For a pathogen to successfully transmit among hosts and become established in the population, it must ‘run the gauntlet’ of defences within individual hosts (Ewald, 1983; Handel & Rohani, 2015; van Baalen & Sabelis, 1995). Intuitively, the strong connections between within‐host infection dynamics and transmission success make sense. Mathematical models and the few empirical studies (e.g. helminths, HIV, avian influenza, rodent malaria) that explicitly link within‐host and between‐host processes confirm this intuition (Coombs, Gilchrist, & Ball, 2007; Handel & Rohani, 2015; Mideo, Alizon, & Day, 2008; van Leeuwen, Budischak, Graham, & Cressler, 2019). Yet, while the importance of these cross‐scale connections is widely acknowledged, for most infectious diseases our understanding of within‐host dynamics remains limited—even for model organisms that are amenable to sophisticated and finely controlled laboratory experiments (Grant et al., 2008; Mideo, Day, & Read, 2008; Westwood et al., 2019).

A quantitative understanding of within‐host infection dynamics remains challenging for at least two key reasons. First, and most obvious, the logistical constraints associated with tracking pathogen dynamics that are concealed within a host hinders our understanding of the entire life history of disease, which typically proceeds in a step‐wise fashion such that earlier steps influence later steps (Fenton, Antonovics, & Brockhurst, 2012; Hall, Bento, & Ebert, 2017). Second, how hosts respond to and cope with infection depends sensitively on the host's age (reviewed by: Ben‐Ami, 2019). As hosts grow and develop, differences in body size govern energy acquisition and allocation, encounter rates with parasites and pathogens, susceptibility to infection, and immune responses. Host age can therefore influence all steps of infection from pathogen exposure and establishment to clearance and recovery (Ashby & Bruns, 2018; Hall, Sivars‐Becker, Duffy, Tessier, & Caceres, 2007; Tate & Rudolf, 2012).

Indeed, host age affects the manifestation and pathophysiology of plant (Ashby & Bruns, 2018; Develey‐Rivière & Galiana, 2007; Sharabani et al., 2013), wildlife (Caraco et al., 2002; Dwyer, 1991; Hall et al., 2007) and human diseases (Carran, Ferrari, & Reluga, 2018; Keeling & Rohani, 2008). Such, age‐specific processes within‐hosts can scale up to fundamentally alter epidemiological dynamics as seen, for example, in recent outbreaks of measles (Cutts et al., 2020) and coronavirus disease (SARS‐CoV‐2; Wu et al., 2020). Therefore, an important objective for empirical studies is to follow the entire disease life history—across different stages of host ontogeny (Ben‐Ami, 2019). Such information will help foster a more comprehensive and biologically realistic understanding of within‐host interactions and how they influence population‐level dynamics.

In this issue of Functional Ecology, Izhar, Gilboa, and Ben‐Ami (2020) examine how within‐host infection dynamics vary in hosts that are infected at different ages. The authors focus on a model system, the zooplankton Daphnia magna and the castrating bacterial pathogen Pasteuria ramosa . Their results indicate that juvenile and adult Daphnia , like many hosts, differ in their susceptibility to pathogens, which subsequently affects host and pathogen fitness. By explicitly examining links between age at exposure, physiological mechanisms of defence, and pathogen traits over the entire infection cycle, the authors uncover novel mechanisms that govern age‐specific patterns of virulence and transmission (i.e. disease life history: Day, 2003; Mideo et al., 2011).

Because the zooplankton host is transparent, the authors were able to use a combination of compound and fluorescent microscopy techniques to collect high‐resolution measurements of host physiology and pathogen developmental cycles over the entire course of infection (approximately 30–35 days). This detailed dissection of within‐host infection dynamics at such a high degree of temporal resolution allowed the authors to quantify links between host age and key epidemiological parameters, such as the length of time it takes for the pathogen to establish an infection, the development and growth of pathogen life‐history stages within hosts, and whether a host was able to clear the infection and recover.

This sophisticated examination of within‐host infection dynamics is rare and provides key insight into the physiological underpinnings of host defence. Traditionally, research on host defence has largely focused on immune functions. The emerging picture, however, indicates that host defence involves an arsenal comprised of immune cells, physiology, metabolism, gut microbes, and behavioural changes. For instance, in many organisms, oesophageal molting is an important part of defence and is particularly common in arthropods. This physiological mechanism is also a crucial component of defence for the focal host–pathogen system. In order to spread to other parts of the host's body cavity, the pathogen must encounter the host, activate and penetrate the cuticula in the host's oesophagus (Hall et al., 2017).

Importantly, Izhar et al. (2020) were able to track the timing of molting events in the host, which sheds its exoskeleton, including its oesophagus, in order to grow. This work demonstrates that while juvenile (5‐day‐old, pre‐reproductive) hosts molt earlier and more frequently (which could enhance their chances of escaping pathogen penetration), the pathogen is able to penetrate the oesophagus much more quickly in juveniles relative to adults. These differences may arise due to ontogenetic differences in the thickness or composition of the oesophageal cuticle. Regardless of the specific traits, this tension means that juvenile hosts are susceptible during three quarters of their molting cycles, whereas their adult counterparts are susceptible for only half of their molting cycles.

The stronger physiological defence in adults resulted in fewer adults becoming infected in the first place. In adults that did become infected, pathogen development was delayed and typically did not progress beyond the first developmental cycle. In other words, adults were able to clear infections, reduce overall pathogen load (despite the larger host size) and prevent the pathogen from developing mature transmission stages. Thus, Izhar et al. (2020) provide compelling evidence that the timing of key processes within hosts alters how hosts and pathogens interact, affecting host recovery and pathogen load, all of which can scale up to shape between‐host transmission.

This study raises several important questions for evolutionary epidemiology more generally. For example, the authors' series of detailed experiments demonstrates that when hosts become infected as adults, they deploy both anti‐infection and anti‐growth mechanisms of resistance (Boots, Best, Miller, & White, 2009; Donnelly, White, & Boots, 2015; Gandon, Mackinnon, Nee, & Read, 2001). Theory predicts, and empirical studies show, that both anti‐growth and anti‐infection resistance can favour faster growing and more virulent pathogens that overcome these defences (de Roode, Fernandez de Castillejo, Faits, & Alizon, 2011). This system is ideal for testing these (and other) questions to better understand how host demography (age structure) and within‐host infection dynamics scale up to alter host–pathogen (co)‐evolution, with implications for both conservation and public health policies (Gandon et al., 2001; Mideo, Alizon, et al., 2008; van Leeuwen et al., 2019; Westwood et al., 2019). Clearly, we still have a long way to go in this endeavour but the study by Izhar et al. (2020) bring us closer to that goal.

为了使病原体成功地在宿主之间传播并在种群中确立地位，它必须在单个宿主内“运行防御系统”（Ewald，  1983； Handel＆Rohani，  2015； van Baalen＆Sabelis，  1995）。直观地讲，宿主内部感染动态与传播成功之间的紧密联系是有道理的。数学模型和少数实证研究（例如蠕虫，HIV，禽流感，啮齿动物疟疾）明确地将寄主内和寄主之间的过程联系起来，证实了这种直觉（Coombs，Gilchrist和＆Ball，  2007； Handel和Rohani，  2015； Mideo ，Alizon和Day，  2008年； van Leeuwen，Budischak，Graham和Cressler，  2019年）。然而，尽管这些跨尺度联系的重要性已得到广泛认可，但对于大多数传染病，我们对宿主内动力学的理解仍然有限，即使对于适合于复杂且精细控制的实验室实验的模型生物（Grant等人，  2008年） ; Mideo，Day，＆Read，  2008 ; Westwood等，  2019）。

至少有两个关键原因，对宿主内感染动态的定量了解仍然具有挑战性。首先，也是最明显的是，与隐藏在宿主内的病原体动态追踪相关的后勤约束阻碍了我们对疾病整个生命史的理解，这通常以逐步的方式进行，以至于较早的步骤会影响后续的步骤（Fenton， Antonovics和Brockhurst，  2012； Hall，Bento和Ebert，  2017）。其次，寄主如何应对和应对感染敏感地取决于寄主的年龄（综述：Ben‐Ami，  2019年）。随着宿主的生长和发育，体型的差异决定着能量的获取和分配，与寄生虫和病原体的发生率，对感染的敏感性以及免疫反应。因此，宿主年龄可以影响从病原体接触和建立到清除和恢复的所有感染步骤（Ashby和Bruns，  2018年; Hall，Sivars-Becker，Duffy，Tessier和Caceres，  2007年; Tate和Rudolf，  2012年）。

实际上，寄主年龄会影响植物的表现和病理生理（Ashby＆Bruns，  2018；Develey-Rivière＆Galiana，  2007； Sharabani等，  2013），野生动植物（Caraco等，  2002； Dwyer，  1991； Hall等） （  2007年）和人类疾病（卡兰，法拉利和雷鲁加，  2018年；基林和罗哈尼，  2008年）。这种寄主内部特定年龄的过程可以扩大规模，从根本上改变流行病学动态，例如，在最近爆发的麻疹（Cutts等人，2020年）和冠状病毒病（SARS-CoV-2； Wu等人）中可见 。 ，  2020）。因此，实证研究的一个重要目标是在宿主个体发育的不同阶段追踪整个疾病的生活史（Ben‐Ami，  2019）。这些信息将有助于促进对宿主内部相互作用及其如何影响种群水平动态的更全面的生物学认识。

在本期《功能生态学》中， Izhar，Gilboa和Ben-Ami（2020）研究了在不同年龄感染的宿主中宿主内部感染动态如何变化。作者专注于一个模型系统，浮游动物水蚤（Daphnia magna）和cast割细菌病原体巴斯德氏菌。他们的结果表明，青少年和成人水蚤像许多宿主一样，它们对病原体的敏感性不同，这随后会影响宿主和病原体的适应性。通过明确检查暴露年龄，防御的生理机制以及整个感染周期中病原体特征之间的联系，作者发现了控制年龄特定毒力和传播方式的新机制（即疾病生活史：Day，  2003； Mideo等）等人，  2011年）。

由于浮游动物宿主是透明的，因此作者能够使用化合物和荧光显微镜技术的组合来收集整个感染过程（约30-35天）内宿主生理和病原体发育周期的高分辨率测量结果。在如此高的时间分辨率下对宿主内部感染动态的详细剖析，使作者能够量化宿主年龄与关键流行病学参数之间的联系，例如病原体建立感染所花费的时间长度，发展和宿主内病原体生命史阶段的增长，以及宿主是否能够清除感染并恢复。

对宿主内部感染动态的这种复杂检查很少见，并且可以深入了解宿主防御的生理基础。传统上，关于宿主防御的研究主要集中在免疫功能上。然而，新出现的情况表明，宿主防御涉及包含免疫细胞，生理学，新陈代谢，肠道微生物和行为改变的武器库。例如，在许多生物中，食管蜕皮是防御的重要组成部分，在节肢动物中尤为常见。这种生理机制也是局灶宿主-病原体系统防御的重要组成部分。为了传播到宿主体腔的其他部位，病原体必须遇到宿主，激活并穿透宿主食道的角质层（Hall等，  2017）。

重要的是，Izhar等。（2020）能够追踪宿主蜕皮事件的发生时间，该宿主脱落其外骨骼（包括食道）以使其生长。这项工作表明，虽然幼体（5天大，生殖前）宿主更早，更频繁地蜕皮（这可能增加其逃脱病原体渗透的机会），但相对于幼体，病原体能够更快地穿透食道。大人。这些差异可能是由于食管表皮厚度或组成的个体发育差异引起的。不管具体特征如何，这种张力意味着未成年寄主在换羽周期的四分之三期间易感，而成年同伴仅在换羽周期的一半易感。

成人具有更强的生理防御能力，因此首先感染的成年人减少了。在确实被感染的成年人中，病原体的发育被延迟，并且通常在第一个发育周期之后才进展。换句话说，成年人能够清除感染，减少总体病原体负荷（尽管宿主更大）并防止病原体发展成成熟的传播阶段。因此，伊扎尔等。（2020年）提供了令人信服的证据，表明宿主内部关键过程的时间改变了宿主与病原体之间的相互作用方式，影响了宿主的恢复和病原体负荷，所有这些因素都可以扩大以影响宿主之间的传播。

这项研究更普遍地提出了进化流行病学的几个重要问题。例如，作者的一系列详细实验表明，成年宿主感染宿主后，它们会同时部署抗感染和抗生长的抗药性机制（Boots，Best，Miller，＆White，  2009 ; Donnelly，White，＆Boots ，  2015； Gandon，Mackinnon，Nee和Read，  2001）。理论预测和实证研究表明，抗增长和抗感染性都可以促进生长更快且更具毒性的病原体，从而克服这些防御问题（de Roode，Fernandez de Castillejo，Faits和Alizon，  2011年））。该系统是测试这些（以及其他）问题的理想选择，以更好地了解宿主人口统计学（年龄结构）和宿主内部感染动态如何扩大以改变宿主-病原体（共同）的演变，从而对保护和公共卫生政策产生影响（Gandon等人，  2001； Mideo，Alizon等人，2008； van Leeuwen等人，  2019； Westwood等人，  2019）。显然，我们在这项工作中还有很长的路要走，但是Izhar等人的研究表明。（2020）使我们更接近那个目标。