Voorstel stage Bio-informatica (2017-18):
Invloed van zandwinning op de bacteriële gemeenschap in de zeebodem langsheen een verstoringsgradiënt.
Op het Belgisch deel van de Noordzee wordt jaarlijks rond de 3 miljoen m³ zand gewonnen voor zowel de bouwindustrie als voor het ophogen van onze stranden ter bescherming van de kustzone. Door zandwinning wordt de zeebodem en de biologische gemeenschappen die er leven echter onvermijdelijk verstoord. Verschillende studies tonen een duidelijk effect op het macrobenthos (organismen > 1mm) in de meest intensief ontgonnen zones maar over de invloed op de bacteriële gemeenschappen is tot nog toe weinig tot niets gekend. Nochtans zijn micro-organismen onmisbaar in een gezond ecosysteem en spelen ze een heel belangrijke rol in de biochemische processen van de zeebodem. Daarom werd in het najaar van 2015, gelijktijdig met het bemonsteren van het macrobenthos, de bacteriële gemeenschap bemonsterd in intensief ontgonnen, gematigd ontgonnen en nooit ontgonnen gebieden.De bacteriële gemeenschap van deze locaties werd bepaald door amplicon sequenering van het V3-V4 fragment van het 16S rRNA door middel van Illumina technologie. De verwerking van de ruwe data set zou antwoord moeten bieden op de volgende vragen: 1/ Beïnvloedt zandwinning de bacteriële gemeenschap en welke groepen bacteriën worden vnl. beïnvloedt? 2/ Kan het gebruik van bacteriële gemeenschappen een ‘quick scan’ methode zijn om het effect van verstoring door zandwinning te bepalen?
Bacterial and meiofaunal communities from marine sediments can be described by analyzing 16S and 18S rRNA gene sequences using a DNA metabarcoding approach with the Illumina Miseq platform. However, the PCR step in this approach suffers from a number of limitations such as PCR bias, primer mismatch, chimera formation and polymerase errors. Primer free methods could greatly improve the characterization of these communities. The long read sequencing technology of Oxford Nanopore may combat the limitations that Miseq has, but the development for biodiversity assessment is still in its infancy. The goal of this internship was to develop a bioinformatic pipeline to analyze long read minion data from environmental samples, and compare the community composition between Minion and Miseq methods to evaluate whether primer free methods are a suitable and better alternative then DNA metabarcoding.
Samples from marine sediments with different intensities of sand extraction in the North Sea have been sampled and sequenced using the Miseq and Minion. Minion data is provided as FAST5 output, this file contains the raw signal data. To process this information, base calling is required. This was done using the tool Guppy. Guppy converts the electric signal to nucleotides. FAST5 files are converted to a FASTQ file, containing Qscore, sequence length and barcodes. A summary file with data from the minion run is also generated. Demultiplexing of samples using nanopore barcodes was done using guppy_barcoder. Filtlong was used to filter out the sequences that had a read length lower than 500 base pairs. The cutoff is set to 500 bp because reads that have less base pairs will be difficult to assign a taxonomy. A total of 1676 sequences were kept, which means that 41.9% of all reads had a read length longer than 500bp. To check the amount of reads that were removed and to analyze overall read quality, the tool Nanoplot was used. This tool generates plots and text files that nicely visualize the read data.
The samples contain DNA originating from a mix of bacterial and small metazoan species. The goal was to find a way to combine the sequences from each species together. Various clustering tools (ONTtrack, vsearch and isONclust) were tried out to form clusters from the sequences that in theory should come from the same species. Our data was too divergent and contained too little reads to form a decent amount of clusters with an acceptable identity grade. Another method to group the same sequences together was required.
A local Blastn was performed against the SILVA 138 SSU Ref NR 99 (Bacteria, Archaea and Eukarya 18S/16S rRNA) database. The e-value cutoff was set at 0.05, this yielded a total of 1022 hits. This blast result generates taxonomic information for each hit. The taxonomic information in the SILVA database is not normalized in terms of taxonomic ranks, some results contain subtaxa while others do not. This makes analyzing the taxonomic levels very difficult. An R script was developed to normalize all taxonomic levels. In total 448 sequences are classified as archeae and bacteria. 505 Sequences are classified as eukaryota, 69 sequences had not been assigned a taxonomy.
Many species had more than one sequence. From these sequences a consensus sequence was generated at species level with SPOA v3.0.1 (github.com/rvaser/spoa), which is based on a partial order alignment (POA) algorithm. The consensus sequences were again blasted again the SILVA database and set to the correct taxonomy. The blastn is performed again because the consensus sequences should be more correct and can yield a different result than the original blast. The blast accuracy improved with 21%, the mean value of the bitscore from the original blast was 346.40 while the bitscore from the blast containing consensus sequences was 437.34. In order to analyse the E-value, the log value was taken and the 0 scores were ignored. The original blast had an average of -160.407, an average of -183.055 was calculated for the consensus blast. The lower the value the better.
The Minion pipeline resulted in the detection of 81 unique metazoan genera and 24 unique bacterial genera. The same DNA samples were also processed with the Miseq data, which yielded a total of 568 unique metazoan genera. In total 27 genera were found in both Miseq and Minion data. A total of 249 unique bacteria was found with Miseq. Twelve of these genera were found in both sequencing techniques.
A semi-automated pipeline was developed to process and analyze long read data from mixed environmental samples. Consensus sequences have shown to be of higher quality than original raw Minion sequences. We expected to receive more genera from Minion data in comparison with Miseq because we should not have a bias from primers, but this was not the case. A reasoning behind this may be that the Minion wet-lab protocol is not fully optimized, hence the low amount of sequences that were left after filtering.
Biodiversity plays an important role in a healthy seabed. The goal of the experiment is to examine biodiversity into a single seabed. One way to do this is with DNA metabarcoding, where DNA is extracted directly from the sediment and a genetic marker is amplified through PCR. Depending on the genetic marker, it is possible to study bacteria (16S) or small multicellular organisms (18S). After the analysis with the Illumina Miseq. The outcome is a lot of sequences per sample. They must then be filtered and corrected. Contamination can also occur in the samples during the lab experiment, which must be removed to obtain a reliable estimate of the biodiversity using metabarcode data.
This project was aimed at investigating the biodiversity in one sandbank (Thorntonbank) in which 15 different samples were taken at 3 different sand extraction impact zones (HIGH, MED, LOW). The names of impact zones indicate the different heights were the samples were taken. It was also examined whether total DNA gave the same results as iDNA
After receiving the 16S and 18S datasets, the primers were removed be using a bash script. Several bioinformatic pipelines were performed in Rstudio. The first was DADA2. This pipeline is determining the amplicon sequence variants (ASV) and will also remove the chimeras. During this pipeline, the taxonomy was assigned using an existing RPD database to determine the different genera. The total amount of ASVs for 16S was 1653 after DADA2 and for 18S it was 3529 ASVs. In 16S they were 227 unique genera and in 18S they were 282 unique genera.
In a next step the phyloseq pipeline was performed. This provides a visual representation of the DADA2 data. One of the most important plots was the Non-metric Multi-dimensional Scaling plot (NMDS plot), from which was deduced that for most samples there was no difference between the total DNA and iDNA. For most cases there were also clusters of the samples taken at the same impact zone. In addition, the pipeline also gave a barplot with the different Genera that occurred per sample and a heatmap with the ASVs per sample. In 16S the species where filtered on phylum and there was no clear difference between the reference and the different impact zones and in 18S there was a higher percentage Opisthokonta in the reference and HIGH samples then the other samples.
The decontam pipeline was carried out after phyloseq, this removed the contaminants. We worked with 2 methods, the frequency method and the Prevalence. The frequency method takes into account the distribution of the frequency of each sequence feature as a function of the input DNA concentration is used to identify contaminants.
The Prevalence method will check which ASVs are present in the negative control and will then remove them from the other samples. When the ASV appeared as contaminant in both methods, they were removed from the dataset. In 16S two ASVs were flagged as contaminants and in 18S zero ASVs were flagged as contaminants.
The last pipeline performed was the LULU pipeline. The purpose of LULU is to reduce the number of ASVs to achieve more realistic biodiversity metrics. This is done by evaluating the co-occurrence patterns of ASVs among samples. LULU identifies ASVs that consistently satisfy some user selected criteria as errors of more abundant ASVs and merges these. Four different combinations of criteria were tested, minimum_match of 80 or 90 and minimum_relative_co-occurrence of 0.1 or 0.9 (figure1). After looking at the number of genera, 4 unique genera were removed in 16S and 3 genera in 18S with criteria1 with a minimum_match of 80 and minimum_relative_co-occurrence of 0.1.
From the different pipelines we can conclude that it is possible to extract ASVs from fastq files, filter the data and assign a taxonomy to the ASVs. We also managed to extract the contaminants using 2 different methods, very little contamination was detected. It was also shown that total DNA can be used for these experiments in the future as results were similar to iDNA because it requires less work in the laboratory. With LULU, we have also successfully clustered similar ASVs which lead to reduction of 1.3% for 16S and 1.4% for 18S of genera. The next step the experiment would be to perform tag switching on the data.
Abstract Bachelor Project 1 FBT 2019-2020: IMPACT OF SAND EXTRACTION ON SEABED COMMUNITIES IN THE NORTH SEA
Human activities like sand extraction at sea are increasing and this can be a problem for the marine ecosystem and its biodiversity. Sediment composition can change, resulting in a different biodiversity. It is very important that human activities are regulated so that the marine system remains healthy. In order to manage human activities, there must be a thorough scientific basis to support any actions that could be taken for the benefit of the marine ecosystem.
The aim of this study is to investigate whether there is an impact of sand extraction on the benthic bacterial community and if the benthic bacterial community can recover when sand extraction has stopped. Samples from two sandbanks, the Thortonbank and the Buitenratel, were analysed in this work. For the Thortonbank, we investigated whether there is an impact on the bacterial community in impacted zones compared to reference zones without impact and whether the impact differs between different depth layers of the sediment. In addition, we compared bacterial communities based on intracellular DNA (iDNA) and extracellular DNA from the sediment. When detritus sinks to the bottom of the sea, DNA is released that is no longer intracellular. Extracellular DNA needs to be removed in order to avoid misrepresentation of DNA and so of impact. For the Buitenratel, a previous study showed differences in bacterial community between the highly impacted zones and the reference zones. Since then, sand extraction has stopped five years ago, so we investigated whether the benthic bacterial community recovered during this time.
The chosen method is DNA-metabarcoding due to its cost-efficiency. First, the DNA is extracted from the sediment samples. From the Thortonbank, the extracellular DNA and iDNA is extracted separated using a different method and kit. From the Buitenratel total DNA is extracted using a kit. Amplicon PCR is performed on all the samples using 16S primers with an adapter overhang to bind with the flow cells of the Illumina sequencer. After the amplicon PCR, the contaminants (for example the primers) are removed with AMPure magnetic beads. Index PCR is performed on the purified amplicon PCR to identify each sample with an index (barcode) and purified again with AMPure magnetic beads. The index PCR samples are equimolarly pooled, using the Quantus to measure the concentration, and quality control is performed with capillary electrophoresis. An external company sequences the samples with Illumina Miseq bridge amplification. Data-analysis is performed in R-studio with the Dada2 pipeline and the Phyloseq package.
The capillary electrophoresis shows that the DNA-extraction, the adapter PCR and the index PCR were successful. The sequences from the Thortonbank contain on average approximately 50000 reads with a few outliers. The sequences from the Buitenratel contain on average approximately 85000 reads with some small variation. The number of reads should be the same because the index PCR-products are pooled equimolar, but due to Quantus-, PCR- and pipetting mistakes there can be a variation. Biodiversity is measured with the number of amplicon sequence variants (ASV’s). The more biodiversity, the more species, the more ASV’s. From the samples of the Thortonbank, the reference impact group contains the highest ASV’s, less ASV’s are found in the moderate impact group and the high impact group contains the least ASV’s. This means that the more sand extraction has happened, the less species are found. No correlation has been found between the number of reads and extracellular DNA and iDNA, nor between the depths. This is the same for the number of ASV’s. Heatmaps were made to look at taxonomy clusters. For the Thortonbank three clusters were seen, one for each impact group illustrating that the bacterial communities are impacted by sand extraction activity. The non-metric multidimensional scaling (nMDS) plot, which shows the genetic distances between locations, shows three clusters corresponding to the impact groups in the Thortonbank. A few samples from the high impact group belonged to the moderate impact group cluster, as seen in the heatmap. No clustering was found between the extracellular DNA and the iDNA samples. There is a clustering based on the different depths, meaning that comparisons can only be made between samples with the same depth. For the Buitenratel, the number of ASV’s do not differ between the reference groups and the impact groups. In addition, no clusters were seen between the impact groups in the heatmap. The nMDS plot also did not contain separated clusters between the impact groups. All results strongly indicate that the bacterial community has recovered from the sand extraction activities.
This study shows that sand extraction has impact on the benthic bacterial communities and that the benthic bacterial communities can recover from it after 5 years. In the future, separating extracellular DNA and iDNA is not needed. Vigilance is still needed to protect the biodiversity.
Abstract Bachelor Project 2 FBT 2019-2020: Chemical taste characterization of algae by analyzing free amino acids with HPLC-MS/MS
The aim of this study is to determine the free amino acids composition in microalgae which can be related to their taste. A hydrophilic interaction liquid chromatography (HILIC) method was validated for analyzing the free amino acids in two different microalgae species, Nannochloropsis and Tetraselmis, by ultrahigh performance liquid chromatography coupled with tandem mass spectrometry (UHPLC-MS/MS).
Precision, accuracy and limits of detection and quantification were evaluated for all individual amino acids. The results shows an acceptable accuracy and precision for most amino acids of Tetraselmis. All amino acids can be detected from Tetraselmis. However, it’s not possible to quantify phenylalanine, methionine, valine and threonine due to the low concentration in this species. The remaining amino acids are quantifiable.
For Nannochloropsis is was not possible to detect following amino acids: taurine, tyrosine, leucine, methionine, isoleucine, threonine, alanine, glycine, aspartic acid, histidine, glutamine, asparagine and tryptophan. Phenylalanine, valine and proline can be detected but not quantified. Only glutamic acid and lysine can be quantified.
The highest concentrations of free amino acids of Tetraselmis are alanine and glutamic acid which are both known to give an umami taste. It can therefore be concluded that Tetraselmis will have an umami taste. Nannochloropsis also has an umami taste because of the high concentration of glutamic acid.
Sole (Solea solea) and cod (Gadus morhua) are both very known and important fish in the Belgian fish industry. The combination of sole being expensive and cod being a public’s favorite comes with the opportunity for fraudsters to emerge. Both fishes are sensitive to fraud, previous research showed that mislabeling occurred in 13,1 % of the cod samples and in 11,1 % of the sole samples in Belgium. The main aim of this study is to detect fraud in the Belgian fish industry using DNA barcoding and qPCR. In this study 97 cod samples and 20 sole samples were collected.
The samples were collected by various staff members at ILVO. The samples were all bought in Belgium and cover the different branches of the Belgian fish industry such as retail, catering and specialist stores. The kit used for DNA extractions was the NucleoSpin® food kit, this kit gave the best results for processed samples. The DNeasy kit was tested as well, but the results were not quite as good for processed samples. To amplify the DNA, either the qPCR or the regular PCR was used. Three primer sets were tested using the regular PCR, one for the CO1 gene, one for the cytb gene and one for the cytb gene with small fragments (mini-barcoding). The samples were sent to an external company for Sanger Sequencing. 80,3 % of the samples sent for Sanger sequencing resulted in a high quality CO1 sequence. For the cytb gene the standard primers gave in 75 % of the cases a valid result. The best result came from the mini-barcoding cytb primers, with a success ratio of 84,7 %. The cytb mini-barcoding primers were able to deliver a result for samples that failed using the CO1 primers or the standard cytb primers. The preferred technique for analyzing cod was qPCR, because this technique is faster and cheaper. The fraudulent qPCR results always need to be double checked with DNA barcoding to prevent a false result. At the time of the investigation there was no qPCR kit available for sole, so every sole sample was analyzed using DNA barcoding. Two sole samples failed the sequencing and did not have a valid barcoding result. Five out of the eighteen successfully sequenced sole samples were mislabeled or 27,78 %. For cod there was only one sample out of 97 that was mislabeled or 1,03 %, this low percentage might be due to cod being mostly imported and undergoing several controls before the fish reaches the Belgian fish market. The fraud rate for sole was a lot higher than what previous research showed, this may be explained by the high delivery price of sole in Belgium. The substitutes for both cod and sole are cheaper fish. This translates to financial gain for the seller, thus fraud is present in the Belgian fish industry.
Antifouling paints with booster biocides are used to avoid fouling on the hull of ships. Booster biocides can be toxic to the marine environment and some can also be persistent in marine sediment. Since there is no information available on booster biocide concentrations at the Belgian part of the North Sea, the goal of this thesis is to validate a method for analysis of 6 booster biocides and to apply it to measure these compounds in marine sediment of the Belgian part of the North Sea. Booster biocides selected were irgarol, diuron, sea-nine, tolylfluanide, dichlofluanide and medetomidine.
The total organic carbon content (TOC) and grain size distribution was determined on 29 samples by respectively a redox titration by Mebius and a sieving procedure followed by laserdiffraction by a Malvern Mastersizer. For the analysis of the booster biocides, pressurized liquid extraction (PLE) was used to extract the booster biocides, after which they were analysed with an HPLC-device coupled with a tandem MS-detector. The method passed the validation for the booster biocides irgarol, diuron, sea-nine 211, dichlofluanide and tolyfluanide as limits for trueness and reproducibility were met. The limit of detection ranged from 0,04-1,14 ng/g. For medetomidine, the method will not be applied as validation failed. For this compound, too high variability was noted between samples, probably due to matrix effects.
The TOC and grain size analysis showed that samples from the dredging spoil disposal sites LZO and LOO along with the shipping track near port Zeebrugge had a high amount of silt and organic components. Three boosterbiocides, irgarol, diuron and sea-nine 211 were detected at these locations with a respective normalized maximum concentration of 15 ng/g, 4 ng/g and <LOQ. The presence of these boosterbiocides confirms that risk assessment is needed to have a view on the impact of these compounds on the marine environment.
The Belgian Part of the North Sea (BPNS) is used extensively by humans. The activities focused in this research are sand and gravel extraction. To assist with spatial planning, granting exploitation licences and implementation of European regulations, it’s imperative to understand the individual and cumulative impacts of these various human activities on the marine ecosystem. Our goal was to determine if sand extraction activities have influenced the “Buitenratel” (a part of the BPNS) on a microbiological scale. Sediment samples of the “Buitenratel” have been collected on the 25th of September 2015. Samples were procured from thirteen areas of high extraction and six high extraction reference locations. Other samples were taken from two areas with moderate extraction and seven reference locations for moderate extraction. After DNA extraction, 16S RNA libraries were prepared to sequence on the Illumina Miseq sequencer (PE 300bp).
Until recently operational taxonomic unit (OTU) clustering was standard in the pipeline for the determination of bacteriological communities. The now recently developed DADA2 pipeline presents many improvements. It records the number of times each exact amplicon sequence variant was observed in each sample. It makes models and corrects amplicon errors. This is to improve the overview and quality of the results. As the last step of the DADA2 pipeline the sequences are assigned a taxonomy and are put into an abundance table. The DADA2 pipeline was optimised for the “Buitenratel” dataset.
The outputted table is used to build a taxonomic plot and a multidimensional scaling (MDS) plot to see differences between conditions. To determine the credibility of the produced plots the following statistical test were used: permanova, permdisp and posthoc pairwise comparison. The pipeline and the tests were conducted on normalised data and on rarefied data.
The taxonomic assignments were preformed using three different microbiological databases namely Silva 132, Greengenes 16_8 and the Ribosomal database project training set 16 (RDP). This was to see if the taxonomy of a sequence was equal in each database, which wasn’t the case. Some taxonomic assignments were different when other databases were used. Other sequences could only be predicted with only one of the three databases. On the genus rank, the differences between each dataset increased. The eucarya were removed from the Silva database to decrease interference of non-important sequences. Silva is still the best database as it is large, manually curated and recently updated.
On the MDS there is a clear difference between areas of high sand extraction and their references sites without sand extraction. On the other hand, there is no difference between the areas of moderate sand extraction and their references sites without sand extraction. There is also a clear contrast between high and moderate areas. The results of the statistical tests confirm this. It can be clearly seen that excavating sandbanks changes the bacteriological communities, both the rarefied data and the normalised data produced the same result. In the future an indicator analysis could be preformed to identify which taxa are most affected.
The taxonomic assignment could be changed. It’s still unclear what the true taxonomy is of some sequences. Adjustments could be done by limiting each dataset to only the bacteria present in the sea or sediment. This lowers the interference of non-relevant sequences.
In the marine environment, there is vegetation on the hull of ships and boats of algae and aquatic animals. To counteract this, various antifouling products are applied to the hull of ships and boats. These products contain booster biocides that can be toxic to the marine environment. Within this bachelor’s report, a method was developed to determine seven booster biocides in the sediment matrix: medetomidine, zinc pyrithione, Irgarol 1051, diuron, Sea-Nine 211, tolylfluanid and dichlofluanid.
The sediment is extracted with pressurized liquid extraction (PLE). Booster biocides are then separated using the ultra-high-performance liquid chromatography (UHPLC) which is linked to a tandem mass spectrometry (MS/MS) as detector. Two ionization methods are used, namely electrospray ionization (ESI) and atmospheric pressure chemical ionization (APCI).
The LC-MS settings, including the multiple reaction monitoring (MRM) settings with determination of mass/charge (m/z) ratios and fragmentation patterns has already been optimized. The derivatisation method of zinc pyrithione is also created, since it does not give a response without derivatisation. In this bachelor’s report, the PLE solvent was optimized to hexane:acetone 2:1. Purification was tested with gel permeation chromatography (GPC), aluminium oxide (Al2O3) and silicon dioxide (SiO2). The latter purification technique gave the best results, but was not appropriate for medetomidine. When the analysis was done without purification, good linear responses were obtained for medetomidine as well as other booster biocides, while the extract was pure enough for injection onto the UHPLC.
The method was finalized for following booster biocides with ESI: medetomidine, Irgarol 1051, diuron and Sea-Nine 211, and with APCI: tolylfluanid and dichlofluanid. Precise results were obtained by quantification by standard addition, except for medetomidine, for which the spiked amount still must be increased. The derivatisation for zinc pyrithione with ESI is successful, but should be further improved to apply on real samples.
On the Belgian market many fish products are sold whose origin are not always known. This allows food processors to add cheaper fish varieties to their products without the consumer knowing. To prevent this, products must be checked. This is often done by DNA control. Each product, in this case fish, has unique DNA sequences that can be checked. But this is not always easy in stored products. These products are often sold after they have been processed. This means that products are steamed, smoked or they are stored in oils and sauces, among other things. Because products are processed, the DNA of the fish in the product is processed or broken down. Because the DNA is often broken in very short sequences, is it difficult to find specific sequences for certain species. This makes fraud much more difficult to detect. This study looks for a way in which this fraud can be detected. This research is mainly done for the interest of the consumer. A consumer pays for quality and expects to receive this quality. As mentioned earlier, food processors can use cheaper variants instead of their more expensive variety that is offered so to speak.
In this study, several tuna samples are bought in a supermarket where a DNA extraction will take place with two different DNA extraction kits. The DNA extraction will also determine which DNA extraction kit is the most efficient to extract the most DNA from these different products. There will also be an extra step included in de DNA extraction protocol with a chloroform/methanol/water solution. This will be checked by comparing DNA concentrations and the purity of the DNA. This DNA extraction will also run on a gel electrophoresis. Next, primers and probes are developed tested to investigate the fraud in tuna products on the Belgian market based on Real-Time Polymerase Chain Reaction (qPCR). To make sure fraud is excluded the result should match with the label of the product.
For processed samples we can decide that de DNeasy® Food kit with a chloroform step is the best method. The Food kit is also the best option for extracting DNA from pure samples. In case of pure samples, no chloroform step is needed. We developed our own probes and primers for identification of specific tuna species. There are three species who can already be identified. This at an amplification temperature of 62°C and a 100/400 probe/primer concentration. With those conditions T. alalunga, T. albacares and K. pelamis can be identified out of an unidentified sample. Also in mixed samples with different tuna species, each species can be successfully identified while the concentration of sample is only 50% or 33%. As a conclusion we can say that different tuna species can be identified by using qPCR and a TaqMan assay.
On January 1, 2014, a new law was introduced, namely the landing obligation. The landing obligation requires all catches of regulated commercial species of fish on-board to be landed and counted against quota. Because of this new law there is a big amount of fish and fish waste that cannot be used for human consumption. A solution for this problem is to use the fish materials as food for animals. One of the processes that has been done is with chemical preservation by acid addition. The product of this process is called fish silage. Several studies on fish silage have been done in Northern Europe but it’s unique in Belgium.
The aim of this study is to analyze the stability and quality of fish silage made in Belgium.
The parameters were analyzed for different kinds of fish silage. These go from raw material to pasteurized (wet sample) and dried silages (concentrated sample). The dry matter (DM) was determined with a freeze drier by using a temperature of -84°C and vacuum. The overall degradation was determined based on the total volatile basic nitrogen (TVBN), the method is based on an extraction with perchloric acid, a steam distillation and titration with hydrochloric acid. The bacterial degradation is based on the measurement of trimethylamine (TMA). The method is the same as for TVBN except that formaldehyde needs to be added to block the primary and secondary amines. The degree of hydrolysis is defined as the percentage of peptide bonds in a protein which have been cleaved during hydrolysis. It’s based on the reaction of TNBS and N-terminal amino groups that is measured spectrophotometric. The quality of lipids is based on the lipid oxidation. The method is based on a distillation and reaction of malonaldehyde (MA) with TBA that is measured spectrophotometric.
There was a big difference in DM content between the different silages going from 22,55% for raw material to 26,44% for pasteurized silages. The dried sample varies from 90,17% to 94,68% DM. The protein content decreases slightly over time. The TVBN value for raw material was under the limit of 50 mg N/100g but increased over time. The dried silages contained more than 195 mg N/100g, this because the TVBN values are more concentrated. TMA values were above the limit of 10 mg N/100g but were relatively stable. This means that the increase of TVBN is mainly due to NH3 production, which corresponds with the protein decrease. The degree of hydrolysis reached a maximum of 68,04%. The lipid oxidation reached a value of 10,62 mg MA/kg for the hydrolyzed sample, the other silages were below the limit.
More research needs to be done to improve the quality and stability of fish silage. Fresher raw material should be used to minimize the TMA values. An anti-oxidant can be added to prevent lipid oxidation. To produce a more stable product, the oil fraction can be removed from the fish silage.
Fishery and aquaculture products are an important food source for humans. One of the major concerns in the seafood trading is renaming and mislabelling of species. Mislabelling involves providing inaccurate information about the identification of the product, most often because the product is a cheaper or a more easily available species. The results of which include degradation of fisheries resources, consumer losses, undermining of the ecological market, and the adverse effects on human health.
This paper examines the application of the correct commercial and scientific names and the authenticity of 70 Belgian seafood samples using DNA barcoding. Furthermore, the focus of this research is the optimization of the DNA barcoding method to identify a greater range of seafood species on the Belgian market.
The fresh, frozen and processed seafood samples were purchased from different Belgian supermarkets and retailers. The extracted DNA, using the Spin Column method and the Chelex method, was used for amplification of the mitochondrial COI, cytb and 16S rRNA and the nuclear rhod genes. The obtained PCR products were then loaded on a 1,5 % agarosegel. After electrophoresis the PCR products that result in one band were purified. The purified fragments were then send to Macrogen Europe Laboratories for Sanger sequencing. The resulting sequences are further processed with BioNumerics 7.6 software and analyzed with the BLAST tool in the NCBI database. After identification of the seafood products the usage of the commercial designation and the scientific name on the package was checked using the World Register of Marine Species and the Policy Informative Note: Scientific and trade names for fishery and aquaculture products on the Belgian market.
The DNA barcoding method revealed that the use of 16S rRNA primers yielded the most identifications of fishes, bivalves, crustaceans and cephalopods. The 16S rRNA primers do not always provide identification to species level, which makes the use of other primers necessary. Of the examined seafood samples in this study 52 % contained an invalid or an unacceptable commercial designation or scientific name and 6 % of these species were genetically mislabelled. The retail chains should be supported more so that the last accepted commercial designation or scientific names can be applied. The use of one name per species could provide a solution for better traceability and transparency in the fisheries sector.
Perennial ryegrass (Lolium perenne) is an important grass species, utilized as hay, silage and pasture. It’s a plant that has a rapid establishment, high yields, tolerance of grazing and a long growing season. ILVO is strongly involved in ryegrass breeding, but this is still a challenge. L. perenne is an outbreeding species, which makes the genome highly polymorphic. Therefore genetic variation has been identified by sequencing a large collection of individual plants. Because the genome of L. perenne is too large, targeted resequencing was used. For targeted resequencing by probe capture in 746 genotypes, 500 genes known to regulate plant growth and quality were selected. The raw sequencing data was first prepared, by trimming the adapter, mapping the reads on a reference genome, sorting the reads, marking duplicates and ultimately with an InDel realignment.
Variant calling is a process used to identify genetic variation. This process is very hard. Firstly, because different tools give different results, and secondly some forms of variation are very hard to detect correctly. That’s why in this project a comparing study is done for variant calling tools to identify genetic variation in L. perenne.
For the variant calling BCFtools, VarScan, Platypus and GATK were used. Variant calling was ultimately done for 67 genes. For BCFtools, VarScan and Platypus different methods were tested in order to select parameter settings that are best suited for our dataset. Results from best suited methods were then compared. The average variant density over all gene regions was calculated per tool.
Platypus performed the worst, by not even being able to identify variants for 21 genes, where all other tools did. Platypus also found much less SNPs compared to the other tools. BCFtools has the biggest overlap from SNPs with the other tools, but also found ten times more unique SNPs than GATK. This big number can be an indication that BCFtools identified a lot of false positive variants. VarScan has the biggest overlap of InDels with the other tools and identified the lowest number of unique InDels, which may be an indication that this tool is precise when calling InDels. This can be checked in a genome browser, by manually looking for traces of called InDels in the reads. GATK however has the biggest overall variant-density, and the biggest InDel-density.
More research is needed to be able to make a good and reliable variant dataset. Future steps are required, and may involve taking into account variant call quality and genotype call quality and verifying through a genome browser if variants are true- or false-positive.
Mike van 't Land