In der physiologischen Schwangerschaft ist ein Abfall der Thrombozyten um ca. 10 % normwertig. In etwa 7-12 % aller Schwangerschaften kommt es zu einer Thrombozytopenie (<150000/µl). Durch einen erhöhten Thrombozytenverbrauch gekennzeichnet sind die Präeklampsie und das HELLP Syndrom. Beide Erkrankungen sind eine häufige Ursache fetaler und maternaler Mortalität mit steigender Inzidenz. Zwischen der Anzahl reifer Thrombozyten und deren Vorstufen, den immature platelets, konnte eine Korrelation nachgewiesen werden. Beide Zelltypen können labormedizinisch einfach quantifiziert werden. Die immature platelet fraction (IPF) kann hier als Marker für den Plättchenturnover dienen. Um eine Aussage über die Bedeutung der unreifen Plättchen bei einer hypertensiven Erkrankung in der Schwangerschaft treffen zu können, ist die Bestimmung der Werte in der physiologischen Schwangerschaft essentiell. Die vorliegende Arbeit untersucht daher die Anzahl unreifen Thrombozyten, der IPF, bei gesunden Schwangeren zwischen der 20. und 40. Schwangerschaftswoche. Bei 27 Schwangeren zeigte sich ein Anstieg der unreifen Plättchen von 8,1/nl (3,2 %) in der 20. bis 23. Schwangerschaftswoche auf 13,6/nl (4,85 %) in der 36. bis 40. Schwangerschaftswoche. Zudem war ein leichter Abfall der reifen Thrombozyten zu verzeichnen. Im Kollektiv 42 gesunder Patientinnen, bei denen Einzelmessungen durchgeführt wurden, zeigten sich ein Anstieg der IPF von 4,1% auf 5,0% sowie ein Abfall der Thrombozyten. Die Absolutwerte der unreifen Plättchen blieben in dieser Gruppe konstant. In beiden Gruppen bestand eine negative Korrelation zwischen den IPF-Werten und den reifen Thrombozyten. Eine größere Kohorte an Patienten ist nötig, um diese Ergebnisse zu bestätigen.  :In der physiologischen Schwangerschaft ist ein Abfall der Thrombozyten um ca. 10 % normwertig. In etwa 7-12 % aller Schwangerschaften kommt es zu einer Thrombozytopenie (<150000/µl). Durch einen erhöhten Thrombozytenverbrauch gekennzeichnet sind die Präeklampsie und das HELLP Syndrom. Beide Erkrankungen sind eine häufige Ursache fetaler und maternaler Mortalität mit steigender Inzidenz. Zwischen der Anzahl reifer Thrombozyten und deren Vorstufen, den immature platelets, konnte eine Korrelation nachgewiesen werden. Beide Zelltypen können labormedizinisch einfach quantifiziert werden. Die immature platelet fraction (IPF) kann hier als Marker für den Plättchenturnover dienen. Um eine Aussage über die Bedeutung der unreifen Plättchen bei einer hypertensiven Erkrankung in der Schwangerschaft treffen zu können, ist die Bestimmung der Werte in der physiologischen Schwangerschaft essentiell. Die vorliegende Arbeit untersucht daher die Anzahl unreifen Thrombozyten, der IPF, bei gesunden Schwangeren zwischen der 20. und 40. Schwangerschaftswoche. Bei 27 Schwangeren zeigte sich ein Anstieg der unreifen Plättchen von 8,1/nl (3,2 %) in der 20. bis 23. Schwangerschaftswoche auf 13,6/nl (4,85 %) in der 36. bis 40. Schwangerschaftswoche. Zudem war ein leichter Abfall der reifen Thrombozyten zu verzeichnen. Im Kollektiv 42 gesunder Patientinnen, bei denen Einzelmessungen durchgeführt wurden, zeigten sich ein Anstieg der IPF von 4,1% auf 5,0% sowie ein Abfall der Thrombozyten. Die Absolutwerte der unreifen Plättchen blieben in dieser Gruppe konstant. In beiden Gruppen bestand eine negative Korrelation zwischen den IPF-Werten und den reifen Thrombozyten. Eine größere Kohorte an Patienten ist nötig, um diese Ergebnisse zu bestätigen.

Chronic lung diseases including Chronic Obstructive Pulmonary Disease (COPD), Idiopathic Pulmonary Fibrosis (IPF) and lung cancer are major causes of morbidity and mortality in the United States due to high incidence and limited therapeutic options. In order to address this critical issue, I have leveraged RNA sequencing and integrative genomics to define disease-associated transcriptomic changes which could be potentially targeted to lead to new therapeutics. We sequenced the lung transcriptome of subjects with IPF (n=19), emphysema (n=19, a subtype of COPD), or neither (n=20). The expression levels of 1770 genes differed between IPF and control lung, and 220 genes differed between emphysema and control lung (p<0.001). Upregulated genes in both emphysema and IPF were enriched for the p53/hypoxia pathway. These results were validated by immunohistochemistry of select p53/hypoxia proteins and by GSEA analysis of independent expression microarray experiments. To identify regulatory events, I constructed an integrative miRNA target prediction and anticorrelation miRNA-mRNA network, which highlighted several miRNA whose expression levels were the opposite of genes differentially expressed in both IPF and emphysema. MiR-96 was a highly connected hub in this network and was subsequently overexpressed in cell lines to validate several potential regulatory connections. Building upon these successful experiments, I next sought to define gene expression changes and the miRNA-mRNA regulatory network in never smoker lung cancer. Large and small RNA was sequenced from matched lung adenocarcinoma tumor and adjacent normal lung tissue obtained from 22 subjects (8 never, 14 current and former smokers). I identified 120 genes whose expression was modified uniquely in never smoker lung tumors. Using a repository of gene-expression profiles associated with small bioactive molecules, several compounds which counter the never smoker tumor signature were identified in silico. Leveraging differential expression information, I again constructed an mRNA-miRNA regulatory network, and subsequently identified a potential never smoker oncomir has-mir-424 and its transcription factor target FOXP2. In this thesis, I have identified genes, pathways and the miRNA-mRNA regulatory network that is altered in COPD, IPF, and lung adenocarcinoma among never smokers. My findings may ultimately lead to improved treatment options by identifying targetable pathways, regulators, and therapeutic drug candidates. / 2017-02-01T00:00:00Z

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The initial stage of a lung examination involves the segmentation of a CT image, a process that has been put under a lot of pressure with the high demand for chest scans and accurate segmentations. Current automatic segmentation algorithms are either non-robust for different datasets, not easily accessible, or time-consuming. Furthermore, classification of lung diseases such as IPF and NSIP is a difficult task often requiring decision-making between pathologists, radiologists and clinicians to make an accurate prognosis.  Therefore, this thesis aims to create two algorithms easily accessible through a common medical software, 3D Slicer, with simple user interfaces for more efficient lung analysis. The first one is a fully automatic segmentation algorithm with a manual adjustment option. It is robust and developed on a diverse dataset, demonstrating a high accuracy with a median Dice score of0,967. The second one is a lung vessel tree morphometry algorithm which computes various parameters correlated to the vessel tree and its structure, providing insight into morphological changes. It shows great usability but has certain limitations, making it not entirely finished for clinical research but acts as an excellent starting point for a future project. The segmentation algorithm was developed using classical image processing techniques making it comprehensible. The distinctive feature of this algorithm is the entropy map used, enabling an effective way in distinguishing between the fibrotic regions of the lungs with surrounding soft tissue and therefore increasing its applicability on lungs with various diseases. The lung vessel tree morphometry algorithm utilized a segmentation of the lung vessels to organize them into a tree-like structure. The structure was divided into branches where each branch was used to calculate different parameters such as its level within the tree hierarchy, the length of the branch and more. These parameters were displayed and color-coded for further analysis. The obtained result underscores the substantial potential and importance of these developed algorithms for clinical research by providing user-friendly, robust and reliable methods. / Det inledande skedet av en lungundersökning involverar segmenteringen av en CT-bild, en process som har satts under mycket press på grund utav den höga efterfrågan på bröstskanningar och noggrann segmentering. Aktuella automatiska segmenteringsalgoritmer är antingen icke-robusta för olika dataset, ej lättillgängliga eller tidskrävande. Dessutom är klassificering av lungsjukdomar som IPF och NSIP en svår uppgift som ofta kräver beslutsfattande mellan patologer, radiologer och kliniker för att göra en korrekt prognos. Därför syftar denna rapport till att skapa två lättillgängliga algoritmer genom en ofta användmedicinsk programvara, 3D Slicer, bestående utav enkla användargränssnitt för en effektivare analys av lungorna. Den första är en helautomatisk segmenteringsalgoritm med ett manuellt justeringsalternativ. Den är robust och utvecklad på ett mångsidigt dataset som har demonstrerat en hög noggrannhet med en median Dice-score på 0,967. Den andra är en morfometri algoritm för lungkärlsträd som beräknar olika parametrar korrelerade till kärlträdet och dess struktur, vilket ger insikt i morfologiska förändringar. Den visar stor användbarhet men innehåller begränsningar, vilket gör den ej helt färdig för klinisk forskning utan fungerar som en utmärkt utgångspunkt för framtida arbete. Segmenteringsalgoritmen utvecklades med hjälp av klassiska bildbehandlingsmetoder vilket gör den mer lättförstådd. Det utmärkande för denna algoritm är entropikartan som används, vilket möjliggör ett effektivt sätt att skilja mellan de fibrotiska regionerna i lungorna med omgivande mjukdelar, detta gör den mer användbar på lungor med olika sjukdomar. Algoritmen för lungkärlsträdets morfometri använde en segmentering av lungkärlen för att sedanorganiseras i en trädliknande struktur. Strukturen var uppdelad i grenar där varje gren användes för att beräkna olika parametrar såsom dess nivå inom trädhierarkin, grenens längd med mera. Dessutom uppvisades dessa parametrar och färgkodades för vidare analys. Det erhållna resultatet understryker den substantiella potential och betydelse som dessa utvecklade algoritmer kommer att ha i klinisk forskning genom att tillhandahålla användarvänliga, robusta och pålitliga metoder

Idiopathic pulmonary fibrosis (IPF) is one of the most aggressive and severe interstitial lung diseases (ILDs) for which there is no cure. IPF is characterized by an excessive accumulation of fibroblasts which secrete an abundance of extracellular proteins such as collagen. These processes lead to repetitive tissue scarring and fibrosis in the lung parenchyma. As a result, lungs become rigid limiting oxygen intake and gas exchange. Once diagnosed, IPF is fatal within 2-3 years. There is no known cause or proven treatment that significantly improves outcomes. Although the cause is unknown, the current model of IPF suggests that an overactive epithelial repair mechanism caused by genetic and epigenetic factors as well as environmental exposures is responsible for the chronic fibrosis and scarring characteristic of IPF. The transforming growth factor beta (TGF-B) signaling pathway has been implicated as a major contributor in activating this chronic fibrosis. An upstream activator of the TGF-B pathway, avB6, has been identified as a potential therapeutic target. My collaborators in Dr. David Baker's lab at the University of Washington have created a novel avB6 integrin inhibitor (BP2_disulf) whose efficacy in improving IPF outcomes has yet to be tested. In my study, I test the ability of BP2_disulf to combat IPF through the use of the standard IPF murine model and translatable end points like non-invasive uCT scans, pulmonary function tests, bronchoalveolar lavage fluid (BALF) profiles, and histology. With these methods, I demonstrate that intraperitoneal injection of BP2_disulf in bleomycin-injured mice has the ability to decrease rate of fibrotic progression and pulmonary function decline compared to mice treated with bleomycin alone. These results prove that BP2_disulf is a promising therapeutic not only for IPF but other ILDs as well. Further efficacy validation and investigation into an aerosolized delivery method will advance this drug to clinical trials and make it accessible to those in need.

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BACKGROUND: The course of disease for patients with idiopathic pulmonary fibrosis (IPF) is highly heterogeneous. Prognostic models rely on demographic and clinical characteristics and are not reproducible. Integrating data from genomic analyses may identify novel prognostic models and provide mechanistic insights into IPF. METHODS: Total RNA of peripheral blood mononuclear cells was subjected to microarray profiling in a training (45 IPF individuals) and two independent validation cohorts (21 IPF/10 controls, and 75 IPF individuals, respectively). To identify a gene set predictive of IPF prognosis, we incorporated genomic, clinical, and outcome data from the training cohort. Predictor genes were selected if all the following criteria were met: 1) Present in a gene co-expression module from Weighted Gene Co-expression Network Analysis (WGCNA) that correlated with pulmonary function (p < 0.05); 2) Differentially expressed between observed "good" vs. "poor" prognosis with fold change (FC) >1.5 and false discovery rate (FDR) < 2 %; and 3) Predictive of mortality (p < 0.05) in univariate Cox regression analysis. "Survival risk group prediction" was adopted to construct a functional genomic model that used the IPF prognostic predictor gene set to derive a prognostic index (PI) for each patient into either high or low risk for survival outcomes. Prediction accuracy was assessed with a repeated 10-fold cross-validation algorithm and independently assessed in two validation cohorts through multivariate Cox regression survival analysis. RESULTS: A set of 118 IPF prognostic predictor genes was used to derive the functional genomic model and PI. In the training cohort, high-risk IPF patients predicted by PI had significantly shorter survival compared to those labeled as low-risk patients (log rank p < 0.001). The prediction accuracy was further validated in two independent cohorts (log rank p < 0.001 and 0.002). Functional pathway analysis revealed that the canonical pathways enriched with the IPF prognostic predictor gene set were involved in T-cell biology, including iCOS, T-cell receptor, and CD28 signaling. CONCLUSIONS: Using supervised and unsupervised analyses, we identified a set of IPF prognostic predictor genes and derived a functional genomic model that predicted high and low-risk IPF patients with high accuracy. This genomic model may complement current prognostic tools to deliver more personalized care for IPF patients.

Lors de la fibrose pulmonaire idiopathique (FPI), la différenciation fibroblastique s’accompagne d’une accumulation excessive des composants de la matrice extracellulaire ainsi qu’à un dérèglement de la balance protéases / antiprotéases. Nous avons étudié le rôle des cathepsines à cystéine dans la myofibrogenèse et leur contribution potentielle à la physiopathologie de la fibrose pulmonaire chez l’Homme. Pour cela, le profil d’expression des cathepsines ainsi que de leurs inhibiteurs naturels a été évalué dans un modèle cellulaire expérimental, puis dans des myofibroblastes primaires et enfin dans des liquides de lavage broncho-Alvéolaires (LBA) de patients atteints de FPI. Nos résultats montrent que lors de la FPI la cystatine C (inhibiteur naturel des protéases à cystéine) régule les activités protéolytiques des cathepsines extracellulaires et pourrait ainsi contribuer à l’accumulation de collagènes. Elle serait un biomarqueur potentiel de la FPI. D’autre part la cathepsine B participe à la différentiation fibroblastique et son inhibition retarde la myofibrogenèse. / During idiopathic pulmonary fibrosis (IPF), fibroblast differentiation is accompanied by an excessive accumulation of extracellular matrix components as well as an imbalance between proteases and theirs inhibitors. We evaluated the role of human cysteine cathepsins in myofibrogenesis and their potential contribution to the pathogenesis of IPF. Expression of cathepsins and their natural inhibitors have been studied in an experimental cell model, but also in primary myofibroblasts and in bronchoalveolar lavage fluids (BALF) of patients suffering from IPF. Our results show that cystatin C (a natural inhibitor of cysteine proteases) regulates the extracellular proteolytic activities of cathepsins and could contribute to the accumulation of collagens. Cystatin C could also be a potential biomarker of IPF. On the other hand, cathepsin B participates in fibroblast differentiation and its inhibition delays myofibrogenesis.