Evaluation of four different strategies to characterize plasma membrane proteins from banana roots

Plasma membrane proteins constitute a very important class of proteins. They are involved in the transmission of external signals to the interior of the cell and selective transport of water, nutrients and ions across the plasma membrane. However, the study of plasma membrane proteins is challenging because of their poor solubility in aqueous media and low relative abundance. In this work, we evaluated four different strategies for the characterization of plasma membrane proteins from banana roots: (i) the aqueous-polymer two-phase system technique (ATPS) coupled to gelelectrophoresis (gel-based), and (ii) ATPS coupled to LC-MS/MS (gel free), (iii) a microsomal fraction and (iv) a full proteome, both coupled to LC-MS/ MS. Our results show that the gel-based strategy is useful for protein visualization but has major limitations in terms of time reproducibility and efficiency. From the gel-free strategies, the microsomal-based strategy allowed the highest number of plasma membrane proteins to be identified, followed by the full proteome strategy and by the ATPS based strategy. The high yield of plasma membrane proteins provided by the microsomal fraction can be explained by the enrichment of membrane proteins in this fraction and the high throughput of the gel-free approach combined with the usage of a fast high-resolution mass spectrometer for the identification of proteins

2019 Bibliography

Updated with additional entries on Jan. 4, 2021. Note: This unedited version, published early for the benefit of researchers, will be updated following the Marian Library\u27s proofreading process

Annunciation and Contemporary Challenges: The Pandemic and Social Injustices

This paper by Father Sebastien B. Abalodo, S.M., represents a portion of the presentation The Annunciation and Contemporary Injustices by Abalodo; Corinne Daprano; and Miranda Cady Hallett

2020 Bibliography

Note: This unedited version, published early for the benefit of researchers, will be updated following the Marian Library\u27s proofreading process

Disease Progression Modeling and Prediction through Random Effect Gaussian Processes and Time Transformation

The development of statistical approaches for the joint modelling of the temporal changes of imaging, biochemical, and clinical biomarkers is of paramount importance for improving the understanding of neurodegenerative disorders, and for providing a reference for the prediction and quantification of the pathology in unseen individuals. Nonetheless, the use of disease progression models for probabilistic predictions still requires investigation, for example for accounting for missing observations in clinical data, and for accurate uncertainty quantification. We tackle this problem by proposing a novel Gaussian process-based method for the joint modeling of imaging and clinical biomarker progressions from time series of individual observations. The model is formulated to account for individual random effects and time reparameterization, allowing non-parametric estimates of the biomarker evolution, as well as high flexibility in specifying correlation structure, and time transformation models. Thanks to the Bayesian formulation, the model naturally accounts for missing data, and allows for uncertainty quantification in the estimate of evolutions, as well as for probabilistic prediction of disease staging in unseen patients. The experimental results show that the proposed model provides a biologically plausible description of the evolution of Alzheimer's pathology across the whole disease time-span as well as remarkable predictive performance when tested on a large clinical cohort with missing observations.Comment: 13 pages, 2 figure