Testing whether stutter and low-level DNA peaks are additive.

Peaks in an electropherogram could represent alleles, stutter product, or a combination of allele and stutter. Continuous probabilistic genotyping (PG) systems model the heights of peaks in an additive manner: for a shared or composite peak, PG models assume that the peak height is the sum of the allelic component and the stutter component. In this work we examine the assumption that the heights of overlapping alleles from a minor contributor and stutter peaks from a major contributor are additive. Any peak below the analytical threshold is considered unobserved; hence, in any dataset and particularly in low-template DNA profiles, some or many peaks may be unobserved or missing. Using simulation and empirical data, we show that an additive model can explain the heights of overlapping alleles from a minor contributor and stutter peaks from a major contributor as long as missing data are carefully considered. We use a naive method of imputation for the missing data which appears to perform adequately in this case. If missing data are ignored then the sum of stutter and allelic peaks is expected to be an overestimate of the average height of the composite peaks, as was observed in this study.

Validation of probabilistic genotyping software for use in forensic DNA casework: Definitions and illustrations.

A number of new computer programs have recently been developed to facilitate the interpretation and statistical weighting of complex DNA profiles in forensic casework. Acceptance of such software in the user community, and subsequent acceptance by the court, relies heavily upon their validation. To date, few guidelines exist that describe the appropriate and sufficient validation of such software used in forensic DNA casework. In this paper, we discuss general principles of software validation and how they could be applied to the interpretation software now being introduced into the forensic community. Importantly, we clarify the relationship between a statistical model and its implementation via software. We use the LRmix program to provide specific examples of how these principles can be implemented.

An assessment of the information content of likelihood ratios derived from complex mixtures.

With the increasing sensitivity of DNA typing methodologies, as well as increasing awareness by law enforcement of the perceived capabilities of DNA typing, complex mixtures consisting of DNA from two or more contributors are increasingly being encountered. However, insufficient research has been conducted to characterize the ability to distinguish a true contributor (TC) from a known non-contributor (KNC) in these complex samples, and under what specific conditions. In order to investigate this question, sets of six 15-locus Caucasian genotype profiles were simulated and used to create mixtures containing 2-5 contributors. Likelihood ratios were computed for various situations, including varying numbers of contributors and unknowns in the evidence profile, as well as comparisons of the evidence profile to TCs and KNCs. This work was intended to illustrate the best-case scenario, in which all alleles from the TC were detected in the simulated evidence samples. Therefore the possibility of drop-out was not modeled in this study. The computer program DNAMIX was then used to compute LRs comparing the evidence profile to TCs and KNCs. This resulted in 140,000 LRs for each of the two scenarios. These complex mixture simulations show that, even when all alleles are detected (i.e. no drop-out), TCs can generate LRs less than 1 across a 15-locus profile. However, this outcome was rare, 7 of 140,000 replicates (0.005%), and associated only with mixtures comprising 5 contributors in which the numerator hypothesis includes one or more unknown contributors. For KNCs, LRs were found to be greater than 1 in a small number of replicates (75 of 140,000 replicates, or 0.05%). These replicates were limited to 4 and 5 person mixtures with 1 or more unknowns in the numerator. Only 5 of these 75 replicates (0.004%) yielded an LR greater than 1,000. Thus, overall, these results imply that the weight of evidence that can be derived from complex mixtures containing up to 5 contributors, under a scenario in which no drop-out is required to explain any of the contributors, is remarkably high. This is a useful benchmark result on top of which to layer the effects of additional factors, such as drop-out, peak height, and other variables.

Lab Retriever: a software tool for calculating likelihood ratios incorporating a probability of drop-out for forensic DNA profiles.

BACKGROUND: Technological advances have enabled the analysis of very small amounts of DNA in forensic cases. However, the DNA profiles from such evidence are frequently incomplete and can contain contributions from multiple individuals. The complexity of such samples confounds the assessment of the statistical weight of such evidence. One approach to account for this uncertainty is to use a likelihood ratio framework to compare the probability of the evidence profile under different scenarios. While researchers favor the likelihood ratio framework, few open-source software solutions with a graphical user interface implementing these calculations are available for practicing forensic scientists. RESULTS: To address this need, we developed Lab Retriever, an open-source, freely available program that forensic scientists can use to calculate likelihood ratios for complex DNA profiles. Lab Retriever adds a graphical user interface, written primarily in JavaScript, on top of a C++ implementation of the previously published R code of Balding. We redesigned parts of the original Balding algorithm to improve computational speed. In addition to incorporating a probability of allelic drop-out and other critical parameters, Lab Retriever computes likelihood ratios for hypotheses that can include up to four unknown contributors to a mixed sample. These computations are completed nearly instantaneously on a modern PC or Mac computer. CONCLUSIONS: Lab Retriever provides a practical software solution to forensic scientists who wish to assess the statistical weight of evidence for complex DNA profiles. Executable versions of the program are freely available for Mac OSX and Windows operating systems.

Structural insight into host recognition by aggregative adherence fimbriae of enteroaggregative Escherichia coli.

Enteroaggregative Escherichia coli (EAEC) is a leading cause of acute and persistent diarrhea worldwide. A recently emerged Shiga-toxin-producing strain of EAEC resulted in significant mortality and morbidity due to progressive development of hemolytic-uremic syndrome. The attachment of EAEC to the human intestinal mucosa is mediated by aggregative adherence fimbria (AAF). Using X-ray crystallography and NMR structures, we present new atomic resolution insight into the structure of AAF variant I from the strain that caused the deadly outbreak in Germany in 2011, and AAF variant II from archetype strain 042, and propose a mechanism for AAF-mediated adhesion and biofilm formation. Our work shows that major subunits of AAF assemble into linear polymers by donor strand complementation where a single minor subunit is inserted at the tip of the polymer by accepting the donor strand from the terminal major subunit. Whereas the minor subunits of AAF have a distinct conserved structure, AAF major subunits display large structural differences, affecting the overall pilus architecture. These structures suggest a mechanism for AAF-mediated adhesion and biofilm formation. Binding experiments using wild type and mutant subunits (NMR and SPR) and bacteria (ELISA) revealed that despite the structural differences AAF recognize a common receptor, fibronectin, by employing clusters of basic residues at the junction between subunits in the pilus. We show that AAF-fibronectin attachment is based primarily on electrostatic interactions, a mechanism not reported previously for bacterial adhesion to biotic surfaces.

Analysis of allelic drop-out using the Identifiler(®) and PowerPlex(®) 16 forensic STR typing systems.

Low-template (LT) DNA profiles continue to present interpretational challenges to the forensic community. Whether the LT contribution comprises the main profile, or whether it is present as the minor component of a mixture, ambiguity arises from the possibility that alleles present in the biological sample may not be detected in the resulting DNA profile. This phenomenon is known as allelic drop-out. This ambiguity complicates both the assessment of the potential number of contributors and estimation of the weight of the DNA evidence for or against specific propositions. One solution to estimating the weight of the evidence is to use a likelihood ratio (LR) that incorporates the probability of allelic drop-out P(DO) estimated for the specific evidence sample under consideration. However, although a vast repository of data exists, few empirical studies to determine allelic drop-out probabilities have been performed to date. Here we characterized patterns of allelic drop-out in single-source samples using both universal and run-specific analytical thresholds. Not surprisingly, we found fewer instances of apparent drop-out when using a lower (run-specific) detection threshold. Also, unsurprisingly, a positive correlation exists between allele drop-out and allele length, even in good quality samples. We used logistic regression to model the fraction of alleles that dropped out of a profile as a function of the average height of the detected peaks. The equation derived from the logistic regression model allowed us to estimate the expected drop-out probability for an evidentiary sample based on the average peak height of the profile. We show that the LRs calculated using the estimated drop-out probabilities were similar to those calculated using the benchmark drop-out probabilities, suggesting that the estimates of the drop-out probability are accurate and useful. This trend holds even when using the data from the PowerPlex(®) 16 typing system to estimate the drop-out probability for an Identifiler(®) profile, and vice versa. Thus we demonstrate that use of a LR that incorporates empirically estimated allelic drop-out probabilities provides a reliable means for extracting additional information from LT forensic DNA profiles.

Complete 1H, 13C and 15N NMR assignments for donor-strand complemented AafA, the major pilin of aggregative adherence fimbriae (AAF/II) from enteroaggregative E. coli.

Aggregative adherence fimbriae (AAF) are the primary adhesive factors of enteroaggregative Escherichia coli (EAEC) and are required for intestinal colonization. They mediate binding to extracellular matrix proteins of the enteric mucosa and display proinflammatory effects on epithelial cells in vitro. Among the simplest of bacterial fimbriae, these passive hairlike appendages are composed primarily of a single 16-kDa structural and adhesive subunit, AafA. Oligomerization occurs by incorporating the N-terminal strand of each AafA subunit into an otherwise incomplete ß-sheet of an adjacent AafA subunit. We have engineered a highly soluble AafA monomer by positioning the N-terminal "donor strand" at the C-terminus, following a turn and short linker that were introduced to allow access of the donor strand to the recipient cleft of the same subunit. The resulting "donor-strand complemented" AafA subunit, or AafA-dsc folds autonomously, is monodisperse in solution, and yields high quality NMR spectral data. Here, we report the (1)H, (13)C, and (15)N chemical shift assignments for AafA-dsc.

Magnitude-dependent variation in peak height balance at heterozygous STR loci.

When the smaller of two peaks at an STR locus is less than 70% the magnitude of the larger peak at that locus, the disparity is typically taken to be an indication that there is more than one contributor of template DNA to the sample being tested. An analysis of 1,763 heterozygous allele pairs suggests that a peak height imbalance threshold that varies with the magnitude of the peaks being evaluated at a locus is superior to a fixed threshold. Identifying samples that are likely to be mixtures and those that are likely to have arisen from a single source is accomplished more reliably when a statistically based, magnitude-dependent peak height imbalance threshold is used. The amelogenin locus was found to behave in a similar fashion and was also found to have no systematic bias that favored the amplification of Y or X alleles.

The major pilin subunit of the AAF/II fimbriae from enteroaggregative Escherichia coli mediates binding to extracellular matrix proteins.

Enteroaggregative Escherichia coli (EAEC) adherence to human intestinal tissue is mediated by aggregative adherence fimbriae (AAF); however, the receptors involved in EAEC adherence remain uncharacterized. Adhesion to extracellular matrix proteins is commonly observed among enteric pathogens, so we addressed the hypothesis that EAEC may bind to extracellular matrix proteins commonly found in the intestine. We found that EAEC prototype strain 042 adhered more abundantly to surfaces that were precoated with the extracellular matrix proteins fibronectin, laminin, and type IV collagen. Differences in fibronectin binding of almost 2 orders of magnitude were observed between EAEC 042 and a mutant in the AAF/II major pilin gene, aafA. Purified AafA, refolded as a donor strand complementation construct, bound fibronectin in a dose-dependent manner. Addition of fibronectin to the apical surfaces of polarized T84 cell monolayers augmented EAEC 042 adherence, and this effect required expression of aafA. Finally, increased bacterial adherence was observed when apical secretion of fibronectin was induced by adenosine in polarized T84 cells. Binding to fibronectin may contribute to colonization of the gastrointestinal tract by EAEC.

Solution structure of the novel dispersin protein of enteroaggregative Escherichia coli.

Enteroaggregative Escherichia coli (EAEC), increasingly recognized as an important cause of infant and travelers' diarrhoea, exhibits an aggregative, stacked-brick pattern of adherence to epithelial cells. Adherence is mediated by aggregative adherence fimbriae (AAFs), which are encoded on the pAA virulence plasmid. We recently described a highly prevalent pAA plasmid-borne gene, aap, which encodes a protein (nicknamed dispersin) that is secreted to the bacterial cell surface. Dispersin-null mutants display a unique hyper-aggregating phenotype, accompanied by collapse of AAF pili onto the bacterial cell surface. To study the mechanism of this effect, we solved the structure of dispersin from EAEC strain 042 using solution NMR, revealing a stable beta-sandwich with a conserved net positive surface charge of +3 to +4 among 23 dispersin alleles. Experimental data suggest that dispersin binds non-covalently to lipopolysaccharide on the surface of the bacterium. We also show that the AAF organelles contribute positive charge to the bacterial surface, suggesting that dispersin's role in fimbrial function is to overcome electrostatic attraction between AAF and the bacterial surface.

Run-specific limits of detection and quantitation for STR-based DNA testing.

STR-based DNA profiling is an exceptionally sensitive analytical technique that is often used to obtain results at the very limits of its sensitivity. The challenge of reliably distinguishing between signal and noise in such situations is one that has been rigorously addressed in numerous other analytical disciplines. However, an inability to determine accurately the height of electropherogram baselines has caused forensic DNA profiling laboratories to utilize alternative approaches. Minimum thresholds established during laboratory validation studies have become the de facto standard for distinguishing between reliable signal and noise/technical artifacts. These minimum peak height thresholds generally fail to consider variability in the sensitivity of instruments, reagents, and the skill of human analysts involved in the DNA profiling process over the course of time. Software (BatchExtract) made publicly available by the National Center for Biotechnology Information now provides an alternative means of establishing limits of detection and quantitation that is more consistent with those used in other analytical disciplines. We have used that software to determine the height of each data collection point for each dye along a control sample's electropherogram trace. These values were then used to determine a limit of detection (the average amount of background noise plus three standard deviations) and a limit of quantitation (the average amount of background noise plus 10 standard deviations) for each control sample. Analyses of the electropherogram data associated with the positive, negative, and reagent blank controls included in 50 different capillary electrophoresis runs validate that this approach could be used to determine run-specific thresholds objectively for use in forensic DNA casework.

Solution NMR structure of S100B bound to the high-affinity target peptide TRTK-12.

The solution NMR structure is reported for Ca(2+)-loaded S100B bound to a 12-residue peptide, TRTK-12, from the actin capping protein CapZ (alpha1 or alpha2 subunit, residues 265-276: TRTKIDWNKILS). This peptide was discovered by Dimlich and co-workers by screening a bacteriophage random peptide display library, and it matches exactly the consensus S100B binding sequence ((K/R)(L/I)XWXXIL). As with other S100B target proteins, a calcium-dependent conformational change in S100B is required for TRTK-12 binding. The TRTK-12 peptide is an amphipathic helix (residues W7 to S12) in the S100B-TRTK complex, and helix 4 of S100B is extended by three or four residues upon peptide binding. However, helical TRTK-12 in the S100B-peptide complex is uniquely oriented when compared to the three-dimensional structures of other S100-peptide complexes. The three-dimensional structure of the S100B-TRTK peptide complex illustrates that residues in the S100B binding consensus sequence (K4, I5, W7, I10, L11) are all involved in the S100B-peptide interface, which can explain its orientation in the S100B binding pocket and its relatively high binding affinity. A comparison of the S100B-TRTK peptide structure to the structures of apo- and Ca(2+)-bound S100B illustrates that the binding site of TRTK-12 is buried in apo-S100B, but is exposed in Ca(2+)-bound S100B as necessary to bind the TRTK-12 peptide.

Three-dimensional solution structure of the calcium-signaling protein apo-S100A1 as determined by NMR.

S100A1, a member of the S100 protein family, is an EF-hand containing Ca(2+)-binding protein (93 residues per subunit) with noncovalent interactions at its dimer interface. Each subunit of S100A1 has four alpha-helices and a small antiparallel beta-sheet consistent with two helix-loop-helix calcium-binding domains [Baldiserri et al. (1999) J. Biomol. NMR 14, 87-88]. In this study, the three-dimensional structure of reduced apo-S100A1 was determined by NMR spectroscopy using a total of 2220 NOE distance constraints, 258 dihedral angle constraints, and 168 backbone hydrogen bond constraints derived from a series of 2D, 3D, and 4D NMR experiments. The final structure was found to be globular and compact with the four helices in each subunit aligning to form a unicornate-type four-helix bundle. Intermolecular NOE correlations were observed between residues in helices 1 and 4 from one subunit to residues in helices 1' and 4' of the other subunit, respectively, consistent with the antiparallel alignment of the two subunits to form a symmetric X-type four-helix bundle as found for other members of the S100 protein family. Because of the similarity of the S100A1 dimer interface to that found for S100B, it was possible to calculate a model of the S100A1/B heterodimer. This model is consistent with a number of NMR chemical shift changes observed when S100A1 is titrated into a sample of (15)N-labeled S100B. Helix 3 (and 3') of S100A1 was found to have an interhelical angle of -150 degrees with helix 4 (and 4') in the apo state. This crossing angle is quite different (>50 degrees ) from that typically found in other EF-hand containing proteins such as apocalmodulin and apotroponin C but more similar to apo-S100B, which has an interhelical angle of -166 degrees. As with S100B, it is likely that the second EF-hand of apo-S100A1 reorients dramatically upon the addition of Ca(2+), which can explain the Ca(2+) dependence that S100A1 has for binding several of its biological targets.