Most research on human pancreatic islets is conducted on samples obtained from normoglycaemic or diseased brain-dead donors and thus cannot accurately describe the molecular changes of pancreatic islet beta cells as they progress towards a state of deficient insulin secretion in type 2 diabetes (T2D). Here, we conduct a comprehensive multi-omics analysis of pancreatic islets obtained from metabolically profiled pancreatectomized living human donors stratified along the glycemic continuum, from normoglycemia to T2D. We find that islet pools isolated from surgical samples by laser-capture microdissection display remarkably more heterogeneous transcriptomic and proteomic profiles in patients with diabetes than in non-diabetic controls. The differential regulation of islet gene expression is already observed in prediabetic individuals with impaired glucose tolerance. Our findings demonstrate a progressive, but disharmonic, remodelling of mature beta cells, challenging current hypotheses of linear trajectories toward precursor or transdifferentiation stages in T2D. Furthermore, through integration of islet transcriptomics with preoperative blood plasma lipidomics, we define the relative importance of gene coexpression modules and lipids that are positively or negatively associated with HbA1c levels, pointing to potential prognostic markers.

A primary challenge in the analysis of high-throughput biological data is the abundance of correlated variables. A small change to a gene's expression or a protein's binding availability can cause significant downstream effects. The existence of such chain reactions presents challenges in numerous areas of analysis. By leveraging knowledge of the network interactions that underlie this type of data, we can often enable better understanding of biological phenomena. This dissertation will examine network-based statistical approaches to the problems of mechanism-of-action inference, characterization of gene expression changes, and prediction of drug response. First, we develop a method for multi-target perturbation detection in multi-omics biological data. We estimate a joint Gaussian graphical model across multiple data types using penalized regression, and filter for network effects. Next, we apply a set of likelihood ratio tests to identify the most likely site of the original perturbation. We also present a conditional testing procedure to allow for detection of secondary perturbations. Second, we address the problem of characterization of cellular phenotypes via Bayesian regression in the Gene Ontology (GO). In our model, we use the structure of the GO to assign changes in gene expression to functional groups, and to model the covariance between these groups. In addition to describing changes in expression, we use these functional activity estimates to predict the expression of unobserved genes. We further determine when such predictions are likely to be inaccurate by identifying GO terms with poor agreement to gene-level estimates. In a case study, we identify GO terms relevant to changes in the growth rate of S. cerevisiae. Lastly, we consider the prediction of drug sensitivity in cancer cell lines based on pathway-level activity estimates from ASSIGN, a Bayesian factor analysis model. We use penalized regression to predict response to various cancer treatments based on cancer subtype, pathway activity, and 2-way interactions thereof. We also present network representations of these interaction models and examine common patterns in their structure across treatments.

No description available.

Schizophrenia and bipolar disorder are two severe mental disorders that affect more than 65 million individuals worldwide. The aim of thisproject was to find co-prediction mechanisms for genes associated with schizophrenia and bipolar disorder using a multi-omics data set and a transparent machine learning approach. The overall purpose of theproject was to further understand the biological mechanisms of these complex disorders. In this work, publicly available multi-omics data collected from post-mortem brain tissue were used. The omics types included were gene expression, DNA methylation, and SNP array data. The data consisted of samples from individuals with schizophrenia, bipolar disorder, and healthy controls. Individuals with schizophrenia or bipolar disorder were considered as a combined CASE class. Using machine learning techniques, a multi-omics pipeline was developedto integrate these data in a manner such that all types were adequately represented. A feature selection was performed on methylation and SNP data, where the most important sites were estimated and mapped to their corresponding genes. Next, those genes were intersected with the gene expression data, and another feature selection was performed on the gene expression data. The most important genes were used to develop an interpretable rule-based model with an accuracy of 88%. The model wasthen visualized as a network. The graph highlighted genes that may be of biological importance, including CACNG8, RTN4, TERT, OSBPL8, and ANTXR1. Moreover, strong co-predictions were found, most notable between CNKSR4 and KDM4C in CASE samples. However, further investigations would need to be performed in order to prove that these are real biological interactions. Through the methods developed and the results found in this project, we hope to shed new light towards analyzing multi-omics data as well as to reveal more about the underlying mechanisms of psychiatric disorders.

Cyanobacteria are photosynthetic organisms with the potential to sustainably produce carbon-based end products by fixing carbon dioxide from the atmosphere. Optimizing the growth or biochemical production in cyanobacteria is an ongoing challenge in metabolic engineering. Rational design of metabolic pathways requires a deep understanding of regulatory mechanisms. Hence, a deeper understanding of photosynthetic regulation of the influence of the environment on metabolic fluxes provides exciting possibilities for enhancing the photosynthetic Calvin-Benson-Bassham cycle. One approach to study metabolic processes is to use omic-level techniques, such as proteomics and fluxomics, to characterize varying phenotypes that result from different environmental conditions or different genetic perturbations.<br><br>This dissertation examines the influence of light intensity on enzymatic abundances and the resulting Calvin-Benson-Bassham cycle fluxes using a combined proteomic and fluxomic approach in the model cyanobacteria Synechocystis sp. PCC 6803. The correlation between light intensity and enzymatic abundances is evaluated to determine which reactions are more regulated by enzymatic abundance. Additionally, carbon enrichment data from isotopic labelling experiments strongly suggest metabolite channeling as a flexible and light-dependent regulatory mechanism present in cyanobacteria. We propose and substantiate biological mechanisms that explains the formation of metabolite channels under specific redox conditions. <br><br>The same multi-omic approach was used to examine genetically modified cyanobacteria. Specifically, genetically engineered and conditionally growth-enhanced Synechocystis strains overexpressing the central Calvin-Benson-Bassham cycle enzymes FBP/SBPase or transketolase were evaluated. We examined the effect of the heterologous expression of each of these enzymes on the Calvin-Benson-Bassham cycle, as well as on adjacent central metabolic pathways. Using both proteomics and fluxomics, we demonstrate distinct increases in Calvin-Benson-Bassham cycle efficiency as a result of lowered oxidative pentose phosphate pathway activity. This work demonstrates the utility of a multi-omic approach in characterizing the differing phenotypes arising from environmental and genetic changes.<br><br>

Thesis advisor: Tim van Opijnen / Thesis advisor: Welkin Johnson / Pathogenic bacteria can experience various stress factors during an infection including antibiotics and the host immune system. Whether a pathogen will establish an infection largely depends on its survival-success while enduring these stress factors. We reasoned that the ability to predict whether a pathogen will survive under and/or adapt to a stressful condition will provide great diagnostic and prognostic value. However, it is unknown what information is needed to enable such predictions. We hypothesized that under a stressful condition, a bacterium triggers responses that indicate how the stress is experienced in the genome, thereby correctly identifying a stress response holds the key to enabling such predictions. Bacterial stress responses have long been studied by determining how small groups of individual genes or pathways respond to certain environmental triggers. However, the conservation of these genes and the manner in which they respond to a stress can vary widely across species. Thus, this thesis sought to achieve a genome-wide and systems-level understanding of a bacterial stress response with the goal to identify signatures that enable predictions of survival and adaptation outcomes in a pathogen- and stress-independent manner. Here, we first set up a multi-omics framework that maps out a stress response on a genome-wide level using the human respiratory pathogen Streptococcus pneumoniae as a model organism. Under an environmental stress, gene fitness changes are determined by transposon insertion sequencing (Tn-Seq) which represents the phenotypic response. Differential expression is profiled by RNA-Seq which represents as the transcriptional response. Much to our surprise, the phenotypic response and transcriptional response are separated on different genes, meaning that differentially expressed genes are poor indicators of genes that contribute to the fitness of the bacterium. By devising and performing topological network analysis, we show that phenotypic and transcriptional responses are coordinated under evolutionary familiar stress, such as nutrient depletion and host infection, in both Gram-positive and -negative pathogens. However, such coordination is lost under the relatively unfamiliar stress of antibiotic treatment. We reasoned that this could mean that a generalizable stress response signature might exist that indicates the level to which a bacterium is adapted to a stress. By extending stress response profiling to 9 antibiotics and 3 nutrient depletion conditions, we found that such a signature indeed exists and can be captured by the level of transcriptomic disruption, defined by us as transcriptomic entropy. Centered on entropy, we constructed predictive models that perform with high accuracy for both survival outcomes and antibiotic sensitivity across 7 species. To further develop these models with the goal to eventually enable predictions on disease progression, we developed a dual RNA-Seq technique that maps out the transcriptomic responses of both S. pneumoniae and its murine host during lung infection. Preliminary data show that a high entropy is observed in the pathogen’s transcriptome during clearance (a failed infection) compared to a successful/severe infection, while the host transcriptome exhibits a pro-inflammatory and active immune response under the severe infection. Lastly, we characterized evolutionary trajectories that lead to long-term survival success of S. pneumoniae, for instance this means that the bacterium successfully adapts to the presence of an antibiotic and becomes resistant or can grow successfully in the absence of a formerly critical nutrient. These trajectories show that adaptive mutations tend to occur in genes closely related to the adapted stress. Additionally, independent of the stress, adaptation triggers rewiring of transcriptional responses resulting in a change in entropy from high to low. Most importantly, we demonstrate that by combining multi-omics profiles with additional genomic data including gene conservation and expression plasticity, and feeding this into machine learning models, that adaptive evolution can become (at least partially) predictable. Additionally, the genetic diversity in bacterial genomes across different strains and species can indeed influence a bacterium’s adaptation trajectory. In conclusion, this thesis presents a substantial collection of multi-omics stress response profiles of S. pneumoniae and other pathogenic bacteria under various environmental and clinically-relevant stresses. By demonstrating the feasibility of predictions on bacterial survival and adaptive outcomes, this thesis paves the way towards future improvements on infectious disease prognostics and forecasting the emergence of antibiotic resistance. / Thesis (PhD) — Boston College, 2020. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Biology.

Advanced molecular profiling technologies, utilizing the entire human genome, have opened new avenues to study biological systems. In recent decades, the generation of vast volumes of multi-omics data, spanning a broad range of phenotypes. Development of advanced bioinformatics tools to identify informative biomarkers from these data becomes increasingly important. These tools are crucial to extract meaningful biomarkers from this data, especially for understanding the biological pathways responsible for disease development. The identification of signature genes and the analysis of differentially networked genes are two fundamental and critically important tasks. However, many current methodologies employ test statistics that don't align perfectly with the signature definition, potentially leading to the identification of imprecise signatures. It may be challenging because the test statistics employed by many prevailing methods fall short of fulfilling the exact definition of a marker genes, inherently leaving them susceptible to deriving inaccurate features. The problem is further compounded when attempting to identify marker genes across biologically diverse samples, especially when comparing more than two biological conditions. Additionally, traditional differential group analysis or co-expression analysis under singular conditions often falls short in certain scenarios. For instance, the subtle expression levels of transcription factors (TFs) make their detection daunting, despite their pivotal role in guiding gene expression. Pinpointing the intricate network landscape of complex ailments and isolating core genes for subsequent analysis are challenging tasks. Yet, these marker genes are instrumental in identifing potential pivotal pathways. Multi-omics data, with its inherent complexity and diversity, presents unique challenges that traditional methods might struggle to address effectively. Recognizing this, our team sought to introduce new and innovative techniques specifically designed to handle this intricate dataset. To overcome these challenges, it is vital to develop and adopt innovative methods tailored to handle the complexity and diversity inherent in multi-omics data. In response to these challenges, we have pioneered the Cosine-based One-sample Test (COT), a method meticulously crafted for the analysis of biologically diverse samples. Tailored to discern marker genes across a spectrum of subtypes using their expression profiles, COT employs a one-sample test framework. The test statistic within COT utilizes cosine similarity, comparing a molecule's expression profile across various subtypes with the precise mathematical representation of ideal marker genes. To ensure ease of application and accessibility, we've encapsulated the COT workflow within a Python package. To assess its effectiveness, we undertook an exhaustive evaluation, juxtaposing the marker genes detection capabilities of COT against its contemporaries. This evaluation employed realistic simulation data. Our findings indicated that COT was not only adept at handling gene expression data but was also proficient with proteomics data. This data, sourced from enriched tissue or cell subtype samples, further accentuated COT's superior performance. We demonstrated the heightened effectiveness of COT when applied to gene expression and proteomics data originating from distinct tissue or cell subtypes. This led to innovative findings and hypotheses in several biomedical case studies. Additionally, we have enhanced the Differential Dependency Network (DDN) framework to detect network rewiring between different conditions where significantly rewired network modes serve as informative biomarkers. Using cross-condition data and a block-wise Lasso network model, DDN detects significant network rewiring together with a subnetwork of hub molecular entities. In DDN 3.0, we took the imbalanced sample size into the consideration, integrated several acceleration strategies to enable it to handle large datasets, and enhanced the network presentation for more informative network displays including color-coded differential dependency network and gradient heatmap. We applied it to the simulated data and real data to detect critical changes in molecular network topology. The current tool stands as a valuable blueprint for the development and validation of mechanistic disease models. This foundation aids in offering a coherent interpretation of data, deepening our understanding of disease biology, and sparking new hypotheses ripe for subsequent validation and exploration. As we chart our future course, our vision is to expand the scope of tools like COT and DDN 3.0, explore the vast realm of multi-omics data, including those from longitudinal studies or clinical trials. We're looking at incorporating datasets from longitudinal studies and clinical trials – domains where data complexity scales to new heights. We believe that these tools can facilitate more nuanced and comprehensive understanding of disease development and progression. Furthermore, by integrating these methods with other advanced bioinformatics and machine learning tools, we aim to create a holistic pipeline that will allow for seamless extraction of significant biomarkers and actionable insights from multi-omics data. This is a promising step towards precision medicine, where individual genomic information can guide personalized treatment strategies. / Doctor of Philosophy / Recent advances in technology have allowed us to study human biology on a much larger scale than ever before. These technologies have produced a lot of data on many different types of traits. As a result, it's becoming increasingly important to develop tools that can sift through this data and find meaningful biomarkers – essentially, indicators that can help us understand what causes diseases. Two key parts of this process are identifying 'signature genes' and analyzing groups of genes that work together differently depending on the circumstances. But, current methods have their drawbacks – they don't always pick out the right genes and can struggle when comparing more than two groups at once. There are also other challenges when it comes to identifying groups of genes that express differently or work together under one set of conditions. For instance, some important genes – known as transcription factors (TFs) – control the activity of other genes. But because TFs are often expressed at low levels, they're hard to detect, even though they play a key role in controlling gene activity. And, it can be tough to identify 'hub' genes, which are central to gene networks and can help us understand the potential key pathways in diseases. To address these challenges, we introduced the Cosine based One-sample Test (COT), a novel approach to identify pivotal genes across diverse samples. COT gauges the alignment of a gene's expression profile with the quintessential marker genes' definition. Our evaluations underscore COT's robust performance, paving the way for deeper disease understanding. Further enhancing our toolkit, we've refined the Differential Dependency Network (DDN), a method to unravel the dynamic interplay of genes under diverse conditions. DDN 3.0 is a more robust iteration, adept at accommodating varied sample sizes, efficiently processing vast datasets, and offering richer visualizations of gene networks. Its prowess in pinpointing crucial alterations in gene networks is noteworthy. The Cosine based One-sample Test (COT) and the Differential Dependency Network (DDN) are revolutionary tools, poised to significantly elevate genomics research. COT, with its precision in gauging the alignment of a gene's expression pattern with predefined ideal gene markers, emerges as an invaluable asset in the hunt for marker genes. It acts as a fine-tuned sieve, meticulously screening vast datasets to unveil these crucial genetic signposts. On the other hand, DDN offers a comprehensive framework to decipher the intricate web of gene interactions under diverse conditions. It meticulously analyzes the interplay between genes, spotlighting potential 'hub' genes and highlighting shifts in their dynamic relationships. Together, COT and DDN not only pave the way for the identification of pivotal marker genes but also furnish a richer, more nuanced understanding of the genomic landscape. By leveraging these tools, researchers are empowered to unravel the intricate tapestry of genes, laying the foundation for groundbreaking discoveries in genomics. Looking to the future, we plan to apply COT and DDN 3.0 to more complex datasets. We believe these tools will give us a better understanding of how diseases develop and progress. By integrating these methods with other advanced tools, we're aiming to create a complete system for extracting important biomarkers and insights from this complex data. This is a big step towards precision medicine, where a person's unique genetic information could guide their treatment strategy.

Insights into the modes of action (MoAs) of xenobiotics are of utmost importance for the definition of adverse outcome pathways (AOPs), which are essential for a mechanism-based risk assessment. A well-established strategy to reveal MoAs of xenobiotics is the use of omics. However, often an even more comprehensive approach is needed, which can be achieved using multi-omics. Since the immune system plays a central role in the defense against foreign substances and pathogens, with the innate immune system building a first barrier, we systematically reviewed multi-omics studies investigating the effects of xenobiotics on macrophages. Surprisingly, only nine publications were identified, combining proteomics with transcriptomics or metabolomics. We summarized pathways and single proteins, transcripts, or metabolites, which were described to be affected upon treatment with xenobiotics in the reviewed studies, thus revealing a broad range of effects. In summary, we show that macrophages are a relevant model system to investigate the toxicological effects induced by xenobiotics. Furthermore, the multi-omics approaches led to a more comprehensive overview compared to only one omics layer with slight advantages for combinations that complement each other directly, e.g., proteome and metabolome.

Mining interspecies interactions remain a challenge due to the complex nature of microbial communities and the need for computational power to handle big data. Our meta-analysis indicates that genetic potential alone does not resolve all issues involving mining of microbial interactions. Nevertheless, it can be used as the starting point to infer synergistic interspecies interactions and to limit the search space (i.e., number of species and metabolic reactions) to a manageable size. A reduced search space decreases the number of additional experiments necessary to validate the inferred putative interactions. As validation experiments, we examine how multi-omics and state of the art imaging techniques may further improve our understanding of species interactions’ role in ecosystem processes. Finally, we analyze pros and cons from the current methods to infer microbial interactions from genetic potential and propose a new theoretical framework based on: (i) genomic information of key members of a community; (ii) information of ecosystem processes involved with a specific hypothesis or research question; (iii) the ability to identify putative species’ contributions to ecosystem processes of interest; and, (iv) validation of putative microbial interactions through integration of other data sources.

Pathway analysis is an important task for gaining novel insights into the molecular architecture of many complex diseases. With the advancement of new sequencing technologies, a large amount of quantitative gene expression data have been continuously acquired. The springing up omics data sets such as proteomics has facilitated the investigation on disease relevant pathways. Although much work has previously been done to explore the single omics data, little work has been reported using multi-omics data integration, mainly due to methodological and technological limitations. While a single omic data can provide useful information about the underlying biological processes, multi-omics data integration would be much more comprehensive about the cause-effect processes responsible for diseases and their subtypes. This project investigates the combination of miRNAseq, proteomics, and RNAseq data on seven types of muscular dystrophies and control group. These unique multi-omics data sets provide us with the opportunity to identify disease-specific and most relevant biological pathways. We first perform t-test and OVEPUG test separately to define the differential expressed genes in protein and mRNA data sets. In multi-omics data sets, miRNA also plays a significant role in muscle development by regulating their target genes in mRNA dataset. To exploit the relationship between miRNA and gene expression, we consult with the commonly used gene library - Targetscan to collect all paired miRNA-mRNA and miRNA-protein co-expression pairs. Next, by conducting statistical analysis such as Pearson's correlation coefficient or t-test, we measured the biologically expected correlation of each gene with its upstream miRNAs and identify those showing negative correlation between the aforementioned miRNA-mRNA and miRNA-protein pairs. Furthermore, we identify and assess the most relevant disease-specific pathways by inputting the differential expressed genes and negative correlated genes into the gene-set libraries respectively, and further characterize these prioritized marker subsets using IPA (Ingenuity Pathway Analysis) or KEGG. We will then use Fisher method to combine all these p-values derived from separate gene sets into a joint significance test assessing common pathway relevance. In conclusion, we will find all negative correlated paired miRNA-mRNA and miRNA-protein, and identifying several pathophysiological pathways related to muscular dystrophies by gene set enrichment analysis. This novel multi-omics data integration study and subsequent pathway identification will shed new light on pathophysiological processes in muscular dystrophies and improve our understanding on the molecular pathophysiology of muscle disorders, preventing and treating disease, and make people become healthier in the long term. / Master of Science / Identification of biological pathways play a central role in understanding both human health and diseases. A biological pathway is a series of information processing steps via interactions among molecules in a cell that partially determines the phenotype of a cell. Specifically, identifying disease-specific pathway will guide focused studies on complex diseases, thus potentially improve the prevention and treatment of diseases. To identify disease-specific pathways, it is crucial to develop computational methods and statistical tests that can integrate multi-omics (multiple omes such as genome, proteome, etc) data. Compared to single omics data, multi-omics data will help gaining a more comprehensive understanding on the molecular architecture of disease processes. In this thesis, we propose a novel data analytics pipeline for multi-omics data integration. We test and apply our method on/to the real proteomics data sets on muscular dystrophy subtypes, and identify several biologically plausible pathways related to muscular dystrophies.