Biology subjects

Fernandez-Lanza, V.

Publications and source records attributed to Fernandez-Lanza, V..

4 recordsLinked to original sources

bioRxiv2024

Single-cell RNA sequencing on formalin-fixed and paraffin-embedded (FFPE) tissue identified multi-ciliary cells in breast cancer

The purpose of this study was to evaluate the suitability of paraffin-embedded (FFPE) samples and fixed fresh (FF) samples for single-cell RNA sequencing (scRNAseq). To this end, we compared whether single-cell profiles from FFPE and matched FF tissue samples from one invasive carcinoma of no especial type carcinoma (invasive ductal carcinoma -IDC) and one invasive lobular carcinoma (ILC) were similar when captured using the common immunohistochemical (IHC) and immune features of the tumors. FF and FFPE tissue derived libraries showed high-quality parameters. The cell heterogeneity obtained was similar, although, there were some differences in the number of cells captured, but there were no specific cell populations captured exclusively by one of the two different approaches. The combined analysis of the four samples identified six types of epithelial cells (normal basal cells, four subpopulations of neoplastic cells in the IDC and one in the ILC), and different tumoral microenvironment populations. scRNAseq results from epithelial neoplastic cells were concordant with common IHC markers. In addition, after quantitative assessment of the whole slide images using QuPath, the proportion of immune cells identified by IHQ in FFPE sections were similar to those obtained by scRNAseq. Confirming the suitability of scRNAseq using FFPE tissue, this technique identified one subpopulation of neoplastic cells present only in the IDC, characterized by the expression of genes related with multi-ciliated cell (MCC) differentiation (FOXJ1, ROPN1L, TPPP3, CFAP45). IHC analysis of FOXJ1 expression confirmed the presence of positive cells only in the IDC in a similar proportion to that obtained by scRNAseq. Analysis of FOXJ1 in a cohort of 214 ER-positive invasive carcinomas demonstrated protein expression in at least 1% of cells in 33% of tumors, suggesting frequent focal MCC differentiation. Our results support the suitability of scRNAseq analysis using FFPE tissue and identified a subpopulation of neoplastic MCC in breast cancer.

cancer biology↗

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↗

bioRxiv2020

Simulating the Influence of Conjugative Plasmids Kinetic Values on the Multilevel Dynamics of Antimicrobial Resistance in a Membrane Computing Model

Plasmids harboring antibiotic resistance genes differ in their kinetic values as plasmid conjugation rate, segregation rate by incompatibility with related plasmids, rate of stochastic loss during replication, cost reducing the host-cell fitness, and frequency of compensatory mutations to reduce plasmid cost, depending on the cell mutation frequency. How variation in these values influence the success of a plasmid and their resistance genes in complex ecosystems, as the microbiota? Genes are located in plasmids, plasmids in cells, cells in populations. These populations are embedded in ensembles of species in different human hosts, are able to exchange between them bacterial ensembles during cross-infection and are located in the hospital or the community setting, under various levels of antibiotic exposure. Simulations using new membrane computing methods help predict the influence of plasmid kinetic values on such multilevel complex system. In our simulation, conjugation frequency needed to be at least 10-3 to clearly influence the dominance of a strain with a resistant plasmid. Host strains able to stably maintain two copies of similar plasmids harboring different resistances, coexistence of these resistances can occur in the population. Plasmid loss rates of 10-4 or 10-5 or plasmid fitness costs [≥]0.06 favor the plasmids located in the most abundant species. The beneficial effect of compensatory mutations for plasmid fitness cost is proportional to this cost, only at high mutation frequencies (10-3-10-5). Membrane computing helps set a multilevel landscape to study the effect of changes in plasmid kinetic values on the success of resistant organisms in complex ecosystems.

microbiology↗

bioRxiv2018

Multi-Hierarchical Dynamics of Antimicrobial Resistance Simulated in a Nested Membrane Computing Model

Membrane Computing is a bio-inspired computing paradigm, whose devices are the so-called membrane systems or P systems. The P system designed in this work reproduces complex biological landscapes in the computer world. It uses nested \"membrane-surrounded entities\" able to divide, propagate and die, be transferred into other membranes, exchange informative material according to flexible rules, mutate and being selected by external agents. This allows the exploration of hierarchical interactive dynamics resulting from the probabilistic interaction of genes (phenotypes), clones, species, hosts, environments, and antibiotic challenges. Our model facilitates analysis of several aspects of the rules that govern the multi-level evolutionary biology of antibiotic resistance. We examine a number of selected landscapes where we predict the effects of different rates of patient flow from hospital to the community and viceversa, cross-transmission rates between patients with bacterial propagules of different sizes, the proportion of patients treated with antibiotics, antibiotics and dosing in opening spaces in the microbiota where resistant phenotypes multiply. We can also evaluate the selective strength of some drugs and the influence of the time-0 resistance composition of the species and bacterial clones in the evolution of resistance phenotypes. In summary, we provide case studies analyzing the hierarchical dynamics of antibiotic resistance using a novel computing model with reciprocity within and between levels of biological organization, a type of approach that may be expanded in the multi-level analysis of complex microbial landscapes.

microbiology↗