The evaluation of bacterial adhesive properties at a single-cell level is critical for under standing the role of phenotypic heterogeneity in bacterial attachment and community formation. Bacterial population exhibits a wide variety of adhesive properties at the single-cell level, suggesting that bacterial adhesion is a rather complex process and some bacteria are prone to phenotypic heterogeneity. This heterogeneity was more pronounced for Escherichia coli, where two subpopulations were detected. Subpopulations exhibiting higher adhesion forces may be better adapted to colonize a new surface, especially during sudden changes in environmental conditions. Escherichia coli was characterized by a higher adhesion force, a stronger ability to form biofilm and larger heterogeneity index calculated in comparison with Bacillus subtilis. Higher adhesion forces are associated with a more efficient attachment of bacteria observed in an adhesion assay and might provide a basis for successful colonization, survival and multiplications in changing environment. The atomic force microscopy provides a platform for investigation of the adhesion heterogeneity of individual cells within a population, which may be expected to underpin further elucidation of the adaptive significance of phenotypic heterogeneity in a natural environment.
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http://dx.doi.org/10.1111/1758-2229.12978 | DOI Listing |
Randomized controlled trials (RCTs) evaluating anti-cancer agents often lack generalizability to real-world oncology patients. Although restrictive eligibility criteria contribute to this issue, the role of selection bias related to prognostic risk remains unclear. In this study, we developed TrialTranslator, a framework designed to systematically evaluate the generalizability of RCTs for oncology therapies.
View Article and Find Full Text PDFNat Commun
January 2025
Hopp Children's Cancer Center (KiTZ), Heidelberg, Germany.
Human cancer cell lines are the mainstay of cancer research. Recent reports showed that highly mutated adult carcinoma cell lines (mainly HeLa and MCF-7) present striking diversity across laboratories and that long-term continuous culturing results in genomic/transcriptomic heterogeneity with strong phenotypical implications. Here, we hypothesize that oligomutated pediatric sarcoma cell lines mainly driven by a fusion transcription factor, such as Ewing sarcoma (EwS), are genetically and phenotypically more stable than the previously investigated adult carcinoma cell lines.
View Article and Find Full Text PDFTrends Cancer
January 2025
Cancer Immunity Laboratory, Molecular Oncology Program, Spanish National Cancer Research Center (CNIO), Madrid, Spain. Electronic address:
Macrophages are myeloid cells that receive, integrate, and respond to tumoral cues. Tumors evolve and are shaped by macrophages, with tumor-associated macrophage (TAM)-tumor sculpting capacities going beyond an increase in their cellular mass. Longitudinal and local heterogeneity of TAM states is now possible with the use of single-cell and spatial transcriptomics.
View Article and Find Full Text PDFRev Alerg Mex
December 2024
Departamento de Inmunología, Hospital Infantil de Especialidades de Chihuahua; Facultad de Medicina y Ciencias Biomédicas, Universidad Autónoma de Chihuahua.
Background: 22q11 deletion syndrome consists of a variable grouping of phenotypic features and immunological defects secondary to the loss of genetic material located in the 22q11.2 band. The 22q11 deletion spectrum encompasses different syndromes related to the same etiology and with overlapping anomalies, including DiGeorge syndrome, velocardiofacial syndrome, among others.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
University of Pennsylvania, Philadelphia, PA, USA.
Background: Alzheimer's disease (AD), characterized by significant brain volume reduction, is influenced by genetic predispositions related to brain volumetric phenotypes. While genome-wide association studies (GWASs) have linked brain imaging-derived phenotypes (IDPs) with AD, existing polygenic risk scores (PRSs) based models inadequately capture this relationship. We develop BrainNetScore, a network-based model enhancing AD risk prediction by integrating genetic associations between multiple brain IDPs and AD incidence.
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