Biology subjects

Giannakis, K.

Publications and source records attributed to Giannakis, K..

8 recordsLinked to original sources

bioRxiv2024

Identifying parsimonious pathways of accumulation and convergent evolution from binary data

How stereotypical, and hence predictable, are evolutionary and accumulation dynamics? Here we consider processes - from genome evolution to cancer progression - involving the irreversible accumulation of binary features (characters), which can be modelled as Markov processes on a hypercubic transition network. We seek subgraphs of such networks that can generate a given set of paired before-after observations and minimize a topological cost function, involving criteria on out-branching which are interpretable in terms of biological parsimony. A transition network supporting a single, deterministic dynamic pathway is maximally simple and lowest cost, and branches (corresponding to possibly different next steps) increase cost, particularly if these branches are "deep", occurring at early stages in the dynamics. In this sense, the lowest-cost subgraph measures how stereotypical the evolutionary or accumulation process is, and also identifies good start points for likelihood-based inference. The problem is solvable in polynomial time for cross-sectional observations by building on an existing method due to Gutin, and we provide a polynomial-time estimate in the more general case of pairs of observed states. We use this approach to define a "stereotypy index" reflecting the extent of evolutionary predictability. We demonstrate use cases in the evolution of antimicrobial resistance, organelle genomes, and cancer progression, and provide a software implementation at https://github.com/StochasticBiology/hyperDAGs.

evolutionary biology↗

bioRxiv2024

HyperTraPS-CT: Inference and prediction for accumulation pathways with flexible data and model structures

Accumulation processes, where many potentially coupled features are acquired over time, occur throughout the sciences, from evolutionary biology to disease progression, and particularly in the study of cancer progression. Existing methods for learning the dynamics of such systems typically assume limited (often pairwise) relationships between feature subsets, cross-sectional or untimed observations, small feature sets, or discrete orderings of events. Here we introduce HyperTraPS-CT (Hypercubic Transition Path Sampling in Continuous Time) to compute posterior distributions on continuous-time dynamics of many, arbitrarily coupled, traits in unrestricted state spaces, accounting for uncertainty in observations and their timings. We demonstrate the capacity of HyperTraPS-CT to deal with cross-sectional, longitudinal, and phylogenetic data, which may have no, uncertain, or precisely specified sampling times. HyperTraPS-CT allows positive and negative interactions between arbitrary subsets of features (not limited to pairwise interactions), supporting Bayesian and maximum-likelihood inference approaches to identify these interactions, consequent pathways, and predictions of future and unobserved features. We also introduce a range of visualisations for the inferred outputs of these processes and demonstrate model selection and regularisation for feature interactions. We apply this approach to case studies on the accumulation of mutations in cancer progression and the acquisition of anti-microbial resistance genes in tuberculosis, demonstrating its flexibility and capacity to produce predictions aligned with applied priorities.

bioinformatics↗

bioRxiv2024

Comparing structure and dynamics of transition graphs by the symmetric difference metric over an edge-filtration

Transition graphs or transition diagrams, describing the rates and probabilities with which a system changes between discrete states, are common throughout the sciences. In many cases, parameterisations of transition graphs are inferred from different datasets, for example in the context of Markov or hidden Markov models. An important task for followup analysis is to find efficient and effective ways to compare transition graphs with different parameterisations. Here, we introduce the Weight-Filtration Comparison Curve (WFCC), an approach by which the differences between two or more parameterisations of a transition graph can be quantified and compared. Borrowing from topological data analysis, the WFCC allows graphs learned from different datasets and/or null models to be systematically compared, and differences in both the fine- and coarse-grained structure and dynamics of transition graphs to be quantitatively assessed. We demonstrate WFCC with simple illustrative cases and real-world cases of transition graphs inferred from global data on the evolution of antimicrobial resistance in different countries, showing how different inferred dynamics, and different levels of uncertainty, are reported by structural aspects of these comparison curves.

bioinformatics↗

bioRxiv2023

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 has 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 chloroplast genome reduction. Controlling for differences between clades and pseudoreplication due to relatedness, we find that mtDNA and ptDNA gene retention are related across taxa, in a generally positive correlation that appears to differ quantitatively across eukaryotes, for example, between algal and non-algal 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.

evolutionary biology↗

bioRxiv2023

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 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. We 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. 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.

evolutionary biology↗

bioRxiv2022

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

Mitochondrial DNA (mtDNA) heteroplasmy samples can shed light on vital developmental and genetic processes shaping mtDNA populations. The sample mean 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 mtDNA 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 mtDNA mechanisms - all published experimental datasets we re-analysed, 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 mtDNA heteroplasmy.

genetics↗

bioRxiv2021

Universal features shaping organelle gene retention

Mitochondria and plastids power complex life, and retain their own organelle DNA (oDNA) genomes, with highly reduced gene contents compared to their endosymbiont ancestors. Why some protein-coding genes are retained in oDNA and some lost remains a debated question. Here we harness over 15k oDNA sequences and over 300 whole genome sequences with tools from structural biology, bioinformatics, machine learning, and Bayesian model selection to reveal the properties of genes, and associated underlying mechanisms, that shape oDNA evolution. Striking symmetry exists between the two organelle types: gene retention patterns in both are predicted by the hydrophobicity of a protein product and its energetic centrality within its protein complex, with additional influences of nucleic acid and amino acid biochemistry. Remarkably, retention principles from one organelle type successfully and quantitatively predict retention in the other, supporting this universality; these principles also distinguish gene profiles in independent endosymbiotic relationships. The identification of these features shaping organelle gene retention both provides quantitative support for several existing evolutionary hypotheses, and suggests new biochemical and biophysical mechanisms influencing organelle genome evolution.

evolutionary biology↗

bioRxiv2021

Encounter networks from collective mitochondrial dynamics support the emergence of effective mtDNA genomes in plant cells

Mitochondria in plant cells form strikingly dynamic populations of largely individual organelles. Each mitochondrion contains on average less than a full copy of the mitochondrial DNA (mtDNA) genome. Here, we asked whether mitochondrial dynamics may allow individual mitochondria to collect a full copy of the mtDNA genome over time, by facilitating exchange between individuals. Akin to trade on a social network, exchange of mtDNA fragments across organelles may lead to the emergence of full effective genomes in individuals over time. We characterise the collective dynamics of mitochondria in Arabidopsis thaliana hypocotyl cells using a recent approach combining single-cell timelapse microscopy, video analysis, and network science. We then use a quantitative model to predict the capacity for the sharing and accumulation of genetic information through the networks of encounters between mitochondria. We find that biological encounter networks are strikingly well predisposed to support the collection of full genomes over time, outperforming a range of other networks generated from theory and simulation. Using results from the coupon collectors problem, we show that the upper tail of the degree distribution is a key determinant of an encounter networks performance at this task and discuss how features of mitochondrial dynamics observed in biology facilitate the emergence of full effective genomes.

plant biology↗