Proteomics Analysis Identifies Molecular Targets Related to Diabetes Mellitus-associated Bladder Dysfunction

Protein expression profiles in rat bladder smooth muscle were compared between animal models of streptozotocin-induced diabetes mellitus (STZ-DM) and age-matched controls at 1 week and 2 months after induction of hyper-glycemia with STZ treatment. At each time point, protein samples from four STZ-DM and four age-matched control rat bladder tissues were prepared independently and analyzed together across multiple DIGE gels using a pooled internal standard sample to quantify expression changes with statistical confidence. A total of 100 spots were determined to be significantly changing among the four experimental groups. A subsequent mass spectrometry analysis of the 100 spots identified a total of 56 unique proteins. Of the proteins identified by two-dimensional DIGE/MS, 10 exhibited significant changes 1 week after STZ-induced hyperglycemia, whereas the rest showed differential expression after 2 months. A network analysis of these proteins using MetaCore™ suggested induction of transcriptional factors that are too low to be detected by two-dimensional DIGE and identified an enriched cluster of down-regulated proteins that are involved in cell adhesion, cell shape control, and motility, including vinculin, intermediate filaments, Ppp2r1a, and extracellular matrix proteins. The proteins that were up-regulated include proteins involved in muscle contraction (e.g. MrIcb and Ly-GDI), in glycolysis (e.g. α-enolase and Taldo1), in mRNA processing (e.g. heterogeneous nuclear ribonucleoprotein A2/B1), in inflammatory response (e.g. S100A9, Annexin 1, and apoA-1), and in chromosome segregation and migration (e.g. Tuba1 and Vil2). Our results suggest that the development of diabetes-related complications in this model involves the down-regulation of structural and extracellular matrix proteins in smooth muscle that are essential for normal muscle contraction and relaxation but also induces proteins that are associated with cell proliferation and inflammation that may account for some of the functional deficits known to occur in diabetic complications of bladder

Systems Analytics and Integration of Big Omics Data

A “genotype"" is essentially an organism's full hereditary information which is obtained from its parents. A ""phenotype"" is an organism's actual observed physical and behavioral properties. These may include traits such as morphology, size, height, eye color, metabolism, etc. One of the pressing challenges in computational and systems biology is genotype-to-phenotype prediction. This is challenging given the amount of data generated by modern Omics technologies. This “Big Data” is so large and complex that traditional data processing applications are not up to the task. Challenges arise in collection, analysis, mining, sharing, transfer, visualization, archiving, and integration of these data. In this Special Issue, there is a focus on the systems-level analysis of Omics data, recent developments in gene ontology annotation, and advances in biological pathways and network biology. The integration of Omics data with clinical and biomedical data using machine learning is explored. This Special Issue covers new methodologies in the context of gene–environment interactions, tissue-specific gene expression, and how external factors or host genetics impact the microbiome

Computational Methods for the Analysis of Genomic Data and Biological Processes

In recent decades, new technologies have made remarkable progress in helping to understand biological systems. Rapid advances in genomic profiling techniques such as microarrays or high-performance sequencing have brought new opportunities and challenges in the fields of computational biology and bioinformatics. Such genetic sequencing techniques allow large amounts of data to be produced, whose analysis and cross-integration could provide a complete view of organisms. As a result, it is necessary to develop new techniques and algorithms that carry out an analysis of these data with reliability and efficiency. This Special Issue collected the latest advances in the field of computational methods for the analysis of gene expression data, and, in particular, the modeling of biological processes. Here we present eleven works selected to be published in this Special Issue due to their interest, quality, and originality

Role of adipose tissue in the pathogenesis and treatment of metabolic syndrome

© Springer International Publishing Switzerland 2014. Adipocytes are highly specialized cells that play a major role in energy homeostasis in vertebrate organisms. Excess adipocyte size or number is a hallmark of obesity, which is currently a global epidemic. Obesity is not only the primary disease of fat cells, but also a major risk factor for the development of Type 2 diabetes, cardiovascular disease, hypertension, and metabolic syndrome (MetS). Today, adipocytes and adipose tissue are no longer considered passive participants in metabolic pathways. In addition to storing lipid, adipocytes are highly insulin sensitive cells that have important endocrine functions. Altering any one of these functions of fat cells can result in a metabolic disease state and dysregulation of adipose tissue can profoundly contribute to MetS. For example, adiponectin is a fat specific hormone that has cardio-protective and anti-diabetic properties. Inhibition of adiponectin expression and secretion are associated with several risk factors for MetS. For this purpose, and several other reasons documented in this chapter, we propose that adipose tissue should be considered as a viable target for a variety of treatment approaches to combat MetS

Mucin and Splice Variant Profiles of Pancreatic Adenocarcinoma Predict Patient Survival and Subtyping

PDAC is a pancreatic epithelial malignancy and demonstrates aggressive progression and bleak patient prognosis. Despite decades of research, the evolution of novel diagnostics and intervention modalities for PDAC is stagnant. This dissertation explores the characteristic aberrant and elevated expression of mucins in PDAC. Beginning with the hypothesis that mucins are associated with disease aggressiveness, analysis of PDAC patient survival in TCGA revealed no associations between single mucin expression and patient survival. This led to the underlying issue of PDAC tumor cellularity since this disease demonstrates variability in the proportion of cancer cells within the tumor. Tumor purity assessed with the ABSOLUTE computational algorithm is reported for all patient samples in the TCGA PDAC dataset. Using these purity scores, a mathematical correction of epithelial-specific mucin expression was devised. Again, no significant association between PDAC patient survival and mucin expression was found. Therefore, I investigated combinatorial expression of mucins by Spearman’s nonparametric PCA, which resulted in four groups of mutual expression: Group One= MUC7/12/17, Group Two= MUC1/3/13/19/20, Group Three= MUC6/15/22, and Group Four= MUC2/4/5AC/5B/16/21. These four groups were associated significantly with survival outcomes. To determine the biological implications of vi these four groups, PCA scores for all patients were correlated to whole transcriptomes. Significantly correlated genes were assessed for biological pathway upregulation. The four pathway composites revealed potential pathological signatures unrelated to previous PDAC classifications, representing novel PDAC subtypes. The role of mucin splice variants (SVs) was assessed and correlated to PDAC patient survival. Bioinformatic studies revealed 12 total mucin SVs significantly associated with survival. Better survival was correlated with expression of four MUC1, one MUC13, and one MUC20 SVs. High expression of two MUC4, one MUC15, one MUC16, one MUC21, and one MUC22 SVs were correlated with worse survival. The correlation between MUC4-sv-215 and MUC13-sv-201 SVs and survival were PCR validated in PDAC patient samples. These MUC4Δ6 prognostic findings contributed to in vitro studies and the development of a novel nanoparticle assay that detects MUC4-sv-215 in patient biofluids. The cumulative impact of the results described here may advance the clinical utility of mucins and associated SVs for improved diagnosis of PDAC

Functional Role of DREAM and DYRK1A in High-Grade Serous Ovarian Cancer Cell Dormancy

High-grade serous ovarian cancer (HGSOC) is the most common form of ovarian cancer. The majority of women are disproportionately diagnosed at an advanced stage (stage III-IV) of the disease when tumours have progressed beyond the ovaries or fallopian tubes and into the peritoneal cavity. Survival rates at late-stage are as low as 25% and chemoresistant disease recurrence is common, affecting up to 90% of patients. Multicellular clusters called spheroids contribute to dormancy, chemoresistance, and metastases and are a major challenge to treatment of HGSOC. Spheroid cells undergo reversible quiescence to evade chemotherapy in a process mediated by the mammalian DREAM complex and its initiating kinase, DYRK1A. Depletion of DYRK1A reduces spheroid cell survival and increases sensitivity to chemotherapy, highlighting it as an attractive therapeutic target. Herein we demonstrate the long-term consequences of DREAM loss in adult mice. DREAM deficient mice do not have proliferative control defects but develop systemic amyloidosis as a result of overexpression of apolipoproteins Apoa1 and Apoa4. Overexpression of Apoa1 and Apo4 were marked with increased B-MYB-MuvB (MMB) and decreased H2AZ deposition within gene bodies. The prolonged latency before developing amyloidosis suggests depriving cells of quiescence is tolerable for short periods of time. To broadly identify genetic vulnerabilities in spheroid cells, we employed an integrated strategy in which we investigated the transcriptional programming and also performed a loss-of-function genome-wide CRISPR screen in HGSOC spheroid cells. Towards this aim, we developed novel bioinformatic tools and methodology to facilitate high-throughput discovery of essential genes and pathways and anticipate these tools will have broad usability in transcriptional and loss-of-function studies. Using these tools, we identified the netrin signaling pathway as an essential mediator of HGSOC spheroid cell survival. Specifically, components of netrin signaling are upregulated in spheroid cells and depletion of netrin ligands or receptors was sufficient to reduce spheroid cell viability. Our work highlights netrin signaling as a potential target for new metastatic ovarian cancer therapies. Taken together, the work presented herein provide more insight into the roles of DREAM and DYRK1A in HGSOC spheroid survival as well as implications of therapeutically targeting this pathway. HGSOC is a very deadly disease and there is an urgent need to develop new therapeutic strategies that can specifically target dormant chemoresistant spheroids in patients to treat or prevent relapse

Metaproteomics of the Human Intestinal Tract to Assess Microbial Functionality and Interactions with the Host

Human physiological processes are complemented by those of the microbiota, the collection of all microbes living in and on our body. The human intestinal microbiota is one of the most prominent representatives and many associations with a wide spectrum of human diseases have been identified. Analysing faecal material with nucleic acid based approaches revealed the species richness of the intestinal microbiota and its individuality, being unique to each human being. In addition, to date approximately ten million unique genes have been identified from the human intestinal microbiota. These genes add an enormous additional genetic potential to the human genome, but little is known about which of these genes can be expressed into proteins and the conditions under which the protein synthesis occurs. The focus of this thesis work was to increase the knowledge of the biological processes taking place in-vivo, and to establish a baseline of these functions in the intestine of a healthy adult. Faecal material was used to study the metabolic reactions in the lower intestine, thus avoiding invasive sampling like biopsies. The proteins contained in the faecal material, which represent the molecules of most biological reactions, were targeted. At first, a method to access and analyse faecal proteins was developed, a so called metaproteomics approach. Proteins were analysed by mass spectrometry and the vast amount of resulting data was analysed with a wide range of computational methods to get a comprehensive overview of the intestinal functions. Altogether, 81 biological samples collected from 48 adults were analysed. As the main result, it was shown that individuals can be separated by their specific faecal protein profiles. This, in turn, indicates that the collection of intestinal microbial functions taking place in each of us are unique. In addition, the faecal protein profiles from obese individuals were found to be different from those of non-obese individuals. On a phylum level, it appeared that in obese individuals Bacteroidetes were biologically more active than the phylogenetic analysis suggested. This thesis work has identified several core intestinal proteins and helps to understand the functional significance of the intestinal microbiota. Next, we have to address these proteins in well concerted studies and still need to learn more about many of the encoded functions contained in the intestinal microbial genes.Suolistossamme elää monimuotoinen mikrobisto, joka toiminnoillaan täydentää ihmisen fysiologisia prosesseja. Suolistomikrobit osallistuvat muun muassa ruuansulatukseen, tuottavat vitamiineja ja säätelevät immuunijärjestelmän toimintaa. Teknologisen kehityksen myötä erilaisia mikrobipopulaatioita pystytään tutkimaan aiempaa kattavammin molekulaarisilla menetelmillä, jotka eivät vaadi bakteerien kasvattamista laboratoriossa. Metagenomiikka pyrkii DNA-sekvensoinnilla selvittämään kokonaisen ekosysteemin geenistön. Suolistomikrobiston metagenomitutkimukset ovat osoittaneet, että yksittäisen ihmisen suolistobakteeristo koodaa jopa miljoonaa geeniä, jotka osallistuvat kehomme toimintojen säätelyyn ihmisen perimän n. 20 000 geenin ohella. Satoja ihmisiä tutkimalla suolistomikrobistosta on tunnistettu yhteensä lähes 10 miljoonaa bakteeriperäistä geeniä. Tämän valtavan geneettisen kapasiteetin paljastuminen sekä suolistobakteeriston piirteiden yhteydet lukuisiin sairauksiin ovatkin nostaneet suolistobakteeritutkimuksen aktiiviseksi tutkimusalaksi niin mikrobiologiassa kuin biolääketieteessä. Laajat perimäaineksen sekvensointitutkimukset ovat paljastaneet suolistomikrobiston lajikoostumuksen ja geneettisen monimuotoisuuden. Mikrobiston geenien toimintojen sekä isännän ja mikrobiston vuorovaikutusmekanismien tuntemus on kuitenkin vielä alkutekijöissään, vaikka juuri näiden oletetaan selittävän, miten suolistomikrobisto liittyy erilaisiin sairauksiin. Tämän väitöskirjan tavoitteena oli selvittää ihmisen suolistossa tapahtuvia toimintoja ja biologisia prosesseja tutkimalla proteiineja, solujen toiminnoista vastaavia molekyylejä. Menetelmänä käytettiin metaproteomiikkaa, jolla haluttiin tutkia paksusuolen proteiinikokonaisuutta. Proteiinit eristettiin ulostenäytteistä ja analysoitiin massaspektometrialla. Suuria tulosaineistoja analysoitiin erilaisin laskennallisin keinoin proteiinien tunnistamiseksi ja niiden tehtävien ennustamiseksi. Ensimmäisessä osatyössä kehitettiin ulostenäytteiden proteomianalyysiin soveltuva näytteenkäsittely- ja proteiinien tunnistusprotokolla. Tätä menetelmää sovellettiin yhteensä 81:een 48:sta aikuisesta kerättyyn ulostenäytteeseen. Näytteistä tunnistettiin tuhansia bakteeri- ja ihmisperäisiä proteiineja, joiden runsaus ja esiintyvyys eri henkilöissä vaihteli merkittävästi. Ulosteen proteiiniprofiilien ja siten suoliston toimintojen todettiin olevan yksilöllisiä ja ajallisesti suhteellisen vakaita. Vertaamalla lihavien ja normaalipainoisten ulosteen proteiiniprofiileja havaittiin eroja, jotka viittaavat Bacteroidetes-ryhmän bakteerien olevan toiminnallisesti aktiivisempia lihavien ihmisten suolistossa. Väitöskirjatyössä tehty peruskartoitus terveen aikuisen suoliston proteiineista auttaa ymmärtämään suolistobakteeriston toiminnallista merkitystä ja luo pohjan mm. eri sairauksiin liittyvien suolistoperäisten proteiiniprofiilin muutosten tunnistamiselle

Statistical Methods for Analyzing Population-scale Genomic and Transcriptomic Data

The study of genetics is an integral part to understanding the biology behind our complex traits and can be approached in a variety of ways. Technological advancements in the field of genomics have enabled unprecedented large-scale studies which have identified numerous statistical associations between many diseases and our genes. Recently, studies involving gene expression have become an increasingly popular approach to understanding the biological pathways underlying statistical associations. In this dissertation, I address specific challenges related to the study of gene expression, including meta-imputation of expression across multiple datasets with only summary-level imputation models available, correcting for technical biases towards reference alleles in array-based expression assays, and identifying tissue-specific and population-specific regulatory variants and trait-associated loci in the context of systems genetics with whole genome sequencing, transcriptomics profiles, morphometric traits, and clinical endpoints. In Chapter 2, I develop a method which leverages multiple datasets to accurately impute tissue-specific gene expression levels. Our method, Smartly Weighted Averaging across Multiple Tissues (SWAM) does not train directly from data, but rather performs a meta-imputation by combines extant imputation models by assigning weights based on their predictive performance and similarity to the tissue of interest. I demonstrate that when using the same set of resources, SWAM improves imputation accuracy compared to existing approaches that impute tissue-specific expression by training directly from raw data. The major benefit of using the SWAM meta-imputation framework is the flexibility to combine multiple pre-trained imputation models trained from privacy-protected raw datasets. Indeed, prediction accuracy is substantially improved when integrating multiple datasets, highlighting the importance of using multiple datasets. In Chapter 3, I examine the benefits of using deep whole genome sequencing to empower and refine existing microarray-based eQTL studies. I revisited a well-known hybridization bias that arises in microarray studies caused by genetic polymorphisms within target probe sequences. In this chapter, I interrogated the impact of genetic variants from whole genome sequencing to accurately identify and characterize this bias at both the probe and probeset level. I evaluated several approaches to account for hybridization bias, including methods to remove variant-overlapping probes, and a novel method to adjust hybridization bias for each probe. I demonstrate that accounting for variant-overlapping probes when quantifying expression levels reduces reference bias and false positives in cis-eQTL analyses. I also demonstrate that adjusting for hybridization bias with deeply sequenced genomes is ideal to avoid reference bias, although leveraging publicly available variant catalogues such as the 1000 Genomes data provides comparable benefits. In Chapter 4, I performed a systems genetic study of Pima Native Americans enrolled in a diabetic nephropathy study. I integrate whole genome sequences, transcriptomic profiles, and morphometric traits derived from two micro-dissected renal compartments – glomerular and tubulointerstitial – and clinical phenotypes to identify significant associations between these molecular and complex traits. I identified thousands of eQTLs, including kidney-specific and population-specific eQTLs. I also identified many transcriptional associations with morphometric and clinical phenotypes enriched for kidney-specific biological pathways. Moreover, through dimension reduction techniques, I identified genome-wide significant genetic associations with a morphometric trait (podocyte volume), and with a composite trait representing albumin-creatin ration and glomerular surface volume, which was obtained from dimensionality reduction techniques. Studying this unique and richly-phenotyped cohort resulted many population- and tissue-specific regulatory variants, genes, and pathways implicated for renal disease progression.PHDBiostatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/170016/1/aeyliu_1.pd

Multi-omics biomarkers of metabolic homeostasis of risk factors associated to non-communicable diseases

Les malalties no transmissibles, com l'obesitat, la síndrome metabòlica, les malalties cardiovasculars, el càncer i les malalties neurodegeneratives, es consideren malalties multifactorials. Per aquesta raó, s'ha proposat que l'aparició d’aquestes malalties es deu a un desequilibri de processos globals. El seguiment d'aquests processos obre la porta a la possibilitat de modular-los i, per tant, prevenir-los mitjançant el disseny d'intervencions/tractaments personalitzats més precisos. No obstant això, els biomarcadors actuals no tenen la capacitat d'avaluar les alteracions primerenques que podrien conduir al desenvolupament de la malaltia, la qual cosa posa de manifest la necessitat de definir nous biomarcadors. Per tant, en el present treball es presenta una signatura metabòlica característica de processos específics obtinguda mitjançant l'ús de tecnologies òmiques: disfunció de carbohidrats, hiperlipèmia, hipertensió i dysbiosis intestinal, com a representatius de l'estrès metabòlic; l'estrès inflamatori; l'estrès oxidatiu i l'estrès psicològic. Per això, s'han desenvolupat diferents models animals i s'ha avaluat el perfil metabòlic dels diferents factors de risc d'interès en plasma i orina. Els resultats indiquen que els lípids i els intermediaris del cicle del TCA són els metabòlits més prometedors del perfil metabòlic. En tots els factors de risc, els diacilglicerols (DG) són els biomarcadors lipídics amb major impacte: en concret, el DG 36:4 i el DG 34:2 vinculen els factors de risc amb el metabolisme de l'àcid araquidònic. En inflamació, estrès oxidatiu i psicològic, l'altre protagonista és el cicle del TCA a causa del seu paper clau en el mitocondri amb l'alfa-cetoglutarat com l'intermediari més prometedor. En conseqüència, el perfil metabòlic presentat és una eina potencial per al seguiment dels factors de risc i podria obrir una finestra per a orientar l'aparició de malalties i intentar prevenir-les i tractar-les.Las enfermedades no transmisibles, como la obesidad, el síndrome metabólico, las enfermedades cardiovasculares, el cáncer y las enfermedades neurodegenerativas, se consideran enfermedades multifactoriales. Por esta razón, se ha propuesto que la aparición de estas enfermedades se debe a un desequilibrio de procesos globales. El seguimiento de estos procesos abre la puerta a la posibilidad de modularlos y, por lo tanto, prevenirlos mediante el diseño de intervenciones/tratamientos personalizados más precisos. Sin embargo, los biomarcadores actuales no tienen la capacidad de evaluar las alteraciones tempranas que podrían conducir al desarrollo de la enfermedad, lo que pone de manifiesto la necesidad de definir nuevos biomarcadores. Por lo tanto, en el presente trabajo se presenta una firma metabólica característica de procesos específicos obtenida mediante el uso de tecnologías ómicas: disfunción de carbohidratos, hiperlipidemia, hipertensión y disbiosis intestinal, como representativos del estrés metabólico; el estrés inflamatorio; el estrés oxidativo y el estrés psicológico. Para ello se han desarrollado diferentes modelos animales y se ha evaluado el perfil metabólico de los diferentes factores de riesgo de interés en plasma y orina. Los resultados indicaron que los lípidos y los intermediarios del ciclo del TCA son los metabolitos más prometedores del perfil metabólico. En todos los factores de riesgo, los diacilgliceroles (DG) son el biomarcador lipídico con mayor impacto: en concreto, el DG 36:4 y el DG 34:2 vinculan los factores de riesgo con el metabolismo del ácido araquidónico. En inflamación, estrés oxidativo y psicológico, el otro protagonista es el ciclo del TCA debido a su papel clave en la mitocondria con el alfa-cetoglutarato como el intermediario más prometedor. En consecuencia, el perfil metabólico presentado es una herramienta potencial para el seguimiento de los factores de riesgo y podría abrir una ventana para orientar la aparición de enfermedades e intentar prevenirlas y tratarlas.Non-communicable diseases, such as obesity, metabolic syndrome, cardiovascular diseases, cancer and neurodegenerative diseases, are considered multifactorial diseases. For this reason, it has been proposed that the occurrence of these diseases is due to an imbalance of overarching processes (i.e., metabolic, inflammatory, oxidative, and psychological stress). Monitoring these overarching processes opens the door to the possibility of modulating them, and thus preventing the occurrence of different process through the design of more precise personalised interventions or treatments. However, current biomarkers of disease cannot assess early alterations that could lead to the development of disease, highlighting the need to define new biomarkers. Thus, the present work presents a characteristic metabolic signature for the detection of specific processes using omic technologies: carbohydrate dysfunction, hyperlipidaemia, hypertension and intestinal dysbiosis, as representative of metabolic stress; inflammatory stress; oxidative stress and psychological stress. For this purpose, different animal models have been developed and the metabolic profile in plasma and urine has been evaluated in the different risk factors of interest. The results indicated that lipids and TCA cycle intermediates are the most promising metabolites of the metabolic profile. In all the risk factors, diacylglycerols (DG) are the lipidic biomarker with the greatest impact on metabolic profiles: specifically, DG 36:4 and DG 34:2 linking risk factors to arachidonic acid metabolism. In inflammation, oxidative and psychological stress, the other protagonist is the TCA cycle due to its multiple roles in mitochondrial metabolism: being alpha-ketoglutarate one of the most promising intermediate. In consequence, the presented metabolic profile is a potential tool for the monitoring of risk factors and could open a window to target the onset of diseases and try to prevent and treat them

Drug Repurposing

This book focuses on various aspects and applications of drug repurposing, the understanding of which is important for treating diseases. Due to the high costs and time associated with the new drug discovery process, the inclination toward drug repurposing is increasing for common as well as rare diseases. A major focus of this book is understanding the role of drug repurposing to develop drugs for infectious diseases, including antivirals, antibacterial and anticancer drugs, as well as immunotherapeutics