The adrenal glands are two major endocrine organs responsible for multiple physiological processes, including the stress response, modulating the immune system and metabolism. The adrenal is composed of an outer cortex and an inner medulla with distinct developmental origin and function. While tissue-specific stem/progenitor populations of the adrenal cortex have been widely identified and characterised, the presence of a functional stem/progenitor population in the medulla is unclear. Establishing cell hierarchy of the adrenal medulla essential to understand normal homeostasis, disease pathogenesis and establishing regenerative medicine strategies, therefore the identification of a stem/progenitor population would provide an important starting point for further basic and translational studies. Cell composition of the adrenal medulla includes three main cell types: chromaffin cells, which secrete catecholamines, neurons, which stimulate catecholamine production, and a third cell type with an unspecified “support” function called sustentacular cells. Using transcriptomics and genetic approaches in mouse, I established that a population of sustentacular cells express the stem/progenitor marker SOX2. These cells are present throughout life and have a developmental origin congruent with the rest of the gland. Through genetic lineage-tracing using the Sox2CreERT2 strain, I demonstrate that SOX2+ cells are an expanding population, capable of giving rise to the catecholamine-producing chromaffin cells, consistent with a stem cell role in vivo. I further demonstrate the self-renewal potential of SOX2+ cells through in vitro isolation and expansion, using a Sox2eGFP mouse line. Analysis of SOX2+ cells in physiological organ challenge suggests potential involvement of these cells in the response to perturbation of normal homeostasis. Through analysis of FFPE sections of human adrenals, I confirm the presence of SOX2+ cells in the normal adult organ, as well as in pheochromocytomas. Taken together, these data support the identification of a previously undescribed stem cell population in the mammalian adrenal medulla and confirm its functional relevance.

Familial risk and environmental stress promote the development of alcohol dependence. We investigated whether a positive family history of alcoholism affects the neuroendocrine response to a standardized laboratory stress test in healthy subjects without alcohol use disorders. Twenty-four high-risk subjects with a paternal history of alcoholism (PHA) and 16 family history negative (FHN) controls were evaluated. Psychosocial stress was induced by having subjects deliver a 5-min speech and mental arithmetics in front of an audience on separate days, after drinking either placebo or ethanol (0.6 g/kg) in a randomized sequence. Adrenocorticotropin (ACTH) was measured in 10 plasma samples covering up to 75 min after the stress test. Plasma arginine vasopressin (AVP) was determined before the stressor, at the time of maximum ACTH secretion, and at 75 min after stress onset. The stress test induced a phasic increase in ACTH secretion. At the time of maximum ACTH, AVP was significantly increased in relation to baseline. Compared to placebo, alcohol administration significantly attenuated maximum ACTH concentration in PHA but not FHN subjects, and decreased AVP measured in the same samples in PHA but not FHN subjects. We conclude that activation of the hypothalamic–pituitary–adrenal system by psychosocial stress is accompanied by an increase in peripheral plasma AVP levels. Secretion of both ACTH and AVP suggest that alcohol attenuates the stress response selectively in PHA but not FHN subjects. This might imply some short-term positive alcohol effect in sons of alcoholics, but also constitute a mechanism by which their risk to develop alcohol use disorders is increased.

Transdisciplinary research in general, and stress research in particular, requires an efficient integration of methodological knowledge of all involved academic disciplines, in order to obtain conclusions of incremental value about the investigated constructs. From a psychologist’s point of view, biochemistry and quantitative neuroendocrinology are of particular importance for the investigation of endocrine stress systems (i.e., the HPA axis, and the SNS). Despite of their fundamental role for the adequate assessment of endocrine activity, both topics are rarely covered by conventional psychological curriculae. Consequently, the transfer of the respective knowledge has to rely on other, less efficient channels of scientific exchange. The present thesis sets out to contribute to this exchange, by highlighting methodological issues that are repeatedly encountered in research on stress-related endocrine activity, and providing solutions to these issues. As outlined within this thesis, modern stress research tends to fall short of an adequate quantification of the kinetics and dynamics of bioactive cortisol. Cortisol has gained considerable popularity during the last decades, as its bioactive fraction is supposed to be reliably determinable from saliva and is therefore the most conveniently obtainable marker of HPA activity. However, a substantial fraction of salivary cortisol is metabolized to its inactivated form cortisone by the enzyme 11β-HSD2 in the parotid glands, which is likely to restrict its utility. Although the commonly used antibody-based quantification methods (i.e. immunoassays) might “involuntarily” qualify this issue to some degree (due to their inherent cross-reactivity with matrix components that are structurally-related to cortisol; e.g., cortisone), they also cause differential within-immunoassay measurement bias: Salivary cortisone has (as compared to salivary cortisol) a substantially longer half-life, which leads to an overestimation of cortisol levels the more time has passed since the onset of the prior HPA secretory episode, and thus tends to distort any inference on the kinetics of bioactive cortisol. Furthermore, absolute cortisol levels also depend on the between-immunoassay variation of antibodies. Consequently, raw signal comparisons between laboratories and studies, which are favorable as compared to effect comparisons, can hardly be performed. This finding also highlights the need for the long-sought standardization of biochemical measurement procedures. The presumably only way to circumvent both issues is to rely on quantification of ultrafiltrated blood cortisol by mass-spectrometric methods. Being partly related to biochemical considerations with research on HPA activity, a second topic arises concerning the operationalization of the construct itself: In contrast to the simple outcome measures like averaged reaction times, inclined stress researchers can only indirectly infer on the sub-processes being involved in HPA activity from longitudinally sampled hormone concentrations. HPA activity can be quantified either by (a) discrete-time, or by (b) continuous-time models. Although the former is the most popular and more convenient approach (as indicated by the overly frequent encounter of ANOVAs and trapezoidal AUC calculations in the field of psychobiological stress research), most discrete time models form rather data-driven, descriptive approaches to quantify HPA activity, that assume the existence of some endocrine resting-state (i.e., a baseline) at the first sampling point and disregard any mechanistic hormonal change occurring in between all following sampling points. Even if one ignores the fact, that such properties are unlikely to pertain to endocrine systems in general, many generic discrete time models fail to account for the specific structure of endocrine data that results from biochemical hormone measurement, as well as from the dynamics of the investigated system. More precisely speaking, cortisol time series violate homoscedasticity, residual normality, and sphericity, which need to be present in order to enable (mixed effects) GLM-based analyses. Neglecting these prerequisites may lead to inference bias unless counter-measures are taken. Such counter-measures usually involve alteration of the scale of hormone concentrations via transformation techniques. As such, a fourth-root transformation of salivary cortisol (being determined by a widely used, commercially available immunoassay) is shown to yield the optimal tradeoff for generating homoscedasticity and residual normality simultaneously. Although the violation of sphericity could be partly accounted for by several correction techniques, many modern software packages for structural equation modeling (e.g., Mplus, OpenMX, Lavaan) also offer the opportunity to easily specify more appropriate moment structures via path notation and therefore to relax the modeling assumptions of GLM approaches to the analysis of longitudinal hormone data. Proceeding from this reasoning, this thesis illustrates how one can additionally incorporate hypotheses about HPA functioning, and thus model all relevant sub-processes that give rise to HPA kinetics and dynamics. The ALT modeling framework being advocated within this thesis, is shown to serve well for this purpose: ALT modeling can recover HPA activity parameters, which are directly interpretable within a physiological framework, that is, distinct growth factors representing the amount of secreted cortisol and velocity of cortisol elimination can serve to interpret HPA reactivity and regulation in a more unambiguous way, as compared to GLM effect measures. For illustration of these advantages on a content level, cortisol elimination after stress induction was found to be elevated as compared to its known pharmacokinetics. While the mechanism behind this effect requires further investigation, its detection would obviously have been more difficult upon application of conventional GLM methods. Further extension of the ALT framework allowed to address a methodological question, which had previously been dealt with by a mere rule of thumb; what’s the optimal threshold criterion, that enables a convenient but comparably accurate classification of individuals whose HPA axis is or is not activated upon encountering a stressful situation? While a rather arbitrarily chosen baseline-to-peak threshold of 2.5 nmol/L was commonly used to identify episodes of secretory HPA activity in time series of salivary cortisol concentrations, a reanalysis of a TSST meta- dataset by means of ALT mixture modeling suggested that this 2.5 nmol/L criterion is overly conservative with modern biochemical measurement tools and should be lowered according to the precision of the utilized assay (i.e., 1.5 nmol/L). In sum, parametric ALT modeling of endocrine activity can provide a convenient alternative to the commonly utilized GLM-based approaches that enables the inference on and quantification of distinct HPA components on a theoretical foundation, and thus to bridge the gap between discrete- and continuous-time modeling frameworks. The implementation of the outlined modeling approaches by the respective statistical syntaxes and practical guidelines being derived from the comparison of cortisol assays mentioned above, are provided in the appendix of the present thesis, which will hopefully help stress researchers to directly quantify the construct they actually intend to assess.

Background/Aims: Little research effort has so far been dedicated to the analysis of the hypothalamic-pituitary-adrenal axis of aetiologically differing subgroups of patients with panic disorder (PD). The current study aimed at a deeper understanding of the cortisol stress response in cannabis-induced PD (CIPD) patients. Methods: Matched groups of 7 PD patients (mean age ± SD: 32.95 ± 9.04 years), 7 CIPD patients (31.94 ± 8.40 years), and 7 healthy controls (HC) (31.13 ± 8.57 years) were included in the study. The Trier Social Stress Test (TSST) was used for stress induction. Salivary cortisol samples were collected and panic- and depression-related questionnaires were applied. Results: A stress response to the TSST was found in 28.6% of PD patients, in 51.1% of CIPD patients, and in 100% of HC subjects. Statistical analyses revealed a cortisol hyporesponsiveness in PD and CIPD patients. While cortisol values of PD patients and HC participantsdiffered significantly, CIPD patients’ cortisol courses balanced between those of PD patients and HC subjects. Conclusions: Current findings show a distinctive pattern of the stress-induced cortisol reaction in CIPD patients, which is markedly different from the hormonal response in PD patients as well as HC subjects. Previous findings of cortisol hyporesponsiveness in PD patients compared to HC subjects were confirmed.

Familial risk and environmental stress promote the development of alcohol dependence. We investigated whether a positive family history of alcoholism affects the neuroendocrine response to a standardized laboratory stress test in healthy subjects without alcohol use disorders. Twenty-four high-risk subjects with a paternal history of alcoholism (PHA) and 16 family history negative (FHN) controls were evaluated. Psychosocial stress was induced by having subjects deliver a 5-min speech and mental arithmetics in front of an audience on separate days, after drinking either placebo or ethanol (0.6 g/kg) in a randomized sequence. Adrenocorticotropin (ACTH) was measured in 10 plasma samples covering up to 75 min after the stress test. Plasma arginine vasopressin (AVP) was determined before the stressor, at the time of maximum ACTH secretion, and at 75 min after stress onset. The stress test induced a phasic increase in ACTH secretion. At the time of maximum ACTH, AVP was significantly increased in relation to baseline. Compared to placebo, alcohol administration significantly attenuated maximum ACTH concentration in PHA but not FHN subjects, and decreased AVP measured in the same samples in PHA but not FHN subjects. We conclude that activation of the hypothalamic–pituitary–adrenal system by psychosocial stress is accompanied by an increase in peripheral plasma AVP levels. Secretion of both ACTH and AVP suggest that alcohol attenuates the stress response selectively in PHA but not FHN subjects. This might imply some short-term positive alcohol effect in sons of alcoholics, but also constitute a mechanism by which their risk to develop alcohol use disorders is increased.

Transdisciplinary research in general, and stress research in particular, requires an efficient integration of methodological knowledge of all involved academic disciplines, in order to obtain conclusions of incremental value about the investigated constructs. From a psychologist’s point of view, biochemistry and quantitative neuroendocrinology are of particular importance for the investigation of endocrine stress systems (i.e., the HPA axis, and the SNS). Despite of their fundamental role for the adequate assessment of endocrine activity, both topics are rarely covered by conventional psychological curriculae. Consequently, the transfer of the respective knowledge has to rely on other, less efficient channels of scientific exchange. The present thesis sets out to contribute to this exchange, by highlighting methodological issues that are repeatedly encountered in research on stress-related endocrine activity, and providing solutions to these issues. As outlined within this thesis, modern stress research tends to fall short of an adequate quantification of the kinetics and dynamics of bioactive cortisol. Cortisol has gained considerable popularity during the last decades, as its bioactive fraction is supposed to be reliably determinable from saliva and is therefore the most conveniently obtainable marker of HPA activity. However, a substantial fraction of salivary cortisol is metabolized to its inactivated form cortisone by the enzyme 11β-HSD2 in the parotid glands, which is likely to restrict its utility. Although the commonly used antibody-based quantification methods (i.e. immunoassays) might “involuntarily” qualify this issue to some degree (due to their inherent cross-reactivity with matrix components that are structurally-related to cortisol; e.g., cortisone), they also cause differential within-immunoassay measurement bias: Salivary cortisone has (as compared to salivary cortisol) a substantially longer half-life, which leads to an overestimation of cortisol levels the more time has passed since the onset of the prior HPA secretory episode, and thus tends to distort any inference on the kinetics of bioactive cortisol. Furthermore, absolute cortisol levels also depend on the between-immunoassay variation of antibodies. Consequently, raw signal comparisons between laboratories and studies, which are favorable as compared to effect comparisons, can hardly be performed. This finding also highlights the need for the long-sought standardization of biochemical measurement procedures. The presumably only way to circumvent both issues is to rely on quantification of ultrafiltrated blood cortisol by mass-spectrometric methods. Being partly related to biochemical considerations with research on HPA activity, a second topic arises concerning the operationalization of the construct itself: In contrast to the simple outcome measures like averaged reaction times, inclined stress researchers can only indirectly infer on the sub-processes being involved in HPA activity from longitudinally sampled hormone concentrations. HPA activity can be quantified either by (a) discrete-time, or by (b) continuous-time models. Although the former is the most popular and more convenient approach (as indicated by the overly frequent encounter of ANOVAs and trapezoidal AUC calculations in the field of psychobiological stress research), most discrete time models form rather data-driven, descriptive approaches to quantify HPA activity, that assume the existence of some endocrine resting-state (i.e., a baseline) at the first sampling point and disregard any mechanistic hormonal change occurring in between all following sampling points. Even if one ignores the fact, that such properties are unlikely to pertain to endocrine systems in general, many generic discrete time models fail to account for the specific structure of endocrine data that results from biochemical hormone measurement, as well as from the dynamics of the investigated system. More precisely speaking, cortisol time series violate homoscedasticity, residual normality, and sphericity, which need to be present in order to enable (mixed effects) GLM-based analyses. Neglecting these prerequisites may lead to inference bias unless counter-measures are taken. Such counter-measures usually involve alteration of the scale of hormone concentrations via transformation techniques. As such, a fourth-root transformation of salivary cortisol (being determined by a widely used, commercially available immunoassay) is shown to yield the optimal tradeoff for generating homoscedasticity and residual normality simultaneously. Although the violation of sphericity could be partly accounted for by several correction techniques, many modern software packages for structural equation modeling (e.g., Mplus, OpenMX, Lavaan) also offer the opportunity to easily specify more appropriate moment structures via path notation and therefore to relax the modeling assumptions of GLM approaches to the analysis of longitudinal hormone data. Proceeding from this reasoning, this thesis illustrates how one can additionally incorporate hypotheses about HPA functioning, and thus model all relevant sub-processes that give rise to HPA kinetics and dynamics. The ALT modeling framework being advocated within this thesis, is shown to serve well for this purpose: ALT modeling can recover HPA activity parameters, which are directly interpretable within a physiological framework, that is, distinct growth factors representing the amount of secreted cortisol and velocity of cortisol elimination can serve to interpret HPA reactivity and regulation in a more unambiguous way, as compared to GLM effect measures. For illustration of these advantages on a content level, cortisol elimination after stress induction was found to be elevated as compared to its known pharmacokinetics. While the mechanism behind this effect requires further investigation, its detection would obviously have been more difficult upon application of conventional GLM methods. Further extension of the ALT framework allowed to address a methodological question, which had previously been dealt with by a mere rule of thumb; what’s the optimal threshold criterion, that enables a convenient but comparably accurate classification of individuals whose HPA axis is or is not activated upon encountering a stressful situation? While a rather arbitrarily chosen baseline-to-peak threshold of 2.5 nmol/L was commonly used to identify episodes of secretory HPA activity in time series of salivary cortisol concentrations, a reanalysis of a TSST meta- dataset by means of ALT mixture modeling suggested that this 2.5 nmol/L criterion is overly conservative with modern biochemical measurement tools and should be lowered according to the precision of the utilized assay (i.e., 1.5 nmol/L). In sum, parametric ALT modeling of endocrine activity can provide a convenient alternative to the commonly utilized GLM-based approaches that enables the inference on and quantification of distinct HPA components on a theoretical foundation, and thus to bridge the gap between discrete- and continuous-time modeling frameworks. The implementation of the outlined modeling approaches by the respective statistical syntaxes and practical guidelines being derived from the comparison of cortisol assays mentioned above, are provided in the appendix of the present thesis, which will hopefully help stress researchers to directly quantify the construct they actually intend to assess.:1. Introduction 2. The hypothalamus-pituitary-adrenal (HPA) axis 3. Induction and quantification of HPA activity 4. The pitfalls of SCC measurement 5. Creating normality and homoscedasticity: GLM-based analyses 6. Relaxing sphericity: moment structure analyses 7. General conclusion

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