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Should I design a primer to detect virus based on NCBI genebank?

Should I design a primer to detect virus based on NCBI genebank?


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When I searched HIV on NCBI gene bank, I found 2 results from 1997: http://www.ncbi.nlm.nih.gov/genome/?term=HIV. HIV can change every day, so I think the sequence from 1997 is not useful. Is it?


Any other database better than NCBI ?

A lot of people overlook some sub-databases inside NCBI and search the main nucleotide database only.

If You never tried it, you can blast the HIV sequence which You have found, against other databases. On the ncbi blast webpage, just choose the options other than nucleotide collection (nr/nt). I tend to find new sequences in Expressed sequence tags (EST), Genomic survey sequences (GSS) or Whole genome shotgun contigs (WGS). Some of these databases cover unfinished sequencing project and should be a source of new data.

(I am a plant person, not virus person, so maybe you will find out, that different databases are worth searching for you. Or you will find it a waste of time.)

BTW, now some time had passed since your question. How did you solve Your HIV sequence problem ?


Metagenomic Analysis of Plant Viruses Associated With Papaya Ringspot Disease in Carica papaya L. in Kenya

Carica papaya L. is an important fruit crop grown by small- and large-scale farmers in Kenya for local and export markets. However, its production is constrained by papaya ringspot disease (PRSD). The disease is believed to be caused by papaya ringspot virus (PRSV). Previous attempts to detect PRSV in papaya plants showing PRSD symptoms, using enzyme-linked immunosorbent assay (ELISA) and reverse transcriptase-polymerase chain reaction (RT-PCR) procedures with primers specific to PRSV, have not yielded conclusive results. Therefore, the nature of viruses responsible for PRSD was elucidated in papaya leaves collected from 22 counties through Illumina MiSeq next-generation sequencing (NGS) and validated by RT-PCR and Sanger sequencing. Viruses were detected in 38 out of the 48 leaf samples sequenced. Sequence analysis revealed the presence of four viruses: a Potyvirus named Moroccan watermelon mosaic virus (MWMV) and three viruses belonging to the genus Carlavirus. The Carlaviruses include cowpea mild mottle virus (CpMMV) and two putative Carlaviruses-closely related but distinct from cucumber vein-clearing virus (CuVCV) with amino acid and nucleotide sequence identities of 75.7-78.1 and 63.6-67.6%, respectively, in the coat protein genes. In reference to typical symptoms observed in the infected plants, the two putative Carlaviruses were named papaya mottle-associated virus (PaMV) and papaya mild mottle-associated virus (PaMMV). Surprisingly, and in contrast to previous studies conducted in other parts of world, PRSV was not detected. The majority of the viruses were detected as single viral infections, while a few were found to be infecting alongside another virus (for example, MWMV and PaMV). Furthermore, the NGS and RT-PCR analysis identified MWMV as being strongly associated with ringspot symptoms in infected papaya fruits. This study has provided the first complete genome sequences of these viruses isolated from papaya in Kenya, together with primers for their detection-thus proving to be an important step towards the design of long-term, sustainable disease management strategies.

Keywords: Carlavirus Potyvirus diagnostic primers next-generation sequencing papaya ringspot disease.

Copyright © 2020 Mumo, Mamati, Ateka, Rimberia, Asudi, Boykin, Machuka, Njuguna, Pelle and Stomeo.

Figures

Counties in Kenya sampled during…

Counties in Kenya sampled during this study.

Diversity in PRSD symptoms observed…

Diversity in PRSD symptoms observed during the field survey. (A) Puckering. (B) Mottling.…

Phylogenetic relationships among MWMV isolates…

Phylogenetic relationships among MWMV isolates and closely related potyviruses. The phylogenetic tree was…

Phylogenetic analysis of coat protein…

Phylogenetic analysis of coat protein amino acid sequences among CpMMV, PaMMV, PaMV, and…

Phylogenetic analysis of RdRp amino…

Phylogenetic analysis of RdRp amino acid sequences among CpMMV, PaMMV, PaMV, and closely…


Introduction

Multiplex polymerase chain reaction (PCR) is a fundamental approach to get many several fragment copies of a single DNA molecule simultaneously. It has a lot of applications in different science areas, from kinship determination [1] and pathogen detection [2] to next-generation sequencing (NGS) library preparation [3,4]. The primer design process involves two main steps. 1) Choosing target regions, while a researcher converts gene names into sequences. 2) For each target region, primers should be constructed taking into consideration a lot of parameters (amplicon and primer lengths, melting temperature, whole primer and only 3’-end GC-content, poly-N tract presence, secondary structures: homo- and heterodimers, hairpins, hybridization to non-target regions, overlapping variable positions). However, a transition from monoplex to multiplex reactions requires consideration of more factors that may influence PCR efficiency (secondary structure formation by oligonucleotides and non-target hybridization, elongation, and amplification with primers from different pairs). The number of factors to consider grows exponentially with the number of target sequences that make the manual primer design labor-intensive.

To facilitate the primer design process, many tools have been developed (S1 Table). However, none of them can completely simplify the process of primer design considering all factors listed above, particularly for developing new amplicon-based NGS panels, that require additional checking for secondary structures due to the adapter sequences at the 5’-ends of each primer. At the same time, the amplicon-based NGS panels have become widespread among both researchers and commercial companies due to its simplicity of utilization by end-user and highly efficient target enrichment for DNA samples of different quality, including to detect somatic mutations with a low tumor frequency. And two specific approaches are used to increase the yield of a genome region studied (nested PCR) or to get all possible sequences of a variable region (anchored PCR). Nested multiplex PCR is applied in molecular biology where higher specificity is necessary [5], e.g. during the detection of low-frequent somatic mutations in tumors [6] or pathogen identification analyzing conservative sequences with enough variability [7]. The use of the nested PCR increases the assay specificity by amplifying only regions that contain both internal and external primers (S1 Fig) that also increases the number of parameters to analyze. The anchored PCR was developed to study rare transcripts with sequence unknown partially in 1988 [8], however, recently, it has gained relevance again due to the need to detect particular gene fusions with an unknown partner [9]. And this type of PCR requires the program to be able to design primers on one side only that is commonly not supported by other primer design tools.

Here we describe a new tool for automatic primer design for multiplex PCR, including for creating new amplicon-based targeted NGS-panels, NGS-PrimerPlex. The program is implemented in Python, takes into consideration all parameters listed above, and was tested on different sets of target sequences, including bacterial and human genomes. Primers designed with NGS-PrimerPlex were validated experimentally for studying the LRRK2 gene coding sequences.

Design and implementation

NGS-PrimerPlex is implemented in Python using free-available Python-modules (S2 Table) and the BWA program [10]. It can be run as a standalone program (only for macOS and Linux users) or inside a docker image with or without a graphical user interface (GUI) (for users of any OS, S2 Fig). Docker image allows someone not to install and not to download additional files but use it immediately after downloading the main package. NGS-PrimerPlex and its manual with screenshots are available at the GitHub server (https://github.com/aakechin/NGS-PrimerPlex, https://github.com/aakechin/NGS-PrimerPlex/wiki) and in the S1 Text.

NGS-PrimerPlex implementation

Getting genome coordinates based on a list of genes and their parts.

NGS-PrimerPlex contains script getGeneRegions.py that reads the table of genes from the input file. For each input gene, the chromosome is determined from the comma-separated values (CSV) file that is created automatically from the reference genome GenBank- and FASTA-files. Then exon/codon coordinates are extracted from the corresponding GenBank-file if it is required. User can specify numbers of exons or codons which are necessary to be included. Human reference genome versions hg19 or hg38 or reference genome of other organisms can be used. The output file can be used in the next script for the primer design NGS-primerplex.py. Whole NGS-PrimerPlex package functionalities are reflected in Fig 1.

Solid arrows denote obligatory steps dotted arrows denote optional steps that can be applied by the user. The primer design process starts from the list of sequence regions (e.g. regions of a reference genome), however, such region coordinates can be automatically obtained with getGeneRegions.py script. NGS-PrimerPlex tries to design primers for all input regions saving primers constructed to the XLS-file (“draft” file 1). If it is necessary, all primers are verified for the forming secondary structures due to the additional adapter sequences at the 5'-ends of primers. If all target regions are covered, the primers are verified for non-target hybridization and amplification. Primers that do not hybridize to many targets and do not form non-target amplicons are saved to distinct XLS-file (“draft” file 2), others are removed. If all target regions are still covered, the primers are verified for overlapping genome regions with variable positions. Primers that do not overlap variable positions with defined parameters (the frequency of the alternative allele and the distance to the 3'-end) are saved to the new XLS-file (“draft” file 3), others are removed. If all targets are still covered, primers are distributed to the defined number of pools. The diagram was drawn in https://app.diagrams.net.

Multi-step primer design.

By default, NGS-PrimerPlex splits input genome regions onto distinct positions and tries to construct primers for each of them using primer3-py that includes the primer3 library [11]. For each position, the program tries to design three types of primers: so that the right primer was close to the studied position, the left primer was close to the studied position, and without any of these restrictions (S3 Fig). This is necessary for subsequent successful joining of primers into a set of primer pairs that amplify a single extended genome region (e.g. whole exon). Unlike other programs (e.g. hi-plex) that design primer pairs not overlapping at all, NGS-PrimerPlex allows overlapping but considers it during distribution into multiplex reactions. This increases the chances of successful primer design, particularly for genome regions with complex structures. At the same time, NGS-PrimerPlex allows users to forbid splitting of some regions, e.g. during primer design to detect EGFR exon 19 deletion [12].

Another feature of the NGS-PrimerPlex that may be useful for complex genome regions is a multi-step primer design. Frequently, existing tools suggest users choose parameters of primer design and at the end of the design process program can give an error that primers could not be constructed with the defined parameters. After choosing less strict parameters, the user has to start the process from scratch that significantly slows down parameter optimization. NGS-PrimerPlex saves primers that could be designed with more strict parameters and constructs primers only for other regions. On the other hand, this feature can be used to create personalized NGS-panels and expanding existing ones that are targeted for patient-specific genome regions, when primers for some of these regions have been obtained earlier that can not be done with other programs.

Non-target hybridization in the genome and overlapping with SNPs.

On-target is one of the main characteristics of targeted NGS-panels [13] and it depends on whether the primers hybridize to non-target genome regions, including substitutions/insertions/deletions as well as with other primers from the same multiplex reaction. For doing it, NGS-PrimerPlex uses the BWA program that can readily find all regions to which a nucleotide sequence can be mapped. For the similar regions (but not necessarily identical ones, because the user can search for primer targets with mismatches), the program checks if the primer has the same last two nucleotides from 3’-end as the non-target region, that is not performed by existing tools (e.g. MPD and hi-plex). This allows identifying regions that will give non-target amplicons but not only have homology with primers. Comparing genome coordinates for different primers, NGS-PrimerPlex filters out primer pairs that can lead to non-target amplification in a multiplex reaction.

Another significant characteristic of NGS-panels is uniformity of coverage [13] which depends, among other things, on the sequence homogeneity of the sequence complementary to primer among different samples. Therefore, NGS-PrimerPlex checks if any of the primers designed overlaps with SNPs from the dbSNP database [14] using the pysam Python-module and dbSNP variant call format (VCF) file. The user has an opportunity to check only part of the primer for overlapping with SNP (e.g. only the last 10 nucleotides) and to define the minimal frequency of SNP in the population for which primers will be checked. Both features are unique for NGS-PrimerPlex.

Nested and anchored multiplex PCR.

NGS-PrimerPlex allows users to design primers for nested PCR with subsequent distribution of four primers among multiplex reactions considering both secondary structure and non-target product formation for internal and external primers.

Anchored multiplex PCR that applies one primer hybridizing to the gene-specific region and one primer been complement to the adapter sequence allows amplifying regions with unknown or partially highly variable sequences (S1 Fig), e.g. to detect gene fusion mutations without prior knowledge of the fusion partners [6,15]. And NGS-PrimerPlex can also design primers for this type of multiplex PCR.

Distribution of primers among multiplex reactions.

The final step of the primer design is the distribution of the constructed primers among the user-defined number of multiplex reactions. NGS-PrimerPlex supports distribution of all primers into any number of multiplexes (unlike e.g. MPD that is capable of distributing into small multiplexes of about 2–15 primer pairs) as well as the distribution of some regions into specific groups of multiplexes (e.g. when it is necessary to separate some genes into different multiplexes). The distribution is performed using the networkx Python module which creates a graph, edges of which mean two primer pairs not producing any secondary structures, not overlapping by the product, and not producing non-target amplicons. For joining primer pairs into a set of primer pairs that amplify a single extended genome region (e.g. whole exon), it searches for the shortest path from the start of such region to the end, both of which are also included into the graph. For the subsequent distribution, NGS-PrimerPlex tries to find a clique in the constructed graph, i.e. such a subset of vertices that every two distinct vertices in the clique are adjacent. Searching for clique is performed until all primer pairs are distributed.

NGS-PrimerPlex throughput

To evaluate NGS-PrimerPlex throughput, we designed primers for studying BRCA1 and BRCA2 coding sequences. Coding exon regions sorted by the length were added one by one to the NGS-panel and the computer time was registered.


Discussion

Early detection of pathogens is crucial to disease prevention 5 and containment, especially during epidemic outbreaks 6 . PCR is a reliable and relatively accessible molecular method that directly recognizes pathogen-derived material from patients samples 7 . However, PCR protocols' optimization is strongly dependent on primers' specificity and efficiency 8 . This reason, combined with the increasing number of SARS-CoV-2 sequences available and its crescent polymorphism, led us to design a set of new primers that can address very conserved regions of the virus genomes.

Therefore, to aid PCR optimization, the UFRN_primers were designed to present Tm values that were as close as possible. These settings will probably enable the use of at least two systems using the same thermal cycling parameters. In this way, it would be possible to perform the PCR test identifying different viral genome regions simultaneously, according to the protocols already described for the PD_primers. In this context, possibly the systems UFRN_3 and UFRN_4 will have different thermal cycling parameters compared to the other systems since, in this case, the probe Tm is similar to the primers (Table 1). Probably these systems will depend on more annealing time to ensure that the probe has interacted in the DNA template before the amplification starts.

The higher specificity of UFRN_primers confirmed by in silico analysis is mainly due to the availability of 2.341 genome sequences, which made it possible to identify the conserved regions with greater accuracy from the alignment. The UFRN_6 and UFRN_7 primers differ only by one base and have overlapping probes. However, these discrete differences were sufficient to alter the sequences in which these primers interact (Table 1). Only 12 sequences did not anneal with the designed primers. Among them, seven were isolated from pangolins and one from bats, all from China provinces. The other four sequences are from Australia and Nigeria and presented a high percentage of N bases, which might have caused negative results.

Another striking result is that UFRN_primers presented a higher potential to identify the main SARS-CoV-2 recent variants of concern than the PD_primers, significantly the B.1.351, B.1.427, B.1.429, B.1.525, and P.1. In silico predictions indicate that the UFRN_primers are potentially less prone to generate false-negative results. Its application could represent a significant difference to Covid-19 diagnostic and epidemiology since the Food and Drugs Administration (FDA) has recently warned of the negative impact of SARS-CoV-2 genetic variants on molecular detection tests available 9 .

The use of universal primers makes it possible to identify several virus variants using the same PCR protocol. UFRN_primers are strong candidates to simplify the procedures and supply chain for detecting SARS-CoV-2, allowing, for example, the mass production of primers and kits that could be applied in different parts of the world with equivalent efficiency. However, the primers presented here still depend on in vitro validation. The availability of these sequences at this time will be crucial so that these new protocols can be validated promptly to assist in the control of the SARS-CoV-2 pandemic.

Another critical point is that primers presented here were tested against the updated RNA sequences databases from bacteria, fungi, and protozoa and did not generate nonspecific amplicons in any case. Although executed through in silico analyses, this lack of prediction increases the potential for applying these primers to different samples such as blood, feces, or even environmental samples. Currently, the most suitable sample for detecting SARS-CoV-2 is the human nasal swab however, there are already studies that have shown digestive symptoms (e.g. diarrhea and vomiting) 10,11 and other less frequent symptoms (e.g. conjunctivitis) in patients who tested positive for SARS-CoV-2 12,13,14 . This diversity of symptoms makes clinical diagnosis difficult, and testing new types of samples may be needed quickly. The application of UFRN_primers to detect SARS-CoV-2 in blood or fecal samples is likely efficient since these primers should not interact non-specifically with RNAs of the main protozoa and bacteria that cause health problems in humans.

Quite possibly, at the time of publication of this work, a considerably larger number of additional sequences will be available, which may reveal new polymorphic sites in the target regions of UFRN_primers and PD_primers. In this way, our research group will continue this bioinformatics work, and whenever relevant, we will report new updates on the primer sequences or new primers.


3 RESULTS AND DISCUSSION

The untargeted nature of SMg enables hypothesis-free approaches for exploration, surveillance and characterization of the porcine virome. Using this approach, we detected PPIV-1, an unexpected and rare virus, not previously seen in the Dutch–German border region, representing a major pig farming area in Europe (Figure 1a). Additionally, we designed a specific RT-qPCR to test all available samples (both previously tested with and without SMg) to screen for PPIV-1 in other farms.

Using the SMg approach, PPIV-1 was detected in 5/53 samples (9.43%) from 3 different German farms and 2 different Dutch farms (5/26 farms 19.2%) (Table 1). PPIV-1 was not detected in the negative control. Using the RT-qPCR approach, PPIV-1 was detected in an additional 6 samples (OF3-8) from two Dutch farms and four German farms from the SMg samples with sufficient sample left (n = 50), along with the extra 17 OF samples (Table 1). Positive farms are illustrated in Figure 1b. The PPIV-1 RT-qPCR resulted in Ct values varying from 27 to 37 (Table 1). One sample (OF-2) was positive using the SMg and RT-qPCR approaches. Sample BS-1 did have a Ct value of 44, and however, this was above the RT-qPCR cut-off (Ct ≤40). The design of specific PPIV-1 primers proved to be challenging due to the limited amount of available PPIV-1 sequences on GenBank. Although the RT-qPCR assay did reveal positive detection in samples not previously tested by SMg, it could not confirm PPIV-1 detection in all SMg-positive samples. A recently published paper designed a PPIV-1 RT-qPCR assay with primers based on the NCBI GenBank sequences, but contrary to our design, targeted the hemagglutinin–neuraminidase gene (Li et al., 2021 ). This RT-qPCR approach should be able to detect the strains described in our study (in silico prediction). In the future, it could be applied to screen for PPIV-1 and to pre-select samples for NGS to obtain further complete or near-complete sequences, not only to refine RT-qPCR assays but also to ensure optimal surveillance. Overall, PPIV-1 was detected in 1/34 BS samples (covering 10 farms), 2/4 NS samples (covering 4 farms) and 8/32 OF samples (covering 21 farms). In total, it was detected in 11 herds (31.4%) from 7 German farms and 4 Dutch farms by SMg and/or RT-qPCR (Table 1).

Sample ID SMg PPIV−1 detection RT-qPCR Ct value Country of origin Date of sampling Age (pen) Symptoms (pen)
PPIV−1 5 5 PPIV-1, porcine parainfluenza virus 1.
SIV 6 6 SIV, swine influenza virus.
PRRSV 7 7 PRRSV, porcine reproductive and respiratory syndrome virus.
BS 1 1 BS, blood serum (pooled).
−1
Yes 44* * Above cut-off (Ct ≤40).
NP 4 4 NP, not performed.
26 Germany 10/2018 11 weeks NA 8 8 NA, not available.
NS 2 2 NS, nasal swab (pooled).
−1
Yes Neg 21 NP 4 4 NP, not performed.
Germany 10/2018 Pig NA 8 8 NA, not available.
NS 2 2 NS, nasal swab (pooled).
−2
Yes Neg 19 NP 4 4 NP, not performed.
Germany 10/2018 Pre-fattening Respiratory
OF 3 3 OF, oral fluid (pen-based)
−1
Yes NP 4 4 NP, not performed.
36 30 Netherlands 08/2017 7 weeks Respiratory
OF 3 3 OF, oral fluid (pen-based)
−2
Yes 29 31 31 Netherlands 06/2017 Fattening pig NA 8 8 NA, not available.
OF 3 3 OF, oral fluid (pen-based)
−3
No 27 30 33 Netherlands 07/2017 9 weeks NA 8 8 NA, not available.
OF 3 3 OF, oral fluid (pen-based)
−4
No 35 34 29 Germany 08/2017 8 weeks Respiratory
OF 3 3 OF, oral fluid (pen-based)
−5
No 37 27 Neg Germany 08/2017 Pig NA 8 8 NA, not available.
OF 3 3 OF, oral fluid (pen-based)
−6
NP 4 4 NP, not performed.
29 25 Neg Netherlands 08/2017 8 weeks Respiratory
OF 3 3 OF, oral fluid (pen-based)
−7
NP 4 4 NP, not performed.
32 29 Neg Germany 08/2017 NA 8 8 NA, not available.
Respiratory
OF 3 3 OF, oral fluid (pen-based)
−8
NP 4 4 NP, not performed.
29 27 34 Germany 08/2017 6 weeks NA 8 8 NA, not available.
  • 1 BS, blood serum (pooled).
  • 2 NS, nasal swab (pooled).
  • 3 OF, oral fluid (pen-based)
  • 4 NP, not performed.
  • 5 PPIV-1, porcine parainfluenza virus 1.
  • 6 SIV, swine influenza virus.
  • 7 PRRSV, porcine reproductive and respiratory syndrome virus.
  • 8 NA, not available.
  • * Above cut-off (Ct ≤40).

PPIV-1 genome coverage obtained using SMg ranged from 5.5% to 99.7% (Table 2). The sequencing of host, environmental and non-pathogenic nucleic acids, along with sequences of interest, reduces the sensitivity of SMg (Greninger, 2018 ). In this study, SMg did not detect viruses with Ct above 30. Therefore, viral enrichment strategies to obtain near- or complete viral sequences have been applied previously, including cell culture (Lau et al., 2013 Palinski et al., 2016 ) followed by depletion of ribosomal background RNA (Agüero et al., 2020 ). Application of oligonucleotide capture probes for viral enrichment on sample BS-1, which had the highest number of reads, resulted in a 22.8-fold increase in PPIV-1 reads and enabled the assembly of a near-complete genome sequence (15,344 nt) (GenBank accession: MT995732). This corresponded to 99.7% of the genome with a sequencing depth of 9,793 times. Furthermore, within BS-1 we were also able to identify a co-infection with PRRSV. Co-infections with PRRSV and/or SIV have been reported previously (Lau et al., 2013 Welch et al., 2017 ), with PPIV-1 being speculated to play a synergistic role within the porcine respiratory disease complex. However, the role of these co-infections in the porcine respiratory disease complex remains to be ascertained (Park et al., 2019 Welch et al., 2017 ).

Sample ID NGS Platform Best BLAST reference (length) Identity (%) Genome coverage (%) Average sequencing depth
BS 1 1 BS, blood serum.
−1
NextSeq S033N (15,396 nt) 96.0 99.7 9,793
NS 2 2 NS, nasal swab.
−1
MinION S033N (15,396 nt) 95.1 7.6 2
NS 2 2 NS, nasal swab.
−2
NextSeq Gd2018 (15,396 nt) 97.0 2.4 1
OF 3 3 OF, oral fluid.
−1
MinION S033N (15,396 nt) 96.2 33.0 3
OF 3 3 OF, oral fluid.
−2
NextSeq S033N (15,396 nt) 89.5 5.5 1

NCBI BLASTn analysis revealed that 4 out of the 5 PPIV-1 sequences obtained from SMg had the highest similarity (89.5%–96.2%) to the Chinese PPIV-1 strain S033N (GenBank accession: JX857410.1) (Table 2), which was first characterized from deceased pigs in Hong Kong in 2013 (Lau et al., 2013 ). Phylogenetic analysis of BS-1 (MT995732) with other available complete or near-complete genomes revealed clustering with strain S033N (Figure 1c). An additional phylogenetic analysis of partial F and L sequences from BS-1, along with the 3 available PPIV-1 strains from Hungary (Dénes et al., 2020 ), also showed the closest similarity to strain S033N (Figure S1). This could indicate that strains genetically related to S033N are established within Central Europe. In comparison, sample NS-2 from a German farm showed the highest similarity (97.0%) to another Chinese PPIV-1 strain, Gd2018 (GenBank accession: MK395271.1), which was collected in 2018.

Previously detected PPIV-1 sequences were obtained from OF, NS and lung samples, with the upper respiratory tract being suggested to be the most suitable sampling site for detection (Agüero et al., 2020 Lau et al., 2013 Park et al., 2019 ). To the best of our knowledge, we have reported the first detection of PPIV-1 in a BS sample. Interestingly, among the samples analysed with SMg, PPIV-1 had the highest sequence read count in the BS sample, indicating an additional potential sampling matrix. Nevertheless, PPIV-1 was only detected within 1/32 BS, compared to 2/4 NS and 8/32 OF samples. Additionally, in a study performed by Li and colleagues, RT-qPCR analysis of 49 BS samples of PPIV-1 infected pigs yielded no positive BS results (Li et al., 2021 ). As a result, it would suggest a limited suitability for detecting PPIV-1 in BS, compared to NS and OF samples.

The aim of this study within the Food Protects project was to develop early warning methods for detection of swine pathogens. We chose SMg as the method to evaluate. The study design implied two limitations: firstly, farms had to be pre-selected based on the suitability for long-term monitoring and samples were further pre-selected based on positive RT-qPCR results for two clinically relevant pathogens, PRRSV and SIV. This pre-selection bias prevents the breakdown of epidemiological links of PPIV-1. Secondly, samples were either pooled (BS and NS) or pen-based (OF) prior to testing. This is a trade-off between an efficient method to monitor circulating pathogens on a herd level, however, at the same time, pathogens are unable to be linked to an individual animal within a herd.

In conclusion, to the best of our knowledge, we report the first detection of PPIV-1 in Germany and the Netherlands, as well as the first near-complete genome in Europe. Moreover, this is the first detection of PPIV-1 using a targeted and untargeted NGS approach directly from the sample. As the PPIV-1 sequences from Hungary, Germany and the Netherlands were closely related to strains previously found in China, it suggests there may have been PPIV-1 transmission between Europe and China. Furthermore, as PPIV-1 was detected in pigs from 11 different farms (5 using SMg and 6 using RT-qPCR), it could confirm its circulation in Central Europe. Additional research is required to determine the extent of dissemination of PPIV-1 in Europe, to assess its relevance in the porcine respiratory disease complex and its ability to cross host species barriers.


Design of high-quality primers for multiple target sequences is essential for qPCR experiments, but is challenging due to the need to consider both homology tests on off-target sequences and the same stringent filtering constraints on the primers. Existing web servers for primer design have major drawbacks, including requiring the use of BLAST-like tools for homology tests, lack of support for ranking of primers, TaqMan probes and simultaneous design of primers against multiple targets. Due to the large-scale computational overhead, the few web servers supporting homology tests use heuristic approaches or perform homology tests within a limited scope. Here, we describe the MRPrimerW, which performs complete homology testing, supports batch design of primers for multi-target qPCR experiments, supports design of TaqMan probes and ranks the resulting primers to return the top-1 best primers to the user. To ensure high accuracy, we adopted the core algorithm of a previously reported MapReduce-based method, MRPrimer, but completely redesigned it to allow users to receive query results quickly in a web interface, without requiring a MapReduce cluster or a long computation. MRPrimerW provides primer design services and a complete set of 341 963 135 in silico validated primers covering 99% of human and mouse genes. Free access: http://MRPrimerW.com.

Polymerase chain reaction (PCR) is a widely adopted technique for fast mass duplication of specific DNA sequences. As a standard laboratory technique, PCR is used in a wide variety of applications including phylogenetic analysis (1–3), genetic testing ( 4) and DNA cloning ( 5). In particular, quantitative PCR (qPCR), also known as real-time PCR, is commonly used to confirm the results of high-throughput experiments by validating changes in the expression of multiple selected genes ( 6).

Optimal primer design is essential for best results in all PCR applications. Manual design of primers is time-consuming and may easily yield incorrect results due to the need to simultaneously consider a large number of filtering constraints on each primer and primer pair ( 7). Another important consideration in primer design is homology testing, i.e. verifying that the designed primers will only amplify the target sequence(s) rather than off-target sequences such tests usually require an additional BLAST-like tool. Fast automatic design of high-quality primers that satisfy both filtering constraints and homology tests remains a challenge that has not yet been completely solved, especially when simultaneously designing a large number of primers for qPCR that satisfy the same set of stringent and uniform constraints. For qPCR experiments, in addition to the above SYBR Green primers, TaqMan probes are also commonly used to detect products and they can significantly increase the specificity of detection however, this requires extreme care in the design of both probes and primers to ensure they satisfy both the filtering constraints and the homology tests ( 7).

To aid in designing primers for PCR experiments, many websites have been developed, including Primer3Plus ( 8, 9), BatchPrimer3 ( 10), Primique ( 11), QuantPrime ( 12), primer-BLAST ( 13) and PrimerBank ( 6, 7). Primer3Plus, a web interface of Primer3, is one of the most widely used tools it allows users to specify a set of filtering constraints for a single target gene. BatchPrimer3, which adopts the Primer3 core algorithm, can design primers in batches for multiple target genes. However, neither server performs homology tests on off-target sequences, requiring users to perform time-consuming homology tests on each candidate primer pair using extrinsic alignment tools. Primique performs homology tests using BLAST in a limited scope, i.e. only on a small secondary set of off-target sequences uploaded by the user. Due to a high-computation overhead of homology testing, the maximum size of this secondary database is limited to 10 MB, much smaller than a whole genome sequence database and therefore too small for the design of high-quality primers. QuantPrime performs homology testing for primer pairs designed by Primer3 against the whole transcriptome (mRNA) and genome database using BLAST. Both Primique and QuantPrime rely on a local alignment algorithm for homology testing. However, a heuristic approach based on local alignment cannot accurately count the number of mismatches between a primer and an off-target sequence ( 13) as a result, these methods could yield suboptimally specific primer pairs. On the contrary, Primer-BLAST performs homology tests with a global alignment algorithm to ensure full primer-target alignment accordingly, Primer-BLAST tends to return more target-specific primer pairs. Although Primer-BLAST exhibits better performance in terms of homology testing, it does not rank the designed primer pairs by their penalty scores, but ranks them by their specificity moreover, it cannot support batch design for multi-target qPCR due to the large computational overhead required for more accurate homology tests. Some websites, including PrimerBank ( 6, 14), RTPrimerDB (15–17) and qPrimerDepot ( 18), simply search a database of pre-designed primers, rather than designing primers in real time in response to user queries. In particular, PrimerBank is one of the largest databases of primers built and updated over the past several years. Because the specificities of the primers of PrimerBank have been experimentally validated under uniform conditions, these primers are fairly effective in real PCR experiments. However, because PrimerBank relies on the pre-designed primers, it does not allow users to adjust the filtering constraints, which might be important in the context of qPCR experiments requiring a full set of primer pairs that satisfy the same constraints.

Here, we describe a new website, MRPrimerW, for batch design of primers for qPCR experiments. This tool checks filtering constraints, performs rigorous homology testing against a whole genome database, and ranks the resultant primer pairs according to their penalty scores to pick the best one for each target sequence. MRPrimerW supports the design of not only SYBR Green primers, but also TaqMap probes. A comparison of MRPrimerW with other existing tools is summarized in Table 1. MRPrimerW is an online processing method based on our previously proposed offline processing method MRPrimer ( 18), which returns all feasible and valid primer pairs for a DNA database at once. MRPrimer performs a fairly complex, large-scale computation based on the MapReduce framework, resulting in design of very high-quality primers. Through qPCR analysis using 343 primer pairs and corresponding sequencing and comparative analyses, we showed that the primer pairs designed by MRPrimer are very stable and effective in qPCR experiments. However, although MRPrimer can design very high-quality primers, routine use is inconvenient because it runs on a cluster of computers and requires several hours of runtime when the filtering constraints are adjusted. MRPrimerW solved this problem completely. On the MRPrimerW website, users can rapidly design primers of the same high quality without using their own computer cluster, typically within a minute, while instantly and freely adjusting filtering constraints. To achieve this level of performance, we adopted an approach based on Google's search system. In particular, we reorganized the complex MRPrimer algorithm, which consists of seven MapReduce rounds, into two parts: offline processing and online processing (Figure 1). We built index structures using the results of offline processing and loaded them into the MRPrimerW web server. Using these indices, the online processing stage can quickly design high-quality primers against a user-specified target, as in a Google keyword search.

Overall flow of the MRPrimerW method. MRPrimerW mainly consists of offline processing part, (A and B), and online processing part (C). (A) Offline processing part, which is irrelevant to queries, designs validated candidate primers and probes (five MapReduce rounds). (B) Then the resultant primers, probes and gene annotation data are converted into indices and the indices are loaded into database. (C) Online processing part, which the website performs, searches primers applied user-defined filtering constraints and then outputs the best primer pairs for the targets.

Overall flow of the MRPrimerW method. MRPrimerW mainly consists of offline processing part, (A and B), and online processing part (C). (A) Offline processing part, which is irrelevant to queries, designs validated candidate primers and probes (five MapReduce rounds). (B) Then the resultant primers, probes and gene annotation data are converted into indices and the indices are loaded into database. (C) Online processing part, which the website performs, searches primers applied user-defined filtering constraints and then outputs the best primer pairs for the targets.

Comparison among websites for primer design

Method . Batch designing . Filtering constraints . Homology test . Scoring (Ranking) . TaqMan probes .
Primer3Plus ( 9) X O X O O
BatchPrimer3 ( 10) O O X O O
Primique ( 11) O O Δ O X
QuantPrime ( 12) O O Δ O X
Primer-BLAST ( 13) X O O Δ a O
PrimerBank ( 6, 7) X X O X X
MRPrimerW O O O O O
Method . Batch designing . Filtering constraints . Homology test . Scoring (Ranking) . TaqMan probes .
Primer3Plus ( 9) X O X O O
BatchPrimer3 ( 10) O O X O O
Primique ( 11) O O Δ O X
QuantPrime ( 12) O O Δ O X
Primer-BLAST ( 13) X O O Δ a O
PrimerBank ( 6, 7) X X O X X
MRPrimerW O O O O O

a Primer-BLAST ranks the designed primer pairs not by penalty scores, but by specificity.

Method . Batch designing . Filtering constraints . Homology test . Scoring (Ranking) . TaqMan probes .
Primer3Plus ( 9) X O X O O
BatchPrimer3 ( 10) O O X O O
Primique ( 11) O O Δ O X
QuantPrime ( 12) O O Δ O X
Primer-BLAST ( 13) X O O Δ a O
PrimerBank ( 6, 7) X X O X X
MRPrimerW O O O O O
Method . Batch designing . Filtering constraints . Homology test . Scoring (Ranking) . TaqMan probes .
Primer3Plus ( 9) X O X O O
BatchPrimer3 ( 10) O O X O O
Primique ( 11) O O Δ O X
QuantPrime ( 12) O O Δ O X
Primer-BLAST ( 13) X O O Δ a O
PrimerBank ( 6, 7) X X O X X
MRPrimerW O O O O O

a Primer-BLAST ranks the designed primer pairs not by penalty scores, but by specificity.


Genomic DNA PCR - is it the same as a cDNA PCR (Aug/28/2008 )

We wish to detect a DNA virus in samples in paraffin sections. To do this we have isolated genomic DNA are planned to do a PCR. I have a PCR protocol that works perfectly for amplifying cDNA, but when I have used it for amplifying similar housekeeping genes from the genomic PCR I don't get an bands.

Can I just use the same PCR protocol that I used for cDNA for amplification for genomic DNA amplification? If not what parameters are the most important to look at?

Thanks in advance for any advice
Brett

cDNA is DNA reverse transcribed from RNA. As such, it has all the introns removed (if you're dealing with a sample from a eukaryote). The introns, however, are still present in the genomic DNA, thus if either of your primers crosses a cDNA exon-exon boundry, it will not find its target on a genomic DNA template, and the PCR will fail.

Are you working with eukaryotic or prokaryotic DNA?

Thanks for the reply. I am using Eukaryotic DNA. We are looking at tumors from humans FFPE samples to see if a virus infection is correlated to prognosis.

May understanding would be that if my primers span an intron then I would get a band much larger than originally expected (including the whole exon-intron-exon amplification), rather than no band at all.

Is this assumption wrong? Is so why is that, I am rather new to working with DNA/RNA.

Is there a good website that provides information on where intron sites are located in each gene?

To give you an update (if your interested ), I tried dropping the annealling temperature right down to 45 C, which the paper I am following used but I thought was too low, and I seemed to get some very faint bands at the expected size. I also however got a band in my -RT lane.

So are these band promising, and if so, which parameters can be adjusted to increase expression? Or is it likely to just be a contamination problem and I need to think again?

Ok, thats it.
Sorry if its too much for one post, but I have so many questions and no one to ask!
Thanks,
Brett

Do you know the size of the amplicon in your genomic DNA PCR?

For the amplification of DNA extracted from FFPE samples, amplicon size should be around 300bp or smaller.

Yes the housekeeping gene (Ubiquitin C) I am using to optimize the PCR should be 132 bp, and eventually when I use the virus primers the amplicon will be 60 bp, which I know is very small so I think I need to have the PCR working well to be able to confirm such a small size.

This is my point -- if either of your primers spans an exon-intron boundry (in other words, either one anneals to part of an exon and also to part of the adjacent intron), then they were designed for use with a eukaryotic DNA template, and would fail using a cDNA template (because in cDNA, the introns are gone).

On the other hand, if either of your primers crosses an exon-exon boundry, they were designed for use with a cDNA template, and will fail using a genomic DNA template, because in genomic DNA, exon-exon boundries are still separated by intervening introns, and therfore the contiguous sequence to which your primer is designed does not exist.

Ahhhhhh ofcourse, I see now. Thanks for helping with that.

Are the any good websites you know of that define the intron exon sites of genes?

NCBI has this information available, though I find it a bit hard to find. For example, the entry for Human glyceraldehyde-3-phosphate dehydrogenase (GAPDH) is here. There is a graphical representation of the intron-exon pattern there, in the "Genomic regions, transcripts, and products" panel. If you click on the "CCDS8549.1" link (it's to the right of the schematic map, and the tooltip balloon says "Link to Consensus CDS Report"), it takes you here, which has the cDNA nucleotide sequence in the "CCDS Sequence Data" panel, shaded as to alternate exons.

If you go back to the original page (here, hit the back button), and scroll down to the "NCBI Reference Sequences (RefSeq)" panel, then click on the GenBank link in the Genomic section, you'll get this, the sequence of the genomic DNA in GenBank format. If you scroll down to where it says

CDS join(343..371,2004..2103,2194..2300,2430..2520,2611..2726,
2819..2900,3094..3506,3611..3680)

The "join" instructions show you how to go from the genomic DNA (with introns) to the cDNA (exons only) by defining the exons. For example, the first exon is 343..371, and the second is 2004..2103, thus the intervening intron is 372..2003.

I'm a bacterial guy, and I'm sure there must be an easier way to get this information, but (coincidently) I had to design some primers for the human GAPDH gene, and this is the way I found this info.

Thanks very much Homebrew, that has been extremely useful, and very well explained.

I appreciate the time you put into that reply.

Working with DNA and cDNA is very different. Remember that the cDNA is a copy of the genes that were active at the time of the sample collection. PCR of the housekeeping genes is easier because they are more active so more more cDNA you got if compare with genomic or viral DNA. For the reaction you will need a higher concentration of template. And a primer set with a high annealing (60C at least) will be better so you avoid that they anneal to anything. If you'r amplicon is so small (60bp) use metaphor agarose (2-3%) for a better resolution or acrylamide gel. Many persons use the one step RT,but I prefer the 2 step so you can have a cDNA library useful for another assays.


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Early detection of a highly invasive bivalve based on environmental DNA (eDNA)

Management of non-indigenous invasive species (NIS) is challenging owing in part to limitations of early detection and identification. The advent of environmental DNA (eDNA) techniques provides an efficient way to detect NIS when their abundance is extremely low. However, eDNA-based methods often suffer from uncertain detection sensitivity, which requires detailed testing before applying these methods in the field. Here we developed an eDNA tool for early detection of the highly invasive golden mussel, Limnoperna fortunei, based on the mitochondrial cytochrome c oxidase subunit I gene (COI). Further, we tested technical issues, including sampling strategy and detection sensitivity, based on a laboratory experiment. We then applied the method to field samples collected from water bodies in China where this mussel has or is expected to colonize. Results showed that the detection limit varied extensively among our newly developed primer pairs, ranging from 4 × 10 −2 to 4 × 10 −6 ng of total genomic DNA. Laboratory detection was affected by the availability of eDNA (i.e., both mussel abundance and incubation time). Detection capacity was higher in laboratory samples containing re-suspended matter from the bottom layer versus that collected from the surface. Among 25 field sites, detection was 100% at sites with high mussel abundance and as low as 40% at sites with low abundance when tested using our most sensitive primer pair. Early detection of NIS present at low abundance in nature requires not only sensitive primers, but also an optimized sampling strategy to reduce the occurrence of false negatives. Careful selection and detailed testing of primer pairs ensures effective eDNA-based species detection in surveillance and management programs.

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