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Aiming at the goal of individualized medicine, this dissertation develops a generic methodology to individualize risk factors and phenotypes via metabolomic data from the urine. As metabolomic data can be seen as a holistic representation of the metabolism of an organism at certain time point, metabolomic data contain not only information about current life-style factors like diet and smoking but also about latent genetic traits. Utilizing this integrative attribute, the dissertation delivers a metric for biological age (the metabolic age score) which was shown to be informative beyond chronological age in three independent samples. It was associated with a broad range of age-related comorbidities in two large population-based cohorts, predicted independently of classical risk factors mortality and, moreover, it predicted weight loss subsequently to bariatric surgery in a small sample of heavily obese individuals.
Subsequently to this work, the dissertation built a definitional framework justifying the procedure underlying the metabolic age score, delivering a general framework for the construction of individualized phenotypes and thereby an operationalization of individualization in statistical terms. Conceptualizing individualization of the process of differentiation of individuals showing the same phenotype despite different underlying biological traits, it was shown formally that the prediction error of a statistical model approximating a phenotype is always informative about the underlying biology beyond the phenotype if the predictors fulfill certain statistical requirements. Thus, the prediction error facilitates the meaningful differentiation of individuals showing the same phenotype. The definitional framework presented here is not restricted to any kind of data and is therefore applicable to a broad range of medical research questions.
However, when utilizing metabolomic data, technical factors, data-preprocessing, pre-analytic features introduce unwanted variance into the statistical modeling. Thus, it is unclear whether predictive models like the metabolic age score are stable enough for clinical application. The third part of this doctoral thesis provided two statistical criteria to decide which normalization method to remove the dilution variance from urinary metabolome data performs best in terms of erroneous variance introduced by the different methods, aiding the minimization of biological irrelevant variance in metabolomic analyses.
In conclusion, this doctoral thesis developed a general, applicable, definitional framework for the construction of individualized phenotypes and demonstrated the value of the methodology for clinical phenotypes on metabolomic data, improving on the way the statistical treatment of urinary data regarding the dilution correction.
The intracellular life cycle of the human immunodeficiency virus (HIV) is modelled using ordinary differential equations (ODEs). Model parameters are obtained from the literature or fitted to experimental data using parameter estimation procedures. Key steps in the life cycle are inhibited singly and in combination to show the effects on viral replication. The results validate the success of highly active antiretroviral therapy (HAART), and in addition DNA nuclear import is identified as a novel influential therapeutic target.
Discovering Latent Structure in High-Dimensional Healthcare Data: Toward Improved Interpretability
(2022)
This cumulative thesis describes contributions to the field of interpretable machine learning in the healthcare domain. Three research articles are presented that lie at the intersection of biomedical and machine learning research. They illustrate how incorporating latent structure can provide a valuable compression of the information hidden in complex healthcare data.
Methodologically, this thesis gives an overview of interpretable machine learning and the discovery of latent structure, including clusters, latent factors, graph structure, and hierarchical structure. Different workflows are developed and applied to two main types of complex healthcare data (cohort study data and time-resolved molecular data). The core result builds on Bayesian networks, a type of probabilistic graphical model. On the application side, we provide accurate predictive or discriminative models focusing on relevant medical conditions, related biomarkers, and their interactions.
Gram-negative bacteria secrete lipopolysaccharides (LPS), leading to a host immune
response of proinflammatory cytokine secretion. Those proinflammatory cytokines are
TNF-α and IFN-γ, which induce the production of indoleamine 2,3-dioxygenase (IDO). IDO production is increased during severe sepsis, and septic shock. High IDO
levels are associated with increased mortality. This enzyme catalyzes the degradation of tryptophan (TRP) to kynurenine (KYN) along the kynurenine pathway (KP).
KYN is further degraded to kynurenic acid (KYNA). Increased IDO levels accompany
with increased levels of KYNA, which is associated with immunoparalysis.
Due to its central role, the KP is a potential target of therapeutic intervention.
The degradation of TRP to KYN by IDO was intervened by 1-Methyltryptophan (1-
MT), which is assumed to inhibit IDO. By administering 1-MT, the survival of
1-MT-treated mice suffering from sepsis increased compared to mice not treated with
1-MT. The levels of downstream metabolites such as KYN and KYNA were
expected to be decreased. Surprisingly, in healthy mice and pigs, an increase in KYNA
after 1-MT administration was reported. Those unexpected metabolite alterations after 1-MT administration, and the mode of action, were not the focus of recent
research. Hence, there is no explanation for KYNA increase, while KYN did not change.
This thesis aims to postulate a possible degradation pathway of 1-MT along the KP
with the help of ordinary differential equation (ODE) systems.
Moreover, the developed ODE models were used to determine the ability of 1-MT to
inhibit IDO in vivo. Therefore, a multiplicity of ODE models were developed, including
a model of the KP, an extension by lipopolysaccharide (LPS) administration, and 1-MT
administration.
Moreover, seven ODE models were developed, all considering possible degradation pathways of 1-MT. The most likely degradation pathway was combined with the ODE model
of LPS administration, including the inhibitory effects of 1-MT.
Those models consist of several dependent equations describing the dynamics of the KP.
For each component of the KP, one equation describes the alterations over time. Equations for TRP, KYN, KYNA, and quinolinic acid (QUIN) were developed.
Moreover, the alterations of serotonin (SER) were also included. All together belong
to the TRP metabolism. They include the degradation of TRP to SER and to KYN,
which is further degraded to KYNA and QUIN. Every degradation is catalyzed by an enzyme. Therefore, Michaelis-Menten (MM) equations were used employing the substrate
constant Km and the maximal degradation velocity Vmax. To reduce the complexity of
parameter calculation, Km values of the different enzymes were fixed to literature values.
The remaining parameters of the equations were determined so that the trajectories of
the calculated metabolite levels correspond to data. The parameters of different models were determined. To propose a degradation pathway of 1-MT leading to increased
KYNA levels, seven models were developed and compared. The most likely model was
extended to test whether the inhibitory effects of 1-MT on IDO can be determined.
Three different approaches determined the ODE model parameters of the different hypothesis of 1-MT degradation. In the first approach, ODE model parameters were fixed
to values fitted to an independent data set. In the second approach, parameters were
fitted to a subset of the data set, which was used for simulations of the different hypotheses. The third approach calculated ODE model parameters 100 times without
fixed parameters. The parameter set ending up in trajectories of the TRP metabolites,
which have the smallest distance to the data, was assumed to be the most likely. The
ODE model parameters were fitted to data measured in pigs. Two different
experimental models delivered data used in this thesis. The first experimental model
activates IDO by LPS administration in pigs. The second one combines the IDO
activation by LPS with the administration of 1-MT in pigs.
The most likely hypothesis, according to approach 1 was the degradation of 1-MT to
KYNA and TRP. For the second data set the most likely one was the direct degradation of 1-MT to KYNA. With approach 2 the most likely degradation pathways were
the combination of all degradation pathways and the degradation of 1-MT to TRP and
TRP to KYNA. With approach 3 the most likely way of KYNA increase was given by
the direct degradation of 1-MT to KYNA. In summary, the three approaches revealed
hypothesis 2, the direct degradation of 1-MT to KYNA most frequently. A cell-free
assay validated this result. This experiment combined 1-MT or TRP with or without
the enzyme kynurenine aminotransferase (KAT). KAT was already shown to degrade
TRP directly to KYNA. The levels of TRP, KYN and KYNA were measured. The
highest KYNA levels were yielded with an assay adding KAT to 1-MT, corresponding
to hypothesis 2. The models describing the inhibitory effects of 1-MT revealed that
the model without inhibitory effects of 1-MT on IDO was more likely for all three approaches.
The correctness of hypothesis 2 has to be confirmed by further in vitro experiments. It
also has to be investigated which reactions promote the degradation of 1-MT to KYNA.
The missing inhibitory properties of 1-MT on IDO, determined by the in silico ODE
models, align with previous research. It was shown that the saturation of 1-MT was too
low, e.g. in pigs, to inhibit IDO efficiently.
In this study, the first possible degradation pathway of 1-MT along the KP is proposed.
The reliability of the results depends on the quality of the experimental data, and the
season, when data were measured. Moreover, the results vary between the different
approaches of parameter fitting. Different approaches of parameter fitting have to be
included in the analysis to get more evidence for the correctness of the results.
Plus‐strand RNA [(+)RNA] viruses are the largest group of viruses, medically highly relevant human pathogens, and are a socio‐economic burden. The current global pandemic of the severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) shows how a virus has been rapidly spreading around the globe and that– without an antiviral treatment– virus trans mission is solely dependent on human behavior. However, other (+)RNA viruses such as rhino‐, noro‐, dengue‐ (DENV), Zika, and hepatitis C virus (HCV) are constantly spreading and expanding geographically. As in the case of hepatitis C, since its first identification in the 1970s, it took more than 30 years to understand the HCV structure, genome organiza t ion, life cycle, and virus‐host interplay leading to the cure of a chronic and life‐threatening disease. However, no vaccination or antiviral treatment exists for most (+)RNA viruses. Con sequently, a precise and comprehensive analysis of the viruses, their life cycles, and parasitic interactions with their hosts remains an important field of research. In the presented thesis, we use mathematical modeling to study the life cycles of (+)RNA viruses. We analyze replication strategies of closely related (+)RNA viruses, namely HCV, DENV, and coxsackievirus B3 (CVB3), to compare their life cycles in the presence and ab sence of the host’s immune response and antiviral drug treatment and consider different viral spreading mechanisms. Host dependency factors shape the viral life cycle, contribut ing to permissiveness and replication efficiency. Our mathematical models predicted that host dependency factors, such as ribosomes, and thus the virus’ ability to hijack the host cell’s translation machinery play an essential role in the viral genome replication efficiency. Furthermore, our mathematical model suggested that the availability of ribosomes in the vi ral life cycle is a crucial factor in disease outcome: the development of an acute or chronic disease. Even though the host developed strategies to attack the virus, e.g., by degrading the viral genome, blocking the viral protein production, and preventing viral spread, viruses found strategies to countermeasure those so‐called host restriction factors derived from the immune system. Our mathematical models predicted that DENV might be highly effective in blocking the cell’s attempts to recognize the invader. Moreover, we found ongoing HCV RNAreplication even with highly effective antiviral drugs that block processes in the viral life cycle. Furthermore, we found alternative pathways of infection spread, e.g., by HCV RNA carrying exosomes, which may be a possible explanation for reported plasma HCV RNA at the end of treatment, found in a subset of patients. Hence, the mathematical models presented in this thesis provide valuable tools to study the viral replication mechanism in detail. Even though being a simplification of reality, our model predictions confirm and explain known and suggest novel biological mechanisms. In the pre sented thesis, I will summarize and discuss key findings and contextualize model predictions in the broader scientific literature to improve our understanding of the viral dynamics and the virus‐host interplay.
Age is the single biggest risk factor for most major human diseases. As such, understanding the intricate molecular changes that drive biological aging holds great promise in attempting to slow
the onset of systemic diseases and thereby increase the effective health-span in modern societies.
This thesis explores several computational approaches to capture and analyze the molecular biological alterations triggered by intrinsic and extrinsic aging using skin as a model tissue to deliver genes and pathways as potential targets for intervention strategies.
Publication 1 demonstrates the utility of multi-omics data integration strategies for aging research, leading to the identification of four latent aging phases in skin tissue through an integrated cluster analysis of gene expression and DNA methylation data. The four phases improved the detection of molecular aging signals and were shown to be associated with sunbathing habits of the test subjects. Deeper analysis revealed extensive non-linear alterations in various biological pathways particularly at the transition into the fourth aging phase, coinciding with menopause, with potentially wide-reaching functional implications. Publication 2 describes the development of a novel type of age clock, that provides a new level of interpretability by embedding biological pathway information in the architecture of an artificial neural network. The clock not only generates meaningful biological age estimates from gene expression data, but further allows simultaneous monitoring of the aging states of various biological processes through the activations of intermediate neurons. Analyses of the inner workings of the clock revealed a wide-spread impact of aging on the global pathway landscape. Simulation experiments using the transcriptomic clock recapitulated known functional aging gene associations and allowed deciphering of the pathways by which accelerated aging conditions such as chronic sun exposure and Hutchinson-Gilford progeria syndrome exert their effects. Publication 3 further explores the molecular alterations caused by the pro-aging effector UV irradiation in the skin. The multi-omics data analysis of repetitively irradiated skin revealed signs of the immediate acquisition of aging- and cancer-related epigenetic signatures and concurrent wide-spread transcriptional changes across various biological processes. Investigations into the varying resilience to irradiation between subjects revealed prognostic biomarker signatures capable of predicting individual UV tolerances, with accuracies far surpassing the traditional Fitzpatrick classification scheme. Further analysis of the transcripts and pathways associated with UV tolerance identified a form of melanin-independent DNA damage protection in individuals with higher innate UV resilience.
Together, the approaches and findings described in this thesis explore several new angles to advance our understanding of aging processes and external drivers of aging such as UV irradiation in the human skin and deliver new insight on target genes and pathways involved.