Many people - especially during their elderly - consume multiple drugs for the treatment of complex or co-existing diseases. Identifying side effects caused by polypharmacy is crucial for reducing mortality and morbidity of the patients which will lead to improvement in their quality of life. Since there is immense space for possible drug combinations, it is infeasible to examine them entirely in the lab. In silico models can offer a convenient solution, however, due to the lack of a sufficient amount of homogenous data it is difficult to develop both reliable and scalable models in its ability to accurately predict Polypharmacy Side Effect. Recent advancement in the field of representational learning has utilized the power of graph networks to harmonize information from the heterogeneous biological databases and interactomes. This thesis takes advantage of those techniques and incorporates them with the state-of-the-art Graph Neural Network algorithms to implement a Deep learning pipeline capable of predicting the Adverse Drug Reaction of any given paired drug combinations.

The Selection during Niche AdaPtation (SNAP) hypothesis aims to explain how the gene order in bacterial chromosomes can change as the result of bacteria adapting to a new environment. It starts with a duplication of a chromosomal segment that includes some genes providing a fitness advantage. The duplication of these genes is preserved by positive selection. However, the rest of the duplicated segment accumulates mutations, including deletions. This results in a rearranged gene order. In this work, we develop a method to identify SNAP in bacterial chromosomes. The method was tested in Salmonella and Bartonella genomes. First, each gene was assigned an orthologous group (OG). For each genus, single-copy panorthologs (SCPos), the OGs that were present in most of the genomes as one copy, were targeted. If these SCPos were present twice or more in a genome, they were used to build duplicated regions within said genome. The resulting regions were visualized and their possible compatibility with the SNAP hypothesis was discussed. Even though the method proved to be effective on Bartonella genomes, it was less efficient on Salmonella. In addition, no strong evidence of SNAP was detected in Salmonella genomes.

Between closely related species there are genomic regions with a higher level of differentiation compared to the rest of the genome. For a time it was believed that these regions harbored loci important for speciation but it has now been shown that these patterns can arise from other mechanisms, like recombination. The aim of this project was to estimate the recombination landscape for red-breasted flycatcher (Ficedula parva) and taiga flycatcher (F. albicilla) using patterns of linkage disequilibrium. For the analysis, 15 red-breasted and 65 taiga individuals were used. Scaffolds on autosomes were phased using fastPHASE and the population recombination rate was estimated using LDhelmet. To investigate the accuracy of the phasing, two re-phasings were done for one scaffold. The correlation between the rephases were weak on the fine-scale, and strong between means in 200 kb windows. 2,176 recombination hotspots were detected in red-breasted flycatcher and 2,187 in taiga flycatcher. Of those 175 hotspots were shared, more than what was expected by chance if the species were completely independent (31 hotspots). Both species showed a small increase in the rate at hotspots unique to the other species. The low number of shared hotspots might indicate that the recombination landscape is less conserved between red-breasted and taiga flycatchers than found between collared and pied flycatcher. However, the investigation of the phasing step indicate that the fine-scale estimation, on which hotspots are found, might not be reliable. For future analysis, it is important to use high-quality data and carefully chose methods.

Eimeria tenella is a eukaryotic, intracellular parasite that, along with six other Eimeria species, causes coccidiosis in chickens. This disease can result in weight loss or even death and is estimated to cause 2 billion euros of damages to the chicken industry each year. While much is known of the life cycle of E. tenella in the chicken, less is known about molecular mechanisms of infection and the chicken immune response. In this study, we produced a pipeline for dual RNA-sequencing analysis of a mixed chicken and E. tenella dataset.  We then carried out an analysis on an in vitro infection of the chicken macrophage HD-11 cell line.  This was followed by a differential expression analysis across six time points, 2, 4, 12, 24, 48, and 72 hours post-infection, in order to elucidate these mechanisms. The results showed clear patterns of expression for the chicken immune genes, with strong down-regulation of genes across the immune system at 24 hours and a repetition of early patterns at 72 hours, indicating that reinfection by a second generation of parasite cells was occurring. Several genes that may have important roles in the immune reaction of the chicken were identified, such as MRC2, ITGB3 and ITGA9, along with genes with known roles, such as TLR15. The expression of surface antigen genes in E. tenella was also examined, showing a clear upregulation in the late stages of merogony, suggesting important roles for merozoites. Finally, a co-expression analysis was carried out, showing considerable co-expression among the two organisms.  One of the gene co-expression networks identified appeared to be enriched with both infection specific genes from E. tenella and chicken immune genes. These results, along with the pipeline, will be used in further studies on E. tenella infections and bring us closer to the eventual goal of a vaccine for coccidiosis.

Adrenal steroid hormones, which regulate a plethora of physiological functions, are produced via tightly controlled pathways. Adrenal hormone excess associates with clinical conditions impacting metabolism and cardiovascular and immune function. Aldosterone and cortisol producing enzymes, CYP11B2 and CYP11B1, share 93% homology requiring highly selective drugs for pharmacological treatment. Investigations of these pathways, based on experimental data, can be facilitated by computational modeling for calculations of metabolic rate alterations. Such systems can be utilized in a variety of applications including investigating effects of endocrine-disrupting chemicals, drugs, gene manipulations. On a human level, regression modelling involving use of clinical data can contribute in defining effects of such diseases or providing reference data for characterizing normal values of physiological processes. The main subject of this thesis was the development of modeling techniques that would benefit basic and clinical research by supplying means for investigating adrenal related dysfunctions and disease. As a first approach, we used a model system, based on mass balance and mass reaction equations, to kinetically evaluate adrenal steroidogenesis in human adrenal cortex-derived NCI H295R cells. For this purpose a panel of 10 steroids was measured by liquid chromatographic-tandem mass spectrometry. Time-dependent changes in cell incubate concentrations of steroids were measured after incubation with angII, forskolin and abiraterone. Model parameters were estimated based on experimental data using weighted least square fitting. Time-dependent angII- and forskolin-induced changes were observed for incubate concentrations of precursor steroids with peaks that preceded maximal increases in aldosterone and cortisol. Inhibition of 17-alpha-hydroxylase/17,20-lyase with abiraterone resulted in increases in upstream precursor steroids and decreases in downstream products. Derived model parameters, including rate constants of enzymatic processes, appropriately quantified observed and expected changes in metabolic pathways at multiple conversion steps. Our data from our first approach demonstrated limitations of single time point measurements and the importance of assessing pathway dynamics in studies of adrenal cortical cell line steroidogenesis. Despite the benefits from a computational approach in comparison to the single time point studies, this kind of modeling demonstrated certain limitations regarding effort, reproducibility and costs. Therefor a second study was conducted in order to address such limitations. As a second approach we introduced an effective in vitro assay for evaluation of steroidogenic enzyme kinetics based on intracellular flux calculations. H295RA cells were cultured in chambers (µ-Slide, Ibidi) under constant medium flow. Four hourly samples were collected (control samples), followed by collections over an additional four hours after treatment with either fadrozole (10nM), metyrapone (10uM), ASI_191 (5nM), a novel CYP11B2 inhibitor or ASI_254 (100nM), a newly synthesized CYP17 inhibitor. Mass spectrometric measurements of multiple steroids combined with linear system computational modeling facilitated calculation of intracellular flux rates at different steroidogenic pathway steps and assessment of the selectivity of drugs for those specific steps. While treatment with fadrozole, metyrapone and ASI_191 all resulted in reductions in fluxes of aldosterone, corticosterone and cortisol production, treatment with ASI_254 led to increased flux through the mineralocorticoid pathway and increased production of aldosterone with reduced production of steroids downstream of CYP17. Comparisons of changes in intracellular fluxes revealed much higher selectivity of ASI_191 for CYP11B2 over CYP11B1 compared to fadrozole or metyrapone. Our study demonstrates the advantages of continuous culture systems over static systems for studying effects of steroidogenic inhibitors. By culturing cells under perfusion the methodology establishes a more realistic model for investigating drug effects, provides for simple and rapid calculations of intracellular fluxes and offers a robust method for drug screening or in vitro investigations of metabolic mechanisms. As a third approach we utilized LC-MS/MS derived plasma concentrations for each of 525 normotensive and hypertensive volunteers with (n=227) and without (n=298) hypertension in combination with regression modeling for the extraction of age and gender-adjusted reference intervals. Values of 8 steroids (pregnenolone, 11-deoxycorticosterone, corticosterone, 17-hydroxyprogesterone, cortisone, dehydroepiandrosterone, dehydroepiandrosterone-sulfate, androstenedione) versus age and gender were modelled via multivariate fractional polynomial analysis successfully providing with 0.5 and 99.5% reference intervals as a function of age and gender.:Contents Acknowledgements 4 Publication Note 5 Contents 6 Abbreviations 9 1. Introduction 10 1.1. The adrenal gland 10 1.1.1. Adrenal cortex steroids regulation 10 1.1.2. Physiological metabolic processes of steroidogenesis 13 1.2. Adrenal cortical dysfunction and therapeutic challenges 15 1.3. The adrenocortical cell line NCI –H295R 16 1.4. Chemical regulators of adrenal steroidogenesis 17 1.5. Computational mechanistic modelling of adrenal metabolism 18 1.5.1. Static cell culture models for characterizing pathway dynamics 18 1.5.2. Steady state as a tool for intracellular fluxes estimation 19 2. Hypothesis and aims 20 3. Materials and methods 22 3.1. Experimental overview 22 3.1.1. Computational mechanistic modeling of steroid metabolism 22 3.1.2 A steady state system for in vitro evaluation of steroidogenic pathway dynamics 24 3.1.3 Calculation of age and gender adjusted reference intervals for plasma adrenal steroids for healthy population 27 3.2 Liquid chromatography – tandem mass spectrometry 29 3.3. Static cell culture mechanistic model 29 3.3.1. Cell culture conditions 29 3.3.2. Computational model representation for steroid metabolism and cell proliferation using ODE systems 29 3.3.3. Metabolic pathways 30 3.3.4. Transport processes 32 3.3.5. Parameter estimation 34 3.4. Steady state model system for computation of pathway kinetics 35 3.4.1. Cell culture conditions for continuous flow culture system 35 3.4.2 Steroid secretion rates and intracellular fluxes calculation under steady state conditions 37 3.4.3 Statistical analysis 40 3.5. Regression and classification models for diagnostic clinical studies 40 3.5.1. Age and sex adjusted reference intervals for adrenal steroids in plasma – patient data collection 40 3.5.2. Mathematical description of multivariate fractional polynomial analysis 41 4. Results 43 4.1. Computational analysis of steroid profiling in NCI H295R cells 43 4.1.1. Transport and metabolic pathway modeling 43 4.1.2. Angiotensin II and forskolin stimulation 43 4.1.3. Abiraterone treatment 46 4.2. Steady state model for in vitro evaluation of steroidogenic pathway dynamics 48 4.2.1. Continuous flow culture steroid profiling 48 4.2.2. Secretion rates, intracellular flux rates and relative changes in rate constants 51 4.2.3. Cell number and viability 53 4.3. Reference intervals for adrenal steroid plasma concentrations 55 5. Discussion 61 Appendix A – Static culture model 70 A1. ODE system equations of the static culture model 70 A2. Jsim MML example code of the ODE system implementation of the static culture model 74 A3. Supplementary data for static culture model derived data 79 Appendix B – Steady state model 84 B1. Mathematical derivation of analytical solution for intracellular flux calculation of the steady state model 84 B2. Equations describing steroid production in cells and flowing medium of the steady state model 86 B3. Quadratic programming formulation and solution of upstream steroidogenic pathways system of the steady state model 91 B4. Calculation of reaction rate constant relative changes 92 B5. Python implementation of secretion rates, flux rates and rate constant relative changes calculation 94 B6. Numerical example of intracellular flux calculation. 98 B7. Supplementary material for steady state model derived data 103 Appendix C – Age and gender-adjusted reference intervals 110 Zusammenfasung 112 Summary 113 Literature 116

Thesis advisor: Kenneth Williams / Due to overuse and misuse of antibiotics, the global threat of antibiotic resistance is a growing crisis. Three critical issues surrounding antibiotic resistance are the lack of rapid testing, treatment failure, and evolution of resistance. However, with new technology facilitating data collection and powerful statistical learning advances, our understanding of the bacterial stress response to antibiotics is rapidly expanding. With a recent influx of omics data, it has become possible to develop powerful computational methods that make the best use of growing systems-level datasets. In this work, I present several such approaches that address the three challenges around resistance. While this body of work was motivated by the antibiotic resistance crisis, the approaches presented here favor generalization, that is, applicability beyond just one context. First, I present ShinyOmics, a web-based application that allow visualization, sharing, exploration and comparison of systems-level data. An overview of transcriptomics data in the bacterial pathogen Streptococcus pneumoniae led to the hypothesis that stress-susceptible strains have more chaotic gene expression patterns than stress-resistant ones. This hypothesis was supported by data from multiple strains, species, antibiotics and non-antibiotic stress factors, leading to the development of a transcriptomic entropy based, general predictor for bacterial fitness. I show the potential utility of this predictor in predicting antibiotic susceptibility phenotype, and drug minimum inhibitory concentrations, which can be applied to bacterial isolates from patients in the near future. Predictors for antibiotic susceptibility are of great value when there is large phenotypic variability across isolates from the same species. Phenotypic variability is accompanied by genomic diversity harbored within a species. I address the genomic diversity by developing BFClust, a software package that for the first time enables pan-genome analysis with confidence scores. Using pan-genome level information, I then develop predictors of essential genes unique to certain strains and predictors for genes that acquire adaptive mutations under prolonged stress exposure. Genes that are essential offer attractive drug targets, and those that are essential only in certain strains would make great targets for very narrow-spectrum antibiotics, potentially leading the way to personalized therapies in infectious disease. Finally, the prediction of adaptive outcome can lead to predictions of future cross-resistance or collateral sensitivities. Overall, this body of work exemplifies how computational methods can complement the increasingly rapid data generation in the lab, and pave the way to the development of more effective antibiotic stewardship practices. / Thesis (PhD) — Boston College, 2020. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Biology.

There is an emerging global epidemic of obesity and related complications, such as type 2diabetes (T2D). Alterations in body composition (adipose tissue, muscle volume and fatcontents) are known to be associated with an increased metabolic risk. Understanding of theunderlying mechanisms is key for development of novel intervention strategies. One study investigating the effect on body composition by different diets is Lipogain1. In this study, it was found that a small weight gain induced by polyunsaturated fats (PUFA, n=19) or saturated fats (SFA, n=20) had very different effects on body fat, liver fat and lean tissue mass respectively. The SFA group gained more liver fat and fat mass in general, while the PUFA group gained more muscle mass. These results were determined by magnetic resonance imaging.  The goal of this project was to visualize the results from Lipogain1 by utilizing the noveltechnique Imiomics. Imiomics is a method for statistical analysis of whole-body medical images. By utilizing image registration, all images are transformed to a common reference space. This enables point-wise comparisons between all images included in the analysis. In this project, mean images of the alterations in fat content and local volume change of the two groups were created. These were used to visualize the alterations in body composition from the study. Additionally, statistical tests were used to visualize statistically significant differences between the groups.  Differences between the groups could be seen in the mean images. Mainly a higher fat content increase was seen in SFA in comparison to PUFA. There was also a larger volume expansion in fat tissue in SFA than in PUFA, while PUFA instead had a larger volume expansion in muscles. An unexpected result was also found; the liver had expanded in PUFA but not in SFA. Unfortunately, few significant differences could be visualized between the groups when the statistical test was performed. The conclusion was that this method is promising for visualization of these kinds of studies, especially due to the potential of finding new, unexpected results. However, a somewhat larger cohort and possibly larger alterations in body composition might be needed to be able to visualize and quantify statistically significant differences between the groups on a voxel-wise level.

Clustering of single-cell RNA sequencing data is often used to show what states and subtypes cells have. Using this technique, striatal cells were clustered into subtypes using different clustering algorithms. Previously known subtypes were confirmed and new subtypes were found. One of them is a third medium spiny neuron subtype. Using the observed heterogeneity, as a second task, this project questions whether or not differences in individual neurons have an impact on the network dynamics. By clustering spiking activity from a neural network model, inconclusive results were found. Both algorithms indicating low heterogeneity, but by altering the quantity of a subtype between a low and high number, and clustering the network activity in each case, results indicate that there is an increase in the heterogeneity. This project shows a list of potential striatal subtypes and gives reasons to keep giving attention to biologically observed heterogeneity.

From creation to its degradation, the RNA molecule is the action field of many binding proteins with different roles in regulation and RNA metabolism. Since these proteins are involved in a large number of processes, a variety of diseases are related to abnormalities occurring within the binding mechanisms. One of the experimental methods for detecting the binding sites of these proteins is PAR-CLIP built on the next generation sequencing technology. Due to its size and intrinsic noise, PAR-CLIP data analysis requires appropriate pre-processing and thorough statistical analysis. The present work has two main goals. First, to develop a modular pipeline for preprocessing PAR-CLIP data and extracting necessary signals for further analysis. Second, to devise a novel statistical model in order to carry out inference about presence of protein binding sites based on the signals extracted in the pre-processing step.

How do bacteria grow and replicate rapidly? How do viruses and phages adapt to their host environments? Bacteria require efficient translation to grow and replicate rapidly, and translation is often rate-limited by initiation. A feature that is conserved across bacterial lineages is the Shine-Dalgarno (SD) sequence at the mRNA 5’ UTR, which pairs with the anti-SD sequence located at the 3’ end of mature 16S rRNA. Nonetheless, much about this interaction remains unclear. Chapter 2 reveals evolutionary differences between Cyanobacteria and chloroplast translation initiation using a new model (DtoStart) that better define optimal SD sequence and an RNA-Seq-based approach that reliably characterize the 3’ end of mature 16S rRNAs. Efficacy of translation elongation depends much on tRNA-mediated codon adaptation. In Escherichia coli, selection favours major codons because they are rapidly decoded by abundantly available cognate tRNAs. Nonetheless, the degree codon bias correlates with tRNA availability is unclear in many bacterial species because tRNA abundance is often inadequately approximated by gene copy numbers. To better understand tRNA-mediated codon bias, Chapter 3 describes an RNA-Seq-based approach to robustly quantify tRNA abundance. Finally, Chapter 4 evaluates the degree optimal translation initiation and elongation signals affect ribosome dynamics. The emergence of COVID-19 pandemic poses a serious global health emergency. To establish infection during cell entry, the coronavirus Spike protein binds to the host ACE2 receptor, and a high binding potential between these two players is key to infectivity. While SARS-CoV-2 transmits efficiently in humans, it is less clear which other mammals are at risk of being infected. Chapter 5 investigates the host range of SARS-CoV-2 through comparative sequence analyses at the ACE2 receptors and the Spike proteins. As obligate parasites, coronaviruses regularly infect host tissues that express antiviral proteins (AVPs) in abundance and must evade or adapt to the host cellular environments post-entry. Two AVPs that shape viral genomes are ZAP that binds to CpG dinucleotides to facilitate viral transcript degradation, and APOBEC3 which deaminates C into U leading to dysfunctional transcripts. Chapter 6 shows that coronavirus genomes are CpG deficient to evade ZAP and are subjected to constant C to U deamination by APOBEC3. This thesis examines two key concepts of microbial genome evolution: 1) coevolution between gene features and the translation machinery in bacteria, and 2) adaptation of viruses to the hosts they infect. Chapters 2, 3, and 4 are aimed at improving our understanding in bacterial gene expression in the applications of transgenic biosynthesis and phage therapy. Chapters 5 and 6 are aimed at improving our understanding in the origin and evolution of SARS-CoV-2 and our ability to control the spread of infection.