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?
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