Digital duplex versus real-time PCR for the determination of meat proportions from sausages containing pork and beef

Discovery unbalanced DNA mixtures and evaluation mixing ratio via a droplet digital PCR method

BACKGROUND

Small amounts of DNA from a perpetrator collected during crime-scene investigations can be masked by large amounts of DNA from the victim. These samples can provide important information for the perpetrator's conviction. Short tandem repeat (STR) detection system is not sensitive enough to detect trace amounts of minor components in unbalanced mixed DNA. We developed a system using droplet digital polymerase chain reaction (ddPCR) capable of discovering trace components and accurately determining the ratio of mixed DNA in extremely unbalanced mixtures.

METHODS

The non-recombining regions of the X chromosome and Y chromosome were quantified in the DNA of male and female mixtures using duplex ddPCR. Absolute quantification of low-abundance portions of trace samples and unbalanced mixtures was done using different mixing ratios.

RESULTS

The ddPCR system could be used to detect low-abundance samples with < 5 copies of DNA components in an extremely unbalanced mixture at a mixing ratio of 10000:1. The high sensitivity and specificity of the system could identify the mixing ratio of mixed DNA accurately.

CONCLUSIONS

A ddPCR system was developed for evaluation of mixed samples of male DNA and female DNA. Our system could detect DNA quantities as low as 5 copies in extremely unbalanced mixed samples with good specificity and applicability. This method could assist forensic investigators in avoiding the omission of important physical evidence, and evaluating the ratio of mixed male/female trace samples.

Nanoplate-based digital PCR for highly sensitive pork DNA detection targeting multi-copy nuclear and mitochondrial genes

The inclusion of ingredients derived from pigs in highly processed consumer products poses a significant challenge for DNA-targeted analytical enforcement, which could be overcome by using digital PCR. However, most species detection methods use digital PCR to target single-copy nuclear genes, which limits their sensitivity. In this work, we examined the performance of a nanoplate-based digital PCR method that targets multi-copy nuclear (MPRE42) and mitochondrial (Cytb) genes. Poor separation of positive and negative partitions, as well as a 'rain effect' were obtained in the porcine-specific MPRE42 assay. Among the optimization strategies examined, the inclusion of restriction enzymes slightly improved the separation of positive and negative partitions, but a more extensive 'rain effect' was observed. The high copy number of the MPRE42 amplicon is hypothesized to contribute to the saturation of the positive signal. In contrast, the porcine-specific Cytb assay achieved perfect separation of positive and negative partitions with no 'rain effect'. This assay can detect as little as 0.4 pg of pork DNA, with a sensitivity of 0.05% (w/w) in a pork-chicken mixture, proving its applicability for detecting pork in meat and meat-based products. For the MPRE42 assay, potential applications in highly degraded products such as gelatin and lard are anticipated.

Development of Seven New dPCR Animal Species Assays and a Reference Material to Support Quantitative Ratio Measurements of Food and Feed Products

Laboratory testing methods to confirm the identity of meat products and eliminate food fraud regularly rely on PCR amplification of extracted DNA, with most published assays detecting mitochondrial sequences, providing sensitive presence/absence results. By targeting single-copy nuclear targets instead, relative quantification measurements are achievable, providing additional information on the proportions of meat species detected. In this Methods paper, new assays for horse, donkey, duck, kangaroo, camel, water buffalo and crocodile have been developed to expand the range of species that can be quantified, and a previously published reference assay targeting the myostatin gene has been modified to include marsupials and reptiles. The accuracy of this ratio measurement approach was demonstrated using dPCR with mixtures of meat DNA down to 0.1%. However, the limit of detection (LOD) of this approach is not just determined by the assay targets, but by the samples themselves, with food or feed ingredients and processing impacting the DNA yield and integrity. In routine testing settings, the myostatin assay can provide multiple quality control roles, including monitoring the yield and purity of extracted DNA, identifying the presence of additional meats not detected by the suite of species-specific assays and potentially estimating a sample-specific LOD based on measured copy numbers of the myostatin target. In addition to the myostatin positive control assay, a synthetic DNA reference material (RM) has been designed, containing PCR targets for beef, pork, sheep, chicken, goat, kangaroo, horse, water buffalo and myostatin, to be used as a positive template control. The availability of standardised measurement methods and associated RMs significantly improves the reliability, comparability and transparency of laboratory testing, leading to greater confidence in results.

Detection and Quantification of Adulterated Beef and Mutton Products by Multiplex Droplet Digital PCR

In order to seek high profit, businesses mix beef and mutton with cheap meat, such as duck, pork, and chicken. Five pairs of primers were designed for quintuple droplet digital PCR (qddPCR) of specific genomic regions from five selected species and specificity and amplification efficiency were determined. The mixed DNA template with an equal copy number was used for detecting the accuracy and limit of multiplex PCR. The results showed that the primers and probes of the five selected species had good specificity with the minimum number of detection copies: 0.15 copies/µL beef (Bos taurus), 0.28 copies/μL duck (Anas platyrhynchos), 0.37 copies/μL pork (Sus scrofa), 0.39 copies/μL chicken (Gallus gallus), and 0.41 copies/μL mutton (Ovis aries), respectively. The five sets of primers and probes could quickly judge whether the specified meat components existed in the food commodities.

Comparison of Real-Time PCR Quantification Methods in the Identification of Poultry Species in Meat Products

Poultry meat is consumed worldwide and is prone to food fraud because of large price differences among meat from different poultry species. Precise and sensitive analytical methods are necessary to control poultry meat products. We chose species-specific sequences of the cytochrome b gene to develop two multiplex real-time polymerase chain reaction (real-time PCR) systems: one for chicken (Gallus gallus), guinea fowl (Numida meleagris), and pheasant (Phasianus colchicus), and one for quail (Coturnix japonica) and turkey (Meleagris gallopavo). For each species, added meat could be detected down to 0.5 % w/w. No cross reactions were seen. For these two real-time PCR systems, we applied three different quantification methods: (A) with relative standard curves, (B) with matrix-specific multiplication factors, and (C) with an internal DNA reference sequence to normalize and to control inhibition. All three quantification methods had reasonable recovery rates from 43% to 173%. Method B had more accepted recovery rates, i.e., in the range 70-130%, namely 83% compared to 75% for method A or C.

A big data approach to metagenomics for all-food-sequencing

BACKGROUND

All-Food-Sequencing (AFS) is an untargeted metagenomic sequencing method that allows for the detection and quantification of food ingredients including animals, plants, and microbiota. While this approach avoids some of the shortcomings of targeted PCR-based methods, it requires the comparison of sequence reads to large collections of reference genomes. The steadily increasing amount of available reference genomes establishes the need for efficient big data approaches.

RESULTS

We introduce an alignment-free k-mer based method for detection and quantification of species composition in food and other complex biological matters. It is orders-of-magnitude faster than our previous alignment-based AFS pipeline. In comparison to the established tools CLARK, Kraken2, and Kraken2+Bracken it is superior in terms of false-positive rate and quantification accuracy. Furthermore, the usage of an efficient database partitioning scheme allows for the processing of massive collections of reference genomes with reduced memory requirements on a workstation (AFS-MetaCache) or on a Spark-based compute cluster (MetaCacheSpark).

CONCLUSIONS

We present a fast yet accurate screening method for whole genome shotgun sequencing-based biosurveillance applications such as food testing. By relying on a big data approach it can scale efficiently towards large-scale collections of complex eukaryotic and bacterial reference genomes. AFS-MetaCache and MetaCacheSpark are suitable tools for broad-scale metagenomic screening applications. They are available at https://muellan.github.io/metacache/afs.html (C++ version for a workstation) and https://github.com/jmabuin/MetaCacheSpark (Spark version for big data clusters).