Biology subjects

Perez, M. F.

Publications and source records attributed to Perez, M. F..

13 recordsLinked to original sources

bioRxiv2026

An atlas of the C. elegans starvation response reveals that transcription is necessary to initiate transcriptional silencing in quiescent primordial germ cells

Starvation profoundly impacts development, but how different tissues respond to starvation is unclear. We generated a cellular atlas of gene expression in fed and starved C. elegans L1 larvae. We identified 79 cell types in both conditions, and we detected [~]98% of protein-coding genes, 44% of which were differentially expressed. Starvation affected translation-related genes across the animal, and most "housekeeping" genes were downregulated. However, tissue-specific patterns of differential expression, transcription factor activity, and GO term enrichments are widespread, suggesting tissue-specific gene regulatory mechanisms and functional consequences. We inferred tissue-specific effects on transcription factor activity, identifying known and novel putative nutrient-dependent transcriptional regulators. Surprisingly, we found extensive transcription in starved primordial germ cells (PGCs), which are known to hyper-compact their chromatin and silence transcription during L1 starvation. We validated PGC transcription of aak-1/AMPK, and we showed that zygotic aak-1/AMPK is required for PGC chromatin hyper-compaction, supporting reproductive success upon recovery. This work provides a valuable resource characterizing nutritional control of transcription in an animal, and it reveals that transcription of aak-1/AMPK is necessary to silence transcription in quiescent PGCs.

genomics↗

bioRxiv2026

Physiological and anatomical leaf acclimation of understory trees subjected to a through-fall precipitation exclusion in a temperate rain forest in southern South America.

Water input is a key component of the ecosystems. Water defines the functionality, composition, and structure of biomes; therefore, any change in its availability would have an impact on ecosystem features. Moreover, a change in the water balance of an ecosystem affects its persistence as well as biochemical cycles, such as the carbon and nitrogen. Forests are ecosystems structured using high amounts of water. Thus, trees, the oldest living plants, are the prime species in these ecosystems and are the main managers of this abiotic element. Trees uptake water from the soil, store it in their biomass, and exchange it with the environment through leaf stomata. They also intercepted rainfall and fog with their canopies. All of this water is also transmitted to the entire biological diversity that inhabits these ecosystems. Any change in water input affects the web described above. The ability of trees to modify their anatomy or processes, that is, to acclimate to novel climates, is of great advantage in maintaining the characteristics of ecosystems. In this study, we took advantage of a precipitation exclusion experiment to reveal the acclimation of shade-tolerant understory trees, which will be the main component of a cold temperate rainforest in the future. We evaluated different anatomical and physiological leaf traits involved in the use of water by these species. We hypothesized that, as observed in similar experiments, species would adopt more conservative water-use strategies by adjusting their functional traits accordingly. Contrary to our hypotheses, we found that understory tree species inhabiting this temperate ecosystem will not become more conservative when using water. In minimal, but significant differences, most of the studied species displayed traits, in the precipitation exclusion treatment, that were demonstrated to be water spender, rather than conservative. We attributed these contrasting changes to root metabolism alleviation due to the flooded soils of Chiloe inhabited by these forests.

ecology↗

bioRxiv2026

In vivo functional classification of PTEN variants reveals context-dependent oncogenicity and interindividual variability

Gene variants, secondary mutations, and stochastic individual variability complicate cancer diagnosis, prognosis, and treatments. Here, we systematically assess the functional impact of PTEN cancer-related missense mutations in mammalian cell lines, yeast, and Caenorhabditis elegans. While cell-based assays revealed alterations in lipid phosphatase activity, CRISPR-based engineering of orthologous mutations in C. elegans enabled classification of variants based on organismal phenotypes and transcriptional profiles, providing a rapid framework to predict oncogenic potential. We further show that secondary mutations, such as gain-of-function of cdc-25.1/CDC25A, can enhance the phenotypic impact of specific daf-18/PTEN variants, revealing context-dependent oncogenicity. Finally, single-worm transcriptomic analyses uncovered substantial interindividual variability among isogenic animals with identical cdc-25.1 and daf-18 mutations, linking transcriptional states to divergent phenotypic outcomes. Together, our results establish C. elegans as a powerful in vivo platform to integrate genetic, functional, and transcriptional information for the interpretation of cancer-associated variants.

cancer biology↗

bioRxiv2025

Integrative taxonomy using traits and genomic data for Species Delimitation with Deep learning

Recognizing species boundaries in complex speciation scenarios, including those involving gene flow and demographic fluctuations, remains a challenge, particularly given the diversity of existing species concepts. Promising recent approaches adopt an integrative taxonomy that combines multiple sources of evidence (e.g., genetic, morphology, geographic distributions), reflecting different properties associated with the dynamics of the speciation continuum. The use of statistical inference methods for model comparison, such as approximate Bayesian computation, approximate likelihood approaches, and machine learning, has improved the better assessment of species boundaries in such contexts. However, most existing approaches involve analyzing genetic and phenotypic/geographical information separately, followed by visual/qualitative comparison. Methods that integrate genetic information with other sources of evidence remain limited to simple evolutionary models and are typically unable to analyze more than a few hundred loci across a maximum of a few tens of samples. Here, we present a deep learning approach (DeepID) that combines two convolutional neural networks to integrate genomic data (thousands of loci or single nucleotide polymorphisms, SNPs) and trait information into a unified framework. Using both simulated and empirical data sets, we evaluate the power and accuracy of this approach for discriminating among competing divergence speciation scenarios (with minimal ongoing gene flow) across a varying number of SNPs and traits, as well as different levels of missing data. Analyses based on genomic or trait data alone yielded a slight lower accuracy, whereas integrating genomic and trait data resulted in improved performance. When we violated the speciation model by including extensive migration, approaches incorporating trait data were less affected than those relying solely on genomic information. Together, these results suggest that combining genomic and trait data may capture complementary signals associated with different stages of the speciation process. Moreover, our approach successfully recovered the expected delimitation scenarios in empirical data sets from a plant (Euphorbia balsamifera) and a fish (Lepomis megalotis) species complex. We argue that our method is a flexible and promising approach, allowing for complex scenario comparison and the use of multiple types of data. Combining genomic and trait data likely captures complementary signals associated with different stages of the speciation process, reflecting the fact that speciation is a continuum in which genetic and phenotypic divergence may proceed at different rates.

evolutionary biology↗

bioRxiv2024

PhyloCNN: Improving tree representation and neural network architecture for deep learning from trees in phylodynamics and diversification studies

Phylodynamics and diversification studies using complex evolutionary models can be challenging, especially with traditional likelihood-based approaches. As an alternative, likelihood-free simulation-based approaches have been proposed due to their ability to incorporate complex models and scenarios. Here, we propose a new simulation-based deep learning (DL) method capable of analyzing large datasets and accurately estimating parameter values for birth-death models in both phylodynamics and diversification studies. Our approach involves encoding trees by extracting a vector of local features for all nodes of the input phylogeny. We also developed a dedicated convolutional neural network architecture called PhyloCNN. Using simulations, we compared the accuracy of PhyloCNN when using feature vectors with a variable number of generations to describe the local context of nodes and leaves. The number of generations had a greater impact when considering smaller training sets, with a broader context showing higher accuracy, especially for complex evolutionary models. Compared to other recently developed DL approaches, PhyloCNN showed higher or similar accuracies for all parameters when used with training sets one or two orders of magnitude smaller (10,000 to 100,000 simulated training trees, instead of millions). We applied PhyloCNN with compelling results to two real-world phylodynamics and diversification datasets, related to HIV superspreaders in Zurich and to primates and their ecological role as seed dispersers. The high accuracy and computational efficiency of our method opens new possibilities for phylodynamics and diversification studies that need to account for idiosyncratic phylogenetic histories with specific parameter spaces and sampling scenarios not considered in more general approaches.

bioinformatics↗

bioRxiv2024

CelEsT: a unified gene regulatory network for estimating transcription factor activities in C. elegans

Transcription factors (TFs) play a pivotal role in orchestrating the intricate patterns of gene regulation critical for development and health. Although gene expression is complex, differential expression of many genes is often due to regulation by just a handful of TFs. Despite extensive efforts to elucidate TF-target regulatory relationships in C. elegans, existing experimental datasets cover distinct subsets of TFs and leave data integration challenging. Here I introduce CelEsT, a unified gene regulatory network (GRN) designed to estimate the activity of 487 distinct C. elegans TFs - [~]58% of the total - from gene expression data. To integrate data from ChIP-seq, DNA-binding motifs, and eY1H screens, different GRNs were benchmarked against a comprehensive set of TF perturbation RNA-seq experiments and identified optimal processing of each data type. Moreover, I showcase how leveraging conservation of TF binding motifs in the promoters of candidate target orthologues across genomes of closely-related species can distil targets into a select set of highly informative interactions, a strategy which can be applied to many model organisms. Combined analyses of multiple datasets from commonly-studied conditions including heat shock, bacterial infection and male-vs-female comparison validates CelEsTs performance and highlights previously overlooked TFs that likely play major roles in co-ordinating the transcriptional response to these conditions. CelEsT can be used to infer TF activity on a standard laptop computer within minutes. Furthermore, an R Shiny app is provided for the community to perform rapid analysis with minimal coding experience required. I anticipate that widespread adoption of CelEsT will significantly enhance the interpretive power of transcriptomic experiments, both present and retrospective, thereby advancing our understanding of gene regulation in C. elegans and beyond.

genomics↗

bioRxiv2023

Histone methylation has a direct metabolic role in human cells

The N-terminal tails of eukaryotic histones are frequently post-translationally modified. The role of these modifications in transcriptional regulation is well-documented. However, the extent to which the enzymatic process of histone post-translational modification itself contributes to metabolic regulation is less clear. Here we investigated the metabolic role of histone methylation using metabolomics, proteomics and RNA-seq data from cancer cell lines, primary tumour samples and healthy tissue samples. In cancer the transcription of histone methyltransferases was inversely correlated to the activity of NNMT, an enzyme previously characterised as a methyl sink that disposes of excess methyl groups carried by the universal methyl donor S-adenosyl methionine (SAM or AdoMet). In healthy tissues histone methylation was inversely correlated to the levels of an alternative methyl sink, PEMT. These associations affected the levels of multiple histone marks on chromatin genome-wide but had no detectable impact on transcriptional regulation. We show that histone methyltransferases with a variety of different associations to transcription are co-regulated by the Retinoblastoma (Rb) tumour suppressor in human cells. Total HMT expression is increased in Rb-mutant cancers, and this leads to NNMT downregulation. Together, our results suggest a direct metabolic role for histone methylation in SAM homeostasis, independent of transcriptional regulation.

bioinformatics↗

bioRxiv2022

Expanding the stdpopsim species catalog, and lessons learned forrealistic genome simulations

Simulation is a key tool in population genetics for both methods development and empirical research, but producing simulations that recapitulate the main features of genomic data sets remains a major obstacle. Today, more realistic simulations are possible thanks to large increases in the quantity and quality of available genetic data, and to the sophistication of inference and simulation software. However, implementing these simulations still requires substantial time and specialized knowledge. These challenges are especially pronounced for simulating genomes for species that are not well-studied, since it is not always clear what information is required to produce simulations with a level of realism sufficient to confidently answer a given question. The community-developed framework stdpopsim seeks to lower this barrier by facilitating the simulation of complex population genetic models using up-to-date information. The initial version of stdpopsim focused on establishing this framework using six well-characterized model species (Adrion et al., 2020). Here, we report on major improvements made in the new release of stdpopsim (version 0.2), which includes a significant expansion of the species catalog and substantial additions to simulation capabilities. Features added to improve the realism of the simulated genomes include non-crossover recombination and provision of species-specific genomic annotations. Through community-driven efforts, we expanded the number of species in the catalog more than three-fold and broadened coverage across the tree of life. During the process of expanding the catalog, we have identified common sticking points and developed best practices for setting up genome-scale simulations. We describe the input data required for generating a realistic simulation, suggest good practices for obtaining the relevant information from the literature, and discuss common pitfalls and major considerations. These improvements to stdpopsim aim to further promote the use of realistic whole-genome population genetic simulations, especially in non-model organisms, making them available, transparent, and accessible to everyone.

bioinformatics↗

bioRxiv2022

Fluctuations in chromatin state at regulatory loci occur spontaneously under relaxed selection and are associated with epigenetically inherited variation in C. elegans gene expression.

Some epigenetic information can be transmitted between generations without changes in the underlying DNA sequence. Changes in epigenetic regulation, termed epimutations, can occur spontaneously and be propagated in populations in a manner reminiscent of DNA mutations. Epimutations mediated by small non-coding RNAs occur in C. elegans and persist for around 3-5 generations on average. Here, we explored whether chromatin states also undergo spontaneous change and whether this could be a potential alternative mechanism for transgenerational epigenetic inheritance. We compared the chromatin and gene expression profiles at matched time points from three independent lineages of C. elegans propagated at minimal population size. Spontaneous changes in chromatin occurred in around 1% of regulatory regions each generation. Some were heritable epimutations and were significantly enriched for heritable changes in expression of nearby protein-coding genes. Most chromatin-mediated epimutations were short-lived but a subset had longer duration. Genes subject to long-lived epimutations were enriched for multiple components of xenobiotic response pathways. This points to a possible role for epimutations in adaptation to environmental stressors.

molecular biology↗

bioRxiv2021

Malignancy and NF-kB signalling strengthen coordination between the expression of mitochondrial and nuclear-encoded oxidative phosphorylation genes

Mitochondria are ancient endosymbiotic organelles crucial to eukaryotic growth and metabolism. Mammalian mitochondria carry a small genome containing thirteen protein-coding genes with the remaining mitochondrial proteins encoded by the nuclear genome. Little is known about how coordination between the two sets of genes is achieved. Correlation analysis of RNA-seq expression data from large publicly-available datasets is a common method to leverage genetic diversity to infer gene co-expression modules. Here we use this method to investigate nuclear-mitochondrial gene expression coordination. We identify a pitfall in correlation analysis that results from the large variation in the proportion of transcripts from the mitochondrial genome in RNA-seq data. Commonly used normalization techniques based on total read count (such as FPKM or TPM) produce artefactual negative correlations between mitochondrial- and nuclear-encoded transcripts. This also results in artefactual correlations between pairs of nuclear-encoded genes, thus having important consequences for inferring co-expression modules beyond mitochondria. We show that these effects can be overcome by normalizing using the median-ratio normalization (MRN) or trimmed mean of M values (TMM) methods. Using these normalizations, we find only weak and inconsistent correlations between mitochondrial and nuclear-encoded mitochondrial genes in the majority of healthy human tissues from the GTEx database. However, a subset of healthy tissues with high expression of NF-{kappa}B show significant coordination supporting a role for NF-{kappa}B in retrograde signalling. Contrastingly, most cancer types show robust coordination of nuclear and mitochondrial OXPHOS gene expression, identifying this as a feature of gene regulation in cancer.

genomics↗

bioRxiv2020

Coalescent-based species delimitation meets deep learning: Insights from a highly fragmented cactus system

Delimiting species boundaries is a major goal in evolutionary biology. An increasing body of literature has focused on the challenges of investigating cryptic diversity within complex evolutionary scenarios of speciation, including gene flow and demographic fluctuations. New methods based on model selection, such as approximate Bayesian computation, approximate likelihoods, and machine learning are promising tools arising in this field. Here, we introduce a framework for species delimitation using the multispecies coalescent model coupled with a deep learning algorithm based on convolutional neural networks (CNNs). We compared this strategy with a similar ABC approach. We applied both methods to test species boundary hypotheses based on current and previous taxonomic delimitations as well as genetic data (sequences from 41 loci) in Pilosocereus aurisetus, a cactus species complex with a sky-island distribution and taxonomic uncertainty. To validate our method, we also applied the same strategy on data from widely accepted species from the genus Drosophila. The results show that our CNN approach has high capacity to distinguish among the simulated species delimitation scenarios, with higher accuracy than ABC. For the cactus dataset, a splitter hypothesis without gene flow showed the highest probability in both CNN and ABC approaches, a result agreeing with previous taxonomic classifications and in line with the sky-island distribution and low dispersal features of P. aurisetus. Our results highlight the cryptic diversity within the P. aurisetus complex and show that CNNs are a promising approach for distinguishing complex evolutionary histories, even outperforming the accuracy of other model-based approaches such as ABC.

evolutionary biology↗

bioRxiv2020

Neuronal perception of the social environment intergenerationally controls germline development and generation time in C. elegans

An old and controversial question in biology is whether information perceived by the nervous system of an animal can cross the Weismann barrier to alter the phenotypes and fitness of their progeny. Here we show that such intergenerational transmission of sensory information occurs in the model organism, C. elegans, with a major effect on fitness. Specifically, that perception of social pheromones by chemosensory neurons controls the post-embryonic timing of development of one tissue - the germline - relative to others in an animals progeny. Neuronal perception of the social environment thus intergenerationally controls the generation time of this animal.

genetics↗

bioRxiv2019

Seed plant families with diverse mycorrhizal states have higher diversification rates

A crucial innovation in plant evolution was the association with soil fungi during land colonization. Today, this symbiotic interaction is present in most plants species and can be classified in four types: arbuscular (AM), Ecto (EM), Orchid (OM) and Ericoid Mycorrhiza (ER). Since the AM ancestral state, some plants lineages have switched partner (EM, OM and ER) or lost the association (no-association: NM). Evolutionary transitions to a novel mycorrhizal state (MS) might allow plant lineages to access new resources, enhancing diversification rates. However, some clades are not restricted to one MS, and this variability might promote diversification. In this study we address the relationship between MS and diversification rates of seed plant families. For this, we used the recently published FungalRoot database, which compiled data for 14,870 species and their mycorrhizal partners. We assigned a MS to each plant family, calculated the MS heterogeneity and estimated their diversification rates using the method-of-moments. Families with mixed MS had the highest diversification rates and there was a positive relationship between MS heterogeneity and diversification rates. These results support the hypothesis that MS lability promotes diversification and highlight the importance of the association with soil fungi for the diversification of plants.

evolutionary biology↗