Computational approaches in high-throughput proteomics data analysis

Proteins are key components in biological systems as they mediate the signaling responsible for information processing in a cell and organism. In biomedical research, one goal is to elucidate the mechanisms of cellular signal transduction pathways to identify possible defects that cause disease. Advancements in technologies such as mass spectrometry and flow cytometry enable the measurement of multiple proteins from a system. Proteomics, or the large-scale study of proteins of a system, thus plays an important role in biomedical research. The analysis of all high-throughput proteomics data requires the use of advanced computational methods. Thus, the combination of bioinformatics and proteomics has become an important part in research of signal transduction pathways. The main objective in this study was to develop and apply computational methods for the preprocessing, analysis and interpretation of high-throughput proteomics data. The methods focused on data from tandem mass spectrometry and single cell flow cytometry, and integration of proteomics data with gene expression microarray data and information from various biological databases. Overall, the methods developed and applied in this study have led to new ways of management and preprocessing of proteomics data. Additionally, the available tools have successfully been used to help interpret biomedical data and to facilitate analysis of data that would have been cumbersome to do without the use of computational methods.Proteiineilla on tärkeä merkitys biologisissa systeemeissä sillä ne koordinoivat erilaisia solujen ja organismien prosesseja. Yksi biolääketieteellisen tutkimuksen tavoitteista on valottaa solujen viestintäreittejä ja niiden toiminnassa tapahtuvia muutoksia eri sairauksien yhteydessä, jotta tällaisia muutoksia voitaisiin korjata. Proteomiikka on proteiinien laajamittaista tutkimista solusta, kudoksesta tai organismista. Proteomiikan menetelmät kuten massaspektrometria ja virtaussytometria ovat keskeisiä biolääketieteellisen tutkimuksen menetelmiä, joilla voidaan mitata näytteestä samanaikaisesti useita proteiineja. Nykyajan kehittyneet proteomiikan mittausteknologiat tuottavat suuria tulosaineistoja ja edellyttävät laskennallisten menetelmien käyttöä aineiston analyysissä. Bioinformatiikan menetelmät ovatkin nousseet tärkeäksi osaksi proteomiikka-analyysiä ja viestintäreittien tutkimusta. Tämän tutkimuksen päätavoite oli kehittää ja soveltaa tehokkaita laskennallisia menetelmiä laajamittaisten proteomiikka-aineistojen esikäsittelyyn, analyysiin ja tulkintaan. Tässä tutkimuksessa kehitettiin esikäsittelymenetelmä massaspektrometria-aineistolle sekä automatisoitu analyysimenetelmä virtaussytometria-aineistolle. Proteiinitason tietoa yhdistettiin mittauksiin geenien transkriptiotasoista ja olemassaolevaan biologisista tietokannoista poimittuun tietoon. Väitöskirjatyö osoittaa, että laskennallisilla menetelmillä on keskeinen merkitys proteomiikan aineistojen hallinnassa, esikäsittelyssä ja analyysissä. Tutkimuksessa kehitetyt analyysimenetelmät edistävät huomattavasti biolääketieteellisen tiedon laajempaa hyödyntämistä ja ymmärtämistä

Comprehensive proteome and phosproteome analysis of human LRRK2 Drosophila model of Parkinson's disease

Gene mutations in the leucine-rich repeat kinase 2 (LRRK2) are the most common cause of autosomal dominant Parkinson`s Disease (PD) and elevated levels of hLRRK2 mutant variants in Drosophila induces PD. Here, we introduced the human LRRK2 (R1441C) variant in dopaminergic neurons of flies and observed a reduced locomotor activity, an age dependent degeneration of dopaminergic neurons, and shorter lifetime. To better understand the hLRRK2 (R1441C) induced pathobiology, we performed stable isotope labeling in fly to accurately quantify the proteome and phosphoproteome dynamics. We quantified almost 3000 proteins and found several regulated cytoskeletal, mitochondrial, and synaptic vesicle (SV) proteins in our PD fly model. To explore the hLRRK2 (R1441C) function more precisely, we compared our model to three different alpha-Synuclein overexpressing fly strains (WT,A30P, A53T), which show a similar PD phenotype. For example, synaptotagmin, syntaxin and rab3 were only affected in hLRRK2 (R1441C) flies compared to all other tested fly strains. Moreover, our global phosphoproteome analysis revealed several synaptic vesicle proteins with enhanced phosphorylation, including synaptojanin (pT1131) and the microtubule-associated protein futsch (pS4106). Consistently, a protein-protein interaction screen confirmed that hLRRK2 is tightly associated with synaptic vesicle proteins. Thus, our results provide a systemic view on the pathobiology mechanism caused by hLRRK and S overexpression and suggest that the increased kinase activity of the hLRRK2 (R1441C) mutant results in enhanced phosphorylation of synaptojanin. These findings may contribute to develop new therapeutic strategies to prevent hLRRK2-induced Parkinson disease

Comprehensive Overview of Bottom-up Proteomics using Mass Spectrometry

Proteomics is the large scale study of protein structure and function from biological systems through protein identification and quantification. "Shotgun proteomics" or "bottom-up proteomics" is the prevailing strategy, in which proteins are hydrolyzed into peptides that are analyzed by mass spectrometry. Proteomics studies can be applied to diverse studies ranging from simple protein identification to studies of proteoforms, protein-protein interactions, protein structural alterations, absolute and relative protein quantification, post-translational modifications, and protein stability. To enable this range of different experiments, there are diverse strategies for proteome analysis. The nuances of how proteomic workflows differ may be challenging to understand for new practitioners. Here, we provide a comprehensive overview of different proteomics methods to aid the novice and experienced researcher. We cover from biochemistry basics and protein extraction to biological interpretation and orthogonal validation. We expect this work to serve as a basic resource for new practitioners in the field of shotgun or bottom-up proteomics

Mass Spectrometry-Based Proteomics Workflows in Cancer Research: The Relevance of Choosing the Right Steps

The qualitative and quantitative evaluation of proteome changes that condition cancer development can be achieved with liquid chromatography–mass spectrometry (LC-MS). LC-MSbased proteomics strategies are carried out according to predesigned workflows that comprise several steps such as sample selection, sample processing including labeling, MS acquisition methods, statistical treatment, and bioinformatics to understand the biological meaning of the findings and set predictive classifiers. As the choice of best options might not be straightforward, we herein review and assess past and current proteomics approaches for the discovery of new cancer biomarkers. Moreover, we review major bioinformatics tools for interpreting and visualizing proteomics results and suggest the most popular machine learning techniques for the selection of predictive biomarkers. Finally, we consider the approximation of proteomics strategies for clinical diagnosis and prognosis by discussing current barriers and proposals to circumvent them.Research Council of Norway INFRASTRUKTUR-program (project number: 295910

Quantitative proteomics in the characterization of T helper lymphocyte differentiation

The term proteome is used to define the complete set of proteins expressed in cells or tissues of an organism at a certain timepoint. Respectively, proteomics is used to describe the methods, which are used to study such proteomes. These methods include chromatographic and electrophoretic techniques for protein or peptide fractionation, mass spectrometry for their identification, and use of computational methods to assist the complicated data analysis. A primary aim in this Ph.D. thesis was to set-up, optimize, and develop proteomics methods for analysing proteins extracted from T-helper (Th) lymphocytes. First, high-throughput LC-MS/MS and ICAT labeling methods were set-up and optimized for analysing the microsomal fraction proteins extracted from Th lymphocytes. Later, iTRAQ method was optimized to study cytokine regulated protein expression in the nuclei of Th lymphocytes. High-throughput LC-MS/MS analyses, like ICAT and iTRAQ, produce large quantities of data and robust software and data analysis pipelines are needed. Therefore, different software programs used for analysing such data were evaluated. Moreover, a pre-filtering algorithm was developed to classify good-quality and bad-quality spectra prior to the database searches. Th-lymphocytes can differentiate into Th1 or Th2 cells based on surrounding antigens, co-stimulatory molecules, and cytokines. Both subsets have individual cytokine secretion profiles and specific functions. Th1 cells participate in the cellular immunity against intracellular pathogens, while Th2 cells have important role in the humoral immunity against extracellular parasites. An abnormal response of Th1 and Th2 cells and imbalance between the subsets are charasteristic of several diseases. Th1 specific reactions and cytokines have been detected in autoimmune diseases, while Th2 specific response and cytokine profile is common in allergy and asthma. In this Ph. D. thesis mass spectrometry-based proteomics was used to study the effects of Th1 and Th2 promoting cytokines IL-12 and IL-4 on the proteome of Th lymphocytes. Characterization of microsomal fraction proteome extracted from IL-12 treated lymphobasts and IL-4 stimulated cord blood CD4+ cells resulted in finding of cytokine regulated proteins. Galectin-1 and CD7 were down-regulated in IL-12 treated cells, while IL-4 stimulation decreased the expression of STAT1, MXA, GIMAP1, and GIMAP4. Interestingly, the transcription of both GIMAP genes was up-regulated in Th1 polarized cells and down-regulated in Th2 promoting conditions.Proteomilla tarkoitetaan organismin solujen tai kudosten tietyllä ajanhetkellä ilmentämiä proteiineja. Proteomiikka puolestaan käsittää menetelmät, joiden avulla tutkitaan proteomeja. Näihin menetelmiin kuuluvat kromatografiset ja elektroforeettiset tekniikat proteiinien ja peptidien fraktiointiin, massaspektrometria niiden tunnistamiseen sekä tietojenkäsittely menetelmät avustamaan tietojen analysointia. Väitöskirjan osatöiden tarkoituksena oli pystyttää, optimoida ja kehittää proteomiikan tutkimusmenetelmiä T-auttajasoluista (Th) eristettyjen proteiinien tutkimiseen. Aluksi pystytettiin ja optimoitiin LC-MS/MS- ja ICAT-menetelmät. Th-solujen mikrosomaalisten fraktioiden analysoimiseksi. Myöhemmin iTRAQ-menetelmä optimoitiin Th-solujen tuman proteomin tutkimiseksi. ICAT- ja iTRAQ-menetelmien kaltaiset LC-MS/MS-menetelmät tuottavat paljon tietoa, minkä analysoimiseksi tarvitaan tehokkaita tietokoneohjelmia ja tietojen analysointijärjestelmiä. Tämän vuoksi väitöskirjatyössä arvioitiin massaspektrometrillä mitattujen spektrien analysointiin soveltuvia tietokoneohjelmia. Lisäksi kehitettiin algoritmi, jonka avulla voidaan erotella laadultaan hyvät ja huonot spektrit toisistaan ennen tietokantahakuja. Th-solut voivat erilaistua Th1- tai Th2-soluiksi ympäröivien antigeenien, ko-stimuloivien molekyylien ja sytokiinien vaikutuksesta. Molemmilla alatyypeillä on yksilölliset sytokiinien tuottoprofiilit ja spesifiset tehtävät. Th1-solut osallistuvat soluvälitteiseen immuniteettiin solunsisäisiä taudinaiheuttajia vastaan, kun taas Th2-soluilla on tärkeä rooli vasta-ainevälitteisessä immuniteetissä solunulkoisia loisia vastaan. Th1- ja Th2-solujen epänormaali vaste ja solupopulaatioiden välinen epätasapaino voivat johtaa sairauksiin. Th1-soluille ominaisia reaktioita ja sytokiineja on havaittu autoimmuunisairauksissa, kun taas Th2-soluille tyypillinen vaste ja sytokiiniprofiili ovat ominaisia allergiassa ja astmassa. Tässä väitöskirjatyössä tutkittiin massaspektrometriaan perustuvan proteomiikan avulla Th1 ja Th2 erilaistavien sytokiinien IL-12 ja IL-4 vaikutusta Th-solujen proteomiin. IL-12:lla stimuloiduista perifeerisen veren Th-soluista ja IL-4:llä stimuloiduista napaveren Th-soluista eristettiin mikrosomaaliset fraktiot, joiden proteomeissa havaittiin sytokiinien säätelemiä proteiineja. Galektiini-1:n ja CD7:n ekspressio väheni IL-12:n vaikutuksesta ja IL-4 puolestaan vähensi STAT1:n, MXA:n, GIMAP1:n ja GIMAP4:n ilmenemistä. Molempien GIMAP geenien transkription havaittiin vähenevän IL-4:n vaikutuksesta ja lisääntyvän IL-12:n vaikutuksesta.Siirretty Doriast

Mass Spectrometry-Based Proteomics Workflows in Cancer Research: The Relevance of Choosing the Right Steps

Mass spectrometry; Proteomics; WorkflowsEspectrometría de masas; Proteómica; Flujos de trabajoEspectrometria de masses; Proteòmica; Fluxos de treballThe qualitative and quantitative evaluation of proteome changes that condition cancer development can be achieved with liquid chromatography–mass spectrometry (LC-MS). LC-MS-based proteomics strategies are carried out according to predesigned workflows that comprise several steps such as sample selection, sample processing including labeling, MS acquisition methods, statistical treatment, and bioinformatics to understand the biological meaning of the findings and set predictive classifiers. As the choice of best options might not be straightforward, we herein review and assess past and current proteomics approaches for the discovery of new cancer biomarkers. Moreover, we review major bioinformatics tools for interpreting and visualizing proteomics results and suggest the most popular machine learning techniques for the selection of predictive biomarkers. Finally, we consider the approximation of proteomics strategies for clinical diagnosis and prognosis by discussing current barriers and proposals to circumvent them.This research was funded by the Research Council of Norway INFRASTRUKTUR-program (project number: 295910)