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Empowering precision medicine through high performance computing clusters

Castrignano T

SuperComputing Applications and Innovation Department, CINECA, SCAI, Rome, Italy

E-mail : bhuvaneswari.bibleraaj@uhsm.nhs.uk

Parisi V

Physics Department, Sapienza Università di Roma, P.le A.Moro 5, 00185, Rome, Italy

Chillemi G

Department for Innovation in Biological, Agro-food and Forest systems, DIBAF, University of Tuscia, via S. Camillo de Lellis s.n.c., 01100 Viterbo, Italy

National Council of Research, CNR, Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies, Bari, Italy

DOI: 10.15761/JTS.1000268

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Abstract

The role of High Performance Computing (HPC) in Medicine is greatly increase in these last years, moving from basic research to the clinics. With the advent of Next Generation Sequencing (NGS) technologies, diverse areas of human health have been investigated through different omics techniques. The extensive use of these NGS platforms to high throughput profile human health issues in a cost-efficient manner, is generating huge amount of sequencing data pushing bioinformatic research in the big-data field. Speed, accuracy and reproducibility of massively sequencing analysis have allowed to transfer molecular biology knowledge into precision medicine. Furthermore, Molecular Dynamics (MD) earned a great importance in aiding genome research. Sequencing studies of cancer have allowed to detect and characterize mutated genes that drive tumorigenesis. As a complementary approach, from a biophysical perspective, MD simulations, executed on HPC architectures, have permitted to investigate the role played by pathological mutations on the molecular mechanism of activation.

Big Data Next-Generation Sequencing for translational research

The goal of genomics research is to identify genetic variants associated with disease, response to treatment, or future patient prognosis. Whole Genome Sequencing (WGS) is a genomics technique that allows to detect all types of genetic variations (single nucleotide and deletion/insertion polymorphism) across the entire genome. This powerful feature joined to the maps of genetic variation in populations is a very robust and effective tool for identifying pathogenic variants thus enabling the integration of diagnosis, genetic counselling into treatment decision-making. In 2015 Taylor et al. extensively applied whole-genome sequencing as tool for diagnosis of genetic disorders in routine clinical practice on 500 patients (including 156 independent cases) [1]. They identified of at least one variant with a high level of evidence of pathogenicity in 21% of cases (33/156) using several analysis strategies that improved the accuracy of variant calling and detection rates. More in general WGS provides a picture of the whole landscape of driver mutation and mutational signature in diseases. Several HPC bioinformatic pipelines have been developed to characterize and prioritize genetics variant [2-3].

Whole-exome sequencing (WES) is a genomic technique for sequencing all of the protein-coding genes in a genome (also known as the exome) [4-5]. It has been applied to cancer and rare diseases to identify both the actionable somatic variants in the coding regions and efficiently detect the Mendelian disorder variants; WES has been used extensively to diagnose novel diseases and find novel causative mutations for known disease phenotypes [6]. WES has been also applied for diagnosis of young patients without all spectrum of symptoms [7] and prenatal diagnosis [8]. Furthermore, detecting the causative mutation can suggest how to modify the treatment and prevent more invasive tests, confirming diagnoses and open the access to clinical trials.

Targeted-exome sequencing (TES) is a genomic technique for which a subset of genes or regions of the genome are isolated and sequenced. This technique allows researchers to focus data analysis on specific genomic ranges of interest and enables sequencing at much higher coverage levels. In this way, specific gene panels [9-10] become valuable tools to detect mutations in genes or genomic regions that are known or suspected to be associated to the disease of interest; the panel can be custom designed to amplify the regions of interest. TES offers a more sensitive approach for the analysis of the cancer genome. It eliminates in short time much of the background noise generated by WES, since it provides higher coverage at a lower cost. This feature makes TES an ideal tool for translational medicine and clinical settings.

RNA sequencing (RNA-Seq) is a sequencing technique able to reveal the presence and quantity of RNA in a biological sample at a given moment and specific experimental condition. RNA-Seq is used to analyse the continuously changing cellular transcriptome. It has been extensively applied to patients to identify the molecular bases of many biological processes and diseases, including cancer [11-12]. In particular, transcriptome-wide gene expression profiling has provided a better comprehension of the molecular mechanisms underlying prognosis and drug sensitivity. It addresses several aspects of the expression process (e.g. identification and quantification of expressed genes and transcripts, alternative splicing and polyadenylation, fusion genes and trans-splicing, post-transcriptional events, etc.) [13-17].

The Cancer Genome Atlas consortium [18] provides access to a big-data secure repository for storing, cataloguing and querying cancer genome ‘omics data. Through the TCGA Data Portal (https://tcga-data.nci.nih.gov) cancer genome sequences, alignments, mutation information and molecular changes in cancer genome datasets, such as new aberrations in several cancer types, are now available to scientific community. Another two available big-data resources on cancer are the Cancer Cell Line Encyclopedia (CCLE) [19] and the Genomics of Drug Sensitivity in Cancer [20]. As translational immediate impact on precision medicine, link among genomic biomarkers and drug sensitivity in hundreds of cancer cell lines are available for patients. With particular reference to CCLE, a big-data HPC analysis has been extensively performed on 935 paired-end RNA-seq experiments downloaded from CCLE repository, aiming at addressing novel putative cell-line specific gene fusion events in human malignancies [21]. Several gene fusion detection algorithms have been applied to the CCLE dataset in order to provide in silico a reliable consensus result set of about 1,700 predicted novel fusion gene candidates in all the human malignant cell lines. Such results, querieble on gene fusion database web portal (Ligea - http://hpc-bioinformatics.cineca.it/fusion) could represent the starting point for detecting in wet lab novel cancer biomarkers and specific drug targets. In (Figure 1) a composition of screenshots of the Ligea portal are shown.

Figure 1. Screenshots of Ligea Portal: a) A ’Search by Cell line’ form allows to navigate the database by indicating a specific cell line name; b) the corresponding results table reporting the gene fusion prediction for each used algorithm (Fusioncatcher, EricScript, Tophat Fusion, Jaffa); c) by clicking on the light blue button (corresponding to fusioncatcher algorithm result), a popup window opens showing details about the putative detected gene fusion event; d) the Venn diagram shows the intersection of the putative gene fusion events identified by the four algorithms; e) the number of the different cell lines derived from the same diseases. Both d) and e) panels can be visualized in the Ligea home page.

The availability of human gene expression profiles for normal (GTEx), tumor (TCGA) and cancer cell line (CCLE) tissues provide a first picture of the structure of global gene expression. However, the complexity functional tumour molecular profiles poses great challenges to translate information contained in the big-data bioinformatics repositories into new cancer drugs and molecular diagnostics. The role of HPC in bioinformatics and computational biology is essential to reach these goals in a reasonable time.

ChIP-seq is a sequencing technique that combines chromatin immunoprecipitation (ChIP) with massively parallel DNA sequencing. It is a powerful method to identify genome-wide DNA binding sites for transcription factors and other proteins. Furthermore, it can be used to precisely map global binding sites for any protein of interest [22-24].

Epigenetic alterations are modifications in gene expression that are independent of the DNA gene sequence. They are considered to be very influential in both the normal and disease states of an organism. In particular they may influence the epigenetic inheritance and epigenetic carcinogenesis, or any other disease related to alterations in an organism. Main epigenetic mechanisms modificating gene expression are: DNA methylation [25], histone modifications [26], chromatin remodeling and microRNAs that act as regulatory molecules [27].    Epigenetics changes provides a molecular profiling of interactions between genomic and environmental conditions [28]; they are responsible for the regulation of specific gene expression networks that differ in behaviour between normal and diseased phenotype. In case of pancreatic ductal adenocarcinoma (PDAC) subtypes the study of epigenomic landscapes integrated with data of Chip-seq and RNAseq has allowed to predict aggressiveness and survival in some subtype of PADC [29], thus providing potential new markers and therapeutic targets.

Metagenomics is a sequencing technique that allows to study the genetic material recovered directly from environmental samples. It has been extensively applied to characterize virus genome heterogeneity, without in vitro replication biases, in the microbial community present in the clinical samples. High-throughput pyrosequencing has been used to detect and characterize 2009 pandemic influenza A (H1N1) virus directly in nasopharyngeal swabs in the context of the microbial community [30-33].

Nowadays another translational clinical field is growing in metagenomic research area: the study of human microbiome, responsible for influencing individuals in both health and disease. It is a major player in the immune system, since researchers believes that immune reactions are closely linked to the distribution of microbial communities throughout a person’s life [34].

Structural characterization of pathogenic mutations

Historically, the HPC role in Medicine is even precedent to the NGS revolution, starting in the ‘90 with the availability of accurate in silico models for the simulation of biological macromolecules (first proteins in aqueous environment and then nucleic acids and membrane proteins).

HPC, in particular, has been widely applied in cancer research with Molecular Dynamics simulations characterizing cancer related proteins [35-38]; evaluating the impact of somatic mutations or the activity of anticancer drugs [39-42]. MD has been also applied for the characterization of viral proteins [43-44].

The growing availability of genomic information, and in particular non-synonymous SNPs obtained by NGS and microarray-based platforms, has increased the need for in silico methods capable to provide information at atomic level on the structural and dynamic alterations produced in mutated proteins. MD simulation is routinely complemented by other complementary methods such as Homology modelling, Molecular docking, and Drug Design. Application of these methods has become a standard tool in human genome research, since they proved to be able to rationalize the impact of pathogenic mutations [45-47].

MD simulations, in particular, allows one to address specific questions about structural properties and long-range dynamics of protein and nucleic acids, thus allowing the formulation of rational hypothesis of clinical data [48-51]. In (Figure 2) location of clinically relevant tubulin cofactor D (TBCD) variants and MD simulations results showing the structural perturbation induced by the Ala586Val clinically observed substitution.

Figure 2. (a) Location of disease-associated amino acid substitutions in tubulin cofactor D (TBCD). The three variants described in 45-46 have the lateral chain highlighted in pink. (b) Ala586 is a buried residue located in a region of α helices. (c) MD simulations performed to investigate the structural perturbation induced by the Ala586Val substitution identified a local rearrangement of these helices, resulting in a substantial rearrangement of their relative orientation.

Acknowledgment

This research was supported by:The “Departments of Excellence-2018” Program (Dipartimenti di Eccellenza) of the Italian Ministry of Education, University and Research-MIUR, DIBAF-Department Project “Landscape 4.0 – food, wellbeing and environment; The “Fondi di Ateneo per la ricerca 2017”, University of Rome, Sapienza.

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Editorial Information

Editor-in-Chief

Terry Lichtor
Tsuyoshi Hirata
Shinya Mizuno
Giacomo Corrado

Article Type

Mini Review

Publication history

Received date: July 03, 2018
Accepted date: July 09, 2018
Published date: July 11, 2018

Copyright

© 2018 Castrignano T. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Citation

Castrignano T, Parisi V, Chillemi G (2018) Empowering precision medicine through high performance computing clusters. J Transl Sci 5: DOI: 10.15761/JTS.1000268

Corresponding author

Tiziana Castrignano

Super Computing Applications and Innovation Department, CINECA, SCAI, Rome, Italy

E-mail : bhuvaneswari.bibleraaj@uhsm.nhs.uk

Figure 1. Screenshots of Ligea Portal: a) A ’Search by Cell line’ form allows to navigate the database by indicating a specific cell line name; b) the corresponding results table reporting the gene fusion prediction for each used algorithm (Fusioncatcher, EricScript, Tophat Fusion, Jaffa); c) by clicking on the light blue button (corresponding to fusioncatcher algorithm result), a popup window opens showing details about the putative detected gene fusion event; d) the Venn diagram shows the intersection of the putative gene fusion events identified by the four algorithms; e) the number of the different cell lines derived from the same diseases. Both d) and e) panels can be visualized in the Ligea home page.

Figure 2. (a) Location of disease-associated amino acid substitutions in tubulin cofactor D (TBCD). The three variants described in 45-46 have the lateral chain highlighted in pink. (b) Ala586 is a buried residue located in a region of α helices. (c) MD simulations performed to investigate the structural perturbation induced by the Ala586Val substitution identified a local rearrangement of these helices, resulting in a substantial rearrangement of their relative orientation.