Proteomic characterization of host response to viral infection

Proteomics is defined as large-scale study of proteins, and with current proteomic methods thousands of proteins can be characterized in a single experiment. Mass spectrometry (MS) has an important role in the characterization of complex protein samples. In addition, various bioinformatics tools have become increasingly important in the interpretation of complex proteomic data. The combination of proteomics and bioinformatics is nowadays an important tool to study cellular signaling mechanisms. When host cell recognizes the invading virus, multiple cell signaling cascades are activated resulting in antiviral immune responses, inflammation and programmed cell death of the infected cell. The detailed mechanisms of host cell defense responses activated upon viral infection are still partially unknown. The aim of this project was to develop and utilize proteomic and bioinformatic methods to characterize host responses to viral infection. Three different proteomic approaches were used in this project to study virus-induced changes in human epithelial cells and macrophages. Viral RNA-induced responses in HaCaT keratinocytes were studied using two-dimensional gel electrophoresis and MS. Influenza A virus-induced changes in the intracellular compartments and secretomes of human primary macrophages were characterized using iTRAQ labeling-based quantitative proteomics. Finally, viral RNA-triggered protein secretion from human primary macrophages was studied using SDS-PAGE, liquid chromatography and MS. In addition, two computational tools, Compid and Pripper, were developed to simplify the analysis of our proteomic data. Our studies showed that both influenza A virus and viral RNA trigger significant changes in the proteomes of macrophages and HaCaT keratinocytes. Virus-induced changes in the expression of 14-3-3 signaling proteins as well as rearrangement of host cell cytoskeleton were detected in HaCaT keratinocytes. Caspase-3-dependent apoptosis was detected in HaCaT keratinocytes and macrophages after viral stimulation. Our studies with macrophages also showed that several inflammatory pathways, and especially the NLRP3 inflammasome, are activated as a result of viral RNA and influenza A virus infection. Additionally, we showed that cathepsins, src tyrosine kinase and P2X7 receptor were involved in the inflammasome activation. Finally, we showed that viral stimulation triggered extensive protein secretion from macrophages. In conclusion, our proteomic experiments have given an in-depth view of cellular events activated in human primary macrophages and HaCaT keratinocytes after viral infection.Proteiinit osallistuvat lähes kaikkiin solun toimintoihin ja niitä säädellään koko ajan usealla eri tavalla. Proteomiikalla tarkoitetaan solun tai kudoksen proteiinien laajamittaista tutkimista. Proteomiikan menetelmien avulla voidaan tutkia eri proteiinien määriä ja sijaintia solussa sekä proteiinien välisiä vuorovaikutuksia. Lisäksi proteomiikalla voidaan kerätä tietoa proteiinien translaation jälkeisestä muokkauksesta. Kaikki nämä asiat vaikuttavat proteiinien toimintaan solussa ja siten kertovat solun toiminnasta tiettynä ajankohtana. Tässä tutkimuksessa erilaisia proteomiikan menetelmiä on käytetty virusinfektion aikaansaamien vasteiden tutkimiseen ihmisen soluissa. Viruksen tunkeuduttua soluun isäntäsolun luontaisen immuunivasteen reseptorit tunnistavat sen. Tämän seurauksena isäntäsolussa aktivoituu lukuisia eri signalointireittejä, jotka johtavat antiviraalisen immuunivasteen ja tulehdusvasteen aktivoitumiseen sekä lopulta infektoidun solun ohjattuun kuolemaan. Luontaisen immuunivasteen signalointireitit muodostavat monimutkaisen verkoston, jonka tarkoituksena on suojata isäntäorganismia infektion alkuvaiheessa. Vaikka luontaisen immuunivasteen aktivoitumista on tutkittu paljon, on suuri osa signalointireittien yksityiskohdista edelleen epäselviä. Tässä työssä on tutkittu influenssa A viruksen ja virusperäisen RNA:n aiheuttamia muutoksia isäntäsolun proteiinien ilmentymisessä. Kvantitatiivisissa ja kvalitatiivisissa analyyseissä on käytetty yksi- ja kaksisuuntaista geelielektroforeesia sekä nestekromatografisia menetelmiä yhdistettynä massaspektrometrisiin analyyseihin. Lisäksi kerätyn tiedon analyysissä on käytetty erilaisia bioinformatiikan työkaluja. Työn yhteydessä kehitettiin myös kaksi uutta bioinformatiikan työkalua, Compid ja Pripper, helpottamaan monimutkaisen proteomiikkatiedon analyysiä. Tutkimuksemme osoittivat, että sekä influenssa A virus infektio että virusperäinen RNA aiheuttavat suuria muutoksia isäntäsolun proteiinikoostumuksessa. Virusinfektion havaittiin aiheuttavan mm. solutukirangan uudelleenjärjestäytymistä ja eri tulehdusvastereittien aktivoitumista sekä johtavan lopulta ohjattuun solukuolemaan. Tutkimme erityisesti tulehdusvasteen muodostumisessa keskeisen proteiinikompleksin, NLRP3 inflammasomin, aktivoitumista sekä siihen liittyviä proteiineja. Lisäksi osoitimme, että virusinfektio laukaisee lukuisien eri proteiinien erityksen ulos isäntäsoluista. Kaiken kaikkiaan tutkimuksemme antavat kattavan kuvan virusinfektion laukaisemista tapahtumista isäntäsolussa

Kohti yksilöllistä hoitoa - proteomiikan näkymät diagnostiikassa

Sairaudet aiheuttavat muutoksia proteiinien ilmentymisessä, ja monet diagnostiset testit perustuvat proteiinien mittaamiseen näytteestä. Valtaosalla testeistä mitataan yksittäisiä proteiineja, vaikka sairauksien aiheuttamat muutokset elimistössä ovat usein moninaisia ja vaikuttavat useiden proteiinien ilmentymiseen. Proteomiikan hyödyntäminen diagnostiikassa mahdollistaisi lukuisien eri proteiinien, esimerkiksi tiettyyn signalointireittiin kuuluvien proteiinien, samanaikaisen mittaamisen näytteestä. Kattavammat testit voisivat tarjota tarkemman diagnoosin sekä yksilöllistä tietoa esimerkiksi sairauden vaiheesta ja mahdollisista liitännäissairauksista. Proteomiikassa käytetään nykyään yleisimmin massaspektrometriaan perustuvia menetelmiä, jotka mahdollistavat tuhansien proteiinien samanaikaisen mittaamisen näytteestä. Menetelmiä on käytetty menestyksekkäästi erilaisissa biomarkkeritutkimuksissa, ja ensimmäiset massaspektrometriaan perustuvat kliiniset proteomiikkatestit on otettu käyttöön. Proteomiikan menetelmät tarjoavat lukuisia ratkaisuja ja mahdollisuuksia käytännön lääketieteeseen

An additive Gaussian process regression model for interpretable non-parametric analysis of longitudinal data

Biomedical research typically involves longitudinal study designs where samples from individuals are measured repeatedly over time and the goal is to identify risk factors (covariates) that are associated with an outcome value. General linear mixed effect models are the standard workhorse for statistical analysis of longitudinal data. However, analysis of longitudinal data can be complicated for reasons such as difficulties in modelling correlated outcome values, functional (time-varying) covariates, nonlinear and non-stationary effects, and model inference. We present LonGP, an additive Gaussian process regression model that is specifically designed for statistical analysis of longitudinal data, which solves these commonly faced challenges. LonGP can model time-varying random effects and non-stationary signals, incorporate multiple kernel learning, and provide interpretable results for the effects of individual covariates and their interactions. We demonstrate LonGP's performance and accuracy by analysing various simulated and real longitudinal -omics datasets.</p

An additive Gaussian process regression model for interpretable non-parametric analysis of longitudinal data

Biomedical research typically involves longitudinal study designs where samples from individuals are measured repeatedly over time and the goal is to identify risk factors (covariates) that are associated with an outcome value. General linear mixed effect models are the standard workhorse for statistical analysis of longitudinal data. However, analysis of longitudinal data can be complicated for reasons such as difficulties in modelling correlated outcome values, functional (time-varying) covariates, nonlinear and non-stationary effects, and model inference. We present LonGP, an additive Gaussian process regression model that is specifically designed for statistical analysis of longitudinal data, which solves these commonly faced challenges. LonGP can model time-varying random effects and non-stationary signals, incorporate multiple kernel learning, and provide interpretable results for the effects of individual covariates and their interactions. We demonstrate LonGP’s performance and accuracy by analysing various simulated and real longitudinal -omics datasets

Characterization and non-parametric modeling of the developing serum proteome during infancy and early childhood

Children develop rapidly during the first years of life, and understanding the sources and associated levels of variation in the serum proteome is important when using serum proteins as markers for childhood diseases. The aim of this study was to establish a reference model for the evolution of a healthy serum proteome during early childhood. Label-free quantitative proteomics analyses were performed for 103 longitudinal serum samples collected from 15 children at birth and between the ages of 3–36 months. A flexible Gaussian process-based probabilistic modelling framework was developed to evaluate the effects of different variables, including age, living environment and individual variation, on the longitudinal expression profiles of 266 reliably identified and quantified serum proteins. Age was the most dominant factor influencing approximately half of the studied proteins, and the most prominent age-associated changes were observed already during the first year of life. High inter-individual variability was also observed for multiple proteins. These data provide important details on the maturing serum proteome during early life, and evaluate how patterns detected in cord blood are conserved in the first years of life. Additionally, our novel modelling approach provides a statistical framework to detect associations between covariates and non-linear time series data

Characterization and non-parametric modeling of the developing serum proteome during infancy and early childhood

Children develop rapidly during the first years of life, and understanding the sources and associated levels of variation in the serum proteome is important when using serum proteins as markers for childhood diseases. The aim of this study was to establish a reference model for the evolution of a healthy serum proteome during early childhood. Label-free quantitative proteomics analyses were performed for 103 longitudinal serum samples collected from 15 children at birth and between the ages of 3-36 months. A flexible Gaussian process-based probabilistic modelling framework was developed to evaluate the effects of different variables, including age, living environment and individual variation, on the longitudinal expression profiles of 266 reliably identified and quantified serum proteins. Age was the most dominant factor influencing approximately half of the studied proteins, and the most prominent age-associated changes were observed already during the first year of life. High inter-individual variability was also observed for multiple proteins. These data provide important details on the maturing serum proteome during early life, and evaluate how patterns detected in cord blood are conserved in the first years of life. Additionally, our novel modelling approach provides a statistical framework to detect associations between covariates and non-linear time series data

Persistent coxsackievirus B1 infection triggers extensive changes in the transcriptome of human pancreatic ductal cells.

Enteroviruses, particularly the group B coxsackieviruses (CVBs), have been associated with the development of type 1 diabetes. Several CVB serotypes establish chronic infections in human cells in vivo and in vitro. However, the mechanisms leading to enterovirus persistency and, possibly, beta cell autoimmunity are not fully understood. We established a carrier-state-type persistent infection model in human pancreatic cell line PANC-1 using two distinct CVB1 strains and profiled the infection-induced changes in cellular transcriptome. In the current study, we observed clear changes in the gene expression of factors associated with the pancreatic microenvironment, the secretory pathway, and lysosomal biogenesis during persistent CVB1 infections. Moreover, we found that the antiviral response pathways were activated differently by the two CVB1 strains. Overall, our study reveals extensive transcriptional responses in persistently CVB1-infected pancreatic cells with strong opposite but also common changes between the two strains. </p

Pancreas Whole Tissue Transcriptomics Highlights the Role of the Exocrine Pancreas in Patients With Recently Diagnosed Type 1 Diabetes

Although type 1 diabetes (T1D) is primarily a disease of the pancreatic beta-cells, understanding of the disease-associated alterations in the whole pancreas could be important for the improved treatment or the prevention of the disease. We have characterized the whole-pancreas gene expression of patients with recently diagnosed T1D from the Diabetes Virus Detection (DiViD) study and non-diabetic controls. Furthermore, another parallel dataset of the whole pancreas and an additional dataset from the laser-captured pancreatic islets of the DiViD patients and non-diabetic organ donors were analyzed together with the original dataset to confirm the results and to get further insights into the potential disease-associated differences between the exocrine and the endocrine pancreas. First, higher expression of the core acinar cell genes, encoding for digestive enzymes, was detected in the whole pancreas of the DiViD patients when compared to non-diabetic controls. Second, In the pancreatic islets, upregulation of immune and inflammation related genes was observed in the DiViD patients when compared to non-diabetic controls, in line with earlier publications, while an opposite trend was observed for several immune and inflammation related genes at the whole pancreas tissue level. Third, strong downregulation of the regenerating gene family (REG) genes, linked to pancreatic islet growth and regeneration, was observed in the exocrine acinar cell dominated whole-pancreas data of the DiViD patients when compared with the non-diabetic controls. Fourth, analysis of unique features in the transcriptomes of each DiViD patient compared with the other DiViD patients, revealed elevated expression of central antiviral immune response genes in the whole-pancreas samples, but not in the pancreatic islets, of one DiViD patient. This difference in the extent of antiviral gene expression suggests different statuses of infection in the pancreas at the time of sampling between the DiViD patients, who were all enterovirus VP1+ in the islets by immunohistochemistry based on earlier studies. The observed features, indicating differences in the function, status and interplay between the exocrine and the endocrine pancreas of recent onset T1D patients, highlight the importance of studying both compartments for better understanding of the molecular mechanisms of T1D.publishedVersionPeer reviewe

Pancreas Whole Tissue Transcriptomics Highlights the Role of the Exocrine Pancreas in Patients With Recently Diagnosed Type 1 Diabetes

Although type 1 diabetes (T1D) is primarily a disease of the pancreatic beta-cells, understanding of the disease-associated alterations in the whole pancreas could be important for the improved treatment or the prevention of the disease. We have characterized the whole-pancreas gene expression of patients with recently diagnosed T1D from the Diabetes Virus Detection (DiViD) study and non-diabetic controls. Furthermore, another parallel dataset of the whole pancreas and an additional dataset from the laser-captured pancreatic islets of the DiViD patients and non-diabetic organ donors were analyzed together with the original dataset to confirm the results and to get further insights into the potential disease-associated differences between the exocrine and the endocrine pancreas. First, higher expression of the core acinar cell genes, encoding for digestive enzymes, was detected in the whole pancreas of the DiViD patients when compared to non-diabetic controls. Second, In the pancreatic islets, upregulation of immune and inflammation related genes was observed in the DiViD patients when compared to non-diabetic controls, in line with earlier publications, while an opposite trend was observed for several immune and inflammation related genes at the whole pancreas tissue level. Third, strong downregulation of the regenerating gene family (REG) genes, linked to pancreatic islet growth and regeneration, was observed in the exocrine acinar cell dominated whole-pancreas data of the DiViD patients when compared with the non-diabetic controls. Fourth, analysis of unique features in the transcriptomes of each DiViD patient compared with the other DiViD patients, revealed elevated expression of central antiviral immune response genes in the whole-pancreas samples, but not in the pancreatic islets, of one DiViD patient. This difference in the extent of antiviral gene expression suggests different statuses of infection in the pancreas at the time of sampling between the DiViD patients, who were all enterovirus VP1+ in the islets by immunohistochemistry based on earlier studies. The observed features, indicating differences in the function, status and interplay between the exocrine and the endocrine pancreas of recent onset T1D patients, highlight the importance of studying both compartments for better understanding of the molecular mechanisms of T1D.</p