Jose Luis Lavín

Selenoproteins are characterized by the incorporation of selenocysteine (Sec) via UGA codon recoding, play vital roles in redox homeostasis and enzymatic catalysis. Despite this biological significance, their computational identification remains challenging, and recent assessments confirm that public databases suffer from widespread annotation errors, often misclassifying these proteins. Current predictive tools frequently operate as “black boxes” with limited interpretability or lack integrated functional validation layers, which contributes to high false-positive rates and persistent ambiguities in the annotation of the selenoproteome. We present SelCysML, an accessible and highly accurate computational pipeline that integrates an ensemble of machine learning (ML) models with a parallel functional validation module using BLAST. By utilizing curated physicochemical descriptors—Amino Acid Composition (AAC) and Composition, Transition, Distribution (CTDC)—the model achieves a robust performance, with an AUC-ROC of 0.968 and a Matthews Correlation Coefficient (MCC) of 0.787. SelCysML not only classifies protein sequences but also provides functional evidence through sequence similarity, effectively bridging the gap between automated prediction and experimental biological insight. SelCysML provides a user-friendly, interpretable, and precise solution for the identification of bacterial selenoproteins in proteomic datasets, serving as a reliable tool for both bioinformaticians and experimental biologists.

RNA extraction demands careful sample handling and processing due to the lack of stability of RNA in the environment. However, these conditions are sometimes challenging to achieve when working with free-ranging animals. The main objective of this study was to evaluate the efficiency of three commercial RNA extraction kits for biological samples obtained from 6 wild bird species (black-headed gull, griffon vulture, house sparrow, mallard, song thrush and common starling). We extracted 24 samples (faeces, oropharyngeal swabs, and cloacal swabs) using RNeasy PowerMicrobiome Kit, RNeasy PowerFecal Pro Kit, and RNeasy Mini Kit (Qiagen). The extraction performance was assessed based on total RNA yield, RNA integrity, and purity ratios (260/280 and 260/230). Moreover, an analysis to determine the inhibitory effect of the extracted samples and a multiple linear regression model were performed.RNA extraction performance varied considerably among species, sample types and extraction kits. Overall, the RNeasy PowerFecal Pro Kit yielded higher RNA integrity and purity values and showed lower RT-qPCR inhibition than the other kits, particularly in faecal samples. These findings highlight the importance of selecting extraction methods according to both species and sample type when working with wildlife samples.

Background: Understanding how the respiratory microbiota matures with age is key to improving poultry health and pathogen surveillance, yet the ecological processes shaping this transition remain elusive. We aimed to develop an interpretable machine-learning framework capable of identifying age-associated microbial signatures within the chicken nasal microbiota across heterogeneous datasets. Results: We compiled data from five independent chicken studies and normalized microbial abundances using Counts Per Million (CPM). To address dataset imbalance and ensure cross-study generalizability, we implemented SMOTE over-sampling and a Leave-One-Study-Out (LOSO) cross-validation framework. Within this architecture, we utilized Recursive Feature Elimination (RFE) to identify a stable consensus signature composed of taxa persisting in at least 70% of the iterations. We benchmarked five algorithms: Classification and Regression Trees (CART), k-nearest neighbors (kNN), Support Vector Machines (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). RF emerged as the best model, achieving a balanced accuracy of 0.965 and a Kappa of 0.920. Consequently, the contribution of each feature was quantified through SHapley Additive exPlanations (SHAP) values on the selected RF model, enabling transparent interpretation of age-dependent microbial patterns. This approach distilled a compact set of predictive taxa, including Corynebacterium , Kocuria , and members of the Micrococcaceae . External validation with longitudinal samples from a Highly Pathogenic Avian Influenza Virus (HPAIV) infection confirmed full generalization, with all 57 samples from 22 chickens correctly classified even under viral-induced conditions. Conclusions: The proposed workflow combining LOSO-based feature selection, class-balancing, and interpretable machine learning provides a transferable framework for microbiota-based age inference. Such approaches may inform health monitoring, management of poultry production systems, and wildlife surveillance, illustrating the power of interpretable artificial intelligence to reveal conserved host-microbe dynamics across avian systems.

The online version contains supplementary material available at 10.1038/s41598-025-33989-2.

Influenza A infection is frequently detected in marine mammals causing mortality events of different magnitude. In recent years, the highly pathogenic avian influenza (HPAI) H5N1 virus (clade 2.3.4.4b) has been rapidly expanding and infecting a new variety of avian and mammalian species, including marine mammals. Given this new epidemiological context, the objective of this work was to evaluate the influenza A virus infection of marine mammals stranded on the Basque Country coast. 38 marine mammals were studied (2012–2024), most of them (79%) being dolphins (striped dolphin, common dolphin, and common bottlenose dolphin). At necropsy, samples were collected from lung, intestine, and central nervous system when possible. During 2024, serum samples and oropharyngeal, nasal, and faeces/rectum swabs were also collected. Real-time RT-qPCR was applied for influenza A virus detection in tissue and swab samples, and commercial enzyme-linked immunosorbent assay (ELISA) was used to detect antibodies against influenza A viruses. All samples tested negative by PCR and ELISA. Despite the negative results obtained, we consider the continuation of the monitoring of influenza viruses in marine mammals essential by establishing collaboration networks and standardized protocols that allow us to gain insight into the epidemiology of influenza viruses in these species.

Campylobacter fetus is an animal pathogen that contains 2 mammal-associated subspecies: Campylobacter fetus subsp. fetus (Cff) and Campylobacter fetus subsp. venerealis (Cfv) including its biovar intermedius that exhibit different biochemical traits and differences in pathogenicity. Although plasmids are important in the horizontal transfer of antimicrobial resistance genes and virulence factors, C. fetus plasmids are understudied. Here, the closed sequences of 12 plasmids from Spanish C. fetus isolates were compared with the publicly available DNA sequences of C. fetus plasmids and other members of the Campylobacterales order. Sizes of C. fetus plasmids from Spanish isolates ranged between 4 and 50 kb and most of them (10/12) were potentially conjugative. Comparative analysis of the plasmids’ gene content revealed a close genetic relationship between the plasmids of C. fetus isolated in Spain and those from other geographical regions, while being clearly distinct from plasmids of other Campylobacter species. Furthermore, C. fetus plasmids were grouped into two main clusters regardless of their geographic location or lineage. The distribution pattern of relaxase, replicase, and single-stranded DNA binding SSB protein encoding genes showed a clustering comparable to that resulting from plasmid whole gene content analysis, suggesting its potential use for the classification of C. fetus plasmids. Most of the larger plasmids harbored mobile genetic elements. These results can help to better understand the evolutionary dynamics and pathogenic implications of C. fetus plasmids.

Myelination is essential for neuronal function and health. In peripheral nerves, >100 causative mutations have been identified that cause Charcot-Marie-Tooth disease, a disorder that can affect myelin sheaths. Among these, a number of mutations are related to essential targets of the posttranslational modification neddylation, although how these lead to myelin defects is unclear. Here, we demonstrate that inhibiting neddylation leads to a notable absence of peripheral myelin and axonal loss both in developing and regenerating mouse nerves. Our data indicate that neddylation exerts a global influence on the complex transcriptional and posttranscriptional program by simultaneously regulating the expression and function of multiple essential myelination signals, including the master transcription factor EGR2 and the negative regulators c-Jun and Sox2, and inducing global secondary changes in downstream pathways, including the mTOR and YAP/TAZ signaling pathways. This places neddylation as a critical regulator of myelination and delineates the potential pathogenic mechanisms involved in CMT mutations related to neddylation.

Campylobacter fetuscomprises two closely related mammal-associated subspecies:Campylobacter fetussubsp.fetus(Cff) andCampylobacter fetussubsp.venerealis(Cfv). The latter causes bovine genital campylobacteriosis, a sexually-transmitted disease endemic in Spain that results in significant economic losses in the cattle industry. Here, 33C. fetusSpanish isolates were whole-genome sequenced and compared with 62 publicly availableC. fetusgenomes from other countries. Genome-based taxonomic identification revealed high concordance with in silico PCR, confirming Spanish isolates asCff(n = 4),Cfv(n = 9) andCfvbiovarintermedius(Cfvi, n = 20). MLST analysis assigned the Spanish isolates to 6 STs, including three novel: ST-76 and ST-77 forCfvand ST-78 forCff. Core genome SNP phylogenetic analysis of the 95 genomes identified multiple clusters, revealing associations at subspecies and biovar level between genomes with the same ST and separating theCfvigenomes from Spain and other countries. A genome-wide association study identifiedpqqLas aCfv-specific gene and a potential candidate for more accurate identification methods. Functionality analysis revealed variations in the accessory genome ofC. fetussubspecies and biovars that deserve further studies. These results provide valuable information about the regional variants ofC. fetuspresent in Spain and the genetic diversity and predicted functionality of the different subspecies.