David H. Ardell

Structure is three to ten times more conserved than sequence--a study of structural response in protein cores.

Protein structures change during evolution in response to mutations. Here, we analyze the mapping between sequence and structure in a set of structurally aligned protein domains. To avoid artifacts, we restricted our attention only to the core components of these structures. We found that on average, using different measures of structural change, protein cores evolve linearly with evolutionary distance (amino acid substitutions per site). This is true irrespective of which measure of structural change we used, whether RMSD or discrete structural descriptors for secondary structure, accessibility, or contacts. This linear response allows us to quantify the claim that structure is more conserved than sequence. Using structural alphabets of similar cardinality to the sequence alphabet, structural cores evolve three to ten times slower than sequences. Although we observed an average linear response, we found a wide variance. Different domain families varied fivefold in structural response to evolution. An attempt to categorically analyze this variance among subgroups by structural and functional category revealed only one statistically significant trend. This trend can be explained by the fact that beta‐sheets change faster than alpha‐helices, most likely due to that they are shorter and that change occurs at the ends of the secondary structure elements. Proteins 2009.

Computational analysis of tRNA identity.

I review recent developments in computational analysis of tRNA identity. I suggest that the tRNA–protein interaction network is hierarchically organized, and coevolutionarily flexible. Its functional specificity of recognition and discrimination persists despite generic structural constraints and perturbative evolutionary forces. This flexibility comes from its arbitrary nature as a self‐recognizing shape code. A revisualization of predicted Proteobacterial tRNA identity highlights open research problems. tRNA identity elements and their coevolution with proteins must be mapped structurally over the Tree of Life. These traits can also resolve deep roots in the Tree. I show that histidylation identity elements phylogenetically reposition Pelagibacter ubique within alpha‐Proteobacteria.

An iterative strategy combining biophysical criteria and duration hidden Markov models for structural predictions of Chlamydia trachomatis σ66 promoters

BackgroundPromoter identification is a first step in the quest to explain gene regulation in bacteria. It has been demonstrated that the initiation of bacterial transcription depends upon the stability and topology of DNA in the promoter region as well as the binding affinity between the RNA polymerase σ-factor and promoter. However, promoter prediction algorithms to date have not explicitly used an ensemble of these factors as predictors. In addition, most promoter models have been trained on data from Escherichia coli. Although it has been shown that transcriptional mechanisms are similar among various bacteria, it is quite possible that the differences between Escherichia coli and Chlamydia trachomatis are large enough to recommend an organism-specific modeling effort.ResultsHere we present an iterative stochastic model building procedure that combines such biophysical metrics as DNA stability, curvature, twist and stress-induced DNA duplex destabilization along with duration hidden Markov model parameters to model Chlamydia trachomatis σ66 promoters from 29 experimentally verified sequences. Initially, iterative duration hidden Markov modeling of the training set sequences provides a scoring algorithm for Chlamydia trachomatis RNA polymerase σ66/DNA binding. Subsequently, an iterative application of Stepwise Binary Logistic Regression selects multiple promoter predictors and deletes/replaces training set sequences to determine an optimal training set. The resulting model predicts the final training set with a high degree of accuracy and provides insights into the structure of the promoter region. Model based genome-wide predictions are provided so that optimal promoter candidates can be experimentally evaluated, and refined models developed. Co-predictions with three other algorithms are also supplied to enhance reliability.ConclusionThis strategy and resulting model support the conjecture that DNA biophysical properties, along with RNA polymerase σ-factor/DNA binding collaboratively, contribute to a sequences ability to promote transcription. This work provides a baseline model that can evolve as new Chlamydia trachomatis σ66 promoters are identified with assistance from the provided genome-wide predictions. The proposed methodology is ideal for organisms with few identified promoters and relatively small genomes.

FAST: FAST Analysis of Sequences Toolbox.

FAST (FAST Analysis of Sequences Toolbox) provides simple, powerful open source command-line tools to filter, transform, annotate and analyze biological sequence data. Modeled after the GNU (GNUs Not Unix) Textutils such as grep, cut, and tr, FAST tools such as fasgrep, fascut, and fastr make it easy to rapidly prototype expressive bioinformatic workflows in a compact and generic command vocabulary. Compact combinatorial encoding of data workflows with FAST commands can simplify the documentation and reproducibility of bioinformatic protocols, supporting better transparency in biological data science. Interface self-consistency and conformity with conventions of GNU, Matlab, Perl, BioPerl, R, and GenBank help make FAST easy and rewarding to learn. FAST automates numerical, taxonomic, and text-based sorting, selection and transformation of sequence records and alignment sites based on content, index ranges, descriptive tags, annotated features, and in-line calculated analytics, including composition and codon usage. Automated content- and feature-based extraction of sites and support for molecular population genetic statistics make FAST useful for molecular evolutionary analysis. FAST is portable, easy to install and secure thanks to the relative maturity of its Perl and BioPerl foundations, with stable releases posted to CPAN. Development as well as a publicly accessible Cookbook and Wiki are available on the FAST GitHub repository at https://github.com/tlawrence3/FAST. The default data exchange format in FAST is Multi-FastA (specifically, a restriction of BioPerl FastA format). Sanger and Illumina 1.8+ FastQ formatted files are also supported. FAST makes it easier for non-programmer biologists to interactively investigate and control biological data at the speed of thought.

Eye movements during listening reveal spontaneous grammatical processing.

Recent research using eye-tracking typically relies on constrained visual contexts in particular goal-oriented contexts, viewing a small array of objects on a computer screen and performing some overt decision or identification. Eyetracking paradigms that use pictures as a measure of word or sentence comprehension are sometimes touted as ecologically invalid because pictures and explicit tasks are not always present during language comprehension. This study compared the comprehension of sentences with two different grammatical forms: the past progressive (e.g., was walking), which emphasizes the ongoing nature of actions, and the simple past (e.g., walked), which emphasizes the end-state of an action. The results showed that the distribution and timing of eye movements mirrors the underlying conceptual structure of this linguistic difference in the absence of any visual stimuli or task constraint: Fixations were shorter and saccades were more dispersed across the screen, as if thinking about more dynamic events when listening to the past progressive stories. Thus, eye movement data suggest that visual inputs or an explicit task are unnecessary to solicit analog representations of features such as movement, that could be a key perceptual component to grammatical comprehension.

Ancestral genome organization: an alignment approach.

We present a comparative genomics approach for inferring ancestral genome organization and evolutionary scenarios, based on present-day genomes represented as ordered gene sequences with duplicates. We develop our methodology for a model of evolution restricted to duplication and loss, and then show how to extend it to other content-modifying operations, and to inversions. From a combinatorial point of view, the main consequence of ignoring rearrangements is the possibility of formulating the problem as an alignment problem. On the other hand, duplications and losses are asymmetric operations that are applicable to one of the two aligned sequences. Consequently, an ancestral genome can directly be inferred from a duplication-loss scenario attached to a given alignment. Although alignments are a priori simpler to handle than rearrangements, we show that a direct approach based on dynamic programming leads, at best, to an efficient heuristic. We present an exact pseudo-boolean linear programming algorithm to search for the optimal alignment along with an optimal scenario of duplications and losses. Although exponential in the worst case, we show low running times on real datasets as well as synthetic data. We apply our algorithm (*) in a phylogenetic context to the evolution of stable RNA (tRNA and rRNA) gene content and organization in Bacillus genomes. Our results lead to various biological insights, such as rates of ribosomal RNA proliferation among lineages, their role in altering tRNA gene content, and evidence of tRNA class conversion.

Evolution of genome organization by duplication and loss: an alignment approach

We present a comparative genomics approach for inferring ancestral genome organization and evolutionary scenarios, based on a model accounting for content-modifying operations. More precisely, we focus on comparing two ordered gene sequences with duplicated genes that have evolved from a common ancestor through duplications and losses; our model can be grouped in the class of Block Edit models. From a combinatorial point of view, the main consequence is the possibility of formulating the problem as an alignment problem. On the other hand, in contrast to symmetrical metrics such as the inversion distance, duplications and losses are asymmetrical operations that are applicable to one of the two aligned sequences. Consequently, an ancestral genome can directly be inferred from a duplication-loss scenario attached to a given alignment. Although alignments are a priori simpler to handle than rearrangements, we show that a direct approach based on dynamic programming leads, at best, to an efficient heuristic. We present an exact pseudo-boolean linear programming algorithm to search for the optimal alignment along with an optimal scenario of duplications and losses. Although exponential in the worst case, we show low running times on real datasets as well as synthetic data. We apply our algorithm in a phylogenetic context to the evolution of stable RNA (tRNA and rRNA) gene content and organization in Bacillus genomes. Our results lead to various biological insights, such as rates of ribosomal RNA proliferation among lineages, their role in altering tRNA gene content, and evidence of tRNA class conversion.

Initiator tRNA genes template the 3′ CCA end at high frequencies in bacteria

BackgroundWhile the CCA sequence at the mature 3′ end of tRNAs is conserved and critical for translational function, a genetic template for this sequence is not always contained in tRNA genes. In eukaryotes and Archaea, the CCA ends of tRNAs are synthesized post-transcriptionally by CCA-adding enzymes. In Bacteria, tRNA genes template CCA sporadically.ResultsIn order to understand the variation in how prokaryotic tRNA genes template CCA, we re-annotated tRNA genes in tRNAdb-CE database version 0.8. Among 132,129 prokaryotic tRNA genes, initiator tRNA genes template CCA at the highest average frequency (74.1%) over all functional classes except selenocysteine and pyrrolysine tRNA genes (88.1% and 100% respectively). Across bacterial phyla and a wide range of genome sizes, many lineages exist in which predominantly initiator tRNA genes template CCA. Convergent and parallel retention of CCA templating in initiator tRNA genes evolved in independent histories of reductive genome evolution in Bacteria. Also, in a majority of cyanobacterial and actinobacterial genera, predominantly initiator tRNA genes template CCA. We also found that a surprising fraction of archaeal tRNA genes template CCA.ConclusionsWe suggest that cotranscriptional synthesis of initiator tRNA CCA 3′ ends can complement inefficient processing of initiator tRNA precursors, “bootstrap” rapid initiation of protein synthesis from a non-growing state, or contribute to an increase in cellular growth rates by reducing overheads of mass and energy to maintain nonfunctional tRNA precursor pools. More generally, CCA templating in structurally non-conforming tRNA genes can afford cells robustness and greater plasticity to respond rapidly to environmental changes and stimuli.

Adaptive Partitioning of the tRNA Interaction Interface by Aminoacyl-tRNA-Synthetases

We introduce rugged fitness landscapes called match landscapes for the coevolution of feature-based assortative interactions between P ≥ 2 cognate pairs of tRNAs and aminoacyl-tRNA synthetases (aaRSs) in aaRS-tRNA interaction networks. Our genotype-phenotype-fitness maps assume additive feature-matching energies, a macroscopic theory of aminoacylation kinetics including proofreading, and selection for translational accuracy in multiple, perfectly encoded site-types. We compute the stationary genotype distributions of finite panmictic, asexual populations of haploid aaRs-tRNA interaction networks evolving under mutation, genetic drift, and selection for cognate matching and non-cognate mismatching of aaRS-tRNA pairs. We compared expected genotype frequencies under different matching rules and fitness functions, both with and without linked site-specific modifiers of interaction. Under selection for translational accuracy alone, our model predicts no selection on modifiers to eliminate non-cognate interactions, so long as they are compensated by tighter cognate interactions. Only under combined selection for both translational accuracy and rate do modifiers adaptively eliminate cross-matching in non-cognate aaRS/tRNA pairs. We theorize that the encoding of macromolecular interaction networks is a genetic language that symbolically maps identifying structural and dynamic features of genes and gene-products to functions within cells. Our theory helps explain 1) the remarkable divergence in how aaRSs bind tRNAs, 2) why interaction-informative features are phylogenetically informative, 3) why the Statistical Tree of Life became more tree-like after the Darwinian Transition, and 4) an approach towards computing the probability of the random origin of an interaction network.

Whole RNA-Sequencing and Transcriptome Assembly of Candida albicans and Candida africana under Chlamydospore-Inducing Conditions

Abstract Candida albicans is the most common cause of life-threatening fungal infections in humans, especially in immunocompromised individuals. Crucial to its success as an opportunistic pathogen is the considerable dynamism of its genome, which readily undergoes genetic changes generating new phenotypes and shaping the evolution of new strains. Candida africana is an intriguing C. albicans biovariant strain that exhibits remarkable genetic and phenotypic differences when compared with standard C. albicans isolates. Candida africana is well-known for its low degree of virulence compared with C. albicans and for its inability to produce chlamydospores that C. albicans, characteristically, produces under certain environmental conditions. Chlamydospores are large, spherical structures, whose biological function is still unknown. For this reason, we have sequenced, assembled, and annotated the whole transcriptomes obtained from an efficient C. albicans chlamydospore-producing clinical strain (GE1), compared with the natural chlamydospore-negative C. africana clinical strain (CBS 11016). The transcriptomes of both C. albicans (GE1) and C. africana (CBS 11016) clinical strains, grown under chlamydospore-inducing conditions, were sequenced and assembled into 7,442 (GE1 strain) and 8,370 (CBS 11016 strain) high quality transcripts, respectively. The release of the first assembly of the C. africana transcriptome will allow future comparative studies to better understand the biology and evolution of this important human fungal pathogen.