Biology subjects

Cruz, C. D.

Publications and source records attributed to Cruz, C. D..

7 recordsLinked to original sources

bioRxiv2026

Heavy metal resistance promotes higher biomass formation in multidrug-resistant wastewater Escherichia coli isolates from Finland

Antimicrobial resistance alone is a serious threat to global public health, but even more so when multidrug-resistant bacteria harbour heavy metal resistance genes, as these can drive co-selection of antibiotics and produce biofilms. The presence of such genes, combined with the bacterias ability to form biofilms, is strongly linked to treatment failure, persistent infections, and reduced therapeutic options. Here, we used the resazurin-crystal violet combination assay to screen a representative cohort of whole-genome sequenced Escherichia coli isolates (n=20) obtained from wastewater surveillance. The specific biofilm formation (SBF) index was used to grade the intensity of biofilm formation as strong, moderate, weak, and non-biofilm producers. Correlation analysis was used to test the association between the intensity of biofilm formation and genotypic features. The SBF index revealed that most of the wastewater E. coli isolates (n=13/20) were weak/non-biofilm producers, four isolates produced moderate biofilms, and three isolates (ST1434: A-O18ab:H55; ST401:A-H25; and ST399:A-O19:H12) produced strong biofilms. The diversity of virulence factors was similar in most of the isolates, except for the three isolates, which had fewer abundant virulence factors. The correlation analysis showed that there was no association between the expression of virulence genes and the formation of strong biofilms by the isolates (p > 0.05). Drug resistance profile was not correlated with higher biofilm production (p > 0.05), as 68.8% (n=11/16) of multi-drug resistant (MDR) and 50% (n=2/4) of non-MDR isolates had weak or no biofilm formation. Similarly, the SBF index was not associated with the number of plasmids in each of the E. coli genomes (p = 0.334). However, there was a positive association between the presence of two or more heavy metal resistance genes (HMRGs) and the strong biofilm formation in our isolates (p = 0.002). Our findings revealed the low occurrence of strong biofilm producers among wastewater E. coli isolates. Further studies are needed to evaluate the impact of the presence of HMRGs and their direct or indirect contribution to enhancing biofilm production and persistence in environmental reservoirs.

microbiology↗

bioRxiv2025

Disassembly of the Escherichia coli AcrABZ-TolC efflux pump by ligand-mediated disruption of TolC-AcrA interfacial contacts

The outer membrane factor TolC is an essential component of various efflux pump complexes in E. coli and represents a potential target for antibiotic adjuvants. By means of a virtual screen for TolC-binding compounds, we identified the kinase inhibitor CEP- 37440 to shift the minimum inhibitory concentration of antibiotics piperacillin and levofloxacin in E. coli. To determine the substructure relevant for TolC binding, a hit deconstruction approach was applied, resulting in a fragment-like compound with low affinity for TolC and AcrB (LP-115). Dynamic light scattering revealed LP-115 to reduce the hydrodynamic radius of AcrABZ-TolC, indicating a disassembly of the efflux pump complex. A cryo-EM structure demonstrated LP-115 to bind at the TolC-AcrA interface within the AcrABZ-TolC complex, thereby disordering the interface and inducing a closed conformation of TolC. Our results suggest that ligand-mediated TolC-AcrA interface disruption represents a novel mechanism of efflux pump inhibition.

microbiology↗

bioRxiv2025

Differences Among Fungal Microbiomes Associated with Tar Spot of Corn in Ecuador, Guatemala, and Indiana, USA

Tar spot of corn, caused by the obligate fungal pathogen Phyllachora maydis, was previously found to be associated with Monographella maydis (syn. Microdochium maydis) and Coniothyrium phyllachorae in Mexico. While M. maydis was once believed to cause fisheye lesions and C. phyllachorae was described as a mycoparasite of P. maydis, their presence and roles in tar spot remain uncertain. This study hypothesizes that the tar spot-associated fungal microbiome varies geographically and includes fungal species with inconsistent associations with the disease. To test this, tar spot-infected leaves were collected in Guatemala, Ecuador, and the USA. This study shows that the distribution and abundance of these fungi exhibit a geographical pattern and are not consistently associated with tar spot lesions in all locations. We confirmed the presence of Microdochium in some parts of Ecuador and Guatemala but not in the USA. The role of Microdochium remains unclear and whether it can be a primary pathogen needs to be tested by additional research, as its occurrence and abundance depend more on geographical locations rather than the presence of fisheye lesions. Coniothyrium was detected at low abundance in samples from Ecuador and Guatemala but was absent in the USA. Additionally, we identified the likely mycoparasite Paraphaeosphaeria as highly prevalent across samples from all three countries. Microbiome diversity analysis grouped tar spot samples into four distinct clusters: two dominated by P. maydis, one primarily associated with Microdochium, and a fourth characterized by a combination of P. maydis and Paraphaeosphaeria.

plant biology↗

bioRxiv2024

Functional Changes to Achilles Tendon and Enthesis in a Mouse Model of an Adolescent Masculine Gender-Affirming Hormone Treatment

Many transgender youth seek gender affirming care, such as puberty suppression, to prolong decision-making and to align their physical sex characteristics with their gender identity. During peripubertal growth, connective tissues such as tendon rapidly adapt to applied mechanical loads (e.g., exercise) yet if and how tendon adaptation is influenced by sex and gender affirming hormone therapy during growth remains unknown. The goal of this study was to understand the how pubertal suppression influences the structural and functional properties of the Achilles tendon using an established mouse model of transmasculine gender affirming hormone therapy. C57BL/6N female-born mice were assigned to experimental groups to mimic gender-affirming hormone therapy in human adolescents, and treatment was initiated prior to the onset of puberty (at postnatal day 26, P26). Experimental groups included controls and mice serially treated with gonadotropin release hormone analogue (GnRHa), delayed Testosterone (T), or GnRHa followed by T. We found that puberty suppression using GnRHa, with and without T, improved the overall tendon load capacity in female-born mice. Treatment with T resulted in an increase in the maximum load that tendon can withstand before failure. Additionally, we found that GnRHa, but not T, treatment resulted in a significant increase in cell density at the Achilles enthesis. NEW & NOTEWORTHYThese findings demonstrate that puberty suppression or testosterone does not negatively influence tendon structural or functional properties in a mouse model of transmasculine gender affirming care. In all treatment groups, the ability of the tendon to withstand load was significantly increased. Puberty suppression with GnRHa significantly increased enthesis cell density, suggesting an extended growth phase. These findings elucidate the effects of gender affirming care on the structural and functional properties of the tendon and enthesis.

physiology↗

bioRxiv2024

Trait prediction through computational intelligence and machine learning applied to soybean (Glycine max) breeding in shaded environments

This study aims to identify more relevant predictors traits, considering different prediction approaches in soybean under different shading levels in the field, using methodologies based on artificial intelligence and machine learning. The experiments were carried out under different shading levels in a greenhouse and in the field, using sixteen cultivars. We have evaluated grain yield, which was used as a response trait, and 22 other attributes as explanatory traits. Three levels of shading were used to restrict photosynthetically active radiation (RPAR): 0%, 25%, and 48%. At full sun level (0% RPAR), the traits that presented better predictive performances using a multilayer perceptron were specific leaf area, plant height and number of pods. In the three levels of shading, the plant height trait exhibited the best performance for the radial base function network. Plant height showed the best predictive efficiency for grain yield at 25% and 48% RPAR, for all machine learning methodologies. Computational intelligence and machine learning methodologies have proven to be efficient in predicting soybean grain yield, regardless of shading level.

genetics↗

bioRxiv2021

Multi-trait and multi-environment Bayesian analysis to predict the G x E interaction in flood-irrigated rice

The biggest challenge for the reproduction of flood-irrigated rice is to identify superior genotypes that present development of high-yielding varieties with specific grain qualities, resistance to abiotic and biotic stresses in addition to superior adaptation to the target environment.Thus, the objectives of this study were to propose a multi-trait and multi-environment Bayesian model to estimate genetic parameters for the flood-irrigated rice crop. To this end, twenty-five rice genotypes belonging to the flood-irrigated rice breeding program were evaluated. Grain yield and flowering were evaluated in the agricultural year 2017/2018. The experimental design used in all experiments was a randomized block design with three replications. The Markov Chain Monte Carlo algorithm was used to estimate genetic parameters and genetic values. The flowering is highly heritable by the Bayesian credibility interval: h2 = 0.039-0.80, and 0.02-0.91, environment 1 and 2, respectively. The genetic correlation between traits was significantly different from zero in the two environments (environment 1: -0.80 to 0.74; environment 2: -0.82 to 0.86. The relationship of CVe and CVg higher for flowering in the reduced model (CVg/CVe= 5.83 and 13.98, environments 1 and 2, respectively). For the complete model, this trait presented an estimate of the relative variation index of: CVe= 4.28 and 4.21, environments 1 and 2, respectively. In summary, the multi-trait and multi-environment Bayesian model allowed a reliable estimate of the genetic parameter of flood-irrigated rice. Bayesian analyzes provide robust inference of genetic parameters. Therefore, we recommend this model for genetic evaluation of flood-irrigated rice genotypes, and their generalization, in other crops. Precise estimates of genetic parameters bring new perspectives on the application of Bayesian methods to solve modeling problems in the genetic improvement of flood-irrigated rice.

plant biology↗

bioRxiv2018

Subset selection of markers for genome-enabled prediction of genetic val-ues using radial basis function neural networks

This paper aimed to evaluate the efficiency of subset selection of markers for genome-enabled prediction of genetic values using radial basis function neural networks (RBFNN). For this purpose, an F1 population from hybridization of divergent parents with 500 individuals genotyped with 1,000 SNP-type markers was simulated. Phenotypic traits were determined by adopting three different gene action models - additive, additive-dominant, and epistasic, complying with two dominance situations: partial and complete with quantitative traits admitting heritability (h2) equal to 30 and 60%, each one controlled by 50 loci, considering two alleles per locus, totaling 12 different scenarios. To evaluate the predictive ability of RR_BLUP and the neural networks, a cross-validation procedure with five replicates were trained using 80% of the individuals of the population. Two methods were used: dimensionality reduction and stepwise regression. The square of the correlation between the predicted genomic estimated breeding value (GEBV) and the phenotype value was used to measure predictive reliability. For h2 = 0.3 in the additive scenario, the R2 values were 59% for neural network (RBFNN) and 57% for RR-BLUP, and in the epistatic scenario, R2 values were 50% and 41%, respectively. Additionally, when analyzing the mean-squared error root, the difference in performance between the techniques is even greater. For the additive scenario, the estimates were 91 for RR-BLUP and 5 for neural networks and, in the most critical scenario, they were 427 for RR-BLUP and 20 for neural network. The results showed that the use of neural networks and variable selection techniques allows capturing epistasis interactions, leading to an improvement in the accuracy of prediction of the genetic value and, mainly, to a large reduction of the mean square error, which indicates greater genomic value.

genetics↗