Per- and polyfluoroalkyl substances (PFASs) are omnipresent and have been shown to induce a wide range of adverse health effects, including hepatotoxicity, developmental toxicity, and immunotoxicity. The aim of the present work was to assess whether human HepaRG liver cells can be used to obtain insight into differences in hepatotoxic potencies of a series of PFASs. Therefore, the effects of 18 PFASs on cellular triglyceride accumulation (AdipoRed assay) and gene expression (DNA microarray for PFOS and RT-qPCR for all 18 PFASs) were studied in HepaRG cells. BMDExpress analysis of the PFOS microarray data indicated that various cellular processes were affected at the gene expression level. From these data, ten genes were selected to assess the concentration–effect relationship of all 18 PFASs using RT-qPCR analysis. The AdipoRed data and the RT-qPCR data were used for the derivation of in vitro relative potencies using PROAST analysis. In vitro relative potency factors (RPFs) could be obtained for 8 PFASs (including index chemical PFOA) based on the AdipoRed data, whereas for the selected genes, in vitro RPFs could be obtained for 11–18 PFASs (including index chemical PFOA). For the readout OAT5 expression, in vitro RPFs were obtained for all PFASs. In vitro RPFs were found to correlate in general well with each other (Spearman correlation) except for the PPAR target genes ANGPTL4 and PDK4. Comparison of in vitro RPFs with RPFs obtained from in vivo studies in rats indicate that best correlations (Spearman correlation) were obtained for in vitro RPFs based on OAT5 and CXCL10 expression changes and external in vivo RPFs. HFPO-TA was found to be the most potent PFAS tested, being around tenfold more potent than PFOA. Altogether, it may be concluded that the HepaRG model may provide relevant data to provide insight into which PFASs are relevant regarding their hepatotoxic effects and that it can be applied as a screening tool to prioritize other PFASs for further hazard and risk assessment.

Per- and polyfluoroalkyl substances (PFASs) are omnipresent in the environment, food chain, and humans. Epidemiological studies have shown a positive association between serum levels of perfluorooctanoic acid (PFOA) and perfluorooctane sulfonic acid (PFOS), and increased serum cholesterol and, in some cases, also triglyceride levels. However, causality has been questioned, as animal studies, as well as a human trial, showed a decrease in serum cholesterol and no effects or a decrease in plasma triglycerides. To obtain more insight into the effects of PFASs on these processes, the present study investigated the effects of PFOA, PFOS, and perfluorononanoic acid (PFNA) on intracellular triglyceride and cholesterol levels in human HepaRG liver cells. DNA microarray analyses were performed to provide insight into underlying mechanisms. All PFASs induced an increase in cellular triglyceride levels, but had no effect on cholesterol levels. Gene set enrichment analysis (GSEA) of the microarray data indicated that gene sets related to cholesterol biosynthesis were repressed by PFOA, PFOS, and PFNA. Other gene sets commonly affected by all PFAS were related to PERK/ATF4 signaling (induced), tRNA amino-acylation (induced), amino acid transport (induced), and glycolysis/gluconeogenesis (repressed). Moreover, numerous target genes of peroxisome proliferator-activated receptor α (PPARα) were found to be upregulated. Altogether, the present study shows that PFOA, PFOS, and PFNA increase triglyceride levels and inhibit cholesterogenic gene expression in HepaRG cells. In addition, the present study indicates that PFASs induce endoplasmic reticulum stress, which may be an important mechanism underlying some of the toxic effects of these chemicals.

Traditional medicines (TMs) are globally traded and the consumer market is estimated to be $83 billion per annum. The diversity of TM matrices and poor quality of DNA extracted from highly processed TMs makes it challenging to apply standardized DNA-based procedures for ingredient analysis. In the present study, multiple DNA extraction methods were compared for the ability to obtain amplifiable DNA from TMs belonging to different matrices. The best performing DNA extraction was used to successfully obtain DNA from 18 TMs that were subsequently analyzed with a multi-locus DNA metabarcoding method to assess the species composition. In the analysis mini-barcodes accounted for the identification of most of the taxa in the TMs. The plant (ITS2) and animal (mini-16S) mini-barcode markers showed to allow species level identification of targets. In a few cases, full-length barcode markers, requiring higher quality DNA, proved to be critically informative at this level. The applied strategy resulted in the identification of a wide range of declared and undeclared ingredients, including endangered species (Ursus arctos and Aloe sp.). In 14 TMs less than 65% of the identified taxa matched the product label, and in two TMs none of the identified species matched the ingredients list. The current study shows that a multi-locus DNA metabarcoding approach is an informative analytical tool for species identification in TMs, including the potential identification of endangered species.

• MinION DNA metabarcoding is a promising tool for species identification in food. • MinION and Illumina MiSeq sequencing platforms perform equally accurate. • Species identification with MinION sequencing requires dedicated bioinformatics.

Fungi of the genus Botrytis (presently containing ~ 35 species) are able to infect more than 1400 different plant species and cause losses in a wide range of crops of economic importance. The best studied species is B. cinerea, which has a broad host range and is one of the best studied necrotrophic plant pathogenic fungi. Most other Botrytis spp. have a narrow host range and have been studied in less detail. To characterize genomic variation among different representatives of Botrytis spp., we sequenced and annotated the draft genomes of nine Botrytis species: B. calthae, B. convoluta, B. elliptica, B. galanthina, B. hyacinthi, B. narcissicola, B. paeoniae, B. porri and B. tulipae. Bioinformatics and comparative genomics tools were applied to determine a core of 7668 shared protein families in all Botrytis species, which grouped them in two distinct phylogenetic clades. The secretome of all nine Botrytis spp. was similar in number (ranging from 716 to 784 predicted proteins). A detailed analysis of the molecular functions of the secretome revealed that shared activities were highly similar. Orthologs to effectors functionally studied in B. cinerea were also present in the other Botrytis species. A complex pattern of presence/absence of secondary metabolite biosynthetic key enzymes was observed. Comparative genomics of Botrytis show that overall, species share the main signatures and protein families in the secreted proteins, and of known effectors. Our study provides leads to study host range determinants in the genus Botrytis and provides a stepping stone to elucidate the roles of effector candidates in the infection process of these species.

The objective of this study was to quantitatively assess potato omics profiles of new varieties for meaningful differences from analogous profiles of commercial varieties through the SIMCA one-class classification model. Analytical profiles of nine commercial potato varieties, eleven experimental potato varieties, one GM potato variety that had acquired Phytophtora resistance based on a single insert with potato-derived DNA sequences, and its non-GM commercial counterpart were generated. The ten conventional varieties were used to construct the one-class model. Omics profiles from experimental non-GM and GM varieties were assessed using the one-class SIMCA models. No potential unintended effects were identified in the case of the GM variety. The model showed that varieties that were genetically more distant from the commercial varieties were recognized as aberrant, highlighting its potential in determining whether additional evaluation is required for the risk assessment of materials produced from any breeding technique, including genetic modification.

The development and commercialization of Genetically Modified Organisms (GMOs) and its related products have been increasing in the last two decades. This challenges the currently applied time-consuming and expensive qPCR screening procedure from a practical perspective, due to the necessity to develop and validate additional targets at a regular pace and the increasing number of targets included in a single screening. In this study we developed a next generation sequencing (NGS)-based GMO screening approach covering 96 GMO targets and compared it to the two-step qPCR GMO screening approach; the two approaches were evaluated with five feed samples known to contain GMOs. The amplicons obtained from the feed samples were analyzed using 150-bp Paired-End sequencing, Illumina HiSeq 4000 platform. A dedicated data analysis pipeline was developed, which allows automated identification of GMOs and associated genetic elements and constructs. The result of the NGS-based screening were compared with the qPCR approach, indicating that 92% of the targets were commonly identified between the qPCR and NGS-based screening. The remaining 8% of the targets had discrepancies in detection between the two methods. This was mainly observed for targets that were detected in qPCR with high Cq values (above 36), which could not be detected in NGS-based screening. Additionally, due to the more extensive screening in the NGS-based strategy, in total 43 additional GMOs and related targets were identified compared to the standard qPCR screening. From the commonly identified targets in both approaches, 8 targets could not be associated with the detected GMOs. These targets had late Cq values (above 36) and could indicate traces of unknown GMOs in the samples. The current study shows the applicability of NGS as a novel, broad and reliable screening strategy for GMOs and its potential to improve current screening methods.

Genetically modified (GM) maize and their non-modified counterparts were compared using MON810 varieties, the only GMO event cultivated in Europe. The differences in grain samples were analysed by omics profiles, including transcriptomics, proteomics and metabolomics. Other cultivated maize varieties were analysed as a reference for the variability that will exist between cultivated varieties. The observed differences between modified and non-modified maize varieties do not exceed typical differences between non-modified varieties. The use of these advanced analytical approaches to analyse novel plant materials as compared to the results from animal feeding trials with whole foods is assessed. No indications were observed for changes in the GM varieties that warrant further investigations. Furthermore, it was shown that such indications will be obtained if maize samples of inferior quality are analysed similarly. Omics data provide detailed analytical information of the plant material, which facilitates a risk assessment procedure of new (GM) plant varieties.

The aim of the project is to compare the genomes of nine species of plant pathogens in the genus Botrytis with distinct host specificity, in order to identify genes involved in host range determination. Six of the nine fungal species infect ornamental bulb flower crops.

Technology is now being developed that is able to handle vast amounts of structured and unstructured data from diverse sources and origins. These technologies are often referred to as big data, and open new areas of research and applications that will have an increasing impact in all sectors of our society. In this paper we assessed to which extent big data is being applied in the food safety domain and identified several promising trends. In several parts of the world, governments stimulate the publication on internet of all data generated in public funded research projects. This policy opens new opportunities for stakeholders dealing with food safety to address issues which were not possible before. Application of mobile phones as detection devices for food safety and the use of social media as early warning of food safety problems are a few examples of the new developments that are possible due to big data.

The majority of feed products in industrialised countries contains materials derived from genetically modified organisms (GMOs). In parallel, the number of reports of unauthorised GMOs (UGMOs) is gradually increasing. There is a lack of specific detection methods for UGMOs, due to the absence of detailed sequence information and reference materials. In this research, an adapted genome walking approach was developed, called ALF: Amplification of Linearly-enriched Fragments. Coupling of ALF to NGS aims for simultaneous detection and identification of all GMOs, including UGMOs, in one sample, in a single analysis. The ALF approach was assessed on a mixture made of DNA extracts from four reference materials, in an uneven distribution, mimicking a real life situation. The complete insert and genomic flanking regions were known for three of the included GMO events, while for MON15985 only partial sequence information was available. Combined with a known organisation of elements, this GMO served as a model for a UGMO. We successfully identified sequences matching with this organisation of elements serving as proof of principle for ALF as new UGMO detection strategy. Additionally, this study provides a first outline of an automated, web-based analysis pipeline for identification of UGMOs containing known GM elements.

Background: DNA metabarcoding provides great potential for species identification in complex samples such as food supplements and traditional medicines. Such a method would aid CITES (the Convention on International Trade in Endangered Species of Wild Fauna and Flora) enforcement officers to combat wildlife crime by preventing illegal trade of endangered plant and animal species. The objective of this research was to develop a multi-locus DNA metabarcoding method for forensic wildlife species identification and to evaluate the applicability and reproducibility of this approach across different laboratories. Results: A DNA metabarcoding method was developed that makes use of 12 DNA barcode markers that have demonstrated universal applicability across a wide range of plant and animal taxa, and that facilitate the identification of species in samples containing degraded DNA. The DNA metabarcoding method was developed based on Illumina MiSeq amplicon sequencing of well-defined experimental mixtures, for which a bioinformatics pipeline with user-friendly web interface was developed. The performance of the DNA metabarcoding method was assessed in an international validation trial by 16 laboratories, in which the method was found to be highly reproducible and sensitive enough to identify species present in a mixture at 1% dry weight content. Conclusion:The advanced multi-locus DNA metabarcoding method assessed in this study provides reliable and detailed data on the composition of complex food products, including information on the presence of CITES-listed species. The method can provide improved resolution for species identification, while verifying species with multiple DNA barcodes contributes to an enhanced quality assurance.

Species identification using DNA barcodes has been widely adopted by forensic scientists as an effective molecular tool for tracking adulterations in food and for analysing samples from alleged wildlife crime incidents. DNA barcoding is an approach that involves sequencing of short DNA sequences from standardized regions and comparison to a reference database as a molecular diagnostic tool in species identification. In recent years, remarkable progress has been made towards developing DNA metabarcoding strategies, which involves next-generation sequencing of DNA barcodes for the simultaneous detection of multiple species in complex samples. Metabarcoding strategies can be used in processed materials containing highly degraded DNA e.g. for the identification of endangered and hazardous species in traditional medicine. This review aims to provide insight into advances of plant and animal DNA barcoding and highlights current practices and recent developments for DNA metabarcoding of food and wildlife forensic samples from a practical point of view. Special emphasis is placed on new developments for identifying species listed in the Convention on International Trade of Endangered Species (CITES) appendices for which reliable methods for species identification may signal and/or prevent illegal trade. Current technological developments and challenges of DNA metabarcoding for forensic scientists will be assessed in the light of stakeholders’ needs.

Before commercial release, new potato (Solanum tuberosum) varieties must be evaluated for content of toxic compounds such as glycoalkaloids (GAs), which are potent poisons. GA biosynthesis proceeds via the cholesterol pathway to a-chaconine and a-solanine. The goal of this study was to evaluate the relationship between total glycoalkaloid (TGA) content and the expression of GAME, SGT1, and SGT3 genes in potato tubers. TGA content was measured by HPLC MS, and reverse transcription quantitative polymerase chain reactions were performed to determine the relative expression of GAME, SGT1, and SGT3 genes. We searched for cis-elements of the transcription start site using the PlantPAN database. There was a relationship between TGA content and the relative expression of GAME, SGT1, and SGT3 genes in potato tubers. Putative promoter regions showed the presence of several cis-elements related to biotic and abiotic stresses and light. These findings provide an important step toward understanding TGA regulation and variation in potato tubers.

Proteomics methodology has seen increased application in food authentication, including tandem mass spectrometry of targeted species-specific peptides in raw, processed, or mixed food products. We have previously described an alternative principle that uses untargeted data acquisition and spectral library matching, essentially spectral counting, to compare and identify samples without the need for genomic sequence information in food species populations. Here, we present an interlaboratory comparison demonstrating how a method based on this principle performs in a realistic context. We also increasingly challenge the method by using data from different types of mass spectrometers, by trying to distinguish closely related and commercially important flatfish, and by analyzing heavily contaminated samples. The method was found to be robust in different laboratories, and 94-97% of the analyzed samples were correctly identified, including all processed and contaminated samples.

Herbarium genomics is proving promising as next-generation sequencing approaches are well suited to deal with the usually fragmented nature of archival DNA. We show that routine assembly of partial plastome sequences from herbarium specimens is feasible, from total DNA extracts and with specimens up to 146 years old. We use genome skimming and an automated assembly pipeline, Iterative Organelle Genome Assembly, that assembles paired-end reads into a series of candidate assemblies, the best one of which is selected based on likelihood estimation. We used 93 specimens from 12 different Angiosperm families, 73 of which were from herbarium material with ages up to 146 years old. For 84 specimens, a sufficient number of paired-end reads were generated (in total 9.4 × 1012 nucleotides), yielding successful plastome assemblies for 74 specimens. Those derived from herbarium specimens have lower fractions of plastome-derived reads compared with those from fresh and silica-gel-dried specimens, but total herbarium assembly lengths are only slightly shorter. Specimens from wet-tropical conditions appear to have a higher number of contigs per assembly and lower N50 values. We find no significant correlation between plastome coverage and nuclear genome size (C value) in our samples, but the range of C values included is limited. Finally, we conclude that routine plastome sequencing from herbarium specimens is feasible and cost-effective (compared with Sanger sequencing or plastome-enrichment approaches), and can be performed with limited sample destruction.

Potato (Solanum tuberosum) yield has increased dramatically over the last 50 years and this has been achieved by a combination of improved agronomy and biotechnology efforts. Gene studies are taking place to improve new qualities and develop new cultivars. Reverse transcriptase quantitative polymerase chain reaction (RT-qPCR) is a bench-marking analytical tool for gene expression analysis, but its accuracy is highly dependent on a reliable normalization strategy of an invariant reference genes. For this reason, the goal of this work was to select and validate reference genes for transcriptional analysis of edible tubers of potato. To do so, RT-qPCR primers were designed for ten genes with relatively stable expression in potato tubers as observed in RNA-Seq experiments. Primers were designed across exon boundaries to avoid genomic DNA contamination. Differences were observed in the ranking of candidate genes identified by geNorm, NormFinder and BestKeeper algorithms. The ranks determined by geNorm and NormFinder were very similar and for all samples the most stable candidates were C2, exocyst complex component sec3 (SEC3) and ATCUL3/ATCUL3A/CUL3/CUL3A (CUL3A). According to BestKeeper, the importin alpha and ubiquitin-associated/ts-n genes were the most stable. Three genes were selected as reference genes for potato edible tubers in RT-qPCR studies. The first one, called C2, was selected in common by NormFinder and geNorm, the second one is SEC3, selected by NormFinder, and the third one is CUL3A, selected by geNorm. Appropriate reference genes identified in this work will help to improve the accuracy of gene expression quantification analyses by taking into account differences that may be observed in RNA quality or reverse transcription efficiency across the samples.

The Leguminosae has emerged as a model for studying angiosperm plastome evolution because of its striking diversity of structural rearrangements and sequence variation. However, most of what is known about legume plastomes comes from few genera representing a subset of lineages in subfamily Papilionoideae. We investigate plastome evolution in subfamily Mimosoideae based on two newly sequenced plastomes (Inga and Leucaena) and two recently published plastomes (Acacia and Prosopis), and discuss the results in the context of other legume and rosid plastid genomes. Mimosoid plastomes have a typical angiosperm gene content and general organization as well as a generally slow rate of protein coding gene evolution, but they are the largest known among legumes. The increased length results from tandem repeat expansions and an unusual 13 kb IR-SSC boundary shift in Acacia and Inga. Mimosoid plastomes harbor additional interesting features, including loss of clpP intron1 in Inga, accelerated rates of evolution in clpP for Acacia and Inga, and dN/dS ratios consistent with neutral and positive selection for several genes. These new plastomes and results provide important resources for legume comparative genomics, plant breeding, and plastid genetic engineering, while shedding further light on the complexity of plastome evolution in legumes and angiosperms.