Biology subjects

Saint-Antoine, M. M.

Publications and source records attributed to Saint-Antoine, M. M..

9 recordsLinked to original sources

bioRxiv2025

Defining and detecting global transcriptional amplitude in circadian gene expression

Many genes exhibit circadian rhythms in expression. The amplitude of oscillation, both in core clock and circadian output genes, may differ from person to person. Mutations in core clock genes are known to alter global rhythmic properties, and researchers often informally discuss "circadian amplitude." Yet, it remains unclear whether, in the general population, differences in transcriptional amplitude are largely gene-specific, or if they reflect a global, transcriptome-wide pattern -- whether some individuals have globally higher or lower amplitude across the set of all rhythmic genes. We used Cosinor regression to reanalyze four human skin time-series transcriptomic datasets (paired epidermis/dermis samples, N=11, N=19) and found that, using either absolute or relative amplitude measures, distributions of gene amplitudes tended to cluster by subject. Using a non-parametric, permutation-based statistical test, we found that in many subjects this global amplitude trend was statistically significant (p [≤] 0.01). Furthermore, we found that when rhythmic genes were divided into two sets based on peak time (genes peaking before-noon and after-noon), the subjects global amplitude in one set predicted global amplitude in the other set (p [≤] 0.05). We also found that in the paired epidermis/dermis datasets, subjects global amplitude in epidermis predicted their global amplitude in the dermis (p [≤] 0.05). After identifying these trends in the skin datasets, we then found that evidence for subject-specific transcriptional rhythm strength replicated across 6 additional human time-course datasets from adipose, muscle, and blood. Perhaps surprisingly, across datasets, we found that neither established metrics of core clock transcriptional organization nor the amplitude of core clock transcription were strongly correlated with subject-specific global amplitude.

cell biology↗

bioRxiv2025

Comparative circadian transcriptome analysis reveals dampened and phase-advanced rhythms in sun-exposed human skin

BackgroundDaily molecular rhythms modulate skin physiology. However, the effects of chronic sun exposure on these rhythms remain unstudied. ObjectivesThis study aimed to identify and compare rhythmic genes and pathways in photoprotected and chronically photoexposed human skin in vivo. MethodsTwenty healthy White women, aged 51-63, with moderate-severe photoageing of the dorsal forearm were recruited. Skin biopsies (3mm) were taken from photoprotected (upper buttock) and photoexposed (dorsal forearm) skin of each individual at noon, 6PM, midnight, and 6AM, across a 24-hour cycle. Skin biopsies were analysed by RNA sequencing. Cosinor analysis identified cycling genes along with their amplitudes and peak expression phases. Nested models were used to identify genes that were differentially rhythmic between the photoprotected and photoexposed sites. Phase set and gene set analyses identified pathways enriched among rhythmic transcripts or altered between the two sites. ResultsIn the photoprotected buttock skin, 1546 genes (12%) met the criteria for cycling. In photoexposed forearm skin, the number was reduced to 959 (8%). As a group, transcripts that cycled in both sites had overall higher amplitude in photoprotected skin (p < 2.2e-16). Peak expression times for these transcripts showed a pronounced bimodal distribution and were clustered in the early morning and mid-afternoon. Distributions of peak times were significantly different between photoexposed and photoprotected skin (p < 0.00025), with peak times advanced in photoexposed skin. We identified 480 genes with significantly different rhythmic properties between the skin sites. Genes involved in DNA repair, MYC targets, E2F and G2M checkpoint pathways were enriched among those that showed higher amplitude oscillations in photoprotected skin. Genes involved in epithelial mesenchymal transition and apical junction pathways showed higher amplitude oscillations in photoexposed skin. ConclusionsTemporal rhythms have a marked influence on skin molecular physiology and are altered in photoaged skin. Temporally advanced cycling patterns and a reduced number of rhythmic genes in photoexposed as compared to photoprotected skin suggest that chronic UV exposure may disrupt and/or reprogram circadian output rhythms to further alter skin physiology.

cell biology↗

bioRxiv2024

The transcription factor ATML1 maintains giant cell identity by inducing synthesis of its own (very) long-chain fatty acid-containing ligands

During development, cells not only adopt specialized identities but also maintain those identities. Endoreduplication is thought to maintain cell identity. High concentrations of ARABIDOPSIS THALIANA MERISTEM LAYER1 (ATML1) specify giant cell identity and induce endoreduplication in sepals. How different concentrations of ATML1 can specify different identities remains unclear. Here, we show that high concentrations of ATML1 induce the biosynthesis of both long-chain and very long-chain fatty acids (LCFAs/VLCFAs), and these fatty acids are required for the maintenance of giant cell identity. Inhibition of VLCFA biosynthesis causes endoreduplicated giant cells to resume division and lose their identity, indicating that endoreduplication is not sufficient to maintain cell identity. Structural predictions suggest that LCFA-containing lipids bind to the START domain 2 of ATML1, causing ATML1 dimerization and its auto-activation. Our data and modeling imply that ATML1 induces biosynthesis of its own lipid ligands in a positive feedback loop, shedding light on the intricate network dynamics that specify and maintain giant cell identity. Teaser: Endoreduplicated cells in Arabidopsis thaliana sepals divide and de-differentiate in the absence of VLCFA biosynthesis.

plant biology↗

bioRxiv2023

Benchmarking Gene Regulatory Network Inference Methods on Simulated and Experimental Data

Although the challenge of gene regulatory network inference has been studied for more than a decade, it is still unclear how well network inference methods work when applied to real data. Attempts to benchmark these methods on experimental data have yielded mixed results, in which sometimes even the best methods fail to outperform random guessing, and in other cases they perform reasonably well. So, one of the most valuable contributions one can currently make to the field of network inference is to benchmark methods on experimental data for which the true underlying network is already known, and report the results so that we can get a clearer picture of their efficacy. In this paper, we report results from the first, to our knowledge, benchmarking of network inference methods on single cell E. coli transcriptomic data. We report a moderate level of accuracy for the methods, better than random chance but still far from perfect. We also find that some methods that were quite strong and accurate on microarray and bulk RNA-seq data did not perform as well on the single cell data. Additionally, we benchmark a simple network inference method (Pearson correlation), on data generated through computer simulations in order to draw conclusions about general best practices in network inference studies. We predict that network inference would be more accurate using proteomic data rather than transcriptomic data, which could become relevant if highthroughput proteomic experimental methods are developed in the future. We also show through simulations that using a simplified model of gene expression that skips the mRNA step tends to substantially overestimate the accuracy of network inference methods, and advise against using this model for future in silico benchmarking studies.

systems biology↗

bioRxiv2023

Limits on Inferring Gene Regulatory Networks Subjected to Different Noise Mechanisms

One of the most difficult and pressing problems in computational cell biology is the inference of gene regulatory network structure from transcriptomic data. Benchmarking network inference methods on model organism datasets has yielded mixed results, in which the methods sometimes perform reasonably well and other times fail to outperform random guessing. In this paper, we analyze the feasibility of network inference under different noise conditions using stochastic simulations. We show that gene regulatory interactions with extrinsic noise appear to be more amenable to inference than those with only intrinsic noise, especially when the extrinsic noise causes the system to switch between distinct expression states. Furthermore, we analyze the problem of false positives between genes that have no direct interaction but share a common upstream regulator, and explore a strategy for distinguishing between these false positives and true interactions based on noise profiles of mRNA expression levels. Lastly, we derive mathematical formulas for the mRNA noise levels and correlation using moment analysis techniques, and show how these levels change as the mean mRNA expression level changes.

systems biology↗

bioRxiv2022

Disrupting cellular memory to overcome drug resistance

Plasticity enables cells to change their gene expression state in the absence of a genetic change. At the single-cell level, these gene expression states can persist for different lengths of time which is a quantitative measurement referred to as gene expression memory. Because plasticity is not encoded by genetic changes, these cell states can be reversible, and therefore, are amenable to modulation by disrupting gene expression memory. However, we currently do not have robust methods to find the regulators of memory or to track state switching in plastic cell populations. Here, we developed a lineage tracing-based technique to quantify gene expression memory and to identify single cells as they undergo cell state transitions. Applied to human melanoma cells, we quantified long-lived fluctuations in gene expression that underlie resistance to targeted therapy. Further, we identified the PI3K and TGF-{beta} pathways as modulators of these state dynamics. Applying the gene expression signatures derived from this technique, we find that these expression states are generalizable to in vivo models and present in scRNA-seq from patient tumors. Leveraging the PI3K and TGF-{beta} pathways as dials on memory between plastic states, we propose a " pretreatment" model in which we first use a PI3K inhibitor to modulate the expression states of the cell population and then apply targeted therapy. This plasticity informed dosing scheme ultimately yields fewer resistant colonies than targeted therapy alone. Taken together, we describe a technique to find modulators of gene expression memory and then apply this knowledge to alter plastic cell states and their connected cell fates.

systems biology↗

bioRxiv2022

A fluctuation-based approach to infer kinetics and topology of cell-state switching

In the noisy cellular environment, RNAs and proteins are subject to considerable stochastic fluctuations in copy numbers over time. As a consequence, single cells within the same isoclonal population can differ in their expression profile and reside in different phenotypic states. The dynamic nature of this intercellular variation, where individual cells can transition between different states over time makes it a particularly hard phenomenon to characterize. Here we propose a novel fluctuation-test approach to infer the kinetics of transitions between cell states. More specifically, single cells are randomly drawn from the population and grown into cell colonies. After growth for a fixed number of generations, the number of cells residing in different states is assayed for each colony. In a simple system with reversible switching between two cell states, our analysis shows that the extent of colony-to-colony fluctuations in the fraction of cells in a given state is monotonically related to the switching kinetics. Several closed-form formulas for inferring the switching rates from experimentally quantified fluctuations are presented. We further extend this approach to multiple cell states where harnessing fluctuation signatures can reveal both the topology and the rates of cell-state switching. In summary, our analysis provides a powerful approach for dissecting cell-state transitions based on a single time point measurement. This is especially important for scenarios where a measurement involves killing the cell (for example, performing single-cell RNA-seq or assaying whether a microbial/cancer cell is in a drug-sensitive or drug-tolerant state), and hence the state of the same cell cannot be measured at different time points.

systems biology↗

bioRxiv2022

Moment-Based Estimation of State-Switching Rates in Cell Populations

In isogenic cell populations, cells can switch back and forth between different gene expression states. These expression states can be biologically relevant. For example, a certain expression state may cause a tumor cell to be resistant to treatment, while another state may leave it vulnerable to treatment. However, estimating the rates of state-switching can be difficult, because experimentally measuring a cells transcriptome often involves destroying the cell, so it can only be measured once. In this paper, we propose a computational method to estimate the rate of switching between expression states, given data from a Luria-Delbruck style fluctuation test that is experimentally simple and feasible. We then benchmark this method using simulated data to test its efficacy, with varying assumptions made about cell cycle timing distribution in the simulations.

cell biology↗

bioRxiv2020

A Simple and Efficient Pipeline for Construction, Merging, Expansion, and Simulation of Large-Scale, Single-Cell Mechanistic Models

Mechanistic models of how single cells respond to different perturbagens can help integrate disparate big data sets or predict response to varied drug combinations. However, the construction and simulation of such models have proved challenging. Our lab previously constructed one of the largest mechanistic models for single mammalian cell regulation of proliferation and death (774 species, 141 genes, 8 ligands, 2400 reactions). However, this, as many other large-scale models, was written using licensed software (MATLAB) with intricate programming structure, impeding alteration, expansion, and sharing. Here, we generated a new foundation for this model, which includes a python-based creation and simulation pipeline converting a few structured text files into an SBML-compatible format. This new open-source model (named SPARCED) is high-performance- and cloud-computing compatible and enables the study of virtual cell population responses at the single-cell level. We applied this new model to a subset of the LINCS MCF10A Data Cube, which observed that IFN{gamma} acts as an anti-proliferative factor, but the reasons why were unknown. After expanding the SPARCED model with an IFN{gamma} signaling module (to 950 species, 150 genes, 9 ligands, 2500 reactions), we ran stochastic single-cell simulations for two different putative crosstalk mechanisms and looked at the number of cycling cells in each case. Our model-based analysis suggested, and experiments support that these observations are better explained by IFN{gamma}-induced SOCS1 expression sequestering activated EGF receptors, thereby downregulating AKT activity, as opposed to direct IFN{gamma}-induced upregulation of p21 expression. This work forms a foundation for increased mechanistic model-based data integration on a single-cell level, an important building block for clinically predictive mechanistic models.

systems biology↗