Disease Ionomics: Understanding the Role of Ions in Complex Disease

Ionomics is a novel multidisciplinary field that uses advanced techniques to investigate the composition and distribution of all minerals and trace elements in a living organism and their variations under diverse physiological and pathological conditions. It involves both high-throughput elemental profiling technologies and bioinformatic methods, providing opportunities to study the molecular mechanism underlying the metabolism, homeostasis, and cross-talk of these elements. While much effort has been made in exploring the ionomic traits relating to plant physiology and nutrition, the use of ionomics in the research of serious diseases is still in progress. In recent years, a number of ionomic studies have been carried out for a variety of complex diseases, which offer theoretical and practical insights into the etiology, early diagnosis, prognosis, and therapy of them. This review aims to give an overview of recent applications of ionomics in the study of complex diseases and discuss the latest advances and future trends in this area. Overall, disease ionomics may provide substantial information for systematic understanding of the properties of the elements and the dynamic network of elements involved in the onset and development of diseases.     

不同基因型丙型肝炎病毒E1、E2蛋白生物信息学分析

目的对不同基因型的丙型肝炎病毒（HCV）的E1、E2蛋白进行生物信息学比较分析,以找出重要的生物信息学数据。方法从Gene Bank中获取不同基因型HCV的E1、E2蛋白核苷酸与氨基酸序列,运用DNA Star,ClustalX,Bio Edit等国际通用的软件进行氨基酸和核苷酸的序列比对,计算核苷酸和氨基酸同源性。在线软件TMHMM v2.0分析E1、E2蛋白跨膜区。AntheProt5.0软件分析二级结构。结果 E1氨基酸起始于aa 193—aa196和终止于aa 382—aa 383,E2蛋白氨基酸起始于aa 384和终止于aa 744—aa 754。不同基因型间E1、E2蛋白基因核苷酸同源性为59.7%-77.0%,氨基酸同源性为60.6%-82.8%。E1、E2蛋白存在3个跨膜区：E1存在2个跨膜区,位于aa 273—aa 293和aa 363—aa 383;E2蛋白存在1个跨膜区,位于aa 723—aa 744。二级结构分析发现不同型HCV E1、E2蛋白富含α螺旋（21%-30%）,β折叠（26%-36%）和卷曲结构（43%-48%）。结论HCV E1、E2核苷酸和氨基酸序列表现为较大的异质性,其蛋白跨膜区富含α螺旋,胞外区以β折叠为主。     

Genes Involved in the Endoplasmic Reticulum N-Glycosylation Pathway of the Red Microalga Porphyridium sp.: A Bioinformatic Study

N-glycosylation is one of the most important post-translational modifications that influence protein polymorphism, including protein structures and their functions. Although this important biological process has been extensively studied in mammals, only limited knowledge exists regarding glycosylation in algae. The current research is focused on the red microalga Porphyridium sp., which is a potentially valuable source for various applications, such as skin therapy, food, and pharmaceuticals. The enzymes involved in the biosynthesis and processing of N-glycans remain undefined in this species, and the mechanism(s) of their genetic regulation is completely unknown. In this study, we describe our pioneering attempt to understand the endoplasmic reticulum N-Glycosylation pathway in Porphyridium sp., using a bioinformatic approach. Homology searches, based on sequence similarities with genes encoding proteins involved in the ER N-glycosylation pathway (including their conserved parts) were conducted using the TBLASTN function on the algae DNA scaffold contigs database. This approach led to the identification of 24 encoded-genes implicated with the ER N-glycosylation pathway in Porphyridium sp. Homologs were found for almost all known N-glycosylation protein sequences in the ER pathway of Porphyridium sp.; thus, suggesting that the ER-pathway is conserved; as it is in other organisms (animals, plants, yeasts, etc.).     

A cluster-DEE-based strategy to empower protein design

The Medical and Pharmaceutical industries have shown high interest in the precise engineering of protein hormones and enzymes that perform existing functions under a wide range of conditions. Proteins are responsible for the execution of different functions in the cell: catalysis in chemical reactions, transport and storage, regulation and recognition control. Computational Protein Design (CPD) investigates the relationship between 3-D structures of proteins and amino acid sequences and looks for all sequences that will fold into such 3-D structure. Many computational methods and algorithms have been proposed over the last years, but the problem still remains a challenge for Mathematicians, Computer Scientists, Bioinformaticians and Structural Biologists. In this article we present a new method for the protein design problem. Clustering techniques and a Dead-End-Elimination algorithm are combined with a SAT problem representation of the CPD problem in order to design the amino acid sequences. The obtained results illustrate the accuracy of the proposed method, suggesting that integrated Artificial Intelligence techniques are useful tools to solve such an intricate problem.