Looking in the right direction: Carl Woese and evolutionary biology

The discovery of archaea: from observed anomaly to consequential restructuring of the phylogenetic tree

Observational and experimental discoveries of new factual entities such as objects, systems, or processes, are major contributors to some advances in the life sciences. Yet, whereas discovery of theories was extensively deliberated by philosophers of science, very little philosophical attention was paid to the discovery of factual entities. This paper examines historical and philosophical aspects of the experimental discovery by Carl Woese of archaea, prokaryotes that comprise one of the three principal domains of the phylogenetic tree. Borrowing Kuhn's terminology, this discovery of a major biological entity was made during a 'normal science' project of building molecular taxonomy for prokaryotes. Unexpectedly, however, an observed anomaly instigated the discovery of archaea. Substantiation of the existence of the new archaeal entity and consequent reconstruction of the phylogenetic tree prompted replacement of a long-held model of a prokarya and eukarya bipartite tree of life by a new model of a tripartite tree comprising of bacteria, archaea, and eukarya. This paper explores the history and philosophical implications of the progression of Woese's project from normal science to anomaly-instigated model-changing discovery. It is also shown that the consequential discoveries of RNA splicing and of ribozymes were similarly prompted by unexpected irregularities during normal science activities. It is thus submitted that some discoveries of factual biological entities are triggered by unforeseen observational or experimental anomalies.

Scale-invariant topology and bursty branching of evolutionary trees emerge from niche construction

Phylogenetic trees describe both the evolutionary process and community diversity. Recent work has established that they exhibit scale-invariant topology, which quantifies the fact that their branching lies in between the two extreme cases of balanced binary trees and maximally unbalanced ones. In addition, the backbones of phylogenetic trees exhibit bursts of diversification on all timescales. Here, we present a simple, coarse-grained statistical model of niche construction coupled to speciation. Finite-size scaling analysis of the dynamics shows that the resultant phylogenetic tree topology is scale-invariant due to a singularity arising from large niche construction fluctuations that follow extinction events. The same model recapitulates the bursty pattern of diversification in time. These results show how dynamical scaling laws of phylogenetic trees on long timescales can reflect the indelible imprint of the interplay between ecological and evolutionary processes.

Deep learning on butterfly phenotypes tests evolution's oldest mathematical model

Traditional anatomical analyses captured only a fraction of real phenomic information. Here, we apply deep learning to quantify total phenotypic similarity across 2468 butterfly photographs, covering 38 subspecies from the polymorphic mimicry complex of Heliconius erato and Heliconius melpomene. Euclidean phenotypic distances, calculated using a deep convolutional triplet network, demonstrate significant convergence between interspecies co-mimics. This quantitatively validates a key prediction of Müllerian mimicry theory, evolutionary biology's oldest mathematical model. Phenotypic neighbor-joining trees are significantly correlated with wing pattern gene phylogenies, demonstrating objective, phylogenetically informative phenome capture. Comparative analyses indicate frequency-dependent mutual convergence with coevolutionary exchange of wing pattern features. Therefore, phenotypic analysis supports reciprocal coevolution, predicted by classical mimicry theory but since disputed, and reveals mutual convergence as an intrinsic generator for the unexpected diversity of Müllerian mimicry. This demonstrates that deep learning can generate phenomic spatial embeddings, which enable quantitative tests of evolutionary hypotheses previously only testable subjectively.

Microbial community structure of a freshwater system receiving wastewater effluent

Despite our dependency on treatment facilities to condition wastewater for eventual release to the environment, our knowledge regarding the effects of treated water on the local watershed is extremely limited. Responses of lotic systems to the treated wastewater effluent have been traditionally investigated by examining the benthic macroinvertebrate assemblages and community structure; however, these studies do not address the microbial diversity of the water systems. In the present study, planktonic and benthic bacterial community structure were examined at 14 sites (from 60 m upstream to 12,100 m downstream) and at two time points along an aquatic system receiving treated effluent from the Charleston Wastewater Treatment Plant (Charleston, IL). Total bacterial DNA was isolated and 16S rRNA sequences were analyzed using a metagenomics platform. The community structure in planktonic bacterial communities was significantly correlated with dissolved oxygen concentration. Benthic bacterial communities were not correlated with water quality but did have a significant geographic structuring. A local restructuring effect was observed in both planktonic and benthic communities near the treated wastewater effluent, which was characterized by an increase in abundance of sphingobacteria. Sites further downstream from the wastewater facility appeared to be less influenced by the effluent. Overall, the present study demonstrated the utility of targeted high-throughput sequencing as a tool to assess the effects of treated wastewater effluent on a receiving water system, and highlighted the potential for this technology to be used for routine monitoring by wastewater facilities.

Liquid but durable: molecular dynamics simulations explain the unique properties of archaeal-like membranes

Archaeal plasma membranes appear to be extremely durable and almost impermeable to water and ions, in contrast to the membranes of Bacteria and Eucaryota. Additionally, they remain liquid within a temperature range of 0-100°C. These are the properties that have most likely determined the evolutionary fate of Archaea, and it may be possible for bionanotechnology to adopt these from nature. In this work, we use molecular dynamics simulations to assess at the atomistic level the structure and dynamics of a series of model archaeal membranes with lipids that have tetraether chemical nature and "branched" hydrophobic tails. We conclude that the branched structure defines dense packing and low water permeability of archaeal-like membranes, while at the same time ensuring a liquid-crystalline state, which is vital for living cells. This makes tetraether lipid systems promising in bionanotechnology and material science, namely for design of new and unique membrane nanosystems.