Cellular processes associated with LRRK2 function and dysfunction.

Mutations in the leucine-rich repeat kinase 2 (LRRK2) encoding gene are the most common cause of monogenic Parkinson's Disease (PD). The identification of LRRK2 polymorphisms associated with increased risk for sporadic PD, as well as the observation that LRRK2-PD has an almost indistinguishable pathological phenotype from the sporadic form of disease, suggested LRRK2 as the culprit to provide understanding for both familial and sporadic PD cases. LRRK2 is a large protein with both GTPase and kinase functions. Mutations segregating with PD reside within the enzymatic core of LRRK2, suggesting the modification of its activity greatly impacts disease onset and progression. Although progress has been gained since its discovery in 2004, there is still much to be understood regarding LRRK2's physiological and neurotoxic properties. Unsurprisingly, given the presence of multiple enzymatic domains, LRRK2 has been associated with a diverse set of cellular functions and signalling pathways including mitochondrial function, vesicle trafficking together with endocytosis, retromer complex modulation and autophagy. This review will discuss the state of current knowledge for the role of LRRK2 in health and disease with discussion of potential substrates of phosphorylation and functional partners with particular emphasis on signalling mechanisms. As well, the use of immune cells in LRRK2 research and the role of oxidative stress as a regulator of LRRK2 activity and cellular function shall also be discussed. This article is protected by copyright. All rights reserved

Inhibition of cellular protein secretion by norwalk virus nonstructural protein p22 requires a mimic of an endoplasmic reticulum export signal.

Protein trafficking between the endoplasmic reticulum (ER) and Golgi apparatus is central to cellular homeostasis. ER export signals are utilized by a subset of proteins to rapidly exit the ER by direct uptake into COPII vesicles for transport to the Golgi. Norwalk virus nonstructural protein p22 contains a YXΦESDG motif that mimics a di-acidic ER export signal in both sequence and function. However, unlike normal ER export signals, the ER export signal mimic of p22 is necessary for apparent inhibition of normal COPII vesicle trafficking, which leads to Golgi disassembly and antagonism of Golgi-dependent cellular protein secretion. This is the first reported function for p22. Disassembly of the Golgi apparatus was also observed in cells replicating Norwalk virus, which may contribute to pathogenesis by interfering with cellular processes that are dependent on an intact secretory pathway. These results indicate that the ER export signal mimic is critical to the antagonistic function of p22, shown herein to be a novel antagonist of ER/Golgi trafficking. This unique and well-conserved human norovirus motif is therefore an appealing target for antiviral drug development

Representing and analysing molecular and cellular function in the computer

Determining the biological function of a myriad of genes, and understanding how they interact to yield a living cell, is the major challenge of the post genome-sequencing era. The complexity of biological systems is such that this cannot be envisaged without the help of powerful computer systems capable of representing and analysing the intricate networks of physical and functional interactions between the different cellular components. In this review we try to provide the reader with an appreciation of where we stand in this regard. We discuss some of the inherent problems in describing the different facets of biological function, give an overview of how information on function is currently represented in the major biological databases, and describe different systems for organising and categorising the functions of gene products. In a second part, we present a new general data model, currently under development, which describes information on molecular function and cellular processes in a rigorous manner. The model is capable of representing a large variety of biochemical processes, including metabolic pathways, regulation of gene expression and signal transduction. It also incorporates taxonomies for categorising molecular entities, interactions and processes, and it offers means of viewing the information at different levels of resolution, and dealing with incomplete knowledge. The data model has been implemented in the database on protein function and cellular processes 'aMAZE' (http://www.ebi.ac.uk/research/pfbp/), which presently covers metabolic pathways and their regulation. Several tools for querying, displaying, and performing analyses on such pathways are briefly described in order to illustrate the practical applications enabled by the model

Finding The Sign Of A Function Value By Binary Cellular Automaton

Given a continuous function $f(x)$, suppose that the sign of $f$ only has finitely many discontinuous points in the interval $[0,1]$. We show how to use a sequence of one dimensional deterministic binary cellular automata to determine the sign of $f(\rho)$ where $\rho$ is the (number) density of 1s in an arbitrarily given bit string of finite length provided that $f$ satisfies certain technical conditions.Comment: Revtex, uses amsfonts, 10 page

Automatic Programming of Cellular Automata and Artificial Neural Networks Guided by Philosophy

Many computer models such as cellular automata and artificial neural networks have been developed and successfully applied. However, in some cases, these models might be restrictive on the possible solutions or their solutions might be difficult to interpret. To overcome this problem, we outline a new approach, the so-called allagmatic method, that automatically programs and executes models with as little limitations as possible while maintaining human interpretability. Earlier we described a metamodel and its building blocks according to the philosophical concepts of structure (spatial dimension) and operation (temporal dimension). They are entity, milieu, and update function that together abstractly describe cellular automata, artificial neural networks, and possibly any kind of computer model. By automatically combining these building blocks in an evolutionary computation, interpretability might be increased by the relationship to the metamodel, and models might be translated into more interpretable models via the metamodel. We propose generic and object-oriented programming to implement the entities and their milieus as dynamic and generic arrays and the update function as a method. We show two experiments where a simple cellular automaton and an artificial neural network are automatically programmed, compiled, and executed. A target state is successfully evolved and learned in the cellular automaton and artificial neural network, respectively. We conclude that the allagmatic method can create and execute cellular automaton and artificial neural network models in an automated manner with the guidance of philosophy.Comment: 12 pages, 1 figur

Comparative functional genomics approach for the annotation of proteins in Unclassified Halophilic archaeon DL31

The structure, function and sub-cellular location prediction for the unknown proteins from Unclassified Halophilic archaeon DL31 were carried out for characterization of the proteins in their respective families. The 991 genes for hypothetical proteins in Halophilic archaeon DL31 chromosome were predicted by the application of computational methods and Bioinformatics web tools. The structure predictions for 206 unknown proteins were possible whereas functions were predicted in 825 protein sequences. The function prediction for the proteins were done by using Bioinformatics web tools like CDD-BLAST, INTERPROSCAN and PFAM by searching protein databases for the presence of conserved domains. The Sub-cellular location predictions were done for all the unknown proteins by using CELLO v 2.5 server. While tertiary structures were constructed using PS2 Server- Protein Structure Prediction server. This study revealed structural, functional and Sub-cellular localization of unknown proteins in Unclassified Halophilic archaeon DL31chromosome

The relationship between redox enzyme activity and electrochemical potential—cellular and mechanistic implications from protein film electrochemistry

In protein film electrochemistry a redox protein of interest is studied as an electroactive film adsorbed on an electrode surface. For redox enzymes this configuration allows quantification of the relationship between catalytic activity and electrochemical potential. Considered as a function of enzyme environment, i.e., pH, substrate concentration etc., the activity–potential relationship provides a fingerprint of activity unique to a given enzyme. Here we consider the nature of the activity–potential relationship in terms of both its cellular impact and its origin in the structure and catalytic mechanism of the enzyme. We propose that the activity–potential relationship of a redox enzyme is tuned to facilitate cellular function and highlight opportunities to test this hypothesis through computational, structural, biochemical and cellular studies