Biology subjects

Miravet-Verde, S.

Publications and source records attributed to Miravet-Verde, S..

13 recordsLinked to original sources

bioRxiv2026

The Ocean Microbiomics Database reveals thermal niches and projected pressure on marine microbes in a warming ocean

Temperature is a key driver of ocean microbial community structure, yet its role in shaping global distributions and their response to warming remains unclear due to fragmented studies, inconsistent methods, and limited environmental context. We present the Ocean Microbiomics Database (OMDB) containing 274,282 genomes forming 31,058 species-level clusters, searchable at https://omdb.microbiomics.io. We show surface temperature is predicted from community composition, driven by thermal niche partitioning among congeneric taxa. Using these thermal niches to model present-day distributions globally, we find that projected warming results in microbial richness declining faster than relative abundance, with losses concentrated at high latitudes and among poorly characterized lineages. Resolving 5,891 species into four thermal clusters reveals ecological structure correlated with genome streamlining, protein thermostability, and marker-gene physicochemistry, enabling prediction of a microorganism's abundance-maximizing temperature directly from its coding sequence. This positions OMDB as a platform spanning molecular traits to global ecology, offering a genome-resolved foundation for understanding how temperature, shown to be a primary driver of ocean microbial life, will continue to shape it as the ocean warms.

microbiology↗

bioRxiv2026

Planetary structure and drivers of diazotroph communities reveal key reservoirs of nitrogen-fixation potential

Biological nitrogen fixation sustains productivity across Earths ecosystems, yet the diversity, distribution and ecological drivers of nitrogen-fixing prokaryotes (diazotrophs) remain incompletely resolved. Here we screened approximately 40 billion genes to generate a catalogue of 21,157 diazotroph genomes. Using this catalogue, we quantified diazotroph diversity and abundance across 9,163 metagenomes spanning diverse environments. Diazotrophy was distributed across the prokaryotic tree of life, but both gene diversity and metagenomic abundance were dominated by heterotrophic lineages. Freshwater and sedimentary ecosystems emerged as major reservoirs of nitrogen-fixation potential beyond the environments that have historically anchored the field. Diazotroph-rich assemblages were associated with nitrogen and phosphorus limitation together with carbohydrate-rich metabolic regimes, revealing distinct nutrient and energy niches for cyanobacterial and heterotrophic diazotrophs. Together, this framework unifies disparate views of diazotroph diversity, abundance and ecology, providing a foundation for improved prediction of biological nitrogen fixation across Earths ecosystems.

microbiology↗

bioRxiv2026

The ultrasmall ocean microbiome: a reservoir of microbial diversity and nitrogen fixation.

Marine microbiology has long considered ultrasmall (<0.2 {micro}m) size fractions as virus-dominated, yet emerging evidence suggests potential prokaryotic activity with implications for global biogeochemical cycles. Here, we present the first comprehensive genome-resolved survey of this fraction, built on 4,058 metagenome-assembled genomes (MAGs) representing 1,152 species across Bacteria (90.7%) and Archaea (9.3%), dominated by Pseudomonadota, Bacteroidota, and Nanoarchaeota. Over 490 non-redundant MAGs occurred exclusively in the ultrasmall fraction, with 66.8% representing novel taxa, particularly abundant in Arctic surface waters. Beyond known ultrasmall organisms (e.g., DPANN archaea and Patescibacteria), which show expected genomic and metabolic reduction, functional annotation revealed diverse metabolic potential across nitrogen, sulfur, and carbon cycling. Focusing on nitrogen fixation, we identified 13 non-cyanobacterial diazotroph species with complete nitrogenase operons spanning four bacterial classes. These resolved into two ecotypes: cosmopolitan low-abundance taxa, and Arctic ultrasmall-restricted strains reaching up to 16.7% relative abundance. Two lineages (50-400-T64 and Novosphingobium) showed ultrasmall exclusive bipolar distributions across Arctic and Southern Oceans. Beyond nitrogen fixation, these diazotrophs encoded pathways for denitrification, DNRA, sulfur oxidation, and carbon fixation. Our findings reveal an overlooked reservoir of microbial diversity including diazotrophs in the ultrasmall ocean microbiome, with significant implications for polar nitrogen budgets and global biogeochemical models.

microbiology↗

bioRxiv2025

Variations in the latitudinal diversity gradients of the ocean microbiome

Latitudinal diversity gradients (LDGs), typically declining from equator to poles, are a pervasive macroecological pattern, yet their generality and drivers in the ocean microbiome remain widely unresolved. We integrated global-scale metagenomic data with habitat modeling to study marine microbial LDGs across seasons and depths. Surface mixed layer microbiomes exhibited diversity peaks at (sub)tropical latitudes and a poleward decline, whereas mesopelagic communities (200-1,000 m) showed no latitudinal diversity structuring. Taxonomic resolution revealed that the mixed layer LDG was underpinned by Alphaproteobacteria and Cyanobacteriia, while other taxa exhibited distinct or contrasting LDGs. Diversity structuring also varied by seasons and regions, governed by temperature and nutrient availability. Together, these findings highlight that within the ocean microbiome, LDGs are not universal, but lineage-specific ecological strategies and responses to environmental gradients. Our study provides fundamental insights into the structuring of ocean microbiome diversity and lays the foundation for predicting responses to environmental change.

microbiology↗

bioRxiv2025

Transcriptomic profiles of single-copy marker genes enable predicting bacterial growth states in microbial communities

Studying microbial community dynamics is fundamental to better understand ecosystem stability, resilience and environmental change. Community composition changes with the growth of individual members, yet current methods to estimate microbial growth in communities face substantial limitations. For example, genome sequence-based estimates of maximum growth rates may not reflect growth patterns in the natural environment well, and metagenomic in situ growth prediction requires the availability of reference genomes and shows limited accuracy for slow-growing bacteria. Gene expression data provide an information-rich readout of community activity that could reflect growth, however, cross-species comparisons in community settings remain challenging. An approach using expression signatures of universal, single-copy marker genes provides independence from reference genomes and may thereby enable comparability across species. Here, we present a transcriptomic, marker gene-based growth classifier that predicts the growth states of bacterial strains from different phyla cultivated in diverse conditions. We demonstrate its application in vivo in gnotobiotic mice carrying the same bacterial strains, and in a more complex synthetic community, where predicted growth states align with reported growth inhibition induced by systemic inflammatory response. This approach offers a new method for predicting bacterial growth states across species, with potential for broad application in the study of microbial growth dynamics at the whole community level.

microbiology↗

bioRxiv2025

The natural diversity of E. coli transporter-dependent capsules

Serotyping of bacteria using genomic information (in silico serotyping) has increasingly replaced serology. However, the E. coli capsule serotyping system has been largely abandoned since the 1990s, leaving gaps in our knowledge of capsule genetics, diversity, distribution, and epidemiology. To address this, we established a definitive genotype-serotype map for 35 serologically identified and structurally characterized transporter-dependent capsules. We then surveyed >37,000 E. coli genomes, cataloging 85 transporter-dependent capsule types (K-types), including 55 novel ones. We leveraged this catalog to develop an in silico serotyping tool, kTYPr, and applied it to curated sets of >25,000 E. coli genomes and metagenome-assembled genomes spanning diverse environmental and clinical sources. We found novel K-types enriched in under-sampled environments and associated with E. coli disease. This research expands our understanding of E. coli surface structures, supporting efforts for precision targeting with phage therapy or vaccines.

microbiology↗

bioRxiv2025

Quantitative essentiality in a reduced genome: a functional, regulatory and structural fitness map

Essentiality studies have traditionally focused on coding regions, often overlooking other small genetic regulatory elements. To address this, we obtained a high-resolution essentiality map at near-single-nucleotide precision of the genome-reduced bacterium Mycoplasma pneumoniae, combining transposon libraries containing promoter or terminator sequences. By integrating temporal transposon-sequencing data, we developed a novel method of essentiality assessment based on k-means unsupervised clustering, which provides dynamic and quantitative information on the fitness contribution of different genomic regions. We compared the insertion tolerance and persistence of the two engineered libraries, assessing the local impact of transcription and termination on cell fitness. Essentiality assessment at the local base-level revealed essential protein domains and small genomic regions that are either essential or inaccessible to transposon insertion. We also identified structural regions within essential genes that tolerate transposon disruptions, resulting in functionally split proteins. Overall, this study presents a nuanced view of gene essentiality, shifting from static and binary models to a more accurate perspective. Additionally, it provides valuable insights for genome engineering and enhances our understanding of the biology of genome-reduced cells.

systems biology↗

bioRxiv2025

Using single-cell perturbation screens to decode the regulatory architecture of splicing factor programs

Splicing factors shape the isoform pool of most transcribed genes, playing a critical role in cellular physiology. Their dysregulation is a hallmark of diseases like cancer, where aberrant splicing contributes to progression. While exon inclusion signatures accurately assess changes in splicing factor activity, systematically mapping disease-driver regulatory interactions at scale remains challenging. Perturb-seq, which combines CRISPR-based perturbations with single-cell RNA sequencing, enables high-throughput measurement of perturbed gene expression signatures but lacks exon-level resolution, limiting its application for splicing factor activity analysis. Here, we show that shallow artificial neural networks (ANNs) can estimate splicing factor activity from gene expression signatures, bypassing the need for exon-level data. As a case study, we map the genetic interactions regulating splicing factors during carcinogenesis, using the shift in splicing program activity -where oncogenic-like splicing factors become more active than tumor suppressor-like factors- as a molecular reporter of a Perturb-seq screen. Our analysis reveals a cross-regulatory loop among splicing factors, involving protein-protein and splicing-mediated interactions, with MYC linking cancer driver mutations to splicing regulation. This network recapitulates splicing factor modulation during development. Altogether, we establish a versatile framework for studying splicing factor regulation and demonstrate its utility for uncovering disease mechanisms. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=79 SRC="FIGDIR/small/637061v1_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@4f02ddorg.highwire.dtl.DTLVardef@14538e0org.highwire.dtl.DTLVardef@198ffe7org.highwire.dtl.DTLVardef@1c8cee7_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGRAPHICAL ABSTRACTC_FLOATNO C_FIG

systems biology↗

bioRxiv2024

Genome-resolved diversity and biosynthetic potential of the coral reef microbiome

Coral reefs are marine biodiversity hotspots that provide a wide range of ecosystem services. They are also reservoirs of bioactive compounds, many of which are produced by microbial symbionts associated with reef invertebrate hosts. However, for the keystone species of coral reefs--the reef-building corals themselves--we still lack a systematic assessment of their microbially encoded biosynthetic potential, and thus the molecular resources that may be at stake due to the alarming decline in reef biodiversity and cover. Here, we analysed microbial genomes reconstructed from 820 reef-building coral samples of three representative coral genera collected at 99 reefs across 32 islands during a two-year expedition throughout the Pacific Ocean (Tara Pacific). By contextualising our analyses with the microbiomes of other reef species, we found that genomic information was previously available for only 10% of the 4,224 microbial species overall and for less than 1% of the 645 species exclusively identified in Tara Pacific samples. We found reef-building coral microbiomes to be host-specific and their biosynthetic potential to rival or even surpass that found in traditional targets for natural product discovery, such as sponges and soft corals. Fire corals were not only particularly diverse in microbially encoded biosynthetic gene clusters (BGCs), but also in BGC-rich bacteria, including Acidobacteriota spp., which have been recently highlighted for their promising natural product repertoire. Together, this study unveils new candidate sources for bioactive compound discovery, prioritises targets for microbial isolation, and underscores the importance of conservation efforts by linking macro-organismal biodiversity loss to host-specific microbiomes and their biotechnological potential.

microbiology↗

bioRxiv2024

Disentangling the splicing factor programs underlying complex molecular phenotypes

Splicing factors control exon inclusion in messenger RNAs, shaping transcriptome and proteome diversity. Their catalytic activity is regulated by multiple layers, making single-omic measurements on their own fall short in identifying which splicing factors underlie a phenotype. Here, we posit that splicing factor activity can be estimated from changes in exon inclusion. To test this hypothesis, we benchmarked methods for constructing splicing factor[->]exon networks and estimating splicing factor activity. We found that combining RNA-seq perturbation-based networks with VIPER (Virtual Inference of Protein Activity by Enriched Regulon analysis) accurately captures splicing factor activation as modulated by multiple regulatory layers. This approach integrates splicing factor regulation into a single score derived solely from exon inclusion signatures, allowing functional interpretation of heterogeneous conditions. As a proof of concept, we identify recurrent cancer splicing programs, revealing oncogenic- and tumor suppressor-like splicing factors missed by conventional methods. These programs correlate with patient survival and key cancer hallmarks: initiation, proliferation, and immune evasion. Altogether, we show splicing factor activity can be accurately estimated from exon inclusion changes, enabling comprehensive analyses of splicing regulation with minimal data requirements.

systems biology↗

bioRxiv2021

robustica: customizable robust independent component analysis

BackgroundIndependent Component Analysis (ICA) allows the dissection of omic datasets into modules that help to interpret global molecular signatures. The inherent randomness of this algorithm can be overcome by clustering many iterations of ICA together to obtain robust components. Existing algorithms for robust ICA are dependent on the choice of clustering method and on computing a potentially biased and large Pearson distance matrix. ResultsWe present robustica, a Python-based package to compute robust independent components with a fully customizable clustering algorithm and distance metric. Here, we exploited its customizability to revisit and optimize robust ICA systematically. From the 6 popular clustering algorithms considered, DBSCAN performed the best at clustering independent components across ICA iterations. After confirming the bias introduced with Pearson distances, we created a subroutine that infers and corrects the components signs across ICA iterations to enable using Euclidean distance. Our subroutine effectively corrected the bias while simultaneously increasing the precision, robustness, and memory efficiency of the algorithm. Finally, we show the applicability of robustica by dissecting over 500 tumor samples from low-grade glioma (LGG) patients, where we define a new gene expression module with the key modulators of tumor aggressiveness downregulated upon IDH1 mutation. Conclusionrobustica brings precise, efficient, and customizable robust ICA into the Python toolbox. Through its customizability, we explored how different clustering algorithms and distance metrics can further optimize robust ICA. Then, we showcased how robustica can be used to discover gene modules associated with combinations of features of biological interest. Taken together, given the broad applicability of ICA for omic data analysis, we envision robustica will facilitate the seamless computation and integration of robust independent components in large pipelines. Contactmiquel.anglada@crg.eu

systems biology↗

bioRxiv2021

Specialization of the photoreceptor transcriptome by Srrm3-dependent microexons is required for outer segment maintenance and vision

Retinal photoreceptors differ in their transcriptomic profiles from other neuronal subtypes, likely as a reflection of their unique cellular morphology and function in the detection of light thorough the ciliary outer segment. We discovered a new layer of this molecular specialization by revealing that the vertebrate retina expresses the largest number of tissue-enriched microexons of all tissue types. A subset of these microexons is included exclusively in photoreceptor transcripts, particularly in genes involved in cilia biogenesis and in vesicle-mediated transport. This microexon program is regulated by Srrm3, a paralog of the neural microexon regulator Srrm4. Despite both proteins positively regulate retina microexons in vitro, only Srrm3 is highly expressed in mature photoreceptors and its deletion in zebrafish results in widespread downregulation of microexon inclusion, severe photoreceptor alterations and blindness. These results shed light into photoreceptors transcriptomic specialization and functionality, uncovering new cell type-specific roles for Srrm3 and microexons with implication for retinal diseases.

systems biology↗

bioRxiv2020

LoxTnSeq: Transposon mutagenesis coupled with ultra-sequencing to study large random genome reductions

The Cre-Lox system is a highly versatile and powerful DNA recombinase mechanism, mainly used in genetic engineering to insert or remove desired DNA sequences. It is widely utilised across multiple fields of biology, with applications ranging from plants, to mammals, to microbes. A key feature of this system is its ability to allow recombination between mutant lox sites, traditionally named lox66 and lox71, to create a functionally inactive double mutant lox72 site. However, a large portion of the published literature has incorrectly annotated these mutant lox sites, which in turn can lead to difficulties in replication of methods, design of proper vectors, and confusion over the proper nomenclature. Here, we demonstrate common errors in annotations, the impacts they can have on experimental viability, and a standardised naming convention. We also show an example of how this incorrect annotation can induce toxic effects in bacteria that lack optimal DNA repair systems, exemplified by Mycoplasma pneumoniae. Data SummaryThe authors confirm all supporting data, code and protocols have been provided within the article or through supplementary data files.

synthetic biology↗