Francesca Finotello

Global genomic and transcriptomic analysis of human pancreatic islets reveals novel genes influencing glucose metabolism

Significance We provide a comprehensive catalog of novel genetic variants influencing gene expression and metabolic phenotypes in human pancreatic islets. The data also show that the path from genetic variation (SNP) to gene expression is more complex than hitherto often assumed, and that we need to consider that genetic variation can also influence function of a gene by influencing exon usage or splice isoforms (sQTL), allelic imbalance, RNA editing, and expression of noncoding RNAs, which in turn can influence expression of target genes. Genetic variation can modulate gene expression, and thereby phenotypic variation and susceptibility to complex diseases such as type 2 diabetes (T2D). Here we harnessed the potential of DNA and RNA sequencing in human pancreatic islets from 89 deceased donors to identify genes of potential importance in the pathogenesis of T2D. We present a catalog of genetic variants regulating gene expression (eQTL) and exon use (sQTL), including many long noncoding RNAs, which are enriched in known T2D-associated loci. Of 35 eQTL genes, whose expression differed between normoglycemic and hyperglycemic individuals, siRNA of tetraspanin 33 (TSPAN33), 5′-nucleotidase, ecto (NT5E), transmembrane emp24 protein transport domain containing 6 (TMED6), and p21 protein activated kinase 7 (PAK7) in INS1 cells resulted in reduced glucose-stimulated insulin secretion. In addition, we provide a genome-wide catalog of allelic expression imbalance, which is also enriched in known T2D-associated loci. Notably, allelic imbalance in paternally expressed gene 3 (PEG3) was associated with its promoter methylation and T2D status. Finally, RNA editing events were less common in islets than previously suggested in other tissues. Taken together, this study provides new insights into the complexity of gene regulation in human pancreatic islets and better understanding of how genetic variation can influence glucose metabolism.

Computational genomics tools for dissecting tumour-immune cell interactions

Recent breakthroughs in cancer immunotherapy and decreasing costs of high-throughput technologies have sparked intensive research into tumour–immune cell interactions using genomic tools. The wealth of the generated data and the added complexity pose considerable challenges and require computational tools to process, to analyse and to visualize the data. Recently, various tools have been developed and used to mine tumour immunologic and genomic data effectively and to provide novel mechanistic insights. Here, we review computational genomics tools for cancer immunology and provide information on the requirements and functionality in order to assist in the selection of tools and assembly of analytical pipelines.

Measuring differential gene expression with RNA-seq: challenges and strategies for data analysis

RNA-seq is a methodology for RNA profiling based on next-generation sequencing that enables to measure and compare gene expression patterns at unprecedented resolution. Although the appealing features of this technique have promoted its application to a wide panel of transcriptomics studies, the fast-evolving nature of experimental protocols and computational tools challenges the definition of a unified RNA-seq analysis pipeline. In this review, focused on the study of differential gene expression with RNA-seq, we go through the main steps of data processing and discuss open challenges and possible solutions.

Reducing bias in RNA sequencing data: a novel approach to compute counts

BackgroundIn the last decade, Next-Generation Sequencing technologies have been extensively applied to quantitative transcriptomics, making RNA sequencing a valuable alternative to microarrays for measuring and comparing gene transcription levels. Although several methods have been proposed to provide an unbiased estimate of transcript abundances through data normalization, all of them are based on an initial count of the total number of reads mapping on each transcript. This procedure, in principle robust to random noise, is actually error-prone if reads are not uniformly distributed along sequences, as happens indeed due to sequencing errors and ambiguity in read mapping.Here we propose a new approach, called maxcounts, to quantify the expression assigned to an exon as the maximum of its per-base counts, and we assess its performance in comparison with the standard approach described above, which considers the total number of reads aligned to an exon. The two measures are compared using multiple data sets and considering several evaluation criteria: independence from gene-specific covariates, such as exon length and GC-content, accuracy and precision in the quantification of true concentrations and robustness of measurements to variations of alignments quality.ResultsBoth measures show high accuracy and low dependency on GC-content. However, maxcounts expression quantification is less biased towards long exons with respect to the standard approach. Moreover, it shows lower technical variability at low expressions and is more robust to variations in the quality of alignments.ConclusionsIn summary, we confirm that counts computed with the standard approach depend on the length of the feature they are summarized on, and are sensitive to the non-uniform distribution of reads along transcripts. On the opposite, maxcounts are robust to biases due to the non-uniformity distribution of reads and are characterized by a lower technical variability. Hence, we propose maxcounts as an alternative approach for quantitative RNA-sequencing applications.

Neoantigens Generated by Individual Mutations and Their Role in Cancer Immunity and Immunotherapy

Recent preclinical and clinical studies have proved the long-standing hypothesis that tumors elicit adaptive immune responses and that the antigens driving effective T-cell response are neoantigens, i.e., peptides that are generated from somatically mutated genes. Hence, the characterization of neoantigens and the identification of the immunogenic ones are of utmost importance for improving cancer immunotherapy and broadening its efficacy to a larger fraction of patients. In this review, we first introduce the methods used for the quantification of neoantigens using next-generation sequencing data and then summarize results obtained using these tools to characterize the neoantigen landscape in solid cancers. We then discuss the importance of neoantigens for cancer immunotherapy using checkpoint blockers, vaccination, and adoptive T-cell transfer. Finally, we give an overview over emerging aspects in cancer immunity, including tumor heterogeneity and immunoediting, and give an outlook on future prospects.

Genomic comparative analysis and gene function prediction in infectious diseases: application to the investigation of a meningitis outbreak

BackgroundNext generation sequencing (NGS) is being increasingly used for the detection and characterization of pathogens during outbreaks. This technology allows rapid sequencing of pathogen full genomes, useful not only for accurate genotyping and molecular epidemiology, but also for identification of drug resistance and virulence traits.MethodsIn this study, an approach based on whole genome sequencing by NGS, comparative genomics, and gene function prediction was set up and retrospectively applied for the investigation of two N. meningitidis serogroup C isolates collected from a cluster of meningococcal disease, characterized by a high fatality rate.ResultsAccording to conventional molecular typing methods, all the isolates had the same typing results and were classified as outbreak isolates within the same N. meningitidis sequence type ST-11, while full genome sequencing demonstrated subtle genetic differences between the isolates. Looking for these specific regions by means of 9 PCR and cycle sequencing assays in other 7 isolates allowed distinguishing outbreak cases from unrelated cases. Comparative genomics and gene function prediction analyses between outbreak isolates and a set of reference N. meningitidis genomes led to the identification of differences in gene content that could be relevant for pathogenesis. Most genetic changes occurred in the capsule locus and were consistent with recombination and horizontal acquisition of a set of genes involved in capsule biosynthesis.ConclusionsThis study showed the added value given by whole genome sequencing by NGS over conventional sequence-based typing methods in the investigation of an outbreak. Routine application of this technology in clinical microbiology will significantly improve methods for molecular epidemiology and surveillance of infectious disease and provide a bulk of data useful to improve our understanding of pathogens biology.

FunPat: function-based pattern analysis on RNA-seq time series data

BackgroundDynamic expression data, nowadays obtained using high-throughput RNA sequencing, are essential to monitor transient gene expression changes and to study the dynamics of their transcriptional activity in the cell or response to stimuli. Several methods for data selection, clustering and functional analysis are available; however, these steps are usually performed independently, without exploiting and integrating the information derived from each step of the analysis.MethodsHere we present FunPat, an R package for time series RNA sequencing data that integrates gene selection, clustering and functional annotation into a single framework. FunPat exploits functional annotations by performing for each functional term, e.g. a Gene Ontology term, an integrated selection-clustering analysis to select differentially expressed genes that share, besides annotation, a common dynamic expression profile.ResultsFunPat performance was assessed on both simulated and real data. With respect to a stand-alone selection step, the integration of the clustering step is able to improve the recall without altering the false discovery rate. FunPat also shows high precision and recall in detecting the correct temporal expression patterns; in particular, the recall is significantly higher than hierarchical, k-means and a model-based clustering approach specifically designed for RNA sequencing data. Moreover, when biological replicates are missing, FunPat is able to provide reproducible lists of significant genes. The application to real time series expression data shows the ability of FunPat to select differentially expressed genes with high reproducibility, indirectly confirming high precision and recall in gene selection. Moreover, the expression patterns obtained as output allow an easy interpretation of the results.ConclusionsA novel analysis pipeline was developed to search the main temporal patterns in classes of genes similarly annotated, improving the sensitivity of gene selection by integrating the statistical evidence of differential expression with the information on temporal profiles and the functional annotations. Significant genes are associated to both the most informative functional terms, avoiding redundancy of information, and the most representative temporal patterns, thus improving the readability of the results. FunPat package is provided in R/Bioconductor at link: http://sysbiobig.dei.unipd.it/?q=node/79.

TIminer: NGS data mining pipeline for cancer immunology and immunotherapy

Summary: Recently, a number of powerful computational tools for dissecting tumor‐immune cell interactions from next‐generation sequencing data have been developed. However, the assembly of analytical pipelines and execution of multi‐step workflows are laborious and involve a large number of intermediate steps with many dependencies and parameter settings. Here we present TIminer, an easy‐to‐use computational pipeline for mining tumor‐immune cell interactions from next‐generation sequencing data. TIminer enables integrative immunogenomic analyses, including: human leukocyte antigens typing, neoantigen prediction, characterization of immune infiltrates and quantification of tumor immunogenicity. Availability and implementation: TIminer is freely available at http://icbi.i‐med.ac.at/software/timiner/timiner.shtml. Contact: zlatko.trajanoski@i‐med.ac.at Supplementary information: Supplementary data are available at Bioinformatics online.

Targeting immune checkpoints potentiates immunoediting and changes the dynamics of tumor evolution

The cancer immunoediting hypothesis postulates a dual role of the immune system: protecting the host by eliminating tumor cells, and shaping the tumor by editing its genome. Here, we elucidate the impact of evolutionary and immune-related forces on editing the tumor in a mouse model for hypermutated and microsatellite-instable colorectal cancer. Analyses of wild-type and immunodeficient RAG1 knockout mice transplanted with MC38 cells reveal that upregulation of checkpoint molecules and infiltration by Tregs are the major tumor escape mechanisms. Our results show that the effects of immunoediting are weak and that neutral accumulation of mutations dominates. Targeting the PD-1/PD-L1 pathway using immune checkpoint blocker effectively potentiates immunoediting. The immunoediting effects are less pronounced in the CT26 cell line, a non-hypermutated/microsatellite-instable model. Our study demonstrates that neutral evolution is another force that contributes to sculpting the tumor and that checkpoint blockade effectively enforces T-cell-dependent immunoselective pressure.The cancer immunoediting hypothesis assumes the immune system sculpts the cancer genome. Here the authors show, in a mouse model, that neutral evolution outweighs the effects of immunoselection and that immune checkpoint blockade potentiates the immunoediting, switching the system to non-neutral evolution.

New strategies for cancer immunotherapy: targeting regulatory T cells.

The immunosuppressive action of regulatory T (Treg) cells is one mechanism attributed to the limited success of cancer immunotherapies with checkpoint blockers. Two recent studies report distinct transcriptional profiles of tumor-infiltrating Treg cells and expression of specific molecules, suggesting novel strategies to overcome resistance to cancer immunotherapy.