Elaboration of Bayesian phylogenetic inference methods has continued at pace in recent years with major new advances in nearly all aspects of the joint modelling of evolutionary data. It is increasingly appreciated that some evolutionary questions can only be adequately answered by combining evidence from multiple independent sources of data, including genome sequences, sampling dates, phenotypic data, radiocarbon dates, fossil occurrences, and biogeographic range information among others. Including all relevant data into a single joint model is very challenging both conceptually and computationally. Advanced computational software packages that allow robust development of compatible (sub-)models which can be composed into a full model hierarchy have played a key role in these developments. Developing such software frameworks is increasingly a major scientific activity in its own right, and comes with specific challenges, from practical software design, development and engineering challenges to statistical and conceptual modelling challenges. BEAST 2 is one such computational software platform, and was first announced over 4 years ago. Here we describe a series of major new developments in the BEAST 2 core platform and model hierarchy that have occurred since the first release of the software, culminating in the recent 2.5 release. Bayesian phylogenetic inference methods have undergone considerable development in recent years, and joint modelling of rich evolutionary data, including genomes, phenotypes and fossil occurrences is increasingly common. Advanced computational software packages that allow robust development of compatible (sub-)models which can be composed into a full model hierarchy have played a key role in these developments. Developing scientific software is increasingly crucial to advancement in many fields of biology. The challenges range from practical software development and engineering, distributed team coordination, conceptual development and statistical modelling, to validation and testing. BEAST 2 is one such computational software platform for phylogenetics, population genetics and phylodynamics, and was first announced over 4 years ago. Here we describe the full range of new tools and models available on the BEAST 2.5 platform, which expand joint evolutionary inference in many new directions, especially for joint inference over multiple data types, non-tree models and complex phylodynamics.

Recent genomic data have revealed multiple interactions between Neanderthals and modern humans¹, but there is currently little genetic evidence regarding Neanderthal behaviour, diet, or disease. Here we describe the shotgun-sequencing of ancient DNA from five specimens of Neanderthal calcified dental plaque (calculus) and the characterization of regional differences in Neanderthal ecology. At Spy cave, Belgium, Neanderthal diet was heavily meat based and included woolly rhinoceros and wild sheep (mouflon), characteristic of a steppe environment. In contrast, no meat was detected in the diet of Neanderthals from El Sidrón cave, Spain, and dietary components of mushrooms, pine nuts, and moss reflected forest gathering^2,3. Differences in diet were also linked to an overall shift in the oral bacterial community (microbiota) and suggested that meat consumption contributed to substantial variation within Neanderthal microbiota. Evidence for self-medication was detected in an El Sidrón Neanderthal with a dental abscess⁴ and a chronic gastrointestinal pathogen (Enterocytozoon bieneusi). Metagenomic data from this individual also contained a nearly complete genome of the archaeal commensal Methanobrevibacter oralis (10.2× depth of coverage)—the oldest draft microbial genome generated to date, at around 48,000 years old. DNA preserved within dental calculus represents a notable source of information about the behaviour and health of ancient hominin specimens, as well as a unique system that is useful for the study of long-term microbial evolution.

Klebsiella pneumoniae has emerged as an important cause of two distinct public health threats: multi-drug resistant (MDR) healthcare-associated infections and drug susceptible community-acquired invasive infections. These pathotypes are generally associated with two distinct subsets of K. pneumoniae lineages or ‘clones’ that are distinguished by the presence of acquired resistance genes and several key virulence loci. Genomic evolutionary analyses of the most notorious MDR and invasive community-associated (‘hypervirulent’) clones indicate differences in terms of chromosomal recombination dynamics and capsule polysaccharide diversity, but it remains unclear if these differences represent generalised trends. Here we leverage a collection of >2200 K. pneumoniae genomes to identify 28 common clones (n ≥ 10 genomes each), and perform the first genomic evolutionary comparison. Eight MDR and 6 hypervirulent clones were identified on the basis of acquired resistance and virulence gene prevalence. Chromosomal recombination, surface polysaccharide locus diversity, pan-genome, plasmid and phage dynamics were characterised and compared. The data showed that MDR clones were highly diverse, with frequent chromosomal recombination generating extensive surface polysaccharide locus diversity. Additional pan-genome diversity was driven by frequent acquisition/loss of both plasmids and phage. In contrast, chromosomal recombination was rare in the hypervirulent clones, which also showed a significant reduction in pan-genome diversity, largely driven by a reduction in plasmid diversity. Hence the data indicate that hypervirulent clones may be subject to some sort of constraint for horizontal gene transfer that does not apply to the MDR clones. Our findings are relevant for understanding the risk of emergence of individual K. pneumoniae strains carrying both virulence and acquired resistance genes, which have been increasingly reported and cause highly virulent infections that are extremely difficult to treat. Specifically, our data indicate that MDR clones pose the greatest risk, because they are more likely to acquire virulence genes than hypervirulent clones are to acquire resistance genes. Klebsiellla pneumoniae is classified by the World Health Organization as a priority drug-resistant organism because it causes a significant burden of hospital infections that are extremely difficult to treat. However, outside of the hospital setting this bacterium is also an important cause of severe drug-susceptible infections. Until recently these two infection types were associated with distinct subsets of the K. pneumoniae population harbouring high prevalence of drug-resistance and virulence genes, respectively. However, there are now increasing reports of highly-virulent and difficult-to-treat K. pneumoniae strains that carry both resistance and virulence genes. In this study, we used genomic analyses to characterise and compare the evolutionary histories of drug-resistant and virulent K. pneumoniae. We show that the former are highly diverse, frequently acquiring novel genes through the processes of chromosomal recombination, plasmid and bacteriophage acquisition. In comparison, the latter show considerably lower gene content diversity, suggesting that they may be subject to some sort of limitation for gene acquisition. Consequently, we predict that drug-resistant K. pneumoniae are more likely to acquire virulence genes than virulent K. pneumoniae are to acquire drug-resistance genes.

Severe liver abscess infections caused by hypervirulent clonal-group CG23 Klebsiella pneumoniae have been increasingly reported since the mid-1980s. Strains typically possess several virulence factors including an integrative, conjugative element ICEKp encoding the siderophore yersiniabactin and genotoxin colibactin. Here we investigate CG23’s evolutionary history, showing several deep-branching sublineages associated with distinct ICEKp acquisitions. Over 80% of liver abscess isolates belong to sublineage CG23-I, which emerged in ~1928 following acquisition of ICEKp10 (encoding yersiniabactin and colibactin), and then disseminated globally within the human population. CG23-I’s distinguishing feature is the colibactin synthesis locus, which reportedly promotes gut colonisation and metastatic infection in murine models. These data show circulation of CG23 K. pneumoniae decades before the liver abscess epidemic was first recognised, and provide a framework for future epidemiological and experimental studies of hypervirulent K. pneumoniae. To support such studies we present an open access, completely sequenced CG23-I human liver abscess isolate, SGH10.

Estimating the rates at which bacterial genomes evolve is critical to understanding major evolutionary and ecological processes such as disease emergence, long-term host–pathogen associations and short-term transmission patterns. The surge in bacterial genomic data sets provides a new opportunity to estimate these rates and reveal the factors that shape bacterial evolutionary dynamics. For many organisms estimates of evolutionary rate display an inverse association with the time-scale over which the data are sampled. However, this relationship remains unexplored in bacteria due to the difficulty in estimating genome-wide evolutionary rates, which are impacted by the extent of temporal structure in the data and the prevalence of recombination. We collected 36 whole genome sequence data sets from 16 species of bacterial pathogens to systematically estimate and compare their evolutionary rates and assess the extent of temporal structure in the absence of recombination. The majority (28/36) of data sets possessed sufficient clock-like structure to robustly estimate evolutionary rates. However, in some species reliable estimates were not possible even with ‘ancient DNA’ data sampled over many centuries, suggesting that they evolve very slowly or that they display extensive rate variation among lineages. The robustly estimated evolutionary rates spanned several orders of magnitude, from approximately 10⁻⁵ to 10⁻⁸ nucleotide substitutions per site year⁻¹. This variation was negatively associated with sampling time, with this relationship best described by an exponential decay curve. To avoid potential estimation biases, such time-dependency should be considered when inferring evolutionary time-scales in bacteria.