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Emergence of Phenotypically Distinct Subpopulations Is a Factor in Adaptation of Recombinant Saccharomyces cerevisiae under Glucose-Limited Conditions. Appl Environ Microbiol 2022; 88:e0230721. [PMID: 35297727 DOI: 10.1128/aem.02307-21] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Cells cultured in a nutrient-limited environment can undergo adaptation, which confers improved fitness under long-term energy limitation. We have shown previously how a recombinant Saccharomyces cerevisiae strain, producing a heterologous insulin product, under glucose-limited conditions adapts over time at the average population level. Here, we investigated this adaptation at the single-cell level by application of fluorescence-activated cell sorting (FACS) and showed that the following three apparent phenotypes underlie the adaptive response observed at the bulk level: (i) cells that drastically reduced insulin production (23%), (ii) cells with reduced enzymatic capacity in central carbon metabolism (46%), and (iii) cells that exhibited pseudohyphal growth (31%). We speculate that the phenotypic heterogeneity is a result of different mechanisms to increase fitness. Cells with reduced insulin productivity have increased fitness by reducing the burden of the heterologous insulin production, and the populations with reduced enzymatic capacity of the central carbon metabolism and pseudohyphal growth have increased fitness toward the glucose-limited conditions. The results highlight the importance of considering population heterogeneity when studying adaptation and evolution. IMPORTANCE The yeast Saccharomyces cerevisiae is an attractive microbial host for industrial production and is used widely for manufacturing, e.g., pharmaceuticals. Chemostat cultivation mode is an efficient cultivation strategy for industrial production processes as it ensures a constant, well-controlled cultivation environment. Nevertheless, both the production of a heterologous product and the constant cultivation environment in the chemostat impose a selective pressure on the production organism, which may result in adaptation and loss of productivity. The exact mechanisms behind the observed adaptation and loss of performance are often unidentified. We used a recombinant S. cerevisiae strain producing heterologous insulin and investigated the adaptation occurring during chemostat growth at the single-cell level. We showed that three apparent phenotypes underlie the adaptive response observed at the bulk level in the chemostat. These findings highlight the importance of considering population heterogeneity when studying adaptation in industrial bioprocesses.
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Garcia I, Orellana-Muñoz S, Ramos-Alonso L, Andersen AN, Zimmermann C, Eriksson J, Bøe SO, Kaferle P, Papamichos-Chronakis M, Chymkowitch P, Enserink JM. Kel1 is a phosphorylation-regulated noise suppressor of the pheromone signaling pathway. Cell Rep 2021; 37:110186. [PMID: 34965431 DOI: 10.1016/j.celrep.2021.110186] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 11/01/2021] [Accepted: 12/07/2021] [Indexed: 11/26/2022] Open
Abstract
Mechanisms have evolved that allow cells to detect signals and generate an appropriate response. The accuracy of these responses relies on the ability of cells to discriminate between signal and noise. How cells filter noise in signaling pathways is not well understood. Here, we analyze noise suppression in the yeast pheromone signaling pathway and show that the poorly characterized protein Kel1 serves as a major noise suppressor and prevents cell death. At the molecular level, Kel1 prevents spontaneous activation of the pheromone response by inhibiting membrane recruitment of Ste5 and Far1. Only a hypophosphorylated form of Kel1 suppresses signaling, reduces noise, and prevents pheromone-associated cell death, and our data indicate that the MAPK Fus3 contributes to Kel1 phosphorylation. Taken together, Kel1 serves as a phospho-regulated suppressor of the pheromone pathway to reduce noise, inhibit spontaneous activation of the pathway, regulate mating efficiency, and prevent pheromone-associated cell death.
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Affiliation(s)
- Ignacio Garcia
- Department of Molecular Cell Biology, Institute for Cancer Research, The Norwegian Radium Hospital, Montebello, 0379 Oslo, Norway; Centre for Cancer Cell Reprogramming, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, 0318 Oslo, Norway
| | - Sara Orellana-Muñoz
- Department of Molecular Cell Biology, Institute for Cancer Research, The Norwegian Radium Hospital, Montebello, 0379 Oslo, Norway; Centre for Cancer Cell Reprogramming, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, 0318 Oslo, Norway
| | - Lucía Ramos-Alonso
- Section for Biochemistry and Molecular Biology, Faculty of Mathematics and Natural Sciences, University of Oslo, 0316 Oslo, Norway; Department of Microbiology, Oslo University Hospital, 0372 Oslo, Norway
| | - Aram N Andersen
- Department of Molecular Cell Biology, Institute for Cancer Research, The Norwegian Radium Hospital, Montebello, 0379 Oslo, Norway; Centre for Cancer Cell Reprogramming, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, 0318 Oslo, Norway; Section for Biochemistry and Molecular Biology, Faculty of Mathematics and Natural Sciences, University of Oslo, 0316 Oslo, Norway
| | - Christine Zimmermann
- Institute for Virology, University Medical Center of the Johannes Gutenberg-University, 55131 Mainz, Germany
| | - Jens Eriksson
- Department of Medical Biochemistry and Microbiology, Uppsala University, 752 37 Uppsala, Sweden
| | - Stig Ove Bøe
- Department of Microbiology, Oslo University Hospital, 0372 Oslo, Norway
| | - Petra Kaferle
- Institut Curie, PSL Research University, CNRS, UMR3664, Sorbonne Universities, Paris, France
| | - Manolis Papamichos-Chronakis
- Department of Molecular Physiology and Cell Signalling Institute of Systems, Molecular and Integrative Biology University of Liverpool, L69 7BE Liverpool, UK
| | - Pierre Chymkowitch
- Section for Biochemistry and Molecular Biology, Faculty of Mathematics and Natural Sciences, University of Oslo, 0316 Oslo, Norway; Department of Microbiology, Oslo University Hospital, 0372 Oslo, Norway
| | - Jorrit M Enserink
- Department of Molecular Cell Biology, Institute for Cancer Research, The Norwegian Radium Hospital, Montebello, 0379 Oslo, Norway; Centre for Cancer Cell Reprogramming, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, 0318 Oslo, Norway; Section for Biochemistry and Molecular Biology, Faculty of Mathematics and Natural Sciences, University of Oslo, 0316 Oslo, Norway.
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3
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Alcalá-Corona SA, Sandoval-Motta S, Espinal-Enríquez J, Hernández-Lemus E. Modularity in Biological Networks. Front Genet 2021; 12:701331. [PMID: 34594357 PMCID: PMC8477004 DOI: 10.3389/fgene.2021.701331] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 08/23/2021] [Indexed: 01/13/2023] Open
Abstract
Network modeling, from the ecological to the molecular scale has become an essential tool for studying the structure, dynamics and complex behavior of living systems. Graph representations of the relationships between biological components open up a wide variety of methods for discovering the mechanistic and functional properties of biological systems. Many biological networks are organized into a modular structure, so methods to discover such modules are essential if we are to understand the biological system as a whole. However, most of the methods used in biology to this end, have a limited applicability, as they are very specific to the system they were developed for. Conversely, from the statistical physics and network science perspective, graph modularity has been theoretically studied and several methods of a very general nature have been developed. It is our perspective that in particular for the modularity detection problem, biology and theoretical physics/network science are less connected than they should. The central goal of this review is to provide the necessary background and present the most applicable and pertinent methods for community detection in a way that motivates their further usage in biological research.
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Affiliation(s)
- Sergio Antonio Alcalá-Corona
- Computational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico.,Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Santiago Sandoval-Motta
- Computational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico.,Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, Mexico City, Mexico.,National Council on Science and Technology, Mexico City, Mexico
| | - Jesús Espinal-Enríquez
- Computational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico.,Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Enrique Hernández-Lemus
- Computational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico.,Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, Mexico City, Mexico
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Chandra Rajan K, Vengatesen T. Molecular adaptation of molluscan biomineralisation to high-CO 2 oceans - The known and the unknown. MARINE ENVIRONMENTAL RESEARCH 2020; 155:104883. [PMID: 32072987 DOI: 10.1016/j.marenvres.2020.104883] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2019] [Revised: 01/11/2020] [Accepted: 01/19/2020] [Indexed: 06/10/2023]
Abstract
High-CO2 induced ocean acidification (OA) reduces the calcium carbonate (CaCO3) saturation level (Ω) and the pH of oceans. Consequently, OA is causing a serious threat to several ecologically and economically important biomineralising molluscs. Biomineralisation is a highly controlled biochemical process by which molluscs deposit their calcareous structures. In this process, shell matrix proteins aid the nucleation, growth and assemblage of the CaCO3 crystals in the shell. These molluscan shell proteins (MSPs) are, ultimately, responsible for determination of the diverse shell microstructures and mechanical strength. Recent studies have attempted to integrate gene and protein expression data of MSPs with shell structure and mechanical properties. These advances made in understanding the molecular mechanism of biomineralisation suggest that molluscs either succumb or adapt to OA stress. In this review, we discuss the fate of biomineralisation process in future high-CO2 oceans and its ultimate impact on the mineralised shell's structure and mechanical properties from the perspectives of limited substrate availability theory, proton flux limitation model and the omega myth theory. Furthermore, studying the interplay of energy availability and differential gene expression is an essential first step towards understanding adaptation of molluscan biomineralisation to OA, because if there is a need to change gene expression under stressors, any living system would require more energy than usual. To conclude, we have listed, four important future research directions for molecular adaptation of molluscan biomineralisation in high-CO2 oceans: 1) Including an energy budgeting factor while understanding differential gene expression of MSPs and ion transporters under OA. 2) Unraveling the genetic or epigenetic changes related to biomineralisation under stressors to help solving a bigger picture about future evolution of molluscs, and 3) Understanding Post Translational Modifications of MSPs with and without stressors. 4) Understanding carbon uptake mechanisms across taxa with and without OA to clarify the OA theories on Ω.
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Affiliation(s)
- Kanmani Chandra Rajan
- The Swire Institute of Marine Science and School of Biological Sciences, The University of Hong Kong, Hong Kong SAR, China; State Key Laboratory of Marine Pollution, Hong Kong SAR, China.
| | - Thiyagarajan Vengatesen
- The Swire Institute of Marine Science and School of Biological Sciences, The University of Hong Kong, Hong Kong SAR, China; State Key Laboratory of Marine Pollution, Hong Kong SAR, China.
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Moore D, Simoes RDM, Dehmer M, Emmert-Streib F. Prostate Cancer Gene Regulatory Network Inferred from RNA-Seq Data. Curr Genomics 2019; 20:38-48. [PMID: 31015790 PMCID: PMC6446481 DOI: 10.2174/1389202919666181107122005] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2018] [Revised: 09/29/2018] [Accepted: 10/22/2018] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND Cancer is a complex disease with a lucid etiology and in understanding the causation, we need to appreciate this complexity. OBJECTIVE Here we are aiming to gain insights into the genetic associations of prostate cancer through a network-based systems approach using the BC3Net algorithm. METHODS Specifically, we infer a prostate cancer Gene Regulatory Network (GRN) from a large-scale gene expression data set of 333 patient RNA-seq profiles obtained from The Cancer Genome Atlas (TCGA) database. RESULTS We analyze the functional components of the inferred network by extracting subnetworks based on biological process information and interpret the role of known cancer genes within each process. Fur-thermore, we investigate the local landscape of prostate cancer genes and discuss pathological associa-tions that may be relevant in the development of new targeted cancer therapies. CONCLUSION Our network-based analysis provides a practical systems biology approach to reveal the collective gene-interactions of prostate cancer. This allows a close interpretation of biological activity in terms of the hallmarks of cancer.
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Affiliation(s)
| | | | | | - Frank Emmert-Streib
- Address correspondence to this author at the Department of Signal Processing, Predictive Medicine and Data Analytics Laboratory, Tampere University of Technology, Tampere 33720, Finland; Tel: +358503015353;, E-mails: ;
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Kawalia SB, Raschka T, Naz M, de Matos Simoes R, Senger P, Hofmann-Apitius M. Analytical Strategy to Prioritize Alzheimer's Disease Candidate Genes in Gene Regulatory Networks Using Public Expression Data. J Alzheimers Dis 2018; 59:1237-1254. [PMID: 28800327 PMCID: PMC5611835 DOI: 10.3233/jad-170011] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Alzheimer’s disease (AD) progressively destroys cognitive abilities in the aging population with tremendous effects on memory. Despite recent progress in understanding the underlying mechanisms, high drug attrition rates have put a question mark behind our knowledge about its etiology. Re-evaluation of past studies could help us to elucidate molecular-level details of this disease. Several methods to infer such networks exist, but most of them do not elaborate on context specificity and completeness of the generated networks, missing out on lesser-known candidates. In this study, we present a novel strategy that corroborates common mechanistic patterns across large scale AD gene expression studies and further prioritizes potential biomarker candidates. To infer gene regulatory networks (GRNs), we applied an optimized version of the BC3Net algorithm, named BC3Net10, capable of deriving robust and coherent patterns. In principle, this approach initially leverages the power of literature knowledge to extract AD specific genes for generating viable networks. Our findings suggest that AD GRNs show significant enrichment for key signaling mechanisms involved in neurotransmission. Among the prioritized genes, well-known AD genes were prominent in synaptic transmission, implicated in cognitive deficits. Moreover, less intensive studied AD candidates (STX2, HLA-F, HLA-C, RAB11FIP4, ARAP3, AP2A2, ATP2B4, ITPR2, and ATP2A3) are also involved in neurotransmission, providing new insights into the underlying mechanism. To our knowledge, this is the first study to generate knowledge-instructed GRNs that demonstrates an effective way of combining literature-based knowledge and data-driven analysis to identify lesser known candidates embedded in stable and robust functional patterns across disparate datasets.
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Affiliation(s)
- Shweta Bagewadi Kawalia
- Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin, Germany.,Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn-Aachen International Center for Information Technology, Bonn, Germany
| | - Tamara Raschka
- Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin, Germany.,University of Applied Sciences Koblenz, RheinAhrCampus, Remagen, Germany
| | - Mufassra Naz
- Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin, Germany.,Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn-Aachen International Center for Information Technology, Bonn, Germany
| | | | - Philipp Senger
- Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin, Germany
| | - Martin Hofmann-Apitius
- Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin, Germany.,Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn-Aachen International Center for Information Technology, Bonn, Germany
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7
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de Matos Simoes R, Dalleau S, Williamson KE, Emmert-Streib F. Urothelial cancer gene regulatory networks inferred from large-scale RNAseq, Bead and Oligo gene expression data. BMC SYSTEMS BIOLOGY 2015; 9:21. [PMID: 25971253 PMCID: PMC4460634 DOI: 10.1186/s12918-015-0165-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/07/2014] [Accepted: 04/22/2015] [Indexed: 12/18/2022]
Abstract
BACKGROUND Urothelial pathogenesis is a complex process driven by an underlying network of interconnected genes. The identification of novel genomic target regions and gene targets that drive urothelial carcinogenesis is crucial in order to improve our current limited understanding of urothelial cancer (UC) on the molecular level. The inference of genome-wide gene regulatory networks (GRN) from large-scale gene expression data provides a promising approach for a detailed investigation of the underlying network structure associated to urothelial carcinogenesis. METHODS In our study we inferred and compared three GRNs by the application of the BC3Net inference algorithm to large-scale transitional cell carcinoma gene expression data sets from Illumina RNAseq (179 samples), Illumina Bead arrays (165 samples) and Affymetrix Oligo microarrays (188 samples). We investigated the structural and functional properties of GRNs for the identification of molecular targets associated to urothelial cancer. RESULTS We found that the urothelial cancer (UC) GRNs show a significant enrichment of subnetworks that are associated with known cancer hallmarks including cell cycle, immune response, signaling, differentiation and translation. Interestingly, the most prominent subnetworks of co-located genes were found on chromosome regions 5q31.3 (RNAseq), 8q24.3 (Oligo) and 1q23.3 (Bead), which all represent known genomic regions frequently deregulated or aberated in urothelial cancer and other cancer types. Furthermore, the identified hub genes of the individual GRNs, e.g., HID1/DMC1 (tumor development), RNF17/TDRD4 (cancer antigen) and CYP4A11 (angiogenesis/ metastasis) are known cancer associated markers. The GRNs were highly dataset specific on the interaction level between individual genes, but showed large similarities on the biological function level represented by subnetworks. Remarkably, the RNAseq UC GRN showed twice the proportion of significant functional subnetworks. Based on our analysis of inferential and experimental networks the Bead UC GRN showed the lowest performance compared to the RNAseq and Oligo UC GRNs. CONCLUSION To our knowledge, this is the first study investigating genome-scale UC GRNs. RNAseq based gene expression data is the data platform of choice for a GRN inference. Our study offers new avenues for the identification of novel putative diagnostic targets for subsequent studies in bladder tumors.
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Affiliation(s)
- Ricardo de Matos Simoes
- Centre for Cancer Research and Cell Biology (CCRCB), Queens University Belfast, 97 Lisburn Road, Belfast, County Antrim, Northern Ireland, UK.
| | - Sabine Dalleau
- Centre for Cancer Research and Cell Biology (CCRCB), Queens University Belfast, 97 Lisburn Road, Belfast, County Antrim, Northern Ireland, UK.
| | - Kate E Williamson
- Centre for Cancer Research and Cell Biology (CCRCB), Queens University Belfast, 97 Lisburn Road, Belfast, County Antrim, Northern Ireland, UK.
| | - Frank Emmert-Streib
- Computational Medicine and Statistical Learning Laboratory, Department of Signal Processing, Tampere University of Technology, Tampere, 33720, Finland. .,Institute of Biosciences and Medical Technology, Tampere, 33520, Finland.
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de Matos Simoes R, Dehmer M, Emmert-Streib F. B-cell lymphoma gene regulatory networks: biological consistency among inference methods. Front Genet 2013; 4:281. [PMID: 24379827 PMCID: PMC3864360 DOI: 10.3389/fgene.2013.00281] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2013] [Accepted: 11/25/2013] [Indexed: 01/28/2023] Open
Abstract
Despite the development of numerous gene regulatory network (GRN) inference methods in the last years, their application, usage and the biological significance of the resulting GRN remains unclear for our general understanding of large-scale gene expression data in routine practice. In our study, we conduct a structural and a functional analysis of B-cell lymphoma GRNs that were inferred using 3 mutual information-based GRN inference methods: C3Net, BC3Net and Aracne. From a comparative analysis on the global level, we find that the inferred B-cell lymphoma GRNs show major differences. However, on the edge-level and the functional-level—that are more important for our biological understanding—the B-cell lymphoma GRNs were highly similar among each other. Also, the ranks of the degree centrality values and major hub genes in the inferred networks are highly conserved as well. Interestingly, the major hub genes of all GRNs are associated with the G-protein-coupled receptor pathway, cell-cell signaling and cell cycle. This implies that hub genes of the GRNs can be highly consistently inferred with C3Net, BC3Net, and Aracne, representing prominent targets for signaling pathways. Finally, we describe the functional and structural relationship between C3Net, BC3Net and Aracne gene regulatory networks. Our study shows that these GRNs that are inferred from large-scale gene expression data are promising for the identification of novel candidate interactions and pathways that play a key role in the underlying mechanisms driving cancer hallmarks. Overall, our comparative analysis reveals that these GRNs inferred with considerably different inference methods contain large amounts of consistent, method independent, biological information.
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Affiliation(s)
- Ricardo de Matos Simoes
- Computational Biology and Machine Learning Laboratory, Faculty of Medicine, Health and Life Sciences, Center for Cancer Research and Cell Biology, School of Medicine, Dentistry and Biomedical Sciences, Queen's University Belfast Belfast, UK
| | - Matthias Dehmer
- Institute for Bioinformatics and Translational Research, UMIT Hall in Tirol, Austria
| | - Frank Emmert-Streib
- Computational Biology and Machine Learning Laboratory, Faculty of Medicine, Health and Life Sciences, Center for Cancer Research and Cell Biology, School of Medicine, Dentistry and Biomedical Sciences, Queen's University Belfast Belfast, UK
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Layered signaling regulatory networks analysis of gene expression involved in malignant tumorigenesis of non-resolving ulcerative colitis via integration of cross-study microarray profiles. PLoS One 2013; 8:e67142. [PMID: 23825635 PMCID: PMC3692446 DOI: 10.1371/journal.pone.0067142] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2013] [Accepted: 05/15/2013] [Indexed: 01/08/2023] Open
Abstract
Background Ulcerative colitis (UC) was the most frequently diagnosed inflammatory bowel disease (IBD) and closely linked to colorectal carcinogenesis. By far, the underlying mechanisms associated with the disease are still unclear. With the increasing accumulation of microarray gene expression profiles, it is profitable to gain a systematic perspective based on gene regulatory networks to better elucidate the roles of genes associated with disorders. However, a major challenge for microarray data analysis is the integration of multiple-studies generated by different groups. Methodology/Principal Findings In this study, firstly, we modeled a signaling regulatory network associated with colorectal cancer (CRC) initiation via integration of cross-study microarray expression data sets using Empirical Bayes (EB) algorithm. Secondly, a manually curated human cancer signaling map was established via comprehensive retrieval of the publicly available repositories. Finally, the co-differently-expressed genes were manually curated to portray the layered signaling regulatory networks. Results Overall, the remodeled signaling regulatory networks were separated into four major layers including extracellular, membrane, cytoplasm and nucleus, which led to the identification of five core biological processes and four signaling pathways associated with colorectal carcinogenesis. As a result, our biological interpretation highlighted the importance of EGF/EGFR signaling pathway, EPO signaling pathway, T cell signal transduction and members of the BCR signaling pathway, which were responsible for the malignant transition of CRC from the benign UC to the aggressive one. Conclusions The present study illustrated a standardized normalization approach for cross-study microarray expression data sets. Our model for signaling networks construction was based on the experimentally-supported interaction and microarray co-expression modeling. Pathway-based signaling regulatory networks analysis sketched a directive insight into colorectal carcinogenesis, which was of significant importance to monitor disease progression and improve therapeutic interventions.
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Wu MY, Dai DQ, Zhang XF, Zhu Y. Cancer subtype discovery and biomarker identification via a new robust network clustering algorithm. PLoS One 2013; 8:e66256. [PMID: 23799085 PMCID: PMC3684607 DOI: 10.1371/journal.pone.0066256] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2013] [Accepted: 05/02/2013] [Indexed: 11/29/2022] Open
Abstract
In cancer biology, it is very important to understand the phenotypic changes of the patients and discover new cancer subtypes. Recently, microarray-based technologies have shed light on this problem based on gene expression profiles which may contain outliers due to either chemical or electrical reasons. These undiscovered subtypes may be heterogeneous with respect to underlying networks or pathways, and are related with only a few of interdependent biomarkers. This motivates a need for the robust gene expression-based methods capable of discovering such subtypes, elucidating the corresponding network structures and identifying cancer related biomarkers. This study proposes a penalized model-based Student’s t clustering with unconstrained covariance (PMT-UC) to discover cancer subtypes with cluster-specific networks, taking gene dependencies into account and having robustness against outliers. Meanwhile, biomarker identification and network reconstruction are achieved by imposing an adaptive penalty on the means and the inverse scale matrices. The model is fitted via the expectation maximization algorithm utilizing the graphical lasso. Here, a network-based gene selection criterion that identifies biomarkers not as individual genes but as subnetworks is applied. This allows us to implicate low discriminative biomarkers which play a central role in the subnetwork by interconnecting many differentially expressed genes, or have cluster-specific underlying network structures. Experiment results on simulated datasets and one available cancer dataset attest to the effectiveness, robustness of PMT-UC in cancer subtype discovering. Moveover, PMT-UC has the ability to select cancer related biomarkers which have been verified in biochemical or biomedical research and learn the biological significant correlation among genes.
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Affiliation(s)
- Meng-Yun Wu
- Center for Computer Vision and Department of Mathematics, Sun Yat-Sen University, Guangzhou, China
| | - Dao-Qing Dai
- Center for Computer Vision and Department of Mathematics, Sun Yat-Sen University, Guangzhou, China
- * E-mail:
| | - Xiao-Fei Zhang
- Center for Computer Vision and Department of Mathematics, Sun Yat-Sen University, Guangzhou, China
| | - Yuan Zhu
- Center for Computer Vision and Department of Mathematics, Sun Yat-Sen University, Guangzhou, China
- Department of Mathematics, Guangdong University of Business Studies, Guangzhou, China
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de Matos Simoes R, Dehmer M, Emmert-Streib F. Interfacing cellular networks of S. cerevisiae and E. coli: connecting dynamic and genetic information. BMC Genomics 2013; 14:324. [PMID: 23663484 PMCID: PMC3698017 DOI: 10.1186/1471-2164-14-324] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2012] [Accepted: 04/25/2013] [Indexed: 12/11/2022] Open
Abstract
Background In recent years, various types of cellular networks have penetrated biology and are nowadays used omnipresently for studying eukaryote and prokaryote organisms. Still, the relation and the biological overlap among phenomenological and inferential gene networks, e.g., between the protein interaction network and the gene regulatory network inferred from large-scale transcriptomic data, is largely unexplored. Results We provide in this study an in-depth analysis of the structural, functional and chromosomal relationship between a protein-protein network, a transcriptional regulatory network and an inferred gene regulatory network, for S. cerevisiae and E. coli. Further, we study global and local aspects of these networks and their biological information overlap by comparing, e.g., the functional co-occurrence of Gene Ontology terms by exploiting the available interaction structure among the genes. Conclusions Although the individual networks represent different levels of cellular interactions with global structural and functional dissimilarities, we observe crucial functions of their network interfaces for the assembly of protein complexes, proteolysis, transcription, translation, metabolic and regulatory interactions. Overall, our results shed light on the integrability of these networks and their interfacing biological processes.
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Affiliation(s)
- Ricardo de Matos Simoes
- Computational Biology and Machine Learning Laboratory Center for Cancer Research and Cell Biology, School of Medicine, Dentistry and Biomedical Sciences Faculty of Medicine, Health and Life Sciences, Queen's University, 97 Lisburn Road, Belfast, UK
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Emmert-Streib F. Influence of the experimental design of gene expression studies on the inference of gene regulatory networks: environmental factors. PeerJ 2013; 1:e10. [PMID: 23638344 PMCID: PMC3628739 DOI: 10.7717/peerj.10] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2012] [Accepted: 12/31/2012] [Indexed: 12/20/2022] Open
Abstract
The inference of gene regulatory networks gained within recent years a considerable interest in the biology and biomedical community. The purpose of this paper is to investigate the influence that environmental conditions can exhibit on the inference performance of network inference algorithms. Specifically, we study five network inference methods, Aracne, BC3NET, CLR, C3NET and MRNET, and compare the results for three different conditions: (I) observational gene expression data: normal environmental condition, (II) interventional gene expression data: growth in rich media, (III) interventional gene expression data: normal environmental condition interrupted by a positive spike-in stimulation. Overall, we find that different statistical inference methods lead to comparable, but condition-specific results. Further, our results suggest that non-steady-state data enhance the inferability of regulatory networks.
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Affiliation(s)
- Frank Emmert-Streib
- Computational Biology and Machine Learning Laboratory, Center for Cancer Research and Cell Biology, School of Medicine, Dentistry and Biomedical Sciences, Faculty of Medicine, Health and Life Sciences , Queen's University Belfast , Belfast , UK
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Emmert-Streib F, Tripathi S, de Matos Simoes R. Harnessing the complexity of gene expression data from cancer: from single gene to structural pathway methods. Biol Direct 2012; 7:44. [PMID: 23227854 PMCID: PMC3769148 DOI: 10.1186/1745-6150-7-44] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2012] [Accepted: 10/01/2012] [Indexed: 12/22/2022] Open
Abstract
High-dimensional gene expression data provide a rich source of information because they capture the expression level of genes in dynamic states that reflect the biological functioning of a cell. For this reason, such data are suitable to reveal systems related properties inside a cell, e.g., in order to elucidate molecular mechanisms of complex diseases like breast or prostate cancer. However, this is not only strongly dependent on the sample size and the correlation structure of a data set, but also on the statistical hypotheses tested. Many different approaches have been developed over the years to analyze gene expression data to (I) identify changes in single genes, (II) identify changes in gene sets or pathways, and (III) identify changes in the correlation structure in pathways. In this paper, we review statistical methods for all three types of approaches, including subtypes, in the context of cancer data and provide links to software implementations and tools and address also the general problem of multiple hypotheses testing. Further, we provide recommendations for the selection of such analysis methods.
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Affiliation(s)
- Frank Emmert-Streib
- Computational Biology and Machine Learning Laboratory, Queen's University Belfast, Belfast, UK.
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Lecca P, Priami C. Biological network inference for drug discovery. Drug Discov Today 2012; 18:256-64. [PMID: 23147668 DOI: 10.1016/j.drudis.2012.11.001] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2012] [Revised: 10/04/2012] [Accepted: 11/05/2012] [Indexed: 12/31/2022]
Abstract
A better understanding of the pathophysiology should help deliver drugs whose targets are involved in the causative processes underlying a disease. Biological network inference uses computational methods for deducing from high-throughput experimental data, the topology and the causal structure of the interactions among the drugs and their targets. Therefore, biological network inference can support and contribute to the experimental identification of both gene and protein networks causing a disease as well as the biochemical networks of drugs metabolism and mechanisms of action. The resulting high-level networks serve as a foundational basis for more detailed mechanistic models and are increasingly used in drug discovery by pharmaceutical and biotechnology companies. We review and compare recent computational technologies for network inference applied to drug discovery.
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Affiliation(s)
- Paola Lecca
- The Microsoft Research, University of Trento, Centre for Computational and Systems Biology, Piazza Manifattura 1 - 38068 Rovereto, Italy.
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