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Sestan N, State MW. Lost in Translation: Traversing the Complex Path from Genomics to Therapeutics in Autism Spectrum Disorder. Neuron 2019; 100:406-423. [PMID: 30359605 DOI: 10.1016/j.neuron.2018.10.015] [Citation(s) in RCA: 73] [Impact Index Per Article: 14.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2018] [Revised: 09/29/2018] [Accepted: 10/08/2018] [Indexed: 12/24/2022]
Abstract
Recent progress in the genomics of non-syndromic autism spectrum disorder (nsASD) highlights rare, large-effect, germline, heterozygous de novo coding mutations. This distinguishes nsASD from later-onset psychiatric disorders where gene discovery efforts have predominantly yielded common alleles of small effect. These differences point to distinctive opportunities for clarifying the neurobiology of nsASD and developing novel treatments. We argue that the path ahead also presents key challenges, including distinguishing human pathophysiology from the potentially pleiotropic neurobiology mediated by established risk genes. We present our view of some of the conceptual limitations of traditional studies of model organisms, suggest a strategy focused on investigating the convergence of multiple nsASD genes, and propose that the detailed characterization of the molecular and cellular landscapes of developing human brain is essential to illuminate disease mechanisms. Finally, we address how recent advances are leading to novel strategies for therapeutics that target various points along the path from genes to behavior.
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Affiliation(s)
- Nenad Sestan
- Department of Neuroscience and Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, CT 06510, USA; Departments of Genetics, of Psychiatry, and of Comparative Medicine, Program in Cellular Neuroscience, Neurodegeneration and Repair, and Yale Child Study Center, Yale School of Medicine, New Haven, CT 06510, USA.
| | - Matthew W State
- Department of Psychiatry, Langley Porter Psychiatric Institute, Quantitative Biosciences Institute, Institute for Human Genetics, and Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94143, USA.
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52
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Mombaerts I, Ramberg I, Coupland SE, Heegaard S. Diagnosis of orbital mass lesions: clinical, radiological, and pathological recommendations. Surv Ophthalmol 2019; 64:741-756. [PMID: 31276737 DOI: 10.1016/j.survophthal.2019.06.006] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2019] [Revised: 06/21/2019] [Accepted: 06/24/2019] [Indexed: 02/07/2023]
Abstract
The orbit can harbor mass lesions of various cellular origins. The symptoms vary considerably according to the nature, location, and extent of the disease and include common signs of proptosis, globe displacement, eyelid swelling, and restricted eye motility. Although radiological imaging tools are improving, with each imaging pattern having its own differential diagnosis, orbital mass lesions often pose a diagnostic challenge. To provide an accurate, specific, and sufficiently comprehensive diagnosis, to optimize clinical management and estimate prognosis, pathological examination of a tissue biopsy is essential. Diagnostic orbital tissue biopsy is obtained through a minimally invasive orbitotomy procedure or, in selected cases, fine needle aspiration. The outcome of successful biopsy, however, is centered on its representativeness, processing, and interpretation. Owing to the often small volume of the orbital biopsies, artifacts in the specimens should be limited by careful peroperative tissue handling, fixation, processing, and storage. Some orbital lesions can be characterized on the basis of cytomorphology alone, whereas others need ancillary molecular testing to render the most reliable diagnosis of therapeutic, prognostic, and predictive value. Herein, we review the diagnostic algorithm for orbital mass lesions, using clinical, radiological, and pathological recommendations, and discuss the methods and potential pitfalls in orbital tissue biopsy acquisition and analysis.
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Affiliation(s)
- Ilse Mombaerts
- Department of Ophthalmology, University Hospitals Leuven, Leuven, Belgium.
| | - Ingvild Ramberg
- Department of Ophthalmology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark; Section of Eye Pathology, Department of Pathology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Sarah E Coupland
- Department of Cellular and Molecular Pathology, University of Liverpool, Liverpool, UK; Liverpool Clinical Laboratories, Royal Liverpool University Hospital, Liverpool, UK
| | - Steffen Heegaard
- Department of Ophthalmology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark; Section of Eye Pathology, Department of Pathology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
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53
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Colás-Algora N, Millán J. How many cadherins do human endothelial cells express? Cell Mol Life Sci 2019; 76:1299-1317. [PMID: 30552441 PMCID: PMC11105309 DOI: 10.1007/s00018-018-2991-9] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2018] [Revised: 11/16/2018] [Accepted: 12/06/2018] [Indexed: 12/13/2022]
Abstract
The vasculature is the paradigm of a compartment generated by parallel cellular barriers that aims to transport oxygen, nutrients and immune cells in complex organisms. Vascular barrier dysfunction leads to fatal acute and chronic inflammatory diseases. The endothelial barrier lines the inner side of vessels and is the main regulator of vascular permeability. Cadherins comprise a superfamily of 114 calcium-dependent adhesion proteins that contain conserved cadherin motifs and form cell-cell junctions in metazoans. In mature human endothelial cells, only VE (vascular endothelial)-cadherin and N (neural)-cadherin have been investigated in detail. Although both cadherins are essential for regulating endothelial permeability, no comprehensive expression studies to identify which other family members could play a relevant role in endothelial cells has so far been performed. Here, we have reviewed gene and protein expression databases to analyze cadherin expression in mature human endothelium and found that at least 24 cadherin superfamily members are significantly expressed. Based on data obtained from other cell types, organisms and experimental models, we discuss their potential functions, many of them unrelated to the formation of endothelial cell-cell junctions. The expression of this new set of endothelial cadherins highlights the important but still poorly defined roles of planar cell polarity, the Hippo pathway and mitochondria metabolism in human vascular homeostasis.
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Affiliation(s)
- Natalia Colás-Algora
- Centro de Biología Molecular Severo Ochoa, Consejo Superior de Investigaciones Científicas, Universidad Autónoma de Madrid, C/Nicolás Cabrera 1, Cantoblanco, 28049, Madrid, Spain
| | - Jaime Millán
- Centro de Biología Molecular Severo Ochoa, Consejo Superior de Investigaciones Científicas, Universidad Autónoma de Madrid, C/Nicolás Cabrera 1, Cantoblanco, 28049, Madrid, Spain.
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54
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Rapid evolution of protein diversity by de novo origination in Oryza. Nat Ecol Evol 2019; 3:679-690. [PMID: 30858588 DOI: 10.1038/s41559-019-0822-5] [Citation(s) in RCA: 85] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2018] [Accepted: 01/23/2019] [Indexed: 12/22/2022]
Abstract
New protein-coding genes that arise de novo from non-coding DNA sequences contribute to protein diversity. However, de novo gene origination is challenging to study as it requires high-quality reference genomes for closely related species, evidence for ancestral non-coding sequences, and transcription and translation of the new genes. High-quality genomes of 13 closely related Oryza species provide unprecedented opportunities to understand de novo origination events. Here, we identify a large number of young de novo genes with discernible recent ancestral non-coding sequences and evidence of translation. Using pipelines examining the synteny relationship between genomes and reciprocal-best whole-genome alignments, we detected at least 175 de novo open reading frames in the focal species O. sativa subspecies japonica, which were all detected in RNA sequencing-based transcriptomes. Mass spectrometry-based targeted proteomics and ribosomal profiling show translational evidence for 57% of the de novo genes. In recent divergence of Oryza, an average of 51.5 de novo genes per million years were generated and retained. We observed evolutionary patterns in which excess indels and early transcription were favoured in origination with a stepwise formation of gene structure. These data reveal that de novo genes contribute to the rapid evolution of protein diversity under positive selection.
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Wei C, Dong X, Lu H, Tong F, Chen L, Zhang R, Dong J, Hu Y, Wu G, Dong X. LPCAT1 promotes brain metastasis of lung adenocarcinoma by up-regulating PI3K/AKT/MYC pathway. JOURNAL OF EXPERIMENTAL & CLINICAL CANCER RESEARCH : CR 2019; 38:95. [PMID: 30791942 PMCID: PMC6385475 DOI: 10.1186/s13046-019-1092-4] [Citation(s) in RCA: 95] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/09/2018] [Accepted: 02/06/2019] [Indexed: 12/15/2022]
Abstract
Background Brain metastasis (BM) is associated with poor prognosis, recurrence, and death in patients with non-small cell lung cancer (NSCLC). Lysophosphatidylcholine acyltransferase 1 (LPCAT1) has been reported to be involved in the progression, metastasis and recurrence of malignancies. However, the potential role of LPCAT1 in NSCLC remains poorly understood. This study was aimed to identify genes involved in lung adenocarcinoma (LUAD) brain metastasis, and look into the role of LPCAT1 in LUAD progression. Methods We used integrative genomic analysis to identify genes involved in lung adenocarcinomas. LPCAT1 expression was evaluated in tumor tissues from LUAD patients and LUAD cell lines. The role of LPCAT1 was subsequently investigated both in vitro and in vivo. The mechanism underlying the involvement of LPCAT1 in LUAD progression was explored with the activator of PI3K/AKT pathway. RNA sequencing was performed to confirm the involvement of LPCAT1 and associated pathway in LUAD brain metastasis. Results LPCAT1 was up-regulated in LUAD tissues and cell lines. shRNA-mediated depletion of LPCAT1 not only abrogated cell proliferation, migration and invasion in vitro, but also arrested tumor growth and brain metastases in vivo. Notably, LPCAT1 at least partially influenced LUAD progression through PI3K/AKT signal pathway by targeting MYC transcription. Moreover, expression of LPCAT1 was higher in tissues of LUAD patients with BM than those without BM as revealed by IHC staining, RNA-Sequencing and qPCR analysis. Finally, elevated LPCAT1 expression in patients with lung adenocarcinomas was associated with a poor clinical outcome. Conclusions This study showed that LPCAT1 works as a regulator of cell metastasis and may serve as a novel therapeutic target for BM in lung adenocarcinoma. Electronic supplementary material The online version of this article (10.1186/s13046-019-1092-4) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Chunhua Wei
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Xiaomin Dong
- Murdoch Children's Research Institute, Melbourne, VIC, Australia.,Department of Paediatrics, University of Melbourne, Melbourne, VIC, Australia
| | - Hui Lu
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Fan Tong
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Lingjuan Chen
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Ruiguang Zhang
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Jihua Dong
- Medical Research Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Yu Hu
- Institute of Hematology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Gang Wu
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Xiaorong Dong
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
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Wang D, Eraslan B, Wieland T, Hallström B, Hopf T, Zolg DP, Zecha J, Asplund A, Li LH, Meng C, Frejno M, Schmidt T, Schnatbaum K, Wilhelm M, Ponten F, Uhlen M, Gagneur J, Hahne H, Kuster B. A deep proteome and transcriptome abundance atlas of 29 healthy human tissues. Mol Syst Biol 2019; 15:e8503. [PMID: 30777892 PMCID: PMC6379049 DOI: 10.15252/msb.20188503] [Citation(s) in RCA: 418] [Impact Index Per Article: 83.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2018] [Revised: 01/01/2019] [Accepted: 01/08/2019] [Indexed: 11/28/2022] Open
Abstract
Genome-, transcriptome- and proteome-wide measurements provide insights into how biological systems are regulated. However, fundamental aspects relating to which human proteins exist, where they are expressed and in which quantities are not fully understood. Therefore, we generated a quantitative proteome and transcriptome abundance atlas of 29 paired healthy human tissues from the Human Protein Atlas project representing human genes by 18,072 transcripts and 13,640 proteins including 37 without prior protein-level evidence. The analysis revealed that hundreds of proteins, particularly in testis, could not be detected even for highly expressed mRNAs, that few proteins show tissue-specific expression, that strong differences between mRNA and protein quantities within and across tissues exist and that protein expression is often more stable across tissues than that of transcripts. Only 238 of 9,848 amino acid variants found by exome sequencing could be confidently detected at the protein level showing that proteogenomics remains challenging, needs better computational methods and requires rigorous validation. Many uses of this resource can be envisaged including the study of gene/protein expression regulation and biomarker specificity evaluation.
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Affiliation(s)
- Dongxue Wang
- Chair of Proteomics and Bioanalytics, Technische Universität München, Freising, Germany
| | - Basak Eraslan
- Computational Biology, Department of Informatics, Technical University of Munich, Garching bei München, Germany
- Department of Biochemistry, Quantitative Biosciences Munich, Gene Center, Ludwig Maximilian Universität, München, Germany
| | | | - Björn Hallström
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | | | - Daniel Paul Zolg
- Chair of Proteomics and Bioanalytics, Technische Universität München, Freising, Germany
| | - Jana Zecha
- Chair of Proteomics and Bioanalytics, Technische Universität München, Freising, Germany
| | - Anna Asplund
- Science for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Li-Hua Li
- Chair of Proteomics and Bioanalytics, Technische Universität München, Freising, Germany
| | - Chen Meng
- Chair of Proteomics and Bioanalytics, Technische Universität München, Freising, Germany
| | - Martin Frejno
- Chair of Proteomics and Bioanalytics, Technische Universität München, Freising, Germany
| | - Tobias Schmidt
- Chair of Proteomics and Bioanalytics, Technische Universität München, Freising, Germany
| | | | - Mathias Wilhelm
- Chair of Proteomics and Bioanalytics, Technische Universität München, Freising, Germany
| | - Frederik Ponten
- Science for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Mathias Uhlen
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Julien Gagneur
- Computational Biology, Department of Informatics, Technical University of Munich, Garching bei München, Germany
| | | | - Bernhard Kuster
- Chair of Proteomics and Bioanalytics, Technische Universität München, Freising, Germany
- Center for Integrated Protein Science Munich (CIPSM), Munich, Germany
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Bhat MA, Sharma JB, Roy KK, Sengupta J, Ghosh D. Genomic evidence of Y chromosome microchimerism in the endometrium during endometriosis and in cases of infertility. Reprod Biol Endocrinol 2019; 17:22. [PMID: 30760267 PMCID: PMC6375207 DOI: 10.1186/s12958-019-0465-z] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/13/2018] [Accepted: 02/07/2019] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Previous studies, which were primarily based on the fluorescent in-situ hybridisation (FISH) technique, revealed conflicting evidence regarding male foetal microchimerism in endometriosis. FISH is a relatively less sensitive technique, as it is performed on a small portion of the sample. Additionally, the probes used in the previous studies specifically detected centromeric and telomeric regions of Y chromosome, which are gene-sparse heterochromatised regions. In the present study, a panel of molecular biology tools such as qPCR, expression microarray, RNA-seq and qRT-PCR were employed to examine the Y chromosome microchimerism in the endometrium using secretory phase samples from fertile and infertile patients with severe (stage IV) ovarian endometriosis (OE) and without endometriosis. METHODS Microarray expression analysis followed by validation using RNA-seq and qRT-PCR experiments at the RNA levels and further validation at the DNA level by qPCR of target inserts for selected targets in eutopic endometrium samples obtained from control (CON) and stage IV ovarian endometriosis (OE), either from fertile (FCON and FOE; n = 30/each) or infertile (ICON and IOE; n = 30/each) women, were performed. RESULTS Six coding (AMELY, PCDH11, SRY, TGIF2LY, TSPY3, and USP9Y) and 10 non-coding (TTTY2, TTTY4C, TTTY5, TTTYY6, TTTY8, TTTY10, TTTY14, TTTY21, TTTY22, and TTTY23) genes exhibited a bimodal pattern of expression characterised by low expression in samples from fertile patients and high expression in samples from infertile patients. Seven coding MSY-linked genes (BAGE, CD24, EIF1AY, NLGN4Y, PRKY, VCY and ZFY) exhibited differential regulation in microarray analysis, and this change was validated by RNA-seq or qRT-PCR. DNA inserts for 7 genes in various samples were validated by qPCR. The prevalence and concentration of PCR-positive target inserts for BAGE, PRKY, TTTY9A and ZFY displayed higher values in the fertile, control (FCON) patients compared with the fertile, endometriosis patients (FOE). CONCLUSION Several coding and non-coding MSY-linked genes displayed microchimerism as evidenced by the presence of their respective DNA inserts, along with their differential transcript expression, in the endometrium during endometriosis and in cases of infertility.
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Affiliation(s)
- Muzaffer A. Bhat
- Department of Physiology, All India Institute of Medical Sciences, New Delhi, India
| | - Jai B. Sharma
- Department of Obstetrics and Gynaecology, All India Institute of Medical Sciences, New Delhi, India
| | - Kallol K. Roy
- Department of Obstetrics and Gynaecology, All India Institute of Medical Sciences, New Delhi, India
| | - Jayasree Sengupta
- Department of Physiology, All India Institute of Medical Sciences, New Delhi, India
| | - Debabrata Ghosh
- Department of Physiology, All India Institute of Medical Sciences, New Delhi, India
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Nguyen L, Brun V, Combes F, Loux V, Vandenbrouck Y. Designing an In Silico Strategy to Select Tissue-Leakage Biomarkers Using the Galaxy Framework. Methods Mol Biol 2019; 1959:275-289. [PMID: 30852829 DOI: 10.1007/978-1-4939-9164-8_18] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Knowledge-based approaches using large-scale biological ("omics") data are a powerful way to identify mechanistic biomarkers, provided that scientists have access to computational solutions even when they have little programming experience or bioinformatics support. To achieve this goal, we designed a set of tools under the Galaxy framework to allow biologists to define their own strategy for reproducible biomarker selection. These tools rely on retrieving experimental data from public databases, and applying successive filters derived from information relating to disease pathophysiology. A step-by-step protocol linking these tools was implemented to select tissue-leakage biomarker candidates of myocardial infarction. A list of 24 candidates suitable for experimental assessment by MS-based proteomics is proposed. These tools have been made publicly available at http://www.proteore.org , allowing researchers to reuse them in their quest for biomarker discovery.
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Affiliation(s)
- Lien Nguyen
- Université Grenoble Alpes, CEA, Inserm, BGE U1038, Grenoble, France
- INRA, MAIAGE Unit, University Paris-Saclay, Jouy-en-Josas, France
| | - Virginie Brun
- Université Grenoble Alpes, CEA, Inserm, BGE U1038, Grenoble, France
| | - Florence Combes
- Université Grenoble Alpes, CEA, Inserm, BGE U1038, Grenoble, France
| | - Valentin Loux
- INRA, MAIAGE Unit, University Paris-Saclay, Jouy-en-Josas, France
| | - Yves Vandenbrouck
- Université Grenoble Alpes, CEA, Inserm, BGE U1038, Grenoble, France.
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Zimmermann MT. The Importance of Biologic Knowledge and Gene Expression Context for Genomic Data Interpretation. Front Genet 2018; 9:670. [PMID: 30619486 PMCID: PMC6305277 DOI: 10.3389/fgene.2018.00670] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2018] [Accepted: 12/04/2018] [Indexed: 11/13/2022] Open
Abstract
Background: Genomic sequencing, including whole exome sequencing (WES), is enabling a higher resolution for defining diseases, understand mechanisms, and improving the practice of clinical care. However, WES routinely identifies genomic variants with uncertain functional effects. Furthering uncertainty in WES data interpretation is that many genes can express multiple transcripts and their relative expression may differ by body tissue. In order to interpret WES data, we not only need to understand which transcript is most relevant, but what tissue is most relevant. Methods: In this work, we quantify how frequently differences in transcript and tissue expression affect WES data interpretation at gene, pathway, disease, and biologic network levels. We combined and analyzed multiple large and publically available datasets to inform genomic data interpretation. Results: Across well-established biologic pathways and genes with pathogenic disease variants, 54 and 40% have a different protein coding effect by transcript selection for, respectively, 25 and 50% of the genes contained. Additionally, strong differences in human tissue expression levels affect 33 and 19% of the same set of pathways and diseases for, respectively, 25 and 50% of the genes contained. Conclusion: Whole exome sequencing identifies genomic variants, but to interpret the functional effects of those variants in high-resolution, we recommend building transcript selection and cross-tissue gene expression levels into hypotheses and analyses. Using current large-scale data, we show how extensively interpretation of genomic variants may differ according to transcript and tissue, across most pathways and disease. Thus, their inclusion is necessary for WES data interpretation.
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Affiliation(s)
- Michael T. Zimmermann
- Bioinformatics Research and Development Laboratory, Genomic Sciences and Precision Medicine Center, Medical College of Wisconsin, Milwaukee, WI, United States
- Clinical and Translational Sciences Institute, Medical College of Wisconsin, Milwaukee, WI, United States
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Tissue-Specific Profiling of Oxidative Stress-Associated Transcriptome in a Healthy Mouse Model. Int J Mol Sci 2018; 19:ijms19103174. [PMID: 30326626 PMCID: PMC6214011 DOI: 10.3390/ijms19103174] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2018] [Revised: 10/08/2018] [Accepted: 10/11/2018] [Indexed: 11/17/2022] Open
Abstract
Oxidative stress is a common phenomenon and is linked to a wide range of diseases and pathological processes including aging. Tissue-specific variation in redox signaling and cellular responses to oxidative stress may be associated with vulnerability especially to age-related and chronic diseases. In order to provide a basis for tissue-specific difference, we examined the tissue-specific transcriptional features of 101 oxidative stress-associated genes in 10 different tissues and organs of healthy mice under physiological conditions. Microarray analysis results, which were consistent with quantitative polymerase chain reaction (qPCR) results, showed that catalase, Gpx3, and Gpx4 were most highly regulated in the liver, kidney, and testes. We also found the tissue-specific gene expression of SOD1 (liver and kidney), SOD2 (heart and muscle), and SOD3 (lung and kidney). The current results will serve as a reference for animal models and help advance our understanding of tissue-specific variability in oxidative stress-associated pathogenesis.
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Sjöstedt E, Sivertsson Å, Hikmet Noraddin F, Katona B, Näsström Å, Vuu J, Kesti D, Oksvold P, Edqvist PH, Olsson I, Uhlén M, Lindskog C. Integration of Transcriptomics and Antibody-Based Proteomics for Exploration of Proteins Expressed in Specialized Tissues. J Proteome Res 2018; 17:4127-4137. [DOI: 10.1021/acs.jproteome.8b00406] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Affiliation(s)
- Evelina Sjöstedt
- Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH - Royal Institute of Technology, Stockholm SE 171 21, Sweden
- Rudbeck Laboratory, Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala SE 752 37, Sweden
| | - Åsa Sivertsson
- Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH - Royal Institute of Technology, Stockholm SE 171 21, Sweden
| | - Feria Hikmet Noraddin
- Rudbeck Laboratory, Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala SE 752 37, Sweden
| | - Borbala Katona
- Rudbeck Laboratory, Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala SE 752 37, Sweden
| | - Åsa Näsström
- Rudbeck Laboratory, Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala SE 752 37, Sweden
| | - Jimmy Vuu
- Rudbeck Laboratory, Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala SE 752 37, Sweden
| | - Dennis Kesti
- Rudbeck Laboratory, Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala SE 752 37, Sweden
| | - Per Oksvold
- Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH - Royal Institute of Technology, Stockholm SE 171 21, Sweden
| | - Per-Henrik Edqvist
- Rudbeck Laboratory, Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala SE 752 37, Sweden
| | - Ingmarie Olsson
- Rudbeck Laboratory, Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala SE 752 37, Sweden
| | - Mathias Uhlén
- Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH - Royal Institute of Technology, Stockholm SE 171 21, Sweden
| | - Cecilia Lindskog
- Rudbeck Laboratory, Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala SE 752 37, Sweden
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Stamer WD, Williams AM, Pflugfelder S, Coupland SE. Accessibility to and Quality of Human Eye Tissue for Research: A Cross-Sectional Survey of ARVO Members. ACTA ACUST UNITED AC 2018; 59:4783-4792. [DOI: 10.1167/iovs.18-25319] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Affiliation(s)
- W. Daniel Stamer
- Department of Ophthalmology, Duke University Medical Center, Durham, North Carolina, United States
| | - Andrew M. Williams
- Department of Ophthalmology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, United States
| | - Stephen Pflugfelder
- Department of Ophthalmology, Baylor University College of Medicine, Houston, Texas, United States
| | - Sarah E. Coupland
- Molecular and Clinical Cancer Medicine, University of Liverpool, Liverpool, United Kingdom
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Melaine N, Com E, Bellaud P, Guillot L, Lagarrigue M, Morrice NA, Guével B, Lavigne R, Velez de la Calle JF, Dojahn J, Pineau C. Deciphering the Dark Proteome: Use of the Testis and Characterization of Two Dark Proteins. J Proteome Res 2018; 17:4197-4210. [DOI: 10.1021/acs.jproteome.8b00387] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Nathalie Melaine
- Univ Rennes, Inserm, EHESP, Irset (Institut de Recherche en Santé, Environnement et Travail)—UMR S 1085, F-35042 Rennes cedex, France
- Protim, Univ Rennes, F-35042 Rennes, France
| | - Emmanuelle Com
- Univ Rennes, Inserm, EHESP, Irset (Institut de Recherche en Santé, Environnement et Travail)—UMR S 1085, F-35042 Rennes cedex, France
- Protim, Univ Rennes, F-35042 Rennes, France
| | - Pascale Bellaud
- H2P2 Core Facility, UMS BioSit, Univ Rennes, Rennes F-35040, France
| | - Laetitia Guillot
- Univ Rennes, Inserm, EHESP, Irset (Institut de Recherche en Santé, Environnement et Travail)—UMR S 1085, F-35042 Rennes cedex, France
- Protim, Univ Rennes, F-35042 Rennes, France
| | - Mélanie Lagarrigue
- Univ Rennes, Inserm, EHESP, Irset (Institut de Recherche en Santé, Environnement et Travail)—UMR S 1085, F-35042 Rennes cedex, France
- Protim, Univ Rennes, F-35042 Rennes, France
| | - Nick A. Morrice
- Sciex, Phoenix House Lakeside Drive Centre Park, Warrington WA1 1RX, U.K
| | - Blandine Guével
- Univ Rennes, Inserm, EHESP, Irset (Institut de Recherche en Santé, Environnement et Travail)—UMR S 1085, F-35042 Rennes cedex, France
- Protim, Univ Rennes, F-35042 Rennes, France
| | - Régis Lavigne
- Univ Rennes, Inserm, EHESP, Irset (Institut de Recherche en Santé, Environnement et Travail)—UMR S 1085, F-35042 Rennes cedex, France
- Protim, Univ Rennes, F-35042 Rennes, France
| | | | - Jörg Dojahn
- Sciex, Landwehrstr. 54, 64293 Darmstadt, Germany
| | - Charles Pineau
- Univ Rennes, Inserm, EHESP, Irset (Institut de Recherche en Santé, Environnement et Travail)—UMR S 1085, F-35042 Rennes cedex, France
- Protim, Univ Rennes, F-35042 Rennes, France
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Mills KJ, Robinson MK, Sherrill JD, Schnell DJ, Xu J. Analysis of gene expression profiles of multiple skin diseases identifies a conserved signature of disrupted homeostasis. Exp Dermatol 2018; 27:1000-1008. [DOI: 10.1111/exd.13694] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/14/2018] [Indexed: 12/19/2022]
Affiliation(s)
- Kevin J. Mills
- The Procter and Gamble Company; Mason Business Center; Mason OH USA
| | | | | | - Daniel J. Schnell
- Department of Biomedical Informatics; Cincinnati Children's Hospital Medical Center; Cincinnati OH USA
| | - Jun Xu
- Calico, LLC; South San Francisco CA USA
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Hopp L, Loeffler-Wirth H, Nersisyan L, Arakelyan A, Binder H. Footprints of Sepsis Framed Within Community Acquired Pneumonia in the Blood Transcriptome. Front Immunol 2018; 9:1620. [PMID: 30065722 PMCID: PMC6056630 DOI: 10.3389/fimmu.2018.01620] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2018] [Accepted: 06/29/2018] [Indexed: 12/14/2022] Open
Abstract
We analyzed the blood transcriptome of sepsis framed within community-acquired pneumonia (CAP) and characterized its molecular and cellular heterogeneity in terms of functional modules of co-regulated genes with impact for the underlying pathophysiological mechanisms. Our results showed that CAP severity is associated with immune suppression owing to T-cell exhaustion and HLA and chemokine receptor deactivation, endotoxin tolerance, macrophage polarization, and metabolic conversion from oxidative phosphorylation to glycolysis. We also found footprints of host's response to viruses and bacteria, altered levels of mRNA from erythrocytes and platelets indicating coagulopathy that parallel severity of sepsis and survival. Finally, our data demonstrated chromatin re-modeling associated with extensive transcriptional deregulation of chromatin modifying enzymes, which suggests the extensive changes of DNA methylation with potential impact for marker selection and functional characterization. Based on the molecular footprints identified, we propose a novel stratification of CAP cases into six groups differing in the transcriptomic scores of CAP severity, interferon response, and erythrocyte mRNA expression with impact for prognosis. Our analysis increases the resolution of transcriptomic footprints of CAP and reveals opportunities for selecting sets of transcriptomic markers with impact for translation of omics research in terms of patient stratification schemes and sets of signature genes.
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Affiliation(s)
- Lydia Hopp
- Interdisciplinary Centre for Bioinformatics, Universität Leipzig, Leipzig, Germany
| | - Henry Loeffler-Wirth
- Interdisciplinary Centre for Bioinformatics, Universität Leipzig, Leipzig, Germany
| | - Lilit Nersisyan
- Group of Bioinformatics, Institute of Molecular Biology, National Academy of Sciences, Yerevan, Armenia
| | - Arsen Arakelyan
- Group of Bioinformatics, Institute of Molecular Biology, National Academy of Sciences, Yerevan, Armenia
| | - Hans Binder
- Interdisciplinary Centre for Bioinformatics, Universität Leipzig, Leipzig, Germany
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66
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Mardinoglu A, Boren J, Smith U, Uhlen M, Nielsen J. Systems biology in hepatology: approaches and applications. Nat Rev Gastroenterol Hepatol 2018; 15:365-377. [PMID: 29686404 DOI: 10.1038/s41575-018-0007-8] [Citation(s) in RCA: 79] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Detailed insights into the biological functions of the liver and an understanding of its crosstalk with other human tissues and the gut microbiota can be used to develop novel strategies for the prevention and treatment of liver-associated diseases, including fatty liver disease, cirrhosis, hepatocellular carcinoma and type 2 diabetes mellitus. Biological network models, including metabolic, transcriptional regulatory, protein-protein interaction, signalling and co-expression networks, can provide a scaffold for studying the biological pathways operating in the liver in connection with disease development in a systematic manner. Here, we review studies in which biological network models were used to integrate multiomics data to advance our understanding of the pathophysiological responses of complex liver diseases. We also discuss how this mechanistic approach can contribute to the discovery of potential biomarkers and novel drug targets, which might lead to the design of targeted and improved treatment strategies. Finally, we present a roadmap for the successful integration of models of the liver and other human tissues with the gut microbiota to simulate whole-body metabolic functions in health and disease.
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Affiliation(s)
- Adil Mardinoglu
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden. .,Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden.
| | - Jan Boren
- Department of Molecular and Clinical Medicine, University of Gothenburg and Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Ulf Smith
- Department of Molecular and Clinical Medicine, University of Gothenburg and Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Mathias Uhlen
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Jens Nielsen
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden.,Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
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67
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Liu H, Zhao R, Fang H, Cheng F, Fu Y, Liu YY. Entropy-based consensus clustering for patient stratification. Bioinformatics 2018; 33:2691-2698. [PMID: 28369256 DOI: 10.1093/bioinformatics/btx167] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2016] [Accepted: 03/22/2017] [Indexed: 12/20/2022] Open
Abstract
Motivation Patient stratification or disease subtyping is crucial for precision medicine and personalized treatment of complex diseases. The increasing availability of high-throughput molecular data provides a great opportunity for patient stratification. Many clustering methods have been employed to tackle this problem in a purely data-driven manner. Yet, existing methods leveraging high-throughput molecular data often suffers from various limitations, e.g. noise, data heterogeneity, high dimensionality or poor interpretability. Results Here we introduced an Entropy-based Consensus Clustering (ECC) method that overcomes those limitations all together. Our ECC method employs an entropy-based utility function to fuse many basic partitions to a consensus one that agrees with the basic ones as much as possible. Maximizing the utility function in ECC has a much more meaningful interpretation than any other consensus clustering methods. Moreover, we exactly map the complex utility maximization problem to the classic K -means clustering problem, which can then be efficiently solved with linear time and space complexity. Our ECC method can also naturally integrate multiple molecular data types measured from the same set of subjects, and easily handle missing values without any imputation. We applied ECC to 110 synthetic and 48 real datasets, including 35 cancer gene expression benchmark datasets and 13 cancer types with four molecular data types from The Cancer Genome Atlas. We found that ECC shows superior performance against existing clustering methods. Our results clearly demonstrate the power of ECC in clinically relevant patient stratification. Availability and implementation The Matlab package is available at http://scholar.harvard.edu/yyl/ecc . Contact yunfu@ece.neu.edu or yyl@channing.harvard.edu. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Hongfu Liu
- Department of Electrical and Computer Engineering, Northeastern University, Boston, MA 02115, USA
| | - Rui Zhao
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.,School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA
| | - Hongsheng Fang
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.,Department of Statistics, Stanford University, Stanford, CA 94305, USA
| | - Feixiong Cheng
- Center for Complex Network Research and Department of Physics, Northeastern University, Boston, MA 02115, USA.,Center for Cancer Systems Biology, Dana-Farber Cancer Institute, Boston, MA 02115, USA
| | - Yun Fu
- Department of Electrical and Computer Engineering, Northeastern University, Boston, MA 02115, USA.,College of Computer and Information Science, Northeastern University, Boston, MA 02115, USA
| | - Yang-Yu Liu
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.,Center for Cancer Systems Biology, Dana-Farber Cancer Institute, Boston, MA 02115, USA
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68
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Strell C, Hilscher MM, Laxman N, Svedlund J, Wu C, Yokota C, Nilsson M. Placing RNA in context and space - methods for spatially resolved transcriptomics. FEBS J 2018. [PMID: 29542254 DOI: 10.1111/febs.14435] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
Abstract
Single-cell transcriptomics provides us with completely new insights into the molecular diversity of different cell types and the different states they can adopt. The technique generates inventories of cells that constitute the building blocks of multicellular organisms. However, since the method requires isolation of discrete cells, information about the original location within tissue is lost. Therefore, it is not possible to draw detailed cellular maps of tissue architecture and their positioning in relation to other cells. In order to better understand the cellular and tissue function of multicellular organisms, we need to map the cells within their physiological, morphological, and anatomical context and space. In this review, we will summarize and compare the different methods of in situ RNA analysis and the most recent developments leading to more comprehensive and highly multiplexed spatially resolved transcriptomic approaches. We will discuss their highlights and advantages as well as their limitations and challenges and give an outlook on promising future applications and directions both within basic research as well as clinical integration.
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Affiliation(s)
- Carina Strell
- Science for Life Laboratory, Department of Biophysics and biochemistry, Stockholm University, Solna, Sweden
| | - Markus M Hilscher
- Science for Life Laboratory, Department of Biophysics and biochemistry, Stockholm University, Solna, Sweden
| | - Navya Laxman
- Science for Life Laboratory, Department of Biophysics and biochemistry, Stockholm University, Solna, Sweden
| | - Jessica Svedlund
- Science for Life Laboratory, Department of Biophysics and biochemistry, Stockholm University, Solna, Sweden
| | - Chenglin Wu
- Science for Life Laboratory, Department of Biophysics and biochemistry, Stockholm University, Solna, Sweden
| | - Chika Yokota
- Science for Life Laboratory, Department of Biophysics and biochemistry, Stockholm University, Solna, Sweden
| | - Mats Nilsson
- Science for Life Laboratory, Department of Biophysics and biochemistry, Stockholm University, Solna, Sweden
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69
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Dréan A, Rosenberg S, Lejeune FX, Goli L, Nadaradjane AA, Guehennec J, Schmitt C, Verreault M, Bielle F, Mokhtari K, Sanson M, Carpentier A, Delattre JY, Idbaih A. ATP binding cassette (ABC) transporters: expression and clinical value in glioblastoma. J Neurooncol 2018. [PMID: 29520610 DOI: 10.1007/s11060-018-2819-3] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
ATP-binding cassette transporters (ABC transporters) regulate traffic of multiple compounds, including chemotherapeutic agents, through biological membranes. They are expressed by multiple cell types and have been implicated in the drug resistance of some cancer cells. Despite significant research in ABC transporters in the context of many diseases, little is known about their expression and clinical value in glioblastoma (GBM). We analyzed expression of 49 ABC transporters in both commercial and patient-derived GBM cell lines as well as from 51 human GBM tumor biopsies. Using The Cancer Genome Atlas (TCGA) cohort as a training dataset and our cohort as a validation dataset, we also investigated the prognostic value of these ABC transporters in newly diagnosed GBM patients, treated with the standard of care. In contrast to commercial GBM cell lines, GBM-patient derived cell lines (PDCL), grown as neurospheres in a serum-free medium, express ABC transporters similarly to parental tumors. Serum appeared to slightly increase resistance to temozolomide correlating with a tendency for an increased expression of ABCB1. Some differences were observed mainly due to expression of ABC transporters by microenvironmental cells. Together, our data suggest that the efficacy of chemotherapeutic agents may be misestimated in vitro if they are the targets of efflux pumps whose expression can be modulated by serum. Interestingly, several ABC transporters have prognostic value in the TCGA dataset. In our cohort of 51 GBM patients treated with radiation therapy with concurrent and adjuvant temozolomide, ABCA13 overexpression is associated with a decreased progression free survival in univariate (p < 0.01) and multivariate analyses including MGMT promoter methylation (p = 0.05) suggesting reduced sensitivity to temozolomide in ABCA13 overexpressing GBM. Expression of ABC transporters is: (i) detected in GBM and microenvironmental cells and (ii) better reproduced in GBM-PDCL. ABCA13 expression is an independent prognostic factor in newly diagnosed GBM patients. Further prospective studies are warranted to investigate whether ABCA13 expression can be used to further personalize treatments for GBM.
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Affiliation(s)
- Antonin Dréan
- Inserm U 1127, CNRS UMR 7225, Sorbonne Universités, UPMC Univ Paris 04 UMR S 1127, Institut du Cerveau et de la Moelle épinière, ICM, 75013, Paris, France
- Equipe de recherche CarThera, Institut du Cerveau et de la Moelle épinière, Ipeps ICM, 75013, Paris, France
| | - Shai Rosenberg
- Inserm U 1127, CNRS UMR 7225, Sorbonne Universités, UPMC Univ Paris 04 UMR S 1127, Institut du Cerveau et de la Moelle épinière, ICM, 75013, Paris, France
- Hadassah - Hebrew University Medical Center, Jerusalem, Israel
| | - François-Xavier Lejeune
- Inserm U 1127, CNRS UMR 7225, Sorbonne Universités, UPMC Univ Paris 04 UMR S 1127, Institut du Cerveau et de la Moelle épinière, ICM, 75013, Paris, France
| | - Larissa Goli
- Inserm U 1127, CNRS UMR 7225, Sorbonne Universités, UPMC Univ Paris 04 UMR S 1127, Institut du Cerveau et de la Moelle épinière, ICM, 75013, Paris, France
| | - Aravindan Arun Nadaradjane
- Inserm U 1127, CNRS UMR 7225, Sorbonne Universités, UPMC Univ Paris 04 UMR S 1127, Institut du Cerveau et de la Moelle épinière, ICM, 75013, Paris, France
| | - Jérémy Guehennec
- Inserm U 1127, CNRS UMR 7225, Sorbonne Universités, UPMC Univ Paris 04 UMR S 1127, Institut du Cerveau et de la Moelle épinière, ICM, 75013, Paris, France
| | - Charlotte Schmitt
- Inserm U 1127, CNRS UMR 7225, Sorbonne Universités, UPMC Univ Paris 04 UMR S 1127, Institut du Cerveau et de la Moelle épinière, ICM, 75013, Paris, France
| | - Maïté Verreault
- Inserm U 1127, CNRS UMR 7225, Sorbonne Universités, UPMC Univ Paris 04 UMR S 1127, Institut du Cerveau et de la Moelle épinière, ICM, 75013, Paris, France
| | - Franck Bielle
- Inserm U 1127, CNRS UMR 7225, Sorbonne Universités, UPMC Univ Paris 04 UMR S 1127, Institut du Cerveau et de la Moelle épinière, ICM, 75013, Paris, France
- AP-HP, Hôpitaux Universitaires La Pitié Salpêtrière - Charles Foix, Service de Neuropathologie, 75013, Paris, France
- OncoNeuroTek, Hôpitaux Universitaires La Pitié Salpêtrière, Paris, France
| | - Karima Mokhtari
- Inserm U 1127, CNRS UMR 7225, Sorbonne Universités, UPMC Univ Paris 04 UMR S 1127, Institut du Cerveau et de la Moelle épinière, ICM, 75013, Paris, France
- AP-HP, Hôpitaux Universitaires La Pitié Salpêtrière - Charles Foix, Service de Neuropathologie, 75013, Paris, France
- OncoNeuroTek, Hôpitaux Universitaires La Pitié Salpêtrière, Paris, France
| | - Marc Sanson
- Inserm U 1127, CNRS UMR 7225, Sorbonne Universités, UPMC Univ Paris 04 UMR S 1127, Institut du Cerveau et de la Moelle épinière, ICM, 75013, Paris, France
- AP-HP, Hôpitaux Universitaires La Pitié Salpêtrière - Charles Foix, Service de Neurologie 2-Mazarin, 75013, Paris, France
- OncoNeuroTek, Hôpitaux Universitaires La Pitié Salpêtrière, Paris, France
| | - Alexandre Carpentier
- AP-HP, Hôpitaux Universitaires La Pitié Salpêtrière - Charles Foix, Service de Neurochirurgie, 75013, Paris, France
| | - Jean-Yves Delattre
- Inserm U 1127, CNRS UMR 7225, Sorbonne Universités, UPMC Univ Paris 04 UMR S 1127, Institut du Cerveau et de la Moelle épinière, ICM, 75013, Paris, France
- AP-HP, Hôpitaux Universitaires La Pitié Salpêtrière - Charles Foix, Service de Neurologie 2-Mazarin, 75013, Paris, France
- OncoNeuroTek, Hôpitaux Universitaires La Pitié Salpêtrière, Paris, France
| | - Ahmed Idbaih
- Inserm U 1127, CNRS UMR 7225, Sorbonne Universités, UPMC Univ Paris 04 UMR S 1127, Institut du Cerveau et de la Moelle épinière, ICM, 75013, Paris, France.
- AP-HP, Hôpitaux Universitaires La Pitié Salpêtrière - Charles Foix, Service de Neurologie 2-Mazarin, 75013, Paris, France.
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Poverennaya EV, Shargunov AV, Ponomarenko EA, Lisitsa AV. The Gene-Centric Content Management System and Its Application for Cognitive Proteomics. Proteomes 2018; 6:proteomes6010012. [PMID: 29473895 PMCID: PMC5874771 DOI: 10.3390/proteomes6010012] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2017] [Revised: 02/07/2018] [Accepted: 02/20/2018] [Indexed: 12/28/2022] Open
Abstract
The Human Proteome Project is moving into the next phase of creating and/or reconsidering the functional annotations of proteins using the chromosome-centric paradigm. This challenge cannot be solved exclusively using automated means, but rather requires human intelligence for interpreting the combined data. To foster the integration between human cognition and post-genome array a number of specific tools were recently developed, among them CAPER, GenomewidePDB, and The Proteome Browser (TPB). For the purpose of tackling the task of protein functional annotating the Gene-Centric Content Management System (GenoCMS) was expanded with new features. The goal was to enable bioinformaticans to develop self-made applications and to position these applets within the generalized informational canvas supported by GenoCMS. We report the results of GenoCMS-enabled integration of the concordant informational flows in the chromosome-centric framework of the human chromosome 18 project. The workflow described in the article can be scaled to other human chromosomes, and also supplemented with new tracks created by the user. The GenoCMS is an example of a project-oriented informational system, which are important for public data sharing.
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Affiliation(s)
| | | | | | - Andrey V Lisitsa
- Orekhovich Institute of Biomedical Chemistry, Moscow 119191, Russia.
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71
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Byström S, Eklund M, Hong MG, Fredolini C, Eriksson M, Czene K, Hall P, Schwenk JM, Gabrielson M. Affinity proteomic profiling of plasma for proteins associated to area-based mammographic breast density. Breast Cancer Res 2018; 20:14. [PMID: 29444691 PMCID: PMC5813412 DOI: 10.1186/s13058-018-0940-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2017] [Accepted: 01/29/2018] [Indexed: 02/08/2023] Open
Abstract
Background Mammographic breast density is one of the strongest risk factors for breast cancer, but molecular understanding of how breast density relates to cancer risk is less complete. Studies of proteins in blood plasma, possibly associated with mammographic density, are well-suited as these allow large-scale analyses and might shed light on the association between breast cancer and breast density. Methods Plasma samples from 1329 women in the Swedish KARMA project, without prior history of breast cancer, were profiled with antibody suspension bead array (SBA) assays. Two sample sets comprising 729 and 600 women were screened by two different SBAs targeting a total number of 357 proteins. Protein targets were selected through searching the literature, for either being related to breast cancer or for being linked to the extracellular matrix. Association between proteins and absolute area-based breast density (AD) was assessed by quantile regression, adjusting for age and body mass index (BMI). Results Plasma profiling revealed linear association between 20 proteins and AD, concordant in the two sets of samples (p < 0.05). Plasma levels of seven proteins were positively associated and 13 proteins negatively associated with AD. For eleven of these proteins evidence for gene expression in breast tissue existed. Among these, ABCC11, TNFRSF10D, F11R and ERRF were positively associated with AD, and SHC1, CFLAR, ACOX2, ITGB6, RASSF1, FANCD2 and IRX5 were negatively associated with AD. Conclusions Screening proteins in plasma indicates associations between breast density and processes of tissue homeostasis, DNA repair, cancer development and/or progression in breast cancer. Further validation and follow-up studies of the shortlisted protein candidates in independent cohorts will be needed to infer their role in breast density and its progression in premenopausal and postmenopausal women. Electronic supplementary material The online version of this article (10.1186/s13058-018-0940-z) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Sanna Byström
- Science for Life Laboratory, School of Biotechnology, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Martin Eklund
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels väg 12A, -171 77, Stockholm, SE, Sweden
| | - Mun-Gwan Hong
- Science for Life Laboratory, School of Biotechnology, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Claudia Fredolini
- Science for Life Laboratory, School of Biotechnology, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Mikael Eriksson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels väg 12A, -171 77, Stockholm, SE, Sweden
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels väg 12A, -171 77, Stockholm, SE, Sweden
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels väg 12A, -171 77, Stockholm, SE, Sweden.,Department of Oncology, South General Hospital, Stockholm, Sweden
| | - Jochen M Schwenk
- Science for Life Laboratory, School of Biotechnology, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Marike Gabrielson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels väg 12A, -171 77, Stockholm, SE, Sweden.
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72
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High-resolution spatiotemporal transcriptome mapping of tomato fruit development and ripening. Nat Commun 2018; 9:364. [PMID: 29371663 PMCID: PMC5785480 DOI: 10.1038/s41467-017-02782-9] [Citation(s) in RCA: 190] [Impact Index Per Article: 31.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2017] [Accepted: 12/22/2017] [Indexed: 12/20/2022] Open
Abstract
Tomato (Solanum lycopersicum) is an established model for studying fruit biology; however, most studies of tomato fruit growth and ripening are based on homogenized pericarp, and do not consider the internal tissues, or the expression signatures of individual cell and tissue types. We present a spatiotemporally resolved transcriptome analysis of tomato fruit ontogeny, using laser microdissection (LM) or hand dissection coupled with RNA-Seq analysis. Regulatory and structural gene networks, including families of transcription factors and hormone synthesis and signaling pathways, are defined across tissue and developmental spectra. The ripening program is revealed as comprising gradients of gene expression, initiating in internal tissues then radiating outward, and basipetally along a latitudinal axis. We also identify spatial variations in the patterns of epigenetic control superimposed on ripening gradients. Functional studies elucidate previously masked regulatory phenomena and relationships, including those associated with fruit quality traits, such as texture, color, aroma, and metabolite profiles. Cell-type transcriptome profiling greatly elucidate organismal development. Here, the authors report a spatiotemporally resolved comprehensive transcriptome analysis of tomato fruit ontogeny and suggest a new model of fruit maturation which initiates in internal tissues then radiates outwards.
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Abstract
Peptidomics is the comprehensive characterization of peptides from biological sources mainly by HPLC and mass spectrometry. Mass spectrometry allows the detection of a multitude of single peptides in complex mixtures. The term first appeared in full papers in the year 2001, after over 100 years of peptide research with a main focus on one or a few specific peptides. Within the last 15 years, this new field has grown to over 1200 publications. Mass spectrometry techniques, in combination with other analytical methods, were developed for the fast and comprehensive analysis of peptides in proteomics and specifically adjusted to implement peptidomics technologies. Although peptidomics is closely linked to proteomics, there are fundamental differences with conventional bottom-up proteomics. The development of peptidomics is described, including the most important implementations for its technological basis. Different strategies are covered which are applied to several important applications, such as neuropeptidomics and discovery of bioactive peptides or biomarkers. This overview includes links to all other chapters in the book as well as recent developments of separation, mass spectrometric, and data processing technologies. Additionally, some new applications in food and plant peptidomics as well as immunopeptidomics are introduced.
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Söllner JF, Leparc G, Hildebrandt T, Klein H, Thomas L, Stupka E, Simon E. An RNA-Seq atlas of gene expression in mouse and rat normal tissues. Sci Data 2017; 4:170185. [PMID: 29231921 PMCID: PMC5726313 DOI: 10.1038/sdata.2017.185] [Citation(s) in RCA: 85] [Impact Index Per Article: 12.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2017] [Accepted: 10/27/2017] [Indexed: 12/21/2022] Open
Abstract
Gene functionality is closely connected to its expression specificity across tissues and cell types. RNA-Seq is a powerful quantitative tool to explore genome wide expression. The aim of this study is to provide a comprehensive RNA-Seq dataset across the same 13 tissues for mouse and rat, two of the most relevant species for biomedical research. The dataset provides the transcriptome across tissues from three male C57BL6 mice and three male Han Wistar rats. We also describe our bioinformatics pipeline to process and technically validate the data. Principal component analysis shows that tissue samples from both species cluster similarly. We show by comparative genomics that many genes with high sequence identity with respect to their human orthologues also have a highly correlated tissue distribution profile and are in agreement with manually curated literature data for human. In summary, the present study provides a unique resource for comparative genomics and will facilitate the analysis of tissue specificity and cross-species conservation in higher organisms.
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Affiliation(s)
- Julia F Söllner
- Target Discovery, Research Boehringer Ingelheim Pharma GmbH & CoKG, Biberach 88397, Germany.,Integrative Transcriptomics, Center for Bioinformatics, University of Tübingen, 72076 Tübingen, Germany
| | - German Leparc
- Target Discovery, Research Boehringer Ingelheim Pharma GmbH & CoKG, Biberach 88397, Germany
| | - Tobias Hildebrandt
- Target Discovery, Research Boehringer Ingelheim Pharma GmbH & CoKG, Biberach 88397, Germany
| | - Holger Klein
- Target Discovery, Research Boehringer Ingelheim Pharma GmbH & CoKG, Biberach 88397, Germany
| | - Leo Thomas
- Cardiometabolic Research, Research Boehringer Ingelheim Pharma GmbH & CoKG, Biberach 88397, Germany
| | - Elia Stupka
- Target Discovery, Research Boehringer Ingelheim Pharma GmbH & CoKG, Biberach 88397, Germany
| | - Eric Simon
- Target Discovery, Research Boehringer Ingelheim Pharma GmbH & CoKG, Biberach 88397, Germany
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75
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Li X, Wong KC. Multiobjective Patient Stratification Using Evolutionary Multiobjective Optimization. IEEE J Biomed Health Inform 2017; 22:1619-1629. [PMID: 29990162 DOI: 10.1109/jbhi.2017.2769711] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
One of the main challenges in modern medic-ine is to stratify patients for personalized care. Many different clustering methods have been proposed to solve the problem in both quantitative and biologically meaningful manners. However, existing clustering algorithms suffer from numerous restrictions such as experimental noises, high dimensionality, and poor interpretability. To overcome those limitations altogether, we propose and formulate a multiobjective framework based on evolutionary multiobjective optimization to balance the feature relevance and redundancy for patient stratification. To demonstrate the effectiveness of our proposed algorithms, we benchmark our algorithms across 55 synthetic datasets based on a real human transcription regulation network model, 35 real cancer gene expression datasets, and two case studies. Experimental results suggest that the proposed algorithms perform better than the recent state-of-the-arts. In addition, time complexity analysis, convergence analysis, and parameter analysis are conducted to demonstrate the robustness of the proposed methods from different perspectives. Finally, the t-Distributed Stochastic Neighbor Embedding (t-SNE) is applied to project the selected feature subsets onto two or three dimensions to visualize the high-dimensional patient stratification data.
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76
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Li S, He Y, Lin Z, Xu S, Zhou R, Liang F, Wang J, Yang H, Liu S, Ren Y. Digging More Missing Proteins Using an Enrichment Approach with ProteoMiner. J Proteome Res 2017; 16:4330-4339. [PMID: 28960076 DOI: 10.1021/acs.jproteome.7b00353] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Human Proteome Project (HPP) aims at mapping entire human proteins with a systematic effort upon all the emerging techniques, which would enhance understanding of human biology and lay a foundation for development of medical applications. Until now, 2563 missing proteins (MPs, PE2-4) are still undetected even using the most sensitive approach of protein detection. Herein, we propose that enrichment of low-abundance proteins benefits MPs finding. ProteoMiner is an equalizing technique by reducing high-abundance proteins and enriching low-abundance proteins in biological liquids. With triton X-100/TBS buffer extraction, ProteoMiner enrichment, and peptide fractionation, 20 MPs (at least two non-nested unique peptides with more than eight a.a. length) with 60 unique peptides were identified from four human tissues including eight membrane/secreted proteins and five nucleus proteins. Then 15 of them were confirmed with two non-nested unique peptides (≥9 a.a.) identified by matching well with their chemically synthetic peptides in PRM assay. Hence, these results demonstrated ProteoMiner as a powerful means in discovery of MPs.
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Affiliation(s)
- Siqi Li
- BGI-Shenzhen , Beishan Industrial Zone 11th building, Yantian District, Shenzhen, Guangdong 518083, China
| | - Yanbin He
- BGI-Shenzhen , Beishan Industrial Zone 11th building, Yantian District, Shenzhen, Guangdong 518083, China
| | - Zhilong Lin
- BGI-Shenzhen , Beishan Industrial Zone 11th building, Yantian District, Shenzhen, Guangdong 518083, China
| | - Shaohang Xu
- BGI-Shenzhen , Beishan Industrial Zone 11th building, Yantian District, Shenzhen, Guangdong 518083, China
| | - Ruo Zhou
- BGI-Shenzhen , Beishan Industrial Zone 11th building, Yantian District, Shenzhen, Guangdong 518083, China
| | - Feng Liang
- BGI-Shenzhen , Beishan Industrial Zone 11th building, Yantian District, Shenzhen, Guangdong 518083, China
| | - Jian Wang
- BGI-Shenzhen , Beishan Industrial Zone 11th building, Yantian District, Shenzhen, Guangdong 518083, China
| | - Huanming Yang
- BGI-Shenzhen , Beishan Industrial Zone 11th building, Yantian District, Shenzhen, Guangdong 518083, China.,James D. Watson Institute of Genome Sciences, Hangzhou 310008, China
| | - Siqi Liu
- BGI-Shenzhen , Beishan Industrial Zone 11th building, Yantian District, Shenzhen, Guangdong 518083, China
| | - Yan Ren
- BGI-Shenzhen , Beishan Industrial Zone 11th building, Yantian District, Shenzhen, Guangdong 518083, China
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77
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Adeola HA, Van Wyk JC, Arowolo A, Ngwanya RM, Mkentane K, Khumalo NP. Emerging Diagnostic and Therapeutic Potentials of Human Hair Proteomics. Proteomics Clin Appl 2017; 12. [PMID: 28960873 DOI: 10.1002/prca.201700048] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2017] [Revised: 06/09/2017] [Indexed: 01/22/2023]
Abstract
The use of noninvasive human substrates to interrogate pathophysiological conditions has become essential in the post- Human Genome Project era. Due to its high turnover rate, and its long term capability to incorporate exogenous and endogenous substances from the circulation, hair testing is emerging as a key player in monitoring long term drug compliance, chronic alcohol abuse, forensic toxicology, and biomarker discovery, among other things. Novel high-throughput 'omics based approaches like proteomics have been underutilized globally in comprehending human hair morphology and its evolving use as a diagnostic testing substrate in the era of precision medicine. There is paucity of scientific evidence that evaluates the difference in drug incorporation into hair based on lipid content, and very few studies have addressed hair growth rates, hair forms, and the biological consequences of hair grooming or bleaching. It is apparent that protein-based identification using the human hair proteome would play a major role in understanding these parameters akin to DNA single nucleotide polymorphism profiling, up to single amino acid polymorphism resolution. Hence, this work seeks to identify and discuss the progress made thus far in the field of molecular hair testing using proteomic approaches, and identify ways in which proteomics would improve the field of hair research, considering that the human hair is mostly composed of proteins. Gaps in hair proteomics research are identified and the potential of hair proteomics in establishing a historic medical repository of normal and disease-specific proteome is also discussed.
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Affiliation(s)
- Henry A Adeola
- Division of Dermatology, Department of Medicine, Faculty of Health Sciences and Groote Schuur Hospital, University of Cape Town, Cape Town, South Africa.,Hair and Skin Research Laboratory, Groote Schuur Hospital, Cape Town, South Africa
| | - Jennifer C Van Wyk
- Division of Dermatology, Department of Medicine, Faculty of Health Sciences and Groote Schuur Hospital, University of Cape Town, Cape Town, South Africa.,Hair and Skin Research Laboratory, Groote Schuur Hospital, Cape Town, South Africa
| | - Afolake Arowolo
- Division of Dermatology, Department of Medicine, Faculty of Health Sciences and Groote Schuur Hospital, University of Cape Town, Cape Town, South Africa.,Hair and Skin Research Laboratory, Groote Schuur Hospital, Cape Town, South Africa
| | - Reginald M Ngwanya
- Division of Dermatology, Department of Medicine, Faculty of Health Sciences and Groote Schuur Hospital, University of Cape Town, Cape Town, South Africa
| | - Khwezikazi Mkentane
- Division of Dermatology, Department of Medicine, Faculty of Health Sciences and Groote Schuur Hospital, University of Cape Town, Cape Town, South Africa.,Hair and Skin Research Laboratory, Groote Schuur Hospital, Cape Town, South Africa
| | - Nonhlanhla P Khumalo
- Division of Dermatology, Department of Medicine, Faculty of Health Sciences and Groote Schuur Hospital, University of Cape Town, Cape Town, South Africa.,Hair and Skin Research Laboratory, Groote Schuur Hospital, Cape Town, South Africa
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78
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Omenn GS, Lane L, Lundberg EK, Overall CM, Deutsch EW. Progress on the HUPO Draft Human Proteome: 2017 Metrics of the Human Proteome Project. J Proteome Res 2017; 16:4281-4287. [PMID: 28853897 DOI: 10.1021/acs.jproteome.7b00375] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
The Human Proteome Organization (HUPO) Human Proteome Project (HPP) continues to make progress on its two overall goals: (1) completing the protein parts list, with an annual update of the HUPO draft human proteome, and (2) making proteomics an integrated complement to genomics and transcriptomics throughout biomedical and life sciences research. neXtProt version 2017-01-23 has 17 008 confident protein identifications (Protein Existence [PE] level 1) that are compliant with the HPP Guidelines v2.1 ( https://hupo.org/Guidelines ), up from 13 664 in 2012-12 and 16 518 in 2016-04. Remaining to be found by mass spectrometry and other methods are 2579 "missing proteins" (PE2+3+4), down from 2949 in 2016. PeptideAtlas 2017-01 has 15 173 canonical proteins, accounting for nearly all of the 15 290 PE1 proteins based on MS data. These resources have extensive data on PTMs, single amino acid variants, and splice isoforms. The Human Protein Atlas v16 has 10 492 highly curated protein entries with tissue and subcellular spatial localization of proteins and transcript expression. Organ-specific popular protein lists have been generated for broad use in quantitative targeted proteomics using SRM-MS or DIA-SWATH-MS studies of biology and disease.
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Affiliation(s)
- Gilbert S Omenn
- Department of Computational Medicine and Bioinformatics, University of Michigan , 100 Washtenaw Avenue, Ann Arbor, Michigan 48109-2218, United States.,Institute for Systems Biology , 401 Terry Avenue North, Seattle, Washington 98109-5263, United States
| | - Lydie Lane
- CALIPHO Group, SIB Swiss Institute of Bioinformatics and Department of Human Protein Science, University of Geneva , CMU, Michel-Servet 1, 1211 Geneva 4, Switzerland
| | - Emma K Lundberg
- SciLifeLab Stockholm and School of Biotechnology, KTH, Karolinska Institutet Science Park , Tomtebodavägen 23, SE-171 65 Solna, Sweden
| | - Christopher M Overall
- Life Sciences Institute, Faculty of Dentistry, University of British Columbia , 2350 Health Sciences Mall, Room 4.401, Vancouver, British Columbia V6T 1Z3, Canada
| | - Eric W Deutsch
- Institute for Systems Biology , 401 Terry Avenue North, Seattle, Washington 98109-5263, United States
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79
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Improving the economics of NASH/NAFLD treatment through the use of systems biology. Drug Discov Today 2017; 22:1532-1538. [DOI: 10.1016/j.drudis.2017.07.005] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2017] [Revised: 06/27/2017] [Accepted: 07/12/2017] [Indexed: 12/13/2022]
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80
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Sjöstedt E, Bollerslev J, Mulder J, Lindskog C, Pontén F, Casar-Borota O. A specific antibody to detect transcription factor T-Pit: a reliable marker of corticotroph cell differentiation and a tool to improve the classification of pituitary neuroendocrine tumours. Acta Neuropathol 2017; 134:675-677. [PMID: 28823042 DOI: 10.1007/s00401-017-1768-9] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2017] [Revised: 08/08/2017] [Accepted: 08/12/2017] [Indexed: 01/23/2023]
Affiliation(s)
- Evelina Sjöstedt
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm, Sweden
- Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Jens Bollerslev
- Department of Specialized Endocrinology, Rikshospitalet, Oslo University Hospital, Oslo, Norway
- Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Jan Mulder
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm, Sweden
| | - Cecilia Lindskog
- Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Fredrik Pontén
- Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
- Department of Clinical Pathology, Uppsala University Hospital, Uppsala, Sweden
| | - Olivera Casar-Borota
- Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden.
- Department of Clinical Pathology, Uppsala University Hospital, Uppsala, Sweden.
- Department of Pathology, Oslo University Hospital, Oslo, Norway.
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81
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Carapito C, Duek P, Macron C, Seffals M, Rondel K, Delalande F, Lindskog C, Fréour T, Vandenbrouck Y, Lane L, Pineau C. Validating Missing Proteins in Human Sperm Cells by Targeted Mass-Spectrometry- and Antibody-based Methods. J Proteome Res 2017; 16:4340-4351. [DOI: 10.1021/acs.jproteome.7b00374] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Affiliation(s)
- Christine Carapito
- Laboratoire
de Spectrométrie de Masse BioOrganique (LSMBO), IPHC, Université de Strasbourg, CNRS UMR7178, 25 Rue Becquerel, Strasbourg F-67087, France
| | - Paula Duek
- CALIPHO
Group, SIB-Swiss Institute of Bioinformatics, CMU, rue Michel-Servet
1, CH-1211 Geneva
4, Switzerland
| | - Charlotte Macron
- Laboratoire
de Spectrométrie de Masse BioOrganique (LSMBO), IPHC, Université de Strasbourg, CNRS UMR7178, 25 Rue Becquerel, Strasbourg F-67087, France
| | - Marine Seffals
- H2P2
Core facility, UMS BioSit, University of Rennes 1, Rennes F-35040, France
| | - Karine Rondel
- Protim,
Inserm U1085, Irset, Campus de Beaulieu, Rennes F-35042, France
| | - François Delalande
- Laboratoire
de Spectrométrie de Masse BioOrganique (LSMBO), IPHC, Université de Strasbourg, CNRS UMR7178, 25 Rue Becquerel, Strasbourg F-67087, France
| | - Cecilia Lindskog
- Department
of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Thomas Fréour
- Service de
Médecine de la Reproduction, CHU de Nantes, 38 boulevard
Jean Monnet, Nantes F-44093, France
- Inserm UMR1064, Nantes F-44093, France
| | - Yves Vandenbrouck
- CEA, DRF, BIG,
Laboratoire de Biologie à Grande Echelle, 17, rue des Martyrs, Grenoble F-38054, France
- Inserm U1038, Grenoble F-38054, France
- Grenoble-Alpes University, Grenoble F-38054, France
| | - Lydie Lane
- CALIPHO
Group, SIB-Swiss Institute of Bioinformatics, CMU, rue Michel-Servet
1, CH-1211 Geneva
4, Switzerland
- Department
of Human Protein Sciences, Faculty of Medicine, University of Geneva, 1, rue Michel-Servet, 1211 Geneva 4, Switzerland
| | - Charles Pineau
- Protim,
Inserm U1085, Irset, Campus de Beaulieu, Rennes F-35042, France
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82
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Lee S, Zhang C, Liu Z, Klevstig M, Mukhopadhyay B, Bergentall M, Cinar R, Ståhlman M, Sikanic N, Park JK, Deshmukh S, Harzandi AM, Kuijpers T, Grøtli M, Elsässer SJ, Piening BD, Snyder M, Smith U, Nielsen J, Bäckhed F, Kunos G, Uhlen M, Boren J, Mardinoglu A. Network analyses identify liver-specific targets for treating liver diseases. Mol Syst Biol 2017; 13:938. [PMID: 28827398 PMCID: PMC5572395 DOI: 10.15252/msb.20177703] [Citation(s) in RCA: 85] [Impact Index Per Article: 12.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2017] [Revised: 07/19/2017] [Accepted: 07/24/2017] [Indexed: 01/02/2023] Open
Abstract
We performed integrative network analyses to identify targets that can be used for effectively treating liver diseases with minimal side effects. We first generated co-expression networks (CNs) for 46 human tissues and liver cancer to explore the functional relationships between genes and examined the overlap between functional and physical interactions. Since increased de novo lipogenesis is a characteristic of nonalcoholic fatty liver disease (NAFLD) and hepatocellular carcinoma (HCC), we investigated the liver-specific genes co-expressed with fatty acid synthase (FASN). CN analyses predicted that inhibition of these liver-specific genes decreases FASN expression. Experiments in human cancer cell lines, mouse liver samples, and primary human hepatocytes validated our predictions by demonstrating functional relationships between these liver genes, and showing that their inhibition decreases cell growth and liver fat content. In conclusion, we identified liver-specific genes linked to NAFLD pathogenesis, such as pyruvate kinase liver and red blood cell (PKLR), or to HCC pathogenesis, such as PKLR, patatin-like phospholipase domain containing 3 (PNPLA3), and proprotein convertase subtilisin/kexin type 9 (PCSK9), all of which are potential targets for drug development.
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Affiliation(s)
- Sunjae Lee
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Cheng Zhang
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Zhengtao Liu
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Martina Klevstig
- Department of Molecular and Clinical Medicine, University of Gothenburg and Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Bani Mukhopadhyay
- Laboratory of Physiologic Studies, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, USA
| | - Mattias Bergentall
- Department of Molecular and Clinical Medicine, University of Gothenburg and Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Resat Cinar
- Laboratory of Physiologic Studies, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, USA
| | - Marcus Ståhlman
- Department of Molecular and Clinical Medicine, University of Gothenburg and Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Natasha Sikanic
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Joshua K Park
- Laboratory of Physiologic Studies, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, USA
| | - Sumit Deshmukh
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Azadeh M Harzandi
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Tim Kuijpers
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Morten Grøtli
- Department of Chemistry and Molecular Biology, University of Gothenburg, Gothenburg, Sweden
| | - Simon J Elsässer
- Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Brian D Piening
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Michael Snyder
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Ulf Smith
- Department of Molecular and Clinical Medicine, University of Gothenburg and Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Jens Nielsen
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Fredrik Bäckhed
- Department of Molecular and Clinical Medicine, University of Gothenburg and Sahlgrenska University Hospital, Gothenburg, Sweden
| | - George Kunos
- Laboratory of Physiologic Studies, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, USA
| | - Mathias Uhlen
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Jan Boren
- Department of Molecular and Clinical Medicine, University of Gothenburg and Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Adil Mardinoglu
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
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83
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84
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Human protein secretory pathway genes are expressed in a tissue-specific pattern to match processing demands of the secretome. NPJ Syst Biol Appl 2017; 3:22. [PMID: 28845240 PMCID: PMC5562915 DOI: 10.1038/s41540-017-0021-4] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2016] [Revised: 06/17/2017] [Accepted: 06/22/2017] [Indexed: 02/05/2023] Open
Abstract
Protein secretory pathway in eukaryal cells is responsible for delivering functional secretory proteins. The dysfunction of this pathway causes a range of important human diseases from congenital disorders to cancer. Despite the piled-up knowledge on the molecular biology and biochemistry level, the tissue-specific expression of the secretory pathway genes has not been analyzed on the transcriptome level. Based on the recent RNA-sequencing studies, the largest fraction of tissue-specific transcriptome encodes for the secretome (secretory proteins). Here, the question arises that if the expression levels of the secretory pathway genes have a tissue-specific tuning. In this study, we tackled this question by performing a meta-analysis of the recently published transcriptome data on human tissues. As a result, we detected 68 as called “extreme genes” which show an unusual expression pattern in specific gene families of the secretory pathway. We also inspected the potential functional link between detected extreme genes and the corresponding tissues enriched secretome. As a result, the detected extreme genes showed correlation with the enrichment of the nature and number of specific post-translational modifications in each tissue’s secretome. Our findings conciliate both the housekeeping and tissue-specific nature of the protein secretory pathway, which we attribute to a fine-tuned regulation of defined gene families to support the diversity of secreted proteins and their modifications. The secretory pathway, an ubiquitous cellular machinery in human cells, is here shown to have tissue-specific characteristics. A research team led by Prof. Jens Nielsen at the Chalmers University of Technology performed a meta-analysis of the gene expressions of the secretory pathway’ component. They detected that even though most of these components are expressed in all tissues, there exist distinct components with fine-tuned expression. They further evaluated the functional link between the detected tuning and the processes that are demanded to make a set of tissue-specific proteins such as endocrine system hormones or their receptors. These findings open up a new avenue to understand the function of the secretion pathway in human tissues with possible applications for improving production of pharmaceutical proteins and getting more insight into the mechanisms underlying diseases such as diabetes that are connected with the endocrine system.
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85
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Hutchins AP, Yang Z, Li Y, He F, Fu X, Wang X, Li D, Liu K, He J, Wang Y, Chen J, Esteban MA, Pei D. Models of global gene expression define major domains of cell type and tissue identity. Nucleic Acids Res 2017; 45:2354-2367. [PMID: 28426095 PMCID: PMC5389706 DOI: 10.1093/nar/gkx054] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2016] [Accepted: 01/22/2017] [Indexed: 01/22/2023] Open
Abstract
The current classification of cells in an organism is largely based on their anatomic and developmental origin. Cells types and tissues are traditionally classified into those that arise from the three embryonic germ layers, the ectoderm, mesoderm and endoderm, but this model does not take into account the organization of cell type-specific patterns of gene expression. Here, we present computational models for cell type and tissue specification derived from a collection of 921 RNA-sequencing samples from 272 distinct mouse cell types or tissues. In an unbiased fashion, this analysis accurately predicts the three known germ layers. Unexpectedly, this analysis also suggests that in total there are eight major domains of cell type-specification, corresponding to the neurectoderm, neural crest, surface ectoderm, endoderm, mesoderm, blood mesoderm, germ cells and the embryonic domain. Further, we identify putative genes responsible for specifying the domain and the cell type. This model has implications for understanding trans-lineage differentiation for stem cells, developmental cell biology and regenerative medicine.
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Affiliation(s)
- Andrew P Hutchins
- Department of Biology, Southern University of Science and Technology of China, Shenzhen, Guangdong 518055, China.,Key Laboratory of Regenerative Biology of the Chinese Academy of Sciences and Guangdong Provincial Key Laboratory of Stem Cells and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, Guangdong 510530, China
| | - Zhongzhou Yang
- Key Laboratory of Regenerative Biology of the Chinese Academy of Sciences and Guangdong Provincial Key Laboratory of Stem Cells and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, Guangdong 510530, China
| | - Yuhao Li
- Department of Biology, Southern University of Science and Technology of China, Shenzhen, Guangdong 518055, China
| | - Fangfang He
- Department of Biology, Southern University of Science and Technology of China, Shenzhen, Guangdong 518055, China
| | - Xiuling Fu
- Department of Biology, Southern University of Science and Technology of China, Shenzhen, Guangdong 518055, China
| | - Xiaoshan Wang
- Key Laboratory of Regenerative Biology of the Chinese Academy of Sciences and Guangdong Provincial Key Laboratory of Stem Cells and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, Guangdong 510530, China
| | - Dongwei Li
- Key Laboratory of Regenerative Biology of the Chinese Academy of Sciences and Guangdong Provincial Key Laboratory of Stem Cells and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, Guangdong 510530, China
| | - Kairong Liu
- Academy of Mathematics and Systems Science, National Center for Mathematics and Interdisciplinary Sciences, Chinese Academy of Sciences, Beijing 100080, China.,Beihang University, Beijing 100191, China
| | - Jiangping He
- Key Laboratory of Regenerative Biology of the Chinese Academy of Sciences and Guangdong Provincial Key Laboratory of Stem Cells and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, Guangdong 510530, China
| | - Yong Wang
- Academy of Mathematics and Systems Science, National Center for Mathematics and Interdisciplinary Sciences, Chinese Academy of Sciences, Beijing 100080, China
| | - Jiekai Chen
- Key Laboratory of Regenerative Biology of the Chinese Academy of Sciences and Guangdong Provincial Key Laboratory of Stem Cells and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, Guangdong 510530, China
| | - Miguel A Esteban
- Key Laboratory of Regenerative Biology of the Chinese Academy of Sciences and Guangdong Provincial Key Laboratory of Stem Cells and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, Guangdong 510530, China.,Laboratory of RNA, Chromatin and Human disease, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, Guangdong 510530, China
| | - Duanqing Pei
- Key Laboratory of Regenerative Biology of the Chinese Academy of Sciences and Guangdong Provincial Key Laboratory of Stem Cells and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, Guangdong 510530, China
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86
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Hoffman JM, Lyu Y, Pletcher SD, Promislow DEL. Proteomics and metabolomics in ageing research: from biomarkers to systems biology. Essays Biochem 2017; 61:379-388. [PMID: 28698311 PMCID: PMC5743054 DOI: 10.1042/ebc20160083] [Citation(s) in RCA: 52] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2017] [Revised: 05/16/2017] [Accepted: 05/17/2017] [Indexed: 02/07/2023]
Abstract
Age is the single greatest risk factor for a wide range of diseases, and as the mean age of human populations grows steadily older, the impact of this risk factor grows as well. Laboratory studies on the basic biology of ageing have shed light on numerous genetic pathways that have strong effects on lifespan. However, we still do not know the degree to which the pathways that affect ageing in the lab also influence variation in rates of ageing and age-related disease in human populations. Similarly, despite considerable effort, we have yet to identify reliable and reproducible 'biomarkers', which are predictors of one's biological as opposed to chronological age. One challenge lies in the enormous mechanistic distance between genotype and downstream ageing phenotypes. Here, we consider the power of studying 'endophenotypes' in the context of ageing. Endophenotypes are the various molecular domains that exist at intermediate levels of organization between the genotype and phenotype. We focus our attention specifically on proteins and metabolites. Proteomic and metabolomic profiling has the potential to help identify the underlying causal mechanisms that link genotype to phenotype. We present a brief review of proteomics and metabolomics in ageing research with a focus on the potential of a systems biology and network-centric perspective in geroscience. While network analyses to study ageing utilizing proteomics and metabolomics are in their infancy, they may be the powerful model needed to discover underlying biological processes that influence natural variation in ageing, age-related disease, and longevity.
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Affiliation(s)
- Jessica M Hoffman
- Department of Biology, University of Alabama at Birmingham, 1300 University Blvd CH464, Birmingham, AL 35294, U.S.A
| | - Yang Lyu
- Department of Molecular and Integrative Physiology and Geriatrics Center, Biomedical Sciences and Research Building, University of Michigan, Ann Arbor, MI 48109, U.S.A
| | - Scott D Pletcher
- Department of Molecular and Integrative Physiology and Geriatrics Center, Biomedical Sciences and Research Building, University of Michigan, Ann Arbor, MI 48109, U.S.A
| | - Daniel E L Promislow
- Department of Pathology, University of Washington, Box 357705, 1959 NE Pacific Street, Seattle, Washington 98195, U.S.A.
- Department of Biology, University of Washington, Seattle, Washington 98195, U.S.A
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87
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Benfeitas R, Uhlen M, Nielsen J, Mardinoglu A. New Challenges to Study Heterogeneity in Cancer Redox Metabolism. Front Cell Dev Biol 2017; 5:65. [PMID: 28744456 PMCID: PMC5504267 DOI: 10.3389/fcell.2017.00065] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2017] [Accepted: 06/26/2017] [Indexed: 12/13/2022] Open
Abstract
Reactive oxygen species (ROS) are important pathophysiological molecules involved in vital cellular processes. They are extremely harmful at high concentrations because they promote the generation of radicals and the oxidation of lipids, proteins, and nucleic acids, which can result in apoptosis. An imbalance of ROS and a disturbance of redox homeostasis are now recognized as a hallmark of complex diseases. Considering that ROS levels are significantly increased in cancer cells due to mitochondrial dysfunction, ROS metabolism has been targeted for the development of efficient treatment strategies, and antioxidants are used as potential chemotherapeutic drugs. However, initial ROS-focused clinical trials in which antioxidants were supplemented to patients provided inconsistent results, i.e., improved treatment or increased malignancy. These different outcomes may result from the highly heterogeneous redox responses of tumors in different patients. Hence, population-based treatment strategies are unsuitable and patient-tailored therapeutic approaches are required for the effective treatment of patients. Moreover, due to the crosstalk between ROS, reducing equivalents [e.g., NAD(P)H] and central metabolism, which is heterogeneous in cancer, finding the best therapeutic target requires the consideration of system-wide approaches that are capable of capturing the complex alterations observed in all of the associated pathways. Systems biology and engineering approaches may be employed to overcome these challenges, together with tools developed in personalized medicine. However, ROS- and redox-based therapies have yet to be addressed by these methodologies in the context of disease treatment. Here, we review the role of ROS and their coupled redox partners in tumorigenesis. Specifically, we highlight some of the challenges in understanding the role of hydrogen peroxide (H2O2), one of the most important ROS in pathophysiology in the progression of cancer. We also discuss its interplay with antioxidant defenses, such as the coupled peroxiredoxin/thioredoxin and glutathione/glutathione peroxidase systems, and its reducing equivalent metabolism. Finally, we highlight the need for system-level and patient-tailored approaches to clarify the roles of these systems and identify therapeutic targets through the use of the tools developed in personalized medicine.
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Affiliation(s)
- Rui Benfeitas
- Science for Life Laboratory, KTH Royal Institute of TechnologyStockholm, Sweden
| | - Mathias Uhlen
- Science for Life Laboratory, KTH Royal Institute of TechnologyStockholm, Sweden
| | - Jens Nielsen
- Science for Life Laboratory, KTH Royal Institute of TechnologyStockholm, Sweden.,Department of Biology and Biological Engineering, Chalmers University of TechnologyGothenburg, Sweden
| | - Adil Mardinoglu
- Science for Life Laboratory, KTH Royal Institute of TechnologyStockholm, Sweden.,Department of Biology and Biological Engineering, Chalmers University of TechnologyGothenburg, Sweden
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88
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Yamamoto H, Unbehaun A, Spahn CMT. Ribosomal Chamber Music: Toward an Understanding of IRES Mechanisms. Trends Biochem Sci 2017; 42:655-668. [PMID: 28684008 DOI: 10.1016/j.tibs.2017.06.002] [Citation(s) in RCA: 58] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2017] [Revised: 06/05/2017] [Accepted: 06/06/2017] [Indexed: 12/31/2022]
Abstract
Internal initiation is a 5'-end-independent mode of translation initiation engaged by many virus- and putatively some cell-encoded templates. Internal initiation is facilitated by specific RNA tertiary folds, called internal ribosomal entry sites (IRESs), in the 5' untranslated region (UTR) of the respective transcripts. In this review we discuss recent structural insight into how established IRESs first capture and then manipulate the eukaryotic translation machinery through non-canonical interactions and by guiding the intrinsic conformational flexibility of the eukaryotic ribosome. Because IRESs operate with reduced complexity and constitute minimal systems of initiation, comparison with canonical initiation may allow common mechanistic principles of the ribosome to be delineated.
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Affiliation(s)
- Hiroshi Yamamoto
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Institut für Medizinische Physik und Biophysik, Charitéplatz 1, 10117 Berlin, Germany
| | - Anett Unbehaun
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Institut für Medizinische Physik und Biophysik, Charitéplatz 1, 10117 Berlin, Germany
| | - Christian M T Spahn
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Institut für Medizinische Physik und Biophysik, Charitéplatz 1, 10117 Berlin, Germany.
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89
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Seitter H, Koschak A. Relevance of tissue specific subunit expression in channelopathies. Neuropharmacology 2017; 132:58-70. [PMID: 28669898 DOI: 10.1016/j.neuropharm.2017.06.029] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2017] [Revised: 06/22/2017] [Accepted: 06/28/2017] [Indexed: 12/27/2022]
Abstract
Channelopathies are a diverse group of human disorders that are caused by mutations in genes coding for ion channels or channel-regulating proteins. Several dozen channelopathies have been identified that involve both non-excitable cells as well as electrically active tissues like brain, skeletal and smooth muscle or the heart. In this review, we start out from the general question which ion channel genes are expressed tissue-selectively. We mined the human gene expression database Human Protein Atlas (HPA) for tissue-enriched ion channel genes and found 85 genes belonging to the ion channel families. Most of these genes were enriched in brain, testis and muscle and a complete list of the enriched ion channel genes is provided. We further focused on the tissue distribution of voltage-gated calcium channel (VGCC) genes including different brain areas and the retina based on the human gene expression from the FANTOM5 dataset. The expression data is complemented by an overview of the tissue-dependent aspects of L-type calcium channel (LTCC) function, dysfunction and pharmacology, as well as of their splice variants. Finally, we focus on the pathology of tissue-restricted LTCC channelopathies and their treatment options. This article is part of the Special Issue entitled 'Channelopathies.'
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Affiliation(s)
- Hartwig Seitter
- University of Innsbruck, Institute of Pharmacy, Pharmacology and Toxicology, Center for Chemistry and Biomedicine, Innrain 80-82/III, 6020 Innsbruck, Austria
| | - Alexandra Koschak
- University of Innsbruck, Institute of Pharmacy, Pharmacology and Toxicology, Center for Chemistry and Biomedicine, Innrain 80-82/III, 6020 Innsbruck, Austria.
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90
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mTFkb: a knowledgebase for fundamental annotation of mouse transcription factors. Sci Rep 2017; 7:3022. [PMID: 28596516 PMCID: PMC5465081 DOI: 10.1038/s41598-017-02404-w] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2016] [Accepted: 04/12/2017] [Indexed: 12/11/2022] Open
Abstract
Transcription factors (TFs) are well-known important regulators in cell biology and tissue development. However, in mouse, one of the most widely-used model species, currently the vast majority of the known TFs have not been functionally studied due to the lack of sufficient annotations. To this end, we collected and analyzed the whole transcriptome sequencing data from more than 30 major mouse tissues and used the expression profiles to annotate the TFs. We found that the expression patterns of the TFs are highly correlated with the histology of the tissue types thus can be used to infer the potential functions of the TFs. Furthermore, we found that as many as 30% TFs display tissue-specific expression pattern, and these tissue-specific TFs are among the key TFs in their corresponding tissues. We also observed signals of divergent transcription associated with many TFs with unique expression pattern. Lastly, we have integrated all the data, our analysis results as well as various annotation resources to build a web-based database named mTFkb freely accessible at http://www.myogenesisdb.org/mTFkb/. We believe that mTFkb could serve as a useful and valuable resource for TF studies in mouse.
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91
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Robaina-Estévez S, Nikoloski Z. On the effects of alternative optima in context-specific metabolic model predictions. PLoS Comput Biol 2017; 13:e1005568. [PMID: 28557990 PMCID: PMC5469500 DOI: 10.1371/journal.pcbi.1005568] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2016] [Revised: 06/13/2017] [Accepted: 05/10/2017] [Indexed: 11/19/2022] Open
Abstract
The integration of experimental data into genome-scale metabolic models can greatly improve flux predictions. This is achieved by restricting predictions to a more realistic context-specific domain, like a particular cell or tissue type. Several computational approaches to integrate data have been proposed-generally obtaining context-specific (sub)models or flux distributions. However, these approaches may lead to a multitude of equally valid but potentially different models or flux distributions, due to possible alternative optima in the underlying optimization problems. Although this issue introduces ambiguity in context-specific predictions, it has not been generally recognized, especially in the case of model reconstructions. In this study, we analyze the impact of alternative optima in four state-of-the-art context-specific data integration approaches, providing both flux distributions and/or metabolic models. To this end, we present three computational methods and apply them to two particular case studies: leaf-specific predictions from the integration of gene expression data in a metabolic model of Arabidopsis thaliana, and liver-specific reconstructions derived from a human model with various experimental data sources. The application of these methods allows us to obtain the following results: (i) we sample the space of alternative flux distributions in the leaf- and the liver-specific case and quantify the ambiguity of the predictions. In addition, we show how the inclusion of ℓ1-regularization during data integration reduces the ambiguity in both cases. (ii) We generate sets of alternative leaf- and liver-specific models that are optimal to each one of the evaluated model reconstruction approaches. We demonstrate that alternative models of the same context contain a marked fraction of disparate reactions. Further, we show that a careful balance between model sparsity and metabolic functionality helps in reducing the discrepancies between alternative models. Finally, our findings indicate that alternative optima must be taken into account for rendering the context-specific metabolic model predictions less ambiguous.
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Affiliation(s)
- Semidán Robaina-Estévez
- Systems Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany
- Bioinformatics Group, Institute of Biochemistry and Biology, University of Potsdam, Potsdam-Golm, Germany
- * E-mail:
| | - Zoran Nikoloski
- Systems Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany
- Bioinformatics Group, Institute of Biochemistry and Biology, University of Potsdam, Potsdam-Golm, Germany
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92
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Abstract
Metabolism is highly complex and involves thousands of different connected reactions; it is therefore necessary to use mathematical models for holistic studies. The use of mathematical models in biology is referred to as systems biology. In this review, the principles of systems biology are described, and two different types of mathematical models used for studying metabolism are discussed: kinetic models and genome-scale metabolic models. The use of different omics technologies, including transcriptomics, proteomics, metabolomics, and fluxomics, for studying metabolism is presented. Finally, the application of systems biology for analyzing global regulatory structures, engineering the metabolism of cell factories, and analyzing human diseases is discussed.
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Affiliation(s)
- Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE41128 Gothenburg, Sweden; .,Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK2800 Lyngby, Denmark.,Science for Life Laboratory, Royal Institute of Technology, SE17121 Stockholm, Sweden
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93
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Paananen J. Bioinformatics in the Identification of Novel Targets and Pathways in Neurodegenerative Diseases. CURRENT GENETIC MEDICINE REPORTS 2017. [DOI: 10.1007/s40142-017-0115-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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94
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Bergman J, Botling J, Fagerberg L, Hallström BM, Djureinovic D, Uhlén M, Pontén F. The Human Adrenal Gland Proteome Defined by Transcriptomics and Antibody-Based Profiling. Endocrinology 2017; 158:239-251. [PMID: 27901589 DOI: 10.1210/en.2016-1758] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/14/2016] [Accepted: 11/29/2016] [Indexed: 12/22/2022]
Abstract
The adrenal gland is a composite endocrine organ with vital functions that include the synthesis and release of glucocorticoids and catecholamines. To define the molecular landscape that underlies the specific functions of the adrenal gland, we combined a genome-wide transcriptomics approach using messenger RNA sequencing of human tissues with immunohistochemistry-based protein profiling on tissue microarrays. Approximately two-thirds of all putative protein coding genes were expressed in the adrenal gland, and the analysis identified 253 genes with an elevated pattern of expression in the adrenal gland, with only 37 genes showing a markedly greater expression level (more than fivefold) in the adrenal gland compared with 31 other normal human tissue types analyzed. The analyses allowed for an assessment of the relative expression levels for well-known proteins involved in adrenal gland function but also identified previously poorly characterized proteins in the adrenal cortex, such as the FERM (4.1 protein, ezrin, radixin, moesin) domain containing 5 and the nephroblastoma overexpressed (NOV) protein homolog. We have provided a global analysis of the adrenal gland transcriptome and proteome, with a comprehensive list of genes with elevated expression in the adrenal gland and spatial information with examples of protein expression patterns for corresponding proteins. These genes and proteins constitute important starting points for an improved understanding of the normal function and pathophysiology of the adrenal glands.
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Affiliation(s)
- Julia Bergman
- Department of Immunology Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Johan Botling
- Department of Immunology Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Linn Fagerberg
- Science for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Björn M Hallström
- Science for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Dijana Djureinovic
- Department of Immunology Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Mathias Uhlén
- Science for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Fredrik Pontén
- Department of Immunology Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
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95
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Functional Genomics, Genetics, and Bioinformatics 2016. BIOMED RESEARCH INTERNATIONAL 2016; 2016:2625831. [PMID: 27995138 PMCID: PMC5138440 DOI: 10.1155/2016/2625831] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 10/17/2016] [Accepted: 10/17/2016] [Indexed: 12/05/2022]
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96
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97
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Omenn GS, Lane L, Lundberg EK, Beavis RC, Overall CM, Deutsch EW. Metrics for the Human Proteome Project 2016: Progress on Identifying and Characterizing the Human Proteome, Including Post-Translational Modifications. J Proteome Res 2016; 15:3951-3960. [PMID: 27487407 PMCID: PMC5129622 DOI: 10.1021/acs.jproteome.6b00511] [Citation(s) in RCA: 63] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
The HUPO Human Proteome Project (HPP) has two overall goals: (1) stepwise completion of the protein parts list-the draft human proteome including confidently identifying and characterizing at least one protein product from each protein-coding gene, with increasing emphasis on sequence variants, post-translational modifications (PTMs), and splice isoforms of those proteins; and (2) making proteomics an integrated counterpart to genomics throughout the biomedical and life sciences community. PeptideAtlas and GPMDB reanalyze all major human mass spectrometry data sets available through ProteomeXchange with standardized protocols and stringent quality filters; neXtProt curates and integrates mass spectrometry and other findings to present the most up to date authorative compendium of the human proteome. The HPP Guidelines for Mass Spectrometry Data Interpretation version 2.1 were applied to manuscripts submitted for this 2016 C-HPP-led special issue [ www.thehpp.org/guidelines ]. The Human Proteome presented as neXtProt version 2016-02 has 16,518 confident protein identifications (Protein Existence [PE] Level 1), up from 13,664 at 2012-12, 15,646 at 2013-09, and 16,491 at 2014-10. There are 485 proteins that would have been PE1 under the Guidelines v1.0 from 2012 but now have insufficient evidence due to the agreed-upon more stringent Guidelines v2.0 to reduce false positives. neXtProt and PeptideAtlas now both require two non-nested, uniquely mapping (proteotypic) peptides of at least 9 aa in length. There are 2,949 missing proteins (PE2+3+4) as the baseline for submissions for this fourth annual C-HPP special issue of Journal of Proteome Research. PeptideAtlas has 14,629 canonical (plus 1187 uncertain and 1755 redundant) entries. GPMDB has 16,190 EC4 entries, and the Human Protein Atlas has 10,475 entries with supportive evidence. neXtProt, PeptideAtlas, and GPMDB are rich resources of information about post-translational modifications (PTMs), single amino acid variants (SAAVSs), and splice isoforms. Meanwhile, the Biology- and Disease-driven (B/D)-HPP has created comprehensive SRM resources, generated popular protein lists to guide targeted proteomics assays for specific diseases, and launched an Early Career Researchers initiative.
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Affiliation(s)
- Gilbert S. Omenn
- Department of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, Michigan 48109-2218, United States
| | - Lydie Lane
- CALIPHO Group, SIB Swiss Institute of Bioinformatics and Department of Human Protein Science, University of Geneva, CMU, Michel-Servet 1, 1211 Geneva 4, Switzerland
| | - Emma K. Lundberg
- SciLifeLab Stockholm and School of Biotechnology, KTH, Karolinska Institutet Science Park, Tomtebodavägen 23, SE-171 65 Solna, Sweden
| | - Ronald C. Beavis
- Biochemistry & Medical Genetics, University of Manitoba, Winnipeg, MB R3T 2N2, Canada
| | - Christopher M. Overall
- Biochemistry and Molecular Biology, and Oral Biological and Medical Sciences University of British Columbia, 2350 Health Sciences Mall, Room 4.401, Vancouver, BC V6T 1Z3, Canada
| | - Eric W. Deutsch
- Institute for Systems Biology, 401 Terry Avenue North, Seattle, Washington 98109-5263, United States
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98
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Krishnan A, Taroni JN, Greene CS. Integrative Networks Illuminate Biological Factors Underlying Gene–Disease Associations. CURRENT GENETIC MEDICINE REPORTS 2016. [DOI: 10.1007/s40142-016-0102-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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99
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Vandenbrouck Y, Lane L, Carapito C, Duek P, Rondel K, Bruley C, Macron C, Gonzalez de Peredo A, Couté Y, Chaoui K, Com E, Gateau A, Hesse AM, Marcellin M, Méar L, Mouton-Barbosa E, Robin T, Burlet-Schiltz O, Cianferani S, Ferro M, Fréour T, Lindskog C, Garin J, Pineau C. Looking for Missing Proteins in the Proteome of Human Spermatozoa: An Update. J Proteome Res 2016; 15:3998-4019. [PMID: 27444420 DOI: 10.1021/acs.jproteome.6b00400] [Citation(s) in RCA: 58] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
The Chromosome-Centric Human Proteome Project (C-HPP) aims to identify "missing" proteins in the neXtProt knowledgebase. We present an in-depth proteomics analysis of the human sperm proteome to identify testis-enriched missing proteins. Using protein extraction procedures and LC-MS/MS analysis, we detected 235 proteins (PE2-PE4) for which no previous evidence of protein expression was annotated. Through LC-MS/MS and LC-PRM analysis, data mining, and immunohistochemistry, we confirmed the expression of 206 missing proteins (PE2-PE4) in line with current HPP guidelines (version 2.0). Parallel reaction monitoring acquisition and sythetic heavy labeled peptides targeted 36 ≪one-hit wonder≫ candidates selected based on prior peptide spectrum match assessment. 24 were validated with additional predicted and specifically targeted peptides. Evidence was found for 16 more missing proteins using immunohistochemistry on human testis sections. The expression pattern for some of these proteins was specific to the testis, and they could possibly be valuable markers with fertility assessment applications. Strong evidence was also found of four "uncertain" proteins (PE5); their status should be re-examined. We show how using a range of sample preparation techniques combined with MS-based analysis, expert knowledge, and complementary antibody-based techniques can produce data of interest to the community. All MS/MS data are available via ProteomeXchange under identifier PXD003947. In addition to contributing to the C-HPP, we hope these data will stimulate continued exploration of the sperm proteome.
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Affiliation(s)
- Yves Vandenbrouck
- CEA, DRF, BIG, Laboratoire de Biologie à Grande Echelle, 17 rue des martyrs, Grenoble F-38054, France.,Inserm U1038 , 17, rue des Martyrs, Grenoble F-38054, France.,Université de Grenoble , Grenoble F-38054, France
| | - Lydie Lane
- Department of Human Protein Sciences, Faculty of Medicine, University of Geneva , 1, rue Michel-Servet, 1211 Geneva 4, Switzerland.,CALIPHO Group, SIB-Swiss Institute of Bioinformatics, CMU , rue Michel-Servet 1, CH-1211 Geneva 4, Switzerland
| | - Christine Carapito
- Laboratoire de Spectrométrie de Masse BioOrganique (LSMBO), IPHC, Université de Strasbourg, CNRS UMR7178, 25 Rue Becquerel, 67087 Strasbourg, France
| | - Paula Duek
- CALIPHO Group, SIB-Swiss Institute of Bioinformatics, CMU , rue Michel-Servet 1, CH-1211 Geneva 4, Switzerland
| | - Karine Rondel
- Protim, Inserm U1085, Irset, Campus de Beaulieu, Rennes 35042, France
| | - Christophe Bruley
- CEA, DRF, BIG, Laboratoire de Biologie à Grande Echelle, 17 rue des martyrs, Grenoble F-38054, France.,Inserm U1038 , 17, rue des Martyrs, Grenoble F-38054, France.,Université de Grenoble , Grenoble F-38054, France
| | - Charlotte Macron
- Laboratoire de Spectrométrie de Masse BioOrganique (LSMBO), IPHC, Université de Strasbourg, CNRS UMR7178, 25 Rue Becquerel, 67087 Strasbourg, France
| | - Anne Gonzalez de Peredo
- Institut de Pharmacologie et de Biologie Structurale, Université de Toulouse, CNRS, UPS, 31062 Toulouse, France
| | - Yohann Couté
- CEA, DRF, BIG, Laboratoire de Biologie à Grande Echelle, 17 rue des martyrs, Grenoble F-38054, France.,Inserm U1038 , 17, rue des Martyrs, Grenoble F-38054, France.,Université de Grenoble , Grenoble F-38054, France
| | - Karima Chaoui
- Institut de Pharmacologie et de Biologie Structurale, Université de Toulouse, CNRS, UPS, 31062 Toulouse, France
| | - Emmanuelle Com
- Protim, Inserm U1085, Irset, Campus de Beaulieu, Rennes 35042, France
| | - Alain Gateau
- CALIPHO Group, SIB-Swiss Institute of Bioinformatics, CMU , rue Michel-Servet 1, CH-1211 Geneva 4, Switzerland
| | - Anne-Marie Hesse
- CEA, DRF, BIG, Laboratoire de Biologie à Grande Echelle, 17 rue des martyrs, Grenoble F-38054, France.,Inserm U1038 , 17, rue des Martyrs, Grenoble F-38054, France.,Université de Grenoble , Grenoble F-38054, France
| | - Marlene Marcellin
- Institut de Pharmacologie et de Biologie Structurale, Université de Toulouse, CNRS, UPS, 31062 Toulouse, France
| | - Loren Méar
- Protim, Inserm U1085, Irset, Campus de Beaulieu, Rennes 35042, France
| | - Emmanuelle Mouton-Barbosa
- Institut de Pharmacologie et de Biologie Structurale, Université de Toulouse, CNRS, UPS, 31062 Toulouse, France
| | - Thibault Robin
- Proteome Informatics Group, Centre Universitaire d'Informatique , Route de Drize 7, 1227 Carouge, CH, Switzerland
| | - Odile Burlet-Schiltz
- Institut de Pharmacologie et de Biologie Structurale, Université de Toulouse, CNRS, UPS, 31062 Toulouse, France
| | - Sarah Cianferani
- Laboratoire de Spectrométrie de Masse BioOrganique (LSMBO), IPHC, Université de Strasbourg, CNRS UMR7178, 25 Rue Becquerel, 67087 Strasbourg, France
| | - Myriam Ferro
- CEA, DRF, BIG, Laboratoire de Biologie à Grande Echelle, 17 rue des martyrs, Grenoble F-38054, France.,Inserm U1038 , 17, rue des Martyrs, Grenoble F-38054, France.,Université de Grenoble , Grenoble F-38054, France
| | - Thomas Fréour
- Service de Médecine de la Reproduction, CHU de Nantes , 38 boulevard Jean Monnet, 44093 Nantes cedex, France.,INSERM UMR1064 , Nantes 44093, France
| | - Cecilia Lindskog
- CEA, DRF, BIG, Laboratoire de Biologie à Grande Echelle, 17 rue des martyrs, Grenoble F-38054, France.,Inserm U1038 , 17, rue des Martyrs, Grenoble F-38054, France
| | - Jérôme Garin
- CEA, DRF, BIG, Laboratoire de Biologie à Grande Echelle, 17 rue des martyrs, Grenoble F-38054, France.,Inserm U1038 , 17, rue des Martyrs, Grenoble F-38054, France.,Université de Grenoble , Grenoble F-38054, France
| | - Charles Pineau
- Protim, Inserm U1085, Irset, Campus de Beaulieu, Rennes 35042, France
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100
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Robinson JL, Nielsen J. Integrative analysis of human omics data using biomolecular networks. MOLECULAR BIOSYSTEMS 2016; 12:2953-64. [PMID: 27510223 DOI: 10.1039/c6mb00476h] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
High-throughput '-omics' technologies have given rise to an increasing abundance of genome-scale data detailing human biology at the molecular level. Although these datasets have already made substantial contributions to a more comprehensive understanding of human physiology and diseases, their interpretation becomes increasingly cryptic and nontrivial as they continue to expand in size and complexity. Systems biology networks offer a scaffold upon which omics data can be integrated, facilitating the extraction of new and physiologically relevant information from the data. Two of the most prevalent networks that have been used for such integrative analyses of omics data are genome-scale metabolic models (GEMs) and protein-protein interaction (PPI) networks, both of which have demonstrated success among many different omics and sample types. This integrative approach seeks to unite 'top-down' omics data with 'bottom-up' biological networks in a synergistic fashion that draws on the strengths of both strategies. As the volume and resolution of high-throughput omics data continue to grow, integrative network-based analyses are expected to play an increasingly important role in their interpretation.
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Affiliation(s)
- Jonathan L Robinson
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE412 96 Gothenburg, Sweden.
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