

Survival analyses of gene expression data has been a useful and widely used approach in clinical applications. But, in complex diseases, such as cancer, the identification of survival-associated cell processes - rather than single genes - provides more informative results because the efficacy of survival prediction increases when multiple prognostic features are combined to enlarge the possibility of having druggable targets. Moreover, genome-wide screening in molecular medicine has rapidly grown, providing not only gene expression but also multi-omic measurements such as DNA mutations, methylation, expression, and copy number data. In cancer, virtually all these aberrations can contribute in synergy to pathological processes, and their measurements can improve a patient's outcome and help in diagnosis and treatment decisions. Here, we present MOSClip, an R package implementing a new topological pathway analysis tool able to integrate multi-omic data and look for survival-associated gene modules. MOSClip tests the survival association of dimensionality-reduced multi-omic data using multivariate models, providing graphical devices for management, browsing and interpretation of results. Using simulated data we evaluated MOSClip performance in terms of false positives and false negatives in different settings, while the TCGA ovarian cancer dataset is used as a case study to highlight MOSClip's potential.

Expression profiles of PRKG1, SDF2L1 and PPP1R12A are predictive and prognostic factors for therapy response and survival in high-grade serous ovarian cancer.

High-grade serous ovarian cancer (HGS-EOCs) is generally sensitive to front-line platinum (Pt)-based chemotherapy although most patients at an advanced stage relapse with progressive resistant disease. Clinical or molecular data to identify primary resistant cases at diagnosis are not yet available. HGS-EOC biopsies from 105 Pt-sensitive (Pt-s) and 89 Pt-resistant (Pt-r) patients were retrospectively selected from two independent tumor tissue collections. Pathway analysis was done integrating miRNA and mRNA expression profiles. Signatures were further validated in silico on a cohort of 838 HGS-EOC cases from a published dataset. In all, 131 mRNAs and 5 miRNAs belonging to different functionally related molecular pathways distinguish Pt-s from Pt-r cases. Then, 17 out of 23 selected elements were validated by orthogonal approaches (SI signature). As resistance to Pt is associated with a short progression-free survival (PFS) and overall survival (OS), the prognostic role of the SI signature was assessed, and 14 genes associated with PFS and OS, in multivariate analyses (SII signature). The prognostic value of the SII signature was validated in a third extensive cohort. The expression profiles of SDF2L1, PPP1R12A and PRKG1 genes (SIII signature) served as independent prognostic biomarkers of Pt-response and survival. The study identified a prognostic molecular signature based on the combined expression profile of three genes which had never been associated with the clinical outcome of HGS-EOC. This may lead to early identification, at the time of diagnosis, of patients who would not greatly benefit from standard chemotherapy and are thus eligible for novel investigational approaches.

metaGraphite-a new layer of pathway annotation to get metabolite networks.

MOTIVATION: Metabolomics is an emerging 'omics' science involving the characterization of metabolites and metabolism in biological systems. Few bioinformatic tools have been developed for the visualization, exploration and analysis of metabolomic data within the context of metabolic pathways: some of them became rapidly obsolete and are no longer supported, others are based on a single database. A systematic collection of existing annotations has the potential of considerably boosting the investigation and contextualization of metabolomic measurements. RESULTS: We have released a major update of our Bioconductor package graphite which explicitly tracks small molecules within pathway topologies and their interactions with proteins. The package gathers the information stored in eight major databases, oriented both at genes and at metabolites, across 14 different species. Depending on user preferences, all pathways can be retrieved as gene-only, gene metabolite or metabolite-only networks. AVAILABILITY AND IMPLEMENTATION: The new graphite version (1.24) is available on Bioconductor. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Multisite analysis of high-grade serous epithelial ovarian cancers identifies genomic regions of focal and recurrent copy number alteration in 3q26.2 and 8q24.3.

High-grade serous epithelial ovarian cancer (HGS-EOC) is a systemic disease, with marked intra and interpatient tumor heterogeneity. The issue of spatial and temporal heterogeneity has long been overlooked, hampering the possibility to identify those genomic alterations that persist, before and after therapy, in the genome of all tumor cells across the different anatomical districts. This knowledge is the first step to clarify those molecular determinants that characterize the tumor biology of HGS-EOC and their route toward malignancy. In our study, -omics data were generated from 79 snap frozen matched tumor biopsies, withdrawn before and after chemotherapy from 24 HGS-EOC patients, gathered together from independent cohorts. The landscape of somatic copy number alterations depicts a more homogenous and stable genomic portrait than the single nucleotide variant profile. Genomic identification of significant targets in cancer analysis identified two focal and minimal common regions (FMCRs) of amplification in the cytoband 3q26.2 (region α, 193 kb long) and 8q24.3 (region ß, 495 kb long). Analysis in two external databases confirmed regions α and ß are features of HGS-EOC. The MECOM gene is located in region α, and 15 genes are in region ß. No functional data are yet available for the genes in the ß region. In conclusion, we have identified for the first time two FMCRs of amplification in HGS-EOC, opening up a potential biological role in its etiopathogenesis.

Wiring miRNAs to pathways: a topological approach to integrate miRNA and mRNA expression profiles.

The production rate of gene expression data is nothing less than astounding. However, with the benefit of hindsight we can assert that, since we completely ignored the non-coding part of the transcriptome, we spent the last decade to study cell mechanisms having few data in our hands. In this scenario, microRNAs, which are key post-trascriptional regulators, deserve special attention. Given the state of knowledge about their biogenesis, mechanisms of action and the numerous experimentally validated target genes, miRNAs are also gradually appearing in the formal pathway representations such as KEGG and Reactome maps. However, the number of miRNAs annotated in pathway maps are very few and pathway analyses exploiting this new regulatory layer are still lacking. To fill these gaps, we present 'micrographite' a new pipeline to perform topological pathway analysis integrating gene and miRNA expression profiles. Here, micrographite is used to study and dissect the epithelial ovarian cancer gene and miRNA transcriptome defining and validating a new regulatory circuit related to ovarian cancer histotype specificity.

Graphite Web: Web tool for gene set analysis exploiting pathway topology.

Graphite web is a novel web tool for pathway analyses and network visualization for gene expression data of both microarray and RNA-seq experiments. Several pathway analyses have been proposed either in the univariate or in the global and multivariate context to tackle the complexity and the interpretation of expression results. These methods can be further divided into 'topological' and 'non-topological' methods according to their ability to gain power from pathway topology. Biological pathways are, in fact, not only gene lists but can be represented through a network where genes and connections are, respectively, nodes and edges. To this day, the most used approaches are non-topological and univariate although they miss the relationship among genes. On the contrary, topological and multivariate approaches are more powerful, but difficult to be used by researchers without bioinformatic skills. Here we present Graphite web, the first public web server for pathway analysis on gene expression data that combines topological and multivariate pathway analyses with an efficient system of interactive network visualizations for easy results interpretation. Specifically, Graphite web implements five different gene set analyses on three model organisms and two pathway databases. Graphite Web is freely available at http://graphiteweb.bio.unipd.it/.

timeClip: pathway analysis for time course data without replicates.

BACKGROUND: Time-course gene expression experiments are useful tools for exploring biological processes. In this type of experiments, gene expression changes are monitored along time. Unfortunately, replication of time series is still costly and usually long time course do not have replicates. Many approaches have been proposed to deal with this data structure, but none of them in the field of pathway analysis. Pathway analyses have acquired great relevance for helping the interpretation of gene expression data. Several methods have been proposed to this aim: from the classical enrichment to the more complex topological analysis that gains power from the topology of the pathway. None of them were devised to identify temporal variations in time course data. RESULTS: Here we present timeClip, a topology based pathway analysis specifically tailored to long time series without replicates. timeClip combines dimension reduction techniques and graph decomposition theory to explore and identify the portion of pathways that is most time-dependent. In the first step, timeClip selects the time-dependent pathways; in the second step, the most time dependent portions of these pathways are highlighted. We used timeClip on simulated data and on a benchmark dataset regarding mouse muscle regeneration model. Our approach shows good performance on different simulated settings. On the real dataset, we identify 76 time-dependent pathways, most of which known to be involved in the regeneration process. Focusing on the 'mTOR signaling pathway' we highlight the timing of key processes of the muscle regeneration: from the early pathway activation through growth factor signals to the late burst of protein production needed for the fiber regeneration. CONCLUSIONS: timeClip represents a new improvement in the field of time-dependent pathway analysis. It allows to isolate and dissect pathways characterized by time-dependent components. Furthermore, using timeClip on a mouse muscle regeneration dataset we were able to characterize the process of muscle fiber regeneration with its correct timing.

A mitochondrial NADPH-cholesterol axis regulates extracellular vesicle biogenesis to support hematopoietic stem cell fate.

Mitochondrial fatty acid oxidation (FAO) is essential for hematopoietic stem cell (HSC) self-renewal; however, the mechanism by which mitochondrial metabolism controls HSC fate remains unknown. Here, we show that within the hematopoietic lineage, HSCs have the largest mitochondrial NADPH pools, which are required for proper HSC cell fate and homeostasis. Bioinformatic analysis of the HSC transcriptome, biochemical assays, and genetic inactivation of FAO all indicate that FAO-generated NADPH fuels cholesterol synthesis in HSCs. Interference with FAO disturbs the segregation of mitochondrial NADPH toward corresponding daughter cells upon single HSC division. Importantly, we have found that the FAO-NADPH-cholesterol axis drives extracellular vesicle (EV) biogenesis and release in HSCs, while inhibition of EV signaling impairs HSC self-renewal. These data reveal the existence of a mitochondrial NADPH-cholesterol axis for EV biogenesis that is required for hematopoietic homeostasis and highlight the non-stochastic nature of HSC fate determination.

Systems biology approach to the dissection of the complexity of regulatory networks in the S. scrofa cardiocirculatory system.

Genome-wide experiments are routinely used to increase the understanding of the biological processes involved in the development and maintenance of a variety of pathologies. Although the technical feasibility of this type of experiment has improved in recent years, data analysis remains challenging. In this context, gene set analysis has emerged as a fundamental tool for the interpretation of the results. Here, we review strategies used in the gene set approach, and using datasets for the pig cardiocirculatory system as a case study, we demonstrate how the use of a combination of these strategies can enhance the interpretation of results. Gene set analyses are able to distinguish vessels from the heart and arteries from veins in a manner that is consistent with the different cellular composition of smooth muscle cells. By integrating microRNA elements in the regulatory circuits identified, we find that vessel specificity is maintained through specific miRNAs, such as miR-133a and miR-143, which show anti-correlated expression with their mRNA targets.

signifinder enables the identification of tumor cell states and cancer expression signatures in bulk, single-cell and spatial transcriptomic data.

Over the last decade, many studies and some clinical trials have proposed gene expression signatures as a valuable tool for understanding cancer mechanisms, defining subtypes, monitoring patient prognosis, and therapy efficacy. However, technical and biological concerns about reproducibility have been raised. Technical reproducibility is a major concern: we currently lack a computational implementation of the proposed signatures, which would provide detailed signature definition and assure reproducibility, dissemination, and usability of the classifier. Another concern regards intratumor heterogeneity, which has never been addressed when studying these types of biomarkers using bulk transcriptomics. With the aim of providing a tool able to improve the reproducibility and usability of gene expression signatures, we propose signifinder, an R package that provides the infrastructure to collect, implement, and compare expression-based signatures from cancer literature. The included signatures cover a wide range of biological processes from metabolism and programmed cell death, to morphological changes, such as quantification of epithelial or mesenchymal-like status. Collected signatures can score tumor cell characteristics, such as the predicted response to therapy or the survival association, and can quantify microenvironmental information, including hypoxia and immune response activity. signifinder has been used to characterize tumor samples and to investigate intra-tumor heterogeneity, extending its application to single-cell and spatial transcriptomic data. Through these higher-resolution technologies, it has become increasingly apparent that the single-sample score assessment obtained by transcriptional signatures is conditioned by the phenotypic and genetic intratumor heterogeneity of tumor masses. Since the characteristics of the most abundant cell type or clone might not necessarily predict the properties of mixed populations, signature prediction efficacy is lowered, thus impeding effective clinical diagnostics. Through signifinder, we offer general principles for interpreting and comparing transcriptional signatures, as well as suggestions for additional signatures that would allow for more complete and robust data inferences. We consider signifinder a useful tool to pave the way for reproducibility and comparison of transcriptional signatures in oncology.

graphite - a Bioconductor package to convert pathway topology to gene network.

BACKGROUND: Gene set analysis is moving towards considering pathway topology as a crucial feature. Pathway elements are complex entities such as protein complexes, gene family members and chemical compounds. The conversion of pathway topology to a gene/protein networks (where nodes are a simple element like a gene/protein) is a critical and challenging task that enables topology-based gene set analyses.Unfortunately, currently available R/Bioconductor packages provide pathway networks only from single databases. They do not propagate signals through chemical compounds and do not differentiate between complexes and gene families. RESULTS: Here we present graphite, a Bioconductor package addressing these issues. Pathway information from four different databases is interpreted following specific biologically-driven rules that allow the reconstruction of gene-gene networks taking into account protein complexes, gene families and sensibly removing chemical compounds from the final graphs. The resulting networks represent a uniform resource for pathway analyses. Indeed, graphite provides easy access to three recently proposed topological methods. The graphite package is available as part of the Bioconductor software suite. CONCLUSIONS: graphite is an innovative package able to gather and make easily available the contents of the four major pathway databases. In the field of topological analysis graphite acts as a provider of biological information by reducing the pathway complexity considering the biological meaning of the pathway elements.

The Biological Connection Markup Language: a SBGN-compliant format for visualization, filtering and analysis of biological pathways.

MOTIVATION: Many models and analysis of signaling pathways have been proposed. However, neither of them takes into account that a biological pathway is not a fixed system, but instead it depends on the organism, tissue and cell type as well as on physiological, pathological and experimental conditions. RESULTS: The Biological Connection Markup Language (BCML) is a format to describe, annotate and visualize pathways. BCML is able to store multiple information, permitting a selective view of the pathway as it exists and/or behave in specific organisms, tissues and cells. Furthermore, BCML can be automatically converted into data formats suitable for analysis and into a fully SBGN-compliant graphical representation, making it an important tool that can be used by both computational biologists and 'wet lab' scientists. AVAILABILITY AND IMPLEMENTATION: The XML schema and the BCML software suite are freely available under the LGPL for download at http://bcml.dc-atlas.net. They are implemented in Java and supported on MS Windows, Linux and OS X.

Association between miR-200c and the survival of patients with stage I epithelial ovarian cancer: a retrospective study of two independent tumour tissue collections.

BACKGROUND: International Federation of Gynecology and Obstetrics stage I epithelial ovarian cancer (EOC) has a significantly better prognosis than stage III/IV EOC, with about 80% of patients surviving at 5 years (compared with about 20% of those with stage III/IV EOC). However, 20% of patients with stage I EOC relapse within 5 years. It is therefore crucial that the biological properties of stage I EOCs are further elucidated. MicroRNAs (miRNAs) have shown diagnostic and prognostic potential in stage III and IV EOCs, but the small number of patients diagnosed with stage I EOC has so far prevented an investigation of its molecular features. We profiled miRNA expression in stage I EOC tumours to assess whether there is a miRNA signature associated with overall and progression-free survival (PFS) in stage I EOC. METHODS: We analysed tumour samples from 144 patients (29 of whom relapsed) with stage I EOC gathered from two independent tumour tissue collections (A and B), both with a median follow-up of 9 years. 89 samples from tumour tissue collection A were stratified into a training set (51 samples, 15 of which were from patients who relapsed) for miRNA signature generation, and into a validation set (38 samples, seven of which were from patients who relapsed) for signature validation. Tumour tissue collection B (55 samples, seven of which were from patients who relapsed) was used as an independent test set. The Cox proportional hazards model and the log-rank test were used to assess the correlation of quantitative reverse transcription PCR (qRT-PCR)-validated miRNAs with overall survival and PFS. FINDINGS: A signature of 34 miRNAs associated with survival was generated by microarray analysis in the training set. In both the training set and validation set, qRT-PCR analysis confirmed that 11 miRNAs (miR-214, miR-199a-3p, miR-199a-5p, miR-145, miR-200b, miR-30a, miR-30a*, miR-30d, miR-200c, miR-20a, and miR-143) were expressed differently in relapsers compared with non-relapsers. Three of these miRNAs (miR-200c, miR-199a-3p, miR-199a-5p) were associated with PFS, overall survival, or both in multivariate analysis. qRT-PCR analysis in the test set confirmed the downregulation of miR-200c in relapsers compared with non-relapsers, but not the upregulation of miR-199a-3p and miR-199a-5p. Multivariate analysis confirmed that downregulation of miR-200c in the test set was associated with overall survival (HR 0·094, 95% CI 0·012-0·766, p=0·0272) and PFS (0·035, 0·004-0·311; p=0·0026), independent of clinical covariates. INTERPRETATION: miR-200c has potential as a predictor of survival, and is a biomarker of relapse, in stage I EOC. FUNDING: Nerina and Mario Mattioli Foundation, Cariplo Foundation (Grant Number 2010-0744), and the Italian Association for Cancer Research.

Oxidative pentose phosphate pathway controls vascular mural cell coverage by regulating extracellular matrix composition.

Vascular mural cells (vMCs) play an essential role in the development and maturation of the vasculature by promoting vessel stabilization through their interactions with endothelial cells. Whether endothelial metabolism influences mural cell recruitment and differentiation is unknown. Here, we show that the oxidative pentose phosphate pathway (oxPPP) in endothelial cells is required for establishing vMC coverage of the dorsal aorta during early vertebrate development in zebrafish and mice. We demonstrate that laminar shear stress and blood flow maintain oxPPP activity, which in turn, promotes elastin expression in blood vessels through production of ribose-5-phosphate. Elastin is both necessary and sufficient to drive vMC recruitment and maintenance when the oxPPP is active. In summary, our work demonstrates that endothelial cell metabolism regulates blood vessel maturation by controlling vascular matrix composition and vMC recruitment.

Copy number alterations in stage I epithelial ovarian cancer highlight three genomic patterns associated with prognosis.

BACKGROUND: Stage I epithelial ovarian cancer (EOC) encompasses five histologically different subtypes of tumors confined to the ovaries with a generally favorable prognosis. Despite the intrinsic heterogeneity, all stage I EOCs are treated with complete resection and adjuvant therapy in most of the cases. Owing to the lack of robust prognostic markers, this often leads to overtreatment. Therefore, a better molecular characterization of stage I EOCs could improve the assessment of the risk of relapse and the refinement of optimal treatment options. MATERIALS AND METHODS: 205 stage I EOCs tumor biopsies with a median follow-up of eight years were gathered from two independent Italian tumor tissue collections, and the genome distribution of somatic copy number alterations (SCNAs) was investigated by shallow whole genome sequencing (sWGS) approach. RESULTS: Despite the variability in SCNAs distribution both across and within the histotypes, we were able to define three common genomic instability patterns, namely stable, unstable, and highly unstable. These patterns were based on the percentage of the genome affected by SCNAs and on their length. The genomic instability pattern was strongly predictive of patients' prognosis also with multivariate models including currently used clinico-pathological variables. CONCLUSIONS: The results obtained in this study support the idea that novel molecular markers, in this case genomic instability patterns, can anticipate the behavior of stage I EOC regardless of tumor subtype and provide valuable prognostic information. Thus, it might be propitious to extend the study of these genomic instability patterns to improve rational management of this disease.

Summarizing RNA-Seq Data or Differentially Expressed Genes Using Gene Set, Network, or Pathway Analysis.

The main purpose of pathway or gene set analysis methods is to provide mechanistic insight into the large amount of data produced in high-throughput studies. These tools were developed for gene expression analyses, but they have been rapidly adopted by other high-throughput techniques, becoming one of the foremost tools of omics research.Currently, according to different biological questions and data, we can choose among a vast plethora of methods and databases. Here we use two published examples of RNAseq datasets to approach multiple analyses of gene sets, networks and pathways using freely available and frequently updated software. Finally, we conclude this chapter by presenting a survival pathway analysis of a multiomics dataset. During this overview of different methods, we focus on visualization, which is a fundamental but challenging step in this computational field.

Micro-RNA Quantification, Target Gene Identification, and Pathway Analysis.

RNA sequencing has become a powerful tool for profiling the expression level of small RNAs from both solid tissues and liquid biopsies. In conjunction with pathway analysis, it offers exciting possibilities for the identification of disease specific biomarkers. In this chapter, we describe a workflow for processing this type of sequencing data. We start by removing technical sequences (adapters) and by performing quality control, a critical task that is necessary to identify possible issues caused by sample preparation and library sequencing. We then describe read alignment and gene-level abundance estimation. Building on these results, we normalize expression profiles and compute differentially expressed microRNAs between sample groups of interest. We conclude by showing how to employ pathway analysis to identify molecular signatures corresponding to biological processes that are significantly altered by the action for microRNAs.