Biology subjects

del Campo, R.

Publications and source records attributed to del Campo, R..

11 recordsLinked to original sources

bioRxiv2025

Cultivation and genomic characterization of human gut-associated Bdellovibrio reveals natural predatory bacteria with specialized prey interactions

Predatory bacteria from the Bdellovibrio and Like Organisms (BALOs) group represent promising alternatives to conventional antibiotics, yet their presence in the human microbiome has remained unconfirmed through cultivation. While molecular detection methods have identified Bdellovibrionaceae DNA in human-associated environments, viable predatory bacteria had never been successfully isolated from human samples, limiting our understanding of their ecology and therapeutic potential. Here, we report the first successful isolation and characterization of viable Bdellovibrio strains from human fecal samples. Despite extremely low natural abundance requiring enrichment protocols for detection, two of five pooled samples yielded viable predators with characteristic lytic activity. Whole-genome sequencing of two isolates revealed >99% average nucleotide identity to the reference strain HD100 with only 26 total single nucleotide polymorphisms, indicating minimal genomic divergence between human-associated and environmental strains. Comparative genomic analysis of 163 publicly available Bdellovibrio genomes demonstrated that only 10.4% represented true B. bacteriovorus sensu stricto, highlighting substantial cryptic diversity within this genus. Pangenome analysis across 41 genomes revealed a highly conserved core genome ([~]2,500-2,650 genes) contrasting with an expanding accessory genome, reflecting functional constraints of obligate predation alongside ecological adaptation. Notably, human-associated isolates exhibited narrower prey ranges, including some multidrug-resistant isolates. These findings establish that predatory bacteria are naturally associated with the human intestinal environment without acquiring novel virulence factors, support their biosafety profile for therapeutic development, and reveal prey specialization that may reflect adaptation to human microbiome ecology. IMPORTANCEThis work demonstrates that bacterial predation operates as an active ecological process within the human microbiota. The successful isolation of viable Bdellovibrio establishes that predatory bacteria are viable microbiota members maintaining their predatory phenotype. Genomic conservation (>99% ANI to environmental strains) indicates that predation imposes stringent functional constraints superseding niche-specific adaptations. Our findings reframe microbiome understanding by revealing that predation (a top-down regulatory mechanism) operates in human-associated communities, potentially functioning as a keystone ecological process maintaining diversity through predator-prey dynamics. For microbiome research, this establishes a novel framework for host-microbe-microbe interactions where rare predators may exert disproportionate impacts on community assembly and dysbiosis pathogenesis. This work opens unprecedented opportunities for developing ecologically-informed therapeutics that harness natural predation to simultaneously combat multidrug-resistant infections while restoring microbiota homeostasis, positioning predatory bacteria as a cornerstone strategy for antimicrobial resistance management through ecological microbiota restoration.

microbiology↗

bioRxiv2025

Drying halves decomposition rates in river networks by disrupting structure-function linkages

River drying is intensifying worldwide due to climate change and water abstraction, with major consequences for biodiversity and ecosystem functioning. In river networks, drying not only alters local environmental conditions but also disrupts hydrological connectivity, reshaping the movement of organisms and resources at the network scale. Leaf litter decomposition--a key ecosystem function in freshwater systems--is particularly sensitive to changes in the structure of decomposer communities. We hypothesized that spatiotemporal patterns of drying regulate decomposition by altering the diversity and composition of detritivore macroinvertebrates, bacteria and fungi. We combined data from six European drying river networks (DRNs) spanning a wide latitudinal gradient to assess how local drying intensity and regional hydrological connectivity affect decomposition through changes in these decomposer groups. We found that short drying events ([≤] six dry days) reduced decomposition rates by up to 50% by shifting the control of decomposition from a balanced contribution of fungi, bacteria, and detritivores to one dominated by dry-tolerant but less efficient bacteria. These community shifts persisted after flow resumption, leading to sustained reductions in decomposition even under flowing conditions. Regional connectivity alleviated these negative effects of local drying by facilitating the recovery of more efficient aquatic decomposers through dispersal. However, this effect depended on DRN context. In particular, in southern, more arid DRNs, stronger fragmentation hindered the recovery of decomposer communities after flow resumption. Overall, our results provide mechanistic evidence that spatiotemporal patterns of drying can regulate the linkages between community structure and ecosystem functioning in river networks. As drying events become more frequent and prolonged, increasing disruption of these linkages will impact carbon cycling and energy fluxes in freshwater ecosystems under global change.

ecology↗

bioRxiv2024

Modelling biological and resource fluxes in fluvial meta-ecosystems

Meta-ecosystem theory predicts that cross-ecosystem flows of energy, nutrients, and organisms have important implications for local community assembly and ecosystem functioning. Developments in the theory also have the potential to enhance our understanding of biodiversity-ecosystem functioning relationships. Meta-ecosystem theory is particularly well-suited to the study of rivers, because water flow forces strong spatial interrelationships among connected ecosystems. However, models that address flows of both resources and organisms and explicitly link both are lacking. We present a model and associated R-package for cross-ecosystem flows of both resources and organisms that can be used to predict their distribution in river networks, as well as meta-ecosystem functioning. The model incorporates feedbacks between these two crucial components--resource concentrations represent niche dimensions for organisms, modifying the colonisation and extinction dynamics and different locations in the network, and organisms also consume resources, thereby modifying the concentrations that are transported downstream. To illustrate the capabilities of the model, we present an in silico experiment and analysis, as well as providing sample code.

ecology↗

bioRxiv2023

Bdellovibrio's Prey-Independent Growth is Fuelled by Amino Acids as a Carbon Source

Identifying the nutritional requirements and growth conditions of microorganisms is crucial for determining their applicability in industry and understanding their role in clinical ecology. Predatory bacteria such as Bdellovibrio bacteriovorus have emerged as promising tools for combating infections by human bacterial pathogens due to their natural killing features. Bdellovibrios lifecycle occurs inside prey cells, using the cytoplasm as a source of nutrients and energy. However, this lifecycle supposes a challenge when determining the specific uptake of metabolites from the prey to complete the growth inside cells, a process that has not been completely elucidated. Here, following a model-based approach we illuminate the ability of Bdellovibrio bacteriovorus to replicate DNA, increase biomass, and generate adenosine triphosphate (ATP) in an amino acid-based rich media in the absence of prey, keeping intact its predatory capacity. In this culture, we determined the main carbon sources used and their preference, being glutamate, serine, aspartate, isoleucine, and threonine. This study offers new insights into the role of predatory bacteria in natural environments and establishes the basis for developing new Bdellovibrio applications using appropriate metabolic and physiological methodologies.

microbiology↗

bioRxiv2022

The Impact of Colistin Resistance on the Activation of Innate Immunity by Lipopolysaccharide Modification

Colistin resistance is caused by different lipopolysaccharide (LPS) modifications, and we propose to evaluate the effect on the innate immune response of in vivo and in vitro colistin resistance acquisition. We used 2 pairs of isogenic strains: (1) Escherichia coli ATCC25922, susceptible to colistin and its isogenic transconjugant-carrying mcr-1 gene; and (2) OXA-48, CTX-M-15 K. pneumoniae susceptible to colistin (CS-Kp) isolated from a urinary infection and its colistin-resistant variant (CR-Kp) from the same patient after prolonged treatment with colistin. No mutation of described genes for colistin resistance (pmrA, pmrB, mgrB. phoP/Q and crrAB) were found in the CR-Kp genome; however, LPS modifications were characterized by negative-ion MALDI-TOF. The strains were co-cultured with human monocytes to determine their survival after phagocytosis and induction to apoptosis. Also, monocytes were stimulated with bacterial LPS to study cytokine and immunecheckpoint production. The addition of 4-amino-4-deoxy-l-arabinose (Ara4N) to lipid A of CR-Kp accounted for the colistin resistance. CR-Kp survived significantly longer inside human monocytes after being phagocytosed compared with the CS-Kp strain, whereas no significant differences were observed for the E. coli isogenic strains. In addition, LPS from CR-Kp induced both higher apoptosis in monocytes and higher levels of cytokine and immune checkpoint production than LPS from CS-Kp. This effect was strictly the opposite for E. coli. Our data reveal a variable impact of colistin resistance on the innate immune system, depending on the responsible mechanism. Adding Ara4N to LPS increases bacterial survival after phagocytosis and elicits a higher inflammatory response than its colistin-susceptible counterpart.

microbiology↗

bioRxiv2022

Statistical evaluation of metaproteomics and 16s rRNA amplicon sequencing techniques for the study of the gut microbiota establishment of infants with cystic fibrosis

The newborn screening for cystic fibrosis (CF) can identify affected but still asymptomatic infants. The selection of omic technique for gut microbiota study is therefore crucial due to both the small amount of feces available and the low microorganism load. Our aim was to compare the agreement between 16S rRNA amplicon sequencing and metaproteomics by a robust statistical analysis including McNemar (taxa presence) test and Bland-Altman (taxa abundance) data plotting for studying the sequential establishment of the gut microbiota during the first year of life in 8 infants with CF (28 fecal samples). The taxonomic assignation was similar by the two techniques, whereas certain discrepancies were observed in the abundance detection, mostly the underrepresentation of Bifidobacterium and the overestimation of certain Firmicutes and Proteobacteria by amplicon sequencing. During the first months of life, the CF gut microbiota is characterized by poor alpha diversity, a significant enrichment of Ruminococcus gnavus, the expression of certain virulent bacterial traits, and the detection of human inflammation-related proteins. Our study provides an extended comparative analysis with robust statistical support that could optimize the use of both approaches for gut microbiota research. Metaproteomics provides information on composition and functionality, as well as data on host-microbiome interactions. Its strength is the identification and quantification of Actinobacteria and certain classes of Firmicutes. Both techniques detected an aberrant microbiota in infants with CF during their first year of life, dominated by the enrichment of R. gnavus within a human inflammatory environment.

microbiology↗

bioRxiv2021

Discrimination of species within the Enterobacter cloacae complex using MALDI-TOF Mass Spectrometry and Fourier-Transform Infrared Spectroscopy coupled with Machine Learning tools

The Enterobacter cloacae complex (ECC) encompasses heterogeneous clusters of species that have been associated with nosocomial outbreaks. These species may host different acquired antimicrobial resistance and virulence mechanisms and their identification are challenging. This study aims to develop predictive models based on MALDI-TOF MS spectral profiles and machine learning for species-level identification. A total of 198 ECC and 116 K. aerogenes clinical isolates from the University Hospital Ramon y Cajal (Spain) and the University Hospital Basel (Switzerland) were included. The capability of the proposed method to differentiate the most common ECC species (E. asburiae, E. kobei, E. hormaechei, E. roggenkampii, E. ludwigii, E. bugandensis) and K. aerogenes was demonstrated by applying unsupervised hierarchical clustering with PCA pre-processing. We observed a distinctive clustering of E. hormaechei and K. aerogenes and a clear trend for the rest of the ECC species to be differentiated over the development dataset. Thus, we developed supervised, non-linear predictive models (Support Vector Machine with Radial Basis Function and Random Forest). The external validation of these models with protein spectra from the two participating hospitals yielded 100% correct species-level assignment for E. asburiae, E. kobei, and E. roggenkampii and between 91.2% and 98.0% for the remaining ECC species. Similar results were obtained with the MSI database developed recently (https://msi.happy-dev.fr/) except in the case of E. hormaechei, which was more accurately identified by Random Forest. In short, MALDI-TOF MS combined with machine learning demonstrated to be a rapid and accurate method for the differentiation of ECC species.

microbiology↗

bioRxiv2021

Development and Validation of a MALDI-TOF-Based Model to Predict Extended-Spectrum Beta-Lactamase and/or Carbapenemase-Producing in Klebsiella pneumoniae Clinical Isolates

Matrix-Assisted Laser Desorption Ionization Time-Of-Flight (MALDI-TOF) Mass Spectrometry (MS) is a reference method for microbial identification and it can be used to predict Antibiotic Resistance (AR) when combined with artificial intelligence methods. However, current solutions need time-costly preprocessing steps, are difficult to reproduce due to hyperparameter tuning, are hardly interpretable, and do not pay attention to epidemiological differences inherent to data coming from different centres, which can be critical. We propose using a multi-view heterogeneous Bayesian model (KSSHIBA) for the prediction of AR using MALDI-TOF MS data together with their epidemiological differences. KSSHIBA is the first model that removes the ad-hoc preprocessing steps that work with raw MALDI-TOF data. In addition, due to its Bayesian probabilistic nature, it does not require hyperparameter tuning, provides interpretable results, and allows exploiting local epidemiological differences between data sources. To test the proposal, we used data from 402 Klebsiella pneumoniae isolates coming from two different domains and 20 different hospitals located in Spain and Portugal. KSSHIBA outperforms current state-of-the-art approaches in antibiotic susceptibility prediction, obtaining a 0.78 AUC score in Wild Type classification and a 0.90 AUC score in Extended-Spectrum Beta-Lactamases (ESBL)+Carbapenemases (CP)-producers. The proposal consistently removes the need for ad-hoc preprocessing by working with raw MALDI-TOF data, which, in turn, reduces the time needed to obtain the results of the resistance mechanism in microbiological laboratories. The proposed model implementation as well as both data domains are publicly available.

microbiology↗

bioRxiv2021

Predation Efficiency upon Clinical Isolates: Bdellovibrio bacteriovorus is Prey Specific and Origin Dependent

The use of predatory bacteria as live antibiotics has been proposed for managing bacterial infections, especially for those caused by antibiotic multiresistant isolates for which there are few therapeutic options. However, the current knowledge in this field is scarce, with most of the available data based on environmental isolates, with a significant lack of human clinical samples. In this study, we evaluated the predatory spectrum of the reference strain Bdellovibrio bacteriovorus 109J on 13 Serratia marcescens (5 of which were carbapenemase producers) and 78 Pseudomonas aeruginosa clinical isolates from respiratory (colonizing the lungs of patients with cystic fibrosis) or bacteremic infections, differentiated by phenotype (mucoid or not), antibiotic resistance phenotype (including multidrug-resistant isolates), and genetic lineage (frequent and rare sequence types). The source of the isolates was significantly associated with predation efficiency (100% for S. marcescens, 67% for P. aeruginosa from cystic fibrosis, and 25% for P. aeruginosa from bacteremia). In contrast, no correlation with colonial morphotype, genetic background, or antibiotic susceptibility was found. To evaluate the influence of the predator on the predation event, we employed a more aggressive B. bacteriovorus mutant 109J preying upon the same 48 bacteremic P. aeruginosa isolates. The mutants predation efficiency was higher than that of their wild-type counterpart (43% vs. 25%), pointing out that predation is specific to each prey-predator pair of isolates. Our results provide the most extensive study of clinical prey susceptibility published to date and show that the prey-predator interaction is influenced by the origin of the isolates rather than by their genetic background or their antibiotic susceptibility phenotype. IMPORTANCEThe potential usefulness of predatory bacteria in controlling human pathogens, particularly those that are multiresistant to antibiotics, is enormous. Although this possibility has long been suggested, there are still no data on predation susceptibility in clinical strains, and the possible presence of autochthonous predators of the human microbiota has not been investigated. In this study, we employed a reference predator with an environmental origin to study predation phenomena in 3 well-characterized collections of human clinical isolates. Our results demonstrated that predation is a specific consequence of each prey-predator interaction, with the origin of the strains the most relevant factor. In contrast, the genetic background, morphotype, and antibiotic resistance did not appear to influence the predation phenomenon. We also highlight the involvement of a putative polyhydroxyalkanoate depolymerase protein of B. bacteriovorus in determining prey susceptibility. To our knowledge, this study is the largest performed with strains of clinical origin, discriminating between various genera and including strains with multiresistance to antibiotics.

ecology↗

bioRxiv2019

Global genomic population structure of Clostridioides difficile

Clostridioides difficile is the primary infectious cause of antibiotic-associated diarrhea. Local transmissions and international outbreaks of this pathogen have been previously elucidated by bacterial whole-genome sequencing, but comparative genomic analyses at the global scale were hampered by the lack of specific bioinformatic tools. Here we introduce EnteroBase, a publicly accessible database (http://enterobase.warwick.ac.uk) that automatically retrieves and assembles C. difficile short-reads from the public domain, and calls alleles for core-genome multilocus sequence typing (cgMLST). We demonstrate that the identification of highly related genomes is 89% consistent between cgMLST and single-nucleotide polymorphisms. EnteroBase currently contains 13,515 quality-controlled genomes which have been assigned to hierarchical sets of single-linkage clusters by cgMLST distances. Hierarchical clustering can be used to identify populations of C. difficile at all epidemiological levels, from recent transmission chains through to pandemic and endemic strains, and is largely compatible with prior ribotyping. Hierarchical clustering thus enables comparisons to earlier surveillance data and will facilitate communication among researchers, clinicians and public-health officials who are combatting disease caused by C. difficile.

microbiology↗

bioRxiv2019

Early-Career Coordinated Distributed Experiments: Empowerment Through Collaboration

Coordinated distributed experiments (CDEs) enable the study of large-scale ecological patterns in geographically dispersed areas, while simultaneously providing broad academic and personal benefits for the participants. However, the effective involvement of early-career researchers (ECRs) presents major challenges. Here, we analyze the benefits and challenges of the first CDE exclusively led and conducted by ECRs (i.e. ECR-CDE), which sets a baseline for similar CDEs, and we provide recommendations for successful CDE execution. ECR-CDEs achieve most of the outcomes identified in conventional CDEs as well as extensive benefits for the young cohort of researchers, including: (i) receiving scientific credit, (ii) peer-training in new concepts and methods, (iii) developing leadership and communication skills, (iv) promoting a peer network among ECRs, and (v) building on individual engagement and independence. We also discuss the challenges of ECR-CDEs, which are mainly derived from the lack of independence and instability of the participants, and we suggest mechanisms to address them, such as resource re-allocation and communication strategies.

scientific communication and education↗