Time and phenotype-dependent transcriptome analysis in AAV-TGFβ1 and Bleomycin-induced lung fibrosis models

We have previously established a novel mouse model of lung fibrosis based on Adeno-associated virus (AAV)-mediated pulmonary overexpression of TGFβ1. Here, we provide an in-depth characterization of phenotypic and transcriptomic changes (mRNA and miRNA) in a head-to-head comparison with Bleomycin-induced lung injury over a 4-week disease course. The analyses delineate the temporal state of model-specific and commonly altered pathways, thereby providing detailed insights into the processes underlying disease development. They further guide appropriate model selection as well as interventional study design. Overall, Bleomycin-induced fibrosis resembles a biphasic process of acute inflammation and subsequent transition into fibrosis (with partial resolution), whereas the TGFβ1-driven model is characterized by pronounced and persistent fibrosis with concomitant inflammation and an equally complex disease phenotype as observed upon Bleomycin instillation. Finally, based on an integrative approach combining lung function data, mRNA/miRNA profiles, their correlation and miRNA target predictions, we identify putative drug targets and miRNAs to be explored as therapeutic candidates for fibrotic diseases. Taken together, we provide a comprehensive analysis and rich data resource based on RNA-sequencing, along with a strategy for transcriptome-phenotype coupling. The results will be of value for TGFβ research, drug discovery and biomarker identification in progressive fibrosing interstitial lung diseases

Regularisoitu riippuvuuksien mallintaminen geeniekpressio- ja metabolomiikkadatan välillä metabolian säätelyn tutkimuksessa

Fusing different high-throughput data sources is an effective way to reveal functions of unknown genes, as well as regulatory relationships between biological components such as genes and metabolites. Dependencies between biological components functioning in the different layers of biological regulation can be investigated using canonical correlation analysis (CCA). However, the properties of the high-throughput bioinformatics data induce many challenges to data analysis: the sample size is often insufficient compared to the dimensionality of the data, and the data pose multi-collinearity due to, for example, co-expressed and co-regulated genes. Therefore, a regularized version of classical CCA has been adopted. An alternative way of introducing regularization to statistical models is to perform Bayesian data analysis with suitable priors. In this thesis, the performance of a new variant of Bayesian CCA called gsCCA is compared to a classical ridge regression regularized CCA (rrCCA) in revealing relevant information shared between two high-throughput data sets. The gsCCA produces a partly similar regulatory effect as the classical CCA but, in addition, the gsCCA introduces a new type of regularization to the data covariance matrices. Both CCA methods are applied to gene expression and metabolic concentration measurements obtained from an oxidative-stress tolerant Arabidopsis thaliana ecotype Col-0, and an oxidative stress sensitive mutant rcd1 as time series under ozone exposure and in a control condition. The aim of this work is to reveal new regulatory mechanisms in the oxidative stress signalling in plants. For the both methods, rrCCA and gsCCA, the thesis illustrates their potential to reveal both already known and new regulatory mechanisms in Arabidopsis thaliana oxidative stress signalling.Bioinformatiikassa erityyppisten mittausaineistojen yhdistäminen on tehokas tapa selvittää tuntemattomien geenien toiminnallisuutta sekä säätelyvuorovaikutuksia eri biologisten komponenttien, kuten geenien ja metaboliittien, välillä. Riippuvuuksia eri biologisilla säätelytasoilla toimivien komponenttien välillä voidaan tutkia kanonisella korrelaatioanalyysilla (canonical correlation analysis, CCA). Bioinformatiikan tietoaineistot aiheuttavat kuitenkin monia haasteita data-analyysille: näytteiden määrä on usein riittämätön verrattuna aineiston piirteiden määrään, ja aineisto on multikollineaarista johtuen esim. yhdessä säädellyistä ja ilmentyvistä geeneistä. Tästä syystä usein käytetään regularisoitua versiota kanonisesta korrelaatioanalyysistä aineiston tilastolliseen analysointiin. Vaihtoehto regularisoidulle analyysille on bayesilainen lähestymistapa yhdessä sopivien priorioletuksien kanssa. Tässä diplomityössä tutkitaan ja vertaillaan uuden bayesilaisen CCA:n sekä klassisen harjanneregressio-regularisoidun CCA:n kykyä löytää oleellinen jaettu informaatio kahden bioinformatiikka-tietoaineiston välillä. Uuden bayesilaisen menetelmän nimi on ryhmittäin harva kanoninen korrelaatioanalyysi. Ryhmittäin harva CCA tuottaa samanlaisen regularisointivaikutuksen kuin harjanneregressio-CCA, mutta lisäksi uusi menetelmä regularisoi tietoaineistojen kovarianssimatriiseja uudella tavalla. Molempia CCA-menetelmiä sovelletaan geenien ilmentymisaineistoon ja metaboliittien konsentraatioaineistoon, jotka on mitattu Arabidopsis thaliana:n hapetus-stressiä sietävästä ekotyypistä Col-0 ja hapetus-stressille herkästä rcd1 mutantista aika-sarjana, sekä otsoni-altistuksessa että kontrolliolosuhteissa. Diplomityö havainnollistaa harjanneregressio-CCA:n ja ryhmittäin harvan CCA:n kykyä paljastaa jo tunnettuja ja mahdollisesti uusia säätelymekanismeja geenien ja metabolittien välillä kasvisolujen viestinnässä hapettavan stressin aikana

Simultaneous analysis of distinct Omics data sets with integration of biological knowledge: Multiple Factor Analysis approach

<p>Abstract</p> <p>Background</p> <p>Genomic analysis will greatly benefit from considering in a global way various sources of molecular data with the related biological knowledge. It is thus of great importance to provide useful integrative approaches dedicated to ease the interpretation of microarray data.</p> <p>Results</p> <p>Here, we introduce a data-mining approach, Multiple Factor Analysis (MFA), to combine multiple data sets and to add formalized knowledge. MFA is used to jointly analyse the structure emerging from genomic and transcriptomic data sets. The common structures are underlined and graphical outputs are provided such that biological meaning becomes easily retrievable. Gene Ontology terms are used to build gene modules that are superimposed on the experimentally interpreted plots. Functional interpretations are then supported by a step-by-step sequence of graphical representations.</p> <p>Conclusion</p> <p>When applied to genomic and transcriptomic data and associated Gene Ontology annotations, our method prioritize the biological processes linked to the experimental settings. Furthermore, it reduces the time and effort to analyze large amounts of 'Omics' data.</p

MicroRNome Analysis Unravels the Molecular Basis of SARS Infection in Bronchoalveolar Stem Cells

Severe acute respiratory syndrome (SARS), caused by the coronavirus SARS-CoV, is an acute infectious disease with significant mortality. A typical clinical feature associated with SARS is pulmonary fibrosis and associated lung failure. In the aftermath of the SARS epidemic, although significant progress towards understanding the underlying molecular mechanism of the infection has been made, a large gap still remains in our knowledge regarding how SARS-CoV interacts with the host cell at the onset of infection. The rapidly changing viral genome adds another variable to this equation. We have focused on a novel concept of microRNA (miRNA)–mediated host–virus interactions in bronchoalveolar stem cells (BASCs) at the onset of infection by correlating the “BASC–microRNome” with their targets within BASCs and viral genome. This work encompasses miRNA array data analysis, target prediction, and miRNA–mRNA enrichment analysis and develops a complex interaction map among disease-related factors, miRNAs, and BASCs in SARS pathway, which will provide some clues for diagnostic markers to view an overall interplay leading to disease progression. Our observation reveals the BASCs (Sca-1+ CD34+ CD45- Pecam-), a subset of Oct-4+ ACE2+ epithelial colony cells at the broncho-alveolar duct junction, to be the prime target cells of SARS-CoV infection. Upregulated BASC miRNAs-17*, -574-5p, and -214 are co-opted by SARS-CoV to suppress its own replication and evade immune elimination until successful transmission takes place. Viral Nucleocapsid and Spike protein targets seem to co-opt downregulated miR-223 and miR-98 respectively within BASCs to control the various stages of BASC differentiation, activation of inflammatory chemokines, and downregulation of ACE2. All these effectively accounts for a successful viral transmission and replication within BASCs causing continued deterioration of lung tissues and apparent loss of capacity for lung repair. Overall, this investigation reveals another mode of exploitation of cellular miRNA machinery by virus to their own advantage