1
|
Hall M, Skogholt AH, Surakka I, Dalen H, Almaas E. Genome-wide association studies reveal differences in genetic susceptibility between single events vs. recurrent events of atrial fibrillation and myocardial infarction: the HUNT study. Front Cardiovasc Med 2024; 11:1372107. [PMID: 38725839 PMCID: PMC11079265 DOI: 10.3389/fcvm.2024.1372107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Accepted: 04/04/2024] [Indexed: 05/12/2024] Open
Abstract
Genetic research into atrial fibrillation (AF) and myocardial infarction (MI) has predominantly focused on comparing afflicted individuals with their healthy counterparts. However, this approach lacks granularity, thus overlooking subtleties within patient populations. In this study, we explore the distinction between AF and MI patients who experience only a single disease event and those experiencing recurrent events. Integrating hospital records, questionnaire data, clinical measurements, and genetic data from more than 500,000 HUNT and United Kingdom Biobank participants, we compare both clinical and genetic characteristics between the two groups using genome-wide association studies (GWAS) meta-analyses, phenome-wide association studies (PheWAS) analyses, and gene co-expression networks. We found that the two groups of patients differ in both clinical characteristics and genetic risks. More specifically, recurrent AF patients are significantly younger and have better baseline health, in terms of reduced cholesterol and blood pressure, than single AF patients. Also, the results of the GWAS meta-analysis indicate that recurrent AF patients seem to be at greater genetic risk for recurrent events. The PheWAS and gene co-expression network analyses highlight differences in the functions associated with the sets of single nucleotide polymorphisms (SNPs) and genes for the two groups. However, for MI patients, we found that those experiencing single events are significantly younger and have better baseline health than those with recurrent MI, yet they exhibit higher genetic risk. The GWAS meta-analysis mostly identifies genetic regions uniquely associated with single MI, and the PheWAS analysis and gene co-expression networks support the genetic differences between the single MI and recurrent MI groups. In conclusion, this work has identified novel genetic regions uniquely associated with single MI and related PheWAS analyses, as well as gene co-expression networks that support the genetic differences between the patient subgroups of single and recurrent occurrence for both MI and AF.
Collapse
Affiliation(s)
- Martina Hall
- Department of Biotechnology and Food Science, Norwegian University of Science and Technology, Trondheim, Norway
- K.G. Jebsen Center for Genetic Epidemiology, Norwegian University of Science and Technology, Trondheim, Norway
| | - Anne Heidi Skogholt
- K.G. Jebsen Center for Genetic Epidemiology, Norwegian University of Science and Technology, Trondheim, Norway
| | - Ida Surakka
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, United States
| | - Haavard Dalen
- Clinic of Cardiology, St. Olavs University Hospital, Trondheim, Norway
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Medicine, Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway
| | - Eivind Almaas
- Department of Biotechnology and Food Science, Norwegian University of Science and Technology, Trondheim, Norway
- K.G. Jebsen Center for Genetic Epidemiology, Norwegian University of Science and Technology, Trondheim, Norway
| |
Collapse
|
2
|
Pettersen JP, Castillo S, Jouhten P, Almaas E. Genome-scale metabolic models reveal determinants of phenotypic differences in non-Saccharomyces yeasts. BMC Bioinformatics 2023; 24:438. [PMID: 37990145 PMCID: PMC10664357 DOI: 10.1186/s12859-023-05506-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 09/29/2023] [Indexed: 11/23/2023] Open
Abstract
BACKGROUND Use of alternative non-Saccharomyces yeasts in wine and beer brewing has gained more attention the recent years. This is both due to the desire to obtain a wider variety of flavours in the product and to reduce the final alcohol content. Given the metabolic differences between the yeast species, we wanted to account for some of the differences by using in silico models. RESULTS We created and studied genome-scale metabolic models of five different non-Saccharomyces species using an automated processes. These were: Metschnikowia pulcherrima, Lachancea thermotolerans, Hanseniaspora osmophila, Torulaspora delbrueckii and Kluyveromyces lactis. Using the models, we predicted that M. pulcherrima, when compared to the other species, conducts more respiration and thus produces less fermentation products, a finding which agrees with experimental data. Complex I of the electron transport chain was to be present in M. pulcherrima, but absent in the others. The predicted importance of Complex I was diminished when we incorporated constraints on the amount of enzymatic protein, as this shifts the metabolism towards fermentation. CONCLUSIONS Our results suggest that Complex I in the electron transport chain is a key differentiator between Metschnikowia pulcherrima and the other yeasts considered. Yet, more annotations and experimental data have the potential to improve model quality in order to increase fidelity and confidence in these results. Further experiments should be conducted to confirm the in vivo effect of Complex I in M. pulcherrima and its respiratory metabolism.
Collapse
Affiliation(s)
- Jakob P Pettersen
- Department of Biotechnology and Food Science, NTNU-Norwegian University of Science and Technology, Trondheim, Norway.
| | | | - Paula Jouhten
- Department of Bioproducts and Biosystems, Aalto University, Espoo, Finland
| | - Eivind Almaas
- Department of Biotechnology and Food Science, NTNU-Norwegian University of Science and Technology, Trondheim, Norway.
- Department of Public Health and General Practice, K.G. Jebsen Center for Genetic Epidemiology, NTNU- Norwegian University of Science and Technology, Trondheim, Norway.
| |
Collapse
|
3
|
Fiedler AW, Gundersen MS, Vo TP, Almaas E, Vadstein O, Bakke I. Phage therapy minimally affects the water microbiota in an Atlantic salmon (Salmo salar) rearing system while still preventing infection. Sci Rep 2023; 13:19145. [PMID: 37932331 PMCID: PMC10628140 DOI: 10.1038/s41598-023-44987-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 10/14/2023] [Indexed: 11/08/2023] Open
Abstract
Excessive usage of antibiotics threatens the bacterial diversity in the microbiota of animals. An alternative to antibiotics that has been suggested to not disturb the microbiota is (bacterio)phage therapy. In this study, we challenged germ-free and microbially colonized yolk sac fry of Atlantic salmon with Flavobacterium columnare and observed that the mere presence of a microbiota protected the fish against lethal infection. We then investigated the effect of phage- or oxytetracycline treatment on fish survival and rearing water bacterial community characteristics using 16S rRNA gene amplicon sequencing. Phage treatment led to an increased survival of F. columnare-challenged fish and reduced the relative amounts of the pathogen in the water microbiota. In the absence of F. columnare, phage treatment did not affect the composition or the α-diversity of the rearing water microbiota. In the presence of the phage's host, phage treatment induced minor changes to the bacterial community composition, without affecting the α-diversity. Surprisingly, oxytetracycline treatment had no observable effect on the water microbiota and did not reduce the relative abundance of F. columnare in the water. In conclusion, we showed that phage treatment prevents mortality while not negatively affecting the rearing water microbiota, thus suggesting that phage treatment may be a suitable alternative to antibiotics. We also demonstrated a protective effect of the microbiota in Atlantic salmon yolk sac fry.
Collapse
Affiliation(s)
- Alexander W Fiedler
- Department of Biotechnology and Food Science, Norwegian University of Science and Technology, 7491, Trondheim, Norway
| | - Madeleine S Gundersen
- Department of Biotechnology and Food Science, Norwegian University of Science and Technology, 7491, Trondheim, Norway
| | - Toan P Vo
- Department of Biotechnology and Food Science, Norwegian University of Science and Technology, 7491, Trondheim, Norway
| | - Eivind Almaas
- Department of Biotechnology and Food Science, Norwegian University of Science and Technology, 7491, Trondheim, Norway
| | - Olav Vadstein
- Department of Biotechnology and Food Science, Norwegian University of Science and Technology, 7491, Trondheim, Norway
| | - Ingrid Bakke
- Department of Biotechnology and Food Science, Norwegian University of Science and Technology, 7491, Trondheim, Norway.
| |
Collapse
|
4
|
Pettersen JP, Almaas E. Parameter inference for enzyme and temperature constrained genome-scale models. Sci Rep 2023; 13:6079. [PMID: 37055413 PMCID: PMC10102030 DOI: 10.1038/s41598-023-32982-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Accepted: 04/05/2023] [Indexed: 04/15/2023] Open
Abstract
The metabolism of all living organisms is dependent on temperature, and therefore, having a good method to predict temperature effects at a system level is of importance. A recently developed Bayesian computational framework for enzyme and temperature constrained genome-scale models (etcGEM) predicts the temperature dependence of an organism's metabolic network from thermodynamic properties of the metabolic enzymes, markedly expanding the scope and applicability of constraint-based metabolic modelling. Here, we show that the Bayesian calculation method for inferring parameters for an etcGEM is unstable and unable to estimate the posterior distribution. The Bayesian calculation method assumes that the posterior distribution is unimodal, and thus fails due to the multimodality of the problem. To remedy this problem, we developed an evolutionary algorithm which is able to obtain a diversity of solutions in this multimodal parameter space. We quantified the phenotypic consequences on six metabolic network signature reactions of the different parameter solutions resulting from use of the evolutionary algorithm. While two of these reactions showed little phenotypic variation between the solutions, the remainder displayed huge variation in flux-carrying capacity. This result indicates that the model is under-determined given current experimental data and that more data is required to narrow down the model predictions. Finally, we made improvements to the software to reduce the running time of the parameter set evaluations by a factor of 8.5, allowing for obtaining results faster and with less computational resources.
Collapse
Affiliation(s)
- Jakob Peder Pettersen
- Department of Biotechnology and Food Science, NTNU- Norwegian University of Science and Technology, Trondheim, Norway
| | - Eivind Almaas
- Department of Biotechnology and Food Science, NTNU- Norwegian University of Science and Technology, Trondheim, Norway.
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and General Practice, NTNU - Norwegian University of Science and Technology, Trondheim, Norway.
| |
Collapse
|
5
|
Karlsen E, Gylseth M, Schulz C, Almaas E. A study of a diauxic growth experiment using an expanded dynamic flux balance framework. PLoS One 2023; 18:e0280077. [PMID: 36607958 PMCID: PMC9821518 DOI: 10.1371/journal.pone.0280077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 12/20/2022] [Indexed: 01/07/2023] Open
Abstract
Flux balance analysis (FBA) remains one of the most used methods for modeling the entirety of cellular metabolism, and a range of applications and extensions based on the FBA framework have been generated. Dynamic flux balance analysis (dFBA), the expansion of FBA into the time domain, still has issues regarding accessibility limiting its widespread adoption and application, such as a lack of a consistently rigid formalism and tools that can be applied without expert knowledge. Recent work has combined dFBA with enzyme-constrained flux balance analysis (decFBA), which has been shown to greatly improve accuracy in the comparison of computational simulations and experimental data, but such approaches generally do not take into account the fact that altering the enzyme composition of a cell is not an instantaneous process. Here, we have developed a decFBA method that explicitly takes enzyme change constraints (ecc) into account, decFBAecc. The resulting software is a simple yet flexible framework for using genome-scale metabolic modeling for simulations in the time domain that has full interoperability with the COBRA Toolbox 3.0. To assess the quality of the computational predictions of decFBAecc, we conducted a diauxic growth fermentation experiment with Escherichia coli BW25113 in glucose minimal M9 medium. The comparison of experimental data with dFBA, decFBA and decFBAecc predictions demonstrates how systematic analyses within a fixed constraint-based framework can aid the study of model parameters. Finally, in explaining experimentally observed phenotypes, our computational analysis demonstrates the importance of non-linear dependence of exchange fluxes on medium metabolite concentrations and the non-instantaneous change in enzyme composition, effects of which have not previously been accounted for in constraint-based analysis.
Collapse
Affiliation(s)
- Emil Karlsen
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Marianne Gylseth
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Christian Schulz
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Eivind Almaas
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
- K. G. Jebsen Center for Genetic Epidemiology Department of Public Health and General Practice, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
- * E-mail:
| |
Collapse
|
6
|
Jouhten P, Konstantinidis D, Pereira F, Andrejev S, Grkovska K, Castillo S, Ghiachi P, Beltran G, Almaas E, Mas A, Warringer J, Gonzalez R, Morales P, Patil KR. Predictive evolution of metabolic phenotypes using model-designed environments. Mol Syst Biol 2022; 18:e10980. [PMID: 36201279 PMCID: PMC9536503 DOI: 10.15252/msb.202210980] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 08/24/2022] [Accepted: 08/26/2022] [Indexed: 11/04/2022] Open
Abstract
Adaptive evolution under controlled laboratory conditions has been highly effective in selecting organisms with beneficial phenotypes such as stress tolerance. The evolution route is particularly attractive when the organisms are either difficult to engineer or the genetic basis of the phenotype is complex. However, many desired traits, like metabolite secretion, have been inaccessible to adaptive selection due to their trade-off with cell growth. Here, we utilize genome-scale metabolic models to design nutrient environments for selecting lineages with enhanced metabolite secretion. To overcome the growth-secretion trade-off, we identify environments wherein growth becomes correlated with a secondary trait termed tacking trait. The latter is selected to be coupled with the desired trait in the application environment where the trait manifestation is required. Thus, adaptive evolution in the model-designed selection environment and subsequent return to the application environment is predicted to enhance the desired trait. We experimentally validate this strategy by evolving Saccharomyces cerevisiae for increased secretion of aroma compounds, and confirm the predicted flux-rerouting using genomic, transcriptomic, and proteomic analyses. Overall, model-designed selection environments open new opportunities for predictive evolution.
Collapse
Affiliation(s)
- Paula Jouhten
- European Molecular Biology LaboratoryHeidelbergGermany,VTT Technical Research Centre of Finland LtdEspooFinland,Department of Bioproducts and BiosystemsAalto UniversityEspooFinland
| | | | | | | | | | | | - Payam Ghiachi
- Department of Chemistry and Molecular BiologyUniversity of GothenburgGothenburgSweden
| | - Gemma Beltran
- Departament Bioquímica i Biotecnologia, Facultat d'EnologiaUniversitat Rovira i VirgiliTarragonaSpain
| | - Eivind Almaas
- Department of Biotechnology and Food ScienceNTNU – Norwegian University of Science and TechnologyTrondheimNorway
| | - Albert Mas
- Departament Bioquímica i Biotecnologia, Facultat d'EnologiaUniversitat Rovira i VirgiliTarragonaSpain
| | - Jonas Warringer
- Department of Chemistry and Molecular BiologyUniversity of GothenburgGothenburgSweden
| | - Ramon Gonzalez
- Instituto de Ciencias de la Vid y delVino (CSIC, Gobierno de la Rioja, Universidad de La Rioja) Finca La GrajeraLogroñoSpain
| | - Pilar Morales
- Instituto de Ciencias de la Vid y delVino (CSIC, Gobierno de la Rioja, Universidad de La Rioja) Finca La GrajeraLogroñoSpain
| | - Kiran R Patil
- European Molecular Biology LaboratoryHeidelbergGermany,Medical Research Council (MRC) Toxicology UnitUniversity of CambridgeCambridgeUK
| |
Collapse
|
7
|
Simensen V, Seif Y, Almaas E. Phenotypic response of yeast metabolic network to availability of proteinogenic amino acids. Front Mol Biosci 2022; 9:963548. [PMID: 36072429 PMCID: PMC9441596 DOI: 10.3389/fmolb.2022.963548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 07/25/2022] [Indexed: 11/20/2022] Open
Abstract
Genome-scale metabolism can best be described as a highly interconnected network of biochemical reactions and metabolites. The flow of metabolites, i.e., flux, throughout these networks can be predicted and analyzed using approaches such as flux balance analysis (FBA). By knowing the network topology and employing only a few simple assumptions, FBA can efficiently predict metabolic functions at the genome scale as well as microbial phenotypes. The network topology is represented in the form of genome-scale metabolic models (GEMs), which provide a direct mapping between network structure and function via the enzyme-coding genes and corresponding metabolic capacity. Recently, the role of protein limitations in shaping metabolic phenotypes have been extensively studied following the reconstruction of enzyme-constrained GEMs. This framework has been shown to significantly improve the accuracy of predicting microbial phenotypes, and it has demonstrated that a global limitation in protein availability can prompt the ubiquitous metabolic strategy of overflow metabolism. Being one of the most abundant and differentially expressed proteome sectors, metabolic proteins constitute a major cellular demand on proteinogenic amino acids. However, little is known about the impact and sensitivity of amino acid availability with regards to genome-scale metabolism. Here, we explore these aspects by extending on the enzyme-constrained GEM framework by also accounting for the usage of amino acids in expressing the metabolic proteome. Including amino acids in an enzyme-constrained GEM of Saccharomyces cerevisiae, we demonstrate that the expanded model is capable of accurately reproducing experimental amino acid levels. We further show that the metabolic proteome exerts variable demands on amino acid supplies in a condition-dependent manner, suggesting that S. cerevisiae must have evolved to efficiently fine-tune the synthesis of amino acids for expressing its metabolic proteins in response to changes in the external environment. Finally, our results demonstrate how the metabolic network of S. cerevisiae is robust towards perturbations of individual amino acids, while simultaneously being highly sensitive when the relative amino acid availability is set to mimic a priori distributions of both yeast and non-yeast origins.
Collapse
Affiliation(s)
- Vetle Simensen
- Department of Biotechnology and Food Science, NTNU—Norwegian University of Science and Technology, Trondheim, Norway
| | - Yara Seif
- Department of Bioengineering, University of California San Diego, San Diego, CA, United States
| | - Eivind Almaas
- Department of Biotechnology and Food Science, NTNU—Norwegian University of Science and Technology, Trondheim, Norway
- K.G. Jebsen Center for Genetic Epidemiology Department of Public Health and General Practice, NTNU—Norwegian University of Science and Technology, Trondheim, Norway
- *Correspondence: Eivind Almaas,
| |
Collapse
|
8
|
Wolowczyk C, Neckmann U, Aure MR, Hall M, Johannessen B, Zhao S, Skotheim RI, Andersen SB, Zwiggelaar R, Steigedal TS, Lingjærde OC, Sahlberg KK, Almaas E, Bjørkøy G. NRF2 drives an oxidative stress response predictive of breast cancer. Free Radic Biol Med 2022; 184:170-184. [PMID: 35381325 DOI: 10.1016/j.freeradbiomed.2022.03.029] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 03/29/2022] [Accepted: 03/30/2022] [Indexed: 12/12/2022]
Abstract
Many breast cancer patients are diagnosed with small, well-differentiated, hormone receptor-positive tumors. Risk of relapse is not easily identified in these patients, resulting in overtreatment. To identify metastasis-related gene expression patterns, we compared the transcriptomes of the non-metastatic 67NR and metastatic 66cl4 cell lines from the murine 4T1 mammary tumor model. The transcription factor nuclear factor, erythroid 2-like 2 (NRF2, encoded by NFE2L2) was constitutively activated in the metastatic cells and tumors, and correspondingly a subset of established NRF2-regulated genes was also upregulated. Depletion of NRF2 increased basal levels of reactive oxygen species (ROS) and severely reduced ability to form primary tumors and lung metastases. Consistently, a set of NRF2-controlled genes was elevated in breast cancer biopsies. Sixteen of these were combined into a gene expression signature that significantly improves the PAM50 ROR score, and is an independent, strong predictor of prognosis, even in hormone receptor-positive tumors.
Collapse
Affiliation(s)
- Camilla Wolowczyk
- Centre of Molecular Inflammation Research and Department of Cancer Research and Molecular Medicine, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway; Department of Biomedical Laboratory Science, Faculty of Natural Sciences, Norwegian University of Science and Technology, Trondheim, Norway; Clinic of Laboratory Medicine, St. Olavs Hospital, Trondheim, Norway.
| | - Ulrike Neckmann
- Centre of Molecular Inflammation Research and Department of Cancer Research and Molecular Medicine, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway; Department of Biomedical Laboratory Science, Faculty of Natural Sciences, Norwegian University of Science and Technology, Trondheim, Norway; Clinic of Laboratory Medicine, St. Olavs Hospital, Trondheim, Norway
| | - Miriam Ragle Aure
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, The Norwegian Radium Hospital, Oslo, Norway; Department of Medical Genetics, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Martina Hall
- Department of Biotechnology and Food Science, Faculty of Natural Sciences, Norwegian University of Science and Technology, Trondheim, Norway; K.G.Jebsen Center for Genetic Epidemiology, Department of Public Health and General Practice, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Norway
| | - Bjarne Johannessen
- Department of Molecular Oncology, Institute for Cancer Research, Oslo University Hospital-Radiumhospitalet, Oslo, Norway; Norwegian Cancer Genomics Consortium, Oslo, Norway
| | - Sen Zhao
- Department of Molecular Oncology, Institute for Cancer Research, Oslo University Hospital-Radiumhospitalet, Oslo, Norway; Norwegian Cancer Genomics Consortium, Oslo, Norway
| | - Rolf I Skotheim
- Department of Molecular Oncology, Institute for Cancer Research, Oslo University Hospital-Radiumhospitalet, Oslo, Norway; Norwegian Cancer Genomics Consortium, Oslo, Norway
| | - Sonja B Andersen
- Centre of Molecular Inflammation Research and Department of Cancer Research and Molecular Medicine, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway; Department of Biomedical Laboratory Science, Faculty of Natural Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Rosalie Zwiggelaar
- Centre of Molecular Inflammation Research and Department of Cancer Research and Molecular Medicine, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Tonje S Steigedal
- Department of Clinical and Molecular Medicine, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | | | - Kristine Kleivi Sahlberg
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, The Norwegian Radium Hospital, Oslo, Norway; Department of Research, Vestre Viken Hospital Trust, Drammen, Norway
| | - Eivind Almaas
- Department of Biotechnology and Food Science, Faculty of Natural Sciences, Norwegian University of Science and Technology, Trondheim, Norway; K.G.Jebsen Center for Genetic Epidemiology, Department of Public Health and General Practice, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Norway
| | - Geir Bjørkøy
- Centre of Molecular Inflammation Research and Department of Cancer Research and Molecular Medicine, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway; Department of Biomedical Laboratory Science, Faculty of Natural Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| |
Collapse
|
9
|
Pettersen JP, Almaas E. csdR, an R package for differential co-expression analysis. BMC Bioinformatics 2022; 23:79. [PMID: 35183100 PMCID: PMC8858518 DOI: 10.1186/s12859-022-04605-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 02/07/2022] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
Differential co-expression network analysis has become an important tool to gain understanding of biological phenotypes and diseases. The CSD algorithm is a method to generate differential co-expression networks by comparing gene co-expressions from two different conditions. Each of the gene pairs is assigned conserved (C), specific (S) and differentiated (D) scores based on the co-expression of the gene pair between the two conditions. The result of the procedure is a network where the nodes are genes and the links are the gene pairs with the highest C-, S-, and D-scores. However, the existing CSD-implementations suffer from poor computational performance, difficult user procedures and lack of documentation.
Results
We created the R-package aimed at reaching good performance together with ease of use, sufficient documentation, and with the ability to play well with other tools for data analysis. was benchmarked on a realistic dataset with 20,645 genes. After verifying that the chosen number of iterations gave sufficient robustness, we tested the performance against the two existing CSD implementations. was superior in performance to one of the implementations, whereas the other did not run. Our implementation can utilize multiple processing cores. However, we were unable to achieve more than $$\sim$$
∼
2.7 parallel speedup with saturation reached at about 10 cores.
Conclusion
The results suggest that is a useful tool for differential co-expression analysis and is able to generate robust results within a workday on datasets of realistic sizes when run on a workstation or compute server.
Collapse
|
10
|
Simensen V, Schulz C, Karlsen E, Bråtelund S, Burgos I, Thorfinnsdottir LB, García-Calvo L, Bruheim P, Almaas E. Experimental determination of Escherichia coli biomass composition for constraint-based metabolic modeling. PLoS One 2022; 17:e0262450. [PMID: 35085271 PMCID: PMC8794083 DOI: 10.1371/journal.pone.0262450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Accepted: 12/24/2021] [Indexed: 12/03/2022] Open
Abstract
Genome-scale metabolic models (GEMs) are mathematical representations of metabolism that allow for in silico simulation of metabolic phenotypes and capabilities. A prerequisite for these predictions is an accurate representation of the biomolecular composition of the cell necessary for replication and growth, implemented in GEMs as the so-called biomass objective function (BOF). The BOF contains the metabolic precursors required for synthesis of the cellular macro- and micromolecular constituents (e.g. protein, RNA, DNA), and its composition is highly dependent on the particular organism, strain, and growth condition. Despite its critical role, the BOF is rarely constructed using specific measurements of the modeled organism, drawing the validity of this approach into question. Thus, there is a need to establish robust and reliable protocols for experimental condition-specific biomass determination. Here, we address this challenge by presenting a general pipeline for biomass quantification, evaluating its performance on Escherichia coli K-12 MG1655 sampled during balanced exponential growth under controlled conditions in a batch-fermentor set-up. We significantly improve both the coverage and molecular resolution compared to previously published workflows, quantifying 91.6% of the biomass. Our measurements display great correspondence with previously reported measurements, and we were also able to detect subtle characteristics specific to the particular E. coli strain. Using the modified E. coli GEM iML1515a, we compare the feasible flux ranges of our experimentally determined BOF with the original BOF, finding that the changes in BOF coefficients considerably affect the attainable fluxes at the genome-scale.
Collapse
Affiliation(s)
- Vetle Simensen
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Christian Schulz
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Emil Karlsen
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Signe Bråtelund
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Idun Burgos
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Lilja Brekke Thorfinnsdottir
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Laura García-Calvo
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Per Bruheim
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Eivind Almaas
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
- K.G. Jebsen Center for Genetic Epidemiology Department of Public Health and General Practice, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
- * E-mail:
| |
Collapse
|
11
|
Voigt A, Almaas E. Complex Network Analysis in Microbial Systems: Theory and Examples. Methods Mol Biol 2022; 2349:167-191. [PMID: 34718996 DOI: 10.1007/978-1-0716-1585-0_9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
A central driver for the field of systems biology is to develop an understanding of how interactions between components affect the functioning of a system as a whole. Network analysis is an approach that is uniquely suited to uncover patterns and organizing principles in a wide variety of complex systems. In this chapter, we will give a detailed description of basic concepts for characterizing empirical networks, frequently used random network models, and how to compute properties of networks using Python packages. We will demonstrate the application of network analysis by investigating several biological networks.
Collapse
Affiliation(s)
- André Voigt
- Department of Biotechnology, Norwegian University of Science & Technology, NTNU, Trondheim, Norway
| | - Eivind Almaas
- Department of Biotechnology, Norwegian University of Science & Technology, NTNU, Trondheim, Norway.
| |
Collapse
|
12
|
Pettersen JP, Gundersen MS, Almaas E. Robust bacterial co-occurence community structures are independent of r- and K-selection history. Sci Rep 2021; 11:23497. [PMID: 34873246 PMCID: PMC8648916 DOI: 10.1038/s41598-021-03018-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Accepted: 11/19/2021] [Indexed: 11/24/2022] Open
Abstract
Selection for bacteria which are K-strategists instead of r-strategists has been shown to improve fish health and survival in aquaculture. We considered an experiment where microcosms were inoculated with natural seawater and the selection regime was switched from K-selection (by continuous feeding) to r-selection (by pulse feeding) and vice versa. We found the networks of significant co-occurrences to contain clusters of taxonomically related bacteria having positive associations. Comparing this with the time dynamics, we found that the clusters most likely were results of similar niche preferences of the involved bacteria. In particular, the distinction between r- or K-strategists was evident. Each selection regime seemed to give rise to a specific pattern, to which the community converges regardless of its prehistory. Furthermore, the results proved robust to parameter choices in the analysis, such as the filtering threshold, level of random noise, replacing absolute abundances with relative abundances, and the choice of similarity measure. Even though our data and approaches cannot directly predict ecological interactions, our approach provides insights on how the selection regime affects the composition of the microbial community, providing a basis for aquaculture experiments targeted at eliminating opportunistic fish pathogens.
Collapse
Affiliation(s)
- Jakob Peder Pettersen
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Madeleine S Gundersen
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Eivind Almaas
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway.
- Department of Public Health and General Practice, K.G. Jebsen Center for Genetic Epidemiology, NTNU - Norwegian University of Science and Technology, Trondheim, Norway.
| |
Collapse
|
13
|
Joudeh N, Saragliadis A, Schulz C, Voigt A, Almaas E, Linke D. Transcriptomic Response Analysis of Escherichia coli to Palladium Stress. Front Microbiol 2021; 12:741836. [PMID: 34690987 PMCID: PMC8533678 DOI: 10.3389/fmicb.2021.741836] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Accepted: 09/03/2021] [Indexed: 12/13/2022] Open
Abstract
Palladium (Pd), due to its unique catalytic properties, is an industrially important heavy metal especially in the form of nanoparticles. It has a wide range of applications from automobile catalytic converters to the pharmaceutical production of morphine. Bacteria have been used to biologically produce Pd nanoparticles as a new environmentally friendly alternative to the currently used energy-intensive and toxic physicochemical methods. Heavy metals, including Pd, are toxic to bacterial cells and cause general and oxidative stress that hinders the use of bacteria to produce Pd nanoparticles efficiently. In this study, we show in detail the Pd stress-related effects on E. coli. Pd stress effects were measured as changes in the transcriptome through RNA-Seq after 10 min of exposure to 100 μM sodium tetrachloropalladate (II). We found that 709 out of 3,898 genes were differentially expressed, with 58% of them being up-regulated and 42% of them being down-regulated. Pd was found to induce several common heavy metal stress-related effects but interestingly, Pd causes unique effects too. Our data suggests that Pd disrupts the homeostasis of Fe, Zn, and Cu cellular pools. In addition, the expression of inorganic ion transporters in E. coli was found to be massively modulated due to Pd intoxication, with 17 out of 31 systems being affected. Moreover, the expression of several carbohydrate, amino acid, and nucleotide transport and metabolism genes was vastly changed. These results bring us one step closer to the generation of genetically engineered E. coli strains with enhanced capabilities for Pd nanoparticles synthesis.
Collapse
Affiliation(s)
- Nadeem Joudeh
- Department of Biosciences, University of Oslo, Oslo, Norway
| | | | - Christian Schulz
- Department of Biotechnology and Food Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - André Voigt
- Department of Biotechnology and Food Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Eivind Almaas
- Department of Biotechnology and Food Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Dirk Linke
- Department of Biosciences, University of Oslo, Oslo, Norway
| |
Collapse
|
14
|
Voigt A, Martyushenko N, Karlsen E, Hall M, Nyhamar K, Omholt SW, Almaas E. Correction to: Containing pandemics through targeted testing of households. BMC Infect Dis 2021; 21:653. [PMID: 34229601 PMCID: PMC8258475 DOI: 10.1186/s12879-021-06319-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Affiliation(s)
- André Voigt
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Nikolay Martyushenko
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Emil Karlsen
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Martina Hall
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Kristen Nyhamar
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Stig William Omholt
- Department of Circulation and Medical Imaging, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Eivind Almaas
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway. .,K.G. Jebsen Center for Genetic Epidemiology, NTNU - Norwegian University of Science and Technology, Trondheim, Norway.
| |
Collapse
|
15
|
Voigt A, Martyushenko N, Karlsen E, Hall M, Nyhamar K, Omholt SW, Almaas E. Containing pandemics through targeted testing of households. BMC Infect Dis 2021; 21:548. [PMID: 34107917 PMCID: PMC8189703 DOI: 10.1186/s12879-021-06256-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Accepted: 05/24/2021] [Indexed: 11/18/2022] Open
Abstract
Background While invasive social distancing measures have proven efficient to control the spread of pandemics failing wide-scale deployment of vaccines, they carry vast societal costs. The development of a diagnostic methodology for identifying COVID-19 infection through simple testing was a reality only a few weeks after the novel virus was officially announced. Thus, we were interested in exploring the ability of regular testing of non-symptomatic people to reduce cases and thereby offer a non-pharmaceutical tool for controlling the spread of a pandemic. Methods We developed a data-driven individual-based epidemiological network model in order to investigate epidemic countermeasures. This models is based on high-resolution demographic data for each municipality in Norway, and each person in the model is subject to Susceptible-Exposed-Infectious-Recovered (SEIR) dynamics. The model was calibrated against hospitalization data in Oslo, Norway, a city with a population of 700k which we have used as the simulations focus. Results Finding that large households function as hubs for the propagation of COVID-19, we assess the intervention efficiency of targeted pooled household testing (TPHT) repeatedly. For an outbreak with reproductive number R=1.4, we find that weekly TPHT of the 25% largest households brings R below unity. For the case of R=1.2, our results suggest that TPHT with the largest 25% of households every three days in an urban area is as effective as a lockdown in curbing the outbreak. Our investigations of different disease parameters suggest that these results are markedly improved for disease variants that more easily infect young people, and when compliance with self-isolation rules is less than perfect among suspected symptomatic cases. These results are quite robust to changes in the testing frequency, city size, and the household-size distribution. Our results are robust even with only 50% of households willing to participate in TPHT, provided the total number of tests stay unchanged. Conclusions Pooled and targeted household testing appears to be a powerful non-pharmaceutical alternative to more invasive social-distancing and lock-down measures as a localized early response to contain epidemic outbreaks. Supplementary Information The online version contains supplementary material available at (10.1186/s12879-021-06256-8).
Collapse
Affiliation(s)
- André Voigt
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Nikolay Martyushenko
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Emil Karlsen
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Martina Hall
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Kristen Nyhamar
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Stig William Omholt
- Department of Circulation and Medical Imaging, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Eivind Almaas
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway. .,K.G. Jebsen Center for Genetic Epidemiology, NTNU - Norwegian University of Science and Technology, Trondheim, Norway.
| |
Collapse
|
16
|
Schulz C, Kumelj T, Karlsen E, Almaas E. Genome-scale metabolic modelling when changes in environmental conditions affect biomass composition. PLoS Comput Biol 2021; 17:e1008528. [PMID: 34029317 PMCID: PMC8177628 DOI: 10.1371/journal.pcbi.1008528] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 06/04/2021] [Accepted: 04/27/2021] [Indexed: 11/29/2022] Open
Abstract
Genome-scale metabolic modeling is an important tool in the study of metabolism by enhancing the collation of knowledge, interpretation of data, and prediction of metabolic capabilities. A frequent assumption in the use of genome-scale models is that the in vivo organism is evolved for optimal growth, where growth is represented by flux through a biomass objective function (BOF). While the specific composition of the BOF is crucial, its formulation is often inherited from similar organisms due to the experimental challenges associated with its proper determination. A cell’s macro-molecular composition is not fixed and it responds to changes in environmental conditions. As a consequence, initiatives for the high-fidelity determination of cellular biomass composition have been launched. Thus, there is a need for a mathematical and computational framework capable of using multiple measurements of cellular biomass composition in different environments. Here, we propose two different computational approaches for directly addressing this challenge: Biomass Trade-off Weighting (BTW) and Higher-dimensional-plane InterPolation (HIP). In lieu of experimental data on biomass composition-variation in response to changing nutrient environment, we assess the properties of BTW and HIP using three hypothetical, yet biologically plausible, BOFs for the Escherichia coli genome-scale metabolic model iML1515. We find that the BTW and HIP formulations have a significant impact on model performance and phenotypes. Furthermore, the BTW method generates larger growth rates in all environments when compared to HIP. Using acetate secretion and the respiratory quotient as proxies for phenotypic changes, we find marked differences between the methods as HIP generates BOFs more similar to a reference BOF than BTW. We conclude that the presented methods constitute a conceptual step in developing genome-scale metabolic modelling approaches capable of addressing the inherent dependence of cellular biomass composition on nutrient environments. Changes in the environment promote changes in an organism’s metabolism. To achieve balanced growth states for near-optimal function, cells respond through metabolic rearrangements, which may influence the biosynthesis of metabolic precursors for building a cell’s molecular constituents. Therefore, it is necessary to take the dependence of biomass composition on environmental conditions into consideration. While measuring the biomass composition for some environments is possible, and should be done, it cannot be completed for all possible environments. In this work, we propose two main approaches, BTW and HIP, for addressing the challenge of estimating biomass composition in response to environmental changes. We evaluate the phenotypic consequences of BTW and HIP by characterizing their effect on growth, secretion potential, respiratory efficiency, and gene essentiality of a cell. Our work constitutes a first conceptual step in accounting for the influence of growth conditions on biomass composition, and in turn the biomass composition’s effect on metabolic phenotypic traits, within constraint-based modelling. As such, we believe it will improve the relevance of constraint-based methods in metabolic engineering and drug discovery, since the biosynthetic potential of microbes for generating industrially relevant products or drugs often is closely linked to their biomass composition.
Collapse
Affiliation(s)
- Christian Schulz
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Tjasa Kumelj
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Emil Karlsen
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Eivind Almaas
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and General Practice, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
- * E-mail:
| |
Collapse
|
17
|
Sulheim S, Fossheim FA, Wentzel A, Almaas E. Correction to: Automatic reconstruction of metabolic pathways from identified biosynthetic gene clusters. BMC Bioinformatics 2021; 22:199. [PMID: 33874882 PMCID: PMC8056699 DOI: 10.1186/s12859-021-04094-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Affiliation(s)
- Snorre Sulheim
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Sem Sælands vei 8, 7034, Trondheim, Norway. .,Department of Biotechnology and Nanomedicine, SINTEF Industry, Richard Birkelands vei 3, 7034, Trondheim, Norway.
| | - Fredrik A Fossheim
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Sem Sælands vei 8, 7034, Trondheim, Norway
| | - Alexander Wentzel
- Department of Biotechnology and Nanomedicine, SINTEF Industry, Richard Birkelands vei 3, 7034, Trondheim, Norway
| | - Eivind Almaas
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Sem Sælands vei 8, 7034, Trondheim, Norway.,K.G. Jebsen Center for Genetic Epidemiology, NTNU - Norwegian University of Science and Technology, Håkon Jarls gate 11, 7030, Trondheim, Norway
| |
Collapse
|
18
|
Simensen V, Voigt A, Almaas E. High-quality genome-scale metabolic model of Aurantiochytrium sp. T66. Biotechnol Bioeng 2021; 118:2105-2117. [PMID: 33624839 DOI: 10.1002/bit.27726] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Revised: 01/21/2021] [Accepted: 02/14/2021] [Indexed: 01/17/2023]
Abstract
The long-chain, ω-3 polyunsaturated fatty acids (PUFAs) (e.g., eicosapentaenoic acid [EPA] and docosahexaenoic acid [DHA]), are essential for humans and animals, including marine fish species. Presently, the primary source of these PUFAs is fish oils. As the global production of fish oils appears to be reaching its limits, alternative sources of high-quality ω-3 PUFAs is paramount to support the growing aquaculture industry. Thraustochytrids are a group of heterotrophic protists with the capability to synthesize and accrue large amounts of DHA. Thus, the thraustochytrids are prime candidates to solve the increasing demand for ω-3 PUFAs using microbial cell factories. However, a systems-level understanding of their metabolic shift from cellular growth into lipid accumulation is, to a large extent, unclear. Here, we reconstructed a high-quality genome-scale metabolic model of the thraustochytrid Aurantiochytrium sp. T66 termed iVS1191. Through iterative rounds of model refinement and extensive manual curation, we significantly enhanced the metabolic scope and coverage of the reconstruction from that of previously published models, making considerable improvements with stoichiometric consistency, metabolic connectivity, and model annotations. We show that iVS1191 is highly consistent with experimental growth data, reproducing in vivo growth phenotypes as well as specific growth rates on minimal carbon media. The availability of iVS1191 provides a solid framework for further developing our understanding of T66's metabolic properties, as well as exploring metabolic engineering and process-optimization strategies in silico for increased ω-3 PUFA production.
Collapse
Affiliation(s)
- Vetle Simensen
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - André Voigt
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Eivind Almaas
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway.,Department of Public Health and General Practice, K.G. Jebsen Center for Genetic Epidemiology, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| |
Collapse
|
19
|
Sulheim S, Fossheim FA, Wentzel A, Almaas E. Automatic reconstruction of metabolic pathways from identified biosynthetic gene clusters. BMC Bioinformatics 2021; 22:81. [PMID: 33622234 PMCID: PMC7901079 DOI: 10.1186/s12859-021-03985-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Accepted: 01/18/2021] [Indexed: 12/17/2022] Open
Abstract
Background A wide range of bioactive compounds is produced by enzymes and enzymatic complexes encoded in biosynthetic gene clusters (BGCs). These BGCs can be identified and functionally annotated based on their DNA sequence. Candidates for further research and development may be prioritized based on properties such as their functional annotation, (dis)similarity to known BGCs, and bioactivity assays. Production of the target compound in the native strain is often not achievable, rendering heterologous expression in an optimized host strain as a promising alternative. Genome-scale metabolic models are frequently used to guide strain development, but large-scale incorporation and testing of heterologous production of complex natural products in this framework is hampered by the amount of manual work required to translate annotated BGCs to metabolic pathways. To this end, we have developed a pipeline for an automated reconstruction of BGC associated metabolic pathways responsible for the synthesis of non-ribosomal peptides and polyketides, two of the dominant classes of bioactive compounds. Results The developed pipeline correctly predicts 72.8% of the metabolic reactions in a detailed evaluation of 8 different BGCs comprising 228 functional domains. By introducing the reconstructed pathways into a genome-scale metabolic model we demonstrate that this level of accuracy is sufficient to make reliable in silico predictions with respect to production rate and gene knockout targets. Furthermore, we apply the pipeline to a large BGC database and reconstruct 943 metabolic pathways. We identify 17 enzymatic reactions using high-throughput assessment of potential knockout targets for increasing the production of any of the associated compounds. However, the targets only provide a relative increase of up to 6% compared to wild-type production rates. Conclusion With this pipeline we pave the way for an extended use of genome-scale metabolic models in strain design of heterologous expression hosts. In this context, we identified generic knockout targets for the increased production of heterologous compounds. However, as the predicted increase is minor for any of the single-reaction knockout targets, these results indicate that more sophisticated strain-engineering strategies are necessary for the development of efficient BGC expression hosts.
Collapse
Affiliation(s)
- Snorre Sulheim
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Sem Sælands vei 8, 7034, Trondheim, Norway. .,Department of Biotechnology and Nanomedicine, SINTEF Industry, Richard Birkelands vei 3, 7034, Trondheim, Norway.
| | - Fredrik A Fossheim
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Sem Sælands vei 8, 7034, Trondheim, Norway
| | - Alexander Wentzel
- Department of Biotechnology and Nanomedicine, SINTEF Industry, Richard Birkelands vei 3, 7034, Trondheim, Norway
| | - Eivind Almaas
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Sem Sælands vei 8, 7034, Trondheim, Norway.,K.G. Jebsen Center for Genetic Epidemiology, NTNU - Norwegian University of Science and Technology, Håkon Jarls gate 11, 7030, Trondheim, Norway
| |
Collapse
|
20
|
Bernstein DB, Sulheim S, Almaas E, Segrè D. Addressing uncertainty in genome-scale metabolic model reconstruction and analysis. Genome Biol 2021; 22:64. [PMID: 33602294 PMCID: PMC7890832 DOI: 10.1186/s13059-021-02289-z] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Accepted: 02/04/2021] [Indexed: 02/07/2023] Open
Abstract
The reconstruction and analysis of genome-scale metabolic models constitutes a powerful systems biology approach, with applications ranging from basic understanding of genotype-phenotype mapping to solving biomedical and environmental problems. However, the biological insight obtained from these models is limited by multiple heterogeneous sources of uncertainty, which are often difficult to quantify. Here we review the major sources of uncertainty and survey existing approaches developed for representing and addressing them. A unified formal characterization of these uncertainties through probabilistic approaches and ensemble modeling will facilitate convergence towards consistent reconstruction pipelines, improved data integration algorithms, and more accurate assessment of predictive capacity.
Collapse
Affiliation(s)
- David B Bernstein
- Department of Biomedical Engineering and Biological Design Center, Boston University, Boston, MA, USA
| | - Snorre Sulheim
- Bioinformatics Program, Boston University, Boston, MA, USA
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
- Department of Biotechnology and Nanomedicine, SINTEF Industry, Trondheim, Norway
| | - Eivind Almaas
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
- K.G. Jebsen Center for Genetic Epidemiology, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Daniel Segrè
- Department of Biomedical Engineering and Biological Design Center, Boston University, Boston, MA, USA.
- Bioinformatics Program, Boston University, Boston, MA, USA.
- Department of Biology and Department of Physics, Boston University, Boston, MA, USA.
| |
Collapse
|
21
|
Ofaim S, Sulheim S, Almaas E, Sher D, Segrè D. Dynamic Allocation of Carbon Storage and Nutrient-Dependent Exudation in a Revised Genome-Scale Model of Prochlorococcus. Front Genet 2021; 12:586293. [PMID: 33633777 PMCID: PMC7900632 DOI: 10.3389/fgene.2021.586293] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Accepted: 01/14/2021] [Indexed: 12/02/2022] Open
Abstract
Microbial life in the oceans impacts the entire marine ecosystem, global biogeochemistry and climate. The marine cyanobacterium Prochlorococcus, an abundant component of this ecosystem, releases a significant fraction of the carbon fixed through photosynthesis, but the amount, timing and molecular composition of released carbon are still poorly understood. These depend on several factors, including nutrient availability, light intensity and glycogen storage. Here we combine multiple computational approaches to provide insight into carbon storage and exudation in Prochlorococcus. First, with the aid of a new algorithm for recursive filling of metabolic gaps (ReFill), and through substantial manual curation, we extended an existing genome-scale metabolic model of Prochlorococcus MED4. In this revised model (iSO595), we decoupled glycogen biosynthesis/degradation from growth, thus enabling dynamic allocation of carbon storage. In contrast to standard implementations of flux balance modeling, we made use of forced influx of carbon and light into the cell, to recapitulate overflow metabolism due to the decoupling of photosynthesis and carbon fixation from growth during nutrient limitation. By using random sampling in the ensuing flux space, we found that storage of glycogen or exudation of organic acids are favored when the growth is nitrogen limited, while exudation of amino acids becomes more likely when phosphate is the limiting resource. We next used COMETS to simulate day-night cycles and found that the model displays dynamic glycogen allocation and exudation of organic acids. The switch from photosynthesis and glycogen storage to glycogen depletion is associated with a redistribution of fluxes from the Entner–Doudoroff to the Pentose Phosphate pathway. Finally, we show that specific gene knockouts in iSO595 exhibit dynamic anomalies compatible with experimental observations, further demonstrating the value of this model as a tool to probe the metabolic dynamic of Prochlorococcus.
Collapse
Affiliation(s)
- Shany Ofaim
- Bioinformatics Program and Biological Design Center, Boston University, Boston, MA, United States.,Department of Marine Biology, University of Haifa, Haifa, Israel
| | - Snorre Sulheim
- Bioinformatics Program and Biological Design Center, Boston University, Boston, MA, United States.,Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway.,Department of Biotechnology and Nanomedicine, SINTEF Industry, Trondheim, Norway
| | - Eivind Almaas
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway.,K.G. Jebsen Center for Genetic Epidemiology, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Daniel Sher
- Department of Marine Biology, University of Haifa, Haifa, Israel
| | - Daniel Segrè
- Bioinformatics Program and Biological Design Center, Boston University, Boston, MA, United States.,Department of Biomedical Engineering, Boston University, Boston, MA, United States.,Department of Physics, Boston University, Boston, MA, United States.,Department of Biology, Boston University, Boston, MA, United States
| |
Collapse
|
22
|
Hall M, Kültz D, Almaas E. Identification of key proteins involved in stickleback environmental adaptation with system-level analysis. Physiol Genomics 2020; 52:531-548. [PMID: 32956024 DOI: 10.1152/physiolgenomics.00078.2020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Using abundance measurements of 1,490 proteins from four separate populations of three-spined sticklebacks, we implemented a system-level approach to correlate proteome dynamics with environmental salinity and temperature and the fish's population and morphotype. We identified robust and accurate fingerprints that classify environmental salinity, temperature, morphotype, and the population sample origin, observing that proteins with specific functions are enriched in these fingerprints. Highly apparent functions represented in all fingerprints include ion transport, proteostasis, growth, and immunity, suggesting that these functions are most diversified in populations inhabiting different environments. Applying a differential network approach, we analyzed the network of protein interactions that differs between populations. Looking at specific population combinations of differential interaction, we identify sets of connected proteins. We find that these sets and their corresponding enriched functions reflect key processes that have diverged between the four populations. Moreover, the extent of divergence, i.e., the number of enriched functions that differ between populations, is highest when all three environmental parameters are different between two populations. Key nodes in the differential interaction network signify functions that are also inherent in the fingerprints, most prominently proteostasis-related functions. However, the differential interaction network also reveals additional functions that have diverged between populations, notably cytoskeletal organization and morphogenesis. The strength of these analyses is that the results are purely data driven. With such an unbiased approach applied on a large proteomic data set, we find the strongest signals given by the data, making it possible to develop more discriminatory and complex biomarkers for specific contexts of interest.
Collapse
Affiliation(s)
- Martina Hall
- Department of Biotechnology and Food Science, Norwegian University of Science and Technology, Trondheim, Norway.,K. G. Jebsen Center for Genetic Epidemiology, Norwegian University of Science and Technology, Trondheim, Norway
| | - Dietmar Kültz
- Department of Animal Sciences, University of California, Davis, California
| | - Eivind Almaas
- Department of Biotechnology and Food Science, Norwegian University of Science and Technology, Trondheim, Norway.,K. G. Jebsen Center for Genetic Epidemiology, Norwegian University of Science and Technology, Trondheim, Norway
| |
Collapse
|
23
|
Morselli Gysi D, de Miranda Fragoso T, Zebardast F, Bertoli W, Busskamp V, Almaas E, Nowick K. Whole transcriptomic network analysis using Co-expression Differential Network Analysis (CoDiNA). PLoS One 2020; 15:e0240523. [PMID: 33057419 PMCID: PMC7561188 DOI: 10.1371/journal.pone.0240523] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Accepted: 09/29/2020] [Indexed: 01/05/2023] Open
Abstract
Biological and medical sciences are increasingly acknowledging the significance of gene co-expression-networks for investigating complex-systems, phenotypes or diseases. Typically, complex phenotypes are investigated under varying conditions. While approaches for comparing nodes and links in two networks exist, almost no methods for the comparison of multiple networks are available and—to best of our knowledge—no comparative method allows for whole transcriptomic network analysis. However, it is the aim of many studies to compare networks of different conditions, for example, tissues, diseases, treatments, time points, or species. Here we present a method for the systematic comparison of an unlimited number of networks, with unlimited number of transcripts: Co-expression Differential Network Analysis (CoDiNA). In particular, CoDiNA detects links and nodes that are common, specific or different among the networks. We developed a statistical framework to normalize between these different categories of common or changed network links and nodes, resulting in a comprehensive network analysis method, more sophisticated than simply comparing the presence or absence of network nodes. Applying CoDiNA to a neurogenesis study we identified candidate genes involved in neuronal differentiation. We experimentally validated one candidate, demonstrating that its overexpression resulted in a significant disturbance in the underlying gene regulatory network of neurogenesis. Using clinical studies, we compared whole transcriptome co-expression networks from individuals with or without HIV and active tuberculosis (TB) and detected signature genes specific to HIV. Furthermore, analyzing multiple cancer transcription factor (TF) networks, we identified common and distinct features for particular cancer types. These CoDiNA applications demonstrate the successful detection of genes associated with specific phenotypes. Moreover, CoDiNA can also be used for comparing other types of undirected networks, for example, metabolic, protein-protein interaction, ecological and psychometric networks. CoDiNA is publicly available as an R package in CRAN (https://CRAN.R-project.org/package=CoDiNA).
Collapse
Affiliation(s)
- Deisy Morselli Gysi
- Department of Computer Science, Leipzig University, Leipzig, Germany
- * E-mail: (KN); (DMG)
| | | | - Fatemeh Zebardast
- Department of Biology, Chemistry, Pharmacy, Freie Universitaet Berlin, Berlin, Germany
| | - Wesley Bertoli
- Department of Statistics, Federal University of Technology - Paraná, Curitiba, Brazil
| | - Volker Busskamp
- Center for Regenerative Therapies (CRTD), Technical University Dresden, Dresden, Germany
- Dept. of Ophthalmology, Universitäts-Augenklinik Bonn, University of Bonn, Bonn, Germany
| | - Eivind Almaas
- Department of Biotechnology, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
- K.G. Jebsen Centre for Genetic Epidemiology, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Katja Nowick
- Department of Biology, Chemistry, Pharmacy, Freie Universitaet Berlin, Berlin, Germany
- * E-mail: (KN); (DMG)
| |
Collapse
|
24
|
Sulheim S, Kumelj T, van Dissel D, Salehzadeh-Yazdi A, Du C, van Wezel GP, Nieselt K, Almaas E, Wentzel A, Kerkhoven EJ. Enzyme-Constrained Models and Omics Analysis of Streptomyces coelicolor Reveal Metabolic Changes that Enhance Heterologous Production. iScience 2020; 23:101525. [PMID: 32942174 PMCID: PMC7501462 DOI: 10.1016/j.isci.2020.101525] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Revised: 07/19/2020] [Accepted: 08/31/2020] [Indexed: 02/06/2023] Open
Abstract
Many biosynthetic gene clusters (BGCs) require heterologous expression to realize their genetic potential, including silent and metagenomic BGCs. Although the engineered Streptomyces coelicolor M1152 is a widely used host for heterologous expression of BGCs, a systemic understanding of how its genetic modifications affect the metabolism is lacking and limiting further development. We performed a comparative analysis of M1152 and its ancestor M145, connecting information from proteomics, transcriptomics, and cultivation data into a comprehensive picture of the metabolic differences between these strains. Instrumental to this comparison was the application of an improved consensus genome-scale metabolic model (GEM) of S. coelicolor. Although many metabolic patterns are retained in M1152, we find that this strain suffers from oxidative stress, possibly caused by increased oxidative metabolism. Furthermore, precursor availability is likely not limiting polyketide production, implying that other strategies could be beneficial for further development of S. coelicolor for heterologous production of novel compounds.
Collapse
Affiliation(s)
- Snorre Sulheim
- Department of Biotechnology and Nanomedicine, SINTEF Industry, 7034 Trondheim, Norway
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, 7491 Trondheim, Norway
| | - Tjaša Kumelj
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, 7491 Trondheim, Norway
| | - Dino van Dissel
- Department of Biotechnology and Nanomedicine, SINTEF Industry, 7034 Trondheim, Norway
| | - Ali Salehzadeh-Yazdi
- Department of Systems Biology and Bioinformatics, Faculty of Computer Science and Electrical Engineering, University of Rostock, 18057 Rostock, Germany
| | - Chao Du
- Microbial Biotechnology, Institute of Biology, Leiden University, 2300 Leiden, the Netherlands
| | - Gilles P. van Wezel
- Microbial Biotechnology, Institute of Biology, Leiden University, 2300 Leiden, the Netherlands
| | - Kay Nieselt
- Integrative Transcriptomics, Center for Bioinformatics, University of Tübingen, 72070 Tübingen, Germany
| | - Eivind Almaas
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, 7491 Trondheim, Norway
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and General Practice, NTNU - Norwegian University of Science and Technology, 7491 Trondheim, Norway
| | - Alexander Wentzel
- Department of Biotechnology and Nanomedicine, SINTEF Industry, 7034 Trondheim, Norway
| | - Eduard J. Kerkhoven
- Systems and Synthetic Biology, Department of Biology and Biological Engineering, Chalmers University of Technology, 412 96 Gothenburg, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, 412 96 Gothenburg, Sweden
| |
Collapse
|
25
|
Martyushenko N, Almaas E. ErrorTracer: an algorithm for identifying the origins of inconsistencies in genome-scale metabolic models. Bioinformatics 2020; 36:1644-1646. [PMID: 31598631 PMCID: PMC7703767 DOI: 10.1093/bioinformatics/btz761] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2019] [Revised: 09/25/2019] [Accepted: 10/03/2019] [Indexed: 01/24/2023] Open
Abstract
MOTIVATION The number and complexity of genome-scale metabolic models is steadily increasing, empowered by automated model-generation algorithms. The quality control of the models, however, has always remained a significant challenge, the most fundamental being reactions incapable of carrying flux. Numerous automated gap-filling algorithms try to address this problem, but can rarely resolve all of a model's inconsistencies. The need for fast inconsistency checking algorithms has also been emphasized with the recent community push for automated model-validation before model publication. Previously, we wrote a graphical software to allow the modeller to solve the remaining errors manually. Nevertheless, model size and complexity remained a hindrance to efficiently tracking origins of inconsistency. RESULTS We developed the ErrorTracer algorithm in order to address the shortcomings of existing approaches: ErrorTracer searches for inconsistencies, classifies them and identifies their origins. The algorithm is ∼2 orders of magnitude faster than current community standard methods, using only seconds even for large-scale models. This allows for interactive exploration in direct combination with model visualization, markedly simplifying the whole error-identification and correction work flow. AVAILABILITY AND IMPLEMENTATION Windows and Linux executables and source code are available under the EPL 2.0 Licence at https://github.com/TheAngryFox/ModelExplorer and https://www.ntnu.edu/almaaslab/downloads. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
| | - Eivind Almaas
- Department of Biotechnology.,Department of Public Health and General Practice, K.G. Jebsen Center for Genetic Epidemiology, NTNU - Norwegian University of Science and Technology, Trondheim N-7491, Norway
| |
Collapse
|
26
|
Bjerkelund Røkke G, Hohmann-Marriott MF, Almaas E. An adjustable algal chloroplast plug-and-play model for genome-scale metabolic models. PLoS One 2020; 15:e0229408. [PMID: 32092117 PMCID: PMC7039451 DOI: 10.1371/journal.pone.0229408] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Accepted: 02/05/2020] [Indexed: 01/25/2023] Open
Abstract
The chloroplast is a central part of plant cells, as this is the organelle where the photosynthesis, fixation of inorganic carbon, and other key functions related to fatty acid synthesis and amino acid synthesis occur. Since this organelle should be an integral part of any genome-scale metabolic model for a microalgae or a higher plant, it is of great interest to generate a detailed and standardized chloroplast model. Additionally, we see the need for a novel type of sub-model template, or organelle model, which could be incorporated into a larger, less specific genome-scale metabolic model, while allowing for minor differences between chloroplast-containing organisms. The result of this work is the very first standardized chloroplast model, iGR774, consisting of 788 reactions, 764 metabolites, and 774 genes. The model is currently able to run in three different modes, mimicking the chloroplast metabolism of three photosynthetic microalgae-Nannochloropsis gaditana, Chlamydomonas reinhardtii and Phaeodactylum tricornutum. In addition to developing the chloroplast metabolic network reconstruction, we have developed multiple software tools for working with this novel type of sub-model in the COBRA Toolbox for MATLAB, including tools for connecting the chloroplast model to a genome-scale metabolic reconstruction in need of a chloroplast, for switching the model between running in different organism modes, and for expanding it by introducing more reactions either related to one of the current organisms included in the model, or to a new organism.
Collapse
Affiliation(s)
- Gunvor Bjerkelund Røkke
- Department of Biotechnology and Food Science, The Norwegian University of Science and Technology, Trondheim, Norway
| | | | - Eivind Almaas
- Department of Biotechnology and Food Science, The Norwegian University of Science and Technology, Trondheim, Norway
- K. G. Jebsen Center for Genetic Epidemiology, The Norwegian University of Science and Technology, Trondheim, Norway
| |
Collapse
|
27
|
Neckmann U, Wolowczyk C, Hall M, Almaas E, Ren J, Zhao S, Johannessen B, Skotheim RI, Bjørkøy G, Ten Dijke P, Holien T. GREM1 is associated with metastasis and predicts poor prognosis in ER-negative breast cancer patients. Cell Commun Signal 2019; 17:140. [PMID: 31694641 PMCID: PMC6836336 DOI: 10.1186/s12964-019-0467-7] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Accepted: 10/22/2019] [Indexed: 12/25/2022] Open
Abstract
Background In breast cancer, activation of bone morphogenetic protein (BMP) signaling and elevated levels of BMP-antagonists have been linked to tumor progression and metastasis. However, the simultaneous upregulation of BMPs and their antagonist, and the fact that both promote tumor aggressiveness seems contradictory and is not fully understood. Methods We analyzed the transcriptomes of the metastatic 66cl4 and the non-metastatic 67NR cell lines of the 4T1 mouse mammary tumor model to search for factors that promote metastasis. CRISPR/Cas9 gene editing was used for mechanistic studies in the same cell lines. Furthermore, we analyzed gene expression patterns in human breast cancer biopsies obtained from public datasets to evaluate co-expression and possible relations to clinical outcome. Results We found that mRNA levels of the BMP-antagonist Grem1, encoding gremlin1, and the ligand Bmp4 were both significantly upregulated in cells and primary tumors of 66cl4 compared to 67NR. Depletion of gremlin1 in 66cl4 could impair metastasis to the lungs in this model. Furthermore, we found that expression of Grem1 correlated with upregulation of several stem cell markers in 66cl4 cells compared to 67NR cells. Both in the mouse model and in patients, expression of GREM1 associated with extracellular matrix organization, and formation, biosynthesis and modification of collagen. Importantly, high expression of GREM1 predicted poor prognosis in estrogen receptor negative breast cancer patients. Analyses of large patient cohorts revealed that amplification of genes encoding BMP-antagonists and elevation of the corresponding transcripts is evident in biopsies from more than half of the patients and much more frequent for the secreted BMP-antagonists than the intracellular inhibitors of SMAD signaling. Conclusion In conclusion, our results show that GREM1 is associated with metastasis and predicts poor prognosis in ER-negative breast cancer patients. Gremlin1 could represent a novel target for therapy.
Collapse
Affiliation(s)
- Ulrike Neckmann
- Centre of Molecular Inflammation Research, Department of Clinical and Molecular Medicine, Faculty of Medicine and Health Sciences, NTNU - Norwegian University of Science and Technology, Trondheim, Norway.,Department of Biomedical Laboratory Science, Faculty of Natural Sciences, NTNU - Norwegian University of Science and Technology, Trondheim, Norway.,Clinic of Laboratory Medicine, St Olavs Hospital, Trondheim, Norway
| | - Camilla Wolowczyk
- Centre of Molecular Inflammation Research, Department of Clinical and Molecular Medicine, Faculty of Medicine and Health Sciences, NTNU - Norwegian University of Science and Technology, Trondheim, Norway.,Department of Biomedical Laboratory Science, Faculty of Natural Sciences, NTNU - Norwegian University of Science and Technology, Trondheim, Norway.,Clinic of Laboratory Medicine, St Olavs Hospital, Trondheim, Norway
| | - Martina Hall
- Department of Biotechnology and Food Science, Faculty of Natural Sciences, NTNU - Norwegian University of Science and Technology, Trondheim, Norway.,K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and General Practice, Faculty of Medicine and Health Sciences, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Eivind Almaas
- Department of Biotechnology and Food Science, Faculty of Natural Sciences, NTNU - Norwegian University of Science and Technology, Trondheim, Norway.,K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and General Practice, Faculty of Medicine and Health Sciences, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Jiang Ren
- Department of Cell and Chemical Biology, Oncode Institute, Leiden University Medical Center, Leiden, The Netherlands
| | - Sen Zhao
- Department of Molecular Oncology, Institute for Cancer Research, Oslo University Hospital-Radiumhospitalet, Oslo, Norway
| | - Bjarne Johannessen
- Department of Molecular Oncology, Institute for Cancer Research, Oslo University Hospital-Radiumhospitalet, Oslo, Norway
| | - Rolf I Skotheim
- Department of Molecular Oncology, Institute for Cancer Research, Oslo University Hospital-Radiumhospitalet, Oslo, Norway.,Department of Informatics, University of Oslo, Oslo, Norway
| | - Geir Bjørkøy
- Centre of Molecular Inflammation Research, Department of Clinical and Molecular Medicine, Faculty of Medicine and Health Sciences, NTNU - Norwegian University of Science and Technology, Trondheim, Norway.,Department of Biomedical Laboratory Science, Faculty of Natural Sciences, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Peter Ten Dijke
- Department of Cell and Chemical Biology, Oncode Institute, Leiden University Medical Center, Leiden, The Netherlands
| | - Toril Holien
- Department of Clinical and Molecular Medicine (IKOM), NTNU, Gastro Center, Prinsesse Kristinas gt 1, 7030, Trondheim, Norway. .,Department of Hematology, St. Olavs Hospital, Trondheim, Norway.
| |
Collapse
|
28
|
Voigt A, Almaas E. Assessment of weighted topological overlap (wTO) to improve fidelity of gene co-expression networks. BMC Bioinformatics 2019; 20:58. [PMID: 30691386 PMCID: PMC6350380 DOI: 10.1186/s12859-019-2596-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2017] [Accepted: 01/03/2019] [Indexed: 01/21/2023] Open
Abstract
BACKGROUND For more than a decade, gene expression data sets have been used as basis for the construction of co-expression networks used in systems biology investigations, leading to many important discoveries in a wide range of subjects spanning human disease to evolution and the development of organisms. A commonly encountered challenge in such investigations is first that of detecting, then subsequently removing, spurious correlations (i.e. links) in these networks. While access to a large number of measurements per gene would reduce this problem, often only a small number of measurements are available. The weighted Topological Overlap (wTO) measure, which incorporates information from the shared network-neighborhood of a given gene-pair into a single score, is a metric that is frequently used with the implicit expectation of producing higher-quality networks. However, the actual extent to which wTO improves on the accuracy of a co-expression analysis has not been quantified. RESULTS Here, we used a large-sample biological data set containing 338 gene-expression measurements per gene as a reference system. From these data, we generated ensembles consisting of 10, 20 and 50 randomly selected measurements to emulate low-quality data sets, finding that the wTO measure consistently generates more robust scores than what results from simple correlation calculations. Furthermore, for the data sets consisting of only 10 and 20 samples per gene, we find that wTO serves as a better predictor of the correlation scores generated from the full data set. However, we find that using wTO as a score for network building substantially alters several topographical aspects of the resulting networks, with no conclusive evidence that the resulting structure is more accurate. Importantly, we find that the much used approach of applying a soft-threshold modifier to link weights prior to computing the wTO substantially decreases the robustness of the resulting wTO network, but increases the predictive power of wTO networks with regards to the reference correlation (soft threshold) network, particularly as the size of the data sets increases. CONCLUSION Our analysis demonstrates that, in agreement with previous assumptions, the wTO approach is capable of significantly improving the fidelity of co-expression networks, and that this effect is especially evident for cases of low-sample number gene-expression data sets.
Collapse
Affiliation(s)
- André Voigt
- Network Systems Biology Group, Department of Biotechnology, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Eivind Almaas
- Network Systems Biology Group, Department of Biotechnology, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and General Practice, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| |
Collapse
|
29
|
Martyushenko N, Almaas E. ModelExplorer - software for visual inspection and inconsistency correction of genome-scale metabolic reconstructions. BMC Bioinformatics 2019; 20:56. [PMID: 30691403 PMCID: PMC6348647 DOI: 10.1186/s12859-019-2615-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2018] [Accepted: 01/07/2019] [Indexed: 12/25/2022] Open
Abstract
Background Genome-scale metabolic network reconstructions are low level chemical representations of biological organisms. These models allow the system-level investigation of metabolic phenotypes using a variety of computational approaches. The link between a metabolic network model and an organisms’ higher-level behaviour is usually found using a constraint-based analysis approach, such as FBA (Flux Balance Analysis). However, the process of model reconstruction rarely proceeds without error. Often, considerable parts of a model cannot carry flux under any condition. This is termed model inconsistency and is caused by faulty topology and/or stoichiometry of the underlying reconstructed network. While there exist several automated gap-filling tools that may solve some of the inconsistencies, much of the work still needs to be carried out manually. The common “linear list” format of writing biochemical reactions makes it difficult to intuit what is at the root of the inconsistent behaviour. Unfortunately, we have frequently observed that model builders do not correct their models past the abilities of automated tools, leaving many widely used models significantly inconsistent. Results We have developed the software ModelExplorer, which main purpose is to fill this gap by providing an intuitive and visual framework that allows the user to explore and correct inconsistencies in genome-scale metabolic models. The software will automatically visualize metabolic networks as graphs with distinct separation and delineation of cellular compartments. ModelExplorer highlights reactions and species that are unable to carry flux (blocked), with several different consistency checking modes available. Our software also allows the automatic identification of neighbours and production pathways of any species or reaction. Additionally, the user may focus on any chosen inconsistent part of the model on its own. This facilitates a rapid and visual identification of reactions and species responsible for model inconsistencies. Finally, ModelExplorer lets the user freely edit, add or delete model elements, allowing straight-forward correction of discovered issues. Conclusion Overall, ModelExplorer is currently the fastest real-time metabolic network visualization program available. It implements several consistency checking algorithms, which in combination with its set of tracking tools, gives an efficient and systematic model-correction process. Electronic supplementary material The online version of this article (10.1186/s12859-019-2615-x) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Nikolay Martyushenko
- Department of Biotechnology, NTNU - Norwegian University of Science and Technology, Trondheim, N-7491, Norway.
| | - Eivind Almaas
- Department of Biotechnology, NTNU - Norwegian University of Science and Technology, Trondheim, N-7491, Norway. .,K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and General Practice, NTNU - Norwegian University of Science and Technology, Trondheim, N-7491, Norway.
| |
Collapse
|
30
|
Kumelj T, Sulheim S, Wentzel A, Almaas E. Predicting Strain Engineering Strategies Using iKS1317: A Genome‐Scale Metabolic Model of
Streptomyces coelicolor. Biotechnol J 2019; 14:e1800180. [DOI: 10.1002/biot.201800180] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2018] [Revised: 11/15/2018] [Indexed: 11/06/2022]
Affiliation(s)
- Tjaša Kumelj
- Department of Biotechnology and Food ScienceNTNU ‐ Norwegian University of Science and TechnologyTrondheimNorway
| | - Snorre Sulheim
- Department of Biotechnology and Food ScienceNTNU ‐ Norwegian University of Science and TechnologyTrondheimNorway
- SINTEF IndustryDepartment of Biotechnology and NanomedicineTrondheimNorway
| | - Alexander Wentzel
- SINTEF IndustryDepartment of Biotechnology and NanomedicineTrondheimNorway
| | - Eivind Almaas
- Department of Biotechnology and Food ScienceNTNU ‐ Norwegian University of Science and TechnologyTrondheimNorway
- K.G. Jebsen Center for Genetic EpidemiologyNTNU – Norwegian University of Science and TechnologyTrondheimNorway
| |
Collapse
|
31
|
Abstract
BACKGROUND Constraint-based modeling is a widely used and powerful methodology to assess the metabolic phenotypes and capabilities of an organism. The starting point and cornerstone of all such modeling is a genome-scale metabolic network reconstruction. The creation, further development, and application of such networks is a growing field of research thanks to a plethora of readily accessible computational tools. While the majority of studies are focused on single-species analyses, typically of a microbe, the computational study of communities of organisms is gaining attention. Similarly, reconstructions that are unified for a multi-cellular organism have gained in popularity. Consequently, the rapid generation of genome-scale metabolic reconstructed networks is crucial. While multiple web-based or stand-alone tools are available for automated network reconstruction, there is, however, currently no publicly available tool that allows the swift assembly of draft reconstructions of community metabolic networks and consolidated metabolic networks for a specified list of organisms. RESULTS Here, we present AutoKEGGRec, an automated tool that creates first draft metabolic network reconstructions of single organisms, community reconstructions based on a list of organisms, and finally a consolidated reconstruction for a list of organisms or strains. AutoKEGGRec is developed in Matlab and works seamlessly with the COBRA Toolbox v3, and it is based on only using the KEGG database as external input. The generated first draft reconstructions are stored in SBML files and consist of all reactions for a KEGG organism ID and corresponding linked genes. This provides a comprehensive starting point for further refinement and curation using the host of COBRA toolbox functions or other preferred tools. Through the data structures created, the tool also facilitates a comparative analysis of metabolic content in any given number of organisms present in the KEGG database. CONCLUSION AutoKEGGRec provides a first step in a metabolic network reconstruction process, filling a gap for tools creating community and consolidated metabolic networks. Based only on KEGG data as external input, the generated reconstructions consist of data with a directly traceable foundation and pedigree. With AutoKEGGRec, this kind of modeling is made accessible to a wider part of the genome-scale metabolic analysis community.
Collapse
Affiliation(s)
- Emil Karlsen
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Høgskoleringen 1, Trondheim, 7491 Norway
| | - Christian Schulz
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Høgskoleringen 1, Trondheim, 7491 Norway
| | - Eivind Almaas
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Høgskoleringen 1, Trondheim, 7491 Norway
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and General Practice, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| |
Collapse
|
32
|
Gysi DM, Voigt A, Fragoso TDM, Almaas E, Nowick K. wTO: an R package for computing weighted topological overlap and a consensus network with integrated visualization tool. BMC Bioinformatics 2018; 19:392. [PMID: 30355288 PMCID: PMC6201546 DOI: 10.1186/s12859-018-2351-7] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2017] [Accepted: 08/30/2018] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Network analyses, such as of gene co-expression networks, metabolic networks and ecological networks have become a central approach for the systems-level study of biological data. Several software packages exist for generating and analyzing such networks, either from correlation scores or the absolute value of a transformed score called weighted topological overlap (wTO). However, since gene regulatory processes can up- or down-regulate genes, it is of great interest to explicitly consider both positive and negative correlations when constructing a gene co-expression network. RESULTS Here, we present an R package for calculating the weighted topological overlap (wTO), that, in contrast to existing packages, explicitly addresses the sign of the wTO values, and is thus especially valuable for the analysis of gene regulatory networks. The package includes the calculation of p-values (raw and adjusted) for each pairwise gene score. Our package also allows the calculation of networks from time series (without replicates). Since networks from independent datasets (biological repeats or related studies) are not the same due to technical and biological noise in the data, we additionally, incorporated a novel method for calculating a consensus network (CN) from two or more networks into our R package. To graphically inspect the resulting networks, the R package contains a visualization tool, which allows for the direct network manipulation and access of node and link information. When testing the package on a standard laptop computer, we can conduct all calculations for systems of more than 20,000 genes in under two hours. We compare our new wTO package to state of art packages and demonstrate the application of the wTO and CN functions using 3 independently derived datasets from healthy human pre-frontal cortex samples. To showcase an example for the time series application we utilized a metagenomics data set. CONCLUSION In this work, we developed a software package that allows the computation of wTO networks, CNs and a visualization tool in the R statistical environment. It is publicly available on CRAN repositories under the GPL -2 Open Source License ( https://cran.r-project.org/web/packages/wTO/ ).
Collapse
Affiliation(s)
- Deisy Morselli Gysi
- Department of Computer Science, Interdisciplinary Center of Bioinformatics, University of Leipzig, Haertelstrasse 16-18, Leipzig, 04109 Germany
- Swarm Intelligence and Complex Systems Group, Faculty of Mathematics and Computer Science, University of Leipzig, Augustusplatz 10, Leipzig, 04109 Germany
| | - Andre Voigt
- Department of Biotechnology, NTNU - Norwegian University of Science and Technology, Trondheim, N-7049 Norway
| | | | - Eivind Almaas
- Department of Biotechnology, NTNU - Norwegian University of Science and Technology, Trondheim, N-7049 Norway
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health, NTNU - Norwegian University of Science and Technology, Trondheim, N-7049 Norway
| | - Katja Nowick
- Freie Universität Berlin, Human Biology Group, Institute for Zoology, Department of Biology, Chemistry and Pharmacy, Königin-Luise-Straße 1-3, Berlin, D-14195 Germany
| |
Collapse
|
33
|
Olaisen C, Kvitvang HFN, Lee S, Almaas E, Bruheim P, Drabløs F, Otterlei M. The role of PCNA as a scaffold protein in cellular signaling is functionally conserved between yeast and humans. FEBS Open Bio 2018; 8:1135-1145. [PMID: 29988559 PMCID: PMC6026702 DOI: 10.1002/2211-5463.12442] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2018] [Revised: 02/19/2018] [Accepted: 05/01/2018] [Indexed: 12/11/2022] Open
Abstract
Proliferating cell nuclear antigen (PCNA), a member of the highly conserved DNA sliding clamp family, is an essential protein for cellular processes including DNA replication and repair. A large number of proteins from higher eukaryotes contain one of two PCNA-interacting motifs: PCNA-interacting protein box (PIP box) and AlkB homologue 2 PCNA-interacting motif (APIM). APIM has been shown to be especially important during cellular stress. PIP box is known to be functionally conserved in yeast, and here, we show that this is also the case for APIM. Several of the 84 APIM-containing yeast proteins are associated with cellular signaling as hub proteins, which are able to interact with a large number of other proteins. Cellular signaling is highly conserved throughout evolution, and we recently suggested a novel role for PCNA as a scaffold protein in cellular signaling in human cells. A cell-penetrating peptide containing the APIM sequence increases the sensitivity toward the chemotherapeutic agent cisplatin in both yeast and human cells, and both yeast and human cells become hypersensitive when the Hog1/p38 MAPK pathway is blocked. These results suggest that the interactions between APIM-containing signaling proteins and PCNA during the DNA damage response is evolutionary conserved between yeast and mammals and that PCNA has a role in cellular signaling also in yeast.
Collapse
Affiliation(s)
- Camilla Olaisen
- Department of Clinical and Molecular MedicineFaculty of Medicine and Health SciencesNorwegian University of Science and Technology (NTNU)TrondheimNorway
| | - Hans Fredrik N. Kvitvang
- Department of Biotechnology and Food ScienceFaculty of Natural SciencesNorwegian University of Science and Technology (NTNU)TrondheimNorway
| | - Sungmin Lee
- Department of Biotechnology and Food ScienceFaculty of Natural SciencesNorwegian University of Science and Technology (NTNU)TrondheimNorway
| | - Eivind Almaas
- Department of Biotechnology and Food ScienceFaculty of Natural SciencesNorwegian University of Science and Technology (NTNU)TrondheimNorway
| | - Per Bruheim
- Department of Biotechnology and Food ScienceFaculty of Natural SciencesNorwegian University of Science and Technology (NTNU)TrondheimNorway
| | - Finn Drabløs
- Department of Clinical and Molecular MedicineFaculty of Medicine and Health SciencesNorwegian University of Science and Technology (NTNU)TrondheimNorway
| | - Marit Otterlei
- Department of Clinical and Molecular MedicineFaculty of Medicine and Health SciencesNorwegian University of Science and Technology (NTNU)TrondheimNorway
| |
Collapse
|
34
|
Abstract
The application of complex network modeling to analyze large co-expression data sets has gained traction during the last decade. In particular, the use of the weighted gene co-expression network analysis framework has allowed an unbiased and systems-level investigation of genotype-phenotype relationships in a wide range of systems. Since mouse is an important model organism for biomedical research on human disease, it is of great interest to identify similarities and differences in the functional roles of human and mouse orthologous genes. Here, we develop a novel network comparison approach which we demonstrate by comparing two gene-expression data sets from a large number of human and mouse tissues. The method uses weighted topological overlap alongside the recently developed network-decomposition method of s-core analysis, which is suitable for making gene-centrality rankings for weighted networks. The aim is to identify globally central genes separately in the human and mouse networks. By comparing the ranked gene lists, we identify genes that display conserved or diverged centrality-characteristics across the networks. This framework only assumes a single threshold value that is chosen from a statistical analysis, and it may be applied to arbitrary network structures and edge-weight distributions, also outside the context of biology. When conducting the comparative network analysis, both within and across the two species, we find a clear pattern of enrichment of transcription factors, for the homeobox domain in particular, among the globally central genes. We also perform gene-ontology term enrichment analysis and look at disease-related genes for the separate networks as well as the network comparisons. We find that gene ontology terms related to regulation and development are generally enriched across the networks. In particular, the genes FOXE3, RHO, RUNX2, ALX3 and RARA, which are disease genes in either human or mouse, are on the top-10 list of globally central genes in the human and mouse networks.
Collapse
Affiliation(s)
- Marius Eidsaa
- Department of Biotechnology, NTNU - Norwegian University of Science and Technology, N-7491 Trondheim, Norway
| | - Lisa Stubbs
- Institute for Genomic Biology, Neuroscience Program, Cell and Developmental Biology, University of Illinois at Urbana-Champaigne, Urbana, IL 61801, United States of America
| | - Eivind Almaas
- Department of Biotechnology, NTNU - Norwegian University of Science and Technology, N-7491 Trondheim, Norway
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and General Practice, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
- * E-mail:
| |
Collapse
|
35
|
Voigt A, Nowick K, Almaas E. A composite network of conserved and tissue specific gene interactions reveals possible genetic interactions in glioma. PLoS Comput Biol 2017; 13:e1005739. [PMID: 28957313 PMCID: PMC5634634 DOI: 10.1371/journal.pcbi.1005739] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2016] [Revised: 10/10/2017] [Accepted: 08/24/2017] [Indexed: 02/08/2023] Open
Abstract
Differential co-expression network analyses have recently become an important step in the investigation of cellular differentiation and dysfunctional gene-regulation in cell and tissue disease-states. The resulting networks have been analyzed to identify and understand pathways associated with disorders, or to infer molecular interactions. However, existing methods for differential co-expression network analysis are unable to distinguish between various forms of differential co-expression. To close this gap, here we define the three different kinds (conserved, specific, and differentiated) of differential co-expression and present a systematic framework, CSD, for differential co-expression network analysis that incorporates these interactions on an equal footing. In addition, our method includes a subsampling strategy to estimate the variance of co-expressions. Our framework is applicable to a wide variety of cases, such as the study of differential co-expression networks between healthy and disease states, before and after treatments, or between species. Applying the CSD approach to a published gene-expression data set of cerebral cortex and basal ganglia samples from healthy individuals, we find that the resulting CSD network is enriched in genes associated with cognitive function, signaling pathways involving compounds with well-known roles in the central nervous system, as well as certain neurological diseases. From the CSD analysis, we identify a set of prominent hubs of differential co-expression, whose neighborhood contains a substantial number of genes associated with glioblastoma. The resulting gene-sets identified by our CSD analysis also contain many genes that so far have not been recognized as having a role in glioblastoma, but are good candidates for further studies. CSD may thus aid in hypothesis-generation for functional disease-associations.
Collapse
Affiliation(s)
- André Voigt
- Network Systems Biology Group, Department of Biotechnology, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Katja Nowick
- Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, University of Leipzig, Leipzig, Germany
- Bioinformatics, Institute of Animal Science, University of Hohenheim, Stuttgart, Germany
- Human Biology, Institute for Biology, Free University Berlin, Berlin, Germany
| | - Eivind Almaas
- Network Systems Biology Group, Department of Biotechnology, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and General Practice, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| |
Collapse
|
36
|
MacGillivray M, Ko A, Gruber E, Sawyer M, Almaas E, Holder A. Robust Analysis of Fluxes in Genome-Scale Metabolic Pathways. Sci Rep 2017; 7:268. [PMID: 28325918 PMCID: PMC5427939 DOI: 10.1038/s41598-017-00170-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2016] [Accepted: 02/13/2017] [Indexed: 02/04/2023] Open
Abstract
Constraint-based optimization, such as flux balance analysis (FBA), has become a standard systems-biology computational method to study cellular metabolisms that are assumed to be in a steady state of optimal growth. The methods are based on optimization while assuming (i) equilibrium of a linear system of ordinary differential equations, and (ii) deterministic data. However, the steady-state assumption is biologically imperfect, and several key stoichiometric coefficients are experimentally inferred from situations of inherent variation. We propose an approach that explicitly acknowledges heterogeneity and conducts a robust analysis of metabolic pathways (RAMP). The basic assumption of steady state is relaxed, and we model the innate heterogeneity of cells probabilistically. Our mathematical study of the stochastic problem shows that FBA is a limiting case of our RAMP method. Moreover, RAMP has the properties that: A) metabolic states are (Lipschitz) continuous with regards to the probabilistic modeling parameters, B) convergent metabolic states are solutions to the deterministic FBA paradigm as the stochastic elements dissipate, and C) RAMP can identify biologically tolerable diversity of a metabolic network in an optimized culture. We benchmark RAMP against traditional FBA on genome-scale metabolic reconstructed models of E. coli, calculating essential genes and comparing with experimental flux data.
Collapse
Affiliation(s)
- Michael MacGillivray
- Department of Mathematics, Rose-Hulman Institute of Technology, Terre Haute, IN, USA
| | - Amy Ko
- Department of Mathematics, Rose-Hulman Institute of Technology, Terre Haute, IN, USA
| | - Emily Gruber
- Department of Mathematics, Rose-Hulman Institute of Technology, Terre Haute, IN, USA
| | - Miranda Sawyer
- Department of Mathematics, Rose-Hulman Institute of Technology, Terre Haute, IN, USA
| | - Eivind Almaas
- Department of Biotechnology, NTNU - Norwegian University of Science and Technology, Trondheim, Norway.
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and General Practice, NTNU - Norwegian University of Science and Technology, Trondheim, Norway.
| | - Allen Holder
- Department of Mathematics, Rose-Hulman Institute of Technology, Terre Haute, IN, USA.
| |
Collapse
|
37
|
Martyushenko N, Johansen SH, Ghim CM, Almaas E. Hypothetical biomolecular probe based on a genetic switch with tunable symmetry and stability. BMC Syst Biol 2016; 10:39. [PMID: 27266276 PMCID: PMC4895904 DOI: 10.1186/s12918-016-0279-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/23/2015] [Accepted: 04/13/2016] [Indexed: 11/21/2022]
Abstract
Background Genetic switches are ubiquitous in nature, frequently associated with the control of cellular functions and developmental programs. In the realm of synthetic biology, it is of great interest to engineer genetic circuits that can change their mode of operation from monostable to bistable, or even to multistable, based on the experimental fine-tuning of readily accessible parameters. In order to successfully design robust, bistable synthetic circuits to be used as biomolecular probes, or understand modes of operation of such naturally occurring circuits, we must identify parameters that are key in determining their characteristics. Results Here, we analyze the bistability properties of a general, asymmetric genetic toggle switch based on a chemical-reaction kinetic description. By making appropriate approximations, we are able to reduce the system to two coupled differential equations. Their deterministic stability analysis and stochastic numerical simulations are in excellent agreement. Drawing upon this general framework, we develop a model of an experimentally realized asymmetric bistable genetic switch based on the LacI and TetR repressors. By varying the concentrations of two synthetic inducers, doxycycline and isopropyl β-D-1-thiogalactopyranoside, we predict that it will be possible to repeatedly fine-tune the mode of operation of this genetic switch from monostable to bistable, as well as the switching rates over many orders of magnitude, in an experimental setting. Furthermore, we find that the shape and size of the bistability region is closely connected with plasmid copy number. Conclusions Based on our numerical calculations of the LacI-TetR asymmetric bistable switch phase diagram, we propose a generic work-flow for developing and applying biomolecular probes: Their initial state of operation should be specified by controlling inducer concentrations, and dilution due to cellular division would turn the probes into memory devices in which information could be preserved over multiple generations. Additionally, insights from our analysis of the LacI-TetR system suggest that this particular system is readily available to be employed in this kind of probe. Electronic supplementary material The online version of this article (doi:10.1186/s12918-016-0279-y) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Nikolay Martyushenko
- Department of Biotechnology, NTNU - Norwegian University of Science and Technology, Trondheim, N-7491, Norway
| | - Sigurd Hagen Johansen
- Department of Biotechnology, NTNU - Norwegian University of Science and Technology, Trondheim, N-7491, Norway
| | - Cheol-Min Ghim
- School of Life Sciences and Department of Physics, Ulsan National Institute of Science and Technology (UNIST), Ulsan, 44919, Korea.,Mathematical Bioscience Institute, The Ohio State University, Columbus, 43210, USA
| | - Eivind Almaas
- Department of Biotechnology, NTNU - Norwegian University of Science and Technology, Trondheim, N-7491, Norway.
| |
Collapse
|
38
|
Cvijovic M, Höfer T, Aćimović J, Alberghina L, Almaas E, Besozzi D, Blomberg A, Bretschneider T, Cascante M, Collin O, de Atauri P, Depner C, Dickinson R, Dobrzynski M, Fleck C, Garcia-Ojalvo J, Gonze D, Hahn J, Hess HM, Hollmann S, Krantz M, Kummer U, Lundh T, Martial G, dos Santos VM, Mauer-Oberthür A, Regierer B, Skene B, Stalidzans E, Stelling J, Teusink B, Workman CT, Hohmann S. Strategies for structuring interdisciplinary education in Systems Biology: an European perspective. NPJ Syst Biol Appl 2016; 2:16011. [PMID: 28725471 PMCID: PMC5516850 DOI: 10.1038/npjsba.2016.11] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2015] [Revised: 03/10/2016] [Accepted: 03/14/2016] [Indexed: 11/08/2022] Open
Abstract
Systems Biology is an approach to biology and medicine that has the potential to lead to a better understanding of how biological properties emerge from the interaction of genes, proteins, molecules, cells and organisms. The approach aims at elucidating how these interactions govern biological function by employing experimental data, mathematical models and computational simulations. As Systems Biology is inherently multidisciplinary, education within this field meets numerous hurdles including departmental barriers, availability of all required expertise locally, appropriate teaching material and example curricula. As university education at the Bachelor's level is traditionally built upon disciplinary degrees, we believe that the most effective way to implement education in Systems Biology would be at the Master's level, as it offers a more flexible framework. Our team of experts and active performers of Systems Biology education suggest here (i) a definition of the skills that students should acquire within a Master's programme in Systems Biology, (ii) a possible basic educational curriculum with flexibility to adjust to different application areas and local research strengths, (iii) a description of possible career paths for students who undergo such an education, (iv) conditions that should improve the recruitment of students to such programmes and (v) mechanisms for collaboration and excellence spreading among education professionals. With the growing interest of industry in applying Systems Biology approaches in their fields, a concerted action between academia and industry is needed to build this expertise. Here we present a reflection of the European situation and expertise, where most of the challenges we discuss are universal, anticipating that our suggestions will be useful internationally. We believe that one of the overriding goals of any Systems Biology education should be a student's ability to phrase and communicate research questions in such a manner that they can be solved by the integration of experiments and modelling, as well as to communicate and collaborate productively across different experimental and theoretical disciplines in research and development.
Collapse
Affiliation(s)
- Marija Cvijovic
- Department of Mathematical Sciences, Chalmers University of Technology and University of Gothenburg, Gothenburg, Sweden
| | - Thomas Höfer
- Division of Theoretical Systems Biology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jure Aćimović
- Centre for Functional Genomics and Bio-Chips, Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Lilia Alberghina
- University of Milano-Bicocca, Department of Biotechnology and Biosciences, Milano, Italy
| | - Eivind Almaas
- Department of Biotechnology, NTNU – Norwegian University of Science and Technology, Trondheim, Norway
| | - Daniela Besozzi
- Department of Informatics, Systems and Communication, University of Milano-Bicocca and SYSBIO Centre of Systems Biology, Milano, Italy
| | - Anders Blomberg
- Department of Marine Sciences, University of Gothenburg, Gothenburg, Sweden
| | | | - Marta Cascante
- Department of Biochemistry and Molecular Biology, Faculty of Biology, University of Barcelona, Barcelona, Spain
| | | | - Pedro de Atauri
- Department of Biochemistry and Molecular Biology, Faculty of Biology, University of Barcelona, Barcelona, Spain
| | - Cornelia Depner
- Division of Theoretical Systems Biology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | | | - Christian Fleck
- Laboratory for Systems and Synthetic Biology, Wageningen UR, Wageningen, Netherlands
| | - Jordi Garcia-Ojalvo
- Universitat Pompeu Fabra, Department of Experimental and Health Sciences, Barcelona, Spain
| | - Didier Gonze
- Unité de Chronobiologie Théorique, Faculté des Sciences, CP 231 and Interuniversity Institute of Bioinformatics in Brussels (IB)2, Université Libre de Bruxelles, Brussels, Belgium
| | - Jens Hahn
- Theoretical Biophysics, Humboldt-Universität zu Berlin, Berlin, Germany
| | | | | | - Marcus Krantz
- Theoretical Biophysics, Humboldt-Universität zu Berlin, Berlin, Germany
| | | | - Torbjörn Lundh
- Department of Mathematical Sciences, Chalmers University of Technology and University of Gothenburg, Gothenburg, Sweden
| | - Gifta Martial
- BioQuant Center, University of Heidelberg, Heidelberg, Germany
| | | | | | | | | | - Egils Stalidzans
- Institute of Microbiology and Biotechnology, University of Latvia, Riga, Latvia
| | | | - Bas Teusink
- Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | | | - Stefan Hohmann
- Department of Biology and Bioengineering, Chalmers University of Technology, Göteborg, Sweden
| |
Collapse
|
39
|
Ebrahim A, Almaas E, Bauer E, Bordbar A, Burgard AP, Chang RL, Dräger A, Famili I, Feist AM, Fleming RM, Fong SS, Hatzimanikatis V, Herrgård MJ, Holder A, Hucka M, Hyduke D, Jamshidi N, Lee SY, Le Novère N, Lerman JA, Lewis NE, Ma D, Mahadevan R, Maranas C, Nagarajan H, Navid A, Nielsen J, Nielsen LK, Nogales J, Noronha A, Pal C, Palsson BO, Papin JA, Patil KR, Price ND, Reed JL, Saunders M, Senger RS, Sonnenschein N, Sun Y, Thiele I. Do genome-scale models need exact solvers or clearer standards? Mol Syst Biol 2015; 11:831. [PMID: 26467284 PMCID: PMC4631202 DOI: 10.15252/msb.20156157] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Affiliation(s)
- Ali Ebrahim
- Department of Bioengineering, University of California, San Diego, CA, USA
| | - Eivind Almaas
- Department of Biotechnology, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Eugen Bauer
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belval, Luxembourg
| | | | | | - Roger L Chang
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Andreas Dräger
- Department of Bioengineering, University of California, San Diego, CA, USA Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen, Germany
| | | | - Adam M Feist
- Department of Bioengineering, University of California, San Diego, CA, USA
| | - Ronan Mt Fleming
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belval, Luxembourg
| | - Stephen S Fong
- Department of Chemical and Life Science Engineering, Virginia Commonwealth University, Richmond, VA, USA
| | - Vassily Hatzimanikatis
- Laboratory of Computational Systems Biotechnology, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Markus J Herrgård
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
| | - Allen Holder
- Department of Mathematics, Rose-Hulman Institute of Technology, Terre Haute, IN, USA
| | - Michael Hucka
- Department of Computing and Mathematical Science, California Institute of Technology, Pasadena, CA, USA
| | - Daniel Hyduke
- Department of Biological Engineering, Utah State University, Logan, UT, USA
| | - Neema Jamshidi
- Department of Radiology, University of California, Los Angeles, CA, USA Institute of Engineering in Medicine, University of California, San Diego, CA, USA
| | - Sang Yup Lee
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark Department of Chemical and Biomolecular Engineering (BK21 Plus Program), Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea
| | | | - Joshua A Lerman
- Department of Bioengineering, University of California, San Diego, CA, USA
| | - Nathan E Lewis
- Department of Pediatrics, University of California, San Diego, CA, USA
| | - Ding Ma
- Department of Management Science and Engineering, Stanford University, Stanford, CA, USA
| | - Radhakrishnan Mahadevan
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Ontario, Canada
| | - Costas Maranas
- Department of Chemical Engineering, Pennsylvania State University, University Park, PA, USA
| | | | - Ali Navid
- Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory, Livermore, CA, USA
| | - Jens Nielsen
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Lars K Nielsen
- Australian Institute for Bioengineering & Nanotechnology (AIBN), The University of Queensland, Brisbane, Queensland, Australia
| | - Juan Nogales
- Department of Environmental Biology, Centro de Investigaciones Biológicas (CSIC), Madrid, Spain
| | - Alberto Noronha
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belval, Luxembourg
| | - Csaba Pal
- Synthetic and Systems Biology Unit, Biological Research Center, Szeged, Hungary
| | - Bernhard O Palsson
- Department of Bioengineering, University of California, San Diego, CA, USA
| | - Jason A Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA
| | - Kiran R Patil
- European Molecular Biology Laboratory, Heidelberg, Germany
| | | | - Jennifer L Reed
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, WI, USA
| | - Michael Saunders
- Department of Management Science and Engineering, Stanford University, Stanford, CA, USA
| | - Ryan S Senger
- Department of Biological Systems Engineering, Virginia Tech, Blacksburg, VA, USA
| | - Nikolaus Sonnenschein
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
| | - Yuekai Sun
- Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA
| | - Ines Thiele
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belval, Luxembourg
| |
Collapse
|
40
|
Eidsaa M, Almaas E. s-core network decomposition: a generalization of k-core analysis to weighted networks. Phys Rev E Stat Nonlin Soft Matter Phys 2013; 88:062819. [PMID: 24483523 DOI: 10.1103/physreve.88.062819] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2013] [Indexed: 06/03/2023]
Abstract
A broad range of systems spanning biology, technology, and social phenomena may be represented and analyzed as complex networks. Recent studies of such networks using k-core decomposition have uncovered groups of nodes that play important roles. Here, we present s-core analysis, a generalization of k-core (or k-shell) analysis to complex networks where the links have different strengths or weights. We demonstrate the s-core decomposition approach on two random networks (ER and configuration model with scale-free degree distribution) where the link weights are (i) random, (ii) correlated, and (iii) anticorrelated with the node degrees. Finally, we apply the s-core decomposition approach to the protein-interaction network of the yeast Saccharomyces cerevisiae in the context of two gene-expression experiments: oxidative stress in response to cumene hydroperoxide (CHP), and fermentation stress response (FSR). We find that the innermost s-cores are (i) different from innermost k-cores, (ii) different for the two stress conditions CHP and FSR, and (iii) enriched with proteins whose biological functions give insight into how yeast manages these specific stresses.
Collapse
Affiliation(s)
- Marius Eidsaa
- Department of Biotechnology, NTNU - Norwegian University of Science and Technology, N-7491 Trondheim, Norway
| | - Eivind Almaas
- Department of Biotechnology, NTNU - Norwegian University of Science and Technology, N-7491 Trondheim, Norway
| |
Collapse
|
41
|
Navid A, Almaas E. Genome-level transcription data of Yersinia pestis analyzed with a new metabolic constraint-based approach. BMC Syst Biol 2012; 6:150. [PMID: 23216785 PMCID: PMC3572438 DOI: 10.1186/1752-0509-6-150] [Citation(s) in RCA: 59] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/06/2012] [Accepted: 11/28/2012] [Indexed: 01/14/2023]
Abstract
Background Constraint-based computational approaches, such as flux balance analysis (FBA), have proven successful in modeling genome-level metabolic behavior for conditions where a set of simple cellular objectives can be clearly articulated. Recently, the necessity to expand the current range of constraint-based methods to incorporate high-throughput experimental data has been acknowledged by the proposal of several methods. However, these methods have rarely been used to address cellular metabolic responses to some relevant perturbations such as antimicrobial or temperature-induced stress. Here, we present a new method for combining gene-expression data with FBA (GX-FBA) that allows modeling of genome-level metabolic response to a broad range of environmental perturbations within a constraint-based framework. The method uses mRNA expression data to guide hierarchical regulation of cellular metabolism subject to the interconnectivity of the metabolic network. Results We applied GX-FBA to a genome-scale model of metabolism in the gram negative bacterium Yersinia pestis and analyzed its metabolic response to (i) variations in temperature known to induce virulence, and (ii) antibiotic stress. Without imposition of any a priori behavioral constraints, our results show strong agreement with reported phenotypes. Our analyses also lead to novel insights into how Y. pestis uses metabolic adjustments to counter different forms of stress. Conclusions Comparisons of GX-FBA predicted metabolic states with fluxomic measurements and different reported post-stress phenotypes suggest that mass conservation constraints and network connectivity can be an effective representative of metabolic flux regulation in constraint-based models. We believe that our approach will be of aid in the in silico evaluation of cellular goals under different conditions and can be used for a variety of analyses such as identification of potential drug targets and their action.
Collapse
Affiliation(s)
- Ali Navid
- Biosciences & Biotechnology Division, Lawrence Livermore National Laboratory, Livermore, CA 94550-0808, USA.
| | | |
Collapse
|
42
|
Zavareh Z, Almaas E. Complex network analysis in microbial systems: theory and examples. Methods Mol Biol 2012; 881:551-571. [PMID: 22639226 DOI: 10.1007/978-1-61779-827-6_19] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
An essential idea in the area of Systems Biology is that a good understanding of interactions between components is crucial for developing deep knowledge of the functioning of the system as a whole. Network analysis is an approach uniquely suited to uncover patterns and organizing principles in a wide variety of complex systems. In this chapter, we will give a detailed description of central network concepts and their algorithmic implementation, and demonstrate how they may be applied on two biological networks: the protein-interaction network of Mus musculus and the reconstructed genome-scale metabolic network of the bacterium Yersinia pestis.
Collapse
Affiliation(s)
- Zahra Zavareh
- Department of Biotechnology, Norwegian University of Science and Technology, NTNU, Trondheim, Norway
| | | |
Collapse
|
43
|
Abstract
Despite stochastic fluctuations, some genetic switches are able to retain their expression states through multiple cell divisions, providing epigenetic memory. We propose a novel rationale for tuning the functional stability of a simple synthetic gene switch through protein dimerization. Introducing an approximation scheme to access long-time stochastic dynamics of multiple-component gene circuits, we find that the spontaneous switching rate may exhibit greater than 8 orders of magnitude variation. The manipulation of the circuit's biochemical properties offers a practical strategy for designing robust epigenetic memory with synthetic circuits.
Collapse
Affiliation(s)
- C-M Ghim
- Biosciences & Biotechnology Division, Lawrence Livermore National Laboratory, Livermore, California 94550, USA
| | | |
Collapse
|
44
|
Abstract
Microbes exist naturally in a wide range of environments in communities where their interactions are significant, spanning the extremes of high acidity and high temperature environments to soil and the ocean. We present a practical discussion of three different approaches for modeling microbial communities: rate equations, individual-based modeling, and population dynamics. We illustrate the approaches with detailed examples. Each approach is best fit to different levels of system representation, and they have different needs for detailed biological input. Thus, this set of approaches is able to address the operation and function of microbial communities on a wide range of organizational levels.
Collapse
Affiliation(s)
- Ali Navid
- Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory, CA, USA
| | | | | | | | | | | |
Collapse
|
45
|
Jin R, McCallen S, Liu CC, Xiang Y, Almaas E, Zhou XJ. Identifying dynamic network modules with temporal and spatial constraints. Pac Symp Biocomput 2009:203-214. [PMID: 19209702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
Despite the rapid accumulation of systems-level biological data, understanding the dynamic nature of cellular activity remains a difficult task. The reason is that most biological data are static, or only correspond to snapshots of cellular activity. In this study, we explicitly attempt to detangle the temporal complexity of biological networks by using compilations of time-series gene expression profiling data. We define a dynamic network module to be a set of proteins satisfying two conditions: (1) they form a connected component in the protein-protein interaction (PPI) network; and (2) their expression profiles form certain structures in the temporal domain. We develop an efficient mining algorithm to discover dynamic modules in a temporal network. Using yeast as a model system, we demonstrate that the majority of the identified dynamic modules are functionally homogeneous. Additionally, many of them provide insight into the sequential ordering of molecular events in cellular systems. Finally, we note that the applicability of our algorithm is not limited to the study of PPI networks, instead it is generally applicable to the combination of any type of network and time-series data.
Collapse
Affiliation(s)
- Ruoming Jin
- Department of Computer Science, Kent State University, Kent, OH, USA
| | | | | | | | | | | |
Collapse
|
46
|
Navid A, Almaas E. Genome-scale reconstruction of the metabolic network in Yersinia pestis, strain 91001. Mol BioSyst 2009; 5:368-75. [DOI: 10.1039/b818710j] [Citation(s) in RCA: 36] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
|
47
|
Abstract
BACKGROUND Gene expression in a cell entails random reaction events occurring over disparate time scales. Thus, molecular noise that often results in phenotypic and population-dynamic consequences sets a fundamental limit to biochemical signaling. While there have been numerous studies correlating the architecture of cellular reaction networks with noise tolerance, only a limited effort has been made to understand the dynamic role of protein-protein interactions. RESULTS We have developed a fully stochastic model for the positive feedback control of a single gene, as well as a pair of genes (toggle switch), integrating quantitative results from previous in vivo and in vitro studies. In particular, we explicitly account for the fast binding-unbinding kinetics among proteins, RNA polymerases, and the promoter/operator sequences of DNA. We find that the overall noise-level is reduced and the frequency content of the noise is dramatically shifted to the physiologically irrelevant high-frequency regime in the presence of protein dimerization. This is independent of the choice of monomer or dimer as transcription factor and persists throughout the multiple model topologies considered. For the toggle switch, we additionally find that the presence of a protein dimer, either homodimer or heterodimer, may significantly reduce its random switching rate. Hence, the dimer promotes the robust function of bistable switches by preventing the uninduced (induced) state from randomly being induced (uninduced). CONCLUSION The specific binding between regulatory proteins provides a buffer that may prevent the propagation of fluctuations in genetic activity. The capacity of the buffer is a non-monotonic function of association-dissociation rates. Since the protein oligomerization per se does not require extra protein components to be expressed, it provides a basis for the rapid control of intrinsic or extrinsic noise. The stabilization of regulatory circuits and epigenetic memory in general is of direct implications to organism fitness. Our results also suggest possible avenues for the design of synthetic gene circuits with tunable robustness for a wide range of engineering purposes.
Collapse
Affiliation(s)
- Cheol-Min Ghim
- Microbial Systems Biology Group, Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory, 7000 East Avenue Livermore, CA 94550, USA.
| | | |
Collapse
|
48
|
Almaas E, Sorensen D, Reinar L. T08-O-01 Effectiveness of sexological treatment. Sexologies 2008. [DOI: 10.1016/s1158-1360(08)72802-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
|
49
|
Almaas E. Nordic master programme in sexology? Sexologies 2008. [DOI: 10.1016/s1158-1360(08)72505-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
|
50
|
Abstract
An important goal of medical research is to develop methods to recover the loss of cellular function due to mutations and other defects. Many approaches based on gene therapy aim to repair the defective gene or to insert genes with compensatory function. Here, we propose an alternative, network-based strategy that aims to restore biological function by forcing the cell to either bypass the functions affected by the defective gene, or to compensate for the lost function. Focusing on the metabolism of single-cell organisms, we computationally study mutants that lack an essential enzyme, and thus are unable to grow or have a significantly reduced growth rate. We show that several of these mutants can be turned into viable organisms through additional gene deletions that restore their growth rate. In a rather counterintuitive fashion, this is achieved via additional damage to the metabolic network. Using flux balance-based approaches, we identify a number of synthetically viable gene pairs, in which the removal of one enzyme-encoding gene results in a non-viable phenotype, while the deletion of a second enzyme-encoding gene rescues the organism. The systematic network-based identification of compensatory rescue effects may open new avenues for genetic interventions.
Collapse
Affiliation(s)
- Adilson E Motter
- Department of Physics and Astronomy and Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL 60208, USA.
| | | | | | | |
Collapse
|