Showing papers by "Mohamed Al-Rubeai published in 2016"

Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition)

Abstract: In 2008 we published the first set of guidelines for standardizing research in autophagy. Since then, research on this topic has continued to accelerate, and many new scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Accordingly, it is important to update these guidelines for monitoring autophagy in different organisms. Various reviews have described the range of assays that have been used for this purpose. Nevertheless, there continues to be confusion regarding acceptable methods to measure autophagy, especially in multicellular eukaryotes. For example, a key point that needs to be emphasized is that there is a difference between measurements that monitor the numbers or volume of autophagic elements (e.g., autophagosomes or autolysosomes) at any stage of the autophagic process versus those that measure flux through the autophagy pathway (i.e., the complete process including the amount and rate of cargo sequestered and degraded). In particular, a block in macroautophagy that results in autophagosome accumulation must be differentiated from stimuli that increase autophagic activity, defined as increased autophagy induction coupled with increased delivery to, and degradation within, lysosomes (in most higher eukaryotes and some protists such as Dictyostelium) or the vacuole (in plants and fungi). In other words, it is especially important that investigators new to the field understand that the appearance of more autophagosomes does not necessarily equate with more autophagy. In fact, in many cases, autophagosomes accumulate because of a block in trafficking to lysosomes without a concomitant change in autophagosome biogenesis, whereas an increase in autolysosomes may reflect a reduction in degradative activity. It is worth emphasizing here that lysosomal digestion is a stage of autophagy and evaluating its competence is a crucial part of the evaluation of autophagic flux, or complete autophagy. Here, we present a set of guidelines for the selection and interpretation of methods for use by investigators who aim to examine macroautophagy and related processes, as well as for reviewers who need to provide realistic and reasonable critiques of papers that are focused on these processes. These guidelines are not meant to be a formulaic set of rules, because the appropriate assays depend in part on the question being asked and the system being used. In addition, we emphasize that no individual assay is guaranteed to be the most appropriate one in every situation, and we strongly recommend the use of multiple assays to monitor autophagy. Along these lines, because of the potential for pleiotropic effects due to blocking autophagy through genetic manipulation, it is imperative to target by gene knockout or RNA interference more than one autophagy-related protein. In addition, some individual Atg proteins, or groups of proteins, are involved in other cellular pathways implying that not all Atg proteins can be used as a specific marker for an autophagic process. In these guidelines, we consider these various methods of assessing autophagy and what information can, or cannot, be obtained from them. Finally, by discussing the merits and limits of particular assays, we hope to encourage technical innovation in the field.

Erratum to: Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition) (Autophagy, 12, 1, 1-222, 10.1080/15548627.2015.1100356

Abstract: Author(s): Klionsky, DJ; Abdelmohsen, K; Abe, A; Abedin, MJ; Abeliovich, H; Arozena, AA; Adachi, H; Adams, CM; Adams, PD; Adeli, K; Adhihetty, PJ; Adler, SG; Agam, G; Agarwal, R; Aghi, MK; Agnello, M; Agostinis, P; Aguilar, PV; Aguirre-Ghiso, J; Airoldi, EM; Ait-Si-Ali, S; Akematsu, T; Akporiaye, ET; Al-Rubeai, M; Albaiceta, GM; Albanese, C; Albani, D; Albert, ML; Aldudo, J; Algul, H; Alirezaei, M; Alloza, I; Almasan, A; Almonte-Beceril, M; Alnemri, ES; Alonso, C; Altan-Bonnet, N; Altieri, DC; Alvarez, S; Alvarez-Erviti, L; Alves, S; Amadoro, G; Amano, A; Amantini, C; Ambrosio, S; Amelio, I; Amer, AO; Amessou, M; Amon, A; An, Z; Anania, FA; Andersen, SU; Andley, UP; Andreadi, CK; Andrieu-Abadie, N; Anel, A; Ann, DK; Anoopkumar-Dukie, S; Antonioli, M; Aoki, H; Apostolova, N; Aquila, S; Aquilano, K; Araki, K; Arama, E; Aranda, A; Araya, J; Arcaro, A; Arias, E; Arimoto, H; Ariosa, AR; Armstrong, JL; Arnould, T; Arsov, I; Asanuma, K; Askanas, V; Asselin, E; Atarashi, R; Atherton, SS; Atkin, JD; Attardi, LD; Auberger, P; Auburger, G; Aurelian, L; Autelli, R

Online flow cytometry for monitoring apoptosis in mammalian cell cultures as an application for process analytical technology.

Abstract: Apoptosis is the main driver of cell death in bioreactor suspension cell cultures during the production of biopharmaceuticals from animal cell lines. It is known that apoptosis also has an effect on the quality and quantity of the expressed recombinant protein. This has raised the importance of studying apoptosis for implementing culture optimization strategies. The work here describes a novel approach to obtain near real time data on proportion of viable, early apoptotic, late apoptotic and necrotic cell populations in a suspension CHO culture using automated sample preparation in conjunction with flow cytometry. The resultant online flow cytometry data can track the progression of apoptotic events in culture, aligning with analogous manual methodologies and giving similar results. The obtained near-real time apoptosis data are a significant improvement in monitoring capabilities and can lead to improved control strategies and research data on complex biological systems in bioreactor cultures in both academic and industrial settings focused on process analytical technology applications.

Understanding central carbon metabolism of rapidly proliferating mammalian cells based on analysis of key enzymatic activities in GS-CHO cell lines.

Abstract: The central carbon metabolism (glycolysis, the pentose phosphate pathway [PPP], and the tricarboxylic acid [TCA] cycle) plays an essential role in the supply of biosynthetic precursors and energy. How the central carbon metabolism changes with the varying growth rates in the in vitro cultivation of rapidly proliferating mammalian cells, such as cancer cells and continuous cell lines for recombinant protein production, remains elusive. Based on relationships between the growth rate and the activity of seven key enzymes from six cell clones, this work reports finding an important metabolic characteristic in rapidly proliferating GS-CHO cells. The key enzymatic activity involved in the TCA cycle that is responsible for the supply of energy, became elevated as the growth rate exhibited increases, while the activity of key enzymes in metabolic pathways (glycolysis and the PPP), responsible for the supply of biosynthetic precursors, tended to decrease - suggesting that rapidly proliferating cells still depended predominantly on the TCA cycle rather than on aerobic glycolysis for their energetic demands. Meanwhile, the growth-limiting resource was most likely biosynthetic substrates rather than energy provision. In addition, the multifaceted role of glucose-6-phosphate isomerase (PGI) was confirmed, based on a significant correlation between PGI activity and the percentage of G2/M-phase cells. This article is protected by copyright. All rights reserved.

Verhulst and stochastic models for comparing mechanisms of MAb productivity in six CHO cell lines

Abstract: The present study validates previously published methodologies—stochastic and Verhulst—for modelling the growth and MAb productivity of six CHO cell lines grown in batch cultures. Cytometric and biochemical data were used to model growth and productivity. The stochastic explanatory models were developed to improve our understanding of the underlying mechanisms of growth and productivity, whereas the Verhulst mechanistic models were developed for their predictability. The parameters of the two sets of models were compared for their biological significance. The stochastic models, based on the cytometric data, indicated that the productivity mechanism is cell specific. However, as shown before, the modelling results indicated that G2 + ER indicate high productivity, while G1 + ER indicate low productivity, where G1 and G2 are the cell cycle phases and ER is Endoplasmic Reticulum. In all cell lines, growth proved to be inversely proportional to the cumulative G1 time (CG1T) for the G1 phase, whereas productivity was directly proportional to ER. Verhulst’s rule, “the lower the intrinsic growth factor (r), the higher the growth (K),” did not hold for growth across all cell lines but held good for the cell lines with the same growth mechanism—i.e., r is cell specific. However, the Verhulst productivity rule, that productivity is inversely proportional to the intrinsic productivity factor (r x ), held well across all cell lines in spite of differences in their mechanisms for productivity—that is, r x is not cell specific. The productivity profile, as described by Verhulst’s logistic model, is very similar to the Michaelis–Menten enzyme kinetic equation, suggesting that productivity is more likely enzymatic in nature. Comparison of the stochastic and Verhulst models indicated that CG1T in the cytometric data has the same significance as r, the intrinsic growth factor in the Verhulst models. The stochastic explanatory and the Verhulst logistic models can explain the differences in the productivity of the six clones.