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Federico A, Pavel A, Möbus L, McKean D, Del Giudice G, Fortino V, Niehues H, Rastrick J, Eyerich K, Eyerich S, van den Bogaard E, Smith C, Weidinger S, de Rinaldis E, Greco D. The integration of large-scale public data and network analysis uncovers molecular characteristics of psoriasis. Hum Genomics 2022; 16:62. [PMID: 36437479 PMCID: PMC9703794 DOI: 10.1186/s40246-022-00431-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 11/07/2022] [Indexed: 11/29/2022] Open
Abstract
In recent years, a growing interest in the characterization of the molecular basis of psoriasis has been observed. However, despite the availability of a large amount of molecular data, many pathogenic mechanisms of psoriasis are still poorly understood. In this study, we performed an integrated analysis of 23 public transcriptomic datasets encompassing both lesional and uninvolved skin samples from psoriasis patients. We defined comprehensive gene co-expression network models of psoriatic lesions and uninvolved skin. Moreover, we curated and exploited a wide range of functional information from multiple public sources in order to systematically annotate the inferred networks. The integrated analysis of transcriptomics data and co-expression networks highlighted genes that are frequently dysregulated and show aberrant patterns of connectivity in the psoriatic lesion compared with the unaffected skin. Our approach allowed us to also identify plausible, previously unknown, actors in the expression of the psoriasis phenotype. Finally, we characterized communities of co-expressed genes associated with relevant molecular functions and expression signatures of specific immune cell types associated with the psoriasis lesion. Overall, integrating experimental driven results with curated functional information from public repositories represents an efficient approach to empower knowledge generation about psoriasis and may be applicable to other complex diseases.
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Affiliation(s)
- Antonio Federico
- Faculty of Medicine and Health Technology, Tampere University, Kauppi Campus, Arvo Ylpön Katu 34, 33520, Tampere, Finland
- BioMeditech Institute, Tampere University, Tampere, Finland
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Tampere University, Tampere, Finland
- Tampere Institute for Advanced Studies, Tampere University, Tampere, Finland
| | - Alisa Pavel
- Faculty of Medicine and Health Technology, Tampere University, Kauppi Campus, Arvo Ylpön Katu 34, 33520, Tampere, Finland
- BioMeditech Institute, Tampere University, Tampere, Finland
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Tampere University, Tampere, Finland
| | - Lena Möbus
- Faculty of Medicine and Health Technology, Tampere University, Kauppi Campus, Arvo Ylpön Katu 34, 33520, Tampere, Finland
- BioMeditech Institute, Tampere University, Tampere, Finland
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Tampere University, Tampere, Finland
| | - David McKean
- Sanofi Immunology and Inflammation Research Therapeutic Area, Precision Immunology Cluster, Cambridge, Massachusetts, USA
| | - Giusy Del Giudice
- Faculty of Medicine and Health Technology, Tampere University, Kauppi Campus, Arvo Ylpön Katu 34, 33520, Tampere, Finland
- BioMeditech Institute, Tampere University, Tampere, Finland
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Tampere University, Tampere, Finland
| | - Vittorio Fortino
- Institute of Biomedicine, University of Eastern Finland, Kuopio, Finland
| | - Hanna Niehues
- Department of Dermatology, Radboud University Medical Center, Radboud Institute for Molecular Life Sciences, Nijmegen, The Netherlands
| | - Joe Rastrick
- Immunology Therapeutic Area, UCB Pharma, Slough, UK
| | - Kilian Eyerich
- Department of Dermatology and Allergy, Technical University of Munich, Munich, Germany
- Unit of Dermatology and Venerology, Department of Medicine, Karolinska Institute, Karolinska University Hospital, Stockholm, Sweden
| | - Stefanie Eyerich
- ZAUM-Center of Allergy and Environment, Technical University and Helmholtz Center Munich, Munich, Germany
| | - Ellen van den Bogaard
- Department of Dermatology, Radboud University Medical Center, Radboud Institute for Molecular Life Sciences, Nijmegen, The Netherlands
| | - Catherine Smith
- St. John's Institute of Dermatology, King's College London, London, UK
| | | | - Emanuele de Rinaldis
- Sanofi Immunology and Inflammation Research Therapeutic Area, Precision Immunology Cluster, Cambridge, Massachusetts, USA
| | - Dario Greco
- Faculty of Medicine and Health Technology, Tampere University, Kauppi Campus, Arvo Ylpön Katu 34, 33520, Tampere, Finland.
- BioMeditech Institute, Tampere University, Tampere, Finland.
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Tampere University, Tampere, Finland.
- Institute of Biotechnology, University of Helsinki, Helsinki, Finland.
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Altaf R, Nadeem H, Babar MM, Ilyas U, Muhammad SA. Genome-scale meta-analysis of breast cancer datasets identifies promising targets for drug development. JOURNAL OF BIOLOGICAL RESEARCH (THESSALONIKE, GREECE) 2021; 28:5. [PMID: 33593445 PMCID: PMC7885587 DOI: 10.1186/s40709-021-00136-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Accepted: 02/05/2021] [Indexed: 01/19/2023]
Abstract
Background Because of the highly heterogeneous nature of breast cancer, each subtype differs in response to several treatment regimens. This has limited the therapeutic options for metastatic breast cancer disease requiring exploration of diverse therapeutic models to target tumor specific biomarkers. Methods Differentially expressed breast cancer genes identified through extensive data mapping were studied for their interaction with other target proteins involved in breast cancer progression. The molecular mechanisms by which these signature genes are involved in breast cancer metastasis were also studied through pathway analysis. The potential drug targets for these genes were also identified. Results From 50 DEGs, 20 genes were identified based on fold change and p-value and the data curation of these genes helped in shortlisting 8 potential gene signatures that can be used as potential candidates for breast cancer. Their network and pathway analysis clarified the role of these genes in breast cancer and their interaction with other signaling pathways involved in the progression of disease metastasis. The miRNA targets identified through miRDB predictor provided potential miRNA targets for these genes that can be involved in breast cancer progression. Several FDA approved drug targets were identified for the signature genes easing the therapeutic options for breast cancer treatment. Conclusion The study provides a more clarified role of signature genes, their interaction with other genes as well as signaling pathways. The miRNA prediction and the potential drugs identified will aid in assessing the role of these targets in breast cancer.
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Affiliation(s)
- Reem Altaf
- Department of Pharmaceutical Chemistry, Faculty of Pharmaceutical Sciences, Riphah International University, Islamabad, 44000, Pakistan.
| | - Humaira Nadeem
- Department of Pharmaceutical Chemistry, Faculty of Pharmaceutical Sciences, Riphah International University, Islamabad, 44000, Pakistan
| | - Mustafeez Mujtaba Babar
- Shifa College of Pharmaceutical Sciences, Shifa Tameer-E-Millat University, Islamabad, 44000, Pakistan
| | - Umair Ilyas
- Department of Pharmaceutics, Faculty of Pharmaceutical Sciences, Riphah International University, Islamabad, 44000, Pakistan
| | - Syed Aun Muhammad
- Institute of Molecular Biology and Biotechnology, Bahauddin Zakariya University, Multan, 66000, Pakistan
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Federico A, Hautanen V, Christian N, Kremer A, Serra A, Greco D. Manually curated and harmonised transcriptomics datasets of psoriasis and atopic dermatitis patients. Sci Data 2020; 7:343. [PMID: 33051456 PMCID: PMC7555498 DOI: 10.1038/s41597-020-00696-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Accepted: 09/03/2020] [Indexed: 11/08/2022] Open
Abstract
We present manually curated transcriptomics data of psoriasis and atopic dermatitis patients retrieved from the NCBI Gene Expression Omnibus and EBI ArrayExpress repositories. We collected 39 transcriptomics datasets, deriving from DNA microarrays and RNA-Sequencing technologies, for a total of 1677 samples. We provide quality-checked, homogenised and preprocessed gene expression matrices and their corresponding metadata tables along with the estimated surrogate variables. These data represent a ready-made valuable source of knowledge for translational researchers in the dermatology field.
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Affiliation(s)
- Antonio Federico
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- BioMediTech Institute, Tampere University, Tampere, Finland
| | - Veera Hautanen
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- BioMediTech Institute, Tampere University, Tampere, Finland
| | - Nils Christian
- ITTM S.A. - Information Technology for Translational Medicine, Esch-sur-Alzette, Luxembourg
| | - Andreas Kremer
- ITTM S.A. - Information Technology for Translational Medicine, Esch-sur-Alzette, Luxembourg
| | - Angela Serra
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- BioMediTech Institute, Tampere University, Tampere, Finland
| | - Dario Greco
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.
- BioMediTech Institute, Tampere University, Tampere, Finland.
- Institute of Biotechnology, University of Helsinki, Helsinki, Finland.
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Analysis of global gene expression at seven brain regions of patients with schizophrenia. Schizophr Res 2020; 223:119-127. [PMID: 32631700 DOI: 10.1016/j.schres.2020.06.032] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/11/2019] [Revised: 04/14/2020] [Accepted: 06/27/2020] [Indexed: 12/30/2022]
Abstract
Previous transcriptome analyses of brain samples provided several insights into the pathophysiology of schizophrenia. In this study, we aimed to re-investigate gene expression datasets from seven brain regions of patients with schizophrenia and healthy controls by adopting a unified approach. After adjustment for confounding factors, we detected gene expression changes in 2 out of 7 brain regions - the dorsolateral prefrontal cortex (DLPFC) and parietal cortex (PC). We found relatively small effect sizes, not exceeding absolute log fold changes of 1. Gene-set enrichment analysis revealed the following alterations: 1) down-regulation of GABAergic signaling (in DLPFC and PC); 2) up-regulation of interleukin-23 signaling together with up-regulation of transcription mediated by RUNX1 and RUNX3 as well as down-regulation of RUNX2 signaling (in DLPFC) and 3) up-regulation of genes associated with responses to metal ions and RUNX1 signaling (PC). The number of neurons was significantly lower and the number of astrocytes was significantly higher at both brain regions. In turn, the index of microglia was increased in DLPFC and decreased in PC. Finally, our unsupervised analysis demonstrated that cellular composition of the samples was a major confounding factor in the analysis of gene expression across all datasets. In conclusion, our analysis provides further evidence that small but significant changes in the expression of genes related to GABAergic signaling, brain development, neuroinflammation and responses to metal ions might be involved in the pathophysiology of schizophrenia. Cell sorting techniques need to be used by future studies to dissect the effect of cellular content.
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Federico A, Serra A, Ha MK, Kohonen P, Choi JS, Liampa I, Nymark P, Sanabria N, Cattelani L, Fratello M, Kinaret PAS, Jagiello K, Puzyn T, Melagraki G, Gulumian M, Afantitis A, Sarimveis H, Yoon TH, Grafström R, Greco D. Transcriptomics in Toxicogenomics, Part II: Preprocessing and Differential Expression Analysis for High Quality Data. NANOMATERIALS 2020; 10:nano10050903. [PMID: 32397130 PMCID: PMC7279140 DOI: 10.3390/nano10050903] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Revised: 04/29/2020] [Accepted: 05/04/2020] [Indexed: 12/28/2022]
Abstract
Preprocessing of transcriptomics data plays a pivotal role in the development of toxicogenomics-driven tools for chemical toxicity assessment. The generation and exploitation of large volumes of molecular profiles, following an appropriate experimental design, allows the employment of toxicogenomics (TGx) approaches for a thorough characterisation of the mechanism of action (MOA) of different compounds. To date, a plethora of data preprocessing methodologies have been suggested. However, in most cases, building the optimal analytical workflow is not straightforward. A careful selection of the right tools must be carried out, since it will affect the downstream analyses and modelling approaches. Transcriptomics data preprocessing spans across multiple steps such as quality check, filtering, normalization, batch effect detection and correction. Currently, there is a lack of standard guidelines for data preprocessing in the TGx field. Defining the optimal tools and procedures to be employed in the transcriptomics data preprocessing will lead to the generation of homogeneous and unbiased data, allowing the development of more reliable, robust and accurate predictive models. In this review, we outline methods for the preprocessing of three main transcriptomic technologies including microarray, bulk RNA-Sequencing (RNA-Seq), and single cell RNA-Sequencing (scRNA-Seq). Moreover, we discuss the most common methods for the identification of differentially expressed genes and to perform a functional enrichment analysis. This review is the second part of a three-article series on Transcriptomics in Toxicogenomics.
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Affiliation(s)
- Antonio Federico
- Faculty of Medicine and Health Technology, Tampere University, FI-33014 Tampere, Finland; (A.F.); (A.S.); (L.C.); (M.F.); (P.A.S.K.)
- BioMediTech Institute, Tampere University, FI-33014 Tampere, Finland
| | - Angela Serra
- Faculty of Medicine and Health Technology, Tampere University, FI-33014 Tampere, Finland; (A.F.); (A.S.); (L.C.); (M.F.); (P.A.S.K.)
- BioMediTech Institute, Tampere University, FI-33014 Tampere, Finland
| | - My Kieu Ha
- Center for Next Generation Cytometry, Hanyang University, Seoul 04763, Korea; (M.K.H.); (J.-S.C.); (T.-H.Y.)
- Department of Chemistry, College of Natural Sciences, Hanyang University, Seoul 04763, Korea
- Institute of Next Generation Material Design, Hanyang University, Seoul 04763, Korea
| | - Pekka Kohonen
- Institute of Environmental Medicine, Karolinska Institutet, 171 77 Stockholm, Sweden; (P.K.); (P.N.); (R.G.)
- Division of Toxicology, Misvik Biology, 20520 Turku, Finland
| | - Jang-Sik Choi
- Center for Next Generation Cytometry, Hanyang University, Seoul 04763, Korea; (M.K.H.); (J.-S.C.); (T.-H.Y.)
- Department of Chemistry, College of Natural Sciences, Hanyang University, Seoul 04763, Korea
- Institute of Next Generation Material Design, Hanyang University, Seoul 04763, Korea
| | - Irene Liampa
- School of Chemical Engineering, National Technical University of Athens, 157 80 Athens, Greece; (I.L.); (H.S.)
| | - Penny Nymark
- Institute of Environmental Medicine, Karolinska Institutet, 171 77 Stockholm, Sweden; (P.K.); (P.N.); (R.G.)
- Division of Toxicology, Misvik Biology, 20520 Turku, Finland
| | - Natasha Sanabria
- National Institute for Occupational Health, Johannesburg 30333, South Africa; (N.S.); (M.G.)
| | - Luca Cattelani
- Faculty of Medicine and Health Technology, Tampere University, FI-33014 Tampere, Finland; (A.F.); (A.S.); (L.C.); (M.F.); (P.A.S.K.)
- BioMediTech Institute, Tampere University, FI-33014 Tampere, Finland
| | - Michele Fratello
- Faculty of Medicine and Health Technology, Tampere University, FI-33014 Tampere, Finland; (A.F.); (A.S.); (L.C.); (M.F.); (P.A.S.K.)
- BioMediTech Institute, Tampere University, FI-33014 Tampere, Finland
| | - Pia Anneli Sofia Kinaret
- Faculty of Medicine and Health Technology, Tampere University, FI-33014 Tampere, Finland; (A.F.); (A.S.); (L.C.); (M.F.); (P.A.S.K.)
- BioMediTech Institute, Tampere University, FI-33014 Tampere, Finland
- Institute of Biotechnology, University of Helsinki, 00014 Helsinki, Finland
| | - Karolina Jagiello
- QSAR Lab Ltd., Aleja Grunwaldzka 190/102, 80-266 Gdansk, Poland; (K.J.); (T.P.)
- Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80-308 Gdansk, Poland
| | - Tomasz Puzyn
- QSAR Lab Ltd., Aleja Grunwaldzka 190/102, 80-266 Gdansk, Poland; (K.J.); (T.P.)
- Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80-308 Gdansk, Poland
| | - Georgia Melagraki
- Nanoinformatics Department, NovaMechanics Ltd., Nicosia 1065, Cyprus; (G.M.); (A.A.)
| | - Mary Gulumian
- National Institute for Occupational Health, Johannesburg 30333, South Africa; (N.S.); (M.G.)
- Haematology and Molecular Medicine Department, School of Pathology, University of the Witwatersrand, Johannesburg 2050, South Africa
| | - Antreas Afantitis
- Nanoinformatics Department, NovaMechanics Ltd., Nicosia 1065, Cyprus; (G.M.); (A.A.)
| | - Haralambos Sarimveis
- School of Chemical Engineering, National Technical University of Athens, 157 80 Athens, Greece; (I.L.); (H.S.)
| | - Tae-Hyun Yoon
- Center for Next Generation Cytometry, Hanyang University, Seoul 04763, Korea; (M.K.H.); (J.-S.C.); (T.-H.Y.)
- Department of Chemistry, College of Natural Sciences, Hanyang University, Seoul 04763, Korea
- Institute of Next Generation Material Design, Hanyang University, Seoul 04763, Korea
| | - Roland Grafström
- Institute of Environmental Medicine, Karolinska Institutet, 171 77 Stockholm, Sweden; (P.K.); (P.N.); (R.G.)
- Division of Toxicology, Misvik Biology, 20520 Turku, Finland
| | - Dario Greco
- Faculty of Medicine and Health Technology, Tampere University, FI-33014 Tampere, Finland; (A.F.); (A.S.); (L.C.); (M.F.); (P.A.S.K.)
- BioMediTech Institute, Tampere University, FI-33014 Tampere, Finland
- Institute of Biotechnology, University of Helsinki, 00014 Helsinki, Finland
- Correspondence:
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Inference of cell type content from human brain transcriptomic datasets illuminates the effects of age, manner of death, dissection, and psychiatric diagnosis. PLoS One 2018; 13:e0200003. [PMID: 30016334 PMCID: PMC6049916 DOI: 10.1371/journal.pone.0200003] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2017] [Accepted: 06/18/2018] [Indexed: 01/01/2023] Open
Abstract
Psychiatric illness is unlikely to arise from pathology occurring uniformly across all cell types in affected brain regions. Despite this, transcriptomic analyses of the human brain have typically been conducted using macro-dissected tissue due to the difficulty of performing single-cell type analyses with donated post-mortem brains. To address this issue statistically, we compiled a database of several thousand transcripts that were specifically-enriched in one of 10 primary cortical cell types in previous publications. Using this database, we predicted the relative cell type content for 833 human cortical samples using microarray or RNA-Seq data from the Pritzker Consortium (GSE92538) or publicly-available databases (GSE53987, GSE21935, GSE21138, CommonMind Consortium). These predictions were generated by averaging normalized expression levels across transcripts specific to each cell type using our R-package BrainInABlender (validated and publicly-released on github). Using this method, we found that the principal components of variation in the datasets strongly correlated with the predicted neuronal/glial content of the samples. This variability was not simply due to dissection–the relative balance of brain cell types appeared to be influenced by a variety of demographic, pre- and post-mortem variables. Prolonged hypoxia around the time of death predicted increased astrocytic and endothelial gene expression, illustrating vascular upregulation. Aging was associated with decreased neuronal gene expression. Red blood cell gene expression was reduced in individuals who died following systemic blood loss. Subjects with Major Depressive Disorder had decreased astrocytic gene expression, mirroring previous morphometric observations. Subjects with Schizophrenia had reduced red blood cell gene expression, resembling the hypofrontality detected in fMRI experiments. Finally, in datasets containing samples with especially variable cell content, we found that controlling for predicted sample cell content while evaluating differential expression improved the detection of previously-identified psychiatric effects. We conclude that accounting for cell type can greatly improve the interpretability of transcriptomic data.
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Lee HJ, Georgiadou A, Otto TD, Levin M, Coin LJ, Conway DJ, Cunnington AJ. Transcriptomic Studies of Malaria: a Paradigm for Investigation of Systemic Host-Pathogen Interactions. Microbiol Mol Biol Rev 2018; 82:e00071-17. [PMID: 29695497 PMCID: PMC5968457 DOI: 10.1128/mmbr.00071-17] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Transcriptomics, the analysis of genome-wide RNA expression, is a common approach to investigate host and pathogen processes in infectious diseases. Technical and bioinformatic advances have permitted increasingly thorough analyses of the association of RNA expression with fundamental biology, immunity, pathogenesis, diagnosis, and prognosis. Transcriptomic approaches can now be used to realize a previously unattainable goal, the simultaneous study of RNA expression in host and pathogen, in order to better understand their interactions. This exciting prospect is not without challenges, especially as focus moves from interactions in vitro under tightly controlled conditions to tissue- and systems-level interactions in animal models and natural and experimental infections in humans. Here we review the contribution of transcriptomic studies to the understanding of malaria, a parasitic disease which has exerted a major influence on human evolution and continues to cause a huge global burden of disease. We consider malaria a paradigm for the transcriptomic assessment of systemic host-pathogen interactions in humans, because much of the direct host-pathogen interaction occurs within the blood, a readily sampled compartment of the body. We illustrate lessons learned from transcriptomic studies of malaria and how these lessons may guide studies of host-pathogen interactions in other infectious diseases. We propose that the potential of transcriptomic studies to improve the understanding of malaria as a disease remains partly untapped because of limitations in study design rather than as a consequence of technological constraints. Further advances will require the integration of transcriptomic data with analytical approaches from other scientific disciplines, including epidemiology and mathematical modeling.
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Affiliation(s)
- Hyun Jae Lee
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia
| | | | - Thomas D Otto
- Centre of Immunobiology, University of Glasgow, Glasgow, United Kingdom
| | - Michael Levin
- Section of Paediatrics, Imperial College, London, United Kingdom
| | - Lachlan J Coin
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia
| | - David J Conway
- Department of Pathogen Molecular Biology, London School of Hygiene and Tropical Medicine, London, United Kingdom
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Karpinski P, Rossowska J, Sasiadek MM. Immunological landscape of consensus clusters in colorectal cancer. Oncotarget 2017; 8:105299-105311. [PMID: 29285252 PMCID: PMC5739639 DOI: 10.18632/oncotarget.22169] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2017] [Accepted: 09/20/2017] [Indexed: 12/30/2022] Open
Abstract
Recent, large-scale expression–based subtyping has advanced our understanding of the genomic landscape of colorectal cancer (CRC) and resulted in a consensus molecular classification that enables the categorization of most CRC tumors into one of four consensus molecular subtypes (CMS). Currently, major progress in characterization of immune landscape of tumor-associated microenvironment has been made especially with respect to microsatellite status of CRCs. While these studies profoundly improved the understanding of molecular and immunological profile of CRCs heterogeneity less is known about repertoire of the tumor infiltrating immune cells of each CMS. In order to comprehensively characterize the immune landscape of CRC we re-analyzed a total of 15 CRC genome-wide expression data sets encompassing 1597 tumors and 125 normal adjacent colon tissues. After quality filtering, CRC clusters were discovered using a combination of multiple clustering algorithms and multiple validity metrics. CIBERSORT algorithm was used to compute relative proportions of 22 human leukocyte subpopulations across CRC clusters and normal colon tissue. Subsequently, differential expression specific to tumor epithelial cells was calculated to characterize mechanisms of tumor escape from immune surveillance occurring in particular CRC clusters. Our results not only characterize the common and cluster-specific influx of immune cells into CRCs but also identify several deregulated gene targets that may contribute to improvement of immunotherapeutic strategies in CRC.
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Affiliation(s)
- Pawel Karpinski
- Department of Genetics, Wroclaw Medical University, Wroclaw, Poland
| | - Joanna Rossowska
- L. Hirszfeld Institute of Immunology and Experimental Therapy, Polish Academy of Sciences, Wroclaw, Poland
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Karpiński P, Frydecka D, Sąsiadek MM, Misiak B. Reduced number of peripheral natural killer cells in schizophrenia but not in bipolar disorder. Brain Behav Immun 2016; 54:194-200. [PMID: 26872421 DOI: 10.1016/j.bbi.2016.02.005] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/23/2015] [Revised: 01/26/2016] [Accepted: 02/08/2016] [Indexed: 12/27/2022] Open
Abstract
Overwhelming evidence indicates that subthreshold inflammatory state might be implicated in the pathophysiology of schizophrenia (SCZ) and bipolar disorder (BPD). It has been reported that both groups of patients might be characterized by abnormal lymphocyte counts. However, little is known about alterations in lymphocyte proportions that may differentiate SCZ and BPD patients. Therefore, in this study we investigated blood cell proportions quantified by means of microarray expression deconvolution using publicly available data from SCZ and BPD patients. We found significantly lower counts of natural killer (NK) cells in drug-naïve and medicated SCZ patients compared to healthy controls across all datasets. In one dataset from SCZ patients, there were no significant differences in the number of NK cells between acutely relapsed and remitted SCZ patients. No significant difference in the number of NK cells between BPD patients and healthy controls was observed in all datasets. Our results indicate that SCZ patients, but not BPD patients, might be characterized by reduced counts of NK cells. Future studies looking at lymphocyte counts in SCZ should combine the analysis of data obtained using computational deconvolution and flow cytometry techniques.
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Affiliation(s)
- Paweł Karpiński
- Department of Genetics, Wroclaw Medical University, 1 Marcinkowski Street, 50-368 Wroclaw, Poland
| | - Dorota Frydecka
- Department of Psychiatry, Wroclaw Medical University, 10 Pasteur Street, 50-367 Wroclaw, Poland
| | - Maria M Sąsiadek
- Department of Genetics, Wroclaw Medical University, 1 Marcinkowski Street, 50-368 Wroclaw, Poland
| | - Błażej Misiak
- Department of Genetics, Wroclaw Medical University, 1 Marcinkowski Street, 50-368 Wroclaw, Poland; Department of Psychiatry, Wroclaw Medical University, 10 Pasteur Street, 50-367 Wroclaw, Poland.
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Microarray experiments and factors which affect their reliability. Biol Direct 2015; 10:46. [PMID: 26335588 PMCID: PMC4559324 DOI: 10.1186/s13062-015-0077-2] [Citation(s) in RCA: 72] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2015] [Accepted: 08/24/2015] [Indexed: 12/12/2022] Open
Abstract
Oligonucleotide microarrays belong to the basic tools of molecular biology and allow for simultaneous assessment of the expression level of thousands of genes. Analysis of microarray data is however very complex, requiring sophisticated methods to control for various factors that are inherent to the procedures used. In this article we describe the individual steps of a microarray experiment, highlighting important elements and factors that may affect the processes involved and that influence the interpretation of the results. Additionally, we describe methods that can be used to estimate the influence of these factors, and to control the way in which they affect the expression estimates. A comprehensive understanding of the experimental protocol used in a microarray experiment aids the interpretation of the obtained results. By describing known factors which affect expression estimates this article provides guidelines for appropriate quality control and pre-processing of the data, additionally applicable to other transcriptome analysis methods that utilize similar sample handling protocols.
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Foukakis T, Lövrot J, Sandqvist P, Xie H, Lindström LS, Giorgetti C, Jacobsson H, Hedayati E, Bergh J. Gene expression profiling of sequential metastatic biopsies for biomarker discovery in breast cancer. Mol Oncol 2015; 9:1384-91. [PMID: 25888067 DOI: 10.1016/j.molonc.2015.03.011] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2014] [Revised: 03/10/2015] [Accepted: 03/23/2015] [Indexed: 11/17/2022] Open
Abstract
The feasibility of longitudinal metastatic biopsies for gene expression profiling in breast cancer is unexplored. Dynamic changes in gene expression can potentially predict efficacy of targeted cancer drugs. Patients enrolled in a phase III trial of metastatic breast cancer with docetaxel monotherapy versus combination of docetaxel + sunitinib were offered to participate in a translational substudy comprising longitudinal fine needle aspiration biopsies and Positron Emission Tomography imaging before (T1) and two weeks after start of treatment (T2). Aspirated tumor material was used for microarray analysis, and treatment-induced changes (T2 versus T1) in gene expression and standardized uptake values (SUV) were investigated and correlated to clinical outcome measures. Gene expression profiling yielded high-quality data at both time points in 14/18 patients. Unsupervised clustering revealed specific patterns of changes caused by monotherapy vs. combination therapy (p = 0.021, Fisher's exact test). A therapy-induced reduction of known proliferation and hypoxia metagene scores was prominent in the combination arm. Changes in a previously reported hypoxia metagene score were strongly correlated to the objective responses seen by conventional radiology assessments after 6 weeks in the combination arm, Spearman's ρ = 1 (p = 0.017) but not in monotherapy, ρ = -0.029 (p = 1). Similarly, the Predictor Analysis of Microarrays 50 (PAM50) proliferation metagene correlated to tumor changes merely in the combination arm at 6 and 12 weeks (ρ = 0.900, p = 0.083 and ρ = 1, p = 0.017 respectively). Reductions in mean SUV were a reliable early predictor of objective response in monotherapy, ρ = 0.833 (p = 0.008), but not in the combination arm ρ = -0.029 (p = 1). Gene expression profiling of longitudinal metastatic aspiration biopsies was feasible, demonstrated biological validity and provided predictive information.
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Affiliation(s)
- Theodoros Foukakis
- Department of Oncology-Pathology, Cancer Center Karolinska, Karolinska Institutet and University Hospital, Stockholm, Sweden.
| | - John Lövrot
- Department of Oncology-Pathology, Cancer Center Karolinska, Karolinska Institutet and University Hospital, Stockholm, Sweden
| | - Patricia Sandqvist
- Department of Radiology and Nuclear Medicine, Karolinska Institutet and University Hospital, Stockholm, Sweden
| | - Hanjing Xie
- Department of Oncology-Pathology, Cancer Center Karolinska, Karolinska Institutet and University Hospital, Stockholm, Sweden
| | - Linda S Lindström
- Department of Oncology-Pathology, Cancer Center Karolinska, Karolinska Institutet and University Hospital, Stockholm, Sweden; Department of Biosciences and Nutrition, Karolinska Institutet, Stockholm, Sweden; Department of Surgery, University of California at San Francisco, USA
| | | | - Hans Jacobsson
- Department of Radiology and Nuclear Medicine, Karolinska Institutet and University Hospital, Stockholm, Sweden
| | - Elham Hedayati
- Department of Oncology-Pathology, Cancer Center Karolinska, Karolinska Institutet and University Hospital, Stockholm, Sweden
| | - Jonas Bergh
- Department of Oncology-Pathology, Cancer Center Karolinska, Karolinska Institutet and University Hospital, Stockholm, Sweden
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Fasold M, Binder H. Variation of RNA Quality and Quantity Are Major Sources of Batch Effects in Microarray Expression Data. MICROARRAYS 2014; 3:322-39. [PMID: 27600351 PMCID: PMC4979052 DOI: 10.3390/microarrays3040322] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/15/2014] [Revised: 09/30/2014] [Accepted: 12/08/2014] [Indexed: 01/03/2023]
Abstract
The great utility of microarrays for genome-scale expression analysis is challenged by the widespread presence of batch effects, which bias expression measurements in particular within large data sets. These unwanted technical artifacts can obscure biological variation and thus significantly reduce the reliability of the analysis results. It is largely unknown which are the predominant technical sources leading to batch effects. We here quantitatively assess the prevalence and impact of several known technical effects on microarray expression results. Particularly, we focus on important factors such as RNA degradation, RNA quantity, and sequence biases including multiple guanine effects. We find that the common variation of RNA quality and RNA quantity can not only yield low-quality expression results, but that both factors also correlate with batch effects and biological characteristics of the samples.
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Affiliation(s)
- Mario Fasold
- Interdisciplinary Centre for Bioinformatics, Universität Leipzig, Härtelstr. 16-18, 04107 Leipzig, Germany.
- ecSeq Bioinformatics, Brandvorwerkstrasse 43, 04275 Leipzig, Germany.
| | - Hans Binder
- Interdisciplinary Centre for Bioinformatics, Universität Leipzig, Härtelstr. 16-18, 04107 Leipzig, Germany.
- Leipzig Research Center for Civilization Diseases, Universität Leipzig, Philipp-Rosenthal-Straße 27, 04103 Leipzig, Germany.
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