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Starskaia I, Valta M, Pietilä S, Suomi T, Pahkuri S, Kalim UU, Rasool O, Rydgren E, Hyöty H, Knip M, Veijola R, Ilonen J, Toppari J, Lempainen J, Elo LL, Lahesmaa R. Distinct cellular immune responses in children en route to type 1 diabetes with different first-appearing autoantibodies. Nat Commun 2024; 15:3810. [PMID: 38714671 DOI: 10.1038/s41467-024-47918-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 04/12/2024] [Indexed: 05/10/2024] Open
Abstract
Previous studies have revealed heterogeneity in the progression to clinical type 1 diabetes in children who develop islet-specific antibodies either to insulin (IAA) or glutamic acid decarboxylase (GADA) as the first autoantibodies. Here, we test the hypothesis that children who later develop clinical disease have different early immune responses, depending on the type of the first autoantibody to appear (GADA-first or IAA-first). We use mass cytometry for deep immune profiling of peripheral blood mononuclear cell samples longitudinally collected from children who later progressed to clinical disease (IAA-first, GADA-first, ≥2 autoantibodies first groups) and matched for age, sex, and HLA controls who did not, as part of the Type 1 Diabetes Prediction and Prevention study. We identify differences in immune cell composition of children who later develop disease depending on the type of autoantibodies that appear first. Notably, we observe an increase in CD161 expression in natural killer cells of children with ≥2 autoantibodies and validate this in an independent cohort. The results highlight the importance of endotype-specific analyses and are likely to contribute to our understanding of pathogenic mechanisms underlying type 1 diabetes development.
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Affiliation(s)
- Inna Starskaia
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- InFLAMES Research Flagship Center, University of Turku, Turku, Finland
- Turku Doctoral Programme of Molecular Medicine, University of Turku, Turku, Finland
| | - Milla Valta
- Turku Doctoral Programme of Molecular Medicine, University of Turku, Turku, Finland
- Immunogenetics Laboratory, Institute of Biomedicine, University of Turku, Turku, Finland
| | - Sami Pietilä
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - Tomi Suomi
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - Sirpa Pahkuri
- Turku Doctoral Programme of Molecular Medicine, University of Turku, Turku, Finland
- Immunogenetics Laboratory, Institute of Biomedicine, University of Turku, Turku, Finland
| | - Ubaid Ullah Kalim
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - Omid Rasool
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - Emilie Rydgren
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - Heikki Hyöty
- Faculty of Medicine and Health Technology, Tampere University, and Fimlab Laboratories, Tampere, Finland
| | - Mikael Knip
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Center for Child Health Research, Tampere University Hospital, Tampere, Finland
| | - Riitta Veijola
- Department of Pediatrics, Research Unit of Clinical Medicine, Medical Research Centre, Oulu University Hospital and University of Oulu, Oulu, Finland
| | - Jorma Ilonen
- Immunogenetics Laboratory, Institute of Biomedicine, University of Turku, Turku, Finland
| | - Jorma Toppari
- InFLAMES Research Flagship Center, University of Turku, Turku, Finland
- Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland
- Research Centre for Integrative Physiology and Pharmacology, Institute of Biomedicine, University of Turku, Turku, Finland
- Department of Pediatrics, University of Turku and Turku University Hospital, Turku, Finland
| | - Johanna Lempainen
- Immunogenetics Laboratory, Institute of Biomedicine, University of Turku, Turku, Finland.
- Department of Pediatrics, University of Turku and Turku University Hospital, Turku, Finland.
- Clinical Microbiology, Turku University Hospital, Turku, Finland.
| | - Laura L Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.
- InFLAMES Research Flagship Center, University of Turku, Turku, Finland.
- Institute of Biomedicine, University of Turku, Turku, Finland.
| | - Riitta Lahesmaa
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.
- InFLAMES Research Flagship Center, University of Turku, Turku, Finland.
- Institute of Biomedicine, University of Turku, Turku, Finland.
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Moulder R, Välikangas T, Hirvonen MK, Suomi T, Brorsson CA, Lietzén N, Bruggraber SFA, Overbergh L, Dunger DB, Peakman M, Chmura PJ, Brunak S, Schulte AM, Mathieu C, Knip M, Elo LL, Lahesmaa R. Targeted serum proteomics of longitudinal samples from newly diagnosed youth with type 1 diabetes distinguishes markers of disease and C-peptide trajectory. Diabetologia 2023; 66:1983-1996. [PMID: 37537394 PMCID: PMC10542287 DOI: 10.1007/s00125-023-05974-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 06/06/2023] [Indexed: 08/05/2023]
Abstract
AIMS/HYPOTHESIS There is a growing need for markers that could help indicate the decline in beta cell function and recognise the need and efficacy of intervention in type 1 diabetes. Measurements of suitably selected serum markers could potentially provide a non-invasive and easily applicable solution to this challenge. Accordingly, we evaluated a broad panel of proteins previously associated with type 1 diabetes in serum from newly diagnosed individuals during the first year from diagnosis. To uncover associations with beta cell function, comparisons were made between these targeted proteomics measurements and changes in fasting C-peptide levels. To further distinguish proteins linked with the disease status, comparisons were made with measurements of the protein targets in age- and sex-matched autoantibody-negative unaffected family members (UFMs). METHODS Selected reaction monitoring (SRM) mass spectrometry analyses of serum, targeting 85 type 1 diabetes-associated proteins, were made. Sera from individuals diagnosed under 18 years (n=86) were drawn within 6 weeks of diagnosis and at 3, 6 and 12 months afterwards (288 samples in total). The SRM data were compared with fasting C-peptide/glucose data, which was interpreted as a measure of beta cell function. The protein data were further compared with cross-sectional SRM measurements from UFMs (n=194). RESULTS Eleven proteins had statistically significant associations with fasting C-peptide/glucose. Of these, apolipoprotein L1 and glutathione peroxidase 3 (GPX3) displayed the strongest positive and inverse associations, respectively. Changes in GPX3 levels during the first year after diagnosis indicated future fasting C-peptide/glucose levels. In addition, differences in the levels of 13 proteins were observed between the individuals with type 1 diabetes and the matched UFMs. These included GPX3, transthyretin, prothrombin, apolipoprotein C1 and members of the IGF family. CONCLUSIONS/INTERPRETATION The association of several targeted proteins with fasting C-peptide/glucose levels in the first year after diagnosis suggests their connection with the underlying changes accompanying alterations in beta cell function in type 1 diabetes. Moreover, the direction of change in GPX3 during the first year was indicative of subsequent fasting C-peptide/glucose levels, and supports further investigation of this and other serum protein measurements in future studies of beta cell function in type 1 diabetes.
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Affiliation(s)
- Robert Moulder
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - Tommi Välikangas
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - M Karoliina Hirvonen
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - Tomi Suomi
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - Caroline A Brorsson
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Niina Lietzén
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | | | - Lut Overbergh
- Katholieke Universiteit Leuven/Universitaire Ziekenhuizen, Leuven, Belgium
| | - David B Dunger
- Department of Paediatrics, University of Cambridge, Cambridge, UK
| | - Mark Peakman
- Immunology & Inflammation Research Therapeutic Area, Sanofi, Boston, MA, USA
| | - Piotr J Chmura
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Soren Brunak
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | | | - Chantal Mathieu
- Katholieke Universiteit Leuven/Universitaire Ziekenhuizen, Leuven, Belgium
| | - Mikael Knip
- Pediatric Research Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Tampere Center for Child Health Research, Tampere University Hospital, Tampere, Finland
| | - Laura L Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.
- InFLAMES Research Flagship Center, University of Turku, Turku, Finland.
- Institute of Biomedicine, University of Turku, Turku, Finland.
| | - Riitta Lahesmaa
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.
- InFLAMES Research Flagship Center, University of Turku, Turku, Finland.
- Institute of Biomedicine, University of Turku, Turku, Finland.
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Suomi T, Starskaia I, Kalim UU, Rasool O, Jaakkola MK, Grönroos T, Välikangas T, Brorsson C, Mazzoni G, Bruggraber S, Overbergh L, Dunger D, Peakman M, Chmura P, Brunak S, Schulte AM, Mathieu C, Knip M, Lahesmaa R, Elo LL. Gene expression signature predicts rate of type 1 diabetes progression. EBioMedicine 2023; 92:104625. [PMID: 37224769 DOI: 10.1016/j.ebiom.2023.104625] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 04/06/2023] [Accepted: 05/09/2023] [Indexed: 05/26/2023] Open
Abstract
BACKGROUND Type 1 diabetes is a complex heterogenous autoimmune disease without therapeutic interventions available to prevent or reverse the disease. This study aimed to identify transcriptional changes associated with the disease progression in patients with recent-onset type 1 diabetes. METHODS Whole-blood samples were collected as part of the INNODIA study at baseline and 12 months after diagnosis of type 1 diabetes. We used linear mixed-effects modelling on RNA-seq data to identify genes associated with age, sex, or disease progression. Cell-type proportions were estimated from the RNA-seq data using computational deconvolution. Associations to clinical variables were estimated using Pearson's or point-biserial correlation for continuous and dichotomous variables, respectively, using only complete pairs of observations. FINDINGS We found that genes and pathways related to innate immunity were downregulated during the first year after diagnosis. Significant associations of the gene expression changes were found with ZnT8A autoantibody positivity. Rate of change in the expression of 16 genes between baseline and 12 months was found to predict the decline in C-peptide at 24 months. Interestingly and consistent with earlier reports, increased B cell levels and decreased neutrophil levels were associated with the rapid progression. INTERPRETATION There is considerable individual variation in the rate of progression from appearance of type 1 diabetes-specific autoantibodies to clinical disease. Patient stratification and prediction of disease progression can help in developing more personalised therapeutic strategies for different disease endotypes. FUNDING A full list of funding bodies can be found under Acknowledgments.
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Affiliation(s)
- Tomi Suomi
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520, Turku, Finland; InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - Inna Starskaia
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520, Turku, Finland; InFLAMES Research Flagship Center, University of Turku, Turku, Finland; Turku Doctoral Programme of Molecular Medicine, University of Turku, Turku, Finland
| | - Ubaid Ullah Kalim
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520, Turku, Finland; InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - Omid Rasool
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520, Turku, Finland; InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - Maria K Jaakkola
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520, Turku, Finland; InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - Toni Grönroos
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520, Turku, Finland; InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - Tommi Välikangas
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520, Turku, Finland; InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - Caroline Brorsson
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Gianluca Mazzoni
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | | | - Lut Overbergh
- Katholieke Universiteit Leuven/Universitaire Ziekenhuizen, Leuven, Belgium
| | - David Dunger
- Department of Paediatrics, University of Cambridge, Cambridge, England, UK
| | - Mark Peakman
- Immunology & Inflammation Research Therapeutic Area, Sanofi, MA, USA
| | - Piotr Chmura
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Søren Brunak
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | | | - Chantal Mathieu
- Katholieke Universiteit Leuven/Universitaire Ziekenhuizen, Leuven, Belgium
| | - Mikael Knip
- Paediatric Research Centre, University of Helsinki and Helsinki University Hospital, Helsinki, Finland; Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland; Tampere Centre for Child Health Research, Tampere University Hospital, Tampere, Finland
| | - Riitta Lahesmaa
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520, Turku, Finland; InFLAMES Research Flagship Center, University of Turku, Turku, Finland; Institute of Biomedicine, University of Turku, FI-20520, Turku, Finland.
| | - Laura L Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520, Turku, Finland; InFLAMES Research Flagship Center, University of Turku, Turku, Finland; Institute of Biomedicine, University of Turku, FI-20520, Turku, Finland.
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4
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Oras A, Kallionpää H, Suomi T, Koskinen S, Laiho A, Elo LL, Knip M, Lahesmaa R, Aints A, Uibo R. Profiling of peripheral blood B-cell transcriptome in children who developed coeliac disease in a prospective study. Heliyon 2023; 9:e13147. [PMID: 36718152 PMCID: PMC9883278 DOI: 10.1016/j.heliyon.2023.e13147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 12/20/2022] [Accepted: 01/17/2023] [Indexed: 01/22/2023] Open
Abstract
Background In coeliac disease (CoD), the role of B-cells has mainly been considered to be production of antibodies. The functional role of B-cells has not been analysed extensively in CoD. Methods We conducted a study to characterize gene expression in B-cells from children developing CoD early in life using samples collected before and at the diagnosis of the disease. Blood samples were collected from children at risk at 12, 18, 24 and 36 months of age. RNA from peripheral blood CD19+ cells was sequenced and differential gene expression was analysed using R package Limma. Findings Overall, we found one gene, HNRNPL, modestly downregulated in all patients (logFC -0·7; q = 0·09), and several others downregulated in those diagnosed with CoD already by the age of 2 years. Interpretation The data highlight the role of B-cells in CoD development. The role of HNRPL in suppressing enteroviral replication suggests that the predisposing factor for both CoD and enteroviral infections is the low level of HNRNPL expression. Funding EU FP7 grant no. 202063, EU Regional Developmental Fund and research grant PRG712, The Academy of Finland Centre of Excellence in Molecular Systems Immunology and Physiology Research (SyMMyS) 2012-2017, grant no. 250114) and, AoF Personalized Medicine Program (grant no. 292482), AoF grants 292335, 294337, 319280, 31444, 319280, 329277, 331790) and grants from the Sigrid Jusélius Foundation (SJF).
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Affiliation(s)
- Astrid Oras
- Department of Immunology, Institute of Biomedicine and Translational Medicine, University of Tartu, Estonia
| | - Henna Kallionpää
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Finland
| | - Tomi Suomi
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Finland,InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - Satu Koskinen
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Finland
| | - Asta Laiho
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Finland,InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - Laura L. Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Finland,InFLAMES Research Flagship Center, University of Turku, Turku, Finland,Institute of Biomedicine, University of Turku, Finland
| | - Mikael Knip
- Pediatric Research Center, Children's Hospital, University of Helsinki and Helsinki University Hospital, Helsinki, Finland,Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland,Center for Child Health Research, Tampere University Hospital, Tampere, Finland
| | - Riitta Lahesmaa
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Finland,InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - Alar Aints
- Department of Immunology, Institute of Biomedicine and Translational Medicine, University of Tartu, Estonia,Corresponding author. Department of Immunology, Institute of Biomedicine and Translational Medicine, University of Tartu, Ravila 19, EE50411, Tartu, Estonia.
| | - Raivo Uibo
- Department of Immunology, Institute of Biomedicine and Translational Medicine, University of Tartu, Estonia
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Kleino I, Frolovaitė P, Suomi T, Elo LL. Computational solutions for spatial transcriptomics. Comput Struct Biotechnol J 2022; 20:4870-4884. [PMID: 36147664 PMCID: PMC9464853 DOI: 10.1016/j.csbj.2022.08.043] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 08/18/2022] [Accepted: 08/18/2022] [Indexed: 11/18/2022] Open
Abstract
Transcriptome level expression data connected to the spatial organization of the cells and molecules would allow a comprehensive understanding of how gene expression is connected to the structure and function in the biological systems. The spatial transcriptomics platforms may soon provide such information. However, the current platforms still lack spatial resolution, capture only a fraction of the transcriptome heterogeneity, or lack the throughput for large scale studies. The strengths and weaknesses in current ST platforms and computational solutions need to be taken into account when planning spatial transcriptomics studies. The basis of the computational ST analysis is the solutions developed for single-cell RNA-sequencing data, with advancements taking into account the spatial connectedness of the transcriptomes. The scRNA-seq tools are modified for spatial transcriptomics or new solutions like deep learning-based joint analysis of expression, spatial, and image data are developed to extract biological information in the spatially resolved transcriptomes. The computational ST analysis can reveal remarkable biological insights into spatial patterns of gene expression, cell signaling, and cell type variations in connection with cell type-specific signaling and organization in complex tissues. This review covers the topics that help choosing the platform and computational solutions for spatial transcriptomics research. We focus on the currently available ST methods and platforms and their strengths and limitations. Of the computational solutions, we provide an overview of the analysis steps and tools used in the ST data analysis. The compatibility with the data types and the tools provided by the current ST analysis frameworks are summarized.
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Key Words
- AOI, area of illumination
- BICCN, Brain Initiative Cell Census Network
- BOLORAMIS, barcoded oligonucleotides ligated on RNA amplified for multiplexed and parallel in situ analyses
- Baysor, Bayesian Segmentation of Spatial Transcriptomics Data
- BinSpect, Binary Spatial Extraction
- CCC, cell–cell communication
- CCI, cell–cell interactions
- CNV, copy-number variation
- Computational biology
- DSP, digital spatial profiling
- DbiT-Seq, Deterministic Barcoding in Tissue for spatial omics sequencing
- FA, factor analysis
- FFPE, formalin-fixed, paraffin-embedded
- FISH, fluorescence in situ hybridization
- FISSEQ, fluorescence in situ sequencing of RNA
- FOV, Field of view
- GRNs, gene regulation networks
- GSEA, gene set enrichment analysis
- GSVA, gene set variation analysis
- HDST, high definition spatial transcriptomics
- HMRF, hidden Markov random field
- ICG, interaction changed genes
- ISH, in situ hybridization
- ISS, in situ sequencing
- JSTA, Joint cell segmentation and cell type annotation
- KNN, k-nearest neighbor
- LCM, Laser Capture Microdissection
- LCM-seq, laser capture microdissection coupled with RNA sequencing
- LOH, loss of heterozygosity analysis
- MC, Molecular Cartography
- MERFISH, multiplexed error-robust FISH
- NMF (NNMF), Non-negative matrix factorization
- PCA, Principal Component Analysis
- PIXEL-seq, Polony (or DNA cluster)-indexed library-sequencing
- PL-lig, padlock ligation
- QC, quality control
- RNAseq, RNA sequencing
- ROI, region of interest
- SCENIC, Single-Cell rEgulatory Network Inference and Clustering
- SME, Spatial Morphological gene Expression normalization
- SPATA, SPAtial Transcriptomic Analysis
- ST Pipeline, Spatial Transcriptomics Pipeline
- ST, Spatial transcriptomics
- STARmap, spatially-resolved transcript amplicon readout mapping
- Single-cell analysis
- Spatial data analysis frameworks
- Spatial deconvolution
- Spatial transcriptomics
- TIVA, Transcriptome in Vivo Analysis
- TMA, tissue microarray
- TME, tumor micro environment
- UMAP, Uniform Manifold Approximation and Projection for Dimension Reduction
- UMI, unique molecular identifier
- ZipSeq, zipcoded sequencing.
- scRNA-seq, single-cell RNA sequencing
- scvi-tools, single-cell variational inference tools
- seqFISH, sequential fluorescence in situ hybridization
- sequ-smFISH, sequential single-molecule fluorescent in situ hybridization
- smFISH, single molecule FISH
- t-SNE, t-distributed stochastic neighbor embedding
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Affiliation(s)
- Iivari Kleino
- Turku Bioscience Centre, University of Turku and Åbo Akademi University Turku, Turku, Finland
| | - Paulina Frolovaitė
- Turku Bioscience Centre, University of Turku and Åbo Akademi University Turku, Turku, Finland
| | - Tomi Suomi
- Turku Bioscience Centre, University of Turku and Åbo Akademi University Turku, Turku, Finland
| | - Laura L. Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University Turku, Turku, Finland
- Institute of Biomedicine, University of Turku, Turku, Finland
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Välikangas T, Junttila S, Rytkönen KT, Kukkonen-Macchi A, Suomi T, Elo LL. COVID-19-specific transcriptomic signature detectable in blood across multiple cohorts. Front Genet 2022; 13:929887. [PMID: 35991542 PMCID: PMC9388772 DOI: 10.3389/fgene.2022.929887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 06/27/2022] [Indexed: 01/08/2023] Open
Abstract
The coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is spreading across the world despite vast global vaccination efforts. Consequently, many studies have looked for potential human host factors and immune mechanisms associated with the disease. However, most studies have focused on comparing COVID-19 patients to healthy controls, while fewer have elucidated the specific host factors distinguishing COVID-19 from other infections. To discover genes specifically related to COVID-19, we reanalyzed transcriptome data from nine independent cohort studies, covering multiple infections, including COVID-19, influenza, seasonal coronaviruses, and bacterial pneumonia. The identified COVID-19-specific signature consisted of 149 genes, involving many signals previously associated with the disease, such as induction of a strong immunoglobulin response and hemostasis, as well as dysregulation of cell cycle-related processes. Additionally, potential new gene candidates related to COVID-19 were discovered. To facilitate exploration of the signature with respect to disease severity, disease progression, and different cell types, we also offer an online tool for easy visualization of the selected genes across multiple datasets at both bulk and single-cell levels.
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Affiliation(s)
- Tommi Välikangas
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Sini Junttila
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Kalle T. Rytkönen
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- Institute of Biomedicine, University of Turku, Turku, Finland
| | - Anu Kukkonen-Macchi
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Tomi Suomi
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- *Correspondence: Tomi Suomi, ; Laura L. Elo,
| | - Laura L. Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- Institute of Biomedicine, University of Turku, Turku, Finland
- *Correspondence: Tomi Suomi, ; Laura L. Elo,
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Pietilä S, Suomi T, Elo LL. Introducing untargeted data-independent acquisition for metaproteomics of complex microbial samples. ISME Commun 2022; 2:51. [PMID: 37938742 PMCID: PMC9723653 DOI: 10.1038/s43705-022-00137-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 05/27/2022] [Accepted: 06/14/2022] [Indexed: 05/17/2023]
Abstract
Mass spectrometry-based metaproteomics is a relatively new field of research that enables the characterization of the functionality of microbiota. Recently, we demonstrated the applicability of data-independent acquisition (DIA) mass spectrometry to the analysis of complex metaproteomic samples. This allowed us to circumvent many of the drawbacks of the previously used data-dependent acquisition (DDA) mass spectrometry, mainly the limited reproducibility when analyzing samples with complex microbial composition. However, the DDA-assisted DIA approach still required additional DDA data on the samples to assist the analysis. Here, we introduce, for the first time, an untargeted DIA metaproteomics tool that does not require any DDA data, but instead generates a pseudospectral library directly from the DIA data. This reduces the amount of required mass spectrometry data to a single DIA run per sample. The new DIA-only metaproteomics approach is implemented as a new open-source software package named glaDIAtor, including a modern web-based graphical user interface to facilitate wide use of the tool by the community.
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Affiliation(s)
- Sami Pietilä
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520, Turku, Finland
| | - Tomi Suomi
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520, Turku, Finland
| | - Laura L Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520, Turku, Finland.
- Institute of Biomedicine, University of Turku, FI-20520, Turku, Finland.
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8
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Hong Y, Flinkman D, Suomi T, Pietilä S, James P, Coffey E, Elo LL. Correction to: PhosPiR: an automated phosphoproteomic pipeline in R. Brief Bioinform 2022; 23:6565175. [PMID: 35393608 PMCID: PMC9116209 DOI: 10.1093/bib/bbac153] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Ye Hong
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Dani Flinkman
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.,Lund University, Lund, Sweden
| | - Tomi Suomi
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Sami Pietilä
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Peter James
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.,Lund University, Lund, Sweden
| | - Eleanor Coffey
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Laura L Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.,Institute of Biomedicine, University of Turku, Turku, Finland
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9
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Suomi T, Kalim UU, Rasool O, Laiho A, Kallionpää H, Vähä-Mäkilä M, Nurmio M, Mykkänen J, Härkönen T, Hyöty H, Ilonen J, Veijola R, Toppari J, Knip M, Elo LL, Lahesmaa R. Type 1 Diabetes in Children With Genetic Risk May Be Predicted Very Early With a Blood miRNA. Diabetes Care 2022; 45:e77-e79. [PMID: 35134118 PMCID: PMC9016735 DOI: 10.2337/dc21-2120] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 01/20/2022] [Indexed: 02/03/2023]
Affiliation(s)
- Tomi Suomi
- Turku Bioscience Centre, University of Turku and bo Akademi University, Turku, Finland.,InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - Ubaid Ullah Kalim
- Turku Bioscience Centre, University of Turku and bo Akademi University, Turku, Finland.,InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - Omid Rasool
- Turku Bioscience Centre, University of Turku and bo Akademi University, Turku, Finland.,InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - Asta Laiho
- Turku Bioscience Centre, University of Turku and bo Akademi University, Turku, Finland.,InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - Henna Kallionpää
- Turku Bioscience Centre, University of Turku and bo Akademi University, Turku, Finland
| | - Mari Vähä-Mäkilä
- Research Centre for Integrative Physiology and Pharmacology, Institute of Biomedicine, University of Turku, Turku, Finland.,Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland
| | - Mirja Nurmio
- Research Centre for Integrative Physiology and Pharmacology, Institute of Biomedicine, University of Turku, Turku, Finland.,Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland
| | - Juha Mykkänen
- Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland.,Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
| | - Taina Härkönen
- Pediatric Research Center, Children's Hospital, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.,Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Heikki Hyöty
- Department of Virology, Faculty of Medicine and Biosciences, University of Tampere, Tampere, Finland.,Fimlab Laboratories, Pirkanmaa Hospital District, Tampere, Finland
| | - Jorma Ilonen
- Immunogenetics Laboratory, Institute of Biomedicine, University of Turku, Turku, Finland
| | - Riitta Veijola
- Department of Pediatrics, PEDEGO Research Unit, Medical Research Centre, University of Oulu, Oulu, Finland.,Department of Children and Adolescents, Oulu University Hospital, Oulu, Finland
| | - Jorma Toppari
- InFLAMES Research Flagship Center, University of Turku, Turku, Finland.,Research Centre for Integrative Physiology and Pharmacology, Institute of Biomedicine, University of Turku, Turku, Finland.,Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland.,Department of Pediatrics, Turku University Hospital, Turku, Finland
| | - Mikael Knip
- Pediatric Research Center, Children's Hospital, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.,Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland.,Centre for Child Health Research, Tampere University Hospital, Tampere, Finland
| | - Laura L Elo
- Turku Bioscience Centre, University of Turku and bo Akademi University, Turku, Finland.,InFLAMES Research Flagship Center, University of Turku, Turku, Finland.,Institute of Biomedicine, University of Turku, Turku, Finland
| | - Riitta Lahesmaa
- Turku Bioscience Centre, University of Turku and bo Akademi University, Turku, Finland.,InFLAMES Research Flagship Center, University of Turku, Turku, Finland.,Institute of Biomedicine, University of Turku, Turku, Finland
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10
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Suomi T, Elo LL. Statistical and machine learning methods to study human CD4+ T cell proteome profiles. Immunol Lett 2022; 245:8-17. [DOI: 10.1016/j.imlet.2022.03.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 03/11/2022] [Accepted: 03/15/2022] [Indexed: 11/05/2022]
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11
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Hong Y, Flinkman D, Suomi T, Pietilä S, James P, Coffey E, Elo LL. PhosPiR: an automated phosphoproteomic pipeline in R. Brief Bioinform 2021; 23:6456296. [PMID: 34882763 PMCID: PMC8787428 DOI: 10.1093/bib/bbab510] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 10/25/2021] [Accepted: 11/04/2021] [Indexed: 01/01/2023] Open
Abstract
Large-scale phosphoproteome profiling using mass spectrometry (MS) provides functional insight that is crucial for disease biology and drug discovery. However, extracting biological understanding from these data is an arduous task requiring multiple analysis platforms that are not adapted for automated high-dimensional data analysis. Here, we introduce an integrated pipeline that combines several R packages to extract high-level biological understanding from large-scale phosphoproteomic data by seamless integration with existing databases and knowledge resources. In a single run, PhosPiR provides data clean-up, fast data overview, multiple statistical testing, differential expression analysis, phosphosite annotation and translation across species, multilevel enrichment analyses, proteome-wide kinase activity and substrate mapping and network hub analysis. Data output includes graphical formats such as heatmap, box-, volcano- and circos-plots. This resource is designed to assist proteome-wide data mining of pathophysiological mechanism without a need for programming knowledge.
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Affiliation(s)
- Ye Hong
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Dani Flinkman
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.,Lund University, Lund, Sweden
| | - Tomi Suomi
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Sami Pietilä
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Peter James
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.,Lund University, Lund, Sweden
| | - Eleanor Coffey
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Laura L Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.,Institute of Biomedicine, University of Turku, Turku, Finland
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12
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Venäläinen MS, Heervä E, Hirvonen O, Saraei S, Suomi T, Mikkola T, Bärlund M, Jyrkkiö S, Laitinen T, Elo LL. Improved risk prediction of chemotherapy-induced neutropenia-model development and validation with real-world data. Cancer Med 2021; 11:654-663. [PMID: 34859963 PMCID: PMC8817096 DOI: 10.1002/cam4.4465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Revised: 11/07/2021] [Accepted: 11/16/2021] [Indexed: 11/08/2022] Open
Abstract
BACKGROUND The existing risk prediction models for chemotherapy-induced febrile neutropenia (FN) do not necessarily apply to real-life patients in different healthcare systems and the external validation of these models are often lacking. Our study evaluates whether a machine learning-based risk prediction model could outperform the previously introduced models, especially when validated against real-world patient data from another institution not used for model training. METHODS Using Turku University Hospital electronic medical records, we identified all patients who received chemotherapy for non-hematological cancer between the years 2010 and 2017 (N = 5879). An experimental surrogate endpoint was first-cycle neutropenic infection (NI), defined as grade IV neutropenia with serum C-reactive protein >10 mg/l. For predicting the risk of NI, a penalized regression model (Lasso) was developed. The model was externally validated in an independent dataset (N = 4594) from Tampere University Hospital. RESULTS Lasso model accurately predicted NI risk with good accuracy (AUROC 0.84). In the validation cohort, the Lasso model outperformed two previously introduced, widely approved models, with AUROC 0.75. The variables selected by Lasso included granulocyte colony-stimulating factor (G-CSF) use, cancer type, pre-treatment neutrophil and thrombocyte count, intravenous treatment regimen, and the planned dose intensity. The same model predicted also FN, with AUROC 0.77, supporting the validity of NI as an endpoint. CONCLUSIONS Our study demonstrates that real-world NI risk prediction can be improved with machine learning and that every difference in patient or treatment characteristics can have a significant impact on model performance. Here we outline a novel, externally validated approach which may hold potential to facilitate more targeted use of G-CSFs in the future.
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Affiliation(s)
- Mikko S Venäläinen
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Eetu Heervä
- Department of Oncology, Turku University Hospital and FICAN West, Turku, Finland.,University of Turku, Turku, Finland
| | - Outi Hirvonen
- Department of Oncology, Turku University Hospital and FICAN West, Turku, Finland.,Department of Clinical Oncology, University of Turku, Turku, Finland.,Palliative Center, Turku University Hospital, Turku, Finland
| | - Sohrab Saraei
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Tomi Suomi
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Toni Mikkola
- Tays Research Services, Clinical Informatics Team, Tampere University Hospital and University of Tampere, Tampere, Finland
| | - Maarit Bärlund
- Department of Oncology, Tays Cancer Centre, Tampere University Hospital, Tampere, Finland.,Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Sirkku Jyrkkiö
- Department of Oncology, Turku University Hospital and FICAN West, Turku, Finland
| | - Tarja Laitinen
- Department of Pulmonary Medicine, University of Turku and Turku University Hospital, Turku, Finland.,Administration Center, Tampere University Hospital and University of Tampere, Tampere, Finland
| | - Laura L Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.,Institute of Biomedicine, University of Turku, Turku, Finland
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13
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Munne PM, Martikainen L, Räty I, Bertula K, Nonappa, Ruuska J, Ala-Hongisto H, Peura A, Hollmann B, Euro L, Yavuz K, Patrikainen L, Salmela M, Pokki J, Kivento M, Väänänen J, Suomi T, Nevalaita L, Mutka M, Kovanen P, Leidenius M, Meretoja T, Hukkinen K, Monni O, Pouwels J, Sahu B, Mattson J, Joensuu H, Heikkilä P, Elo LL, Metcalfe C, Junttila MR, Ikkala O, Klefström J. Compressive stress-mediated p38 activation required for ERα + phenotype in breast cancer. Nat Commun 2021; 12:6967. [PMID: 34845227 PMCID: PMC8630031 DOI: 10.1038/s41467-021-27220-9] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Accepted: 11/04/2021] [Indexed: 01/01/2023] Open
Abstract
Breast cancer is now globally the most frequent cancer and leading cause of women's death. Two thirds of breast cancers express the luminal estrogen receptor-positive (ERα + ) phenotype that is initially responsive to antihormonal therapies, but drug resistance emerges. A major barrier to the understanding of the ERα-pathway biology and therapeutic discoveries is the restricted repertoire of luminal ERα + breast cancer models. The ERα + phenotype is not stable in cultured cells for reasons not fully understood. We examine 400 patient-derived breast epithelial and breast cancer explant cultures (PDECs) grown in various three-dimensional matrix scaffolds, finding that ERα is primarily regulated by the matrix stiffness. Matrix stiffness upregulates the ERα signaling via stress-mediated p38 activation and H3K27me3-mediated epigenetic regulation. The finding that the matrix stiffness is a central cue to the ERα phenotype reveals a mechanobiological component in breast tissue hormonal signaling and enables the development of novel therapeutic interventions. Subject terms: ER-positive (ER + ), breast cancer, ex vivo model, preclinical model, PDEC, stiffness, p38 SAPK.
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Affiliation(s)
- Pauliina M Munne
- Finnish Cancer Institute, FICAN South Helsinki University Hospital & Translational Cancer Medicine, Medical Faculty, University of Helsinki. Cancer Cell Circuitry Laboratory, PO Box 63 Haartmaninkatu 8, 00014 University of Helsinki, Helsinki, Finland
| | - Lahja Martikainen
- Department of Applied Physics, Molecular Materials Group, Aalto University School of Science, PO Box, 15100, FI-00076, Espoo, Finland
| | - Iiris Räty
- Finnish Cancer Institute, FICAN South Helsinki University Hospital & Translational Cancer Medicine, Medical Faculty, University of Helsinki. Cancer Cell Circuitry Laboratory, PO Box 63 Haartmaninkatu 8, 00014 University of Helsinki, Helsinki, Finland
| | - Kia Bertula
- Department of Applied Physics, Molecular Materials Group, Aalto University School of Science, PO Box, 15100, FI-00076, Espoo, Finland
| | - Nonappa
- Department of Applied Physics, Molecular Materials Group, Aalto University School of Science, PO Box, 15100, FI-00076, Espoo, Finland
- Department of Bioproducts and Biosystems, Aalto University School of Chemical Engineering, Espoo, Finland
| | - Janika Ruuska
- Finnish Cancer Institute, FICAN South Helsinki University Hospital & Translational Cancer Medicine, Medical Faculty, University of Helsinki. Cancer Cell Circuitry Laboratory, PO Box 63 Haartmaninkatu 8, 00014 University of Helsinki, Helsinki, Finland
| | - Hanna Ala-Hongisto
- Finnish Cancer Institute, FICAN South Helsinki University Hospital & Translational Cancer Medicine, Medical Faculty, University of Helsinki. Cancer Cell Circuitry Laboratory, PO Box 63 Haartmaninkatu 8, 00014 University of Helsinki, Helsinki, Finland
| | - Aino Peura
- Finnish Cancer Institute, FICAN South Helsinki University Hospital & Translational Cancer Medicine, Medical Faculty, University of Helsinki. Cancer Cell Circuitry Laboratory, PO Box 63 Haartmaninkatu 8, 00014 University of Helsinki, Helsinki, Finland
| | - Babette Hollmann
- Finnish Cancer Institute, FICAN South Helsinki University Hospital & Translational Cancer Medicine, Medical Faculty, University of Helsinki. Cancer Cell Circuitry Laboratory, PO Box 63 Haartmaninkatu 8, 00014 University of Helsinki, Helsinki, Finland
| | - Lilya Euro
- Research Program of Stem Cells and Metabolism, Biomedicum Helsinki, University of Helsinki, 00290, Helsinki, Finland
| | - Kerim Yavuz
- Applied Tumor Genomics Research Program, Enhancer Biology Laboratory, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Linda Patrikainen
- Finnish Cancer Institute, FICAN South Helsinki University Hospital & Translational Cancer Medicine, Medical Faculty, University of Helsinki. Cancer Cell Circuitry Laboratory, PO Box 63 Haartmaninkatu 8, 00014 University of Helsinki, Helsinki, Finland
| | - Maria Salmela
- Finnish Cancer Institute, FICAN South Helsinki University Hospital & Translational Cancer Medicine, Medical Faculty, University of Helsinki. Cancer Cell Circuitry Laboratory, PO Box 63 Haartmaninkatu 8, 00014 University of Helsinki, Helsinki, Finland
| | - Juho Pokki
- Department of Electrical Engineering and Automation, Aalto University, Espoo, Finland
| | - Mikko Kivento
- Applied Tumor Genomics Research Program, Faculty of Medicine, Oncogenomics Laboratory, University of Helsinki, Helsinki, Finland
| | - Juho Väänänen
- Applied Tumor Genomics Research Program, Faculty of Medicine, Oncogenomics Laboratory, University of Helsinki, Helsinki, Finland
| | - Tomi Suomi
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520, Turku, Finland
| | - Liina Nevalaita
- Finnish Cancer Institute, FICAN South Helsinki University Hospital & Translational Cancer Medicine, Medical Faculty, University of Helsinki. Cancer Cell Circuitry Laboratory, PO Box 63 Haartmaninkatu 8, 00014 University of Helsinki, Helsinki, Finland
| | - Minna Mutka
- Department of Pathology, HUSLAB and Haartman Institute, Helsinki University Central Hospital and University of Helsinki, Helsinki, Finland
| | - Panu Kovanen
- Department of Pathology, HUSLAB and Haartman Institute, Helsinki University Central Hospital and University of Helsinki, Helsinki, Finland
| | - Marjut Leidenius
- Breast Surgery Unit, Helsinki University Central Hospital, Helsinki, Finland
| | - Tuomo Meretoja
- Breast Surgery Unit, Helsinki University Central Hospital, Helsinki, Finland
| | - Katja Hukkinen
- Department of Mammography, Helsinki University Central Hospital, Helsinki, Finland
| | - Outi Monni
- Applied Tumor Genomics Research Program, Faculty of Medicine, Oncogenomics Laboratory, University of Helsinki, Helsinki, Finland
| | - Jeroen Pouwels
- Finnish Cancer Institute, FICAN South Helsinki University Hospital & Translational Cancer Medicine, Medical Faculty, University of Helsinki. Cancer Cell Circuitry Laboratory, PO Box 63 Haartmaninkatu 8, 00014 University of Helsinki, Helsinki, Finland
| | - Biswajyoti Sahu
- Applied Tumor Genomics Research Program, Enhancer Biology Laboratory, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Johanna Mattson
- Department of Oncology, University of Helsinki & Helsinki University Hospital, Helsinki, Finland
| | - Heikki Joensuu
- Department of Oncology, University of Helsinki & Helsinki University Hospital, Helsinki, Finland
| | - Päivi Heikkilä
- Department of Pathology, HUSLAB and Haartman Institute, Helsinki University Central Hospital and University of Helsinki, Helsinki, Finland
| | - Laura L Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520, Turku, Finland
| | - Ciara Metcalfe
- Genentech Inc., 1 DNA Way, South San Francisco, CA, 94080, USA
| | | | - Olli Ikkala
- Department of Applied Physics, Molecular Materials Group, Aalto University School of Science, PO Box, 15100, FI-00076, Espoo, Finland
- Department of Bioproducts and Biosystems, Aalto University School of Chemical Engineering, Espoo, Finland
| | - Juha Klefström
- Finnish Cancer Institute, FICAN South Helsinki University Hospital & Translational Cancer Medicine, Medical Faculty, University of Helsinki. Cancer Cell Circuitry Laboratory, PO Box 63 Haartmaninkatu 8, 00014 University of Helsinki, Helsinki, Finland.
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14
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Yang M, Petralia F, Li Z, Li H, Ma W, Song X, Kim S, Lee H, Yu H, Lee B, Bae S, Heo E, Kaczmarczyk J, Stępniak P, Warchoł M, Yu T, Calinawan AP, Boutros PC, Payne SH, Reva B, Boja E, Rodriguez H, Stolovitzky G, Guan Y, Kang J, Wang P, Fenyö D, Saez-Rodriguez J, Aderinwale T, Afyounian E, Agrawal P, Ali M, Amadoz A, Azuaje F, Bachman J, Bae S, Bhalla S, Carbonell-Caballero J, Chakraborty P, Chaudhary K, Choi Y, Choi Y, Çubuk C, Dhanda SK, Dopazo J, Elo LL, Fóthi Á, Gevaert O, Granberg K, Greiner R, Heo E, Hidalgo MR, Jayaswal V, Jeon H, Jeon M, Kalmady SV, Kambara Y, Kang J, Kang K, Kaoma T, Kaur H, Kazan H, Kesar D, Kesseli J, Kim D, Kim K, Kim SY, Kim S, Kumar S, Lee B, Lee H, Liu Y, Luethy R, Mahajan S, Mahmoudian M, Muller A, Nazarov PV, Nguyen H, Nykter M, Okuda S, Park S, Pal Singh Raghava G, Rajapakse JC, Rantapero T, Ryu H, Salavert F, Saraei S, Sharma R, Siitonen A, Sokolov A, Subramanian K, Suni V, Suomi T, Tranchevent LC, Usmani SS, Välikangas T, Vega R, Zhong H. Community Assessment of the Predictability of Cancer Protein and Phosphoprotein Levels from Genomics and Transcriptomics. Cell Syst 2020; 11:186-195.e9. [DOI: 10.1016/j.cels.2020.06.013] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2019] [Revised: 03/12/2020] [Accepted: 06/29/2020] [Indexed: 10/23/2022]
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15
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Aakko J, Pietilä S, Suomi T, Mahmoudian M, Toivonen R, Kouvonen P, Rokka A, Hänninen A, Elo LL. Data-Independent Acquisition Mass Spectrometry in Metaproteomics of Gut Microbiota—Implementation and Computational Analysis. J Proteome Res 2019; 19:432-436. [DOI: 10.1021/acs.jproteome.9b00606] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Juhani Aakko
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku 20520, Finland
| | - Sami Pietilä
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku 20520, Finland
| | - Tomi Suomi
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku 20520, Finland
| | - Mehrad Mahmoudian
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku 20520, Finland
- Department of Future Technologies, University of Turku, Turku 20014, Finland
| | - Raine Toivonen
- Department of Medical Microbiology and Immunology, University of Turku, Turku 20520, Finland
| | - Petri Kouvonen
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku 20520, Finland
| | - Anne Rokka
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku 20520, Finland
| | - Arno Hänninen
- Department of Medical Microbiology and Immunology, University of Turku, Turku 20520, Finland
- TYKS Microbiology, Turku University Central Hospital, Turku 20521, Finland
| | - Laura L. Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku 20520, Finland
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16
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Suni V, Suomi T, Tsubosaka T, Imanishi SY, Elo LL, Corthals GL. SimPhospho: a software tool enabling confident phosphosite assignment. Bioinformatics 2019; 34:2690-2692. [PMID: 29596608 PMCID: PMC6061695 DOI: 10.1093/bioinformatics/bty151] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2017] [Accepted: 03/26/2018] [Indexed: 11/20/2022] Open
Abstract
Motivation Mass spectrometry combined with enrichment strategies for phosphorylated peptides has been successfully employed for two decades to identify sites of phosphorylation. However, unambiguous phosphosite assignment is considered challenging. Given that site-specific phosphorylation events function as different molecular switches, validation of phosphorylation sites is of utmost importance. In our earlier study we developed a method based on simulated phosphopeptide spectral libraries, which enables highly sensitive and accurate phosphosite assignments. To promote more widespread use of this method, we here introduce a software implementation with improved usability and performance. Results We present SimPhospho, a fast and user-friendly tool for accurate simulation of phosphopeptide tandem mass spectra. Simulated phosphopeptide spectral libraries are used to validate and supplement database search results, with a goal to improve reliable phosphoproteome identification and reporting. The presented program can be easily used together with the Trans-Proteomic Pipeline and integrated in a phosphoproteomics data analysis workflow. Availability and implementation SimPhospho is open source and it is available for Windows, Linux and Mac operating systems. The software and its user’s manual with detailed description of data analysis as well as test data can be found at https://sourceforge.net/projects/simphospho/. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Veronika Suni
- TUCS - Turku Centre for Computer Science, FI-20500 Turku, Finland.,Bioinformatics Unit, Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Tykistökatu 6, FI-20520 Turku, Finland
| | - Tomi Suomi
- Bioinformatics Unit, Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Tykistökatu 6, FI-20520 Turku, Finland
| | | | | | - Laura L Elo
- Bioinformatics Unit, Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Tykistökatu 6, FI-20520 Turku, Finland
| | - Garry L Corthals
- Van't Hoff Institute of Molecular Sciences, 1090 GS Amsterdam, The Netherlands
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17
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Välikangas T, Suomi T, Elo LL. A comprehensive evaluation of popular proteomics software workflows for label-free proteome quantification and imputation. Brief Bioinform 2019; 19:1344-1355. [PMID: 28575146 PMCID: PMC6291797 DOI: 10.1093/bib/bbx054] [Citation(s) in RCA: 54] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2017] [Indexed: 01/15/2023] Open
Abstract
Label-free mass spectrometry (MS) has developed into an important tool applied in various fields of biological and life sciences. Several software exist to process the raw MS data into quantified protein abundances, including open source and commercial solutions. Each software includes a set of unique algorithms for different tasks of the MS data processing workflow. While many of these algorithms have been compared separately, a thorough and systematic evaluation of their overall performance is missing. Moreover, systematic information is lacking about the amount of missing values produced by the different proteomics software and the capabilities of different data imputation methods to account for them.In this study, we evaluated the performance of five popular quantitative label-free proteomics software workflows using four different spike-in data sets. Our extensive testing included the number of proteins quantified and the number of missing values produced by each workflow, the accuracy of detecting differential expression and logarithmic fold change and the effect of different imputation and filtering methods on the differential expression results. We found that the Progenesis software performed consistently well in the differential expression analysis and produced few missing values. The missing values produced by the other software decreased their performance, but this difference could be mitigated using proper data filtering or imputation methods. Among the imputation methods, we found that the local least squares (lls) regression imputation consistently increased the performance of the software in the differential expression analysis, and a combination of both data filtering and local least squares imputation increased performance the most in the tested data sets.
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Affiliation(s)
- Tommi Välikangas
- Computational Biomedicine Group, Turku Centre for Biotechnology Finland
| | - Tomi Suomi
- Computational Biomedicine research group at the Turku Centre for Biotechnology Finland
| | - Laura L Elo
- Biomathematics, Research Director in Bioinformatics and Group Leader in Computational Biomedicine at Turku Centre for Biotechnology, University of Turku, Finland
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18
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19
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Abstract
Data-independent acquisition (DIA) mode of mass spectrometry, such as the SWATH-MS technology, enables accurate and consistent measurement of proteins, which is crucial for comparative proteomics studies. However, there is lack of free and easy to implement data analysis protocols that can handle the different data processing steps from raw spectrum files to peptide intensity matrix and its downstream analysis. Here, we provide a data analysis protocol, named diatools, covering all these steps from spectral library building to differential expression analysis of DIA proteomics data. The data analysis tools used in this protocol are open source and the protocol is distributed at Docker Hub as a complete software environment that supports Linux, Windows, and macOS operating systems.
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Affiliation(s)
- Sami Pietilä
- Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Turku, Finland
| | - Tomi Suomi
- Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Turku, Finland
| | - Juhani Aakko
- Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Turku, Finland
| | - Laura L Elo
- Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Turku, Finland.
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20
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Abstract
To date, mass spectrometry (MS) data remain inherently biased as a result of reasons ranging from sample handling to differences caused by the instrumentation. Normalization is the process that aims to account for the bias and make samples more comparable. The selection of a proper normalization method is a pivotal task for the reliability of the downstream analysis and results. Many normalization methods commonly used in proteomics have been adapted from the DNA microarray techniques. Previous studies comparing normalization methods in proteomics have focused mainly on intragroup variation. In this study, several popular and widely used normalization methods representing different strategies in normalization are evaluated using three spike-in and one experimental mouse label-free proteomic data sets. The normalization methods are evaluated in terms of their ability to reduce variation between technical replicates, their effect on differential expression analysis and their effect on the estimation of logarithmic fold changes. Additionally, we examined whether normalizing the whole data globally or in segments for the differential expression analysis has an effect on the performance of the normalization methods. We found that variance stabilization normalization (Vsn) reduced variation the most between technical replicates in all examined data sets. Vsn also performed consistently well in the differential expression analysis. Linear regression normalization and local regression normalization performed also systematically well. Finally, we discuss the choice of a normalization method and some qualities of a suitable normalization method in the light of the results of our evaluation.
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Affiliation(s)
- Tommi Välikangas
- Computational Biomedicine Group at the Turku Centre for Biotechnology Finland
| | - Tomi Suomi
- Computational Biomedicine research group at the Turku Centre for Biotechnology Finland
| | - Laura L Elo
- Computational Biomedicine at Turku Centre for Biotechnology, University of Turku, Finland
- Corresponding author. Laura L. Elo, Turku Centre for Biotechnology, FI-20520 Turku, Finland. Tel.: +358-2-333-8009; Fax: +358-2-251 8808; E-mail:
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21
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Saraei S, Suomi T, Kauko O, Elo LL, Stegle O. Phosphonormalizer: an R package for normalization of MS-based label-free phosphoproteomics. Bioinformatics 2018; 34:693-694. [PMID: 28968644 DOI: 10.1093/bioinformatics/btx573] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2017] [Accepted: 09/14/2017] [Indexed: 11/14/2022] Open
Abstract
Motivation Global centering-based normalization is a commonly used normalization approach in mass spectrometry-based label-free proteomics. It scales the peptide abundances to have the same median intensities, based on an assumption that the majority of abundances remain the same across the samples. However, especially in phosphoproteomics, this assumption can introduce bias, as the samples are enriched during sample preparation which can mask the underlying biological changes. To address this possible bias, phosphopeptides quantified in both enriched and non-enriched samples can be used to calculate factors that mitigate the bias. Results We present an R package phosphonormalizer for normalizing enriched samples in label-free mass spectrometry-based phosphoproteomics. Availability and implementation The phosphonormalizer package is freely available under GPL ( > =2) license from Bioconductor (https://bioconductor.org/packages/phosphonormalizer). Contact sohrab.saraei@utu.fi or laura.elo@utu.fi. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Sohrab Saraei
- Bioinformatics Unit, Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Tykistökatu 6, FI-20520 Turku, Finland
| | - Tomi Suomi
- Bioinformatics Unit, Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Tykistökatu 6, FI-20520 Turku, Finland
| | - Otto Kauko
- Bioinformatics Unit, Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Tykistökatu 6, FI-20520 Turku, Finland
| | - Laura L Elo
- Bioinformatics Unit, Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Tykistökatu 6, FI-20520 Turku, Finland
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22
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Miikkulainen P, Högel H, Rantanen K, Suomi T, Kouvonen P, Elo LL, Jaakkola PM. HIF prolyl hydroxylase PHD3 regulates translational machinery and glucose metabolism in clear cell renal cell carcinoma. Cancer Metab 2017; 5:5. [PMID: 28680592 PMCID: PMC5496173 DOI: 10.1186/s40170-017-0167-y] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2017] [Accepted: 06/25/2017] [Indexed: 12/03/2022] Open
Abstract
Background A key feature of clear cell renal cell carcinoma (ccRCC) is the inactivation of the von Hippel-Lindau tumour suppressor protein (pVHL) that leads to the activation of hypoxia-inducible factor (HIF) pathway also in well-oxygenated conditions. Important regulator of HIF-α, prolyl hydroxylase PHD3, is expressed in high amounts in ccRCC. Although several functions and downstream targets for PHD3 in cancer have been suggested, the role of elevated PHD3 expression in ccRCC is not clear. Methods To gain insight into the functions of high PHD3 expression in ccRCC, we used PHD3 knockdown by siRNA in 786-O cells under normoxic and hypoxic conditions and performed discovery mass spectrometry (LC-MS/MS) of the purified peptide samples. The LC-MS/MS results were analysed by label-free quantification of proteome data using a peptide-level expression-change averaging procedure and subsequent gene ontology enrichment analysis. Results Our data reveals an intriguingly widespread effect of PHD3 knockdown with 91 significantly regulated proteins. Under hypoxia, the response to PHD3 silencing was wider than under normoxia illustrated by both the number of regulated proteins and by the range of protein expression levels. The main cellular functions regulated by PHD3 expression were glucose metabolism, protein translation and messenger RNA (mRNA) processing. PHD3 silencing led to downregulation of most glycolytic enzymes from glucose transport to lactate production supported by the reduction in extracellular acidification and lactate production and increase in cellular oxygen consumption rate. Moreover, upregulation of mRNA processing-related proteins and alteration in a number of ribosomal proteins was seen as a response to PHD3 silencing. Further studies on upstream effectors of the translational machinery revealed a possible role for PHD3 in regulation of mTOR pathway signalling. Conclusions Our findings suggest crucial involvement of PHD3 in the maintenance of key cellular functions including glycolysis and protein synthesis in ccRCC. Electronic supplementary material The online version of this article (doi:10.1186/s40170-017-0167-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Petra Miikkulainen
- Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Tykistökatu 6, 20520 Turku, Finland.,Department of Medical Biochemistry, Faculty of Medicine, University of Turku, Kiinamyllynkatu 10, 20520 Turku, Finland
| | - Heidi Högel
- Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Tykistökatu 6, 20520 Turku, Finland.,Department of Medical Biochemistry, Faculty of Medicine, University of Turku, Kiinamyllynkatu 10, 20520 Turku, Finland
| | - Krista Rantanen
- Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Tykistökatu 6, 20520 Turku, Finland.,Department of Medical Biochemistry, Faculty of Medicine, University of Turku, Kiinamyllynkatu 10, 20520 Turku, Finland
| | - Tomi Suomi
- Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Tykistökatu 6, 20520 Turku, Finland.,Department of Information Technology, Faculty of Mathematics and Natural Sciences, University of Turku, Vesilinnantie 5, 20520 Turku, Finland
| | - Petri Kouvonen
- Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Tykistökatu 6, 20520 Turku, Finland
| | - Laura L Elo
- Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Tykistökatu 6, 20520 Turku, Finland
| | - Panu M Jaakkola
- Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Tykistökatu 6, 20520 Turku, Finland.,Department of Medical Biochemistry, Faculty of Medicine, University of Turku, Kiinamyllynkatu 10, 20520 Turku, Finland.,Department of Oncology and Radiotherapy, Turku University Hospital, Hämeentie 11, 20520 Turku, Finland
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23
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Suomi T, Seyednasrollah F, Jaakkola MK, Faux T, Elo LL. ROTS: An R package for reproducibility-optimized statistical testing. PLoS Comput Biol 2017; 13:e1005562. [PMID: 28542205 PMCID: PMC5470739 DOI: 10.1371/journal.pcbi.1005562] [Citation(s) in RCA: 77] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2016] [Revised: 06/14/2017] [Accepted: 05/10/2017] [Indexed: 12/21/2022] Open
Abstract
Differential expression analysis is one of the most common types of analyses performed on various biological data (e.g. RNA-seq or mass spectrometry proteomics). It is the process that detects features, such as genes or proteins, showing statistically significant differences between the sample groups under comparison. A major challenge in the analysis is the choice of an appropriate test statistic, as different statistics have been shown to perform well in different datasets. To this end, the reproducibility-optimized test statistic (ROTS) adjusts a modified t-statistic according to the inherent properties of the data and provides a ranking of the features based on their statistical evidence for differential expression between two groups. ROTS has already been successfully applied in a range of different studies from transcriptomics to proteomics, showing competitive performance against other state-of-the-art methods. To promote its widespread use, we introduce here a Bioconductor R package for performing ROTS analysis conveniently on different types of omics data. To illustrate the benefits of ROTS in various applications, we present three case studies, involving proteomics and RNA-seq data from public repositories, including both bulk and single cell data. The package is freely available from Bioconductor (https://www.bioconductor.org/packages/ROTS).
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Affiliation(s)
- Tomi Suomi
- Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Turku, Finland
- Department of Future Technologies, University of Turku, Turku, Finland
- * E-mail: (TS); (LLE)
| | - Fatemeh Seyednasrollah
- Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Turku, Finland
- Department of Mathematics and Statistics, University of Turku, Turku, Finland
| | - Maria K. Jaakkola
- Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Turku, Finland
- Department of Mathematics and Statistics, University of Turku, Turku, Finland
| | - Thomas Faux
- Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Turku, Finland
| | - Laura L. Elo
- Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Turku, Finland
- * E-mail: (TS); (LLE)
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24
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Kohtala S, Theilmann W, Suomi T, Wigren HK, Porkka-Heiskanen T, Elo LL, Rokka A, Rantamäki T. Brief Isoflurane Anesthesia Produces Prominent Phosphoproteomic Changes in the Adult Mouse Hippocampus. ACS Chem Neurosci 2016; 7:749-56. [PMID: 27074656 DOI: 10.1021/acschemneuro.6b00002] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Anesthetics are widely used in medical practice and experimental research, yet the neurobiological basis governing their effects remains obscure. We have here used quantitative phosphoproteomics to investigate the protein phosphorylation changes produced by a 30 min isoflurane anesthesia in the adult mouse hippocampus. Altogether 318 phosphorylation alterations in total of 237 proteins between sham and isoflurane anesthesia were identified. Many of the hit proteins represent primary pharmacological targets of anesthetics. However, findings also enlighten the role of several other proteins-implicated in various biological processes including neuronal excitability, brain energy homeostasis, synaptic plasticity and transmission, and microtubule function-as putative (secondary) targets of anesthetics. In particular, isoflurane increases glycogen synthase kinase-3β (GSK3β) phosphorylation at the inhibitory Ser(9) residue and regulates the phosphorylation of multiple proteins downstream and upstream of this promiscuous kinase that regulate diverse biological functions. Along with confirmatory Western blot data for GSK3β and p44/42-MAPK (mitogen-activated protein kinase; reduced phosphorylation of the activation loop), we observed increased phosphorylation of microtubule-associated protein 2 (MAP2) on residues (Thr(1620,1623)) that have been shown to render its dissociation from microtubules and alterations in microtubule stability. We further demonstrate that diverse anesthetics (sevoflurane, urethane, ketamine) produce essentially similar phosphorylation changes on GSK3β, p44/p42-MAPK, and MAP2 as observed with isoflurane. Altogether our study demonstrates the potential of quantitative phosphoproteomics to study the mechanisms of anesthetics (and other drugs) in the mammalian brain and reveals how already a relatively brief anesthesia produces pronounced phosphorylation changes in multiple proteins in the central nervous system.
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Affiliation(s)
| | | | - Tomi Suomi
- Turku
Centre for Biotechnology, University of Turku, FI-20014 Turku, Finland
| | - Henna-Kaisa Wigren
- Institute
of Biomedicine, University of Helsinki, FI-00014 Helsinki, Finland
| | | | - Laura L. Elo
- Turku
Centre for Biotechnology, University of Turku, FI-20014 Turku, Finland
| | - Anne Rokka
- Turku
Centre for Biotechnology, University of Turku, FI-20014 Turku, Finland
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25
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Abstract
The expression of proteins can be quantified in high-throughput means using different types of mass spectrometers. In recent years, there have emerged label-free methods for determining protein abundance. Although the expression is initially measured at the peptide level, a common approach is to combine the peptide-level measurements into protein-level values before differential expression analysis. However, this simple combination is prone to inconsistencies between peptides and may lose valuable information. To this end, we introduce here a method for detecting differentially expressed proteins by combining peptide-level expression-change statistics. Using controlled spike-in experiments, we show that the approach of averaging peptide-level expression changes yields more accurate lists of differentially expressed proteins than does the conventional protein-level approach. This is particularly true when there are only few replicate samples or the differences between the sample groups are small. The proposed technique is implemented in the Bioconductor package PECA, and it can be downloaded from http://www.bioconductor.org.
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Affiliation(s)
| | - Garry L Corthals
- Van't Hoff Institute for Molecular Sciences, University of Amsterdam , 1090 GD Amsterdam , The Netherlands
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26
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Pursiheimo A, Vehmas AP, Afzal S, Suomi T, Chand T, Strauss L, Poutanen M, Rokka A, Corthals GL, Elo LL. Optimization of Statistical Methods Impact on Quantitative Proteomics Data. J Proteome Res 2015; 14:4118-26. [DOI: 10.1021/acs.jproteome.5b00183] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Anna Pursiheimo
- Turku
Centre for Biotechnology, University of Turku and Åbo Akademi University, Tykistökatu 6, FI-20520 Turku, Finland
- Department
of Mathematics and Statistics, University of Turku, FI-20014 Turku, Finland
| | - Anni P. Vehmas
- Turku
Centre for Biotechnology, University of Turku and Åbo Akademi University, Tykistökatu 6, FI-20520 Turku, Finland
| | - Saira Afzal
- Turku
Centre for Biotechnology, University of Turku and Åbo Akademi University, Tykistökatu 6, FI-20520 Turku, Finland
| | - Tomi Suomi
- Turku
Centre for Biotechnology, University of Turku and Åbo Akademi University, Tykistökatu 6, FI-20520 Turku, Finland
- Department
of Information Technology, University of Turku, FI-20014 Turku, Finland
| | - Thaman Chand
- Turku
Centre for Biotechnology, University of Turku and Åbo Akademi University, Tykistökatu 6, FI-20520 Turku, Finland
| | - Leena Strauss
- Department
of Physiology and Turku Center for Disease Modeling, Institute of
Biomedicine, University of Turku, Kiinamyllynkatu 10, FI-20520 Turku, Finland
| | - Matti Poutanen
- Department
of Physiology and Turku Center for Disease Modeling, Institute of
Biomedicine, University of Turku, Kiinamyllynkatu 10, FI-20520 Turku, Finland
| | - Anne Rokka
- Turku
Centre for Biotechnology, University of Turku and Åbo Akademi University, Tykistökatu 6, FI-20520 Turku, Finland
| | - Garry L. Corthals
- Turku
Centre for Biotechnology, University of Turku and Åbo Akademi University, Tykistökatu 6, FI-20520 Turku, Finland
- Van’t
Hoff Institute for Molecular Sciences, University of Amsterdam, P.O. Box 94157, 1090 GD Amsterdam, The Netherlands
| | - Laura L. Elo
- Turku
Centre for Biotechnology, University of Turku and Åbo Akademi University, Tykistökatu 6, FI-20520 Turku, Finland
- Department
of Mathematics and Statistics, University of Turku, FI-20014 Turku, Finland
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27
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Kannaste O, Suomi T, Salmi J, Uusipaikka E, Nevalainen O, Corthals GL. Cross-Correlation of Spectral Count Ranking to Validate Quantitative Proteome Measurements. J Proteome Res 2014; 13:1957-68. [DOI: 10.1021/pr401096z] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Olli Kannaste
- Turku
Centre for Biotechnology, University of Turku and Åbo Akademi University, 20520 Turku, Finland
| | - Tomi Suomi
- Department
of Information Technology, University of Turku, 20014 Turku, Finland
| | - Jussi Salmi
- Turku
Centre for Biotechnology, University of Turku and Åbo Akademi University, 20520 Turku, Finland
| | - Esa Uusipaikka
- Department
of Statistics, University of Turku, 20014 Turku, Finland
| | - Olli Nevalainen
- Department
of Information Technology, University of Turku, 20014 Turku, Finland
| | - Garry L. Corthals
- Turku
Centre for Biotechnology, University of Turku and Åbo Akademi University, 20520 Turku, Finland
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28
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Virolainen KM, Suomi T, Suhonen J, Kuitunen M. Conservation of vascular plants in single large and several small mires: species richness, rarity and taxonomic diversity. J Appl Ecol 2003. [DOI: 10.1046/j.1365-2664.1998.355344.x] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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29
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Virolainen KM, Ahlroth P, Hyvärinen E, Korkeamäki E, Mattila J, Päiivinen J, Rintala T, Suomi T, Suhonen J. Hot spots, indicator taxa, complementarity and optimal networks of taiga. Proc Biol Sci 2000; 267:1143-7. [PMID: 10885520 PMCID: PMC1690649 DOI: 10.1098/rspb.2000.1120] [Citation(s) in RCA: 50] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
If hot spots for different taxa coincide, priority-setting surveys in a region could be carried out more cheaply by focusing on indicator taxa. Several previous studies show that hot spots of different taxa rarely coincide. However, in tropical areas indicator taxa may be used in selecting complementary networks to represent biodiversity as a whole. We studied beetles (Coleoptera), Heteroptera, polypores or bracket fungi (Polyporaceae) and vascular plants of old growth boreal taiga forests. Optimal networks for Heteroptera maximized the high overall species richness of beetles and vascular plants, but these networks were least favourable options for polypores. Polypores are an important group indicating the conservation value of old growth taiga forests. Random selection provided a better option. Thus, certain groups may function as good indicators for maximizing the overall species richness of some taxonomic groups, but all taxa should be examined separately.
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Affiliation(s)
- K M Virolainen
- Department of Biological and Environmental Science, University of Jyväskylä , Finland.
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