Caratterizzazione genomica di virus influenzali suini in Italia mediante next generation sequencing

I sottotipi H1N1, H1N2 e H3N2 di influenza A virus sono largamente diffusi nella popolazione suina di tutto il mondo. Nel presente lavoro è stato sviluppato un protocollo di sequenziamento di c.d. nuova generazione, su piattaforma Ion Torrent PGM, idoneo per l’analisi di tutti i virus influenzali suini (SIV). Per valutare l’evoluzione molecolare dei SIV italiani, sono stati sequenziati ed analizzati mediante analisi genomica e filogenetica un totale di sessantadue ceppi di SIV appartenenti ai sottotipi H1N1, H1N2 e H3N2, isolati in Italia dal 1998 al 2014. Sono stati evidenziati in sei campioni due fenomeni di riassortimento: tutti i SIV H1N2 esaminati presentavano una neuraminidasi di derivazione umana, diversa da quella dei SIV H1N2 circolanti in Europa, inoltre l’emoagglutinina (HA) di due isolati H1N2 era originata dal riassortimento con un SIV H1N1 avian-like. L’analisi molecolare dell’HA ha permesso di rivelare un’inserzione di due amminoacidi in quattro SIV H1N1 pandemici e una delezione di due aminoacidi in quattro SIV H1N2, entrambe a livello del sito di legame con il recettore cellulare. E’ stata inoltre evidenziata un’elevata omologia di un SIV H1N1 con ceppi europei isolati negli anni ’80, suggerendo la possibile origine vaccinale di questo virus. E’ stato possibile, in aggiunta, applicare il nuovo protocollo sviluppato per sequenziare un virus influenzale aviare altamente patogeno trasmesso all’uomo, direttamente da campione biologico. La diversità genetica nei SIV esaminati in questo studio conferma l’importanza di un continuo monitoraggio della costellazione genomica dei virus influenzali nella popolazione suina.

Targeted next generation sequencing as a diagnostic tool in epileptic disorders

Epilepsies have a highly heterogeneous background with a strong genetic contribution. The variety of unspecific and overlapping syndromic and nonsyndromic phenotypes often hampers a clear clinical diagnosis and prevents straightforward genetic testing. Knowing the genetic basis of a patient's epilepsy can be valuable not only for diagnosis but also for guiding treatment and estimating recurrence risks.

Next-generation sequencing of HIV-1 RNA genomes: determination of error rates and minimizing artificial recombination.

Next-generation sequencing (NGS) is a valuable tool for the detection and quantification of HIV-1 variants in vivo. However, these technologies require detailed characterization and control of artificially induced errors to be applicable for accurate haplotype reconstruction. To investigate the occurrence of substitutions, insertions, and deletions at the individual steps of RT-PCR and NGS, 454 pyrosequencing was performed on amplified and non-amplified HIV-1 genomes. Artificial recombination was explored by mixing five different HIV-1 clonal strains (5-virus-mix) and applying different RT-PCR conditions followed by 454 pyrosequencing. Error rates ranged from 0.04-0.66% and were similar in amplified and non-amplified samples. Discrepancies were observed between forward and reverse reads, indicating that most errors were introduced during the pyrosequencing step. Using the 5-virus-mix, non-optimized, standard RT-PCR conditions introduced artificial recombinants in a fraction of at least 30% of the reads that subsequently led to an underestimation of true haplotype frequencies. We minimized the fraction of recombinants down to 0.9-2.6% by optimized, artifact-reducing RT-PCR conditions. This approach enabled correct haplotype reconstruction and frequency estimations consistent with reference data obtained by single genome amplification. RT-PCR conditions are crucial for correct frequency estimation and analysis of haplotypes in heterogeneous virus populations. We developed an RT-PCR procedure to generate NGS data useful for reliable haplotype reconstruction and quantification.

Functional data analysis approaches for genotype-phenotype association studies from next-generation sequencing

Next-generation DNA sequencing platforms can effectively detect the entire spectrum of genomic variation and is emerging to be a major tool for systematic exploration of the universe of variants and interactions in the entire genome. However, the data produced by next-generation sequencing technologies will suffer from three basic problems: sequence errors, assembly errors, and missing data. Current statistical methods for genetic analysis are well suited for detecting the association of common variants, but are less suitable to rare variants. This raises great challenge for sequence-based genetic studies of complex diseases.^ This research dissertation utilized genome continuum model as a general principle, and stochastic calculus and functional data analysis as tools for developing novel and powerful statistical methods for next generation of association studies of both qualitative and quantitative traits in the context of sequencing data, which finally lead to shifting the paradigm of association analysis from the current locus-by-locus analysis to collectively analyzing genome regions.^ In this project, the functional principal component (FPC) methods coupled with high-dimensional data reduction techniques will be used to develop novel and powerful methods for testing the associations of the entire spectrum of genetic variation within a segment of genome or a gene regardless of whether the variants are common or rare.^ The classical quantitative genetics suffer from high type I error rates and low power for rare variants. To overcome these limitations for resequencing data, this project used functional linear models with scalar response to develop statistics for identifying quantitative trait loci (QTLs) for both common and rare variants. To illustrate their applications, the functional linear models were applied to five quantitative traits in Framingham heart studies. ^ This project proposed a novel concept of gene-gene co-association in which a gene or a genomic region is taken as a unit of association analysis and used stochastic calculus to develop a unified framework for testing the association of multiple genes or genomic regions for both common and rare alleles. The proposed methods were applied to gene-gene co-association analysis of psoriasis in two independent GWAS datasets which led to discovery of networks significantly associated with psoriasis.^

DEVELOPMENT OF NOVEL METHODS TO MINIMIZE THE IMPACT OF SEQUENCING ERRORS IN THE NEXT-GENERATION SEQUENCING DATA ANALYSIS

Next-generation sequencing (NGS) technology has become a prominent tool in biological and biomedical research. However, NGS data analysis, such as de novo assembly, mapping and variants detection is far from maturity, and the high sequencing error-rate is one of the major problems. . To minimize the impact of sequencing errors, we developed a highly robust and efficient method, MTM, to correct the errors in NGS reads. We demonstrated the effectiveness of MTM on both single-cell data with highly non-uniform coverage and normal data with uniformly high coverage, reflecting that MTM’s performance does not rely on the coverage of the sequencing reads. MTM was also compared with Hammer and Quake, the best methods for correcting non-uniform and uniform data respectively. For non-uniform data, MTM outperformed both Hammer and Quake. For uniform data, MTM showed better performance than Quake and comparable results to Hammer. By making better error correction with MTM, the quality of downstream analysis, such as mapping and SNP detection, was improved. SNP calling is a major application of NGS technologies. However, the existence of sequencing errors complicates this process, especially for the low coverage (

Next-generation sequencing of iron-metabolism related genes in Portuguese patients with iron overload: novel pathogenic genetic variants

Objective: In Southern European countries up to one-third of the patients with hereditary hemochromatosis (HH) do not present the common HFE risk genotype. In order to investigate the molecular basis of these cases we have designed a gene panel for rapid and simultaneous analysis of 6 HH-related genes (HFE, TFR2, HJV, HAMP, SLC40A1 and FTL) by next-generation sequencing (NGS). Materials and Methods: Eighty-eight iron overload Portuguese patients, negative for the common HFE mutations, were analysed. A TruSeq Custom Amplicon kit (TSCA, by Illumina) was designed in order to generate 97 amplicons covering exons, intron/exon junctions and UTRs of the mentioned genes with a cumulative target sequence of 12115bp. Amplicons were sequenced in the MiSeq instrument (IIlumina) using 250bp paired-end reads. Sequences were aligned against human genome reference hg19 using alignment and variant caller algorithms in the MiSeq reporter software. Novel variants were validated by Sanger sequencing and their pathogenic significance were assessed by in silico studies. Results: We found a total of 55 different genetic variants. These include novel pathogenic missense and splicing variants (in HFE and TFR2), a very rare variant in IRE of FTL, a variant that originates a novel translation initiation codon in the HAMP gene, among others. Conclusion: The merging of TSCA methodology and NGS technology appears to be an appropriate tool for simultaneous and fast analysis of HH-related genes in a large number of samples. However, establishing the clinical relevance of NGS-detected variants for HH development remains a hard-working task, requiring further functional studies.

Retrotransposon expression profiling: Unveiling the hidden SINE transcriptome through Next-Generation Sequencing data analysis

Of the ~1.7 million SINE elements in the human genome, only a tiny number are estimated to be active in transcription by RNA polymerase (Pol) III. Tracing the individual loci from which SINE transcripts originate is complicated by their highly repetitive nature. By exploiting RNA-Seq datasets and unique SINE DNA sequences, we devised a bioinformatic pipeline allowing us to identify Pol III-dependent transcripts of individual SINE elements. When applied to ENCODE transcriptomes of seven human cell lines, this search strategy identified ~1300 Alu loci and ~1100 MIR loci corresponding to detectable transcripts, with ~120 and ~60 respectively Alu and MIR loci expressed in at least three cell lines. In vitro transcription of selected SINEs did not reflect their in vivo expression properties, and required the native 5’-flanking region in addition to internal promoter. We also identified a cluster of expressed AluYa5-derived transcription units, juxtaposed to snaR genes on chromosome 19, formed by a promoter-containing left monomer fused to an Alu-unrelated downstream moiety. Autonomous Pol III transcription was also revealed for SINEs nested within Pol II-transcribed genes raising the possibility of an underlying mechanism for Pol II gene regulation by SINE transcriptional units. Moreover the application of our bioinformatic pipeline to both RNA-seq data of cells subjected to an in vitro pro-oncogenic stimulus and of in vivo matched tumor and non-tumor samples allowed us to detect increased Alu RNA expression as well as the source loci of such deregulation. The ability to investigate SINE transcriptomes at single-locus resolution will facilitate both the identification of novel biologically relevant SINE RNAs and the assessment of SINE expression alteration under pathological conditions.

Codon-precise, synthetic, antibody fragment libraries built using automated hexamer codon additions and validated through next generation sequencing

We have previously described ProxiMAX, a technology that enables the fabrication of precise, combinatorial gene libraries via codon-by-codon saturation mutagenesis. ProxiMAX was originally performed using manual, enzymatic transfer of codons via blunt-end ligation. Here we present Colibra™: an automated, proprietary version of ProxiMAX used specifically for antibody library generation, in which double-codon hexamers are transferred during the saturation cycling process. The reduction in process complexity, resulting library quality and an unprecedented saturation of up to 24 contiguous codons are described. Utility of the method is demonstrated via fabrication of complementarity determining regions (CDR) in antibody fragment libraries and next generation sequencing (NGS) analysis of their quality and diversity.

Response of spring diatoms to CO2 availability in the Western North Pacific as determined by next-generation sequencing

Next-generation sequencing (NGS) technologies have enabled us to determine phytoplankton community compositions at high resolution. However, few studies have adopted this approach to assess the responses of natural phytoplankton communities to environmental change. Here, we report the impact of different CO2 levels on spring diatoms in the Oyashio region of the western North Pacific as estimated by NGS of the diatom-specific rbcL gene (DNA), which encodes the large subunit of RubisCO. We also examined the abundance and composition of rbcL transcripts (cDNA) in diatoms to assess their physiological responses to changing CO2 levels. A short-term (3-day) incubation experiment was carried out on-deck using surface Oyashio waters under different pCO2 levels (180, 350, 750, and 1000 µatm) in May 2011. During the incubation, the transcript abundance of the diatom-specific rbcL gene decreased with an increase in seawater pCO2 levels. These results suggest that CO2 fixation capacity of diatoms decreased rapidly under elevated CO2 levels. In the high CO2 treatments (750 and 1000 µatm), diversity of diatom-specific rbcL gene and its transcripts decreased relative to the control treatment (350µatm), as well as contributions of Chaetocerataceae, Thalassiosiraceae, and Fragilariaceae to the total population, but the contributions of Bacillariaceae increased. In the low CO2 treatment, contributions of Bacillariaceae also increased together with other eukaryotes. These suggest that changes in CO2 levels can alter the community composition of spring diatoms in the Oyashio region. Overall, the NGS technology provided us a deeper understanding of the response of diatoms to changes in CO2 levels in terms of their community composition, diversity, and photosynthetic physiology.

The application of next generation sequencing to profile microbe related cheese quality defects

High throughput next generation sequencing, together with advanced molecular methods, has considerably enhanced the field of food microbiology. By overcoming biases associated with culture dependant approaches, it has become possible to achieve novel insights into the nature of food-borne microbial communities. In this thesis, several different sequencing-based approaches were applied with a view to better understanding microbe associated quality defects in cheese. Initially, a literature review provides an overview of microbe-associated cheese quality defects as well as molecular methods for profiling complex microbial communities. Following this, 16S rRNA sequencing revealed temporal and spatial differences in microbial composition due to the time during the production day that specific commercial cheeses were manufactured. A novel Ion PGM sequencing approach, focusing on decarboxylase genes rather than 16S rRNA genes, was then successfully employed to profile the biogenic amine producing cohort of a series of artisanal cheeses. Investigations into the phenomenon of cheese pinking formed the basis of a joint 16S rRNA and whole genome shotgun sequencing approach, leading to the identification of Thermus species and, more specifically, the pathway involved in production of lycopene, a red coloured carotenoid. Finally, using a more traditional approach, the effect of addition of a facultatively heterofermentative Lactobacillus (Lactobacillus casei) to a Swiss-type cheese, in which starter activity was compromised, was investigated from the perspective of its ability to promote gas defects and irregular eye formation. X-ray computed tomography was used to visualise, using a non-destructive method, the consequences of the undesirable gas formation that resulted. Ultimately this thesis has demonstrated that the application of molecular techniques, such as next generation sequencing, can provide a detailed insight into defect-causing microbial populations present and thereby may underpin approaches to optimise the quality and consistency of a wide variety of cheeses.

Inter-Laboratory Evaluation of a Next-Generation Sequencing Panel for Acute Myeloid Leukemia

INTRODUCTION: Acute myeloid leukemia (AML) is a heterogeneous clonal disorder often associated with dismal overall survival. The clinical diversity of AML is reflected in the range of recurrent somatic mutations in several genes, many of which have a prognostic and therapeutic value. Targeted next-generation sequencing (NGS) of these genes has the potential for translation into clinical practice. In order to assess this potential, an inter-laboratory evaluation of a commercially available AML gene panel across three diagnostic centres in the UK and Ireland was performed.

METHODS: DNA from six AML patient samples was distributed to each centre and processed using a standardised workflow, including a common sequencing platform, sequencing chips and bioinformatics pipeline. A duplicate sample in each centre was run to assess inter- and intra-laboratory performance.

RESULTS: An average sample read depth of 2725X (range 629-5600) was achieved using six samples per chip, with some variability observed in the depth of coverage generated for individual samples and between centres. A total of 16 somatic mutations were detected in the six AML samples, with a mean of 2.7 mutations per sample (range 1-4) representing nine genes on the panel. 15/16 mutations were identified by all three centres. Allelic frequencies of the mutations ranged from 5.6 to 53.3 % (median 44.4 %), with a high level of concordance of these frequencies between centres, for mutations detected.

CONCLUSION: In this inter-laboratory comparison, a high concordance, reproducibility and robustness was demonstrated using a commercially available NGS AML gene panel and platform.

Understanding next generation sequencing in oncology: A guide for oncologists

DNA sequencing is now faster and cheaper than ever before, due to the development of next generation sequencing (NGS) technologies. NGS is now widely used in the research setting and is becoming increasingly utilised in clinical practice. However, due to evolving clinical commitments, increased workload and lack of training opportunities, many oncologists may be unfamiliar with the terminology and technology involved. This can lead to oncologists feeling daunted by issues such as how to interpret the vast amounts of data generated by NGS and the differences between sequencing platforms. This review article explains common concepts and terminology, summarises the process of DNA sequencing (including data analysis) and discusses the main factors to consider when deciding on a sequencing method. This article aims to improve oncologists' understanding of the most commonly used sequencing platforms and the ongoing challenges faced in expanding the use of NGS into routine clinical practice.

Next-Generation Sequencing of Hereditary Hemochromatosis-Related Genes: Novel Likely Pathogenic Variants Found in the Portuguese Population

Hereditary hemochromatosis (HH) is an autosomal recessive disorder characterized by excessive iron absorption resulting in pathologically increased body iron stores. It is typically associated with common HFE gene mutation (p.Cys282Tyr and p.His63Asp). However, in Southern European populations up to one third of HH patients do not carry the risk genotypes. This study aimed to explore the use of next-generation sequencing (NGS) technology to analyse a panel of iron metabolism-related genes (HFE, TFR2, HJV, HAMP, SLC40A1, and FTL) in 87 non-classic HH Portuguese patients. A total of 1241 genetic alterations were detected corresponding to 53 different variants, 13 of which were not described in the available public databases. Among them, five were predicted to be potentially pathogenic: three novel mutations in TFR2 [two missense (p.Leu750Pro and p.Ala777Val) and one intronic splicing mutation (c.967-1G>C)], one missense mutation in HFE (p.Tyr230Cys), and one mutation in the 5'-UTR of HAMP gene (c.-25G>A). The results reported here illustrate the usefulness of NGS for targeted iron metabolism-related gene panels, as a likely cost-effective approach for molecular genetics diagnosis of non-classic HH patients. Simultaneously, it has contributed to the knowledge of the pathophysiology of those rare iron metabolism-related disorders.