Microbial genomic trait evolution is dominated by frequent and rare pulsed evolution

The Cauchy Process on Phylogenies: A Tractable Model for Pulsed Evolution

Phylogenetic comparative methods use random processes, such as the Brownian Motion, to model the evolution of continuous traits on phylogenetic trees. Growing evidence for non-gradual evolution motivated the development of complex models, often based on Lévy processes. However, their statistical inference is computationally intensive and currently relies on approximations, high-dimensional sampling, or numerical integration. We consider here the Cauchy Process (CP), a particular pure-jump Lévy process in which the trait increment along each branch follows a centered Cauchy distribution with a dispersion proportional to its length. In this work, we derive an exact algorithm to compute both the joint probability density of the tip trait values of a phylogeny under a CP and the ancestral trait values and branch increments posterior densities in quadratic time. A simulation study shows that the CP generates patterns in comparative data that are distinct from any Gaussian process, and that restricted maximum likelihood parameter estimates and root trait reconstruction are unbiased and accurate for trees with 200 tips or less. The CP has only two parameters but is rich enough to capture complex-pulsed evolution. It can reconstruct posterior ancestral trait distributions that are multimodal, reflecting the uncertainty associated with the inference of the evolutionary history of a trait from extant taxa only. Applied on empirical datasets taken from the Evolutionary Ecology and Virology literature, the CP suggests nuanced scenarios for the body size evolution of Greater Antilles Lizards and for the geographical spread of the West Nile Virus epidemics in North America, both consistent with previous studies using more complex models. The method is efficiently implemented in C with an R interface in package cauphy, which is open source and freely available online.

Accounting for 16S rRNA copy number prediction uncertainty and its implications in bacterial diversity analyses

16S rRNA gene copy number (16S GCN) varies among bacterial species and this variation introduces potential biases to microbial diversity analyses using 16S rRNA read counts. To correct the biases, methods have been developed to predict 16S GCN. A recent study suggests that the prediction uncertainty can be so great that copy number correction is not justified in practice. Here we develop RasperGade16S, a novel method and software to better model and capture the inherent uncertainty in 16S GCN prediction. RasperGade16S implements a maximum likelihood framework of pulsed evolution model and explicitly accounts for intraspecific GCN variation and heterogeneous GCN evolution rates among species. Using cross-validation, we show that our method provides robust confidence estimates for the GCN predictions and outperforms other methods in both precision and recall. We have predicted GCN for 592605 OTUs in the SILVA database and tested 113842 bacterial communities that represent an exhaustive and diverse list of engineered and natural environments. We found that the prediction uncertainty is small enough for 99% of the communities that 16S GCN correction should improve their compositional and functional profiles estimated using 16S rRNA reads. On the other hand, we found that GCN variation has limited impacts on beta-diversity analyses such as PCoA, NMDS, PERMANOVA and random-forest test.

Scientific novelty beyond the experiment

Practical experiments drive important scientific discoveries in biology, but theory-based research studies also contribute novel-sometimes paradigm-changing-findings. Here, we appraise the roles of theory-based approaches focusing on the experiment-dominated wet-biology research areas of microbial growth and survival, cell physiology, host-pathogen interactions, and competitive or symbiotic interactions. Additional examples relate to analyses of genome-sequence data, climate change and planetary health, habitability, and astrobiology. We assess the importance of thought at each step of the research process; the roles of natural philosophy, and inconsistencies in logic and language, as drivers of scientific progress; the value of thought experiments; the use and limitations of artificial intelligence technologies, including their potential for interdisciplinary and transdisciplinary research; and other instances when theory is the most-direct and most-scientifically robust route to scientific novelty including the development of techniques for practical experimentation or fieldwork. We highlight the intrinsic need for human engagement in scientific innovation, an issue pertinent to the ongoing controversy over papers authored using/authored by artificial intelligence (such as the large language model/chatbot ChatGPT). Other issues discussed are the way in which aspects of language can bias thinking towards the spatial rather than the temporal (and how this biased thinking can lead to skewed scientific terminology); receptivity to research that is non-mainstream; and the importance of theory-based science in education and epistemology. Whereas we briefly highlight classic works (those by Oakes Ames, Francis H.C. Crick and James D. Watson, Charles R. Darwin, Albert Einstein, James E. Lovelock, Lynn Margulis, Gilbert Ryle, Erwin R.J.A. Schrödinger, Alan M. Turing, and others), the focus is on microbiology studies that are more-recent, discussing these in the context of the scientific process and the types of scientific novelty that they represent. These include several studies carried out during the 2020 to 2022 lockdowns of the COVID-19 pandemic when access to research laboratories was disallowed (or limited). We interviewed the authors of some of the featured microbiology-related papers and-although we ourselves are involved in laboratory experiments and practical fieldwork-also drew from our own research experiences showing that such studies can not only produce new scientific findings but can also transcend barriers between disciplines, act counter to scientific reductionism, integrate biological data across different timescales and levels of complexity, and circumvent constraints imposed by practical techniques. In relation to urgent research needs, we believe that climate change and other global challenges may require approaches beyond the experiment.

Genomic Legacies of Ancient Adaptation Illuminate GC-Content Evolution in Bacteria

Bacterial evolution is characterized by strong purifying selection as well as rapid adaptive evolution in changing environments. In this context, the genomic GC content (genomic GC) varies greatly but presents some level of phylogenetic stability, making it challenging to explain based on current hypotheses. To illuminate the evolutionary mechanisms of the genomic GC, we analyzed the base composition and functional inventory of 11,083 representative genomes. A phylogenetically constrained bimodal distribution of the genomic GC, which mainly originated from parallel divergences in the early evolution, was demonstrated. Such variation of the genomic GC can be well explained by DNA replication and repair (DRR), in which multiple pathways correlate with the genomic GC. Furthermore, the biased conservation of various stress-related genes, especially the DRR-related ones, implies distinct adaptive processes in the ancestral lineages of high- or low-GC clades which are likely induced by major environmental changes. Our findings support that the mutational biases resulting from these legacies of ancient adaptation have changed the course of adaptive evolution and generated great variation in the genomic GC. This highlights the importance of indirect effects of natural selection, which indicates a new model for bacterial evolution. IMPORTANCE GC content has been shown to be an important factor in microbial ecology and evolution, and the genomic GC of bacteria can be characterized by great intergenomic heterogeneity, high intragenomic homogeneity, and strong phylogenetic inertia, as well as being associated with the environment. Current hypotheses concerning direct selection or mutational biases cannot well explain these features simultaneously. Our findings of the genomic GC showing that ancient adaptations have transformed the DRR system and that the resulting mutational biases further contributed to a bimodal distribution of it offer a more reasonable scenario for the mechanism. This would imply that, when thinking about the evolution of life, diverse processes of adaptation exist, and combined effects of natural selection should be considered.