New tools to screen wild peanut species for aflatoxin accumulation and genetic fingerprinting.

BACKGROUND: Aflatoxin contamination in peanut seeds is still a serious problem for the industry and human health. No stable aflatoxin resistant cultivars have yet been produced, and given the narrow genetic background of cultivated peanuts, wild species became an important source of genetic diversity. Wild peanut seeds, however, are not abundant, thus, an effective method of screening for aflatoxin accumulation using minimal seeds is highly desirable. In addition, keeping record of genetic fingerprinting of each accession would be very useful for breeding programs and for the identification of accessions within germplasm collections. RESULTS: In this study, we report a method of screening for aflatoxin accumulation that is applicable to the small-size seeds of wild peanuts, increases the reliability by testing seed viability, and records the genetic fingerprinting of the samples. Aflatoxin levels observed among 20 wild peanut species varied from zero to 19000 ng.g-1 and 155 ng.g-1 of aflatoxin B1 and B2, respectively. We report the screening of 373 molecular markers, including 288 novel SSRs, tested on 20 wild peanut species. Multivariate analysis by Neighbor-Joining, Principal Component Analysis and 3D-Principal Coordinate Analysis using 134 (36 %) transferable markers, in general grouped the samples according to their reported genomes. The best 88 markers, those with high fluorescence, good scorability and transferability, are reported with BLAST results. High quality markers (total 98) that discriminated genomes are reported. A high quality marker with UPIC score 16 (16 out of 20 species discriminated) had significant hits on BLAST2GO to a pentatricopeptide-repeat protein, another marker with score 5 had hits on UDP-D-apiose synthase, and a third one with score 12 had BLASTn hits on La-RP 1B protein. Together, these three markers discriminated all 20 species tested. CONCLUSIONS: This study provides a reliable method to screen wild species of peanut for aflatoxin resistance using minimal seeds. In addition we report 288 new SSRs for peanut, and a cost-effective combination of markers sufficient to discriminate all 20 species tested. These tools can be used for the systematic search of aflatoxin resistant germplasm keeping record of the genetic fingerprinting of the accessions tested for breeding purpose.

Development of a Large Set of Microsatellite Markers in Zapote Mamey (Pouteria sapota (Jacq.) H.E. Moore & Stearn) and Their Potential Use in the Study of the Species.

Pouteria sapota is known for its edible fruits that contain unique carotenoids, as well as for its fungitoxic, anti-inflammatory and anti-oxidant activity. However, its genetics is mostly unknown, including aspects about its genetic diversity and domestication process. We did high-throughput sequencing of microsatellite-enriched libraries of P. sapota, generated 5223 contig DNA sequences, 1.8 Mbp, developed 368 microsatellites markers and tested them on 29 individuals from 10 populations (seven wild, three cultivated) from Mexico, its putative domestication center. Gene ontology BLAST analysis of the DNA sequences containing microsatellites showed potential association to physiological functions. Genetic diversity was slightly higher in cultivated than in the wild gene pool (HE = 0.41 and HE = 0.35, respectively), although modified Garza-Williamson Index and Bottleneck software showed evidence for a reduction in genetic diversity for the cultivated one. Neighbor Joining, 3D Principal Coordinates Analysis and assignment tests grouped most individuals according to their geographic origin but no clear separation was observed between wild or cultivated gene pools due to, perhaps, the existence of several admixed populations. The developed microsatellites have a great potential in genetic population and domestication studies of P. sapota but additional sampling will be necessary to better understand how the domestication process has impacted the genetic diversity of this fruit crop.

Metagenomic analysis of the turkey gut RNA virus community.

Viral enteric disease is an ongoing economic burden to poultry producers worldwide, and despite considerable research, no single virus has emerged as a likely causative agent and target for prevention and control efforts. Historically, electron microscopy has been used to identify suspect viruses, with many small, round viruses eluding classification based solely on morphology. National and regional surveys using molecular diagnostics have revealed that suspect viruses continuously circulate in United States poultry, with many viruses appearing concomitantly and in healthy birds. High-throughput nucleic acid pyrosequencing is a powerful diagnostic technology capable of determining the full genomic repertoire present in a complex environmental sample. We utilized the Roche/454 Life Sciences GS-FLX platform to compile an RNA virus metagenome from turkey flocks experiencing enteric disease. This approach yielded numerous sequences homologous to viruses in the BLAST nr protein database, many of which have not been described in turkeys. Our analysis of this turkey gut RNA metagenome focuses in particular on the turkey-origin members of the Picornavirales, the Caliciviridae, and the turkey Picobirnaviruses.

Colistin Resistance mcr-1-Gene-Bearing Escherichia coli Strain from the United States.

Transmissible colistin resistance in the form of an mcr-1-gene-bearing plasmid has been recently reported in Enterobacteriaceae in several parts of the world. We report here the completed genome sequence of an Escherichia coli strain isolated from swine in the United States that carried the mcr-1 gene on an IncI2-type plasmid.

UPIC: Perl scripts to determine the number of SSR markers to run.

UNLABELLED: We introduce here the concept of Unique Pattern Informative Combinations (UPIC), a decision tool for the cost-effective design of DNA fingerprinting/genotyping experiments using simple-sequence/tandem repeat (SSR/STR) markers. After the first screening of SSR-markers tested on a subset of DNA samples, the user can apply UPIC to find marker combinations that maximize the genetic information obtained by a minimum or desirable number of markers. This allows a cost-effective planning of future experiments. We have developed Perl scripts to calculate all possible subset combinations of SSR markers, and determine based on unique patterns or alleles, which combinations can discriminate among all DNA samples included in a test. This makes UPIC an essential tool for optimizing resources when working with microsatellites. An example using real data from eight markers and 12 genotypes shows that UPIC detected groups of as few as three markers sufficient to discriminate all 12- DNA samples. Should markers for future experiments be chosen based only on polymorphism-information content (PIC), the necessary number of markers for discrimination of all samples cannot be determined. We also show that choosing markers using UPIC, an informative combination of four markers can provide similar information as using a combination of six markers (23 vs. 25 patterns, respectively), granting a more efficient planning of experiments. Perl scripts with documentation are also included to calculate the percentage of heterozygous loci on the DNA samples tested and to calculate three PIC values depending on the type of fertilization and allele frequency of the organism. AVAILABILITY: Perl scripts are freely available for download from http://www.ars.usda.gov/msa/jwdsrc/gbru.