scholarly journals Protein Storage Vacuoles Are Transformed into Lytic Vacuoles in Root Meristematic Cells of Germinating Seedlings by Multiple, Cell Type-Specific Mechanisms

2011 ◽  
Vol 155 (4) ◽  
pp. 2023-2035 ◽  
Author(s):  
Huiqiong Zheng ◽  
L. Andrew Staehelin
Keyword(s):  
Cell Type ◽  
Protein Storage ◽  
Meristematic Cells ◽  
Protein Storage Vacuoles ◽  
Root Meristematic Cells ◽  
Multiple Cell ◽  
Cell Type Specific ◽  
Lytic Vacuoles ◽  
Multiple Cell Type

Multiple cell-type-specific elements regulate Myc protein stability

Oncogene ◽  
2004 ◽  
Vol 23 (21) ◽  
pp. 3863-3871 ◽  
Author(s):  
Andreas Herbst ◽  
Simone E Salghetti ◽  
So Young Kim ◽  
William P Tansey
Keyword(s):  
Protein Stability ◽  
Cell Type ◽  
Multiple Cell ◽  
Cell Type Specific ◽  
Multiple Cell Type

scholarly journals Erratum: Multiple cell-type-specific elements regulate Myc protein stability

Oncogene ◽  
2004 ◽  
Vol 23 (58) ◽  
pp. 9448-9448 ◽  
Author(s):  
Andreas Herbst ◽  
Simone E Salghetti ◽  
So Young Kim ◽  
William P Tansey
Keyword(s):  
Protein Stability ◽  
Cell Type ◽  
Multiple Cell ◽  
Cell Type Specific ◽  
Multiple Cell Type

scholarly journals Incorporation of resident macrophages in engineered tissues: Multiple cell type response to microenvironment controlled macrophage‐laden gelatine hydrogels

2017 ◽  
Vol 12 (2) ◽  
pp. 330-340 ◽  
Author(s):  
Camille Dollinger ◽  
Sait Ciftci ◽  
Helena Knopf‐Marques ◽  
Rabia Guner ◽  
Amir M. Ghaemmaghami ◽  
...  
Keyword(s):  
Cell Type ◽  
Engineered Tissues ◽  
Multiple Cell ◽  
Type Response ◽  
Multiple Cell Type

scholarly journals accuEnhancer: Accurate enhancer prediction by integration of multiple cell type data with deep learning

2020 ◽  
Author(s):  
Yi-An Tung ◽  
Wen-Tse Yang ◽  
Tsung-Ting Hsieh ◽  
Yu-Chuan Chang ◽  
June-Tai Wu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cell Types ◽  
Regulatory Elements ◽  
Sequence Motifs ◽  
Cell Type ◽  
Enhancer Activity ◽  
Multiple Cell ◽  
Type Data ◽  
Different Cell Types ◽  
Multiple Cell Type

AbstractEnhancers are one class of the regulatory elements that have been shown to act as key components to assist promoters in modulating the gene expression in living cells. At present, the number of enhancers as well as their activities in different cell types are still largely unclear. Previous studies have shown that enhancer activities are associated with various functional data, such as histone modifications, sequence motifs, and chromatin accessibilities. In this study, we utilized DNase data to build a deep learning model for predicting the H3K27ac peaks as the active enhancers in a target cell type. We propose joint training of multiple cell types to boost the model performance in predicting the enhancer activities of an unstudied cell type. The results demonstrated that by incorporating more datasets across different cell types, the complex regulatory patterns could be captured by deep learning models and the prediction accuracy can be largely improved. The analyses conducted in this study demonstrated that the cell type-specific enhancer activity can be predicted by joint learning of multiple cell type data using only DNase data and the primitive sequences as the input features. This reveals the importance of cross-cell type learning, and the constructed model can be applied to investigate potential active enhancers of a novel cell type which does not have the H3K27ac modification data yet.AvailabilityThe accuEnhancer package can be freely accessed at: https://github.com/callsobing/accuEnhancer

Fabrication of perfusable 3D hepatic lobule-like constructs through assembly of multiple cell type laden hydrogel microstructures

2018 ◽  
Vol 11 (1) ◽  
pp. 015016 ◽  
Author(s):  
Juan Cui ◽  
Huaping Wang ◽  
Zhiqiang Zheng ◽  
Qing Shi ◽  
Tao Sun ◽  
...  
Keyword(s):  
Cell Type ◽  
Hepatic Lobule ◽  
Multiple Cell ◽  
Multiple Cell Type

The Trafficking Machinery of Lytic and Protein Storage Vacuoles: How Much is Shared and How Much is Distinct?

2021 ◽  
Author(s):  
Xiuxiu Zhang ◽  
Hui Li ◽  
Hai Lu ◽  
Inhwan Hwang
Keyword(s):  
Protein Trafficking ◽  
Molecular Mechanisms ◽  
Protein Storage ◽  
Protein Storage Vacuoles ◽  
Protein Storage Vacuole ◽  
Lytic Vacuoles ◽  
Lytic Vacuole ◽  
The Relationship ◽  
Storage Vacuole ◽  
Made In

Abstract Plant cells contain two types of vacuoles, the lytic vacuole and the protein storage vacuole. Lytic vacuoles (LVs) are present in vegetative cells, whereas protein storage vacuoles (PSVs) are found in seed cells. The physiological functions of the two vacuole types differ. Newly synthesized proteins must be transported to these vacuoles via protein trafficking through the endomembrane system for them to function. Recently, significant advances have been made in elucidating the molecular mechanisms of protein trafficking to these organelles. Despite these advances, the relationship between the trafficking mechanisms in LV and PSVs remains unclear. Some aspects of the trafficking mechanisms are common to both organelles, but certain aspects are specific to trafficking to either LV or PSVs. In this review, we summarize recent findings on the components involved in protein trafficking to both LV and PSVs and compare them to examine the extent of overlap in the trafficking mechanisms. In addition, we discuss the interconnection between the LV and PSVs in protein trafficking machinery and the implication in the identity of these organelles.

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