Warraich Umm-E-Ammara, Hussain Fatma, Kayani Haroon Ur Rashid
Department of Biochemistry, Faculty of Sciences, University of Agriculture, Faisalabad, Pakistan.
Department of Software Engineering, Lahore Garrison University, Lahore, Pakistan.
Heliyon. 2020 May 31;6(5):e04107. doi: 10.1016/j.heliyon.2020.e04107. eCollection 2020 May.
Aging is a degenerative, biological, time-dependent, universally conserved process thus designed as one of the highest known risk factors for morbidity and mortality. Every individual has its own aging mechanisms as both environmental conditions (75%) and genetics (25%) account for aging. Several theories have been proposed until now but not even a single theory solves this mystery. There are still some queries un-answered to the scientific community regarding mechanisms behind aging. However, oxidative stress theory (OST) is considered one of the famous theories that sees mitochondria as one of the leading organelles which largely contribute to the aging process. Many reactive oxygen species (ROS) are produced endogenously and exogenously that are associated with aging. But the mitochondrial ROS contribute largely to the aging process as mitochondrial dysfunction due to oxidative stress is considered one of the contributors toward aging. Although ROS is known to damage cell machinery, new evidence suggests their role in signal transduction to regulate biological and physiological processes. Moreover, besides mitochondria, other important cell organelles such as peroxisome and endoplasmic reticulum also produce ROS that contribute to aging. However, nature has provided humans with free radical scavengers called antioxidants that protect from harmful effects of ROS. Future predictions regarding aging, biochemical mechanisms involved, biomarkers internal and external factors can be easily done with machine learning algorithms and other computational models. This review explains important aspects of aging, the contribution of ROS producing organelles in aging, importance of antioxidants fighting against ROS, different computational models developed to understand the complexities of the aging.
衰老 是一个退行性的、生物学的、时间依赖性的、普遍存在的保守过程,因此被视为已知的发病率和死亡率的最高风险因素之一。每个人都有自己的衰老机制,因为环境条件(75%)和基因(25%)都对衰老有影响。到目前为止,已经提出了几种理论,但没有一种理论能解开这个谜团。科学界对于衰老背后的机制仍有一些问题尚未得到解答。然而,氧化应激理论(OST)被认为是著名的理论之一,该理论认为线粒体是导致衰老过程的主要细胞器之一。内源性和外源性都会产生许多与衰老相关的活性氧(ROS)。但线粒体ROS在很大程度上导致了衰老过程,因为氧化应激引起的线粒体功能障碍被认为是衰老的促成因素之一。虽然已知ROS会损害细胞机制,但新证据表明它们在调节生物和生理过程的信号转导中发挥作用。此外,除了线粒体,其他重要的细胞器如过氧化物酶体和内质网也会产生活导致衰老的ROS。然而,大自然为人类提供了称为抗氧化剂的自由基清除剂,以保护免受ROS的有害影响。利用机器学习算法和其他计算模型,可以轻松地对衰老、涉及的生化机制、生物标志物、内部和外部因素进行未来预测。这篇综述解释了衰老的重要方面、产生活导致衰老的ROS的细胞器的作用、抗氧化剂对抗ROS的重要性以及为理解衰老的复杂性而开发的不同计算模型。