Evolutionary medicine

Evolutionary medicine and bioastronautics: an innovative approach in addressing adverse mental health effects to astronauts during long term space missions

Although evolutionary medicine has produced several novel insights for explaining prevalent health issues, it has yet to sufficiently address possible adverse mental health effects of humans during long-term space missions While evolutionary applications to medicine have increased over the past 20 years, there is scope for the integration of evolutionary applications in the new branch of space medicine called bioastronautics, which analyses the effects on human bodies when in outer space. Evolutionary principles may explain what kinds of space environments increase mental health risks to astronauts, both in the short and long term; secondly, evolutionary principles may provide a more informed understanding of the evolutionary mismatch between terrestrial and space environments in which astronauts exist. This information may assist in developing frameworks for improving mental health of astronauts and future space colonists. Consequently, this paper will focus on some of the major evolutionary mismatches currently confronting astronauts' mental health, with an aim to improve medical knowledge. It will also provide possible therapeutic countermeasures based on evolutionary principles for reducing adverse mental effects on astronauts.

GETdb: A comprehensive database for genetic and evolutionary features of drug targets

The development of an innovative drug is complex and time-consuming, and the drug target identification is one of the critical steps in drug discovery process. Effective and accurate identification of drug targets can accelerate the drug development process. According to previous research, evolutionary and genetic information of genes has been found to facilitate the identification of approved drug targets. In addition, allosteric proteins have great potential as targets due to their structural diversity. However, this information that could facilitate target identification has not been collated in existing drug target databases. Here, we construct a comprehensive drug target database named Genetic and Evolutionary features of drug Targets database (GETdb, http://zhanglab.hzau.edu.cn/GETdb/page/index.jsp). This database not only integrates and standardizes data from dozens of commonly used drug and target databases, but also innovatively includes the genetic and evolutionary information of targets. Moreover, this database features an effective allosteric protein prediction model. GETdb contains approximately 4000 targets and over 29,000 drugs, and is a user-friendly database for searching, browsing and downloading data to facilitate the development of novel targets.

Placental-Heart Axis: An Evolutionary Perspective

To maintain its development, the growing fetus is directly dependent on the placenta, an organ that acts as both a modulator and mediator. As an essential component of pregnancy that is derived from both maternal and fetal tissues, the placenta facilitates the passage of all oxygen and nutrients from the expecting parent to their fetuses. Further, the placenta conveys multiple impacts of the maternal environment to the growing fetus. The timing of placental development parallels that of the fetal cardiovascular system, and placental anomalies are implicated as a potential cause of congenital heart disease. For example, congenital heart disease is more common in pregnancies complicated by maternal preeclampsia, a condition characterized by placental dysfunction. Given the placenta's intermediary links to the maternal environment and fetal health outcomes, it is an emerging focus of evolutionary medicine, which seeks to understand how interactions between humans and the environment affect our biology and give rise to disease. The present review provides an overview of the evolutionary and developmental courses of the placenta as well as their implications on infant health.

Relation Between Obesity and Type 2 Diabetes: Evolutionary Insights, Perspectives and Controversies

PURPOSE OF REVIEW

Since the mid-twentieth century, obesity and its related comorbidities, notably insulin resistance (IR) and type 2 diabetes (T2D), have surged. Nevertheless, their underlying mechanisms remain elusive. Evolutionary medicine (EM) sheds light on these issues by examining how evolutionary processes shape traits and diseases, offering insights for medical practice. This review summarizes the pathogenesis and genetics of obesity-related IR and T2D. Subsequently, delving into their evolutionary connections. Addressing limitations and proposing future research directions aims to enhance our understanding of these conditions, paving the way for improved treatments and prevention strategies.

RECENT FINDINGS

Several evolutionary hypotheses have been proposed to unmask the origin of obesity-related IR and T2D, e.g., the "thrifty genotype" hypothesis suggests that certain "thrifty genes" that helped hunter-gatherer populations efficiently store energy as fat during feast-famine cycles are now maladaptive in our modern obesogenic environment. The "drifty genotype" theory suggests that if thrifty genes were advantageous, they would have spread widely, but proposes genetic drift instead. The "behavioral switch" and "carnivore connection" hypotheses propose insulin resistance as an adaptation for a brain-dependent, low-carbohydrate lifestyle. The thrifty phenotype theory suggests various metabolic outcomes shaped by genes and environment during development. However, the majority of these hypotheses lack experimental validation. Understanding why ancestral advantages now predispose us to diseases may aid in drug development and prevention of disease. EM helps us to understand the evolutionary relation between obesity-related IR and T2D. But still gaps and contradictions persist. Further interdisciplinary research is required to elucidate complete mechanisms.

The Role of Selection and Migration in the Evolution of (Auto)Immunity Genes

The genetic architecture of multiple sclerosis is complicated. Additionally, the disease incidence varies per population or per geographical region. A recent study gives convincing explanations about the north-south incidence gradient of multiple sclerosis in Europe, by analyzing ancient and modern human genomes. Interestingly, the evidence shows that multiple sclerosis associated immunogenetic variants underwent positive selection in Asian and European populations. Lifestyle and pathogen infections probably shaped the overall multiple sclerosis risk. These results complete the findings of previous studies that showed that a high percentage of the autoimmunity associated genetic variants are under selection pressure.

Reinforcement learning informs optimal treatment strategies to limit antibiotic resistance

Antimicrobial resistance was estimated to be associated with 4.95 million deaths worldwide in 2019. It is possible to frame the antimicrobial resistance problem as a feedback-control problem. If we could optimize this feedback-control problem and translate our findings to the clinic, we could slow, prevent, or reverse the development of high-level drug resistance. Prior work on this topic has relied on systems where the exact dynamics and parameters were known a priori. In this study, we extend this work using a reinforcement learning (RL) approach capable of learning effective drug cycling policies in a system defined by empirically measured fitness landscapes. Crucially, we show that it is possible to learn effective drug cycling policies despite the problems of noisy, limited, or delayed measurement. Given access to a panel of 15 [Formula: see text]-lactam antibiotics with which to treat the simulated Escherichia coli population, we demonstrate that RL agents outperform two naive treatment paradigms at minimizing the population fitness over time. We also show that RL agents approach the performance of the optimal drug cycling policy. Even when stochastic noise is introduced to the measurements of population fitness, we show that RL agents are capable of maintaining evolving populations at lower growth rates compared to controls. We further tested our approach in arbitrary fitness landscapes of up to 1,024 genotypes. We show that minimization of population fitness using drug cycles is not limited by increasing genome size. Our work represents a proof-of-concept for using AI to control complex evolutionary processes.

The crucial role of diverse animal models to investigate cochlear aging and hearing loss

Age-related hearing loss affects a large and growing segment of the population, with profound impacts on quality of life. Age-related pathology of the cochlea-the mammalian hearing organ-underlies age-related hearing loss. Because investigating age-related changes in the cochlea in humans is challenging and often impossible, animal models are indispensable to investigate these mechanisms as well as the complex consequences of age-related hearing loss on the brain and behavior. In this review, we advocate for a comparative and interdisciplinary approach while also addressing the challenges of comparing age-related hearing loss across species with varying lifespans. We describe the experimental advantages and limitations as well as areas for future research in well-established models of age-related hearing loss, including mice, rats, gerbils, chinchillas, and birds. We also indicate the need to expand characterization of age-related hearing loss in other established animal models, especially guinea pigs, cats, and non-human primates, in which auditory function is well characterized but age-related cochlear pathology is understudied. Finally, we highlight the potential of emerging animal models for advancing our understanding of age-related hearing loss, including deer mice, with their notably extended lifespans and preserved hearing, naked mole rats, with their exceptional longevity and extensive vocal communications, as well as zebrafish, which offer genetic tractability and suitability for drug screening. Ultimately, a comparative and interdisciplinary approach in auditory research, combining insights from various animal models with human studies, is key to robust and reliable research outcomes that better advance our understanding and treatment of age-related hearing loss.

Reinforcement Learning informs optimal treatment strategies to limit antibiotic resistance

Antimicrobial resistance was estimated to be associated with 4.95 million deaths worldwide in 2019. It is possible to frame the antimicrobial resistance problem as a feedback-control problem. If we could optimize this feedback-control problem and translate our findings to the clinic, we could slow, prevent or reverse the development of high-level drug resistance. Prior work on this topic has relied on systems where the exact dynamics and parameters were known a priori. In this study, we extend this work using a reinforcement learning (RL) approach capable of learning effective drug cycling policies in a system defined by empirically measured fitness landscapes. Crucially, we show that is possible to learn effective drug cycling policies despite the problems of noisy, limited, or delayed measurement. Given access to a panel of 15 β-lactam antibiotics with which to treat the simulated E. coli population, we demonstrate that RL agents outperform two naive treatment paradigms at minimizing the population fitness over time. We also show that RL agents approach the performance of the optimal drug cycling policy. Even when stochastic noise is introduced to the measurements of population fitness, we show that RL agents are capable of maintaining evolving populations at lower growth rates compared to controls. We further tested our approach in arbitrary fitness landscapes of up to 1024 genotypes. We show that minimization of population fitness using drug cycles is not limited by increasing genome size. Our work represents a proof-of-concept for using AI to control complex evolutionary processes.

Evolution-strengthened knowledge graph enables predicting the targetability and druggability of genes

Identifying promising targets is a critical step in modern drug discovery, with causative genes of diseases that are an important source of successful targets. Previous studies have found that the pathogeneses of various diseases are closely related to the evolutionary events of organisms. Accordingly, evolutionary knowledge can facilitate the prediction of causative genes and further accelerate target identification. With the development of modern biotechnology, massive biomedical data have been accumulated, and knowledge graphs (KGs) have emerged as a powerful approach for integrating and utilizing vast amounts of data. In this study, we constructed an evolution-strengthened knowledge graph (ESKG) and validated applications of ESKG in the identification of causative genes. More importantly, we developed an ESKG-based machine learning model named GraphEvo, which can effectively predict the targetability and the druggability of genes. We further investigated the explainability of the ESKG in druggability prediction by dissecting the evolutionary hallmarks of successful targets. Our study highlights the importance of evolutionary knowledge in biomedical research and demonstrates the potential power of ESKG in promising target identification. The data set of ESKG and the code of GraphEvo can be downloaded from https://github.com/Zhankun-Xiong/GraphEvo.

The gut microbiota links disease to human genome evolution

A large number of studies have established a causal relationship between the gut microbiota and human disease. In addition, the composition of the microbiota is substantially influenced by the human genome. Modern medical research has confirmed that the pathogenesis of various diseases is closely related to evolutionary events in the human genome. Specific regions of the human genome known as human accelerated regions (HARs) have evolved rapidly over several million years since humans diverged from a common ancestor with chimpanzees, and HARs have been found to be involved in some human-specific diseases. Furthermore, the HAR-regulated gut microbiota has undergone rapid changes during human evolution. We propose that the gut microbiota may serve as an important mediator linking diseases to human genome evolution.

Phylogenetic articulation of uric acid evolution in mammals and how it informs a therapeutic uricase

The role of uric acid during primate evolution has remained elusive ever since it was discovered over 100 years ago that humans have unusually high levels of the small molecule in our serum. It has been difficult to generate a neutral or adaptive explanation in part because the uricase enzyme evolved to become a pseudogene in apes thus masking typical signals of sequence evolution. Adding to the difficulty is a lack of clarity on the functional role of uric acid in apes. One popular hypothesis proposes that uric acid is a potent antioxidant that increased in concentration to compensate for the lack of vitamin C synthesis in primate species ∼65 million years ago (Mya). Here, we have expanded on our previous work with resurrected ancient uricase proteins to better resolve the reshaping of uricase enzymatic activity prior to ape evolution. Our results suggest that the pivotal death-knell to uricase activity occurred between 20-30 Mya despite small sequential modifications to its catalytic efficiency for the tens of millions of years since primates lost their ability to synthesize vitamin C, and thus the two appear uncorrelated. We also use this opportunity to demonstrate how molecular evolution can contribute to biomedicine by presenting ancient uricases to human immune cells that assay for innate reactivity against foreign antigens. A highly stable and highly catalytic ancient uricase is shown to elicit a lower immune response in more human haplotypes than other uricases currently in therapeutic development.