The cellular basis of feeding-dependent body size plasticity in sea anemones.

Many animals share a lifelong capacity to adapt their growth rates and body sizes to changing environmental food supplies. However, the cellular and molecular basis underlying this plasticity remains only poorly understood. We therefore studied how the sea anemones Nematostella vectensis and Aiptasia (Exaiptasia pallida) respond to feeding and starvation. Combining quantifications of body size and cell numbers with mathematical modelling, we observed that growth and shrinkage rates in Nematostella are exponential, stereotypic and accompanied by dramatic changes in cell numbers. Notably, shrinkage rates, but not growth rates, are independent of body size. In the facultatively symbiotic Aiptasia, we show that growth and cell proliferation rates are dependent on the symbiotic state. On a cellular level, we found that >7% of all cells in Nematostella juveniles reversibly shift between S/G2/M and G1/G0 cell cycle phases when fed or starved, respectively. Furthermore, we demonstrate that polyp growth and cell proliferation are dependent on TOR signalling during feeding. Altogether, we provide a benchmark and resource for further investigating the nutritional regulation of body plasticity on multiple scales using the genetic toolkit available for Nematostella.

The Nitroplast and Its Relatives Support a Universal Model of Features Predicting Gene Retention in Endosymbiont and Organelle Genomes.

Endosymbiotic relationships have shaped eukaryotic life. As endosymbionts coevolve with their host, toward full integration as organelles, their genomes tend to shrink, with genes being completely lost or transferred to the host nucleus. Modern endosymbionts and organelles show diverse patterns of gene retention, and why some genes and not others are retained in these genomes is not fully understood. Recent bioinformatic study has explored hypothesized influences on these evolutionary processes, finding that hydrophobicity and amino acid chemistry predict patterns of gene retention, both in organelles across eukaryotes and in less mature endosymbiotic relationships. The exciting ongoing elucidation of endosymbiotic relationships affords an independent set of instances to test this theory. Here, we compare the properties of retained genes in the nitroplast, recently reported to be an integrated organelle, two related cyanobacterial endosymbionts that form "spheroid bodies" in their host cells, and a range of other endosymbionts, with free-living relatives of each. We find that in each case, the symbiont's genome encodes proteins with higher hydrophobicity and lower amino pKa than their free-living relative, supporting the data-derived model predicting the retention propensity of genes across endosymbiont and organelle genomes.

Connecting Species-Specific Extents of Genome Reduction in Mitochondria and Plastids.

Mitochondria and plastids have both dramatically reduced their genomes since the endosymbiotic events that created them. The similarities and differences in the evolution of the two organelle genome types have been the target of discussion and investigation for decades. Ongoing work has suggested that similar mechanisms may modulate the reductive evolution of the two organelles in a given species, but quantitative data and statistical analyses exploring this picture remain limited outside of some specific cases like parasitism. Here, we use cross-eukaryote organelle genome data to explore evidence for coevolution of mitochondrial and plastid genome reduction. Controlling for differences between clades and pseudoreplication due to relatedness, we find that extents of mtDNA and ptDNA gene retention are related to each other across taxa, in a generally positive correlation that appears to differ quantitatively across eukaryotes, for example, between algal and nonalgal species. We find limited evidence for coevolution of specific mtDNA and ptDNA gene pairs, suggesting that the similarities between the two organelle types may be due mainly to independent responses to consistent evolutionary drivers.

Nucleoside supplements as treatments for mitochondrial DNA depletion syndrome.

Introduction: In mitochondrial DNA (mtDNA) depletion syndrome (MDS), patients cannot maintain sufficient mtDNA for their energy needs. MDS presentations range from infantile encephalopathy with hepatopathy (Alpers syndrome) to adult chronic progressive external ophthalmoplegia. Most are caused by nucleotide imbalance or by defects in the mtDNA replisome. There is currently no curative treatment available. Nucleoside therapy is a promising experimental treatment for TK2 deficiency, where patients are supplemented with exogenous deoxypyrimidines. We aimed to explore the benefits of nucleoside supplementation in POLG and TWNK deficient fibroblasts. Methods: We used high-content fluorescence microscopy with software-based image analysis to assay mtDNA content and membrane potential quantitatively, using vital dyes PicoGreen and MitoTracker Red CMXRos respectively. We tested the effect of 15 combinations (A, T, G, C, AT, AC, AG, CT, CG, GT, ATC, ATG, AGC, TGC, ATGC) of deoxynucleoside supplements on mtDNA content of fibroblasts derived from four patients with MDS (POLG1, POLG2, DGUOK, TWNK) in both a replicating (10% dialysed FCS) and quiescent (0.1% dialysed FCS) state. We used qPCR to measure mtDNA content of supplemented and non-supplemented fibroblasts following mtDNA depletion using 20 µM ddC and after 14- and 21-day recovery in a quiescent state. Results: Nucleoside treatments at 200 µM that significantly increased mtDNA content also significantly reduced the number of cells remaining in culture after 7 days of treatment, as well as mitochondrial membrane potential. These toxic effects were abolished by reducing the concentration of nucleosides to 50 µM. In POLG1 and TWNK cells the combination of ATGC treatment increased mtDNA content the most after 7 days in non-replicating cells. ATGC nucleoside combination significantly increased the rate of mtDNA recovery in quiescent POLG1 cells following mtDNA depletion by ddC. Conclusion: High-content imaging enabled us to link mtDNA copy number with key read-outs linked to patient wellbeing. Elevated G increased mtDNA copy number but severely impaired fibroblast growth, potentially by inhibiting purine synthesis and/or causing replication stress. Combinations of nucleosides ATGC, T, or TC, benefited growth of cells harbouring POLG mutations. These combinations, one of which reflects a commercially available preparation, could be explored further for treatment of POLG patients.

Ecological predictors of organelle genome evolution: Phylogenetic correlations with taxonomically broad, sparse, unsystematized data.

Comparative analysis of variables across phylogenetically linked observations can reveal mechanisms and insights in evolutionary biology. As the taxonomic breadth of the sample of interest increases, challenges of data sparsity, poor phylogenetic resolution, and complicated evolutionary dynamics emerge. Here, we investigate a cross-eukaryotic question where all these problems exist: which organismal ecology features are correlated with gene retention in mitochondrial and chloroplast DNA (organelle DNA or oDNA). Through a wide palette of synthetic control studies, we first characterize the specificity and sensitivity of a collection of parametric and non-parametric phylogenetic comparative approaches to identify relationships in the face of such sparse and awkward datasets. This analysis is not directly focused on oDNA, and so provides generalizable insights into comparative approaches with challenging data. We then combine and curate ecological data coupled to oDNA genome information across eukaryotes, including a new semi-automated approach for gathering data on organismal traits from less systematized open-access resources including encyclopedia articles on species and taxa. The curation process also involved resolving several issues with existing datasets, including enforcing clade-specificity of several ecological features and fixing incorrect annotations. Combining this unique dataset with our benchmarked comparative approaches, we confirm support for several known links between organismal ecology and organelle gene retention, identify several previously unidentified relationships constituting possible ecological contributors to oDNA genome evolution, and provide support for a recently hypothesized link between environmental demand and oDNA retention. We, with caution, discuss the implications of these findings for organelle evolution and of this pipeline for broad comparative analyses in other fields.

Stochastic organelle genome segregation through Arabidopsis development and reproduction.

Organelle DNA (oDNA) in mitochondria and plastids is vital for plant (and eukaryotic) life. Selection against damaged oDNA is mediated in part by segregation - sorting different oDNA types into different cells in the germline. Plants segregate oDNA very rapidly, with oDNA recombination protein MSH1 a key driver of this segregation, but we have limited knowledge of the dynamics of this segregation within plants and between generations. Here, we reveal how oDNA evolves through Arabidopsis thaliana development and reproduction. We combine stochastic modelling, Bayesian inference, and model selection with new and existing tissue-specific oDNA measurements from heteroplasmic Arabidopsis plant lines through development and between generations. Segregation proceeds gradually but continually during plant development, with a more rapid increase between inflorescence formation and the next generation. When MSH1 is compromised, the majority of observed segregation can be achieved through partitioning at cell divisions. When MSH1 is functional, mtDNA segregation is far more rapid; we show that increased oDNA gene conversion is a plausible mechanism quantitatively explaining this acceleration. These findings reveal the quantitative, time-dependent details of oDNA segregation in Arabidopsis. We also discuss the support for different models of the plant germline provided by these observations.

Collective mitochondrial dynamics resolve conflicting cellular tensions: From plants to general principles.

Mitochondria play diverse and essential roles in eukaryotic cells, and plants are no exception. Plant mitochondria have several differences from their metazoan and fungal cousins: they often exist in a fragmented state, move rapidly on actin rather than microtubules, have many plant-specific metabolic features and roles, and usually contain only a subset of the complete mtDNA genome, which itself undergoes frequent recombination. This arrangement means that exchange and complementation is essential for plant mitochondria, and recent work has begun to reveal how their collective dynamics and resultant "social networks" of encounters support this exchange, connecting plant mitochondria in time rather than in space. This review will argue that this social network perspective can be extended to a "societal network", where mitochondrial dynamics are an essential part of the interacting cellular society of organelles and biomolecules. Evidence is emerging that mitochondrial dynamics allow optimal resolutions to competing cellular priorities; we will survey this evidence and review potential future research directions, highlighting that plant mitochondria can help reveal and test principles that apply across other kingdoms of life. In parallel with this fundamental cell biology, we also highlight the translational "One Health" importance of plant mitochondrial behaviour - which is exploited in the production of a vast amount of crops consumed worldwide - and the potential for multi-objective optimisation to understand and rationally re-engineer the evolved resolutions to these tensions.

Stromule geometry allows optimal spatial regulation of organelle interactions in the quasi-2D cytoplasm.

In plant cells, plastids form elongated extensions called stromules, the regulation and purposes of which remain unclear. Here, we quantitatively explore how different stromule structures serve to enhance the ability of a plastid to interact with other organelles: increasing the effective space for interaction and biomolecular exchange between organelles. Interestingly, electron microscopy and confocal imaging showed that the cytoplasm in Arabidopsis thaliana and Nicotiana benthamiana epidermal cells is extremely thin (around 100 nm in regions without organelles), meaning that inter-organelle interactions effectively take place in 2D. We combine these imaging modalities with mathematical modelling and new in planta experiments to demonstrate how different stromule varieties (single or multiple, linear or branching) could be employed to optimise different aspects of inter-organelle interaction capacity in this 2D space. We found that stromule formation and branching provide a proportionally higher benefit to interaction capacity in 2D than in 3D. Additionally, this benefit depends on optimal plastid spacing. We hypothesize that cells can promote the formation of different stromule architectures in the quasi-2D cytoplasm to optimise their interaction interface to meet specific requirements. These results provide new insight into the mechanisms underlying the transition from low to high stromule numbers, the consequences for interaction with smaller organelles, how plastid access and plastid to nucleus signaling are balanced, as well as the impact of plastid density on organelle interaction.

Data science approaches provide a roadmap to understanding the role of abscisic acid in defence.

Abscisic acid (ABA) is a plant hormone well known to regulate abiotic stress responses. ABA is also recognised for its role in biotic defence, but there is currently a lack of consensus on whether it plays a positive or negative role. Here, we used supervised machine learning to analyse experimental observations on the defensive role of ABA to identify the most influential factors determining disease phenotypes. ABA concentration, plant age and pathogen lifestyle were identified as important modulators of defence behaviour in our computational predictions. We explored these predictions with new experiments in tomato, demonstrating that phenotypes after ABA treatment were indeed highly dependent on plant age and pathogen lifestyle. Integration of these new results into the statistical analysis refined the quantitative model of ABA influence, suggesting a framework for proposing and exploiting further research to make more progress on this complex question. Our approach provides a unifying road map to guide future studies involving the role of ABA in defence.

Avoiding misleading estimates using mtDNA heteroplasmy statistics to study bottleneck size and selection.

Mitochondrial DNA heteroplasmy samples can shed light on vital developmental and genetic processes shaping mitochondrial DNA populations. The sample means and sample variance of a set of heteroplasmy observations are typically used both to estimate bottleneck sizes and to perform fits to the theoretical "Kimura" distribution in seeking evidence for mitochondrial DNA selection. However, each of these applications raises problems. Sample statistics do not generally provide optimal fits to the Kimura distribution and so can give misleading results in hypothesis testing, including false positive signals of selection. Using sample variance can give misleading results for bottleneck size estimates, particularly for small samples. These issues can and do lead to false positive results for mitochondrial DNA mechanisms-all published experimental datasets we re-analyzed, reported as displaying departures from the Kimura model, do not in fact give evidence for such departures. Here we outline a maximum likelihood approach that is simple to implement computationally and addresses all of these issues. We advocate the use of maximum likelihood fits and explicit hypothesis tests, not fits and Kolmogorov-Smirnov tests via summary statistics, for ongoing work with mitochondrial DNA heteroplasmy.

Mitochondrial network structure controls cell-to-cell mtDNA variability generated by cell divisions.

Mitochondria are highly dynamic organelles, containing vital populations of mitochondrial DNA (mtDNA) distributed throughout the cell. Mitochondria form diverse physical structures in different cells, from cell-wide reticulated networks to fragmented individual organelles. These physical structures are known to influence the genetic makeup of mtDNA populations between cell divisions, but their influence on the inheritance of mtDNA at divisions remains less understood. Here, we use statistical and computational models of mtDNA content inside and outside the reticulated network to quantify how mitochondrial network structure can control the variances of inherited mtDNA copy number and mutant load. We assess the use of moment-based approximations to describe heteroplasmy variance and identify several cases where such an approach has shortcomings. We show that biased inclusion of one mtDNA type in the network can substantially increase heteroplasmy variance (acting as a genetic bottleneck), and controlled distribution of network mass and mtDNA through the cell can conversely reduce heteroplasmy variance below a binomial inheritance picture. Network structure also allows the generation of heteroplasmy variance while controlling copy number inheritance to sub-binomial levels, reconciling several observations from the experimental literature. Overall, different network structures and mtDNA arrangements within them can control the variances of key variables to suit a palette of different inheritance priorities.

Cellular and environmental dynamics influence species-specific extents of organelle gene retention.

Mitochondria and plastids rely on many nuclear-encoded genes, but retain small subsets of the genes they need to function in their own organelle DNA (oDNA). Different species retain different numbers of oDNA genes, and the reasons for these differences are not completely understood. Here, we use a mathematical model to explore the hypothesis that the energetic demands imposed by an organism's changing environment influence how many oDNA genes it retains. The model couples the physical biology of cell processes of gene expression and transport to a supply-and-demand model for the environmental dynamics to which an organism is exposed. The trade-off between fulfilling metabolic and bioenergetic environmental demands, and retaining genetic integrity, is quantified for a generic gene encoded either in oDNA or in nuclear DNA. Species in environments with high-amplitude, intermediate-frequency oscillations are predicted to retain the most organelle genes, whereas those in less dynamic or noisy environments the fewest. We discuss support for, and insight from, these predictions with oDNA data across eukaryotic taxa, including high oDNA gene counts in sessile organisms exposed to day-night and intertidal oscillations (including plants and algae) and low counts in parasites and fungi.

HyperHMM: efficient inference of evolutionary and progressive dynamics on hypercubic transition graphs.

MOTIVATION: The evolution of bacterial drug resistance and other features in biology, the progression of cancer and other diseases and a wide range of broader questions can often be viewed as the sequential stochastic acquisition of binary traits (e.g. genetic changes, symptoms or characters). Using potentially noisy or incomplete data to learn the sequences by which such traits are acquired is a problem of general interest. The problem is complicated for large numbers of traits, which may, individually or synergistically, influence the probability of further acquisitions both positively and negatively. Hypercubic inference approaches, based on hidden Markov models on a hypercubic transition network, address these complications, but previous Bayesian instances can consume substantial time for converged results, limiting their practical use. RESULTS: Here, we introduce HyperHMM, an adapted Baum-Welch (expectation-maximization) algorithm for hypercubic inference with resampling to quantify uncertainty, and show that it allows orders-of-magnitude faster inference while making few practical sacrifices compared to previous hypercubic inference approaches. We show that HyperHMM allows any combination of traits to exert arbitrary positive or negative influence on the acquisition of other traits, relaxing a common limitation of only independent trait influences. We apply this approach to synthetic and biological datasets and discuss its more general application in learning evolutionary and progressive pathways. AVAILABILITY AND IMPLEMENTATION: Code for inference and visualization, and data for example cases, is freely available at https://github.com/StochasticBiology/hypercube-hmm. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Quantification and uncertainty of root growth stimulation by elevated CO2 in a mature temperate deciduous forest.

Increasing CO2 levels are a major global challenge, and the potential mitigation of anthropogenic CO2 emissions by natural carbon sinks remains poorly understood. The uptake of elevated CO2 (eCO2) by the terrestrial biosphere, and subsequent sequestration as biomass in ecosystems, remain hard to quantify in natural ecosystems. Here, we combine field observations of fine root stocks and flows, derived from belowground imaging and soil cores, with image analysis, stochastic modelling, and statistical inference, to elucidate belowground root dynamics in a mature temperate deciduous forest under free-air eCO2 to 150 ppm above ambient levels. eCO2 led to relatively faster root production (a peak volume fold change of 4.52 ± 0.44 eCO2 versus 2.58 ± 0.21 control), with increased root elongation relative to decay the likely causal mechanism for this acceleration. Physical analysis of 552 root systems from soil cores support this picture, with lengths and widths of fine roots significantly increasing under eCO2. Estimated fine root contributions to belowground net primary productivity increase under eCO2 (mean annual 204 ± 93 g dw m-2 yr-1 eCO2 versus 140 ± 60 g dw m-2 yr-1 control). This multi-faceted approach thus sheds quantitative light on the challenging characterisation of the eCO2 response of root biomass in mature temperate forests.

Organelle bottlenecks facilitate evolvability by traversing heteroplasmic fitness valleys.

Bioenergetic organelles-mitochondria and plastids-retain their own genomes (mtDNA and ptDNA), and these organelle DNA (oDNA) molecules are vital for eukaryotic life. Like all genomes, oDNA must be able to evolve to suit new environmental challenges. However, mixed oDNA populations in cells can challenge cellular bioenergetics, providing a penalty to the appearance and adaptation of new mutations. Here we show that organelle "bottlenecks," mechanisms increasing cell-to-cell oDNA variability during development, can overcome this mixture penalty and facilitate the adaptation of beneficial mutations. We show that oDNA heteroplasmy and bottlenecks naturally emerge in evolutionary simulations subjected to fluctuating environments, demonstrating that this evolvability is itself evolvable. Usually thought of as a mechanism to clear damaging mutations, organelle bottlenecks therefore also resolve the tension between intracellular selection for pure cellular oDNA populations and the "bet-hedging" need for evolvability and adaptation to new environments. This general theory suggests a reason for the maintenance of organelle heteroplasmy in cells, and may explain some of the observed diversity in organelle maintenance and inheritance across taxa.

Evolutionary inference across eukaryotes identifies universal features shaping organelle gene retention.

Mitochondria and plastids power complex life. Why some genes and not others are retained in their organelle DNA (oDNA) genomes remains a debated question. Here, we attempt to identify the properties of genes and associated underlying mechanisms that determine oDNA retention. We harness over 15k oDNA sequences and over 300 whole genome sequences across eukaryotes with tools from structural biology, bioinformatics, machine learning, and Bayesian model selection. Previously hypothesized features, including the hydrophobicity of a protein product, and less well-known features, including binding energy centrality within a protein complex, predict oDNA retention across eukaryotes, with additional influences of nucleic acid and amino acid biochemistry. Notably, the same features predict retention in both organelles, and retention models learned from one organelle type quantitatively predict retention in the other, supporting the universality of these features-which also distinguish gene profiles in more recent, independent endosymbiotic relationships. A record of this paper's transparent peer review process is included in the supplemental information.

Sorting of mitochondrial and plastid heteroplasmy in Arabidopsis is extremely rapid and depends on MSH1 activity.

The fate of new mitochondrial and plastid mutations depends on their ability to persist and spread among the numerous organellar genome copies within a cell (heteroplasmy). The extent to which heteroplasmies are transmitted across generations or eliminated through genetic bottlenecks is not well understood in plants, in part because their low mutation rates make these variants so infrequent. Disruption of MutS Homolog 1 (MSH1), a gene involved in plant organellar DNA repair, results in numerous de novo point mutations, which we used to quantitatively track the inheritance of single nucleotide variants in mitochondrial and plastid genomes in Arabidopsis. We found that heteroplasmic sorting (the fixation or loss of a variant) was rapid for both organelles, greatly exceeding rates observed in animals. In msh1 mutants, plastid variants sorted faster than those in mitochondria and were typically fixed or lost within a single generation. Effective transmission bottleneck sizes (N) for plastids and mitochondria were N ∼ 1 and 4, respectively. Restoring MSH1 function further increased the rate of heteroplasmic sorting in mitochondria (N ∼ 1.3), potentially because of its hypothesized role in promoting gene conversion as a mechanism of DNA repair, which is expected to homogenize genome copies within a cell. Heteroplasmic sorting also favored GC base pairs. Therefore, recombinational repair and gene conversion in plant organellar genomes can potentially accelerate the elimination of heteroplasmies and bias the outcome of this sorting process.

Altered collective mitochondrial dynamics in the Arabidopsis msh1 mutant compromising organelle DNA maintenance.

Mitochondria form highly dynamic populations in the cells of plants (and almost all eukaryotes). The characteristics and benefits of this collective behaviour, and how it is influenced by nuclear features, remain to be fully elucidated. Here, we use a recently developed quantitative approach to reveal and analyse the physical and collective 'social' dynamics of mitochondria in an Arabidopsis msh1 mutant where the organelle DNA maintenance machinery is compromised. We use a newly created line combining the msh1 mutant with mitochondrially targeted green fluorescent protein (GFP), and characterize mitochondrial dynamics with a combination of single-cell time-lapse microscopy, computational tracking, and network analysis. The collective physical behaviour of msh1 mitochondria is altered from that of the wild type in several ways: mitochondria become less evenly spread, and networks of inter-mitochondrial encounters become more connected, with greater potential efficiency for inter-organelle exchange-reflecting a potential compensatory mechanism for the genetic challenge to the mitochondrial DNA population, supporting more inter-organelle exchange. We find that these changes are similar to those observed in friendly, where mitochondrial dynamics are altered by a physical perturbation, suggesting that this shift to higher connectivity may reflect a general response to mitochondrial challenges, where physical dynamics of mitochondria may be altered to control the genetic structure of the mtDNA population.

Symmetry and simplicity spontaneously emerge from the algorithmic nature of evolution.

SignificanceWhy does evolution favor symmetric structures when they only represent a minute subset of all possible forms? Just as monkeys randomly typing into a computer language will preferentially produce outputs that can be generated by shorter algorithms, so the coding theorem from algorithmic information theory predicts that random mutations, when decoded by the process of development, preferentially produce phenotypes with shorter algorithmic descriptions. Since symmetric structures need less information to encode, they are much more likely to appear as potential variation. Combined with an arrival-of-the-frequent mechanism, this algorithmic bias predicts a much higher prevalence of low-complexity (high-symmetry) phenotypes than follows from natural selection alone and also explains patterns observed in protein complexes, RNA secondary structures, and a gene regulatory network.