Biology subjects

Tchourine, K.

Publications and source records attributed to Tchourine, K..

8 recordsLinked to original sources

bioRxiv2025

Macroecological Laws Can Naturally Arise from Chaotic Internal Species Dynamics

Macroecological relationships that connect various statistical descriptors of long-term and short-term species dynamics represent some of the most general laws in ecology and biology. These macroecological laws have been observed across diverse ecologies of plants and animals, and more recently, also in microbiota. Yet it remains unclear why strikingly similar macroecological relationships often arise in very different biological communities and various environmental contexts. Here, we investigated whether chaotic internal dynamics in spatially heterogeneous communities could underlie multiple macroecological relationships. Our analyses reveal that very general constraints on species interactions and spatial migration parameters can simultaneously lead to multiple macroecological laws found in microbial ecosystems without requiring external sources of noise. Our study also identifies the mechanistic origins of many empirically observed macroecological relationships, such as Taylors law, anomalous abundance diffusion, the Laplace distribution of short-term abundance changes, and the distribution of species residence times. Overall, we demonstrate how macroecological laws can arise from interaction-driven chaotic dynamics and common ecological constraints, thereby providing a unifying explanation for their widespread prevalence in nature.

ecology↗

bioRxiv2024

Ecological Scaling of Temporal Fluctuations with Bacterial Abundance in Gut Microbiota Depends on Functional Properties of Individual Microbial Species and Bacterial Communities

Macroecological relationships that describe various statistical associations between species abundances, their spatial, and temporal variability are among the most general laws in ecology and biology. One of the most commonly observed relationships is a power-law scaling between means and variances of temporal species abundances, known in ecology as Taylors law. Taylors law has been observed across many ecosystems, from diverse plant and animal ecosystems to complex microbial communities. While many mathematical models have been proposed to explain the potential origins of Taylors law, what determines its scaling exponents across species and ecosystems is not understood. Here, we use temporal trajectories of human and baboon gut microbiota to analyze the relationship between functional properties of individual bacterial species and microbial communities with the scaling of species-specific and community-level Taylors law. The species Taylor law characterizes - for each individual species - the relationship between the species temporal abundance means and temporal abundance variances across host organisms. On the other hand, community-level Taylors law characterizes - in each host organism - the scaling across multiple species between their temporal abundance means and temporal abundance variances. For community Taylors law, we find that the power law scaling is strongly associated with the microbiota abundance of certain nutrient-degrading enzymes in the gut. Notably, our results demonstrate that the availability of enzymes metabolizing starch glycogen significantly increases Taylors law scaling. We also find that species Taylors law depends on the individual species functional properties. Specifically, we observe lower Taylors law scaling for species with larger metabolic networks, for species that are able to grow on a larger number of carbon sources, and for species with particular metabolic functions, such as glutamine and folate metabolism. Overall, our study reveals that Taylors law scaling is strongly associated with the functional capabilities of bacterial communities and individual microbial species biosynthetic properties, which are likely related to their ecological roles in the gut microbiota.

microbiology↗

bioRxiv2022

Cancer tissue of origin constrains the growth and metabolism of metastases

Metastases arise from a subset of cancer cells that disseminate from the primary tumor; however, the factors that contribute to proliferation of cancer cells in a secondary site are incompletely understood. The ability of cancer cells to thrive in a new tissue site is influenced by genetic and epigenetic changes that are important for disease initiation and progression, but these factors alone do not predict if and where cancers metastasize. Specific cancer types metastasize to consistent subsets of tissues, suggesting that factors associated with the primary tumor influence the tissue environments where cancers can grow. Using pancreatic cancer as a model, we find that primary and metastatic tumors share many metabolic similarities and that the tumor initiating capacity and proliferation of both primary- and metastasis-derived cells is favored in the primary site relative to the metastatic site. Moreover, propagating lung or liver metastatic cells in vivo to enrich for tumor cells adapted to grow in the lung or the liver does not enhance their relative ability to form large tumors in those sites, change their preference to grow in the primary site, nor stably alter some aspects of their metabolism relative to primary tumors. We also analyzed primary liver and lung cancer cells and find that these cells also exhibit a preference to grow in their primary site relative to metastatic sites. Together, these data suggest that the cancer tissue-of-origin influences the metabolism of both primary and metastatic tumors and may impact whether cancer cells can thrive in a metastatic site.

cancer biology↗

bioRxiv2021

Multiple macroecological laws do characterize various aspects of microbiota dynamics

In this letter we address the potential confusion related to our recent demonstration that multiple macroecological laws describe short- and long-term dynamics of microbial communities. Specifically, we clarify that these laws, similarly to many other relationships observed in nature, are characterized not just by the existence of scaling, but also by certain characteristic values of the scaling exponents. By performing proper statistical analysis, we demonstrate that the relationships sensitive to temporal bacterial dynamics are not reproduced in the shuffled data. We also discuss that there is no clear evidence in the data that macroecological relationships in microbiota are primarily driven by external or environmental factors. Proper statistical analyses of the data suggest that the dynamics of gut microbiota, even on a constant diet, contains rich temporal structure. Therefore, it is likely that complex and non-linear internal dynamics may be primarily responsible for the observed macroecological laws in microbiota and other ecological communities.

microbiology↗

bioRxiv2021

Single-cell gene regulatory network inference atscale: The Inferelator 3.0

MotivationGene regulatory networks define regulatory relationships between transcription factors and target genes within a biological system, and reconstructing them is essential for understanding cellular growth and function. Methods for inferring and reconstructing networks from genomics data have evolved rapidly over the last decade in response to advances in sequencing technology and machine learning. The scale of data collection has increased dramatically; the largest genome-wide gene expression datasets have grown from thousands of measurements to millions of single cells, and new technologies are on the horizon to increase to tens of millions of cells and above. ResultsIn this work, we present the Inferelator 3.0, which has been significantly updated to integrate data from distinct cell types to learn context-specific regulatory networks and aggregate them into a shared regulatory network, while retaining the functionality of the previous versions. The Inferelator is able to integrate the largest single-cell datasets and learn cell-type specific gene regulatory networks. Compared to other network inference methods, the Inferelator learns new and informative Saccharomyces cerevisiae networks from single-cell gene expression data, measured by recovery of a known gold standard. We demonstrate its scaling capabilities by learning networks for multiple distinct neuronal and glial cell types in the developing Mus musculus brain at E18 from a large (1.3 million) single-cell gene expression dataset with paired single-cell chromatin accessibility data. AvailabilityThe inferelator software is available on GitHub (https://github.com/flatironinstitute/inferelator) under the MIT license and has been released as python packages with associated documentation (https://inferelator.readthedocs.io/).

systems biology↗

bioRxiv2020

Cancer cells depend on environmental lipids for proliferation when electron acceptors are limited

It is not well understood how physiological environmental conditions and nutrient availability influence cancer cell proliferation. Production of oxidized biomass, which requires regeneration of the cofactor NAD+, can limit cancer cell proliferation1-5. However, it is currently unclear which specific metabolic processes are constrained by electron acceptor availability, and how they affect cell proliferation. Here, we use computational and experimental approaches to demonstrate that de novo lipid biosynthesis can impose an increased demand for NAD+ in proliferating cancer cells. While some cancer cells and tumors synthesize a substantial fraction of their lipids de novo6, we find that environmental lipids are crucial for proliferation in hypoxia or when the mitochondrial electron transport chain is inhibited. Surprisingly, we also find that even the reductive glutamine carboxylation pathway to produce fatty acids is impaired when cancer cells are limited for NAD+. Furthermore, gene expression analysis of 34 heterogeneous tumor types shows that lipid biosynthesis is strongly and consistently negatively correlated with hypoxia, whereas expression of genes involved in lipid uptake is positively correlated with hypoxia. These results demonstrate that electron acceptor availability and access to environmental lipids can play an important role in determining whether cancer cells engage in de novo lipogenesis to support proliferation.

cancer biology↗

bioRxiv2017

Integrative meta-analysis reveals that most yeast proteins are very stable

Measurements for protein half-lives in yeast Saccharomyces cerevisiae reported large discrepancies, with median values between minutes to several hours. We present a unifying analysis that provides a consistent half-life estimate, based on our re-analysis of three published and one new dataset of cells grown under similar conditions. We found that degradation of many proteins can be approximated by exponential decay. Protein disappearance was primarily driven by dilution due to cell division, with cell doubling times ranging from [~]2 to 3.5 hours across the four experiments. After adjusting for doubling time, protein half-lives increased to median values between [~]7.5 to [~]40 hours. Half-lives correlated with cell doubling time even after adjustment, implying that slow growth also slows protein degradation. All estimates were validated by multiple means and were robust to different analysis methods. Overall, protein stability correlated with abundance and showed weak enrichment for degradation signals such as degrons and disordered regions. Long-lived proteins often functioned in oxidation-reduction and amino acid synthesis. Short-lived proteins often functioned in ribosome biogenesis. Despite some overall differences in behavior, all methods were able to resolve subtle difference in half-lives of ribosomal proteins, e.g. the short lifespan of RPL10. Finally, our results help the design of future experiments: time series measurements need to cover at least two to three cell doubling times for accurate estimates, exponential decay provides a reasonable proxy for protein stability, and it can be sufficiently estimated with four measurement points.

systems biology↗

bioRxiv2017

Explicit Modeling of RNA Stability Improves Large-Scale Inference of Transcription Regulation

Inference of eukaryotic transcription regulatory networks remains challenging due to the large number of regu-lators, combinatorial interactions, and redundant pathways. Even in the model system Saccharomyces cerevisiae, inference has performed poorly. Most existing inference algorithms ignore crucial regulatory components, like RNA stability and post-transcriptional modulation of regulators. Here we demonstrate that explicitly modeling tran-scription factor activity and RNA half-lives during inference of a genome-wide transcription regulatory network in yeast not only advances prediction performance, but also produces new insights into gene-and condition-specific variation of RNA stability. We curated a high quality gold standard reference network that we use for priors on network structure and model validation. We incorporate variation of RNA half-lives into the Inferelator inference framework, and show improved performance over previously described algorithms and over implementations of the algorithm that do not model RNA degradation. We recapitulate known condition-and gene-specific trends in RNA half-lives, and make new predictions about RNA half-lives that are confirmed by experimental data.

biophysics↗