Biology subjects

Martinez-Garcia, M.

Publications and source records attributed to Martinez-Garcia, M..

6 recordsLinked to original sources

bioRxiv2026

Single-cell sequencing reveals genome streamlining and functional diversity in ecologically dominant marine protists

A large fraction of marine protists, particularly the smallest ones, belong to uncultured lineages that lack genomic data, limiting insights into their ecological roles and evolutionary strategies. Here, we generated 325 single-cell amplified genomes (SAGs) from 2-5 {micro}m planktonic protists, which resulted in 147 genomes from dominant marine species at varying levels of completeness (40 of them >50%). These genomes matched the in situ community, with Prymnesiophyceae, Mamiellophyceae and Chrysophyceae dominating pigmented cells and MAST, Choanoflagellata and Picozoa prevailing among heterotrophic colourless cells. This resource allowed us to describe the genomic architecture of marine protist species, and revealed a pronounced genome streamlining in ecologically successful lineages. Comparative analyses highlighted unique functions enriched in photosynthetic and heterotrophic taxa (including motility, signal transduction, digestion and secondary metabolism), and revealed a broad distribution of gene families with adaptive traits such as polyketide synthases and rhodopsins. This large-scale single-cell genomics dataset provides a mechanistic foundation for investigating functional diversity, ecological strategies and genome evolution in the ocean.

ecology↗

bioRxiv2025

Illuminating the active virosphere with BONCAT and single virus genomic sequencing technologies

Marine viruses impact biogeochemical cycles through cell lysis, releasing organic matter and nutrients that fuel ocean productivity. Identifying and quantifying the specific viruses active in these processes remains a priority in the field. Here, we introduce a click-chemistry method to fluorescently label, sort, and sequence the genomes of newly produced viral particles released from transcriptionally active host microbial cells, alongside the analysis of co-occurring inactive cells and viruses in environmental samples. This approach, called viral BONCAT-FACS, combines biorthogonal non-canonical amino acid tagging (BONCAT) with environmental sample incubation, followed by single-virus and single-cell sorting by flow cytometry (FACS). Genomic analysis of translationally-active cells and new viral progeny in coastal seawater incubations confirmed BONCAT labeling and successful sorting of diverse marine bacteria, microeukaryotic cells, and virioplankton, with stark differences in the predicted turnover of specific groups of infecting viruses, including Pelagiphages, Methylophages, a Flavobacteriales-associated novel "Far-T4" clade, algae-infecting giant NCLDV viruses, and parasitic virophages. Sequenced BONCAT-active cells showed a strong enrichment in viral contigs relative to the inactive cell fraction, suggestive of a large proportion of translationally-active virocells. This study illustrates the effectiveness of viral BONCAT-FACS for uncovering genome-resolved viral-host dynamics. By providing a direct approach for tracking active viral infections in natural environments, this method enhances our ability to investigate behavior and interactions of these nanoscale predators, expanding our understanding of their role in ecosystem dynamics.

microbiology↗

bioRxiv2023

New avenues for potentially seeking microbial responses to climate change beneath Antarctic ice shelves

The signs of climate change are undeniable, and the impact of these changes on ecosystem function heavily depends on the response of microbes that underpin the food web. Antarctic ice shelf is a massive mass of floating ice that extends from the continent into the ocean, exerting a profound influence on global carbon cycles. Beneath Antarctic ice shelves, marine ice stores valuable genetic information, where marine microbial communities before the industrial revolution are archived. Here, in this proof-of-concept, by employing a combination of single-cell genomics and metagenomics, we have been able to sequence frozen microbial DNA ({asymp}300 years old) stored in the marine ice core B15 collected from the Filchnner-Ronne Ice Shelf. Metagenomic data indicated that Proteobacteria and Thaumarchaeota (e.g. Nitrosopumilus spp.) followed by Actinobacteria (e.g. Actinomarinales) were abundant. Remarkably, our data allow us to travel to the past and calibrate genomic and genetic evolutionary changes for ecologically relevant microbes and functions, such as Nitrosopumilus spp., preserved in the marine ice ({asymp}300 years old) with those collected recently in seawater under an ice shelf (year 2017). The evolutionary divergence for the ammonia monooxygenase gene amoA involved in chemolithoautotrophy was about 0.88 amino acid and 2.8 nucleotide substitution rate per 100 sites in a century, while the accumulated rate of genomic SNPs was 2,467 per 1 Mb of genome and 100 years. Whether these evolutionary changes remained constant over the last 300 years or accelerated during post-industrial periods remains an open question that will be further elucidated. ImportanceSeveral efforts have been undertaken to predict the response of microbes under climate change, mainly based on short-term microcosm experiments under forced conditions. A common concern is that manipulative experiments cannot properly simulate the response of microbes to climate change, which is a long-term evolutionary process. In this proof-of-concept study with a limited sample size, we demonstrate a novel approach yet to be fully explored in science for accessing genetic information from putative past marine microbes preserved under Antarctic Ice shelves before Industrial revolution. This potentially allow us estimating evolutionary changes as exemplified in our study. We advocate for gathering a more comprehensive Antarctic marine ice core datasets across various periods and sites. Such a dataset would enable the establishment of a robust baseline, facilitating a better assessment of the potential effects of climate change on key genetic signatures of microbes.

microbiology↗

bioRxiv2022

Handling ill-conditioned omics data with deep probabilistic models

The advent of high-throughput technologies has produced an increase in the dimensionality of omics datasets, which limits the application of machine learning methods due to the great unbalance between the number of observations and features. In this scenario, dimensionality reduction is essential to extract the relevant information within these datasets and project it in a low-dimensional space, and probabilistic latent space models are becoming popular given their capability to capture the underlying structure of the data as well as the uncertainty in the information. This article aims to provide a general classification and dimensionality reduction method based on deep latent space models that tackles two of the main problems that arise in omics datasets: the presence of missing data and the limited number of observations against the number of features. We propose a semi-supervised Bayesian latent space model that infers a low-dimensional embedding driven by the target label: the Deep Bayesian Logistic Regression (DBLR) model. During inference, the model also learns a global vector of weights that allows to make predictions given the low-dimensional embedding of the observations. Since this kind of datasets is prone to overfitting, we introduce an additional probabilistic regularization method based on the semi-supervised nature of the model. We compared the performance of the DBLR against several state-of-the-art methods for dimensionality reduction, both in synthetic and real datasets with different data types. The proposed model provides more informative low-dimensional representations, outperforms the baseline methods in classification and can naturally handle missing entries. HighlightsO_LIInference of the latent space driven by the label value. The DBLR infers different low-dimensional latent distributions depending on the label value, forcing clustering in the latent space in an informative manner, thus capturing the underlying structure of the data. C_LIO_LIClassification. During inference, the model additionally learns a global vector of weights that allows to make predictions given the low-dimensional representation of the data. C_LIO_LIHandling missing data. As the DBLR is a probabilistic generative model, it can naturally handle partially missing observations during the training process, including not annotated observations as censored samples. In this article we cover the Missing at Random (MAR) case. C_LIO_LIRegularization method to handle small datasets. In order to handle small high-dimensional datasets, which usually entail overfitting problems, we introduced an additional regularization mechanism following a drop-outlike strategy that relies in the generative semi-supervised nature of the model. C_LIO_LIHandling different data types. We have defined and implemented different observation likelihood models that can be used to describe different data types. In particular, we show how to use the DBLR with binary and real-valued features. C_LI

bioinformatics↗

bioRxiv2022

GRAS-1 is a conserved novel regulator of early meiotic chromosome dynamics in C. elegans

Chromosome movements and licensing of synapsis must be tightly regulated during early meiosis to ensure accurate chromosome segregation and avoid aneuploidy, although how these steps are coordinated is not fully understood. Here we show that GRAS-1, the worm homolog of mammalian GRASP/Tamalin and CYTIP, coordinates early meiotic events with cytoskeletal forces outside the nucleus. GRAS-1 localizes close to the nuclear envelope (NE) in early prophase I and interacts with NE and cytoskeleton proteins. Delayed homologous chromosome pairing, synaptonemal complex (SC) assembly, and DNA double-strand break repair progression are partially rescued by the expression of human CYTIP in gras-1 mutants, supporting functional conservation. However, Tamalin, Cytip double knockout mice do not exhibit obvious fertility or meiotic defects, suggesting evolutionary differences between mammals. gras-1 mutants show accelerated chromosome movement during early prophase I, implicating GRAS-1 in regulating chromosome dynamics. GRAS-1-mediated regulation of chromosome movement is DHC-1-dependent, placing it acting within the LINC-controlled pathway, and depends on GRAS-1 phosphorylation at a C-terminal S/T cluster. We propose that GRAS-1 serves as a scaffold for a multi-protein complex coordinating the early steps of homology search and licensing of SC assembly by regulating the pace of chromosome movement in early prophase I.

cell biology↗

bioRxiv2021

A resistome roadmap: from the human body to pristine environments

A comprehensive characterization of the human body resistome (sets of antibiotic resistance genes (ARGs)) is yet to be done and paramount for addressing the antibiotic microbial resistance threat. Here, we study the resistome of 771 samples from five major body parts (skin, nares, vagina, gut and oral cavity) of healthy subjects from the Human Microbiome Project and addressed the potential dispersion of ARGs in pristine environments. A total of 28,731 ARGs belonging to 344 different ARG types were found in the HMP proteome dataset (n=9.1x107 proteins analyzed). Our study reveals a distinct resistome profile (ARG type and abundance) between body sites and high inter-individual variability. Nares had the highest ARG load ({approx}5.4 genes/genome) followed by the oral cavity, while the gut showed one of the highest ARG richness (shared with nares) but the lowest abundance ({approx}1.3 genes/genome). Fluroquinolone resistance genes were the most abundant in the human body, followed by macrolide-lincosamide-streptogramin (MLS) or tetracycline. Most of the ARGs belonged to common bacterial commensals and multidrug resistance trait was predominant in the nares and vagina. Our data also provide hope, since the spread of common ARG from the human body to pristine environments (n=271 samples; 77 Gb of sequencing data and 2.1x108 proteins analyzed) thus far remains very unlikely (only one case found in an autochthonous bacterium from a pristine environment). These findings broaden our understanding of ARG in the context of the human microbiome and the One-Health Initiative of WHO uniting human host-microbes and environments as a whole. ImportanceThe current antibiotic resistance crisis affects our health and wealth at a global scale and by 2050 predictions estimate 10 million deaths attributed to antibiotic resistance worldwide. Remarkably, a comprehensive analysis of ARG diversity and prevalence in different human body sites is yet to be done. Undoubtedly, our body and human built-environment have antibiotic resistant bacteria than can also be transported to other environments. Hence, the analysis of Human Microbiome Project dataset provides us not only the opportunity to explore in detail the ARGs diversity and prevalence in different parts of our body but also to provide some insights into the dispersion of ARGs from human to natural populations inhabiting pristine environments. Thus, our data would help to stablish a baseline in ARG surveillance protocols to asses further changes in antibiotic resistances in our society.

microbiology↗