Automated extraction of potential migraine biomarkers using a semantic graph

Problem Biomedical literature and databases contain important clues for the identification of potential disease biomarkers. However, searching these enormous knowledge reservoirs and integrating findings across heterogeneous sources is costly and difficult. Here we demonstrate how semantically integrated knowledge, extracted from biomedical literature and structured databases, can be used to automatically identify potential migraine biomarkers. Method We used a knowledge graph containing more than 3.5 million biomedical concepts and 68.4 million relationships. Biochemical compound concepts were filtered and ranked by their potential as biomarkers based on their connections to a subgraph of migraine-related concepts. The ranked results were evaluated against the results of a systematic literature review that was performed manually by migraine researchers. Weight points were assigned to these reference compounds to indicate their relative importance. Results Ranked results automatically generated by the knowledge graph were highly consistent with results from the manual literature review. Out of 222 reference compounds, 163 (73%) ranked in the top 2000, with 547 out of the 644 (85%) weight points assigned to the reference compounds. For reference compounds that were not in the top of the list, an extensive error analysis has been performed. When evaluating the overall performance, we obtained a ROC-AUC of 0.974. Discussion Semantic knowledge graphs composed of information integrated from multiple and varying sources can assist researchers in identifying potential disease biomarkers

System biology modeling : the insights for computational drug discovery

Indiana University-Purdue University Indianapolis (IUPUI)Traditional treatment strategy development for diseases involves the identification of target proteins related to disease states, and the interference of these proteins with drug molecules. Computational drug discovery and virtual screening from thousands of chemical compounds have accelerated this process. The thesis presents a comprehensive framework of computational drug discovery using system biology approaches. The thesis mainly consists of two parts: disease biomarker identification and disease treatment discoveries. The first part of the thesis focuses on the research in biomarker identification for human diseases in the post-genomic era with an emphasis in system biology approaches such as using the protein interaction networks. There are two major types of biomarkers: Diagnostic Biomarker is expected to detect a given type of disease in an individual with both high sensitivity and specificity; Predictive Biomarker serves to predict drug response before treatment is started. Both are essential before we even start seeking any treatment for the patients. In this part, we first studied how the coverage of the disease genes, the protein interaction quality, and gene ranking strategies can affect the identification of disease genes. Second, we addressed the challenge of constructing a central database to collect the system level data such as protein interaction, pathway, etc. Finally, we built case studies for biomarker identification for using dabetes as a case study. The second part of the thesis mainly addresses how to find treatments after disease identification. It specifically focuses on computational drug repositioning due to its low lost, few translational issues and other benefits. First, we described how to implement literature mining approaches to build the disease-protein-drug connectivity map and demonstrated its superior performances compared to other existing applications. Second, we presented a valuable drug-protein directionality database which filled the research gap of lacking alternatives for the experimental CMAP in computational drug discovery field. We also extended the correlation based ranking algorithms by including the underlying topology among proteins. Finally, we demonstrated how to study drug repositioning beyond genomic level and from one dimension to two dimensions with clinical side effect as prediction features

Uncovering Molecular Biomarkers That Correlate Cognitive Decline with the Changes of Hippocampus' Gene Expression Profiles in Alzheimer's Disease

Background: Alzheimer’s disease (AD) is characterized by a neurodegenerative progression that alters cognition. On a phenotypical level, cognition is evaluated by means of the MiniMental State Examination (MMSE) and the post-morten examination of Neurofibrillary Tangle count (NFT) helps to confirm an AD diagnostic. The MMSE evaluates different aspects of cognition including orientation, short-term memory (retention and recall), attention and language. As there is a normal cognitive decline with aging, and death is the final state on which NFT can be counted, the identification of brain gene expression biomarkers from these phenotypical measures has been elusive. Methodology/Principal Findings: We have reanalysed a microarray dataset contributed in 2004 by Blalock et al. of 31 samples corresponding to hippocampus gene expression from 22 AD subjects of varying degree of severity and 9 controls. Instead of only relying on correlations of gene expression with the associated MMSE and NFT measures, and by using modern bioinformatics methods based on information theory and combinatorial optimization, we uncovered a 1,372-probe gene expression signature that presents a high-consensus with established markers of progression in AD. The signature reveals alterations in calcium, insulin, phosphatidylinositol and wnt-signalling. Among the most correlated gene probes with AD severity we found those linked to synaptic function, neurofilament bundle assembly and neuronal plasticity. Conclusions/Significance: A transcription factors analysis of 1,372-probe signature reveals significant associations with the EGR/KROX family of proteins, MAZ, and E2F1. The gene homologous of EGR1, zif268, Egr-1 or Zenk, together with other members of the EGR family, are consolidating a key role in the neuronal plasticity in the brain. These results indicate a degree of commonality between putative genes involved in AD and prion-induced neurodegenerative processes that warrants further investigation

Proteomic and clinical insights into polycystic ovary syndrome in adolescents

Despite its high prevalence, our understanding of the pathophysiology of polycystic ovary syndrome (PCOS) is lacking. Consequently, the way we diagnose and manage this common condition is inadequate, which is especially true for adolescents. This thesis aims to expand the body of knowledge regarding PCOS in adolescents. It will explore the clinical phenotype of PCOS, in addition to using proteomic techniques to better understand the biological mechanisms which underpin this condition. However, to do this, we must first comprehend ‘normal’ menstrual patterns in these pubertal years. As such, this thesis begins by seeking to define menstrual and ovulatory ‘normality’ in the first year following menarche, by systematically reviewing relevant literature. Following this, data are presented from a longitudinal study evaluating the clinical presentation and phenotype of adolescents with a suspected diagnosis of PCOS. The latter part of the thesis focuses on the use of proteomic techniques to broaden our understanding of PCOS. Discovery proteomic analysis of urine samples is employed firstly to explore the biological pathways associated with PCOS, and secondly to identify specific proteins which are differentially expressed in adolescents with PCOS, which may form a pool of non-invasive candidate biomarkers. Inflammation was identified as the most significant biological process associated with PCOS in discovery analysis, and these findings were validated in subsequent targeted proteomic panels. Validation studies were undertaken in a larger cohort of adolescents with PCOS, and then comparison was also made to adults with PCOS. Finally, all results from this thesis are summarised, the findings discussed, and their implications considered, alongside future work

Metabolic syndrome, the leptin gene and kidney disease in non-diabetic black South Africans

Includes abstract.Includes bibliographical references (leaves 226-256).Obesity is a worldwide problem and is a factor in the pathogenesis of the metabolic syndrome and kidney disease through the development of obesity-related hypertension and neurohormonal mechanisms that include the action of leptin. As there appear to be no focussed studies that have looked at the association of the LEP gene with kidney disease phenotypes or cardiovascular disease markers like hypertension, the metabolic syndrome and obesity, and especially so in native black Africans, this study sought to establish an association between the obesity gene (LEP) and kidney disease phenotypes (independent of diabetes and hypertension) in a homogenous black African population

Epigenetic mechanisms in the early life programming of obesity

PhD ThesisObesity presents a major public health burden with prevalence rising in both children and adults. This disorder is associated with many adverse health outcomes and improved understanding of the mechanisms is required to develop effective preventive and treatment strategies. It has been hypothesised that environmental exposures such as poor nutrition in utero and during the early post natal period can programme an individual to develop obesity in later life. These early life exposures can be ‘memorised’ by the cell in the form of epigenetic modifications, changes to the biochemical structure and function of DNA. Such modifications include DNA methylation, the addition of a methyl group to cytosine residues which is involved in the regulation of gene transcription. Epigenetic mechanisms therefore represent an attractive mechanism to explain developmental programming phenomena. The overarching aim of this study was to establish the mediating role of epigenetic processes in linking modifiable environmental exposures with subsequent risk of obesity. This was addressed through interrogation of animal models, through the development and application of bioinformatic approaches and through epidemiological investigation of human population studies. Tissue level DNA methylation patterns were investigated in hypothalamus using immunohistochemical staining. No significant differences were discernible between methylation levels in the hypothalami of control rodents when compared to hypothalami from rodents that had been exposed in utero to a dietary regimen that induces metabolic perturbation and obesity in offspring. Bioinformatic approaches were used to develop and apply an in silico workflow to interrogate gene expression dataset, in this instance from a rodent model of dietary manipulation in utero and early postnatal life. The purpose of this in silico interrogation was to identify loci that were strong candidates for epigenetic regulation of gene expression. Four genes, Esr1, Fxn, Igf2r and Rbl2 were identified and the levels of promoter methylation at these loci were assessed in rodent liver tissue from offspring of exposed and unexposed mothers using pyrosequencing. DNA methylation levels in Igf2r were observed to be higher in animals exposed to a maternal obesogenic diet

The value of semantics in biomedical knowledge graphs

Knowledge graphs use a graph-based data model to represent knowledge of the real world. They consist of nodes, which represent entities of interest such as diseases or proteins, and edges, which represent potentially different relations between these entities. Semantic properties can be attached to these nodes and edges, indicating the classes of entities they represent (e.g. gene, disease), the predicates that indicate the types of relationships between the nodes (e.g. stimulates, treats), and provenance that provides references to the sources of these relationships.Modelling knowledge as a graph emphasizes the interrelationships between the entities, making knowledge graphs a useful tool for performing computational analyses for domains in which complex interactions and sequences of events exist, such as biomedicine. Semantic properties provide additional information and are assumed to benefit such computational analyses but the added value of these properties has not yet been extensively investigated.This thesis therefore develops and compares computational methods that use these properties, and applies them to biomedical tasks. These are: biomarker identification, drug repurposing, drug efficacy screening, identifying disease trajectories, and identifying genes targeted by disease-associated SNPs located on the non-coding part of the genome.In general, we find that methods which use concept classes, predicates, or provenance improves achieve a superior performance over methods that do not use them. We thereby demonstrate the added value of these semantic properties for computational analyses performed on biomedical knowledge graphs.<br/

Markers of Bone Turnover In Preclinical Development of Drugs for Skeletal Diseases

Skeletal tissue is constantly remodeled in a process where osteoclasts resorb old bone and osteoblasts form new bone. Balance in bone remodeling is related to age, gender and genetic factors, but also many skeletal diseases, such as osteoporosis and cancer-induced bone metastasis, cause imbalance in bone turnover and lead to decreased bone mass and increased fracture risk. Biochemical markers of bone turnover are surrogates for bone metabolism and may be used as indicators of the balance between bone resorption and formation. They are released during the remodeling process and can be conveniently and reliably measured from blood or urine by immunoassays. Most commonly used bone formation markers include N-terminal propeptides of type I collagen (PINP) and osteocalcin, whereas tartrate-resistant acid phosphatase isoform 5b (TRACP 5b) and C-terminal cross-linked telopeptide of type I collagen (CTX) are common resorption markers. Of these, PINP has been, until recently, the only marker not commercially available for preclinical use. To date, widespread use of bone markers is still limited due to their unclear biological significance, variability, and insufficient evidence of their prognostic value to reflect long term changes. In this study, the feasibility of bone markers as predictors of drug efficacy in preclinical osteoporosis models was elucidated. A non-radioactive PINP immunoassay for preclinical use was characterized and validated. The levels of PINP, N-terminal mid-fragment of osteocalcin, TRACP 5b and CTX were studied in preclinical osteoporosis models and the results were compared with the results obtained by traditional analysis methods such as histology, densitometry and microscopy. Changes in all bone markers at early timepoints correlated strongly with the changes observed in bone mass and bone quality parameters at the end of the study. TRACP 5b correlated strongly with the osteoclast number and CTX correlated with the osteoclast activity in both in vitro and in vivo studies. The concept “resorption index” was applied to the relation of CTX/TRACP 5b to describe the mean osteoclast activity. The index showed more substantial changes than either of the markers alone in the preclinical osteoporosis models used in this study. PINP was strongly associated with bone formation whereas osteocalcin was associated with both bone formation and resorption. These results provide novel insight into the feasibility of PINP, osteocalcin, TRACP 5b and CTX as predictors of drug efficacy in preclinical osteoporosis models. The results support clinical findings which indicate that short-term changes of these markers reflect long-term responses in bone mass and quality. Furthermore, this information may be useful when considering cost-efficient and clinically predictive drug screening and development assays for mining new drug candidates for skeletal diseases.Luun biokemialliset merkkiaineet luustosairauksien prekliinisessä lääkekehityksessä Luun uudismuodostusta tapahtuu koko elämän ajan. Tässä prosessissa osteoklastit hajottavat vanhaa luuta ja osteoblastit muodostavat uutta luuta. Tasapainoon vaikuttaa ikä, sukupuoli ja perinnöllisyys, mutta myös monissa luustosairauksissa, kuten osteoporoosissa ja syövän luustometastaaseissa, tämä tasapaino on järkkynyt johtaen vähentyneeseen luun määrään ja lisääntyneeseen murtumaherkkyyteen. Luun biokemialliset merkkiaineet kertovat luun aineenvaihdunnasta eli hajotuksen ja muodostuksen välisestä tasapainosta. Merkkiaineita vapautuu luun uudismuodostuksessa ja niitä voidaan helposti ja luotettavasti mitata seerumista tai virtsasta immunomääritysmenetelmillä. Yleisesti käytettyjä luun muodostuksen merkkiaineita ovat tyypin I kollageenin aminoterminaalinen propeptidi (PINP) ja osteokalsiini, sekä luun hajotuksen merkkiaineita tartraatti-resistentti hapan fosfataasi alatyyppi 5b (TRACP 5b) ja tyypin I kollageenin karboksiterminaalinen telopeptidi (CTX). Näistä PINP on ainoa merkkiaine, jolle ei ole aiemmin ollut saatavilla prekliiniseen käyttöön soveltuvaa kaupallista immunomääritysmenetelmää. Tällä hetkellä biokemiallisten merkkiaineiden laajamittainen käyttö on vielä rajoittunutta, koska niihin liittyy paljon biologista ja analyyttistä variaatiota eikä niiden merkitsevyydestä ja käyttökelpoisuudesta pitkän aikavälin muutosta ennustavana tekijänä ole riittävästi näyttöä. Tämän tutkimuksen tavoitteena oli selvittää luuston biokemiallisten merkkiaineiden soveltuvuutta lääkemolekyylien tehokkuuden ennustajina prekliinisissä osteoporoositutkimusmalleissa. Tutkimuksessa karakterisoitiin ja validoitiin PINP:lle prekliiniseen käyttöön kehitetty immunomääritysmenetelmä. PINP:n, osteokalsiinin N-terminaalisen keskifragmentin, TRACP 5b:n ja CTX:n tasoja tutkittiin prekliinisissä osteoporoosimalleissa, ja saatuja tuloksia verrattiin perinteisillä menetelmillä kuten histologialla, tiheysmittauksilla ja mikroskopialla saatuihin tuloksiin. Kaikkien tutkittujen luuston merkkiaineiden alkuvaiheen muutosten havaittiin korreloivan kokeen lopussa nähtyihin luuston rakenteellisiin muutoksiin. TRACP 5b korreloi osteoklastien lukumäärään ja CTX osteoklastien aktiivisuuteen sekä in vitro että in vivo kokeissa. CTX/TRACP 5b suhteelle luotiin termi resorptio-indeksi, joka kuvaa osteoklastien keskimääräistä aktiivisuutta. Indeksi antoi tarkempaa tietoa kuin kumpikaan merkkiaine erikseen käytetyissä prekliinisissä osteoporoosimalleissa. PINP:n havaittiin korreloivan vahvasti luun muodostukseen, kun taas osteokalsiini kuvasi sekä luun muodostusta että hajotusta. Tämän tutkimuksen tulokset antavat uutta tietoa luun biokemiallisten merkkiaineiden soveltuvuudesta lääkemolekyylien tehokkuuden ennustajina prekliinisissä osteoporoosimalleissa. Havainnot tukevat kliinisiä tutkimuksia, joissa merkkiaineiden on havaittu korreloivan myöhemmin nähtävien luuston rakenteellisten muutosten kanssa. Tutkimuksessa saatu tieto auttaa suunniteltaessa kliinisen ennustettavuuden kannalta parempia määritysmenetelmiä, joiden avulla voidaan nopeammin ja tehokkaammin löytää uusia toimivia lääkemolekyylejä luustosairauksien hoitoon.Siirretty Doriast