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The provenance of the aminoacyl-tRNA synthetases (aaRS) poses unusually challenging questions because of their role in the emergence and evolution of genetic coding. We investigate evidence about their ancestry from highly curated structure-based multiple sequence alignments of a small “scaffold” that is structurally invariant in all 10 canonical Class I aaRS. Statistically different values of two uncorrelated phylogenetic metrics—residue by residue conservation derived from Clustal and row-by-row cladistic congruence derived from BEAST2—suggest that the Class I scaffold is a mosaic assembled from distinct, successive genetic sources. These data are especially significant in light of: (i) experimental fragmentations of the Class I scaffold into three partitions that retain catalytic activities in proportion to their length; and (ii) multiple sources of evidence that two of these partitions arose from an ancestral Class I aaRS gene encoding a Class II ancestor in frame on the opposite strand. Two additional metrics output by BEAST2 vary in accordance with the presumed functionality endowed by the various modules. The new evidence supplements previous aaRS phylogenies. It identifies a previously characterized 46-residue Class I “protozyme” as preceding the adaptive radiation of the superfamily containing variations of the Rossmann dinucleotide binding fold related to amino acid discrimination, and thus as root of that molecular tree. Such a rooting is consistent with near simultaneous emergence of genetic coding and the origin of the proteome, resolving a conundrum posed by previous inferences that Class I aaRS evolved long after the genetic code had been implemented in an RNA world. Further, it establishes a timeline for the growth of coding from a binary amino acid alphabet by pinpointing discontinuous enhancements of aaRS fidelity.Author SummaryPhylogenetic analysis uncovers evolutionary connections between different protein superfamily members. We describe complementary, uncorrelated, phylogenetic metrics that support multiple evolutionary histories for different segments within members of the Class I aminoacyl-tRNA synthetase superfamily. Using a carefully curated 3D crystal structure superposition as the primary source of the multiple sequence alignment substantially reduced dependence of these metrics on empirical amino acid substitution matrices. Two metrics are derived from the amino acid distribution observed in each successive position. A third depends on how individual sequences distribute into phylogenetic tree branches for each of the ten amino acids activated by the superfamily. All metrics confirm that a segment previously identified as an inserted element is, indeed, a more recent acquisition, despite its structural conservation. The residue-by-residue conservation metrics reveal significant co-variation of mutational frequencies between a core segment that forms the amino acid binding site and a neighboring segment derived from the more recent insertion element. We attribute that covariation to the differentiation of superfamily members as evolutionary divergence enhanced amino acid specificity. Finally, evidence that the insertion element is a recent acquisition implies a new branching order for much of the proteome.

The role of aminoacyl-tRNA synthetases (aaRS) in the emergence and evolution of genetic coding poses challenging questions concerning their provenance. We seek evidence about their ancestry from curated structure-based multiple sequence alignments of a structurally invariant “scaffold” shared by all 10 canonical Class I aaRS. Three uncorrelated phylogenetic metrics—mutation frequency, its uniformity, and row-by-row cladistic congruence—imply that the Class I scaffold is a mosaic assembled from successive genetic sources. Metrics for different modules vary in accordance with their presumed functionality. Sequences derived from the ATP– and amino acid– binding sites exhibit specific two-way coupling to those derived from Connecting Peptide 1, a third module whose metrics suggest later acquisition. The data help validate: (i) experimental fragmentations of the canonical Class I structure into three partitions that retain catalytic activities in proportion to their length; and (ii) evidence that the ancestral Class I aaRS gene also encoded a Class II ancestor in frame on the opposite strand. A 46-residue Class I “protozyme” roots the Class I tree prior to the adaptive radiation of the Rossmann dinucleotide binding fold that refined substrate discrimination. Such rooting implies near simultaneous emergence of genetic coding and the origin of the proteome, resolving a conundrum posed by previous inferences that Class I aaRS evolved after the genetic code had been implemented in an RNA world. Further, pinpointing discontinuous enhancements of aaRS fidelity establishes a timeline for the growth of coding from a binary amino acid alphabet.

Chewing lice (Phthiraptera: Trichodectidae) and the pocket gophers (Rodentia: Geomyidae) that they inhabit have shared an intimate historical association that has made them a textbook study for cophylogeny. Herein, we examine the chewing lice found on pocket gophers of the Cratogeomys fumosus species group using DNA sequence data from the mitochondrial cytochrome c oxidase subunit I gene to determine relationships among lice for comparison to published host phylogeny. Although matrix approaches indicate a correlation between host and parasite genetic distances, cophylogenetic reconstruction methods fail to detect a pattern of widespread cophylogeny. In conclusion, this study provides an exception to the rule of host-parasite cophylogeny that could be the result of the young age of the relationships considered herein and the complex history of the Trans-Mexican Volcanic Belt.

Estimation of epidemiological and population parameters from molecular sequence data has become central to the understanding of infectious disease dynamics. Various models have been proposed to infer details of the dynamics that describe epidemic progression. These include inference approaches derived from Kingman's coalescent theory. Here, we use recently described coalescent theory for epidemic dynamics to develop stochastic and deterministic coalescent SIR tree priors. We implement these in a Bayesian phylogenetic inference framework to permit joint estimation of SIR epidemic parameters and the sample genealogy. We assess the performance of the two coalescent models and also juxtapose results obtained with BDSIR, a recently published birth-death-sampling model for epidemic inference. Comparisons are made by analyzing sets of genealogies simulated under precisely known epidemiological parameters. Additionally, we analyze influenza A (H1N1) sequence data sampled in the Canterbury region of New Zealand and HIV-1 sequence data obtained from known UK infection clusters. We show that both coalescent SIR models are effective at estimating epidemiological parameters from data with large fundamental reproductive number $R_0$ and large population size $S_0$. Furthermore, we find that the stochastic variant generally outperforms its deterministic counterpart in terms of error, bias, and highest posterior density coverage, particularly for smaller $R_0$ and $S_0$. However, each of these inference models are shown to have undesirable properties in certain circumstances, especially for epidemic outbreaks with $R_0$ close to one or with small effective susceptible populations., Comment: Submitted

Bioinformatics, 30 (16), ISSN:1367-4803, ISSN:1460-2059