Testing the intrinsic mechanisms driving the dynamics of Ross River Virus across Australia.

The mechanisms driving dynamics of many epidemiologically important mosquito-borne pathogens are complex, involving combinations of vector and host factors (e.g., species composition and life-history traits), and factors associated with transmission and reporting. Understanding which intrinsic mechanisms contribute most to observed disease dynamics is important, yet often poorly understood. Ross River virus (RRV) is Australia's most important mosquito-borne disease, with variable transmission dynamics across geographic regions. We used deterministic ordinary differential equation models to test mechanisms driving RRV dynamics across major epidemic centers in Brisbane, Darwin, Mandurah, Mildura, Gippsland, Renmark, Murray Bridge, and Coorong. We considered models with up to two vector species (Aedes vigilax, Culex annulirostris, Aedes camptorhynchus, Culex globocoxitus), two reservoir hosts (macropods, possums), seasonal transmission effects, and transmission parameters. We fit models against long-term RRV surveillance data (1991-2017) and used Akaike Information Criterion to select important mechanisms. The combination of two vector species, two reservoir hosts, and seasonal transmission effects explained RRV dynamics best across sites. Estimated vector-human transmission rate (average ß = 8.04x10-4per vector per day) was similar despite different dynamics. Models estimate 43% underreporting of RRV infections. Findings enhance understanding of RRV transmission mechanisms, provide disease parameter estimates which can be used to guide future research into public health improvements and offer a basis to evaluate mitigation practices.

Performance of Akaike Information Criterion and Bayesian Information Criterion in Selecting Partition Models and Mixture Models.

In molecular phylogenetics, partition models and mixture models provide different approaches to accommodating heterogeneity in genomic sequencing data. Both types of models generally give a superior fit to data than models that assume the process of sequence evolution is homogeneous across sites and lineages. The Akaike Information Criterion (AIC), an estimator of Kullback-Leibler divergence, and the Bayesian Information Criterion (BIC) are popular tools to select models in phylogenetics. Recent work suggests that AIC should not be used for comparing mixture and partition models. In this work, we clarify that this difficulty is not fully explained by AIC misestimating the Kullback-Leibler divergence. We also investigate the performance of the AIC and BIC at comparing amongst mixture models and amongst partition models. We find that under nonstandard conditions (i.e. when some edges have small expected number of changes), AIC underestimates the expected Kullback-Leibler divergence. Under such conditions, AIC preferred the complex mixture models and BIC preferred the simpler mixture models. The mixture models selected by AIC had a better performance in estimating the edge length, while the simpler models selected by BIC performed better in estimating the base frequencies and substitution rate parameters. In contrast, AIC and BIC both prefer simpler partition models over more complex partition models under nonstandard conditions, despite the fact that the more complex partition model was the generating model. We also investigated how mispartitioning (i.e., grouping sites that have not evolved under the same process) affects both the performance of partition models compared with mixture models and the model selection process. We found that as the level of mispartitioning increases, the bias of AIC in estimating the expected Kullback-Leibler divergence remains the same, and the branch lengths and evolutionary parameters estimated by partition models become less accurate. We recommend that researchers are cautious when using AIC and BIC to select among partition and mixture models; other alternatives, such as cross-validation and bootstrapping, should be explored, but may suffer similar limitations [AIC; BIC; mispartitioning; partitioning; partition model; mixture model].

The spectral underpinnings of pathogen spread on animal networks.

Predicting what factors promote or protect populations from infectious disease is a fundamental epidemiological challenge. Social networks, where nodes represent hosts and edges represent direct or indirect contacts between them, are important in quantifying these aspects of infectious disease dynamics. However, how network structure and epidemic parameters interact in empirical networks to promote or protect animal populations from infectious disease remains a challenge. Here we draw on advances in spectral graph theory and machine learning to build predictive models of pathogen spread on a large collection of empirical networks from across the animal kingdom. We show that the spectral features of an animal network are powerful predictors of pathogen spread for a variety of hosts and pathogens and can be a valuable proxy for the vulnerability of animal networks to pathogen spread. We validate our findings using interpretable machine learning techniques and provide a flexible web application for animal health practitioners to assess the vulnerability of a particular network to pathogen spread.

Evaluation of the Relative Performance of the Subflattenings Method for Phylogenetic Inference.

The algebraic properties of flattenings and subflattenings provide direct methods for identifying edges in the true phylogeny-and by extension the complete tree-using pattern counts from a sequence alignment. The relatively small number of possible internal edges among a set of taxa (compared to the number of binary trees) makes these methods attractive; however, more could be done to evaluate their effectiveness for inferring phylogenetic trees. This is the case particularly for subflattenings, and the work we present here makes progress in this area. We introduce software for constructing and evaluating subflattenings for splits, utilising a number of methods to make computing subflattenings more tractable. We then present the results of simulations we have performed in order to compare the effectiveness of subflattenings to that of flattenings in terms of split score distributions, and susceptibility to possible biases. We find that subflattenings perform similarly to flattenings in terms of the distribution of split scores on the trees we examined, but may be less affected by bias arising from both split size/balance and long branch attraction. These insights are useful for developing effective algorithms to utilise these tools for the purpose of inferring phylogenetic trees.

Unattained geometric configurations of secondary structure elements in protein structural space.

Discovery of new folds in the Protein Data Bank (PDB) has all but ceased. This could be viewed as evidence that all existing protein folds have been documented. Sampling bias has, however, been presented as an alternative explanation. Furthermore, although we may know of all protein folds that do exist, we may not have documented all protein folds that could exist. While addressing completeness in the context of entire protein structures is extremely difficult, they can be simplified in a number of ways. One such simplification is presented: considering protein structures as a series of α helices and ß sheets and analysing the geometric relationships between these successive secondary structure elements (SSEs) through torsion angles, lengths and distances. We aimed to find out whether all substructures that could be formed by triplets of these successive SSEs were represented in the PDB. When SSEs were defined with the assignment program Promotif, a gap was identified in the represented torsion angles of helix-strand-strand substructures. This was not present when SSEs were defined with an alternative assignment program with a smaller minimum SSE length, DSSP. We also looked at representing proteins as one-dimensional sequences of SSE types and searched for underrepresented motifs. Completely absent motifs occurred more often than expected at random. If a gap in SSE substructure space exists that could be filled or if a physically possible SSE motif is absent, associated gaps in protein structure space are implied, meaning that the PDB as we know it may not be complete.

Establishing a distributed national research infrastructure providing bioinformatics support to life science researchers in Australia.

EMBL Australia Bioinformatics Resource (EMBL-ABR) is a developing national research infrastructure, providing bioinformatics resources and support to life science and biomedical researchers in Australia. EMBL-ABR comprises 10 geographically distributed national nodes with one coordinating hub, with current funding provided through Bioplatforms Australia and the University of Melbourne for its initial 2-year development phase. The EMBL-ABR mission is to: (1) increase Australia's capacity in bioinformatics and data sciences; (2) contribute to the development of training in bioinformatics skills; (3) showcase Australian data sets at an international level and (4) enable engagement in international programs. The activities of EMBL-ABR are focussed in six key areas, aligning with comparable international initiatives such as ELIXIR, CyVerse and NIH Commons. These key areas-Tools, Data, Standards, Platforms, Compute and Training-are described in this article.

Benchmarking Methods of Protein Structure Alignment.

The function of a protein is primarily determined by its structure and amino acid sequence. Many biological questions of interest rely on being able to accurately determine the group of structures to which domains of a protein belong; this can be done through alignment and comparison of protein structures. Dozens of different methods for Protein Structure Alignment (PSA) have been proposed that use a wide range of techniques. The aim of this study is to determine the ability of PSA methods to identify pairs of protein domains known to share differing levels of structural similarity, and to assess their utility for clustering domains from several different folds into known groups. We present the results of a comprehensive investigation into eighteen PSA methods, to our knowledge the largest piece of independent research on this topic. Overall, SP-AlignNS (non-sequential) was found to be the best method for classification, and among the best performing methods for clustering. Methods (where possible) were split into the algorithm used to find the optimal alignment and the score used to assess similarity. This allowed us to largely separate the algorithm from the score it maximizes and thus, to assess their effectiveness independently of each other. Surprisingly, we found that some hybrids of mismatched scores and algorithms performed better than either of the native methods at classification and, in some cases, clustering as well. It is hoped that this investigation and the accompanying discussion will be useful for researchers selecting or designing methods to align protein structures.

Mitochondrial genome sequencing reveals potential origins of the scabies mite Sarcoptes scabiei infesting two iconic Australian marsupials.

BACKGROUND: Debilitating skin infestations caused by the mite, Sarcoptes scabiei, have a profound impact on human and animal health globally. In Australia, this impact is evident across different segments of Australian society, with a growing recognition that it can contribute to rapid declines of native Australian marsupials. Cross-host transmission has been suggested to play a significant role in the epidemiology and origin of mite infestations in different species but a chronic lack of genetic resources has made further inferences difficult. To investigate the origins and molecular epidemiology of S. scabiei in Australian wildlife, we sequenced the mitochondrial genomes of S. scabiei from diseased wombats (Vombatus ursinus) and koalas (Phascolarctos cinereus) spanning New South Wales, Victoria and Tasmania, and compared them with the recently sequenced mitochondrial genome sequences of S. scabiei from humans. RESULTS: We found unique S. scabiei haplotypes among individual wombat and koala hosts with high sequence similarity (99.1% - 100%). Phylogenetic analysis of near full-length mitochondrial genomes revealed three clades of S. scabiei (one human and two marsupial), with no apparent geographic or host species pattern, suggestive of multiple introductions. The availability of additional mitochondrial gene sequences also enabled a re-evaluation of a range of putative molecular markers of S. scabiei, revealing that cox1 is the most informative gene for molecular epidemiological investigations. Utilising this gene target, we provide additional evidence to support cross-host transmission between different animal hosts. CONCLUSIONS: Our results suggest a history of parasite invasion through colonisation of Australia from hosts across the globe and the potential for cross-host transmission being a common feature of the epidemiology of this neglected pathogen. If this is the case, comparable patterns may exist elsewhere in the 'New World'. This work provides a basis for expanded molecular studies into mange epidemiology in humans and animals in Australia and other geographic regions.

Australian spiny mountain crayfish and their temnocephalan ectosymbionts: an ancient association on the edge of coextinction?

Australian spiny mountain crayfish (Euastacus, Parastacidae) and their ecotosymbiotic temnocephalan flatworms (Temnocephalida, Platyhelminthes) may have co-occurred and interacted through deep time, during a period of major environmental change. Therefore, reconstructing the history of their association is of evolutionary, ecological, and conservation significance. Here, time-calibrated Bayesian phylogenies of Euastacus species and their temnocephalans (Temnohaswellia and Temnosewellia) indicate near-synchronous diversifications from the Cretaceous. Statistically significant cophylogeny correlations between associated clades suggest linked evolutionary histories. However, there is a stronger signal of codivergence and greater host specificity in Temnosewellia, which co-occurs with Euastacus across its range. Phylogeography and analyses of evolutionary distinctiveness (ED) suggest that regional differences in the impact of climate warming and drying had major effects both on crayfish and associated temnocephalans. In particular, Euastacus and Temnosewellia show strong latitudinal gradients in ED and, conversely, in geographical range size, with the most distinctive, northern lineages facing the greatest risk of extinction. Therefore, environmental change has, in some cases, strengthened ecological and evolutionary associations, leaving host-specific temnocephalans vulnerable to coextinction with endangered hosts. Consequently, the extinction of all Euastacus species currently endangered (75%) predicts coextinction of approximately 60% of the studied temnocephalans, with greatest loss of the most evolutionarily distinctive lineages.

Simple, rapid and accurate genotyping-by-sequencing from aligned whole genomes with ArrayMaker.

SUMMARY: Whole-genome sequencing has revolutionized the study of genetics. Genotyping-by-sequencing is now a viable method of genotyping, yet the bioinformatics involved can be daunting if not prohibitive for some laboratories. Here we present ArrayMaker, a user-friendly tool that extracts accurate single nucleotide polymorphism genotypes at pre-defined loci from whole-genome alignments and presents them in a standard genotyping format compatible with association analysis software and datasets genotyped on commercial array platforms. Using this tool, geneticists with only basic computing ability can genotype samples at any desired list of markers, facilitating genome-wide association analysis, fine mapping, candidate variant assessment, data sharing and compatibility of data sourced from multiple technologies. AVAILABILITY AND IMPLEMENTATION: ArrayMaker is licensed under The MIT License and can be freely obtained at https://github.com/cw2014/ArrayMaker/. The program is implemented in Perl and runs on Linux operating systems. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. CONTACT: cali.willet@sydney.edu.au.

Evolving nutritional strategies in the presence of competition: a geometric agent-based model.

Access to nutrients is a key factor governing development, reproduction and ultimately fitness. Within social groups, contest-competition can fundamentally affect nutrient access, potentially leading to reproductive asymmetry among individuals. Previously, agent-based models have been combined with the Geometric Framework of nutrition to provide insight into how nutrition and social interactions affect one another. Here, we expand this modelling approach by incorporating evolutionary algorithms to explore how contest-competition over nutrient acquisition might affect the evolution of animal nutritional strategies. Specifically, we model tolerance of nutrient excesses and deficits when ingesting nutritionally imbalanced foods, which we term 'nutritional latitude'; a higher degree of nutritional latitude constitutes a higher tolerance of nutritional excess and deficit. Our results indicate that a transition between two alternative strategies occurs at moderate to high levels of competition. When competition is low, individuals display a low level of nutritional latitude and regularly switch foods in search of an optimum. When food is scarce and contest-competition is intense, high nutritional latitude appears optimal, and individuals continue to consume an imbalanced food for longer periods before attempting to switch to an alternative. However, the relative balance of nutrients within available foods also strongly influences at what levels of competition, if any, transitions between these two strategies occur. Our models imply that competition combined with reproductive skew in social groups can play a role in the evolution of diet breadth. We discuss how the integration of agent-based, nutritional and evolutionary modelling may be applied in future studies to further understand the evolution of nutritional strategies across social and ecological contexts.

Fungal identification using a Bayesian classifier and the Warcup training set of internal transcribed spacer sequences.

Fungi are key organisms in many ecological processes and communities. Rapid and low cost surveys of the fungal members of a community can be undertaken by isolating and sequencing a taxonomically informative genomic region, such as the ITS (internal transcribed spacer), from DNA extracted from a metagenomic sample, and then classifying these sequences to determine which organisms are present. This paper announces the availability of the Warcup ITS training set and shows how it can be used with the Ribosomal Database Project (RDP) Bayesian Classifier to rapidly and accurately identify fungi using ITS sequences. The classifications can be down to species level and use conventional literature-based mycological nomenclature and taxonomic assignments.

Nutritional ecology beyond the individual: a conceptual framework for integrating nutrition and social interactions.

Over recent years, modelling approaches from nutritional ecology (known as Nutritional Geometry) have been increasingly used to describe how animals and some other organisms select foods and eat them in appropriate amounts in order to maintain a balanced nutritional state maximising fitness. These nutritional strategies profoundly affect the physiology, behaviour and performance of individuals, which in turn impact their social interactions within groups and societies. Here, we present a conceptual framework to study the role of nutrition as a major ecological factor influencing the development and maintenance of social life. We first illustrate some of the mechanisms by which nutritional differences among individuals mediate social interactions in a broad range of species and ecological contexts. We then explain how studying individual- and collective-level nutrition in a common conceptual framework derived from Nutritional Geometry can bring new fundamental insights into the mechanisms and evolution of social interactions, using a combination of simulation models and manipulative experiments.

Introducing TreeCollapse: a novel greedy algorithm to solve the cophylogeny reconstruction problem.

BACKGROUND: Cophylogeny mapping is used to uncover deep coevolutionary associations between two or more phylogenetic histories at a macro coevolutionary scale. As cophylogeny mapping is NP-Hard, this technique relies heavily on heuristics to solve all but the most trivial cases. One notable approach utilises a metaheuristic to search only a subset of the exponential number of fixed node orderings possible for the phylogenetic histories in question. This is of particular interest as it is the only known heuristic that guarantees biologically feasible solutions. This has enabled research to focus on larger coevolutionary systems, such as coevolutionary associations between figs and their pollinator wasps, including over 200 taxa. Although able to converge on solutions for problem instances of this size, a reduction from the current cubic running time is required to handle larger systems, such as Wolbachia and their insect hosts. RESULTS: Rather than solving this underlying problem optimally this work presents a greedy algorithm called TreeCollapse, which uses common topological patterns to recover an approximation of the coevolutionary history where the internal node ordering is fixed. This approach offers a significant speed-up compared to previous methods, running in linear time. This algorithm has been applied to over 100 well-known coevolutionary systems converging on Pareto optimal solutions in over 68% of test cases, even where in some cases the Pareto optimal solution has not previously been recoverable. Further, while TreeCollapse applies a local search technique, it can guarantee solutions are biologically feasible, making this the fastest method that can provide such a guarantee. CONCLUSION: As a result, we argue that the newly proposed algorithm is a valuable addition to the field of coevolutionary research. Not only does it offer a significantly faster method to estimate the cost of cophylogeny mappings but by using this approach, in conjunction with existing heuristics, it can assist in recovering a larger subset of the Pareto front than has previously been possible.

Effect of molnupiravir on SARS-CoV-2 evolution in immunocompromised patients: a retrospective observational study.

INTRODUCTION: Continued SARS-CoV-2 infection among immunocompromised individuals is likely to play a role in generating genomic diversity and the emergence of novel variants. Antiviral treatments such as molnupiravir are used to mitigate severe COVID-19 outcomes, but the extended effects of these drugs on viral evolution in patients with chronic infections remain uncertain. This study investigates how molnupiravir affects SARS-CoV-2 evolution in immunocompromised patients with prolonged infections. METHODS: The study included five immunocompromised patients treated with molnupiravir and four patients not treated with molnupiravir (two immunocompromised and two non-immunocompromised). We selected patients who had been infected by similar SARS-CoV-2 variants and with high-quality genomes across timepoints to allow comparison between groups. Throat and nasopharyngeal samples were collected in patients up to 44 days post treatment and were sequenced using tiled amplicon sequencing followed by variant calling. The UShER pipeline and University of California Santa Cruz genome viewer provided insights into the global context of variants. Treated and untreated patients were compared, and mutation profiles were visualised to understand the impact of molnupiravir on viral evolution. FINDINGS: Patients treated with molnupiravir showed a large increase in low-to-mid-frequency variants in as little as 10 days after treatment, whereas no such change was observed in untreated patients. Some of these variants became fixed in the viral population, including non-synonymous mutations in the spike protein. The variants were distributed across the genome and included unique mutations not commonly found in global omicron genomes. Notably, G-to-A and C-to-T mutations dominated the mutational profile of treated patients, persisting up to 44 days post treatment. INTERPRETATION: Molnupiravir treatment in immunocompromised patients led to the accumulation of a distinctive pattern of mutations beyond the recommended 5 days of treatment. Treated patients maintained persistent PCR positivity for the duration of monitoring, indicating clear potential for transmission and subsequent emergence of novel variants. FUNDING: Australian Research Council.

Ancient and Modern Genomes Reveal Microsatellites Maintain a Dynamic Equilibrium Through Deep Time.

Microsatellites are widely used in population genetics, but their evolutionary dynamics remain poorly understood. It is unclear whether microsatellite loci drift in length over time. This is important because the mutation processes that underlie these important genetic markers are central to the evolutionary models that employ microsatellites. We identify more than 27 million microsatellites using a novel and unique dataset of modern and ancient Adélie penguin genomes along with data from 63 published chordate genomes. We investigate microsatellite evolutionary dynamics over 2 timescales: one based on Adélie penguin samples dating to ∼46.5 ka and the other dating to the diversification of chordates aged more than 500 Ma. We show that the process of microsatellite allele length evolution is at dynamic equilibrium; while there is length polymorphism among individuals, the length distribution for a given locus remains stable. Many microsatellites persist over very long timescales, particularly in exons and regulatory sequences. These often retain length variability, suggesting that they may play a role in maintaining phenotypic variation within populations.

Active subnetwork recovery with a mechanism-dependent scoring function; with application to angiogenesis and organogenesis studies.

BACKGROUND: The learning active subnetworks problem involves finding subnetworks of a bio-molecular network that are active in a particular condition. Many approaches integrate observation data (e.g., gene expression) with the network topology to find candidate subnetworks. Increasingly, pathway databases contain additional annotation information that can be mined to improve prediction accuracy, e.g., interaction mechanism (e.g., transcription, microRNA, cleavage) annotations. We introduce a mechanism-based approach to active subnetwork recovery which exploits such annotations. We suggest that neighboring interactions in a network tend to be co-activated in a way that depends on the "correlation" of their mechanism annotations. e.g., neighboring phosphorylation and de-phosphorylation interactions may be more likely to be co-activated than neighboring phosphorylation and covalent bonding interactions. RESULTS: Our method iteratively learns the mechanism correlations and finds the most likely active subnetwork. We use a probabilistic graphical model with a Markov Random Field component which creates dependencies between the states (active or non-active) of neighboring interactions, that incorporates a mechanism-based component to the function. We apply a heuristic-based EM-based algorithm suitable for the problem. We validated our method's performance using simulated data in networks downloaded from GeneGO against the same approach without the mechanism-based component, and two other existing methods. We validated our methods performance in correctly recovering (1) the true interaction states, and (2) global network properties of the original network against these other methods. We applied our method to networks generated from time-course gene expression studies in angiogenesis and lung organogenesis and validated the findings from a biological perspective against current literature. CONCLUSIONS: The advantage of our mechanism-based approach is best seen in networks composed of connected regions with a large number of interactions annotated with a subset of mechanisms, e.g., a regulatory region of transcription interactions, or a cleavage cascade region. When applied to real datasets, our method recovered novel and biologically meaningful putative interactions, e.g., interactions from an integrin signaling pathway using the angiogenesis dataset, and a group of regulatory microRNA interactions in an organogenesis network.

A review of visualisations of protein fold networks and their relationship with sequence and function.

Proteins form arguably the most significant link between genotype and phenotype. Understanding the relationship between protein sequence and structure, and applying this knowledge to predict function, is difficult. One way to investigate these relationships is by considering the space of protein folds and how one might move from fold to fold through similarity, or potential evolutionary relationships. The many individual characterisations of fold space presented in the literature can tell us a lot about how well the current Protein Data Bank represents protein fold space, how convergence and divergence may affect protein evolution, how proteins affect the whole of which they are part, and how proteins themselves function. A synthesis of these different approaches and viewpoints seems the most likely way to further our knowledge of protein structure evolution and thus, facilitate improved protein structure design and prediction.

Hunting alters viral transmission and evolution in a large carnivore.

Hunting can fundamentally alter wildlife population dynamics but the consequences of hunting on pathogen transmission and evolution remain poorly understood. Here, we present a study that leverages a unique landscape-scale quasi-experiment coupled with pathogen-transmission tracing, network simulation and phylodynamics to provide insights into how hunting shapes feline immunodeficiency virus (FIV) dynamics in puma (Puma concolor). We show that removing hunting pressure enhances the role of males in transmission, increases the viral population growth rate and increases the role of evolutionary forces on the pathogen compared to when hunting was reinstated. Changes in transmission observed with the removal of hunting could be linked to short-term social changes while the male puma population increased. These findings are supported through comparison with a region with stable hunting management over the same time period. This study shows that routine wildlife management can have impacts on pathogen transmission and evolution not previously considered.