Biology subjects

Munoz, P.

Publications and source records attributed to Munoz, P..

At least 19 recordsLinked to original sources

bioRxiv2026

Spatiotemporal biogenesis of thylakoid membranes in the green alga Chlamydomonas reinhardtii

Thylakoid membranes are essential for oxygenic photosynthesis, yet the mechanisms underlying their spatial and temporal biogenesis remain poorly understood. Here, using a light-induced membrane regeneration system in Chlamydomonas reinhardtii, we generate a time-resolved map of thylakoid formation and photosynthetic complex assembly at unprecedented spatial and temporal resolution by integrating super-resolution fluorescence microscopy, cryo-electron tomography, proteomics, and spectroscopy. We show that thylakoid biogenesis is globally distributed and multipolar, with new membrane formation occurring at multiple sites across the chloroplast, including regions adjacent to the inner envelope membranes in both basal and lobe regions. We identify F-ATP synthase storage membranes and map the redistribution of translating ribosomes from T-zone enrichment to a chloroplast-wide distribution at early stages of thylakoid formation, consistent with decentralized synthesis of photosynthetic complexes. We further delineate the hierarchical assembly of photosystem supercomplexes, starting with formation of reaction centers. Together, these findings refine the spatial organization of photosynthetic membrane biogenesis and establish a unified spatiotemporal framework linking thylakoid formation, translational activation, and functional maturation, providing a mechanistic basis for plastid engineering to enhance photosynthetic efficiency.

Cell Biology↗

bioRxiv2026

A robust and high-efficiency Rhizobium rhizogenes hairy root transformation platform for Vaccinium

Efficient transformation remains a major constraint to functional genomics and genome editing in Vaccinium, where stable transformation systems are often genotype-dependent and inefficient. Here, we establish a rapid and high-efficiency Rhizobium rhizogenes-mediated hairy root transformation platform optimized for the genus. Using the RUBY visual reporter, transformation efficiency reached 46.7% in leaf explants infected with strain Ar. A4 and cultured on half-strength Woody Plant Medium, with transgenic roots visible within 16 days post-co-cultivation. Comparative evaluation of six R. rhizogenes strains identified Ar. A4 and ATCC15834 as consistently superior across diverse Vaccinium germplasm representing different taxonomic sections, achieving up to 80% efficiency in selected genotypes. While conventional regeneration from transgenic roots was not successful, overexpression of developmental regulators enabled shoot formation with 7% efficiency, demonstrating a path toward stable plant recovery. This platform delivers a rapid, genotype-flexible system for gene validation, metabolic pathway analysis, and genome editing in Vaccinium, substantially expanding the molecular toolkit available for perennial fruit crop research and translational breeding.

plant biology↗

bioRxiv2025

Direct nanopore sequencing of M. tuberculosis on sputa and rescue of suboptimal results to enhance transmission surveillance

Whole-genome sequencing (WGS) enhances precision in predicting antimicrobial resistance and tracking Mycobacterium tuberculosis (MTB) transmission. Due to MTBs slow-growing nature, genomic results are delayed; however, few efforts have sought to accelerate them by performing WGS directly on respiratory specimens. Most culture-free efforts have focused on accelerating resistance prediction. The present study provides further evidence to the only preceding study aiming to accelerate precise delineation of transmission, coupling culture-free WGS to a surveillance programme. Our study is distinguished from its predecessor by being the first to apply flexible nanopore sequencing to further accelerate the process. A total of 71 sputa were selected, in which we applied only a procedure to deplete human DNA, thus avoiding costly and cumbersome capture-bait alternatives. Optimal results (>90% genome covered, mean coverage >45x and >70% genome covered >20x) were obtained from 33.8% of cases, allowing the assignment to transmission clusters close to diagnosis of every new case. A further 12.6% of samples yielded suboptimal results (15.5%-90.92% at >10x), which were exploited through a rescue pipeline. This approach was based on identifying informative SNPs acting as markers for relevant transmission clusters in our population. The pipeline enabled pre-allocation of new cases to pre-existing clusters and, in some cases, precise genomic relationships with the preceding cases in the cluster. In summary, this study demonstrates that epidemiologically valuable information can be obtained directly from sputum in approximately half the samples analysed. It represents a new advancement in the pursuit of faster comparative genomics, with epidemiological purposes, at diagnosis.

genomics↗

bioRxiv2024

Characterization of a nosocomial outbreak caused by VIM-1 Klebsiella michiganensis using Fourier-Transform Infrared (FT-IR) Spectroscopy

Healthcare-associated infections (HAIs) are a significant concern worldwide due to their impact on patient safety and healthcare costs. Klebsiella spp., particularly Klebsiella pneumoniae and Klebsiella oxytoca, are frequently implicated in HAIs and often exhibit multidrug resistance mechanisms, posing challenges for infection control. In this study, we evaluated Fourier-transform Infrared (FT-IR) spectroscopy as a rapid method for characterizing a nosocomial outbreak caused by VIM-1-producing K. oxytoca. A total of 47 isolates, including outbreak strains and controls, were collected from Hospital Universitario Gregorio Maranon, Spain and the University Hospital Basel, Switzerland. FT-IR spectroscopy was employed for bacterial typing, offering rapid and accurate results compared to conventional methods like pulsed-field gel electrophoresis (PFGE) and correlating with whole-genome sequencing (WGS) results. The FT-IR spectra analysis revealed distinct clusters corresponding to outbreak strains, suggesting a common origin. Subsequent WGS analysis identified Klebsiella michiganensis as the causative agent of the outbreak, challenging the initial assumption based on FT-IR results. However, both FT-IR and WGS methods showed high concordance, with an Adjusted Rand index (AR) of 0.882 and an Adjusted Wallace coefficient (AW) of 0.937, indicating the reliability of FT-IR in outbreak characterization. Furthermore, FT-IR spectra visualization highlighted discriminatory features between outbreak and non-outbreak isolates, facilitating rapid screening in case and outbreak is suspected. In conclusion, FT-IR spectroscopy offers a rapid and cost-effective alternative to traditional typing methods, enabling timely intervention and effective management of nosocomial outbreaks. Its integration with WGS enhances the accuracy of outbreak investigations, demonstrating its utility in clinical microbiology and infection control practices.

microbiology↗

bioRxiv2024

SimpleMating: R-package for prediction and optimization of breeding crosses using genomic selection

Selecting parents and crosses is a critical step for a successful breeding program. The ability to design crosses with high means that will maintain genetic variation in the population is the goal for long-term applications. Herein, we describe a new computational package for mate allocation in a breeding program. SimpleMating is a flexible and open-source R package originally designed to predict and optimize breeding crosses in crops with different reproductive systems and breeding designs. Divided into modules, SimpleMating first estimates the cross performance (criterion), such as mean parental average, cross total genetic value, and/or usefulness of a set of crosses. The second module implements an optimization algorithm to maximize a target criterion while minimizing next-generation inbreeding. The software is flexible, allowing users to specify the desired number of crosses, maximum/minimum number of crosses per parent, and the maximum value of the parent relationship for creating crosses. As an outcome, SimpleMating generates a mating plan from the target parental population using single or multi-trait criteria. As example, we implemented and tested SimpleMating in a simulated maize breeding program obtained through stochastic simulations. The crosses designed via SimpleMating showed large genetic mean over time (up to 22% more genetic gain than conventional genomic selection programs, with lower genetic diversity decrease over time), supporting the use of this tool, as well as the use of data-driven decisions in applied breeding programs.

genetics↗

bioRxiv2024

Genome-wide association studies in a diverse strawberry collection unveil loci controlling agronomic and fruit quality traits

Strawberries are cherished for their organoleptic properties and nutritional value. However, breeding new cultivars involves the simultaneous selection of many agronomical and fruit quality traits. The strawberry germplasm collection here studied exhibited extensive phenotypic variation in 26 agronomic and fruit quality traits across three consecutive seasons. Phenotypic correlations and Principal Component Analysis (PCA) revealed relationships among traits and accessions, emphasizing the impact of plant breeding. Genetic diversity analysis on 124 accessions using 44,408 markers denoted a population structure divided into six subpopulations that still retain considerable diversity. Genome-wide association studies (GWAS) for the 26 traits unveiled 121 significant marker-trait associations distributed across 95 quantitative trait loci (QTL). Multiple associations were detected for fruit firmness, a key breeding target, including a prominent locus on chromosome 6A. The candidate gene FaPG1, controlling fruit softening, was identified within this QTL region. Differential expression of FaPG1 confirmed its role as the main contributor to natural variation in fruit firmness. A Kompetitive Allele-Specific PCR (KASP) assay based on SNP AX-184242253, associated with the 6A QTL, predicts a substantial increase in fruit firmness, validating its utility for Marker-Assisted Selection (MAS). In essence, this comprehensive study provides insights into the phenotypic and genetic landscape of the strawberry collection and lays a robust foundation for propelling the development of superior strawberry cultivars through precision breeding. HighlightSeveral QTL controlling agronomic and fruit quality traits detected by genome wide association studies. Natural variation on FaPG1 expression is associated with a mayor and stable QTL for fruit firmness.

plant biology↗

bioRxiv2024

First-in-class Transactivator-Free, Doxycycline-inducible IL-18-engineered CAR-T cells for relapsed/refractory B-cell lymphomas

BackgroundDespite their success treating type B cancers, Chimeric Antigen Receptor (CAR) T cells still showed limited efficacy in certain lymphomas and solid tumors. Reinforcing conventional CAR-T cells to release cytokines can improve their efficacy but also increase safety concerns. Several strategies have been developed to regulate their secretion using minimal promoters that are controlled by chimeric proteins harboring transactivators. However, these chimeric proteins can disrupt the normal physiology of target cells. MethodsCo-transduction with CAR19 and Lent-On-Plus-IL-18 LVs allowed for generating constitutive CAR/Dox-inducible IL-18 CAR-T cells that respond to ultra-low doses of doxycycline (iTRUCK19.18). iTRUCK19.18 were evaluated against an aggressive Burkitt lymphoma model in vitro and in vivo, against primary B-cell tumors and against a CD19-engineered pancreatic tumor model. Patient-derived iTRUCK19.18 cells were also generated. ResultsiTRUCK19.18 controlled IL-18 release through a dual mechanism dependent on doxycycline and T cell activation, thereby enhancing the safety profile. IL-18 release increased the activation state/proinflammatory profile of T cells in a doxycycline-dependent manner without altering cellular fitness, which was translated into an increased CAR-T cell antitumor activity against aggressive hematologic and solid tumor models. In a clinically relevant context, we generated patient-derived iTRUCK19.18 cells able to significantly increase elimination of primary B cells tumors under doxycycline. Furthermore, IL-18-releasing iTRUCK19.18 polarized pro-tumoral M2 macrophages towards an antitumoral phenotype (M1), suggesting the ability to modulate the tumor microenvironment. ConclusionWe have generated the first transactivator-free inducible TRUCKs from healthy donors and B-cell neoplasms patients. iTRUCK19-18 exhibit dual safety control mechanisms for IL-18 secretion and improved antitumoral activity against type-B neoplasms. Inducible IL-18 secretion not only enhanced T cell potency but could also change the tumor microenvironment to a more antitumoral state.

immunology↗

bioRxiv2024

Rapid, automatic typing of Clostridioides difficile Ribotypes Using MALDI-TOF MS

Clostridioides difficile is a major cause of hospital-acquired diarrhea, posing significant clinical challenges due to its high morbi-mortality rates and its involvement in nosocomial outbreaks. Detecting its toxigenic ribotypes rapidly and accurately is crucial for effective outbreak control. This study aimed to create a rapid diagnostic methodology based on MALDI-TOF MS and Machine Learning algorithms to differentiate toxigenic C. difficile RTs. MALDI-TOF spectra were acquired from 379 clinical isolates sourcing from 10 Spanish hospitals and analysed using Clover MSDAS, a specific software for MALDI-TOF spectra analysis, considered as the state-of-the-art tool for this purpose, and AutoCdiff, an ad hoc software developed in this study. Seven biomarker peaks were found to differentiate epidemic RT027 and RT181 strains from other RTs (2463, 3353, 4933, 4993, 6187, 6651 and 6710 m/z). Two peaks (2463 and 4993 m/z) were specifically found in RT027 isolates while combinations of the other 5 peaks allowed the differentiation of RT181 from other ribotypes. Automatic classification tools developed in Clover MSDAS and AutoCdiff using the specific peaks and the entire protein spectra, respectively, showed up to 100% balanced accuracy. Both methods allowed correct ribotype assignment for isolates sourcing from real-time outbreaks. The developed models, available from Clover MSDAS and the AutoCdiff website -https://bacteria.id-offer researchers a valuable tool for quick C. difficile ribotype determination based on MALDI-TOF spectra analysis. Although further validation of the models is still required, they represent rapid and cost-effective methods for standardized C. difficile ribotype assignment.

microbiology↗

bioRxiv2023

A quantitative survey of the blueberry (Vaccinium spp.) nectar microbiome: variation between cultivars, locations, and farm management approaches

Microbes in floral nectar can impact both their host plants and floral visitors, yet little is known about the nectar microbiome of most pollinator-dependent crops. In this study, we examined the abundance and composition of the fungi and bacteria inhabiting Vaccinium spp. nectar, as well as nectar volume and sugar concentrations, hypothesizing that nectar traits and microbial communities would vary between plants. We compared wild V. myrsinites with two field-grown V. corymbosum cultivars collected from two organic and two conventional farms. Differences in nectar traits and microbiomes were identified between V. corymbosum cultivars but not Vaccinium species. The microbiome of cultivated plants also varied greatly between farms, whereas management regime had only subtle effects, with higher fungal populations detected under organic management. Nectars were hexose-dominant, and sugars were depleted in nectar with higher cell densities. Bacteria were more common than fungi in blueberry nectar, although both were frequently detected and co-occurred more often than would be predicted by chance. Cosmopolitan blueberry nectar microbes that were isolated in all plants, including Rosenbergiella sp. and Symmetrospora symmetrica, were identified. This study provides the first systematic report of the blueberry nectar microbiome, which may have important implications for pollinator and crop health. One-sentence summaryParallel analysis of blueberry crops and a wild relative offers insight into the impacts of management and domestication on the nectar microbiome O_FIG O_LINKSMALLFIG WIDTH=183 HEIGHT=200 SRC="FIGDIR/small/556904v1_ufig1.gif" ALT="Figure 1"> View larger version (62K): org.highwire.dtl.DTLVardef@15f36caorg.highwire.dtl.DTLVardef@63496org.highwire.dtl.DTLVardef@1667a6eorg.highwire.dtl.DTLVardef@eff26e_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2023

Frequent emergence of resistance mutations following complex intra-host genomic dynamics in SARS-CoV-2 patients receiving Sotrovimab

The emergence of the Omicron variant of SARS-CoV-2 represented a challenge to the treatment of COVID-19 with monoclonal antibodies. Only Sotrovimab maintained partial activity, allowing it to be used in high-risk patients infected with the Omicron variant. However, the reports of resistance mutations to Sotrovimab call for efforts to better understand the intra-patient emergence of this resistance. A retrospective genomic analysis was conducted on respiratory samples from immunocompromised patients infected with SARS-CoV-2 who received Sotrovimab at our hospital between December 2021 and August 2022. The study involved 95 sequential specimens from 22 patients (1-12 samples/patient; 3-107 days post-infusion (Ct [≤] 32)). Resistance mutations (in P337, E340, K356, and R346) were detected in 68% of cases; the shortest time to detection of a resistance mutation was 5 days after Sotrovimab infusion. The dynamics of resistance acquisition were highly complex, with up to 11 distinct amino acid changes in specimens from the same patient. In two patients, the mutation distribution was compartmentalized in respiratory samples from different sources. This is the first study to examine the acquisition of resistance to Sotrovimab in the BA.5 lineage, enabling us to determine the lack of genomic or clinical differences between Sotrovimab resistance in BA.5 relative to BA.1/2. Across all Omicron lineages, the acquisition of resistance delayed SARS-CoV-2 clearance (40.67 vs 19.5 days). Close, real-time genomic surveillance of patients receiving Sotrovimab should be mandatory to facilitate early therapeutic interventions.

microbiology↗

bioRxiv2023

Using visual scores and categorical data for genomic prediction of complex traits in breeding programs

Most genomic prediction methods are based on assumptions of normality due to their simplicity, robustness, and ease of implementation. However, in plant and animal breeding, target traits are often collected as categorical data, thus violating the normality assumption, which could affect the prediction of breeding values and the estimation of crucial genetic parameters. In this study, we examined the main challenges of categorical phenotypes in genomic prediction and genetic parameter estimation using mixed models, Bayesian approaches, and machine learning techniques. We evaluated these approaches using simulated and real breeding data sets. Our contribution in this study is a five-fold demonstration: (i) collecting data using an intermediate number of categories (1 to 3 and 1 to 5 scores) is the best strategy, even considering errors and subjectivity associated with visual scores; (ii) in the context of genomic prediction, Linear Mixed Models and Bayesian Linear Regression Models are robust to the normality violation, but marginal gains can be achieved when using Bayesian Ordinal Regression Models (BORM) and Random Forest Classification technique; (iii) genetic parameters are better estimated using BORM; (iv) our conclusions using simulated data are also applicable to real data in autotetraploid blueberry, which can guide breeders decisions; and (v) a comparison of continuous and categorical phenotype testing for complex traits with low heritability, found that investing in the evaluation of 600-1000 categorical data points with low error, when it is not feasible to collect continuous phenotypes, is a strategy for improving predictive abilities. Our findings suggest the best approaches for effectively using categorical traits to explore genetic information in breeding programs, and highlight the importance of investing in the training of evaluator teams and in high-quality phenotyping. Key messageAn approach for handling categorical data with potential errors and subjectivity in scores was evaluated in simulated and blueberry recurrent selection breeding schemes to assist breeders in their decision-making.

genetics↗

bioRxiv2022

Identification of Mycobacterium abscessus subspecies by MALDI-TOF Mass Spectrometry and Machine Learning

Mycobacterium abscessus complex is one of the most common and pathogenic nontuberculous mycobacteria (NTM) isolated in clinical laboratories. It consists of three subspecies: M. abscessus subsp. abscessus, M. abscessus subsp. bolletii and M. abscessus subsp. massiliense. Due to their different antibiotic susceptibility pattern, a rapid and accurate identification method is necessary for their differentiation. Although matrix assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) has proven useful for NTM identification, the differentiation of M. abscessus subspecies is challenging. In this study, a collection of 244 clinical isolates of M. abscessus complex was used for MALDI-TOF MS analysis and for the development of machine learning predictive models. Overall, using a Random Forest model with several confidence criteria (samples by triplicate and similarity values >60%), a total of 95.8% of isolates were correctly identified at subspecies level. In addition, differences in culture media, colony morphology and geographic origin of the strains were evaluated, showing that the latter most affected the mass spectra of isolates. Finally, after studying all protein peaks previously reported for this complex, two novel peaks with potential for subspecies differentiation were found. Therefore, machine learning methodology has proven to be a promising approach for rapid and accurate identification of subspecies of the M. abscessus complex using MALDI-TOF MS.

microbiology↗

bioRxiv2022

Chloroplast genome assemblies and comparative analyses of major Vaccinium berry crops

BackgroundVaccinium is an economically important genus of berry crops in the family Ericaceae. Given the numerous hybridizations and polyploidization events among Vaccinium species, the taxonomy of this genus has remained uncertain and the subject of long debate. Therefore, the availability of more genomic resources for Vaccinium can provide useful tools for phylogenetic resolution, species identification, authentication of berry food products, and a framework for genetic engineering. ResultsIn this study, we assembled five Vaccinium chloroplast sequences representing the following berry types: northern highbush blueberry (V. corymbosum), southern highbush blueberry (V. corymbosum hybrids), rabbiteye blueberry (V. virgatum), lowbush blueberry (V. angustifolium), and bilberry (V. myrtillus). Two complete plastid genomes were achieved using long-read PacBio sequencing, while three draft sequences were obtained using short-read Illumina sequencing. Comparative analyses also included other previously available Vaccinium chloroplast sequences, especially the commercially important species V. macrocarpon (cranberry). The Vaccinium chloroplast genomes exhibited a circular quadripartite structure, with an overall highly conserved synteny and sequence identity among them. Despite their high similarity, we identified some polymorphic regions in terms of expansion/contraction of inverted repeats, gene copy number variation, simple sequence repeats, and single nucleotide polymorphisms. Phylogenetic analysis revealed multiple origins of highbush blueberry plastomes, likely due to the hybridization events during northern and southern highbush blueberry domestication. ConclusionsOur results enrich the genomic data availability for new Vaccinium species by sequencing and assembling the chloroplast DNA of major economically important berry types. Additional whole plastome analyses including more samples and wild species will be useful to obtain a refined knowledge of the maternal breeding history of blueberries and increase phylogenetic resolution at low taxonomic levels.

plant biology↗

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

Rapid and reproducible MALDI-TOF-based method for detection Vancomycin-resistant Enterococcus faecium using classifying algorithms

Vancomycin-resistant Enterococcus faecium has become a health threat over the last 20 years due to its ability to rapidly spread and cause outbreaks in hospital settings. Although MALDI-TOF MS has already demonstrated its usefulness for accurate identification of E. faecium, its implementation for antimicrobial resistance detection is still under evaluation. The reproducibility of MALDI-TOF MS for peak analysis and its performance for correct discrimination of vancomycin susceptible isolates (VSE) from those hosting the VanA and VanB resistance mechanisms was evaluated in this study. For the first goal, intra-spot, inter-spot -technical- and inter-day -biological- reproducibility was assayed. The capability of MALDI-TOF to discriminate VSE isolates from VanA VRE and VanB VRE strains was carried out on protein spectra from 178 E. faecium unique clinical isolates -92 VSE, 31 VanA VRE, 55 VanB VRE-, processed with Clover MS Data Analysis software. Unsupervised (Principal Component Analysis -PCA-) and supervised algorithms (Support Vector Machine -SVM-, Random Forest -RF- and Partial Least Squares-Discriminant Analysis -PLS-DA-) were applied. The reproducibility assay showed lower variability for normalized data (p<0.0001) and for the peaks within the 3000-9000 m/z range. Besides, 80.9%, 79.21% and 77.53% VSE vs VRE (VanA + VanB) discrimination was achieved by applying SVM, RF and PLS-DA, respectively. Correct differentiation of VanA from VanB VRE isolates was obtained by SVM in 86.65% cases. The implementation MALDI-TOF MS and peak analysis could represent a rapid and effective tool for VRE screening. However, further improvements are needed to increase the accuracy of this approach.

microbiology↗

bioRxiv2021

Genomic Prediction in Family Bulks Using Different Traits and Cross-Validations in Pine

Genomic prediction (GP) integrates statistical, genomic and computational tools to improve the estimation of breeding values and increase genetic gain. Due to the broad diversity in biology, breeding scheme, propagation method, and unit of selection, no universal GP approach can be applied in all crops. In a genome-wide family prediction (GWFP) approach, the family bulk is the basic unit of selection. We tested GWFP in two loblolly pine (Pinus taeda L.) datasets: a breeding population composed of 63 full-sib families (5-20 individuals per family), and a simulated population with the same pedigree structure. In both populations, phenotypic and genomic data was pooled at the family level in silico. Marker effects were estimated to compute genomic estimated breeding values at the individual (GEBV) and family (GWFP) levels. Less than six individuals per family produced inaccurate estimates of family phenotypic performance and allele frequency. Tested across different scenarios, GWFP predictive ability was higher than those for GEBV in both populations. Validation sets composed of families with similar phenotypic mean and variance as the training population yielded predictions consistently higher and more accurate than other validation sets. Results revealed potential for applying GWFP in breeding programs whose selection unit are family bulks, and for systems where family can serve as training sets. The GWFP approach is well suited for crops that are routinely genotyped and phenotyped at the plot-level, but it can be extended to other breeding programs. Higher predictive ability obtained with GWFP would motivate the application of GP in these situations.

genomics↗

bioRxiv2020

QTL Mapping in Outbred Tetraploid (and Diploid) Diallel Populations

Over the last decade, multiparental populations have become a mainstay of genetics research in diploid species. Our goal was to extend this paradigm to autotetraploids by developing software for quantitative trait locus (QTL) mapping in connected F1 populations derived from a set of shared parents. For QTL discovery, phenotypes are regressed on the dosage of parental haplotypes to estimate additive effects. Statistical properties of the model were explored by simulating half-diallel diploid and tetraploid populations with different population sizes and numbers of parents. Across scenarios, the number of progeny per parental haplotype (pph) largely determined the statistical power for QTL detection and accuracy of the estimated haplotype effects. Multi-allelic QTL with heritability 0.2 were detected with 90% probability at 25 pph and genome-wide significance level 0.05, and the additive haplotype effects were estimated with over 90% accuracy. Following QTL discovery, the software enables a comparison of models with multiple QTL and non-additive effects. To illustrate, we analyzed potato tuber shape in a half-diallel population with 3 tetraploid parents. A well-known QTL on chromosome 10 was detected, for which the inclusion of digenic dominance lowered the Deviance Information Criterion (DIC) by 17 points compared to the additive model. The final model also contained a minor QTL on chromosome 1, but higher order dominance and epistatic effects were excluded based on the DIC. In terms of practical impacts, the software is already being used to select offspring based on the effect and dosage of particular haplotypes in breeding programs.

genetics↗