Nucleolar size regulates nuclear envelope shape in Saccharomyces cerevisiae.

Nuclear shape and size are cell-type specific. Change in nuclear shape is seen during cell division, development and pathology. The nucleus of Saccharomycescerevisiae is spherical in interphase and becomes dumbbell shaped during mitotic division to facilitate the transfer of one nucleus to the daughter cell. Because yeast cells undergo closed mitosis, the nuclear envelope remains intact throughout the cell cycle. The pathways that regulate nuclear shape are not well characterized. The nucleus is organized into various subcompartments, with the nucleolus being the most prominent. We have conducted a candidate-based genetic screen for nuclear shape abnormalities in S. cerevisiae to ask whether the nucleolus influences nuclear shape. We find that increasing nucleolar volume triggers a non-isometric nuclear envelope expansion resulting in an abnormal nuclear envelope shape. We further show that the tethering of rDNA to the nuclear envelope is required for the appearance of these extensions.

The challenge of staying in shape: nuclear size matters.

Cellular organelles have unique morphology and the organelle size to cell size ratio is regulated. Nucleus is one of the most prominent, usually round in shape, organelle of a eukaryotic cell that occupies 8-10% of cellular volume. The shape and size of nucleus is known to undergo remodeling during processes such as cell growth, division and certain stresses. Regulation of protein and lipid distribution at the nuclear envelope is crucial for preserving the nuclear morphology and size. As size and morphology are interlinked, altering one influences the other. In this perspective, we discuss the relationship between size and shape regulation of the nucleus.

Predicting subcellular localization of proteins using protein-protein interaction data.

The knowledge of subcellular localization of proteins can provide useful clues about their functions. The conventional methods to determine the subcellular localization are unable to keep pace with the rate at which the new data is being generated. Thus, though sequence information is available, the localization and function of a number of proteins remains unknown. In this study, we have developed a script that makes use of the physical interactors of a protein and their localization data to predict the subcellular localization. We used the script to predict the localization of yeast proteins for which there is no localization data. Further, we experimentally verified the predicted localization for six arbitrarily chosen proteins and found our predictions to be correct for five of the proteins.