ENDOMET database – A means to identify novel diagnostic and prognostic tools for endometriosis

Endometriosis is a common benign hormone reliant inflammatory gynecological disease that affects fertile aged women and has a considerable economic impact on healthcare systems. Symptoms include intense menstrual pain, persistent pelvic pain, and infertility. It is defined by the existence of endometrium-like tissue developing in ectopic locations outside the uterine cavity and inflammation in the peritoneal cavity. Endometriosis presents with multifactorial etiology, and despite extensive research the etiology is still poorly understood. Diagnostic delay from the onset of the disease to when a conclusive diagnosis is reached is between 7–12 years. There is no known cure, although symptoms can be improved with hormonal medications (which often have multiple side effects and prevent pregnancy), or through surgery which carries its own risk. Current non-invasive tools for diagnosis are not sufficiently dependable, and a definite diagnosis is achieved through laparoscopy or laparotomy. This study was based on two prospective cohorts: The ENDOMET study, including 137 endometriosis patients scheduled for surgery and 62 healthy women, and PROENDO that included 138 endometriosis patients and 33 healthy women. Our long-term goal with the current study was to support the discovery of innovative new tools for efficient diagnosis of endometriosis as well as tools to further understand the etiology and pathogenesis of the disease. We set about achieving this goal by creating a database, EndometDB, based on a relational data model, implemented with PostgreSQL programming language. The database allows e.g., for the exploration of global genome-wide expression patterns in the peritoneum, endometrium, and in endometriosis lesions of endometriosis patients as well as in the peritoneum and endometrium of healthy control women of reproductive age. The data collected in the EndometDB was also used for the development and validation of a symptom and biomarker-based predictive model designed for risk evaluation and early prediction of endometriosis without invasive diagnostic methods. Using the data in the EndometDB we discovered that compared with the eutopic endometrium, the WNT- signaling pathway is one of the molecular pathways that undergo strong changes in endometriosis. We then evaluated the potential role for secreted frizzled-related protein 2 (SFRP-2, a WNT-signaling pathway modulator), in improving endometriosis lesion border detection. The SFRP-2 expression visualizes the lesion better than previously used markers and can be used to better define lesion size and that the surgical excision of the lesions is complete.ENDOMET tietokanta – Keino tunnistaa uusi diagnostinen ja ennustava työkalu endometrioosille Endometrioosi on yleinen hyvänlaatuinen, hormoneista riippuvainen tulehduksellinen lisääntymisikäisten naisten gynekologinen sairaus, joka kuormittaa terveydenhuoltojärjestelmää merkittävästi. Endometrioositaudin oireita ovat mm. voimakas kuukautiskipu, jatkuva lantion alueen kipu ja hedelmättömyys. Sairaus määritellään kohdun limakalvon kaltaisen kudoksen esiintymisenä kohdun ulkopuolella sekä siihen liittyvänä vatsakalvon tulehduksena. Endometrioosin etiologia on monitahoinen, ja laajasta tutkimuksesta huolimatta edelleen huonosti tunnettu. Kesto taudin puhkeamisesta lopullisen diagnoosin saamiseen on usein jopa 7–12 vuotta. Sairauteen ei tunneta parannuskeinoa, mutta oireita voidaan lievittää esimerkiksi hormonaalisilla lääkkeillä (joilla on usein monia sivuvaikutuksia ja jotka estävät raskauden) tai leikkauksella, johon liittyy omat tunnetut riskit. Nykyiset ei-invasiiviset diagnoosityökalut eivät ole riittävän luotettavia sairauden tunnistamiseen, ja varma endometrioosin diagnoosi saavutetaan laparoskopian tai laparotomian avulla. Tämä tutkimus perustui kahteen prospektiiviseen kohorttiin: ENDOMET-tutkimuk-seen, johon osallistui 137 endometrioosipotilasta ja 62 terveellistä naista, sekä PROENDO-tutkimukseen, johon osallistui 138 endometrioosipotilasta ja 33 terveellistä naista. Tässä tutkimuksessa pitkän aikavälin tavoitteemme oli löytää uusia työkalujen endometrioosin diagnosointiin, sekä ymmärtää endometrioosin etiologiaa ja patogeneesiä. Ensimmäisessä vaiheessa loimme EndometDB –tietokannan PostgreSQL-ohjelmointi-kielellä. Tämän osittain avoimeen käyttöön vapautetun tietokannan avulla voidaan tutkia genomin, esimerkiksi kaikkien tunnettujen geenien ilmentymistä peritoneumissa, endo-metriumissa ja endometrioosipotilaiden endometrioosileesioissa EndometDB-tietokantaan kerättyjä tietoja käytettiin oireiden ja biomarkkeripohjaisen ennustemallin kehittämiseen ja validointiin. Malli tuottaa riskinarvioinnin endometrioositaudin varhaiseen ennustamiseen ilman laparoskopiaa. Käyttäen EndometDB-tietokannan tietoja havaitsimme, että endo-metrioositautikudoksessa tapahtui voimakkaita geeni-ilmentymisen muutoksia erityisesti geeneissä, jotka liittyvät WNT-signalointireitin säätelyyn. Keskeisin löydös oli, että SFRP-2 proteiinin ilmentyminen oli huomattavasti koholla endometrioosikudoksessa ja SFRP-2 proteiinin immunohistokemiallinen värjäys erottaa endometrioosin tautikudoksen terveestä kudoksesta aiempia merkkiaineita paremmin. Löydetyllä menetelmällä voidaan siten selvittää tautikudoksen laajuus ja tarvittaessa osoittaa, että leikkauksella on kyetty poistamaan koko sairas kudos

Pacific Symposium on Biocomputing 2023

The Pacific Symposium on Biocomputing (PSB) 2023 is an international, multidisciplinary conference for the presentation and discussion of current research in the theory and application of computational methods in problems of biological significance. Presentations are rigorously peer reviewed and are published in an archival proceedings volume. PSB 2023 will be held on January 3-7, 2023 in Kohala Coast, Hawaii. Tutorials and workshops will be offered prior to the start of the conference.PSB 2023 will bring together top researchers from the US, the Asian Pacific nations, and around the world to exchange research results and address open issues in all aspects of computational biology. It is a forum for the presentation of work in databases, algorithms, interfaces, visualization, modeling, and other computational methods, as applied to biological problems, with emphasis on applications in data-rich areas of molecular biology.The PSB has been designed to be responsive to the need for critical mass in sub-disciplines within biocomputing. For that reason, it is the only meeting whose sessions are defined dynamically each year in response to specific proposals. PSB sessions are organized by leaders of research in biocomputing's 'hot topics.' In this way, the meeting provides an early forum for serious examination of emerging methods and approaches in this rapidly changing field

Incorporating standardised drift-tube ion mobility to enhance non-targeted assessment of the wine metabolome (LC×IM-MS)

Liquid chromatography with drift-tube ion mobility spectrometry-mass spectrometry (LCxIM-MS) is emerging as a powerful addition to existing LC-MS workflows for addressing a diverse range of metabolomics-related questions [1,2]. Importantly, excellent precision under repeatability and reproducibility conditions of drift-tube IM separations [3] supports the development of non-targeted approaches for complex metabolome assessment such as wine characterisation [4]. In this work, fundamentals of this new analytical metabolomics approach are introduced and application to the analysis of 90 authentic red and white wine samples originating from Macedonia is presented. Following measurements, intersample alignment of metabolites using non-targeted extraction and three-dimensional alignment of molecular features (retention time, collision cross section, and high-resolution mass spectra) provides confidence for metabolite identity confirmation. Applying a fingerprinting metabolomics workflow allows statistical assessment of the influence of geographic region, variety, and age. This approach is a state-of-the-art tool to assess wine chemodiversity and is particularly beneficial for the discovery of wine biomarkers and establishing product authenticity based on development of fingerprint libraries

Human Blood Group Systems and Haemoglobinopathies

The past decade has seen remarkable improvements and advances in the fields of blood transfusion and hematology, particularly with regards to advances in science, technology, method development, quality, standardization, and governance. This book provides more evidenced-based insight into the field of blood transfusion and the management of hemoglobinopathies

Metabolome-based studies of virulence factors in Pseudomonas aeruginosa

Pseudomonas aeruginosa is an opportunistic pathogen and an important causative agent of potentially life-threatening nosocomial infections in predisposed patients. The Gram-negative bacterium produces a large and diverse repertoire of small-molecule secondary metabolites that serve as regulators and effectors of its virulence. In this study, a range of mass spectrometry-based bacterial metabolomics approaches was used to investigate these small-molecule virulence factors and their interplay with pseudomonal metabolism as well as with phenotypic traits related to virulence. The groundwork was laid by exploring the metabolite inventory of P. aeruginosa and improving the coverage of its metabolome by the application of a custom software named CluMSID, that clusters analytes based on similarities of their MS² spectra. CluMSID led to the annotation of, i.a., 27 novel members of the class of alkylquinolone quorum sensing signalling molecules, which represent crucial players in the highly complex network that regulates pseudomonal virulence. The tool was developed towards a versatile and user-friendly R package hosted on Bioconductor, whose functionalities and benefits are described in detail. The new findings on the alkylquinolone chemodiversity led to further studies with a mechanistic focus that probed the substrate specificity of the enzyme complex PqsBC. It was demonstrated that PqsBC accepts different medium-chain acyl-coenzyme A substrates for the condensation with 2-aminobenzoylacetate and thereby produces alkylquinolones with various side chain lengths, whose distribution is a function of substrate specificity and substrate availability. Moreover, it was shown that PqsBC also synthesises alkylquinolones with unsaturated side chains. The focus was further broadened from metabolite and pathway-centred questions to a more global perspective on pseudomonal virulence and metabolism, which directed attention at PrmC, an enzyme with a partially unknown function indispensable for in vivo virulence. An untargeted metabolomics experiment yielded insights into the role of PrmC and its influence on the pseudomonal endo- and exometabolome. Finally, clinical P. aeruginosa strains with different virulence phenotypes were examined by untargeted metabolomics in order to disclose metabolic variation and interconnections between virulence and metabolism. The analysis resulted in the discovery of a putative virulence biomarker and enabled the construction of a random forest classification model for certain virulence phenotypes based only on metabolomics data. In summary, this study demonstrated the potential of metabolomics for the investigation of P. aeruginosa virulence factors and thereby contributed towards the comprehension of the complex interplay of metabolism and virulence in this important pathogen.Pseudomonas aeruginosa ist ein wichtiger opportunistischer Erreger potenziell lebensbedrohlicher nosokomialer Infektionen bei prädisponierten Patienten. Das Gram-negative Bakterium produziert ein vielfältiges Repertoire an niedermolekularen Sekundärmetaboliten, die als Regulatoren und Effektoren seiner Virulenz dienen. In dieser Studie wurde eine Reihe von Massenspektrometrie-basierten Ansätzen der bakteriellen Metabolomik verwendet, um diese niedermolekularen Virulenzfaktoren und ihre Wechselwirkungen mit dem pseudomonalen Metabolismus sowie mit virulenzassoziierten phänotypischen Merkmalen zu untersuchen. Die Grundlage bilden die Untersuchung des Metaboliteninventars von P. aeruginosa und die Verbesserung der analytischen Abdeckung des Metaboloms durch die Anwendung einer selbstentwickelten Software namens CluMSID, die MS²-Spektren nach Ähnlichkeit clustert. CluMSID führte zur Annotation von u.a. 27 neuen Mitgliedern der Klasse der Alkylchinolone, die als Quorum-Sensing-Signalmoleküle entscheidende Akteure im hochkomplexen Netzwerk der Virulenzregulation darstellen. Das Tool wurde zu einem R-Paket entwickelt, das auf Bioconductor verfügbar ist und dessen Funktionalitäten und Vorteile ausführlich beschrieben werden. Die neuen Erkenntnisse über die Chemodiversität der Alkylchinolone führten zu weiteren Studien mit mechanistischem Schwerpunkt, die die Substratspezifität des Enzymkomplexes PqsBC untersuchten. Es wurde nachgewiesen, dass PqsBC verschiedene mittelkettige Acyl-Coenzym-A-Substrate für die Kondensation mit 2-Aminobenzoylacetat akzeptiert und dadurch Alkylchinolone mit verschiedenen Seitenkettenlängen produziert, deren Verteilung eine Funktion der Substratspezifität und der Substratverfügbarkeit ist. Zudem konnte gezeigt werden, dass PqsBC auch Alkylchinolone mit ungesättigten Seitenketten synthetisiert. Im Weiteren wurde der Fokus von Metaboliten- und Stoffwechselweg-zentrierten Fragen hin zu einer globaleren Perspektive der pseudomonalen Virulenz und des Metabolismus erweitert, was die Aufmerksamkeit auf PrmC lenkte, ein Enzym mit teilweise unbekannter, für die in vivo-Virulenz unverzichtbarer Funktion. Ein globales Metabolomik-Experiment lieferte Einblicke in die Rolle von PrmC und seinen Einfluss auf das pseudomonale Endo- und Exometabolom. Schließlich wurden klinische P. aeruginosa-Stämme mit unterschiedlichen Virulenzphänotypen mittels ungerichteter Metabolomik untersucht, um metabolische Variationen und Zusammenhänge zwischen Virulenz und Metabolismus aufzudecken. Die Analyse resultierte in der Entdeckung eines putativen Virulenzbiomarkers und ermöglichte die Konstruktion eines Random-Forest-Klassifikationsmodells für bestimmte Virulenzphänotypen, das nur auf Metabolomik-Daten basiert. Zusammenfassend hat diese Studie das Potenzial der Metabolomik für die Untersuchung der Virulenzfaktoren von P. aeruginosa aufgezeigt und damit zum Verständnis des komplexen Zusammenspiels von Metabolismus und Virulenz bei diesem wichtigen Pathogen beigetragen

Reorganizing Biomedical Research : Biobanks as Conditions of Possibility for Personalized Medicine

In recent decades biomedical samples and data have been organized into large depositories such as biobanks. These biobanks have also been founded in Finland to allow for increasingly large-scale, international, and data-intensive biomedical research. Simultaneously expectations of personalized medicine have increased – in the future individuals instead of averages will be treated, and genomic data may be utilized in the clinics or in disease prevention. This study – rooted in science and technology studies, and linking to discussions of the role of expectations and imaginaries – examines biobanks as conditions of possibility for personalized medicine to become reality: that is, how biobanks are expected to make personalized medicine possible. The rearranging of biomedical research through biobanks is investigated against the backdrop of personalized medicine as a sociotechnical imaginary: a vision of a desirable future, which is both built on, and continuously requires, science and technology, and therefore societal efforts, for its fulfillment (Jasanoff and Kim, 2015). Consequently, this study asks: What do the expectations related to biobanks as conditions of possibility for personalized medicine tell us about the knowledge production in which biobanks are supposed to participate, and the role biobanks play in it? To answer this question, biobanking is studied through three different lenses. The analytical sections unpack, first, the claims of high quality samples they store; second, the ideas related to research population(s) seen to be stored in biobanks; and third, their link to the expectations of translational medicine. Thus, it is explored how biobanks are expected and said to contribute to contemporary biomedical knowledge production that takes place in highly regulated settings. The main argument of the study is that the very idea of biobanks is being reshaped as operations, conventions, regulatory frameworks, and new expectations are linked to the imaginary of personalized medicine and require that action be taken. The different layers of stakeholders, regulations, developments, and projects that condition and constrain biobanking and hence knowledge production, have, and continue to have, an effect on what biobanks are considered and understood to be, and the kind of knowledge and scientific practices they could foster. The analytical chapters illustrate the multiplicity of tendencies and linkages attendant on biobanks as they begin to reorganize biomedical research.Biopankkeja on perustettu viime vuosikymmenten aikana organisoimaan ihmisperäisten näytteiden ja niihin liitetyn terveystiedon keräystä, säilytystä ja jakelua. Niitä on pidetty kansainvälisen, data-intensiivisen ja suuriin aineistoihin perustuvan biolääketieteellisen tutkimuksen mahdollistajina. Myös Suomessa on perustettu biopankkeja, joilla nähdään olevan tärkeä rooli yksilöllistetyn, henkilökohtaisen lääketieteen kehittämisessä. Odotus on, että tulevaisuudessa potilaiden hoito voidaan räätälöidä juuri heille sopivaksi. Myös genomitietoa voitaisiin hyödyntää niin potilashoidossa kuin sairauksien ennaltaehkäisyssä. Tässä tieteen ja teknologian tutkimukseen sekä odotusten sosiologiaan kiinnittyvässä tutkimuksessa tarkastellaan biopankkeja henkilökohtaisen lääketieteen toteutumisen edellytyksenä. Biolääketieteellisen tutkimuksen uudelleenjärjestelemistä biopankkien kautta tutkitaan suhteessa henkilökohtaiseen lääketieteeseen. Henkilökohtainen lääketiede nähdään sosioteknisenä kuvitelmana eli näkemyksenä tavoittelemisenarvoisesta tulevaisuudesta (Jasanoff ja Kim, 2015). Tämä tulevaisuuskuva perustuu tieteeseen ja teknologiaan, edellyttää niitä ja näin ollen vaatii siis yhteiskunnallisia toimia toteutuakseen. Tutkimuksessa kysytään: Mitä odotukset biopankeista henkilökohtaisen lääketieteen edellytyksinä paljastavat siitä tiedontuotannosta, johon niiden odotetaan osallistuvan sekä biopankkien itsensä roolista tässä tiedontuotannossa? Kysymykseen vastataan tarkastelemalla biopankkeja kolmesta näkökulmasta. Analyysiosioissa asiaa käsitellään ensinnäkin tarkastelemalla väitettä biopankkien korkealaatuisista näytteistä. Toiseksi huomio kiinnitetään käsityksiin tutkimuspopulaatioista, joita biopankeissa säilytetään. Kolmanneksi analysoidaan biopankkien linkkiä odotuksiin translationaalisesta lääketieteestä. Analyysiosioissa huomio kiinnittyy siihen, miten biopankkien odotetaan osallistuvan tiedontuotantoon ja olevan hyödyksi biolääketieteen tarkkaan säädellyissä käytännöissä. Tutkimuksen pääargumentti on, että idea biopankista muuntuu ja muokkautuu sitä mukaa, kun uusia toimintatapoja, säädöksiä ja odotuksia kiinnittyy kuvitelmaan henkilökohtaisesta lääketieteestä, jonka toteutuminen vaatii toimia. Asiaan eri tavoin liittyvät toimijat, säädökset, kehityskulut, uudet projektit ja avaukset, jotka kaikki ovat samanaikaisesti biopankkitoiminnan jatkuvuuden ehtoja että sen rajoittajia, vaikuttavat siihen mitä biopankkien ymmärretään olevan ja minkälaiseen tiedontuotantoon ne osallistuvat sekä minkälaista tietoa ne voivat tuottaa

Metabolomics Data Processing and Data Analysis—Current Best Practices

Metabolomics data analysis strategies are central to transforming raw metabolomics data files into meaningful biochemical interpretations that answer biological questions or generate novel hypotheses. This book contains a variety of papers from a Special Issue around the theme “Best Practices in Metabolomics Data Analysis”. Reviews and strategies for the whole metabolomics pipeline are included, whereas key areas such as metabolite annotation and identification, compound and spectral databases and repositories, and statistical analysis are highlighted in various papers. Altogether, this book contains valuable information for researchers just starting in their metabolomics career as well as those that are more experienced and look for additional knowledge and best practice to complement key parts of their metabolomics workflows

Computational Toxinology

Venoms are complex mixtures of biological macromolecules and other compounds that are used for predatory and defensive purposes by hundreds of thousands of known species worldwide. Throughout human history, venoms and venom components have been used to treat a vast array of illnesses, causing them to be of great clinical, economic, and academic interest to the drug discovery and toxinology communities. In spite of major computational advances that facilitate data-driven drug discovery, most therapeutic venom effects are still discovered via tedious trial-and-error, or simply by accident. In this dissertation, I describe a body of work that aims to establish a new subdiscipline of translational bioinformatics, which I name “computational toxinology”. To accomplish this goal, I present three integrated components that span a wide range of informatics techniques: (1) VenomKB, (2) VenomSeq, and (3) VenomKB’s Semantic API. To provide a platform for structuring, representing, retrieving, and integrating venom data relevant to drug discovery, VenomKB provides a database-backed web application and knowledge base for computational toxinology. VenomKB is structured according to a fully-featured ontology of venoms, and provides data aggregated from many popular web re- sources. VenomSeq is a biotechnology workflow that is designed to generate new high-throughput sequencing data for incorporation into VenomKB. Specifically, we expose human cells to controlled doses of crude venoms, conduct RNA-Sequencing, and build profiles of differential gene expression, which we then compare to publicly-available differential expression data for known dis- eases and drugs with known effects, and use those comparisons to hypothesize ways that the venoms could act in a therapeutic manner, as well. These data are then integrated into VenomKB, where they can be effectively retrieved and evaluated using existing data and known therapeutic associations. VenomKB’s Semantic API further develops this functionality by providing an intelligent, powerful, and user-friendly interface for querying the complex underlying data in VenomKB in a way that reflects the intuitive, human-understandable mean- ing of those data. The Semantic API is designed to cater to the needs of advanced users as well as laypersons and bench scientists without previous expertise in computational biology and semantic data analysis. In each chapter of the dissertation, I describe how we evaluated these 3 components through various approaches. We demonstrate the utility of VenomKB and the Semantic API by testing a number of practical use-cases for each, designed to highlight their ability to rediscover existing knowledge as well as suggesting potential areas for future exploration. We use statistics and data science techniques to evaluate VenomSeq on 25 diverse species of venomous animals, and propose biologically feasible explanations for significant findings. In evaluating the Semantic API, I show how observations on VenomSeq data can be interpreted and placed into the context of past research by members of the larger toxinology community. Computational toxinology is a toolbox designed to be used by multiple stakeholders (toxinologists, computational biologists, and systems pharmacologists, among others) to improve the return rate of clinically-significant findings from manual experimentation. It aims to achieve this goal by enabling access to data, providing means for easy validation of results, and suggesting specific hypotheses that are preliminarily supported by rigorous inferential statistics. All components of the research I describe are open-access and publicly available, to improve reproducibility and encourage widespread adoptio