Metabolic and translational efficiency in microbial organisms.

Metabolic efficiency, as a selective force shaping proteomes, has been shown to exist in Escherichia coli and Bacillus subtilis and in a small number of organisms with photoautotrophic and thermophilic lifestyles. Earlier attempts at larger-scale analyses have utilized proxies (such as molecular weight) for biosynthetic cost, and did not consider lifestyle or auxotrophy. This study extends the analysis to all currently sequenced microbial organisms that are amenable to these analyses while utilizing lifestyle specific amino acid biosynthesis pathways (where possible) to determine protein production costs and compensating for auxotrophy. The tendency for highly expressed proteins (with adherence to codon usage bias as a proxy for expressivity) to utilize less biosynthetically expensive amino acids is taken as evidence of cost selection. A comprehensive analysis of sequenced genomes to identify those that exhibit strong translational efficiency bias (389 out of 1,700 sequenced organisms) is also presented.

Inferring the number of contributors to mixed DNA profiles.

Forensic samples containing DNA from two or more individuals can be difficult to interpret. Even ascertaining the number of contributors to the sample can be challenging. These uncertainties can dramatically reduce the statistical weight attached to evidentiary samples. A probabilistic mixture algorithm that takes into account not just the number and magnitude of the alleles at a locus, but also their frequency of occurrence allows the determination of likelihood ratios of different hypotheses concerning the number of contributors to a specific mixture. This probabilistic mixture algorithm can compute the probability of the alleles in a sample being present in a 2-person mixture, 3-person mixture, etc. The ratio of any two of these probabilities then constitutes a likelihood ratio pertaining to the number of contributors to such a mixture.

Amino acid biosynthetic cost and protein conservation.

Protein products of highly expressed genes tend to favor amino acids that have lower average biosynthetic costs (i.e., they exhibit metabolic efficiency). While this trend has been observed in several studies, the specific sites where cost-reducing substitutions accumulate have not been well characterized. Toward that end, weighted costs in conserved and variable positions were evaluated across a total of 9,119 homologous proteins in four mammalian orders (primate, carnivore, rodent, and artiodactyls), which together contain a total of 20,457,072 amino acids. Degree of conservation at homologous positions in these mammalian proteins and average-weighted cost across all positions within a single protein are significantly correlated. Dividing human genes into two classes (those with and those without CpG islands in their promoters) suggests that humans also preferentially utilize less costly amino acids in highly expressed genes. In contrast to the intuitive expectation that the relatively weak selective force associated with metabolic efficiency would be a selection pressure in complex multicellular organisms, the overall level of selective constraint within the variable regions of mammalian proteins allows the metabolic efficiency to derive a reduction of overall biosynthetic cost, particularly in genes with the highest levels of expression.

A genetic optimization approach for isolating translational efficiency bias.

The study of codon usage bias is an important research area that contributes to our understanding of molecular evolution, phylogenetic relationships, respiratory lifestyle, and other characteristics. Translational efficiency bias is perhaps the most well-studied codon usage bias, as it is frequently utilized to predict relative protein expression levels. We present a novel approach to isolating translational efficiency bias in microbial genomes. There are several existent methods for isolating translational efficiency bias. Previous approaches are susceptible to the confounding influences of other potentially dominant biases. Additionally, existing approaches to identifying translational efficiency bias generally require both genomic sequence information and prior knowledge of a set of highly expressed genes. This novel approach provides more accurate results from sequence information alone by resisting the confounding effects of other biases. We validate this increase in accuracy in isolating translational efficiency bias on 10 microbial genomes, five of which have proven particularly difficult for existing approaches due to the presence of strong confounding biases.

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.

Automated isolation of translational efficiency bias that resists the confounding effect of GC(AT)-content.

Genomic sequencing projects are an abundant source of information for biological studies ranging from the molecular to the ecological in scale; however, much of the information present may yet be hidden from casual analysis. One such information domain, trends in codon usage, can provide a wealth of information about an organism's genes and their expression. Degeneracy in the genetic code allows more than one triplet codon to code for the same amino acid, and usage of these codons is often biased such that one or more of these synonymous codons are preferred. Detection of this bias is an important tool in the analysis of genomic data, particularly as a predictor of gene expressivity. Methods for identifying codon usage bias in genomic data that rely solely on genomic sequence data are susceptible to being confounded by the presence of several factors simultaneously influencing codon selection. Presented here is a new technique for removing the effects of one of the more common confounding factors, GC(AT)-content, and of visualizing the search-space for codon usage bias through the use of a solution landscape. This technique successfully isolates expressivity-related codon usage trends, using only genomic sequence information, where other techniques fail due to the presence of GC(AT)-content confounding influences.

Do amino acid biosynthetic costs constrain protein evolution in Saccharomyces cerevisiae?

Prokaryotic organisms preferentially utilize less energetically costly amino acids in highly expressed genes. Studies have shown that the proteome of Saccharomyces cerevisiae also exhibits this behavior, but only in broad terms. This study examines the question of metabolic efficiency as a proteome-shaping force at a finer scale, examining whether trends consistent with cost minimization as an evolutionary force are present independent of protein function and amino acid physicochemical property, and consistently with respect to amino acid biosynthetic costs. Inverse correlations between the average amino acid biosynthetic cost of the protein product and the levels of gene expression in S. cerevisiae are consistent with natural selection to minimize costs. There are, however, patterns of amino acid usage that raise questions about the strength (and possibly the universality) of this selective force in shaping S. cerevisiae's proteome.

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.

Amino acid cost and codon-usage biases in 6 prokaryotic genomes: a whole-genome analysis.

For most prokaryotic organisms, amino acid biosynthesis represents a significant portion of their overall energy budget. The difference in the cost of synthesis between amino acids can be striking, differing by as much as 7-fold. Two prokaryotic organisms, Escherichia coli and Bacillus subtilis, have been shown to preferentially utilize less costly amino acids in highly expressed genes, indicating that parsimony in amino acid selection may confer a selective advantage for prokaryotes. This study confirms those findings and extends them to 4 additional prokaryotic organisms: Chlamydia trachomatis, Chlamydophila pneumoniae AR39, Synechocystis sp. PCC 6803, and Thermus thermophilus HB27. Adherence to codon-usage biases for each of these 6 organisms is inversely correlated with a coding region's average amino acid biosynthetic cost in a fashion that is independent of chemoheterotrophic, photoautotrophic, or thermophilic lifestyle. The obligate parasites C. trachomatis and C. pneumoniae AR39 are incapable of synthesizing many of the 20 common amino acids. Removing auxotrophic amino acids from consideration in these organisms does not alter the overall trend of preferential use of energetically inexpensive amino acids in highly expressed genes.

Empirical analysis of the STR profiles resulting from conceptual mixtures.

Samples containing DNA from two or more individuals can be difficult to interpret. Even ascertaining the number of contributors can be challenging and associated uncertainties can have dramatic effects on the interpretation of testing results. Using an FBI genotypes dataset, containing complete genotype information from the 13 Combined DNA Index System (CODIS) loci for 959 individuals, all possible mixtures of three individuals were exhaustively and empirically computed. Allele sharing between pairs of individuals in the original dataset, a randomized dataset and datasets of generated cousins and siblings was evaluated as were the number of loci that were necessary to reliably deduce the number of contributors present in simulated mixtures of four or less contributors. The relatively small number of alleles detectable at most CODIS loci and the fact that some alleles are likely to be shared between individuals within a population can make the maximum number of different alleles observed at any tested loci an unreliable indicator of the maximum number of contributors to a mixed DNA sample. This analysis does not use other data available from the electropherograms (such as peak height or peak area) to estimate the number of contributors to each mixture. As a result, the study represents a worst case analysis of mixture characterization. Within this dataset, approximately 3% of three-person mixtures would be mischaracterized as two-person mixtures and more than 70% of four-person mixtures would be mischaracterized as two- or three-person mixtures using only the maximum number of alleles observed at any tested locus.

Systematic differences in electropherogram peak heights reported by different versions of the GeneScan software.

DNA profiling using STRs on the 310 and 3100 Genetic Analyzers routinely generates electropherograms that are analyzed with the GeneScan software available from the instrument's manufacturer, Applied Biosystems. Users have been able to choose from three different smoothing options that have been known to result in significant differences in the peak heights that are reported. Improvements in the underlying algorithm of the most recent version of the software also result in significant and somewhat predictable differences in peak height values. Laboratories that have performed validation studies using older versions of GeneScan should either reanalyze the data generated in those validation studies with the newest version of the software or otherwise take into consideration the systematically higher peak height values obtained as they begin following the recommendation of the manufacturer and use the new algorithm.