Non-canonical roles of autophagy proteins in endocytosis and exocytosis

2021 ◽  
Author(s):  
Christian Münz
Keyword(s):  
Viral Replication ◽  
Immune Activation ◽  
Human Diseases ◽  
Biological Processes ◽  
Membrane Remodeling ◽  
Biological Functions ◽  
Recent Advances

Autophagy, the pathways that degrade cytoplasmic constituents in lysosomes, contribute to most biological processes from aging and neurodegeneration to pathogen restriction and immunity. In recent years, it was realized that the autophagy machinery serves additional functions, primarily in endo- and exocytosis. In this review, I summarize recent advances in our understanding on how these non-canonical functions differ from canonical macroautophagy, and contribute to immune activation and viral replication. Understanding these pathways will allow us to harness them for the treatment of human diseases, as well as appreciate how cells use modules of membrane remodeling and trafficking for multiple biological functions.

A Dynamic Systems Approach to Personality

2001 ◽  
Vol 6 (3) ◽  
pp. 172-176 ◽  
Author(s):  
Lawrence A. Pervin
Keyword(s):  
Dynamic Systems ◽  
Systems Approach ◽  
Biological Processes ◽  
Recent Advances ◽  
Dynamic View ◽  
The Future ◽  
History Of

David Magnusson has been the most articulate spokesperson for a holistic, systems approach to personality. This paper considers three concepts relevant to a dynamic systems approach to personality: dynamics, systems, and levels. Some of the history of a dynamic view is traced, leading to an emphasis on the need for stressing the interplay among goals. Concepts such as multidetermination, equipotentiality, and equifinality are shown to be important aspects of a systems approach. Finally, attention is drawn to the question of levels of description, analysis, and explanation in a theory of personality. The importance of the issue is emphasized in relation to recent advances in our understanding of biological processes. Integrating such advances into a theory of personality while avoiding the danger of reductionism is a challenge for the future.

Comparison and Analysis of Computational Methods for Identifying N6 - methyladenosine Sites in Saccharomyces Cerevisiae

2020 ◽  
Vol 26 ◽  
Author(s):  
Pengmian Feng ◽  
Lijing Feng ◽  
Chaohui Tang
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Computational Methods ◽  
Computational Analysis ◽  
Computational Prediction ◽  
Experimental Methods ◽  
Biological Processes ◽  
Biological Functions ◽  
Precise Location ◽  
Future Directions ◽  
The Past

Background and Purpose: N 6 -methyladenosine (m6A) plays critical roles in a broad set of biological processes. Knowledge about the precise location of m6A site in the transcriptome is vital for deciphering its biological functions. Although experimental techniques have made substantial contributions to identify m6A, they are still labor intensive and time consuming. As good complements to experimental methods, in the past few years, a series of computational approaches have been proposed to identify m6A sites. Methods: In order to facilitate researchers to select appropriate methods for identifying m6A sites, it is necessary to give a comprehensive review and comparison on existing methods. Results: Since researches on m6A in Saccharomyces cerevisiae are relatively clear, in this review, we summarized recent progresses on computational prediction of m6A sites in S. cerevisiae and assessed the performance of existing computational methods. Finally, future directions of computationally identifying m6A sites were presented. Conclusion: Taken together, we anticipate that this review will provide important guides for computational analysis of m 6A modifications.

Recent advances in multivalent carbon nanoform-based glycoconjugates

2021 ◽  
Vol 28 ◽  
Author(s):  
Javier Ramos-Soriano ◽  
Mattia Ghirardello ◽  
M. Carmen Galan
Keyword(s):  
Carbon Nanotubes ◽  
Molecular Level ◽  
Biological Processes ◽  
Biologically Relevant ◽  
Disease Mechanisms ◽  
Recent Advances

: Multivalent carbohydrate-mediated interactions are fundamental to many biological processes, including disease mechanisms. To study these significant glycan-mediated interactions at a molecular level, carbon nanoforms such as fullerenes, carbon nanotubes, or graphene and their derivatives have been identified as promising biocompatible scaffolds that can mimic the multivalent presentation of biologically relevant glycans. In this minireview, we will summarize the most relevant examples of the last few years in the context of their applications.

scholarly journals PPInfer: a Bioconductor package for inferring functionally related proteins using protein interaction networks

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 1969
Author(s):  
Dongmin Jung ◽  
Xijin Ge
Keyword(s):  
Protein Interaction ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Biological Processes ◽  
Bioconductor Package ◽  
Biological Functions ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Related Proteins

Interactions between proteins occur in many, if not most, biological processes. This fact has motivated the development of a variety of experimental methods for the identification of protein-protein interaction (PPI) networks. Leveraging PPI data available STRING database, we use network-based statistical learning methods to infer the putative functions of proteins from the known functions of neighboring proteins on a PPI network. This package identifies such proteins often involved in the same or similar biological functions. The package is freely available at the Bioconductor web site (http://bioconductor.org/packages/PPInfer/).

scholarly journals Deep6mA: A deep learning framework for exploring similar patterns in DNA N6-methyladenine sites across different species

2021 ◽  
Vol 17 (2) ◽  
pp. e1008767
Author(s):  
Zutan Li ◽  
Hangjin Jiang ◽  
Lingpeng Kong ◽  
Yuanyuan Chen ◽  
Kun Lang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Prediction Accuracy ◽  
Cross Validation ◽  
Biological Processes ◽  
Biological Functions ◽  
Learning Framework ◽  
Wide Range ◽  
Genomic Scale ◽  
Downstream Gene Expression ◽  
Fold Cross Validation

N6-methyladenine (6mA) is an important DNA modification form associated with a wide range of biological processes. Identifying accurately 6mA sites on a genomic scale is crucial for under-standing of 6mA’s biological functions. However, the existing experimental techniques for detecting 6mA sites are cost-ineffective, which implies the great need of developing new computational methods for this problem. In this paper, we developed, without requiring any prior knowledge of 6mA and manually crafted sequence features, a deep learning framework named Deep6mA to identify DNA 6mA sites, and its performance is superior to other DNA 6mA prediction tools. Specifically, the 5-fold cross-validation on a benchmark dataset of rice gives the sensitivity and specificity of Deep6mA as 92.96% and 95.06%, respectively, and the overall prediction accuracy is 94%. Importantly, we find that the sequences with 6mA sites share similar patterns across different species. The model trained with rice data predicts well the 6mA sites of other three species: Arabidopsis thaliana, Fragaria vesca and Rosa chinensis with a prediction accuracy over 90%. In addition, we find that (1) 6mA tends to occur at GAGG motifs, which means the sequence near the 6mA site may be conservative; (2) 6mA is enriched in the TATA box of the promoter, which may be the main source of its regulating downstream gene expression.

scholarly journals PPInfer: a Bioconductor package for inferring functionally related proteins using protein interaction networks

F1000Research ◽  
2018 ◽  
Vol 6 ◽  
pp. 1969 ◽  
Author(s):  
Dongmin Jung ◽  
Xijin Ge
Keyword(s):  
Protein Interaction ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Biological Processes ◽  
Bioconductor Package ◽  
Biological Functions ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Related Proteins

Interactions between proteins occur in many, if not most, biological processes. This fact has motivated the development of a variety of experimental methods for the identification of protein-protein interaction (PPI) networks. Leveraging PPI data available in the STRING database, we use a network-based statistical learning methods to infer the putative functions of proteins from the known functions of neighboring proteins on a PPI network. This package identifies such proteins often involved in the same or similar biological functions. The package is freely available at the Bioconductor web site (http://bioconductor.org/packages/PPInfer/).

scholarly journals Role of Main RNA Methylation in Hepatocellular Carcinoma: N6-Methyladenosine, 5-Methylcytosine, and N1-Methyladenosine

2021 ◽  
Vol 9 ◽  
Author(s):  
Yating Xu ◽  
Menggang Zhang ◽  
Qiyao Zhang ◽  
Xiao Yu ◽  
Zongzong Sun ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cellular Differentiation ◽  
Molecular Mechanisms ◽  
Gene Sequence ◽  
Epigenetic Modification ◽  
Biological Processes ◽  
Biological Functions ◽  
Rna Detection ◽  
Rna Methylation

RNA methylation is considered a significant epigenetic modification, a process that does not alter gene sequence but may play a necessary role in multiple biological processes, such as gene expression, genome editing, and cellular differentiation. With advances in RNA detection, various forms of RNA methylation can be found, including N6-methyladenosine (m6A), N1-methyladenosine (m1A), and 5-methylcytosine (m5C). Emerging reports confirm that dysregulation of RNA methylation gives rise to a variety of human diseases, particularly hepatocellular carcinoma. We will summarize essential regulators of RNA methylation and biological functions of these modifications in coding and noncoding RNAs. In conclusion, we highlight complex molecular mechanisms of m6A, m5C, and m1A associated with hepatocellular carcinoma and hope this review might provide therapeutic potent of RNA methylation to clinical research.

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