Statistical methods for discrimination of STR genotypes using high resolution melt curve data.

Despite the improvements in forensic DNA quantification methods that allow for the early detection of low template/challenged DNA samples, complicating stochastic effects are not revealed until the final stage of the DNA analysis workflow. An assay that would provide genotyping information at the earlier stage of quantification would allow examiners to make critical adjustments prior to STR amplification allowing for potentially exclusionary information to be immediately reported. Specifically, qPCR instruments often have dissociation curve and/or high-resolution melt curve (HRM) capabilities; this, coupled with statistical prediction analysis, could provide additional information regarding STR genotypes present. Thus, this study aimed to evaluate Qiagen's principal component analysis (PCA)-based ScreenClust® HRM® software and a linear discriminant analysis (LDA)-based technique for their abilities to accurately predict genotypes and similar groups of genotypes from HRM data. Melt curves from single source samples were generated from STR D5S818 and D18S51 amplicons using a Rotor-Gene® Q qPCR instrument and EvaGreen® intercalating dye. When used to predict D5S818 genotypes for unknown samples, LDA analysis outperformed the PCA-based method whether predictions were for individual genotypes (58.92% accuracy) or for geno-groups (81.00% accuracy). However, when a locus with increased heterogeneity was tested (D18S51), PCA-based prediction accuracy rates improved to rates similar to those obtained using LDA (45.10% and 63.46%, respectively). This study provides foundational data documenting the performance of prediction modeling for STR genotyping based on qPCR-HRM data. In order to expand the forensic applicability of this HRM assay, the method could be tested with a more commonly utilized qPCR platform.

A quantifiler™ trio-based HRM screening assay for the accurate prediction of single source versus mixed biological samples.

At present, the forensic DNA workflow is not capable of providing information about the contributor status (single source vs. multiple contributors) of evidentiary samples prior to end-point analysis. This exacerbates the challenges inherent to mixtures and low-template DNA samples. If additional sample information could be provided earlier in the workflow, protocols could be implemented to mitigate these challenges. An integrated Quantiplex®- high resolution melt (HRM) assay was shown to be effective in distinguishing between single source and mixture DNA samples; however, integration of the HRM assay into a more commonly used chemistry would be beneficial to the practitioner community. Thus, the assay was redesigned as an integrated Quantifiler™ Trio-HRM assay, which included the identification of a new DNA-binding dye, an increased reaction volume, and the establishment of new data analysis and standard curve metrics for all targets. This redesigned assay produced quantification values and qualitative values that were comparable to those produced when the same samples were tested using the standard Quantifiler™ Trio chemistry and settings. Further, STR profiles generated with quantification values produced from the integrated Quantifiler™ Trio-HRM assay and standard Quantifiler™ Trio chemistry were complete and fully concordant. Most importantly, the integrated Quantifiler™ Trio-HRM assay was able to accurately predict whether a sample was single source or a mixture 79.2% of the time, demonstrating the potential of this approach. With the incorporation of an expanded training set for prediction modeling, and completion of critical developmental validation studies, this assay could prove useful to the forensic DNA practitioner community.

An Assessment of the Performance Limitations of the Integrated QuantifilerTM Trio-HRM Assay: A Forensic Tool Designed to Identify Mixtures at the Quantification Stage.

Although guidelines exist for identifying mixtures, these measures often occur at the end-point of analysis and are protracted. To facilitate early mixture detection, we integrated a high-resolution melt (HRM) mixture screening assay into the qPCR step of the forensic workflow, producing the integrated QuantifilerTM Trio-HRM assay. The assay, when coupled with a prediction tool, allowed for 75.0% accurate identification of the contributor status of a sample (single source vs. mixture). To elucidate the limitations of the developed qPCR-HRM assay, developmental validation studies were conducted assessing the reproducibility and samples with varying DNA ratios, contributors, and quality. From this work, it was determined that the integrated QuantifilerTM Trio-HRM assay is capable of accurately identifying mixtures with up to five contributors and mixtures at ratios up to 1:100. Further, the optimal performance concentration range was found to be between 0.025 and 0.5 ng/µL. With these results, evidentiary-like DNA samples were then analyzed, resulting in 100.0% of the mixture samples being accurately identified; furthermore, every time a sample was predicted as a single source, it was true, giving confidence to any single-source calls. Overall, the integrated QuantifilerTM Trio-HRM assay has exhibited an enhanced ability to discern mixture samples from single-source samples at the qPCR stage under commonly observed conditions regardless of the contributor's sex.

Comparison of DNA typing success in compromised blood and touch samples based on sampling swab composition.

Sample collection at the crime scene can introduce variations in DNA recovery based upon the substrate from which a sample is collected, the material of the collection device used, or the storage conditions after collection. There are many factors during this process that can degrade the sample during drying and storage, and before DNA extraction can be performed. The purpose of this study was to evaluate and compare the performance of standard cotton swab collection with the Bode BioSafe® swab, which includes both a desiccant at the swab head and proprietary compounds to prevent degradation of the sample during sample collection and preservation. Blood and touch DNA samples were collected from porous and nonporous substrates and stored at elevated temperatures to simulate accelerated time. DNA quantification and STR profile data were used to assess the performance of the swabs. BioSafe® swab collection resulted in similar DNA yields from blood samples and significantly higher DNA yields from touch samples when compared to collection with cotton swabs. BioSafe® swabs also resulted in higher DNA integrity during long-term storage, increased STR profile success and improved retention of low-level contributor alleles.

The Transcription Factor Lrp of Pantoea stewartii subsp. stewartii Controls Capsule Production, Motility, and Virulence Important for in planta Growth.

The bacterial phytopathogen Pantoea stewartii subsp. stewartii causes leaf blight and Stewart's wilt disease in susceptible corn varieties. A previous RNA-Seq study examined P. stewartii gene expression patterns during late-stage infection in the xylem, and a Tn-Seq study using a P. stewartii mutant library revealed genes essential for colonization of the xylem. Based on these findings, strains with in-frame chromosomal deletions in the genes encoding seven transcription factors (NsrR, IscR, Nac, Lrp, DSJ_00125, DSJ_03645, and DSJ_18135) and one hypothetical protein (DSJ_21690) were constructed to further evaluate the role of the encoded gene products during in vitro and in planta growth. Assays for capsule production and motility indicate that Lrp plays a role in regulating these two key physiological outputs in vitro. Single infections of each deletion strain into the xylem of corn seedlings determined that Lrp plays a significant role in P. stewartii virulence. In planta xylem competition assays between co-inoculated deletion and the corresponding complementation or wild-type strains as well as in vitro growth curves determined that Lrp controls functions important for P. stewartii colonization and growth in corn plants, whereas IscR may have a more generalized impact on growth. Defining the role of essential transcription factors, such as Lrp, during in planta growth will enable modeling of key components of the P. stewartii regulatory network utilized during growth in corn plants.