Is there any evidence that immunity or resistance is heritable?

Is there any evidence that immunity or resistance is heritable?

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Is any means by which smallpox or any other disease be heritable?

I have heard that smallpox introduced by European colonialists affected (and reduced) a significant proportion of the native American population. How were the native Americans more susceptible to smallpox? Is disease susceptibility or immunity heritable?

TL;DR: Pre-Columbian Native Americans would have been much more susceptible to smallpox than the Europeans who spread it, because of non-heritable factors such as immunity and traditional knowledge, and also because of heritable factors. It's therefore completely believable that smallpox (and other diseases introduced by Europeans, such as measles) could have caused massive deaths among the American populations post-contact.

The title of the question makes an incorrect assumption. Resistance to smallpox would not have to be heritable to have a highly susceptible population. European populations were continually exposed to smallpox and there would have been both maternal immunity and acquired immunity in most populations, leading to a herd immunity effect that would both reduce viral spread and increase resistance among some infected people. (It's commonly believed that herd immunity needs to be very high to lead to protection, but that's a misconception -- the lower the contagiousness of the disease, the lower herd immunity needs to be to lead to protection.)

Smallpox apparently circulated as a childhood disease in northern England and Sweden, even where population densities were low and settlement patterns dispersed.

--The geography of smallpox in England before vaccination: A conundrum resolved

Cultural factors would also be important. Europeans had thousands of years of experience with smallpox, and knew that isolating victims was important to reduce spread.

We concluded that transmission was controlled in southern England by local practices of avoidance and mass inoculation that arose in the seventeenth and eighteenth centuries. Avoidance measures included isolation of victims in pest houses and private homes, as well as cancellation of markets and other public gatherings, and pre-dated the widespread use of inoculation.

--The geography of smallpox in England before vaccination: A conundrum resolved

That said, there is also evidence that European populations did have some innate resistance to smallpox that was lacking in the pre-Columbian Native Americans, and this innate resistance may be even stronger in Africa, which is (possibly) where smallpox originated:

Pathogens that diminish reproductive potential, either through death or poor health, drive selection on genetic variants that affect resistance; selection is likely to be most evident for pathogens with a long-standing relationship with Homo sapiens, including those that cause malaria, smallpox, cholera, tuberculosis and leprosy… We noted that, for 32 viral isolates with documented mortality, death rates are lower in Africa (0.4−12%) than elsewhere (4−38%), even though all isolates were from a single phylogenetic clade. This is consistent with selection for resistance in Africa, where the smallpox virus is predicted to have evolved from a rodent-borne ancestor tens of thousands of years ago and where outbreaks of other poxviruses continue nowadays.

With smallpox eradicated, vaccine response is used as a crude phenotypic proxy for studying host resistance. Two GWASs that included European, African American and Hispanic populations identified 37 SNPs associated with cytokine response to vaccination179, 180 (P < 1 × 10−8). Most of the significant associations (65%) were found in African Americans, even though their sample size was half of that of the European cohorts, which is consistent with a larger effect due to selection in Africa.

--Natural selection and infectious disease in human populations

Some resistance alleles have been tentatively mapped:

However, when examining the population post-contact and into contemporary times, variants of the HLA-DQA1 gene experience a marked frequency change… Although we were unable to precisely identify the selection coefficient necessary to drive the allele frequency change (since the likelihood surface is relatively flat, Supplementary Fig. 8), it is likely that relatively strong negative selection occurred. Such strength would be expected under a time frame of less than seven generations and correlates with the high mortality rates associated with the regional smallpox epidemics of the 1800s, which reached upwards of 70%

--A time transect of exomes from a Native American population before and after European contact

The CCR5 Δ32 mutation is a good example of an advantageous allele with a well-characterized geographic distribution… The mutation is found principally in Europe and western Asia, where average frequencies are approximately 10%… Bubonic plague was initially proposed as the selective agent [2], but subsequent analysis suggested that a disease like smallpox is a more plausible candidate

--The Geographic Spread of the CCR5 Δ32 HIV-Resistance Allele

As usual, @iayork has a great answer. Let me add some additional context.

Host-pathogen interactions

Pathogens are not disease. The result of an interaction between, e.g., a virus and a host is variable. This is a fundamental and extremely important concept in understanding infectious disease. You can read more about it in Chapter 286 of Cecil Medicine and Chapter 119 of Harrison's Internal Medicine. The clinical outcome of a host-pathogen interaction is as much dependent on the host as it is the pathogen.

Host effects are variable and heritable

There is great interindividual and interpopulation variability in host factor side of this graph, the susceptibility/resistance to specific infectious diseases. Much of it is heritable. Sickle cell trait is an excellent example. We refer to it so often to illustrate genetic principles, that the key lesson about host-pathogen interactions is often forgotten. It was not unnoticed in the original paper. This genetic variation in susceptibility is not an exception, see Jean-Laurent Casanova's inaugural article in PNAS for an excellent discussion.

Comment on @iayork's answer

I did enjoy the discussion of the non-genetic (but certainly vertically transmitted) traditional knowledge in the susceptibility of a population to smallpox, as well as the references to single gene associations. Host susceptibility and resistance is certainly heritable, but as s/he points out, there are important non-heritable contributions to consider.

Early life in humans (beginning at the fetal stage and progressing to the first few years of life) is associated with dramatic developmental milestones in the immune system, which makes this stage particularly important and unique. The innate branch of the immune system consists of cells such as neutrophils and macrophages, and is the first response to infection. It lacks memory and generally is activated by recognizing generic pathogen-associated molecular patterns. In contrast, the adaptive branch, consisting of cells such as B and T cells, is targeted, specific, and has memory to previously encountered stimuli. Development of the innate and adaptive branches of the immune system occurs in waves with the earliest tissue resident macrophages observed at 4 weeks of gestation and the earliest T cell development observed between 8 and 12 weeks of gestation. Compared with the adult immune system, which has incurred years of exposure to antigens and environmental stimuli, the newborn immune system emerges from a relatively sterile environment into one filled with bacterial, fungal, and viral challenges.

These differences in exposure to antigens and environmental stimuli have consequences when examining disease susceptibility. For instance, compared with adults and children, infants face increased susceptibility to infection [1, 2]. Yet many of our preventative strategies for neonates rely upon our understanding of the adult immune system because of our limited knowledge of early life immunity. To address the many outstanding questions concerning how early life environment and genetics affect the susceptibility of disease both at the early life stages and later in life requires understanding of the heritability of immune responses and the variability of the responses in a population. Studies of immune system variability in adults highlight the extensive impact the environment has on the immune response. For example, Brodin et al. [3] analyzed the heritability of immune response characteristics in twins and found that the majority of the variation cannot be explained by heritable influences, which suggests the environment plays a considerable role in shaping the adult immune response. Moreover, the variability increased with age. Similarly, in a study focusing on the epigenetics of the immune response, Cheung et al. [4] found that 70% of the inter-individual variability in chromatin modifications in immune cells was due to non-heritable factors. Both of these studies imply a model in which the immune response at early life is largely uniform across individuals and that time and its associated environmental exposures leads to divergence. Unfortunately, immunological studies on newborns tend to be small-scale and focus on only a few parameters because of limited sample volumes and low-throughput techniques. However, high dimensional single-cell technologies such as cytometry by time of flight (CyTOF) and methods to profile hundreds of plasma proteins in small volumes have made possible several new studies on early life immune system development. High-resolution understanding of the early life immune response could lead to vaccines with better efficacy in the young, help identify risk factors for autoimmunity, and improve treatment of early life infectious disease.


This paper aims to consider the relative definitions of resistance and tolerance, as applied to host genetic resistance to disease in livestock, determine the situations when resistance and tolerance are useful breeding goals, and apply the concepts discussed to nematode infections in ruminants. Currently there is considerable debate amongst livestock geneticists on the relative utility of resistance and tolerance when considering the term 𠇍isease resistance” such debates are often unhindered by data or evidence and are even unhindered by a consistent logical thread in the argument. Curiously a parallel debate on the merits of tolerance and resistance has been conducted within the ecological and immunological communities (e.g., Rrg et al., 2007, 2009). However, there has been little cross fertilization between these different groups of researchers. The debate on the relative merits of resistance and tolerance is particularly apposite now, as disease resistance is becoming an ever more ubiquitous goal in many breeding programs and is invariably nominated by breeders as a high priority trait. Further, with the ready availability of DNA from populations of animals that have faced epidemic challenges, genomic selection (albeit with low precision) is now becoming an option for diseases that hitherto would have been difficult to incorporate into breeding programs.

From consideration of literature on disease resistance (from a livestock viewpoint) it is apparent that different authors have different interpretations of the term “resistance”. For example, common usage is to define resistance in terms of susceptibility to infection per se, i.e., liability to becoming infected when faced with an infectious challenge of a parasite or pathogen, with animals that are less susceptible being more resistant. However, this definition does not hold for nematode infections, where faecal egg count (FEC) is often used as the indicator of relative resistance and FEC may be thought of summarizing the net outcome of the host–parasite interaction. The issue of trait definition for resistance has even been avoided on occasions. For example Boddicker et al. (2012), in a study aiming to find QTL for resistance to porcine reproductive and respiratory syndrome (PRRS), simply described their trait (viraemia following infection) as a measure of host response to infection.

The trait definition problem can be clarified to some extent by generalizing the definitions to encapsulate the trait biology, as outlined by Bishop and Stear (2003). Defining infection as the colonization of a host animal by a parasite (or pathogen) and disease as the side effects of infection, these authors then defined resistance as the ability of the individual host to control or influence the parasite (pathogen) lifecycle, and tolerance as the net impact of infection on the performance of host animal, i.e., the disease side-effects. These definitions are consistent with those used elsewhere in this Special Topic. Definitions as broad as this allow the concepts of resistance and tolerance to be applied to any disease, and to be applied equally to any aspect of the host–parasite (pathogen) interaction or any outcome of infection. Full definitions of the terms used in this paper to describe impacts of infection on individual hosts and in populations are shown in Box 1, along with a diagrammatic representation of these terms.

Box 1. Definitions used in paper..

This review article considers the wider implications of resistance and tolerance, when applied to any infectious disease, with a particular focus on nematode infections in ruminants. It is assumed that for most diseases host–parasite interactions are complex and under partial genetic control (e.g., Davies et al., 2009). Further, it is assumed that the complexity of the host–parasite interactions leads to variation in resistance being polygenic in most (but not all) cases.

Results and discussion

We cultured Escherichia coli K12 MG1655 as biofilms in the absence of antibiotics. Briefly, we inoculated E. coli into flow-cells and cultured biofilms using minimal medium with glucose as a sole carbon source. We sampled biofilm populations at 15, 30 and 60 days and isolated ten bacterial clones from three replicate flow cells at each time point. Using the Kirby-Bauer disc diffusion method [34], we characterized each bacterial clone for resistance to twelve antibiotics by measuring the diameters of zones of (growth) inhibition (ZOI) for each clone on each antibiotic (Table 1 see detailed methods below).

We tested whether heritable variation for antibiotic resistance evolved during biofilm development by assessing change in mean ZOI for the biofilm-derived clones relative to the ancestor. We observed the evolution of statistically significant differences in antibiotic resistance in biofilms (one-way MANOVA across all levels of biofilm replicate × time, Wilks’ = 0.016, P < 0.0001 Figure 1), with clones that were more sensitive or more resistant appearing independently in each of our replicates (Figure 2 see Additional file 1: Figure S1 for evidence that this variation is heritable, and Additional file 1: Table S1 for an analysis of correlations across different antibiotics). The data used for the analysis was formatted to create a balanced design matrix. Using a set of nine planned contrasts, we found significant changes in the mean evolved resistance for several antibiotics through time (Table 2). Many evolved clones also showed increased susceptibility to antibiotics. Although resistant clones were uncommon in our experiments, when they appeared in evolved biofilms, they did so at notable frequencies (Figure 2). For some combinations of antibiotics and sampling times, multiple samples from evolved biofilms showed higher variability than seen among multiple samples from the ancestral clone (Figure 3). These findings support hypotheses 1 and 2 genetic variation in levels of resistance to antibiotics evolves during biofilm development in the absence of antibiotics, and this variation includes both resistant and sensitive clones. There was no evidence of increased variation through time in our data (hypothesis 3 linear regression of total multivariate phenotypic variation [disparity] among clones vs. biofilm age, P > 0.05 number of resistant or sensitive clones vs. biofilm age, P > 0.05).

Mean diameter (in pixels) of the zone of inhibition (ZOI), a measure of antibiotic resistance, across ancestor (time zero) and bacteria isolated from biofilms at 15, 30, and 60 days. Individual replicates appear as distinct colors connected with a line. The ZOI of the ancestor is plotted at time 0 as an open circle.

Raw data for zone of inhibition, a measure of antibiotic resistance, across ancestor (time zero) and bacteria isolated from biofilms at 15, 30, and 60 days. Individual clones appear as dots. Red or green squares denote sensitive and resistant forms, respectively, determined as datapoints that are more than two standard deviations above or below the mean. Means for each replicate are marked with black bars and overall means connected with a dotted line. Antibiotics in the same class are followed by matching symbols.

Standard deviation (in pixels) of the diameters of zones of inhibition (ZOI) across ancestor (time zero) and bacteria isolated from biofilms at 15, 30, and 60 days. Individual replicates appear as distinct colors connected with a line. The variance across ZOI measurements of the ancestor is plotted at time 0 as an open circle.

Our results suggest that antibiotic resistance and susceptibility can rapidly evolve in biofilms over relatively short time scales (<15 days), which begs the question of how these rates of mutation accumulation compare to those in well-mixed liquid cultures that support exponential growth. Such a comparison is difficult because estimating the “mutation rate” in the spatially structured bacterial cells of biofilms is problematic. Mutation rates are almost always calculated and compared on a “per-generation” basis (e.g. [35, 36]), but rates of bacterial cell division in biofilms vary widely depending on location within the biofilm matrix. This variation in cellular growth rates is a consequence of nutrient depletion and the creation of strong gradients of substrates, electron acceptors and other resources within the spatially structured environment of biofilms [37, 38]. These gradients cause growth rates to vary tremendously within biofilms, such that cells deep within the biofilm matrix may not divide at all [39]. Because of this, mutation frequency cannot be expressed in the same terms, i.e., per generation, as in well-mixed liquid cultures nor can one calculate a meaningful population-wide average growth rate for cells in biofilms. One can imagine applying models that account for differential growth in biofilms (e.g., [40]), and then using current data to calculate mutation rates that can be compared to rates in well-mixed cultures. However, such calculations require data about mutation rates in in non-growing bacterial cells that is largely lacking, so direct and simple comparisons between biofilms and well-mixed cultures are not possible at this time.

The evolution of antibiotic resistance and susceptibility in bacterial biofilms involves the interaction between mutation, selection, genetic drift, and spatial structure [26, 40]. The data presented here cannot determine the importance of these multiple explanatory factors. It seems likely that evolution in biofilms typically occurs under conditions contrary to what is typically assumed in standard population genetics theory (e.g. strong selection and weak mutation) and rather involves strong mutational mechanisms typical in bacteria under stress [41] coupled with weak selection (see also [26]). Future work combining spatially explicit models for biofilm growth (e.g. [40, 42]) with model-based estimates of mutation rates and effect sizes for bacteria (e.g. [43]) would provide more insight into the details of evolution in biofilms.

Tracking infections

One study hoping to shed light on immunity after a coronavirus infection, posted on medRxiv in June and not yet peer-reviewed, drew on blood samples from healthy control subjects in an ongoing HIV project that began in 1985. [Update: This study has now been peer-reviewed and was published in Nature Medicine on September 14.] Researchers based at the Amsterdam University Medical Center (UMC) and their colleagues at other institutions analyzed stored samples from 10 subjects who had their blood collected every three to six months for at least 10 years, looking for antibodies to proteins from the four known cold-causing coronaviruses that would indicate a recent viral infection.

See “A Brief History of Human Coronaviruses”

The research team knew of the earlier 229E reinfection study, so they weren’t surprised to see multiple 229E infections in the same subjects crop up in their own data, as revealed by increases in antibody levels, says Arthur Edridge, a physician and Amsterdam UMC graduate student who is the paper’s first author. “What was surprising for us is that [reinfection] actually seemed to be a common feature for all the seasonal coronaviruses that we studied,” he says. All but one study subject had been infected with a particular coronavirus multiple times over the period of the study, and in some cases the time between infections with the same virus was as little as six months to a year, indicating an “alarmingly short duration of protective immunity,” the authors write in their paper.

Edridge cautions that it’s not clear whether SARS-CoV-2 will follow the same pattern as these more familiar coronaviruses—but if it does, then the idea that allowing the virus to spread in order to achieve herd immunity wouldn’t be a successful strategy, he adds.

Another recent study to find evidence of coronavirus reinfection was an analysis of data from a respiratory virus monitoring program conducted between 2016 and 2018. That study, which included 214 children and adults in New York City and relied on self-reports of symptoms and viral RNA swabbed from the back of the throat, found 12 instances of reinfection by the same coronavirus, although nine of these were in children, whose immune systems are less developed than those of adults. Reinfections were found for three of the four cold-causing coronaviruses (OC43, HKU1, and 229E).

It’s not clear whether SARS-CoV-2 will follow the same pattern as these more familiar coronaviruses—but if it does, then the idea that allowing the virus to spread in order to achieve herd immunity wouldn’t be a successful strategy.

Marta Galanti, a postdoc at Columbia University and the study’s first author, notes that the reinfections fell into two clusters in terms of timeline: at four to eight weeks after the initial infection, and at 8 to 10 months after the initial infection. She and her coauthor weren’t able to rule out the possibility that the earlier reinfections were in fact persistent first infections, she says, although they’re working on this in a follow-up study.

Like Edridge, Galanti is clear that the reinfection results don’t necessarily apply to SARS-CoV-2. But, she says, they indicate that “we have to be prepared [for] the possibility that the multiple subsequent infections can happen” with the novel coronavirus.

“Maybe it’s possible that if you only have these mild respiratory symptoms [with SARS-CoV-2 infection], you don’t develop a really strong immune response, and you could get reinfected,” says Rachel Roper, an immunologist at East Carolina University who was not involved in either of the studies. But she still thinks there’s uncertainty about whether reinfections occur with endemic coronaviruses, and she adds that infection with murine hepatitis virus, a coronavirus that causes serious disease in mice, confers lifelong immunity, as she suspects more severe cases of COVID-19 would. “If you had a serious infection the first time, all indications are you’ve got a stronger immune response,” and would either be immune to a second infection or experience only mild symptoms the second time around.

The Christensenellaceae are linked to metabolic health

Body composition and metabolic health

Body mass index (BMI) was the first host phenotype associated with the relative abundance of Christensenellaceae in the gut. Goodrich et al. observed that Christensenellaceae was significantly enriched in individuals with a normal BMI (18.5–24.9) compared to obese individuals (BMI ≥ 30) [21]. Since this initial observation, the association of Christensenellaceae with a normal BMI has been corroborated repeatedly in populations from a number of countries that included adult men and women of various ages (Table 3). Consistent with its association with leanness, Christensenellaceae have been shown to increase after diet-induced weight loss [100]. Although obese and lean subjects can often be differentiated using aspects of microbial ecology of the gut, these aspects (e.g., alpha-diversity, or abundances of phyla) have differed between studies [101]: the link between Christensenellaceae and BMI therefore stands as the strongest corroborated association between the gut microbiome and BMI.

BMI is a proxy for adiposity, and consistent with reports linking levels of Christensenellaceae with BMI, studies in which adiposity is more directly measured have also noted strong associations with the abundance of Christensenellaceae in the gut. For instance, Beaumont et al. correlated adiposity measures, determined using dual x-ray absorptiometry (DEXA), with the microbiome in a study of 1313 UK twins. At the family level, the most significant association was with Christensenellaceae, which negatively correlated with visceral fat mass [84], a type of fat that is considered a cardiometabolic risk factor. A similar observation was made by Hibberd et al., who reported significant negative correlations of Christensenellaceae with trunk fat and android fat [102]. Additionally, Christensenellaceae has been negatively correlated with waist circumference and waist to hip ratio, which are direct markers of central adiposity [66, 102,103,104].

In addition to its association with body fat measures, Christensenellaceae is negatively correlated with serum lipids in several studies. In the Dutch LifeLines DEEP cohort (n = 893), Fu et al. reported a negative correlation of Christensenellaceae with BMI, together with a strong association with low triglyceride levels and elevated levels of high density lipoprotein (HDL, or “good cholesterol”) [96]. Other groups have also reported that Christensenellaceae is associated with reduced serum triglycerides [66, 102, 104]. Similarly, this family is also negatively associated with total cholesterol, low density lipoprotein (LDL or “bad cholesterol”), and apolipoprotein B, a component of LDL particles [94, 102].

Christensenellaceae is reported as depleted in individuals with metabolic syndrome (MetS) compared to healthy controls [66, 104]. In addition to excess visceral fat, MetS includes other risk factors such as dyslipidemia and impaired glucose metabolism, and is a risk factor for type 2 diabetes and cardiovascular disease. Christensenellaceae was identified in a cohort of 441 Colombians as positively associated with a lower cardiometabolic risk score [103], and others report it is negatively correlated with blood pressure [66, 104, 105], which is often elevated in MetS [106]. Christensenellaceae has also been associated with healthy glucose metabolism [66, 107] and Christensenellaceae OTUs are reduced in individuals with pre-type 2 diabetes [65]. Given that a high BMI, impaired glucose metabolism, dyslipidemia, and other aspects of MetS are comorbidities, it is not surprising that Christensenellaceae inversely tracks with many of these conditions. The mechanism underlying its negative association with MetS remains to be elucidated.

Metabolic disorders are often linked to dietary patterns. The Christensenellaceae appear to be responsive to diet, and evidence points to a role in protein and fiber fermentation. On a coarse level, large-scale diet studies have associated Christensenellaceae with healthy dietary habits low in refined sugar and high in consumption of fruit and vegetables [108,109,110]. Christensenellaceae is reported higher in relative abundance in humans with an omnivorous diet, relative to vegetarians [71, 111], and has also been associated with dairy consumption [112]. In a more direct link, Christensenellaceae has been shown to respond rapidly to an increase in animal products in the diet [113]. Furthermore, Christensenellaceae has been positively associated with gut metabolites typical of protein catabolism and dietary animal protein [114,115,116]. Christensenellaceae has also been reported to increase in human dietary interventions involving prebiotic fibers such as resistant starch 4, galacto-oligosaccharide, and polydextrose [22, 102, 112]. Similar observations have also been made in rodent models [117,118,119]. Taken together, these studies indicate that the association of Christensenellaceae with health parameters may in part be due to its association with a diet high in protein and fiber.

To test for a causal role for Christensenellaceae in metabolic disease while controlling for diet, Goodrich et al. selected an obese human donor based on almost undetectable levels of Christensenellaceae in the microbiome, and performed fecal transfers to germfree mice that were fed the same fiber-rich chow, but otherwise only differed by whether or not the obese human microbiome inoculum was amended with C. minuta. These experiments showed that amendment with C. minuta reduced the adiposity gains of mice compared to those that received unamended stool (or stool amended with heat-killed C. minuta) [21]. The mechanism underlying the protective effect of C. minuta against excess adiposity gain remains to be elucidated, but may involve re-modeling the microbial community, as a shift in diversity was observed when C. minuta was added. These experiments demonstrated that the activity of C. minuta in the gut microbiome can affect host body composition even when diet is controlled for, possibly via interactions with other members of the microbiota. Indeed, the ecological role of members of the Christensenellaceae and their function in the gut in general remains to be better understood (Box 3).

Inflammation and transit time

In a meta-analysis of inflammatory bowel disease (IBD) that included over 3000 individuals, Mancabelli et al. reported Christensenellaceae as one of five taxa considered a signature of a healthy gut [120]. Indeed, Christensenellaceae were consistently depleted in individuals with Crohn’s disease [121,122,123,124,125,126,127,128,129] and ulcerative colitis [97, 122, 125, 129, 130], the two major sub-types of IBD. In irritable bowel syndrome (IBS), a gastrointestinal disorder characterized by abdominal pain and abnormal bowel movements, a higher relative abundance of Christensenellaceae in healthy controls relative to individuals with IBS has been reported in several studies [131,132,133,134]. Several studies have also noted a positive correlation of Christensenellaceae and longer transit time or even constipation [67, 114, 133, 135, 136]. Thus, the Christensenellaceae appear to be depleted in conditions associated with inflammation and fast transit time.

Given Christensenellaceae’s link with transit time, it is perhaps not surprising that the family has been linked to affective disorders that impact gut motility. For instance, gastric dysfunction, particularly constipation, affects approximately two-thirds of patients with Parkinson’s disease (PD) and multiple sclerosis (MS) [137, 138]. Studies have noted a greater relative abundance of Christensenellaceae in PD and MS patients relative to healthy controls [139,140,141,142]. Since diet is also related to gut transit time, the effects of diet, host status, and host genetics remain to be carefully disentangled to better understand how levels of the Christensenellaceae are controlled.

Damage threshold hypothesis of coral holobiont susceptibility

Immune systems can use resistance or tolerance strategies to promote fitness in the presence of a perturbation 71,72,73,74 (Box 1). The damage threshold hypothesis of insect-pathogen interactions 15 proposes that resistance and tolerance are intimately related to the amount of damage/danger signaling an infection incurs, and its effect on fitness. As such, we can expand upon the damage threshold hypothesis 15 to form a hypothetical and dynamic framework for how coral holobionts may moderate mutualisms, cohabit with commensals, kill pathogens and manage acute abiotic perturbations (Box 1). While the terms tolerance and resistance, and even resilience are often used interchangeably in coral biology 53,54 , they represent different immune strategies and outcomes 72 . Resisting a perturbation incurs high short-term costs, energetically, in rapidly mounting a strong immune response, and in autoimmune damage 24 . Tolerance, on the other hand, incurs a lower but longer-term cost of continual immune activity that physiologically offsets a damage burden, such as oxidative stress, and possibly more strictly moderates the microbiome 15 . The damage threshold hypothesis of coral holobiont susceptibility (Box 1, Fig. 2) demonstrates how more tolerant holobionts, with their higher constituent immunity 13 , may be better able to maintain homeostasis through perturbations (inclusive of a functional microbiome), than holobionts that employ a resistance strategy, which may be more readily overwhelmed. Balancing the trade-offs in immune strategy (Box 1), in the context of life history theory and physiology, may help explain the high variation in susceptibility to perturbations observed among coral holobionts with their dynamic microbiota 13,67,75 (Box 1).

Damage threshold hypothesis of coral susceptibility. Holobiont with a low susceptibility, tolerance strategy (a) has comparatively high constituent immunity, (b) is intermediate, living closer to the lowered damage threshold (dt) and (c) respresents a highly susceptible holobiont, unable to survive the hypothetical perturbation. a indicates constituent immunity levels, while A and B demonstrate homeostatic tolerance of damage, lowering the burden. C and D represent the magnitude of heightened immunity to a perturbation and D and E indicate resilliance (the time taken to return below the damage threshold) with the shorter duration indicating higher resilience

Box 1: Damage threshold hypothesis of coral holobiont susceptibility

The damage threshold hypothesis proposes a model for which a holobiont can coexist with a specific microbiota (tolerance) and remain vigilant to disturbance (resistance). How this is achieved likely varies among holobionts and is dependent upon evolved life history strategy and physiological trade-offs 24 , which combine to determine damage thresholds (Fig. 2). Damage thresholds, defined as the upper limit of damage that can be tolerated without causing harm, are inversely related to susceptibility.

Coral constituent immunity during homeostasis is directly, and inversely, related to disease and bleaching susceptibility 13 . Holobiont constituent immunity—potentially including the benefits conferred by mutualists—can physiologically off-set damage incurred by fluctuations in local or holobiont conditions, and therefore can be used as a proxy for determining damage thresholds. It can be hypothesized that high investment into constituent immunity equates to a high damage threshold and reduced damage burden, and therefore a low susceptibility, high tolerance immune strategy e.g., Porites spp. (Fig. 2a). When a severe perturbation occurs, inducing damage that exceeds the damage threshold, such as with a virulent pathogen, wounding, an acute shift in environmental conditions or dysfunctional mutualists, an immune response is triggered to resist and eliminate the threat, to repair the damage and re-establish homeostasis. For the tolerant strategist, with high constituent immunity, there is a large buffer before damage threshold is breached, suggesting that signs of stress, disease and bleaching will likely be delayed and less severe. Similarly, the immune response will likely be short lived, returning homeostasis rapidly (high resilience) and with lower up-regulation as compared to less tolerant strategists (e.g., Fig. 2b).

For the hypothetical intermediate strategist (Fig. 2b), constituent immunity enables a reasonably high damage threshold, though the damage burden during homeostasis is notably closer to it. This ensures that, at the onset of a perturbation, a higher magnitude response is required, and return to homeostasis (immune activity returning below the damage threshold) takes longer, demonstrating lower resilience. Holobionts with least investment into constituent immunity live the closest to the damage threshold (Fig. 2c) e.g., Acropora spp., and therefore are at highest risk of mortality in the face of a perturbation. Low constituent immunity means that a comparatively higher up-regulation is required, risking autoimmunity and a high short-term energetic cost—the resistance strategist. It may also mean that the holobiont is overcome before physiological measures can compensate for damage, and resistance measures may be overwhelmed, leading to death. While Fig. 2a–c can represent different holobionts with differing life histories, they may also represent one holobiont sliding from low to high susceptibility during a chronic perturbation.

Heavy use of hand sanitizer boosts antimicrobial resistance

Hand sanitisers can contain ingredients that may cause antimicrobial resistance. Credit: Maridav/ Shutterstock

Since the start of the coronavirus pandemic, scientists and governments have been advising people about the best hygiene practices to protect themselves. This advice has caused a significant surge in the sale and use of cleaning products and hand sanitisers. Unfortunately, these instructions rarely come with advice about using them responsibly or of the consequences of misuse.

But as with the misuse of antibiotics, the excessive use of cleaning products and hand sanitisers can lead to antimicrobial resistance in bacteria. There's concern that the sudden overuse in cleaning products and hand sanitisers during the pandemic could lead to an increase in the number of antimicrobial-resistant bacterial species we encounter. This would put a greater strain on our already struggling healthcare systems, potentially leading to more deaths. What's more, the problem could continue long after the current pandemic is over.

Antimicrobials (including antibiotic, antiprotozoal, antiviral and antifungal medicines) are important to our health. They help us fight against infections—particularly if your immune system is weak or compromised. However, some organisms (like bacteria) can change or mutate after being exposed to an antimicrobial. This makes them able to withstand the medicines designed to kill them. As the use and misuse of antimicrobials become more widespread, the number of resistant strains increases. Infections that were once easily treated are now becoming life threatening.

The processes that lead to antimicrobial resistance are many and varied. One route is through mutation. Some mutations occur after the bacteria's DNA has been damaged. This can happen naturally during cell replication, or after exposure to genotoxic chemicals, which damage the cell's DNA. Another route is if bacteria acquires resistant genes from another bacteria.

We usually (and correctly) associate antimicrobial resistance with the misuse of medications, such as antibiotics. Misuse could include failing to complete a course of antibiotics, or ignoring daily dose intervals. Both of these can increase the chance of the most resistant strains of bacteria in a population surviving and multiplying.

But bacteria can also acquire resistance after the inappropriate or excessive use of certain chemicals, including cleaning agents. Diluting sanitising agents, or using them intermittently and inefficiently, can provide a survival advantage to the most resistant strains. This ultimately leads to greater overall resistance.

Making matters worse are internet and social media "experts" offering advice about making homemade hand sanitisers they claim can kill the virus. For most of these products, there's no evidence they're effective. There's also no consideration about any possible adverse effects from using them. What we do know is that many of these homemade products contain ingredients, such as alcohol, that have antibacterial properties in the right quantities. Anything that's antibacterial has the potential to increase antimicrobial resistance.

Common cleaning supplies contain genotoxic chemicals. Credit: Chutima Chaochaiya/ Shutterstock

As a toxicologist, I'm concerned that more microorganisms could become antimicrobially resistant as a result of mutations caused by exposure to chemicals found in store-bought and homemade cleaning and hand sanitising products. Many of these (including phenols and hydrogen peroxide) have the potential to damage or alter DNA, and are sometimes referred to as genotoxic agents.

A small amount of DNA damage from normal cellular metabolism is common, particularly in rapidly multiplying bacteria. This damage is normally repaired properly. But if there's extensive DNA damage, or if the repair isn't correct, the cell may not survive.

However, mutations are more likely to happen in the few cases where the genotoxicity is not accurately repaired and the cell survives. A few of these cells may now have a survival advantage, such as antimicrobial resistance. The more frequent the genotoxic events, the more likely a few surviving cells will acquire antimicrobial resistance.

Many of the ingredients in various cleaning agents and hand sanitisers recommended by the World Health Organization have the potential to cause DNA damage and could lead to mutations necessary for antimicrobial resistance.

Common ingredients in many cleaning supplies and hand sanitisers include alcohols, quaternary ammonium compounds, phenols, hydrogen peroxide, surfactants, benzalkonium chloride and triclosan. The use of some of these compounds have already been linked to an increase in antimicrobial resistance. The current encouragement by the government to use products with these compounds—without clear warnings about the consequences of misuse—needs to be addressed.

When using hand sanitisers and cleaning products, treat them as you would a prescription medication. Read the instructions carefully, as any deviation can render them ineffective. Avoid diluting or combining pre-prepared products with other ones. Only make homemade sanitiser and cleaning products using recipes from government sites with ingredients bought from reputable stores.

Our attempts to protect ourselves from COVID-19 may also be creating an environment where even more antimicrobial resistant microorganisms can emerge. Given that antimicrobial resistance already causes more than 700,000 deaths a year worldwide, it's important we act with caution to prevent further impact.

This article is republished from The Conversation under a Creative Commons license. Read the original article.

We thank the reviewers for their insightful and constructive comments on the manuscript.

Figure S1. Quantity of articles from 𠇋oth” category with possible tag interactions. Total articles per tag are shown next to each tag title. If a tag is applied, it is indicated with a black dot. Black lines connecting dots indicates conditions where two or more tags were applied to the same article. Quantity of articles in each category are shown above the respective bar.

Table S1. Complete analysis of 5,626 articles retrieved from search. The Boolean search was: (TS = ((infection OR immunity OR hemocyte OR imd OR toll) AND drosophila) NOT TI = (bug OR bumblebee OR shrimp OR damesfly OR mollusc OR crab OR squid OR beetle OR baculovirus OR ant OR monochamus OR dastarcus OR cockroach OR crickets OR gryllus OR bemisia OR armyworm OR spodoptera OR mussel OR galleria OR helicoverpa OR amphibian OR manduca OR bee OR honey OR bactrocera OR tenebrio OR zebra OR dugesia OR flesh OR Apis OR house OR glossnia OR jelly OR Andrias OR dragonfly OR pachydiplax OR termite OR leech OR stick OR rhynchophorus OR rhodnius OR pardosa OR plutella OR coleoptera OR zophobas OR glossina OR ceratitis OR suzukii OR diabrotica OR rootworm OR sheep OR whitefly OR bird OR branchiostoma OR lizards OR laodelphax OR ceratopogonidae OR crassostrea OR oyster OR artemia OR freshwater OR calliphoridae OR phytomonas OR acyrthosiphon OR aphid OR crustacean OR parhyale OR hippocampus OR seahorse OR anopheles OR protaetia OR sea OR litopenaeus OR copepod OR swine OR planthopper OR arabidopsis OR circulifer OR leafhopper OR apostichopus OR cryptolaemus OR clam OR paphia OR mollusk OR achaea OR castor OR musca OR salmon OR dog OR echinococcus OR hetaerina OR sarcophaga OR fleshfly OR bovine OR zygoptera OR calopterygidae OR coenagrionidae OR scorpion OR locusta OR harpalus OR culex OR scylla OR firefly OR honeybees OR antheraea OR penaeus OR trichinella OR prawn OR macrobrachium OR ostrinia OR arge OR magnaporthe OR Phaeotabanus OR palm OR ostrinia OR daphnia OR scallop OR Chlamys OR Biomphalaria OR pig OR Anostostomatidae OR Orthoptera OR crayfish OR procambarus OR Platynereis)).


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Q: Can employers, colleges and universities require COVID-19 vaccinations?