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Each year, seasonal influenza results in high mortality and morbidity. The current classification of circulating influenza viruses is mainly focused on the hemagglutinin gene. Whole-genome sequencing (WGS) enables tracking mutations across all influenza segments allowing a better understanding of the epidemiological effects of intra- and inter-seasonal evolutionary dynamics, and exploring potential associations between mutations across the viral genome and patient’s clinical data. In this study, mutations were identified in 253 Influenza A (H3N2) clinical isolates from the 2016-2017 influenza season in Belgium. As a proof of concept, available patient data were integrated with this genomic data, resulting in statistically significant associations that could be relevant to improve the vaccine and clinical management of infected patients. Several mutations were significantly associated with the sampling period. A new approach was proposed for exploring mutational effects in highly diverse Influenza A (H3N2) strains through considering the viral genetic background by using phylogenetic classification to stratify the samples. This resulted in several mutations that were significantly associated with patients suffering from renal insufficiency. This study demonstrates the usefulness of using WGS data for tracking mutations across the complete genome and linking these to patient data, and illustrates the importance of accounting for the viral genetic background in association studies. A limitation of this association study, especially when analyzing stratified groups, relates to the number of samples, especially in the context of national surveillance of small countries. Therefore, we investigated if international databases like GISAID may help to verify whether observed associations in the Belgium A (H3N2) samples, could be extrapolated to a global level. This work highlights the need to construct international databases with both information of viral genome sequences and patient data.

Gene-edited organisms and derived food and feed products commercialized on the European market falls within the scope of the Directive, 2001/18/EC. Therefore, the possibility to specifically detect and quantify them has become a priority. To this end, PCR-based approaches, such as real-time PCR and digital droplet PCR, targeting a single variation point carried by a gene-edited organism are expected to be suitable, even if potentially challenging at the technical level. However, additional issues related to the interpretation of the results can also be encountered. Indeed, given its possible spread, natural or through breeding programs, the presence of this single variation does not automatically prove the presence of the gene-edited organism. To overcome such critical issue, we proposed a general workflow to develop and validate a PCR-based method specific to a gene-edited organism in targeting its single variation point. First, based on in silico analyses, the possibility to technically design the PCR-based method as well as to discriminate the gene-edited organism using it single variation point are assessed. In case such parameters are confirmed, the performance of the developed PCR-based method are then tested in agreement with the minimum performance requirements for GMO testing. The use of the proposed general workflow was successfully illustrated through the development a 2-plex digital droplet PCR method targeting specifically a gene-edited rice carrying a single nucleotide insertion. The proposed workflow was thus considered as a key tool to support the competent authorities regarding the food and feed traceability.

Additional file 2. Results of the meta-analyses, leave-one-out analyses, and funnel plots for the association between the rs12252, rs333, rs1801274, and rs34481144 polymorphisms and influenza disease severity.

Additional file 3. Search strategy using the MEDLINE and EMBASE databases, eligibility criteria, and risk of bias score to assess the quality of included studies for the systematic review and meta-analysis of host genetic factors and influenza disease severity.

Additional file 1. Appendix A: Internal validation. Appendix B: Exploration of other data analysis scenarios. Appendix C: Comparison baseline characteristics to assess selection bias. Appendix D: List of 25 mutations with an unadjusted p-value below 0.05. Appendix E: Manhattan plot. Appendix F: Principle Component Analysis (PCA). Appendix G: Apparent Area Under the ROC curve. Appendix H: Boxplots of the fitted values for the host and the combined model. Appendix I: Genomic model.

Protocol. (PDF 939 kb)

Inclusion and exclusion criteria organized according to the Population-Concept-Context (PCC) principle, as outlined in the Joanna Briggsâ Manual for Scoping Reviews. (PDF 499 kb)

PubMed search string. (PDF 176 kb)

Extraction form listing the characteristics that are extracted from the included studies. (PDF 179 kb)

PRISMA checklist. Completed PRISMA checklist indicating page number in manuscript of relevant content. (PDF 409 kb)

Decision tree used during screening to distinguish between research, individual patient care, and public health practice. (PDF 275 kb)

Visualization of the PCR products from the bidirectional DNA walking method anchored on CryAb/c applied on 2000 HGE of GM events (Bt11, MON87701, MON87751, MON531, Bt176, T304–40, Bt63, MON810, KeFeng-6, MON89034, MON15985 and 281–24-236 × 3006–210-23 events). (DOCX 1205 kb)

Genetic variation of the Belgian population related to other European populations. Principal component (PC) analysis based on 41,083 SNPs from five different European populations (see Fig. 4 for details). (a) Screeplot showing the variability captured by the first 10 PCs. (b) PC plot of five European populations with mapping of the Belgian population and indication of their migration backgrounds. For visualization purposes, the other European populations are shown in gray. (PDF 366 kb)

Provincial genetic variation within the Belgian population. Belgian data were mapped on the European PC model (41,083 SNPs). Panels show plots of the first two PCs with indication of the province of inhabitance as indicated. The central map of Belgium shows the geographical location of each province. Panels with Flemish provinces are shown on top while Walloon province panels are shown on the bottom. (PDF 1806Â kb)

Variant allele frequency correlations between Belgian and other populations. Variant allele frequencies (VAF) for all SNPs were calculated with reference to the human genome build hg19. Plots show the VAF of Belgian versus other continental (a) and European (b) populations. Pearson correlation coefficients are shown on top of each plot. For visualization purposes, only 1000 random points are shown. (PDF 5259 kb)

Supplementary table, supporting data, and supporting information. (DOCX 1229 kb)

Alignement of the targeted Cry1Ab sequences from the T304â 40 event sequenced from the PCR verification assay (1 and 2) with the reference sequence from the T304â 40 event (reference) used for the design of the target-specific primers (surrounded by orange rectangles). (DOCX 506Â kb)

Genetic variation of the Belgian population related to continental populations. Principal component (PC) analysis using 47,802 SNPs from five continental populations with mapping of the Belgian population. (a) Screeplot showing the variability captured by the first 10 PCs. (b) Pairwise plots of the first four PCs. Populations are indicated below the plot (AFR: African; AMR: American; EAS: East Asian; EUR: European; SAS: South Asian; BEL: Belgian). (PDF 7654Â kb)

Sequences of the sequenced amplicons from the processed food matrix (GeM MP17) coming from a GeMMA Scheme Proficiency Test. (DOCX 14Â kb)

Sequences of the sequenced amplicons (see Tables 2 and 3). (DOCX 20 kb)

Sequences of oligonucleotides provided by the APAgeneTM GOLD Genome Walking Kit from BIO S&T. (DOCX 13Â kb)

Sequences, obtained by PCR, of the 3â transgene flanking regions of Bt rice on the rice chromosome II and III. The rice genome and the transgenic cassette are indicated respectively in small letter and capital letter. (DOCX 15 kb)

Sequences obtained from the 100 % Bt rice sample using the bidirectional p35S and tNOS DNA walking methods. The number of the corresponding amplicons observed in Fig. 1a is indicated in brackets. The rice genome and the transgenic cassette are indicated respectively in small letter and capital letter. The hpt gene (underlined) is regulated by the promoter p35S (p35S; in italic) while the Cry1B gene (double underlined) is under the control of the tNOS terminator (tNOS; in bold). (DOCX 16 kb)

Results from qPCR SYBR®Green screening on maize samples using the p35S, tNOS and t35S pCAMBIA methods as a decision support system. The obtained positive and negative signals are represented by + and -, respectively. For each positive signal, the corresponding Ct and Tm are indicated. (DOCX 15 kb)

Alignment of 5â transgene flanking region of MON863 coming from Zhu et al., 2008 (A) and the present DNA walking strategy (B) [ 28 ]. The maize genome and the transgenic cassette are indicated respectively in small letter and capital letter. (DOCX 13 kb)

Alignment of 5â transgene flanking region of MON863 coming from Zhu et al., 2008 (A) and the present DNA walking strategy (B) [ 28 ]. The maize genome and the transgenic cassette are indicated respectively in small letter and capital letter. (DOCX 13 kb)

Alignment of sequences of PCR fragment containing the “37 bp gap” between Contig00019 and Contig00022 and the B. subtilis subsp. subtilis str. 168 genome sequence [Genbank:CP010052.1]. Alignment of the obtained sequences (“57-scaf19-F3” and “57-scaf22-R”) of the PCR fragment containing the junction between Contig00019 and Contig00022 to the corresponding region in the B. subtilis subsp. subtilis str. 168 genome sequence [Genbank:CP010052.1] (region taken as displayed in Additional file 1: Figure S1). The nucleotides at the beginning of the obtained sequence are to be ignored, as these are of lower quality, which is known for Sanger sequencing with BigDye Terminator v3.1. The position corresponding to the “37 bp gap” has been indicated with a red box. It is clear that both contigs are adjacent in the GM-Bacillus genome, and that the sequence region corresponding to the “gap” was not present in the reads/contigs obtained through the the NGS analysis. (PDF 534 kb)

Sequence of B. subtilis subsp. subtilis str. 168 genome sequence [Genbank:CP010052.1] corresponding to the region containing part of Contig00019 and Contig00022. There is a gap of 37 basepairs (uncoloured region in the figure) between Contig00019 (pink region in figure) and Contig00022 (yellow region in figure), when aligning the obtained contigs to B. subtilis subsp. subtilis str. 168 genome sequence [Genbank:CP010052.1]. PCR and sequence analysis were used to confirm that the genomic regions present on both contigs are indeed adjacent in the GM-Bacillus genome. Hereto, primers Scaf-19-F3-seq (positioned on Contig00019, indicated in green) and Scaf-22-R-seq (positioned on Contig00022, indicated in blue) were used to amplify the flanking regions of the junction between Contig00019 and Contig00022. Subsequently, the obtained PCR fragment was sequenced. (PDF 101 kb)

Sequences obtained from the GM maize MON863 event using the bidirectional p35S and tNOS DNA walking methods. The number of the corresponding amplicons observed in Fig. 2a is indicated in brackets. The maize genome and the transgenic cassette are designated respectively in small letter and capital letter. The nptII gene (underlined) is under the control of the p35S promoter (p35S; in italic) and the tNOS terminator (tNOS; in bold). The 4-AS1 promoter (p-4AS1; dashed underlined) is followed by the Wheat major chlorophyll a/b binding protein gene (wtCAB; wave underlined) and Rice Actin Intron (rAct; dotted underlined) to regulate the Cry3Bb1 gene (double underlined). (DOCX 16 kb)

Exposure to the air pollutant particulate matter (PM) is associated with increased risks of respiratory diseases and enhancement of airway inflammation in children. In the context of large scale air pollution studies, it can be challenging to measure fractional exhaled nitric oxide (FeNO) as indicator of lung inflammation. Urinary CC16 (U-CC16) is a potential biomarker of increased lung permeability and toxicity, increasing following short-term PM2.5 exposure. The single nucleotide polymorphism (SNP) CC16 G38A (rs3741240) affects CC16 levels and respiratory health. Our study aimed at assessing the use of U-CC16 (incl. CC16 G38A from saliva) as potential alternative for FeNO by investigating their mutual correlation in children exposed to PM. Samples from a small-scale study conducted in 42 children from urban (n = 19) and rural (n = 23) schools examined at two time points, were analysed. When considering recent (lag1) low level exposure to PM2.5 as air pollution measurement, we found that U-CC16 was positively associated with FeNO (8 = 0.23; 95% CI [-0.01; 0.47]; p = 0.06) in an adjusted analysis using a linear mixed effects model. Further, we observed a positive association between PM2.5 and FeNO (8 = 0.56; 95% CI [0.02; 1.09]; p = 0.04) and higher FeNO in urban school children as compared to rural school children (8 = 0.72; 95% CI [0.12; 1.31]; p = 0.02). Although more investigations are needed, our results suggest that inflammatory responses evidenced by increased FeNO are accompanied by potential increased lung epithelium permeability and injury, evidenced by increased U-CC16. In future large scale studies, where FeNO measurement is less feasible, the integrated analysis of U-CC16 and CC16 G38A, using noninvasive samples, might be a suitable alternative to assess the impact of air pollution exposure on the respiratory health of children, which is critical for policy development at population level. The authors would like to thank the Belgian Federal Science Policy Office (BELSPO) and Sciensano who funded this research through respectively the project PMOLLUGENIX-V2 (BELSPO, BR/165/PI/ PMOLLUGENIX) and the research project Respikid (Sciensano PJ/RP). They would like to thank the colleagues from the department of Transversal Activities of Applied Genomics at Sciensano for their help during the field study, including the sampling and measurements. They would also like to thank all the participating Belgian schools and the associated ‘pupil guidance centers (CLBs) for their enthusiastic collaboration during the field study. Finally, the authors would also like to thank Irceline and Leefmilieu Brussel-BIM (Het Brussels Instituut voor Milieubeheer) for making pollutant data publicly available.