Extensive cross-environment fitness variation lies along few axes of genetic variation in the model alga, Chlamydomonas reinhardtii

Genus-Wide Transcriptional Landscapes Reveal Correlated Gene Networks Underlying Microevolutionary Divergence in Diatoms

Marine microbes like diatoms make up the base of marine food webs and drive global nutrient cycles. Despite their key roles in ecology, biogeochemistry, and biotechnology, we have limited empirical data on how forces other than adaptation may drive diatom diversification, especially in the absence of environmental change. One key feature of diatom populations is frequent extreme reductions in population size, which can occur both in situ and ex situ as part of bloom-and-bust growth dynamics. This can drive divergence between closely related lineages, even in the absence of environmental differences. Here, we combine experimental evolution and transcriptome landscapes (t-scapes) to reveal repeated evolutionary divergence within several species of diatoms in a constant environment. We show that most of the transcriptional divergence can be captured on a reduced set of axes, and that repeatable evolution can occur along a single major axis of variation defined by core ortholog expression comprising common metabolic pathways. Previous work has associated specific transcriptional changes in gene networks with environmental factors. Here, we find that these same gene networks diverge in the absence of environmental change, suggesting these pathways may be central in generating phenotypic diversity as a result of both selective and random evolutionary forces. If this is the case, these genes and the functions they encode may represent universal axes of variation. Such axes that capture suites of interacting transcriptional changes during diversification improve our understanding of both global patterns in local adaptation and microdiversity, as well as evolutionary forces shaping algal cultivation.

Multivariate trait analysis reveals diatom plasticity constrained to a reduced set of biological axes

Trait-based approaches to phytoplankton ecology have gained traction in recent decades as phenotypic traits are incorporated into ecological and biogeochemical models. Here, we use high-throughput phenotyping to explore both intra- and interspecific constraints on trait combinations that are expressed in the cosmopolitan marine diatom genus Thalassiosira. We demonstrate that within Thalassiosira, phenotypic diversity cannot be predicted from genotypic diversity, and moreover, plasticity can create highly divergent phenotypes that are incongruent with taxonomic grouping. Significantly, multivariate phenotypes can be represented in reduced dimensional space using principal component analysis with 77.7% of the variance captured by two orthogonal axes, here termed a 'trait-scape'. Furthermore, this trait-scape can be recovered with a reduced set of traits. Plastic responses to the new environments expanded phenotypic trait values and the trait-scape, however, the overall pattern of response to the new environments was similar between strains and many trait correlations remained constant. These findings demonstrate that trait-scapes can be used to reveal common constraints on multi-trait plasticity in phytoplankton with divergent underlying phenotypes. Understanding how to integrate trait correlational constraints and trade-offs into theoretical frameworks like biogeochemical models will be critical to predict how microbial responses to environmental change will impact elemental cycling now and into the future.

A High-Throughput Assay for Quantifying Phenotypic Traits of Microalgae

High-throughput methods for phenotyping microalgae are in demand across a variety of research and commercial purposes. Many microalgae can be readily cultivated in multi-well plates for experimental studies which can reduce overall costs, while measuring traits from low volume samples can reduce handling. Here we develop a high-throughput quantitative phenotypic assay (QPA) that can be used to phenotype microalgae grown in multi-well plates. The QPA integrates 10 low-volume, relatively high-throughput trait measurements (growth rate, cell size, granularity, chlorophyll a, neutral lipid content, silicification, reactive oxygen species accumulation, and photophysiology parameters: ETRmax, Ik, and alpha) into one workflow. We demonstrate the utility of the QPA on Thalassiosira spp., a cosmopolitan marine diatom, phenotyping six strains in a standard nutrient rich environment (f/2 media) using the full 10-trait assay. The multivariate phenotypes of strains can be simplified into two dimensions using principal component analysis, generating a trait-scape. We determine that traits show a consistent pattern when grown in small volume compared to more typical large volumes. The QPA can thus be used for quantifying traits across different growth environments without requiring exhaustive large-scale culturing experiments, which facilitates experiments on trait plasticity. We confirm that this assay can be used to phenotype newly isolated diatom strains within 4 weeks of isolation. The QPA described here is highly amenable to customisation for other traits or unicellular taxa and provides a framework for designing high-throughput experiments. This method will have applications in experimental evolution, modelling, and for commercial applications where screening of phytoplankton traits is of high importance.

The evolution of trait correlations constrains phenotypic adaptation to high CO2 in a eukaryotic alga

Microbes form the base of food webs and drive biogeochemical cycling. Predicting the effects of microbial evolution on global elemental cycles remains a significant challenge due to the sheer number of interacting environmental and trait combinations. Here, we present an approach for integrating multivariate trait data into a predictive model of trait evolution. We investigated the outcome of thousands of possible adaptive walks parameterized using empirical evolution data from the alga Chlamydomonas exposed to high CO₂. We found that the direction of historical bias (existing trait correlations) influenced both the rate of adaptation and the evolved phenotypes (trait combinations). Critically, we use fitness landscapes derived directly from empirical trait values to capture known evolutionary phenomena. This work demonstrates that ecological models need to represent both changes in traits and changes in the correlation between traits in order to accurately capture phytoplankton evolution and predict future shifts in elemental cycling.

Hyper-Heuristic Capacitance Array Method for Multi-Metal Wear Debris Detection

Online detection of fatigued wear debris in the lubricants of aero-engines can provide warning of engine failure during flight, thus having great economic and social benefits. In this paper, we propose a capacitance array sensor and a hyper-heuristic partial differential equation (PDE) inversion method for detecting multiple micro-scale metal debris, combined with self-adaptive cellular genetic (SA-CGA) and morphological algorithms. Firstly, different from the traditional methods, which are limited in multi-induction-Dirac-boundary-inversion, a mathematical model with non-local boundary conditions is established. Furthermore, a hyper-heuristic method based on prior knowledge is also proposed to extract the wear character. Moreover, a 12-plate array circulating sensor and corresponding detection system are designed. The experimental results were compared with the optical microscopy. The results show that under the conditions of 1~3 wear debris with diameters of between 250–900 μm, the accuracy of the proposed method is 10–38% higher than those of the traditional methods. The recognition error of the wear debris counts decreases to 0.

The effect of sex on the repeatability of evolution in different environments

The adaptive function of sex has been extensively studied, while less consideration has been given to the potential downstream consequences of sex on evolution. Here, we investigate one such potential consequence, the effect of sex on the repeatability of evolution. By affecting the repeatability of evolution, sex could have important implications for biodiversity, and for our ability to make predictions about the outcome of environmental change. We allowed asexual and sexual populations of Chlamydomonas reinhardtii to evolve in novel environments and monitored both their change in fitness and variance in fitness after evolution. Sex affected the repeatability of evolution by changing the importance of the effect of selection, chance, and ancestral constraints on the outcome of the evolutionary process. In particular, the effects of sex were highly dependent on the initial genetic composition of the population and on the environment. Given the lack of a consistent effect of sex on repeatability across the environments used here, further studies to dissect in more detail the underlying reasons for these differences as well as studies in additional environments are required if we are to have a general understanding of the effects of sex on the repeatability of evolution.

Single-cell mass spectrometry reveals the importance of genetic diversity and plasticity for phenotypic variation in nitrogen-limited Chlamydomonas

Phenotypic variation is vital for microbial populations to survive environmental perturbations. Both genetic and non-genetic factors contribute to an organism's phenotypic variation and therefore its fitness. To investigate the correlation between genetic diversity and phenotypic variation, we applied our recently developed mass spectrometry method that allows for the simultaneous measurement of more than 25 different lipids and pigments with high throughput in the unicellular microalga Chlamydomonas reinhardtii. We monitored the impact of nitrogen limitation on a genetically diverse wild-type strain CC-1690 and two isoclonal isolates from CC-1690 named ANC3 and ANC5. Measuring molecular composition of thousands of single cells at different time points of the experiment allowed us to capture a dynamic picture of the phenotypic composition and adaptation of the populations over time. Although the genetically diverse population maintained phenotypic variation over the whole time course of the experiment, the isoclonal cultures showed higher synchronicity in their phenotypic response. Furthermore, the genetically diverse population showed equal or greater phenotypic variation over the whole time range in multidimensional trait space compared with isoclonal populations. However, along individual trait axes non-genetic variance was higher in isoclonal populations.