Biology subjects

Tikhonov, M.

Publications and source records attributed to Tikhonov, M..

14 recordsLinked to original sources

bioRxiv2026

Survivorship Bias Explains Age-Dependent Extinction in Fossil Genera

Fossil genera exhibit pronounced age-dependent extinction: older genera are more likely to survive extinction events than younger ones. Many biological mechanisms have been proposed that could make lineages progressively more resistant to extinction through time. However, the same pattern can also arise from survivorship bias: as more vulnerable genera are progressively eliminated, the surviving pool becomes increasingly enriched in robust lineages, even if the properties of individual lineages never change. Using the Sepkoski marine genus compendium, we construct a quantitative model based solely on survivorship bias and ask whether additional age-dependent changes are required to explain the fossil record. This model with a single fitting parameter quantitatively reproduces the full set of age-conditioned survival probabilities, together with the overall dependence of extinction risk on genus age and the genus lifetime distribution. Allowing extinction resistance to change systematically through time yields no measurable improvement, indicating that, at least at the aggregate statistical level, explicit age-dependent changes in lineage properties are not required to explain the observed patterns of extinction. These results show that our minimal one-parameter model provides a quantitative null model for age-dependent extinction, against which proposed biological mechanisms can now be tested.

paleontology↗

bioRxiv2025

Learning the mechanism of collective microbial function via random community-media pairing

In microbial systems, many biochemical functions arise from pathways encoded and executed at the community level. The collective nature of these functions complicates bottom-up efforts to determine each species contribution. Previous work has shown that regression over randomly sampled datasets of collective functions succeeds at predicting those functions. Building on this top-down idea, this paper asks whether regression can also reveal mechanistic insight into a cross-feeding relationship. For this, we propose extending the random sampling method to vary the growth environment, cultivating each community in the spent medium of another randomly constructed community. With a model-based analysis, we show that the new protocol extracts more mechanistic information, enabling assignment of species to the correct cross-feeding pathway steps and identification of species essential to the collective function, both achieved with simple LASSO regressions. More generally, our work illustrates that the utility of machine learning-based approaches can be greatly enhanced by a synergistic experimental design.

ecology↗

bioRxiv2024

Energy-ordered resource stratification as an agnostic signature of life

The search for extraterrestrial life hinges on identifying biosignatures, often focusing on gaseous metabolic byproducts as indicators. However, most such biosignatures require assuming specific metabolic processes. It is widely recognized that life on other planets may not resemble that of Earth, but identifying biosignatures "agnostic" to such assumptions has remained a challenge. Here, we propose a novel approach by considering the generic outcome of life: the formation of competing ecosystems. We use a minimal model to argue that the presence of ecosystem-level dynamics, characterized by ecological interactions and resource competition, may yield biosignatures independent of specific metabolic activities. Specifically, we propose the emergent stratification of chemical resources in order of decreasing energy content as a candidate new biosignature. While likely inaccessible to remote sensing, this signature could be relevant for sample return missions, or for detection of ancient signatures of life on Earth itself.

ecology↗

bioRxiv2024

Emergent predictability in microbial ecosystems

Microbial ecosystems exhibit a surprising amount of functionally relevant diversity at all levels of taxonomic resolution, presenting a significant challenge for most modeling frameworks. A long-standing hope of theoretical ecology is that some patterns might persist despite community complexity - or perhaps even emerge because of it. A deeper understanding of such "emergent simplicity" could enable new approaches for predicting the behaviors of the complex ecosystems in nature. However, the concept remains partly intuitive with no consistent definition, and most empirical examples described so far afford limited predictive power. Here, we propose an information-theoretic framework for defining and quantifying emergent simplicity in empirical data based on the ability of coarsened descriptions to predict community-level functional properties. Applying this framework to two published datasets, we demonstrate that all five properties measured across both experiments exhibit robust evidence of what we define as "emergent predictability": surprisingly, as community richness increases, simple compositional descriptions become more predictive. We show that standard theoretical models of high-diversity ecosystems fail to recapitulate this behavior. This is in contrast to simple self-averaging, which is well-understood and generic across models. We propose that, counterintuitively, emergent predictability arises when physiological or environmental feedbacks oppose statistical self-averaging along some axes of community variation. As a result, these axes of variation become increasingly predictive of community function at high richness. We demonstrate this mechanism in a minimal model, and argue that explaining and leveraging emergent predictability will require integrating large-N theoretical models with a minimal notion of physiology, which the dominant modeling frameworks currently omit.

ecology↗

bioRxiv2024

Functional phases encode the response of the soil microbiome to environmental change

The metabolic activity of soil microbiomes plays a central role in carbon and nitrogen cycling. Given the changing climate, it is important to understand how the metabolism of natural communities responds to environmental change. However, the ecological, spatial, and chemical complexity of soils makes understanding the mechanisms governing the response of these communities to perturbations challenging. Here, we overcome this complexity by using dynamic measurements of metabolism in microcosms and modeling to reveal regimes where a few key mechanisms govern the response of soils to environmental change. We sample soils along a natural pH gradient, construct >1500 microcosms to perturb the pH, and quantify the dynamics of respiratory nitrate utilization, a key process in the nitrogen cycle. Despite the complexity of the soil microbiome, a minimal mathematical model with two variables, the quantity of active biomass in the community and the availability of a growth-limiting nutrient, quantifies observed nitrate utilization dynamics across soils and pH perturbations. Across environmental perturbations, changes in these two variables give rise to three functional regimes each with qualitatively distinct dynamics of nitrate utilization over time: a regime where acidic perturbations induce cell death that limits metabolic activity, a nutrientlimiting regime where nitrate uptake is performed by dominant taxa that utilize nutrients released from the soil matrix, and a resurgent growth regime in basic conditions, where excess nutrients enable growth of initially rare taxa. The underlying mechanism of each regime is predicted by our interpretable model and tested via amendment experiments, nutrient measurements, and sequencing. Further, our data suggest that the long-term history of environmental variation in the wild influences the transitions between functional regimes. Therefore, quantitative measurements and a mathematical model reveal the existence of qualitative regimes that capture the mechanisms and dynamics of a community responding to environmental change.

ecology↗

bioRxiv2024

Linear-regression-based algorithms can succeed at identifying microbial functional groups despite the nonlinearity of ecological function

Microbial communities play key roles across diverse environments. Predicting their function and dynamics is a key goal of microbial ecology, but detailed microscopic descriptions of these systems can be prohibitively complex. One approach to deal with this complexity is to resort to coarser representations. Several approaches have sought to identify useful groupings of microbial species in a data-driven way. Of these, recent work has claimed some empirical success at de novo discovery of coarse representations predictive of a given function using methods as simple as a linear regression, against multiple groups of species or even a single such group (the EQO approach of Shan et al. [25]). This success seems puzzling, since modeling community function as a linear combination of contributions of individual species appears simplistic. However, the task of identifying a predictive coarsening of an ecosystem is distinct from the task of predicting the function well, and it is conceivable that the former could be accomplished by a simpler methodology than the latter. Here, we use the resource competition framework to design a model where the "correct" grouping to be discovered is well-defined, and use synthetic data to evaluate and compare three regression-based methods, namely, two proposed previously and one we introduce. We find that regression-based methods can recover the groupings even when the function is manifestly nonlinear; that multi-group methods offer an advantage over a single-group EQO; and crucially, that simpler (linear) methods can outperform more complex ones. Author summaryNatural microbial communities are highly complex, making predictive modeling difficult. One appealing approach is to make their description less detailed, rendering modeling more tractable while hopefully still retaining some predictive power. The Tree of Life naturally provides one possible method for building coarser descriptions (instead of thousands of strains, we could think about hundreds of species; or dozens of families). However, it is known that useful descriptions need not be taxonomically coherent, as illustrated, for example, by the so-called functional guilds. This prompted the development of computational methods seeking to propose candidate groupings in a data-driven manner. In this computational study, we examine one class of such methods, recently proposed in the microbial context. Quantitatively testing their performance can be difficult, as the answer they "should" recover is often unknown. Here, we overcome this difficulty by testing these methods on synthetic data from a model where the ground truth is known by construction. Curiously, we demonstrate that simpler approaches, rather than suffering from this simplicity, can in fact be more robust.

ecology↗

bioRxiv2023

Improving the accuracy of bulk fitness assays by correcting barcode processing biases

Measuring the fitnesses of genetic variants is a fundamental objective in evolutionary biology. A standard approach for measuring microbial fitnesses in bulk involves labeling a library of genetic variants with unique sequence barcodes, competing the labeled strains in batch culture, and using deep sequencing to track changes in the barcode abundances over time. However, idiosyncratic properties of barcodes (e.g., GC content) can induce non-uniform amplification or uneven sequencing coverage that cause some barcodes to be over-or under-represented in samples. This systematic bias can result in erroneous read count trajectories and misestimates of fitness. Here we develop a computational method for inferring the effects of processing bias by leveraging the structure of systematic deviations in the data. We illustrate this approach by applying it to fitness assay data collected for a large library of yeast variants, and show that this method estimates and corrects for bias more accurately than standard proxies, such as GC-based corrections. Our method mitigates bias and improves fitness estimates in high-throughput assays with-out introducing additional complexity to the experimental protocols, with potential value in a range of experimental evolution and mutation screening contexts.

evolutionary biology↗

bioRxiv2023

Metabolomic rearrangement controls the intrinsic microbial response to temperature changes

Temperature is one of the key determinants of microbial behavior and survival, whose impact is typically studied under heat- or cold-shock conditions that elicit specific regulation to combat lethal stress. At intermediate temperatures, cellular growth rate varies according to the Arrhenius law of thermodynamics without stress responses, a behavior whose origins have not yet been elucidated. Using single-cell microscopy during temperature perturbations, we show that bacteria exhibit a highly conserved, gradual response to temperature upshifts with a time scale of [~]1.5 doublings at the higher temperature, regardless of initial/final temperature or nutrient source. We find that this behavior is coupled to a temperature memory, which we rule out as being neither transcriptional, translational, nor membrane dependent. Instead, we demonstrate that an autocatalytic enzyme network incorporating temperature-sensitive Michaelis-Menten kinetics recapitulates all temperature-shift dynamics through metabolome rearrangement, which encodes a temperature memory and successfully predicts alterations in the upshift response observed under simple-sugar, low-nutrient conditions, and in fungi. This model also provides a mechanistic framework for both Arrhenius-dependent growth and the classical Monod Equation through temperature-dependent metabolite flux.

cell biology↗

bioRxiv2023

Learning the functional landscape of microbial communities

Microbial consortia exhibit complex functional properties in contexts ranging from soils to bioreactors to human hosts. Understanding how community composition determines emergent function is a major goal of microbial ecology. Here we address this challenge using the concept of community-function landscapes - analogs to fitness landscapes - that capture how changes in community composition alter collective function. Using datasets that represent a broad set of community functions, from production/degradation of specific compounds to biomass generation, we show that statistically-inferred landscapes quantitatively predict community functions from knowledge of strain presence or absence. Crucially, community-function landscapes allow prediction without explicit knowledge of abundance dynamics or interactions between species, and can be accurately trained using measurements from a small subset of all possible community compositions. The success of our approach arises from the fact that empirical community-function landscapes are typically not rugged, meaning that they largely lack high-order epistatic contributions that would be difficult to fit with limited data. Finally, we show this observation is generic across many ecological models, suggesting community-function landscapes can be applied broadly across many contexts. Our results open the door to the rational design of consortia without detailed knowledge of abundance dynamics or interactions.

ecology↗

bioRxiv2023

Transcriptional read through interrupts boundary function in Drosophila

In higher eukaryotes enhancer-promoter interactions are known to be restricted by the chromatin insulators/boundaries that delimit topologically associated domains (TADs); however, there are instances in which enhancer-promoter interactions span one or more boundary elements/TADs. At present, the mechanisms that enable cross-TAD regulatory interaction are not known. In the studies reported here we have taken advantage of the well characterized Drosophila Bithorax complex (BX-C) to study one potential mechanism for controlling boundary function and TAD organization. The regulatory domains of BX-C are flanked by boundaries which function to block crosstalk with their neighboring domains and also to support long distance interactions between the regulatory domains and their target gene. As many lncRNAs have been found in BX-C, we asked whether transcriptional readthrough can impact boundary function. For this purpose, we took advantage of two BX-C boundary replacement platforms, Fab-7attP50 and F2attP, in which the Fab-7 and Fub boundaries, respectively, are deleted and replaced with an attP site. We introduced boundary elements, promoters and polyadenylation signals arranged in different combinations and then assayed for boundary function. Our results show that transcriptional readthrough can interfere with boundary activity. Since lncRNAs represent a significant fraction of Pol II transcripts in multicellular eukaryotes, it is possible that many of them may function in the regulation of TAD organization. Author SummaryRecent studies have shown that much genome in higher eukaryotes is transcribed into non-protein coding lncRNAs. It is though that lncRNAs may preform important regulatory functions, including the formation of protein complexes, organization of functional interactions between enhancers and promoters and the maintenance of open chromatin. Here we examined how transcription from promoters inserted into the Drosophila Bithorax complex can impact the boundaries that are responsible for establishing independent regulatory domains. Surprisingly, we found that even a relatively low level of transcriptional readthrough can impair boundary function. Transcription also affects the activity of enhancers located in BX-C regulatory domains. Taken together, our results raise the possibility that transcriptional readthrough may be a widely used mechanism to alter chromosome structure and regulate gene expression.

genetics↗

bioRxiv2019

Photo sensing and quorum sensing are integrated to control bacterial collective behaviors.

Pseudomonas aeruginosa transitions between the free-swimming state and the sessile biofilm mode during its pathogenic lifestyle. We show that quorum sensing represses P. aeruginosa biofilm formation and virulence by activating expression of genes encoding the KinB-AlgB two-component system. Phospho-AlgB represses biofilm and virulence genes, while KinB dephosphorylates, and thereby, inactivates AlgB. We discover that the photoreceptor BphP is the kinase that, in response to light, phosphorylates and activates AlgB. Indeed, exposing P. aeruginosa to light represses biofilm formation and virulence gene expression. To our knowledge, P. aeruginosa was not previously known to detect light. The KinB-AlgB-BphP module is present in all Pseudomonads, and we demonstrate that AlgB is the cognate response regulator for BphP in diverse bacterial phyla. We propose that KinB-AlgB-BphP constitutes a \"three-component\" system and AlgB is the node at which varied sensory information is integrated. This study sets the stage for light-mediated control of P. aeruginosa infectivity.

microbiology↗

bioRxiv2019

Modeling the interplay between plastic tradeoffs and evolution in changing environments

Performance tradeoffs are ubiquitous in both ecological and evolutionary modeling, yet are usually postulated and built into fitness and ecological landscapes. But tradeoffs depend on genetic background and evolutionary history, and can themselves evolve. We present a simple model capable of capturing the key feedback loop: evolutionary history shapes tradeoff strength, which, in turn, shapes evolutionary future. One consequence of this feedback is that genomes with identical fitness can have different evolutionary properties, shaped by prior environmental exposure. Another is that, generically, the best adaptations to one environment may evolve in another. Our minimal model highlights the need for analysis of simple models capable of incorporating explicit dependence on environment, and can serve as a rich playground for investigating evolution in multiple or changing environments.

evolutionary biology↗

bioRxiv2018

Cohesiveness in microbial community coalescence

Microbial invasions exhibit many unique properties; notably, entire microbial communities often invade one another, a phenomenon known as community coalescence. In spite of the potential importance of this process for the dynamics and stability of microbiome assembly, our understanding of it is still very limited. Recent theoretical and empirical work has proposed that large microbial communities may exhibit an emergent cohesiveness, as a result of collective consumer-resource interactions and metabolic feedbacks between microbial growth and the environment. A fundamental prediction of this proposal is the presence of ecological co-selection during community coalescence, where the invasion success of a given taxon is determined by its community members. To establish the generality of this prediction in experimental microbiomes, we have performed over one hundred invasion and coalescence experiments with environmental communities of different origins that had spontaneously and stably assembled in two different synthetic aerobic environments. We show that the dominant species of the coalesced communities can both recruit their community members (top-down co-selection) and be recruited by them (bottom-up co-selection) into the coalesced communities. Our results provide direct evidence that collective invasions generically produce ecological co-selection of interacting species, emphasizing the importance of community-level interactions during microbial community assembly.

ecology↗

bioRxiv2017

Emergent Simplicity in Microbial Community Assembly

Microbes assemble into complex, dynamic, and species-rich communities that play critical roles in human health and in the environment. The complexity of natural environments and the large number of niches present in most habitats are often invoked to explain the maintenance of microbial diversity in the presence of competitive exclusion. Here we show that soil and plant-associated microbiota, cultivated ex situ in minimal synthetic environments with a single supplied source of carbon, universally re-assemble into large and dynamically stable communities with strikingly predictable coarse-grained taxonomic and functional compositions. We find that generic, non-specific metabolic cross-feeding leads to the assembly of dense facilitation networks that enable the coexistence of multiple competitors for the supplied carbon source. The inclusion of universal and non-specific cross-feeding in ecological consumer-resource models is sufficient to explain our observations, and predicts a simple determinism in community structure, a property reflected in our experiments.

ecology↗