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TECHNIQUES FOR STUDYING INTEGRATED IMMUNE FUNCTION IN BIRDS

2006· article· en· W2176285847 on OpenAlexaff
Katrina G. Salvante

Bibliographic record

VenueThe Auk · 2006
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsFunction (biology)Immune systemBiologyZoologyGeographyEcologyEvolutionary biologyImmunology

Abstract

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Evolutionary physiologists and ecologists seek to understand the mechanisms that underlie trade-offs involving life-history traits. These trade-offs arise when resources are limited, and allocation of resources to certain traits limits the amount of resources available for other traits (Williams 1966, Stearns 1992). Recent studies have found that maintaining or activating immune function can be resource-dependent (Tsiagbe et al. 1987; Saino et al. 1997b, 2003; Alonso-Alvarez and Tella 2001) and metabolically costly (Demas et al. 1997, Lochmiller and Deerenberg 2000, Ots et al. 2001, Martin et al. 2002) and, therefore, may compete with life-history traits for nutrient or energetic resources (for reviews see Sheldon and Verhulst 1996, Lochmiller and Deerenberg 2000). Therefore, the study of immunocompetence (i.e. the ability of a host to prevent or control infection by pathogens and parasites) has become the focus of many studies on fitness-related trade-offs in free-living birds (Saino et al. 1997a; Hõrak et al. 2000; Norris and Evans 2000; Hanssen et al. 2003, 2004). In their seminal review, Norris and Evans (2000) contrasted the techniques currently used by ecologists to measure immunocompetence with the techniques used by immunologists. Reviewing examples of trade-offs between immune-system maintenance and resource allocation to life-history traits, they concluded that future studies need to (1) assess multiple components of the immune system to make conclusions about how these components interact and (2) manipulate immunocompetence to measure the potential fitness consequences. Although many authors have since stressed the importance of examining integrated immunity (i.e. how the various components of the immune system work together; Keil et al. 2001, Sandland and Minchella 2003, Adamo 2004), few studies on free-living birds have fully appreciated the complexity of this system. Here I discuss alternative immunological techniques, currently used in studies of fish, mammals, and domestic poultry, that can be used to examine integrated immune function in free-living birds. I emphasize the importance of an integrated approach when examining the relationship between immune defense and fitness. The immune system can be divided into three components: innate, cell-mediated, and humoral immunity (Roitt et al. 1998). The innate immune response involves nonspecific recognition, binding, internalization, and destruction of foreign material by phagocytotic cells (Roitt et al. 1998). Cell-mediated immune responses are mediated by T-lymphocytes (T-cells) that either regulate the function of B-lymphocytes (B-cells) and phagocytes or destroy infected host cells through interactions with antigens present on the surface of these cells (Roitt et al. 1998). Finally, the humoral, or adaptive, immune response involves production of specific antibodies (i.e. immunoglobulins [Ig]) by B-cells against antigens associated with pathogen infection; the response improves with repeated exposure to specific pathogens (Roitt et al. 1998). Although a multitude of assays is available for assessing the activation of each of these components of the immune system (mammals: Luster et al. 1988, 1992, 1993; poultry: Norris and Evans 2000; fish: Rice and Arkoosh 2002), evolutionary ecologists and physiologists studying free-living birds have chosen to focus on a small proportion of these immunological tests (Table 1). These tests were likely chosen on the basis of the ease of sample collection, the nondestructive nature of sampling, and the ease of sample analysis. However, results from some of these techniques, especially those that assess innate immunity, are ambiguous. For example, elevated numbers of leukocytes (i.e. white blood cells [WBC]) or a large leukocrit value could indicate an individual in good condition, with a healthy immune system, or an individual currently fighting infection. There is also evidence that WBC number does not necessarily correlate with WBC activity (Ladics et al. 1998, Wilson et al. 2001). Therefore, techniques that assess WBC concentration (e.g. WBC counts or Ig enzyme-linked immunosorbent assays [ELISA]) may not be reliable measures of immune function. By contrast, studies on immune function in other taxa use a variety of techniques, most of which are less ambiguous, are as easy to perform as those used in avian studies, and are equally nondestructive (i.e. many assays require ≈150 μL of blood, and a combination of assays can be performed on only one 150-μL blood sample; Table 2). Techniques used to measure immunocompetence in wild birds (adapted from Norris and Evans 2000). Techniques used to measure immunocompetence in wild birds (adapted from Norris and Evans 2000). Alternative techniques to measure immunocompetence in wild birds. Alternative techniques to measure immunocompetence in wild birds. Many authors have cautioned against making generalizations about host immunity and disease resistance on the basis of results from a single immunocompetence assay that examines a single component of the immune system (Lochmiller 1995, Sheldon and Verhulst 1996, Zuk and Johnsen 1998, Norris and Evans 2000, Keil et al. 2001, Sandland and Minchella 2003, Adamo 2004). For example, a decline in the measured component of the immune system may be offset by up-regulation of the unmeasured components of the immune system or other aspects of the measured component (i.e. different cell types within the same arm of the immune system could compensate for declines in the measured cell type; Keil et al. 2001). Norris and Evans (2000) pointed out that, at the time of their review, studies on free-living birds that assessed more than one component of the immune system were nonexistent (but see Spinu et al. 1999, Szép and Møller 1999). Since 2000, only a handful of studies on wild birds have simultaneously examined more than one component of the immune system (Hõrak et al. 2000, Saino et al. 2003, Müller et al. 2004, Matson et al. 2005). To assess integrated immunity, studies on laboratory mammals have examined hyperthermia (Nava et al. 1997, Raghavendra et al. 1999, Bilbo and Nelson 2002) and wound healing (Rojas et al. 2002, Kinsey et al. 2003). However, these techniques are not realistically applicable to field studies on birds because of repeated-measurement requirements, lethal endpoints, or effects on other aspects of physiology or behavior, such as sickness-related anorexia. By contrast, other studies on humans and laboratory mammals have measured circulating cytokine levels following lipopolysaccharide (LPS) challenge as a measure of integrated immunity (Lee et al. 1992, Sacco et al. 1998, Baykal et al. 2000). Cytokines (e.g. interferons, interleukins, tumor necrosis factor) function as signals between different components of the immune system; they are synthesized de novo by a variety of leukocytes in response to an immune stimulus to mediate inflammation by regulating leukocyte proliferation and differentiation (Roitt et al. 1998). Incorporation of this and other techniques (see Table 2) into the repertoire of immunological studies on free-living birds would simplify the simultaneous measurement of two, or even all three, components of the immune system. Studies examining the role of immunocompetence in fitness-related trade-offs assume that immune function is energetically costly and may “trade off” with other energetically demanding, fitness-related traits (e.g. growth, reproduction) during periods of limited resources. However, because of the complexity of the immune system, trade-offs can arise both within and between components of the immune system. Consequently, examining a trade-off between life-history traits and only one component of the immune system can be misleading. Therefore, studies examining immunology in the field should step back and first try combining a variety of techniques to determine how the various components that make up the immune system interact. Because these types of experiments may require multiple sampling, large samples, or destructive sampling, preliminary experiments could be carried out in the laboratory. Once the basis for how the different components of the immune system function together is understood, field studies can be planned to examine how these interactions are modulated in response to changes in environmental conditions and physiological state. Moreover, greater knowledge of how the different components of the immune system work together could allow evolutionary ecologists and physiologists, who are interested in examining immunocompetence in a fitness context, to work with immunologists to develop more applicable tests of integrated immunity for use in the field. Once the technical aspects of examining integrated immune function in the field are addressed, questions regarding the relationship between immune function modulation and fitness can be raised. However, because organisms modulate their immune responses to adaptively respond to their condition, physiological state, or environment (i.e. short-term declines in immune function may be adaptive at certain times, whereas enhanced immune function is required at other times), comparisons of the immune responses of individuals in different environmental or physiological conditions (e.g. nestlings vs. adults, nonbreeding vs. laying females, capital- vs. income-breeders, individuals in good vs. poor condition) can also be misleading. This may explain why results from studies that have examined the relationship between immune function and an index of fitness (e.g. return rate as an index of survival) are not consistent. In a metaanalysis using results from studies on immune function and survival in passerine birds, survival was correlated with stronger immune responses (Møller and Saino 2004). Similarly, in breeding female Common Eiders (Somateria mollissima), a capital breeder with precocial young, nonchallenged lymphocyte levels measured late in the breeding season were positively correlated with the probability of returning the next year (i.e. survival to the next breeding season; Hanssen et al. 2003). By contrast, when incubating female Common Eiders were challenged with multiple antigens, females that responded by activating the humoral component of the immune system by producing antibodies against sheep red blood cells and diphtheria, thus exhibiting a stronger immune response than nonresponders, were less likely to return to the breeding grounds in the next two years, and were assumed to have died (Hanssen et al. 2004). Consequently, to properly understand how immune function and fitness are related, future studies need to examine integrated immunity (1) in a variety of organisms, (2) at various physiological stages, (3) throughout the lifetime of organisms, and (4) in a variety of environmental conditions. Furthermore, these studies must also measure aspects of fitness (e.g. mortality and lifetime reproductive success). While this task is complex, it is the future of evolutionary immunology. I thank O. P. Love and T. D. Williams for stimulating discussions regarding the relationships between immunocompetence, life-history theory, and fitness. This manuscript benefited greatly from helpful comments by O. P. Love and two anonymous reviewers.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.031
GPT teacher head0.244
Teacher spread0.213 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations68
Published2006
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