Biology subjects

Razo-Mejia, M.

Publications and source records attributed to Razo-Mejia, M..

8 recordsLinked to original sources

bioRxiv2025

Unannotated translation products are widespread in model E. coli

Genomes contain orders of magnitude more open reading frames (ORFs) than known protein coding genes, and recent work suggests there may be unannotated proteins present in even the best studied organisms. To address this gap, we used a high throughput reverse genetic toolkit to construct precise C-terminal fusions of a reporter (and control) to >120,000 ORFs in model E. coli. We found hundreds of unannotated significant hits, and individually detected >50 novel polypeptides by western blot, including ORFs within tRNA loci. Many ORFs overlap annotated genes in the sense orientation, and we found these are likely chimeric polypeptides produced by ribosomal frameshifting. Using degron based knockdowns, we identified unannotated proteins that have putative fitness effects, and we found a novel small protein that displays phenotypes consistent with a role in the mRNA degradosome. The observation of a range of unannotated translation products should lead to better annotation and understanding of the bacterial domain of life and motivates the continued exploration of genomes broadly.

microbiology↗

bioRxiv2025

Learning the Shape of Evolutionary Landscapes: Geometric Deep Learning Reveals Hidden Structure in Phenotype-to-Fitness Maps

Elucidating the complex relationships between genotypes, phenotypes, and fitness remains one of the fundamental challenges in evolutionary biology. Part of the difficulty arises from the enormous number of possible genotypes and the lack of understanding of the underlying phenotypic differences driving adaptation. Here, we present a computational method that takes advantage of modern high-throughput fitness measurements to learn a map from high-dimensional fitness profiles to a low-dimensional latent space in a geometry-informed manner. We demonstrate that our approach using a Riemannian Hamiltonian Variational Autoencoder (RHVAE) outperforms traditional linear dimensionality reduction techniques by capturing the nonlinear structure of the phenotype-fitness map. When applied to simulated adaptive dynamics, we show that the learned latent space retains information about the underlying adaptive phenotypic space and accurately reconstructs complex fitness landscapes. We then apply this method to a dataset of high-throughput fitness measurements of E. coli under different antibiotic pressures and demonstrate superior predictive power for out-of-sample data compared to linear approaches. Our work provides a data-driven implementation of Fishers geometric model of adaptation, transforming it from a theoretical framework into an empirically grounded approach for understanding evolutionary dynamics using modern deep learning methods.

evolutionary biology↗

bioRxiv2024

Massively parallel experimental interrogation of natural variants in ancient signaling pathways reveals both purifying selection and local adaptation

The nature of standing genetic variation remains a central debate in population genetics, with differing perspectives on whether common variants are almost always neutral as suggested by neutral and nearly neutral theories or whether they can commonly have large functional and fitness effects as proposed by the balance theory. We address this question by mapping the fitness effects of over 9,000 natural variants in the Ras/PKA and TOR/Sch9 pathways--key regulators of cell proliferation in eukaryotes--across four conditions in Saccharomyces cerevisiae. While most variants are neutral in our assay, [~]3,500 exhibited significant fitness effects. These non-neutral variants tend to be missense and to affect conserved, more densely packed, and less solvent-exposed protein regions. While some of these non-neutral variants are younger and rarer, and more often found in heterozygous states--consistent with purifying selection--a substantial fraction is present at high frequencies in the population, which is expected under balancing selection. Indeed, we find that variants with a positive fitness effect in our laboratory measurement show strong signs of local adaptation as they tend to be found specifically in domesticated strains isolated from human-made environments. Our findings support the view that while many common variants might be effectively neutral, a significant proportion have locally adaptive functional consequences and are driven into a subset of the population by local positive selection. This study highlights the potential to combine high-throughput precision genome editing with fitness measurements to explore natural genetic variation on a pathway-wide scale, thereby bridging the gap between population genetics and functional genomics to understand the nature of evolutionary forces in the wild.

evolutionary biology↗

bioRxiv2023

Bayesian inference of relative fitness on high-throughput pooled competition assays

The tracking of lineage frequencies via DNA barcode sequencing enables the quantification of microbial fitness. However, experimental noise coming from biotic and abiotic sources complicates the computation of a reliable inference. We present a Bayesian pipeline to infer relative microbial fitness from high-throughput lineage tracking assays. Our model accounts for multiple sources of noise and propagates uncertainties throughout all parameters in a systematic way. Furthermore, using modern variational inference methods based on automatic differentiation, we are able to scale the inference to a large number of unique barcodes. We extend this core model to analyze multi-environment assays, replicate experiments, and barcodes linked to genotypes. On simulations, our method recovers known parameters within posterior credible intervals. This work provides a generalizable Bayesian framework to analyze lineage tracking experiments. The accompanying open-source software library enables the adoption of principled statistical methods in experimental evolution.

evolutionary biology↗

bioRxiv2020

Reconciling Kinetic and Equilibrium Models of Bacterial Transcription

The study of transcription remains one of the centerpieces of modern biology with implications in settings from development to metabolism to evolution to disease. Precision measurements using a host of different techniques including fluorescence and sequencing readouts have raised the bar for what it means to quantitatively understand transcriptional regulation. In particular our understanding of the simplest genetic circuit is sufficiently refined both experimentally and theoretically that it has become possible to carefully discriminate between different conceptual pictures of how this regulatory system works. This regulatory motif, originally posited by Jacob and Monod in the 1960s, consists of a single transcriptional repressor binding to a promoter site and inhibiting transcription. In this paper, we show how seven distinct models of this so-called simple-repression motif, based both on equilibrium and kinetic thinking, can be used to derive the predicted levels of gene expression and shed light on the often surprising past success of the equilibrium models. These different models are then invoked to confront a variety of different data on mean, variance and full gene expression distributions, illustrating the extent to which such models can and cannot be distinguished, and suggesting a two-state model with a distribution of burst sizes as the most potent of the seven for describing the simple-repression motif.

biophysics↗

bioRxiv2019

The Energetics of Molecular Adaptation in Transcriptional Regulation

Mutation is a critical mechanism by which evolution explores the functional landscape of proteins. Despite our ability to experimentally inflict mutations at will, it remains difficult to link sequence-level perturbations to systems-level responses. Here, we present a framework centered on measuring changes in the free energy of the system to link individual mutations in an allosteric transcriptional repressor to the parameters which govern its response. We find the energetic effects of the mutations can be categorized into several classes which have characteristic curves as a function of the inducer concentration. We experimentally test these diagnostic predictions using the well-characterized LacI repressor of Escherichia coli, probing several mutations in the DNA binding and inducer binding domains. We find that the change in gene expression due to a point mutation can be captured by modifying only a subset of the model parameters that describe the respective domain of the wild-type protein. These parameters appear to be insulated, with mutations in the DNA binding domain altering only the DNA affinity and those in the inducer binding domain altering only the allosteric parameters. Changing these subsets of parameters tunes the free energy of the system in a way that is concordant with theoretical expectations. Finally, we show that the induction profiles and resulting free energies associated with pairwise double mutants can be predicted with quantitative accuracy given knowledge of the single mutants, providing an avenue for identifying and quantifying epistatic interactions.\n\nSummaryWe present a biophysical model of allosteric transcriptional regulation that directly links the location of a mutation within a repressor to the biophysical parameters that describe its behavior. We explore the phenotypic space of a repressor with mutations in either the inducer binding or DNA binding domains. Using the LacI repressor in E. coli, we make sharp, falsifiable predictions and use this framework to generate a null hypothesis for how double mutants behave given knowledge of the single mutants. Linking mutations to the parameters which govern the system allows for quantitative predictions of how the free energy of the system changes as a result, permitting coarse graining of high-dimensional data into a single-parameter description of the mutational consequences.

biophysics↗

bioRxiv2019

First-principles prediction of the information processing capacity of a simple genetic circuit

Given the stochastic nature of gene expression, genetically identical cells exposed to the same environmental inputs will produce different outputs. This heterogeneity has been hypothesized to have consequences for how cells are able to survive in changing environments. Recent work has explored the use of information theory as a framework to understand the accuracy with which cells can ascertain the state of their surroundings. Yet the predictive power of these approaches is limited and has not been rigorously tested using precision measurements. To that end, we generate a minimal model for a simple genetic circuit in which all parameter values for the model come from independently published data sets. We then predict the information processing capacity of the genetic circuit for a suite of biophysical parameters such as protein copy number and protein-DNA affinity. We compare these parameter-free predictions with an experimental determination of protein expression distributions and the resulting information processing capacity of E. coli cells. We find that our minimal model captures the scaling of the cell-to-cell variability in the data and the inferred information processing capacity of our simple genetic circuit up to a systematic deviation.

biophysics↗

bioRxiv2017

An allosteric theory of transcription factor induction

Allosteric regulation is found across all domains of life, yet we still lack simple, predictive theories that directly link the experimentally tunable parameters of a system to its input-output response. To that end, we present a general theory of allosteric transcriptional regulation using the Monod-Wyman-Changeux model. We rigorously test this model using the ubiquitous simple repression motif in bacteria by first predicting the behavior of strains that span a large range of repressor copy numbers and DNA binding strengths and then constructing and measuring their response. Our model not only accurately captures the induction profiles of these strains but also enables us to derive analytic expressions for key properties such as the dynamic range and [EC50]. Finally, we derive an expression for the free energy of allosteric repressors which enables us to collapse our experimental data onto a single master curve that captures the diverse phenomenology of the induction profiles.

biophysics↗