A framework to validate fluorescently labeled DNA-binding proteins for single-molecule experiments.

Due to the enhanced labeling capability of maleimide-based fluorescent probes, lysine-cysteine-lysine (KCK) tags are frequently added to proteins for visualization. In this study, we employed an in vitro single-molecule DNA flow-stretching assay as a sensitive way to assess the impact of the KCK tag on the property of DNA-binding proteins. Using Bacillus subtilis ParB as an example, we show that, although no noticeable changes were detected by in vivo fluorescence imaging and chromatin immunoprecipitation (ChIP) assays, the KCK tag substantially altered ParB's DNA compaction rates and its response to nucleotide binding and to the presence of the specific sequence (parS) on the DNA. While it is typically assumed that short peptide tags minimally perturb protein function, our results urge researchers to carefully validate the use of tags for protein labeling. Our comprehensive analysis can be expanded and used as a guide to assess the impacts of other tags on DNA-binding proteins in single-molecule assays.

Using DNA flow-stretching assay as a tool to validate the tagging of DNA-binding proteins for single-molecule experiments.

Due to the enhanced labeling capability of maleimide-based fluorescent probes, lysine-cysteine-lysine (KCK) tags are frequently added to proteins for visualization. In this study, we employed in vitro single-molecule DNA flow-stretching assay as a sensitive way to assess the impact of the KCK-tag on the property of DNA-binding proteins. Using Bacillus subtilis ParB as an example, we show that, although no noticeable changes were detected by in vivo fluorescence imaging and chromatin immunoprecipitation (ChIP) assays, the KCK-tag substantially altered ParB's DNA compaction rates, its response to nucleotide binding and to the presence of the specific sequence (parS) on the DNA. While it is typically assumed that short peptide tags minimally perturb protein function, our results urge researchers to carefully validate the use of tags for protein labeling. Our comprehensive analysis can be expanded and used as a guide to assess the impacts of other tags on DNA-binding proteins in single-molecule assays.

Evaluación de modelos matemáticos para estimar el peso y talla en pacientes adultos usando CRM, RMSE, Pearson y Bland Altman / Assessment of mathematical models to estimate weight and height in adultpatients using CRM, RMSE, Pearson and Bland Altman

Introducción: La disponibilidad de datos antropométricos(peso y talla) de pacientes con poca o nula movilidad son importantes en el tratamiento médico y nutricional, para estimaresos valores se han usado modelos matemáticos que reproducen con mayor fidelidad, por lo que es importante evaluarel método de estimación de los modelos.Objetivo: Evaluar los modelos matemáticos de Rabito,Chumlea y HNHU para estimar peso y talla en pacientes adultos usando los métodos de ERM, RMSE, Pearson y Bland Altman. Materiales y métodos: Se considera los datos de 31 pacientes entre 20 y 65 años. Los datos fueron altura de rodilla(AR), circunferencia de brazo (CB), circunferencia abdominal(CA), circunferencia de la pantorrilla (CP), media brazada(MB) y envergadura de brazo (EB) comprendidos en ocho modelos de Rabito para estimar peso y talla, cuatro del HospitalNacional Hipólito Unanue (HNHU) y cuatro de Chumlea. Lacalidad de la estimación fue evaluada por los métodos de Correlación de Pearson, Error Relativo Medio (ERM), RaizCuadrado Medio del Error (RMSE) y Bland Altman. El nivel deasociación entre los métodos fue determinado por Pearson. Los cálculos fueron desarrollados usando el software estadístico R 4.1.0. Resultados: Las mediciones por el método de Pearsonpresenta una variación de 54%, el método ERM de 26.65%,por Bland Altman de 8.49% y RMSE 6.1%. Los métodos deRMSE y Bland Altman presentan una asociación de 0.72. Losmodelos de Rabito 3M (RMSE=4.38) y Rabito 3F(RMSE=4.36) reproducen los valores de peso con mayor fidelidad y para la estimación de la talla los modelos de Rabito 2M(RMSE=3.64) y Rabito 2F (RMSE = 3.82). Conclusiones: Los métodos RMSE y de Bland Altman tienen buena asociación, presentando buena estabilidad en lasevaluaciones. Los modelos matemáticos de Rabito tienenbuena estimación para peso y talla.(AU)

Introduction: The availability of anthropometric data(weight and height) of patients with little or no mobility areimportant for medical and nutritional treatment, to estimatethese values mathematical models that reproduce withgreater fidelity have been used, so it is important to evaluatethe model estimation method. Objective: To Assess the mathematical models of Rabito, Chumlea and HNHU to estimate weight and height in adul patients using the ERM, RMSE, Pearson and Bland Altmanmethods.Materials and methods: Data from 31 patients between20 and 65 years old are considered. The data were kneeheight (RA), arm circumference (AB), abdominal circumference (AC), calf circumference (CP), mean arm length (MB),and arm span (EB) comprised of eight Rabito models for estimate weight and height, four from Hospital Nacional HipólitoUnanue (HNHU) and four from Chumlea. The quality of theestimation was evaluated by the Pearson Correlation, Relative Mean Error (ERM), Root Mean Square Error (RMSE) and Bland Altman methods. The level of association between the methods was determined by Pearson. Calculations were developedusing R 4.1.0 statistical software. Results: The measurements by the Pearson method present a variation of 54%, the ERM method of 26.65%, by BlandAltman of 8.49% and RMSE 6.1%. The RMSE and Bland Altman methods present an association of 0.72. The Rabito3M (RMSE=4.38) and Rabito 3F (RMSE=4.36) models reproduce the weight values with greater fidelity and for height estimation the Rabito 2M (RMSE=3.64) and Rabito 2F (RMSE =3.82) models. Conclusions: The RMSE and Bland Altman methods havea good association, presenting good stability in the evaluations. Rabito’s mathematical models have good estimates forweight and height.(AU)

GC-MS metabolite profiling for specific detection of dwarf somaclonal variation in banana plants.

PREMISE OF THE STUDY: The production of banana (Musa spp.; Musaceae) plants is affected by various types of somaclonal variations (SV), including dwarfism. However, methods for specific detection of SV are still scarce. To overcome this, a metabolite-based method for detection of dwarf variants was evaluated. METHODS: The gas chromatography-mass spectrometry (GC-MS) metabolite profile of dwarf banana variants was investigated and compared to that of normal-healthy (N) and cucumber mosaic virus (CMV)-infected plants using principal components analysis and partial least squares discriminant analysis (PLS-DA). RESULTS: Significant differences among the sample groups were observed in 82 metabolites. Rhamnose was exclusively present in dwarf plants but allothreonine and trehalose were present in all but SV samples. Cellobiose was only detected in N plants, while 45 other metabolites, including methyl-glucopyranoside, allopyranose, lactose, phenylalanine, and l-lysine were detected in all but CMV-infected samples. PLS-DA models were able to detect SV, CMV, and N plants with 100% accuracy and specificity. DISCUSSION: The GC-MS metabolite profile can be used for the rapid, specific detection of SV at early plant production stages. This is the first metabolite-based characterization and detection of somaclonal variation in plants.