1.
Disease Ionomics: Understanding the Role of Ions in Complex Disease.
Zhang, Y, Xu, Y, Zheng, L
International journal of molecular sciences. 2020;(22)
Abstract
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.
2.
Trace Elements and Healthcare: A Bioinformatics Perspective.
Zhang, Y
Advances in experimental medicine and biology. 2017;:63-98
Abstract
Biological trace elements are essential for human health. Imbalance in trace element metabolism and homeostasis may play an important role in a variety of diseases and disorders. While the majority of previous researches focused on experimental verification of genes involved in trace element metabolism and those encoding trace element-dependent proteins, bioinformatics study on trace elements is relatively rare and still at the starting stage. This chapter offers an overview of recent progress in bioinformatics analyses of trace element utilization, metabolism, and function, especially comparative genomics of several important metals. The relationship between individual elements and several diseases based on recent large-scale systematic studies such as genome-wide association studies and case-control studies is discussed. Lastly, developments of ionomics and its recent application in human health are also introduced.
3.
Investigation of the impact of trace elements on anaerobic volatile fatty acid degradation using a fractional factorial experimental design.
Jiang, Y, Zhang, Y, Banks, C, Heaven, S, Longhurst, P
Water research. 2017;:458-465
Abstract
The requirement of trace elements (TE) in anaerobic digestion process is widely documented. However, little is understood regarding the specific requirement of elements and their critical concentrations under different operating conditions such as substrate characterisation and temperature. In this study, a flask batch trial using fractional factorial design is conducted to investigate volatile fatty acids (VFA) anaerobic degradation rate under the influence of the individual and combined effect of six TEs (Co, Ni, Mo, Se, Fe and W). The experiment inoculated with food waste digestate, spiked with sodium acetate and sodium propionate both to 10 g/l. This is followed by the addition of a selection of the six elements in accordance with a 26-2 fractional factorial principle. The experiment is conducted in duplicate and the degradation of VFA is regularly monitored. Factorial effect analysis on the experimental results reveals that within these experimental conditions, Se has a key role in promoting the degradation rates of both acetic and propionic acids; Mo and Co are found to have a modest effect on increasing propionic acid degradation rate. It is also revealed that Ni shows some inhibitory effects on VFA degradation, possibly due to its toxicity. Additionally, regression coefficients for the main and second order effects are calculated to establish regression models for VFA degradation.