| 1. |
- Roshanzamir, Fariba, 1986, et al.
(författare)
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Metastatic triple negative breast cancer adapts its metabolism to destination tissues while retaining key metabolic signatures
- 2022
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Ingår i: Proceedings of the National Academy of Sciences of the United States of America. - : Proceedings of the National Academy of Sciences. - 0027-8424 .- 1091-6490. ; 119:35
-
Tidskriftsartikel (refereegranskat)abstract
- Triple negative breast cancer (TNBC) metastases are assumed to exhibit similar functions in different organs as in the original primary tumor. However, studies of metastasis are often limited to a comparison of metastatic tumors with primary tumors of their origin, and little is known about the adaptation to the local environment of the metastatic sites. We therefore used transcriptomic data and metabolic network analyses to investigate whether metastatic tumors adapt their metabolism to the metastatic site and found that metastatic tumors adopt a metabolic signature with some similarity to primary tumors of their destinations. The extent of adaptation, however, varies across different organs, and metastatic tumors retain metabolic signatures associated with TNBC. Our findings suggest that a combination of anti-metastatic approaches and metabolic inhibitors selected specifically for different metastatic sites, rather than solely targeting TNBC primary tumors, may constitute a more effective treatment approach.
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| 2. |
- Gustafsson, Johan, 1976, et al.
(författare)
-
Cellular limitation of enzymatic capacity explains glutamine addiction in cancers
- 2022
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Metabolism within the tumor microenvironment, where a complex mixture of different cell types resides in a nutrient-deprived surrounding, is not fully understood due to difficulties in measuring metabolic fluxes and exchange of metabolites between different cell types in vivo. Genome-scale metabolic modeling enables estimation of such exchange fluxes as well as an opportunity to gain insight into the metabolic behavior of individual cell types. Here, we estimated the availability of nutrients and oxygen within the tumor microenvironment using concentration measurements from blood together with a metabolite diffusion model. In addition, we developed an approach to efficiently apply enzyme usage constraints in a comprehensive metabolic model of human cells. The combined modeling reproduced severe hypoxic conditions and the Warburg effect, and we found that limitations in enzymatic capacity contribute to cancer cells’ preferential use of glutamine as a substrate to the citric acid cycle. Furthermore, we investigated the common belief that some stromal cells are exploited by cancer cells to produce metabolites useful for the cancer cells. We identified a total of 233 potential metabolites that could support collaboration between cancer cells and cancer associated fibroblasts, but when limiting to metabolites previously identified to participate in such collaboration, no growth advantage was observed. Our work highlights the importance of enzymatic capacity limitations for cell behaviors and exemplifies the utility of enzyme constrained models for accurate prediction of metabolism in cells and tumor microenvironments.
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| 3. |
- Gustafsson, Johan, 1976, et al.
(författare)
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Generation and analysis of context-specific genome-scale metabolic models derived from single-cell RNA-Seq data
- 2023
-
Ingår i: Proceedings of the National Academy of Sciences of the United States of America. - : Proceedings of the National Academy of Sciences. - 0027-8424 .- 1091-6490. ; 120:6
-
Tidskriftsartikel (refereegranskat)abstract
- Single-cell RNA sequencing combined with genome-scale metabolic models (GEMs) has the potential to unravel the differences in metabolism across both cell types and cell states but requires new computational methods. Here, we present a method for generating cell-type-specific genome-scale models from clusters of single-cell RNA-Seq profiles. Specifically, we developed a method to estimate the minimum number of cells required to pool to obtain stable models, a bootstrapping strategy for estimating statistical inference, and a faster version of the task-driven integrative network inference for tissues algorithm for generating context-specific GEMs. In addition, we evaluated the effect of different RNA-Seq normalization methods on model topology and differences in models generated from single-cell and bulk RNA-Seq data. We applied our methods on data from mouse cortex neurons and cells from the tumor microenvironment of lung cancer and in both cases found that almost every cell subtype had a unique metabolic profile. In addition, our approach was able to detect cancer-associated metabolic differences between cancer cells and healthy cells, showcasing its utility. We also contextualized models from 202 single-cell clusters across 19 human organs using data from Human Protein Atlas and made these available in the web portal Metabolic Atlas, thereby providing a valuable resource to the scientific community. With the ever-increasing availability of single-cell RNA-Seq datasets and continuously improved GEMs, their combination holds promise to become an important approach in the study of human metabolism.
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| 4. |
- Gustafsson, Johan, 1976, et al.
(författare)
-
Generation and analysis of context-specific genome-scale metabolic models derived from single-cell RNA-Seq data
- 2022
-
Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Single-cell RNA sequencing has the potential to unravel the differences in metabolism across cell types and cell states in both the healthy and diseased human body. The use of existing knowledge in the form of genome-scale metabolic models (GEMs) holds promise to strengthen such analyses, but the combined use of these two methods requires new computational methods. Here, we present a method for generating cell-type-specific genome-scale models from clusters of single-cell RNA-Seq profiles. Specifically, we developed a method to estimate the number of cells required to pool to obtain stable models, a bootstrapping strategy for estimating statistical inference, and a faster version of the tINIT algorithm for generating context-specific GEMs. In addition, we evaluated the effect of different RNA-Seq normalization methods on model topology and differences in models generated from single-cell and bulk RNA-Seq data. We applied our methods on data from mouse cortex neurons and cells from the tumor microenvironment of lung cancer and in both cases found that almost every cell subtype had a unique metabolic profile, emphasizing the need to study them separately rather than to build models from bulk RNA-Seq data. In addition, our approach was able to detect cancer-associated metabolic differences between cancer cells and healthy cells, showcasing its utility. With the ever-increasing availability of single-cell RNA-Seq datasets and continuously improved GEMs, their combination holds promise to become an important approach in the study of human metabolism.
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| 5. |
- Gustafsson, Johan, 1976, et al.
(författare)
-
Metabolic collaboration between cells in the tumor microenvironment has a negligible effect on tumor growth
- 2024
-
Ingår i: Innovation. - 2666-6758. ; 5:2
-
Tidskriftsartikel (refereegranskat)abstract
- The tumor microenvironment is composed of a complex mixture of different cell types interacting under conditions of nutrient deprivation, but the metabolism therein is not fully understood due to difficulties in measuring metabolic fluxes and exchange of metabolites between different cell types in vivo. Genome-scale metabolic modeling enables estimation of such exchange fluxes as well as an opportunity to gain insight into the metabolic behavior of individual cell types. Here, we estimated the availability of nutrients and oxygen within the tumor microenvironment using concentration measurements from blood together with a metabolite diffusion model. In addition, we developed an approach to efficiently apply enzyme usage constraints in a comprehensive metabolic model of human cells. The combined modeling reproduced severe hypoxic conditions and the Warburg effect, and we found that limitations in enzymatic capacity contribute to cancer cells’ preferential use of glutamine as a substrate to the citric acid cycle. Furthermore, we investigated the common hypothesis that some stromal cells are exploited by cancer cells to produce metabolites useful for the cancer cells. We identified over 200 potential metabolites that could support collaboration between cancer cells and cancer-associated fibroblasts, but when limiting to metabolites previously identified to participate in such collaboration, no growth advantage was observed. Our work highlights the importance of enzymatic capacity limitations for cell behaviors and exemplifies the utility of enzyme-constrained models for accurate prediction of metabolism in cells and tumor microenvironments.
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| 6. |
- Robinson, Jonathan, 1986, et al.
(författare)
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An atlas of human metabolism
- 2020
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Ingår i: Science Signaling. - : American Association for the Advancement of Science (AAAS). - 1945-0877 .- 1937-9145. ; 13:624
-
Tidskriftsartikel (refereegranskat)abstract
- Genome-scale metabolic models (GEMs) are valuable tools to study metabolism and provide a scaffold for the integrative analysis of omics data. Researchers have developed increasingly comprehensive human GEMs, but the disconnect among different model sources and versions impedes further progress. We therefore integrated and extensively curated the most recent human metabolic models to construct a consensus GEM, Human1. We demonstrated the versatility of Human1 through the generation and analysis of cell- and tissue-specific models using transcriptomic, proteomic, and kinetic data. We also present an accompanying web portal, Metabolic Atlas (https://www.metabolicatlas.org/), which facilitates further exploration and visualization of Human1 content. Human1 was created using a version-controlled, open-source model development framework to enable community-driven curation and refinement. This framework allows Human1 to be an evolving shared resource for future studies of human health and disease.
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| 7. |
- Rose Mathew, Nimitha, 1987, et al.
(författare)
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Single-cell BCR and transcriptome analysis after influenza infection reveals spatiotemporal dynamics of antigen-specific B cells
- 2021
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Ingår i: Cell Reports. - : Elsevier BV. - 2211-1247. ; 35:12
-
Tidskriftsartikel (refereegranskat)abstract
- B cell responses are critical for antiviral immunity. However, a comprehensive picture of antigen-specific B cell differentiation, clonal proliferation, and dynamics in different organs after infection is lacking. Here, by combining single-cell RNA and B cell receptor (BCR) sequencing of antigen-specific cells in lymph nodes, spleen, and lungs after influenza infection in mice, we identify several germinal center (GC) B cell subpopulations and organ-specific differences that persist over the course of the response. We discover transcriptional differences between memory cells in lungs and lymphoid organs and organ-restricted clonal expansion. Remarkably, we find significant clonal overlap between GC-derived memory and plasma cells. By combining BCR-mutational analyses with monoclonal antibody (mAb) expression and affinity measurements, we find that memory B cells are highly diverse and can be selected from both low- and high-affinity precursors. By linking antigen recognition with transcriptional programming, clonal proliferation, and differentiation, these finding provide important advances in our understanding of antiviral immunity.
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| 8. |
- Uhlén, Mathias, et al.
(författare)
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The human secretome
- 2019
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Ingår i: Science Signaling. - : American Association for the Advancement of Science (AAAS). - 1945-0877 .- 1937-9145. ; 12:609
-
Tidskriftsartikel (refereegranskat)abstract
- The proteins secreted by human cells (collectively referred to as the secretome) are important not only for the basic understanding of human biology but also for the identification of potential targets for future diagnostics and therapies. Here, we present a comprehensive analysis of proteins predicted to be secreted in human cells, which provides information about their final localization in the human body, including the proteins actively secreted to peripheral blood. The analysis suggests that a large number of the proteins of the secretome are not secreted out of the cell, but instead are retained intracellularly, whereas another large group of proteins were identified that are predicted to be retained locally at the tissue of expression and not secreted into the blood. Proteins detected in the human blood by mass spectrometry-based proteomics and antibody-based immuno-assays are also presented with estimates of their concentrations in the blood. The results are presented in an updated version 19 of the Human Protein Atlas in which each gene encoding a secretome protein is annotated to provide an open-access knowledge resource of the human secretome, including body-wide expression data, spatial localization data down to the single-cell and subcellular levels, and data about the presence of proteins that are detectable in the blood.
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| 9. |
- Azimi, Alireza, et al.
(författare)
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Targeting CDK2 overcomes melanoma resistance against BRAF and Hsp90 inhibitors
- 2018
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Ingår i: Molecular Systems Biology. - : EMBO. - 1744-4292. ; 14:3
-
Tidskriftsartikel (refereegranskat)abstract
- Novel therapies are undergoing clinical trials, for example, the Hsp90 inhibitor, XL888, in combination with BRAF inhibitors for the treatment of therapy-resistant melanomas. Unfortunately, our data show that this combination elicits a heterogeneous response in a panel of melanoma cell lines including PDX-derived models. We sought to understand the mechanisms underlying the differential responses and suggest a patient stratification strategy. Thermal proteome profiling (TPP) identified the protein targets of XL888 in a pair of sensitive and unresponsive cell lines. Unbiased proteomics and phosphoproteomics analyses identified CDK2 as a driver of resistance to both BRAF and Hsp90 inhibitors and its expression is regulated by the transcription factor MITF upon XL888 treatment. The CDK2 inhibitor, dinaciclib, attenuated resistance to both classes of inhibitors and combinations thereof. Notably, we found that MITF expression correlates with CDK2 upregulation in patients; thus, dinaciclib would warrant consideration for treatment of patients unresponsive to BRAF-MEK and/or Hsp90 inhibitors and/or harboring MITF amplification/overexpression.
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| 10. |
- Cansby, Emmelie, 1984, et al.
(författare)
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Silencing of STE20-type kinase STK25 in human aortic endothelial and smooth muscle cells is atheroprotective
- 2022
-
Ingår i: Communications Biology. - : Springer Science and Business Media LLC. - 2399-3642. ; 5, s. 1-14
-
Tidskriftsartikel (refereegranskat)abstract
- Recent studies highlight the importance of lipotoxic damage in aortic cells as the major pathogenetic contributor to atherosclerotic disease. Since the STE20-type kinase STK25 has been shown to exacerbate ectopic lipid storage and associated cell injury in several metabolic organs, we here investigate its role in the main cell types of vasculature. We depleted STK25 by small interfering RNA in human aortic endothelial and smooth muscle cells exposed to oleic acid and oxidized LDL. In both cell types, the silencing of STK25 reduces lipid accumulation and suppresses activation of inflammatory and fibrotic pathways as well as lowering oxidative and endoplasmic reticulum stress. Notably, in smooth muscle cells, STK25 inactivation hinders the shift from a contractile to a synthetic phenotype. Together, we provide several lines of evidence that antagonizing STK25 signaling in human aortic endothelial and smooth muscle cells is atheroprotective, highlighting this kinase as a new potential therapeutic target for atherosclerotic disease.
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| 11. |
- Gowers, G. O. F., et al.
(författare)
-
Starved Escherichia coli preserve reducing power under nitric oxide stress
- 2016
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Ingår i: Biochemical and Biophysical Research Communications. - : Elsevier BV. - 1090-2104 .- 0006-291X. ; 476:1, s. 29-34
-
Tidskriftsartikel (refereegranskat)abstract
- Nitric oxide (NO) detoxification enzymes, such as NO dioxygenase (NOD) and NO reductase (NOR), are important to the virulence of numerous bacteria. Pathogens use these defense systems to ward off immune-generated NO, and they do so in environments that contain additional stressors, such as reactive oxygen species, nutrient deprivation, and acid stress. NOD and NOR both use reducing equivalents to metabolically deactivate NO, which suggests that nutrient deprivation could negatively impact their functionality. To explore the relationship between NO detoxification and nutrient deprivation, we examined the ability of Escherichia coli to detoxify NO under different levels of carbon source availability in aerobic cultures. We observed failure of NO detoxification under both carbon source limitation and starvation, and those failures could have arisen from inabilities to synthesize Hmp (NOD of E. coli) and/or supply it with sufficient NADH (preferred electron donor). We found that when limited quantities of carbon source were provided, NO detoxification failed due to insufficient NADH, whereas starvation prevented Hmp synthesis, which enabled cells to maintain their NADH levels. This maintenance of NADH levels under starvation was confirmed to be dependent on the absence of Hmp. Intriguingly, these data show that under NO stress, carbon-starved E. con are better positioned with regard to reducing power to cope with other stresses than cells that had consumed an exhaustible amount of carbon. (C) 2016 Elsevier Inc. All rights reserved.
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| 12. |
- Gustafsson, Johan, 1976, et al.
(författare)
-
Brain energy metabolism is optimized to minimize the cost of enzyme synthesis and transport
- 2024
-
Ingår i: Proceedings of the National Academy of Sciences of the United States of America. - 0027-8424 .- 1091-6490. ; 121:7
-
Tidskriftsartikel (refereegranskat)abstract
- The energy metabolism of the brain is poorly understood partly due to the complex morphology of neurons and fluctuations in ATP demand over time. To investigate this, we used metabolic models that estimate enzyme usage per pathway, enzyme utilization over time, and enzyme transportation to evaluate how these parameters and processes affect ATP costs for enzyme synthesis and transportation. Our models show that the total enzyme maintenance energy expenditure of the human body depends on how glycolysis and mitochondrial respiration are distributed both across and within cell types in the brain. We suggest that brain metabolism is optimized to minimize the ATP maintenance cost by distributing the different ATP generation pathways in an advantageous way across cell types and potentially also across synapses within the same cell. Our models support this hypothesis by predicting export of lactate from both neurons and astrocytes during peak ATP demand, reproducing results from experimental measurements reported in the literature. Furthermore, our models provide potential explanation for parts of the astrocyte-neuron lactate shuttle theory, which is recapitulated under some conditions in the brain, while contradicting other aspects of the theory. We conclude that enzyme usage per pathway, enzyme utilization over time, and enzyme transportation are important factors for defining the optimal distribution of ATP production pathways, opening a broad avenue to explore in brain metabolism.
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| 13. |
- Gustafsson, Johan, 1976, et al.
(författare)
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BUTTERFLY: addressing the pooled amplification paradox with unique molecular identifiers in single-cell RNA-seq
- 2021
-
Ingår i: Genome Biology. - : Springer Science and Business Media LLC. - 1474-760X .- 1474-7596. ; 22:1
-
Tidskriftsartikel (refereegranskat)abstract
- The incorporation of unique molecular identifiers (UMIs) in single-cell RNA-seq assays makes possible the identification of duplicated molecules, thereby facilitating the counting of distinct molecules from sequenced reads. However, we show that the naïve removal of duplicates can lead to a bias due to a “pooled amplification paradox,” and we propose an improved quantification method based on unseen species modeling. Our correction called BUTTERFLY uses a zero truncated negative binomial estimator implemented in the kallisto bustools workflow. We demonstrate its efficacy across cell types and genes and show that in some cases it can invert the relative abundance of genes.
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| 14. |
- Gustafsson, Johan, 1976, et al.
(författare)
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DSAVE: Detection of misclassified cells in single-cell RNA-Seq data
- 2020
-
Ingår i: PLoS ONE. - : Public Library of Science (PLoS). - 1932-6203 .- 1932-6203. ; 15:12 December
-
Tidskriftsartikel (refereegranskat)abstract
- Single-cell RNA sequencing has become a valuable tool for investigating cell types in complex tissues, where clustering of cells enables the identification and comparison of cell populations. Although many studies have sought to develop and compare different clustering approaches, a deeper investigation into the properties of the resulting populations is lacking. Specifically, the presence of misclassified cells can influence downstream analyses, highlighting the need to assess subpopulation purity and to detect such cells. We developed DSAVE (Down-SAmpling based Variation Estimation), a method to evaluate the purity of single-cell transcriptome clusters and to identify misclassified cells. The method utilizes down-sampling to eliminate differences in sampling noise and uses a log-likelihood based metric to help identify misclassified cells. In addition, DSAVE estimates the number of cells needed in a population to achieve a stable average gene expression profile within a certain gene expression range. We show that DSAVE can be used to find potentially misclassified cells that are not detectable by similar tools and reveal the cause of their divergence from the other cells, such as differing cell state or cell type. With the growing use of single-cell RNA-seq, we foresee that DSAVE will be an increasingly useful tool for comparing and purifying subpopulations in single-cell RNA-Seq datasets.
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| 15. |
- Gustafsson, Johan, 1976, et al.
(författare)
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Sources of variation in cell-type RNA-Seq profiles
- 2020
-
Ingår i: PLoS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 15:9
-
Tidskriftsartikel (refereegranskat)abstract
- Cell-type specific gene expression profiles are needed for many computational methods operating on bulk RNA-Seq samples, such as deconvolution of cell-type fractions and digital cytometry. However, the gene expression profile of a cell type can vary substantially due to both technical factors and biological differences in cell state and surroundings, reducing the efficacy of such methods. Here, we investigated which factors contribute most to this variation. We evaluated different normalization methods, quantified the variance explained by different factors, evaluated the effect on deconvolution of cell type fractions, and examined the differences between UMI-based single-cell RNA-Seq and bulk RNA-Seq. We investigated a collection of publicly available bulk and single-cell RNA-Seq datasets containing B and T cells, and found that the technical variation across laboratories is substantial, even for genes specifically selected for deconvolution, and this variation has a confounding effect on deconvolution. Tissue of origin is also a substantial factor, highlighting the challenge of using cell type profiles derived from blood with mixtures from other tissues. We also show that much of the differences between UMI-based single-cell and bulk RNA-Seq methods can be explained by the number of read duplicates per mRNA molecule in the single-cell sample. Our work shows the importance of either matching or correcting for technical factors when creating cell-type specific gene expression profiles that are to be used together with bulk samples.
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| 16. |
- Hodge, Kenneth, et al.
(författare)
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Deep Proteomic Deconvolution of Interferons and HBV Transfection Effects on a Hepatoblastoma Cell Line
- 2020
-
Ingår i: ACS Omega. - : American Chemical Society (ACS). - 2470-1343. ; 5:27, s. 16796-16810
-
Tidskriftsartikel (refereegranskat)abstract
- Interferons are commonly utilized in the treatment of chronic hepatitis B virus (HBV) infection but are not effective for all patients. A deep understanding of the limitations of interferon treatment requires delineation of its activity at multiple "omic"levels. While myriad studies have characterized the transcriptomic effects of interferon treatment, surprisingly, few have examined interferon-induced effects at the proteomic level. To remedy this paucity, we stimulated HepG2 cells with both IFN-α and IFN-λ and performed proteomic analysis versus unstimulated cells. Alongside, we examined the effects of HBV transfection in the same cell line, reasoning that parallel IFN and HBV analysis might allow determination of cases where HBV transfection counters the effects of interferons. More than 6000 proteins were identified, with multiple replicates allowing for differential expression analysis at high confidence. Drawing on a compendium of transcriptomic data, as well as proteomic half-life data, we suggest means by which transcriptomic results diverge from our proteomic results. We also invoke a recent multiomic study of HBV-related hepatocarcinoma (HCC), showing that despite HBV's role in initiating HCC, the regulated proteomic landscapes of HBV transfection and HCC do not strongly align. Special focus is applied to the proteasome, with numerous components divergently altered under IFN and HBV-transfection conditions. We also examine alterations of other protein groups relevant to HLA complex peptide display, unveiling intriguing alterations in a number of ubiquitin ligases. Finally, we invoke genome-scale metabolic modeling to predict relevant alterations to the metabolic landscape under experimental conditions. Our data should be useful as a resource for interferon and HBV researchers.
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| 17. |
- Limjiasahapong, Suphitcha, et al.
(författare)
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UPLC-ESI-MRM/MS for Absolute Quantification and MS/MS Structural Elucidation of Six Specialized Pyranonaphthoquinone Metabolites From Ventilago harmandiana
- 2021
-
Ingår i: Frontiers in Plant Science. - : Frontiers Media SA. - 1664-462X. ; 11
-
Tidskriftsartikel (refereegranskat)abstract
- Pyranonaphthoquinones (PNQs) are important structural scaffolds found in numerous natural products. Research interest in these specialized metabolites lies in their natural occurrence and therapeutic activities. Nonetheless, research progress has thus far been hindered by the lack of analytical standards and analytical methods for both qualitative and quantitative analysis. We report here that various parts of Ventilago harmandiana are rich sources of PNQs. We developed an ultraperformance liquid chromatography-electrospray ionization multiple reaction monitoring/mass spectrometry method to quantitatively determine six PNQs from leaves, root, bark, wood, and heartwood. The addition of standards in combination with a stable isotope of salicylic acid-D-6 was used to overcome the matrix effect with average recovery of 82% +/- 1% (n = 15). The highest concentration of the total PNQs was found in the root (11,902 mu g/g dry weight), whereas the lowest concentration was found in the leaves (28 mu g/g dry weight). Except for the root, PNQ-332 was found to be the major compound in all parts of V. harmandiana, accounting for similar to 48% of the total PNQs quantified in this study. However, PNQ-318A was the most abundant PNQ in the root sample, accounting for 27% of the total PNQs. Finally, we provide novel MS/MS spectra of the PNQs at different collision induction energies: 10, 20, and 40 eV (POS and NEG). For structural elucidation purposes, we propose complete MS/MS fragmentation pathways of PNQs using MS/MS spectra at collision energies of 20 and 40 eV. The MS/MS spectra along with our discussion on structural elucidation of these PNQs should be very useful to the natural products community to further exploring PNQs in V. harmandiana and various other sources.
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| 18. |
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| 19. |
- Robinson, Jonathan, 1986, et al.
(författare)
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A Systematic Investigation of the Malignant Functions and Diagnostic Potential of the Cancer Secretome
- 2019
-
Ingår i: Cell Reports. - : Elsevier BV. - 2211-1247. ; 26:10, s. 2622-2635.e5
-
Tidskriftsartikel (refereegranskat)abstract
- The collection of proteins secreted from a cell—the secretome—is of particular interest in cancer pathophysiology due to its diagnostic potential and role in tumorigenesis. However, cancer secretome studies are often limited to one tissue or cancer type or focus on biomarker prediction without exploring the associated functions. We therefore conducted a pan-cancer analysis of secretome gene expression changes to identify candidate diagnostic biomarkers and to investigate the underlying biological function of these changes. Using transcriptomic data spanning 32 cancer types and 30 healthy tissues, we quantified the relative diagnostic potential of secretome proteins for each cancer. Furthermore, we offer a potential mechanism by which cancer cells relieve secretory pathway stress by decreasing the expression of tissue-specific genes, thereby facilitating the secretion of proteins promoting invasion and proliferation. These results provide a more systematic understanding of the cancer secretome, facilitating its use in diagnostics and its targeting for therapeutic development.
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| 20. |
- Robinson, Jonathan, 1986, et al.
(författare)
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An integrated network analysis reveals that nitric oxide reductase prevents metabolic cycling of nitric oxide by Pseudomonas aeruginosa
- 2017
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Ingår i: Metabolic Engineering. - : Elsevier BV. - 1096-7176 .- 1096-7184. ; 41, s. 67-81
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Tidskriftsartikel (refereegranskat)abstract
- Nitric oxide (NO) is a chemical weapon within the arsenal of immune cells, but is also generated endogenously by different bacteria. Pseudomonas aeruginosa are pathogens that contain an NO-generating nitrite (NO2 ) reductase (NirS), and NO has been shown to influence their virulence. Interestingly, P. aeruginosa also contain NO dioxygenase (Fhp) and nitrate (NO3 (-)) reductases, which together with NirS provide the potential for NO to be metabolically cycled (NO -> NO3 (-)-> NO2 (-)-> NO). Deeper understanding of NO metabolism in P. aeruginosa will increase knowledge of its pathogenesis, and computational models have proven to be useful tools for the quantitative dissection of NO biochemical networks. Here we developed such a model for P. aeruginosa and confirmed its predictive accuracy with measurements of NO, O-2, NO2 (-), and NO3 (-) in mutant cultures devoid of Fhp or NorCB (NO reductase) activity. Using the model, we assessed whether NO was metabolically cycled in aerobic P. aeruginosa cultures. Calculated fluxes indicated a bottleneck at NO3 (-), which was relieved upon O-2 depletion. As cell growth depleted dissolved O-2 levels, NO3 (-) was converted to NO2 (-) at near-stoichiometric levels, whereas NO2 (-) consumption did not coincide with NO or NO3 (-) accumulation. Assimilatory NO2 reductase (NirBD) or NorCB activity could have prevented NO cycling, and experiments with Delta nirB,Delta nirS, and Delta norC showed that NorCB was responsible for loss of flux from the cycle. Collectively, this work provides a computational tool to analyze NO metabolism in P. aeruginosa, and establishes that P. aeruginosa use NorCB to prevent metabolic cycling of NO.
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| 21. |
- Robinson, Jonathan, 1986, et al.
(författare)
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Anticancer drug discovery through genome-scale metabolic modeling
- 2017
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Ingår i: Current Opinion in Systems Biology. - : Elsevier BV. - 2452-3100. ; 4, s. 1-8
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Forskningsöversikt (refereegranskat)abstract
- Altered metabolism has long been recognized as a defining property of cancer physiology, but is experiencing renewed interest as the importance of such alterations are becoming fully realized. Once regarded merely as a side effect of a damaging mutation or a general increase in proliferation rate, metabolic network rewiring is now viewed as an intentional process to optimize tumor growth and maintenance, and can even drive cancer transformation. This has motivated the search for anticancer targets among enzymes in the metabolic network of cancer cells. Genome-scale metabolic models (GEMs) provide the necessary framework to systematically interrogate this network, and many recent studies have successfully employed GEMs to predict anticancer drug targets in the metabolic networks of various cancer types.
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| 22. |
- Robinson, Jonathan, 1986, et al.
(författare)
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Integrative analysis of human omics data using biomolecular networks
- 2016
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Ingår i: Molecular Biosystems. - : Royal Society of Chemistry (RSC). - 1742-206X .- 1742-2051. ; 12:10, s. 2953-2964
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Forskningsöversikt (refereegranskat)abstract
- High-throughput '-omics' technologies have given rise to an increasing abundance of genome-scale data detailing human biology at the molecular level. Although these datasets have already made substantial contributions to a more comprehensive understanding of human physiology and diseases, their interpretation becomes increasingly cryptic and nontrivial as they continue to expand in size and complexity. Systems biology networks offer a scaffold upon which omics data can be integrated, facilitating the extraction of new and physiologically relevant information from the data. Two of the most prevalent networks that have been used for such integrative analyses of omics data are genome-scale metabolic models (GEMs) and protein-protein interaction (PPI) networks, both of which have demonstrated success among many different omics and sample types. This integrative approach seeks to unite 'top-down' omics data with 'bottom-up' biological networks in a synergistic fashion that draws on the strengths of both strategies. As the volume and resolution of high-throughput omics data continue to grow, integrative network-based analyses are expected to play an increasingly important role in their interpretation.
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| 23. |
- Saghaleyni, Rasool, 1987, et al.
(författare)
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Enhanced metabolism and negative regulation of ER stress support higher erythropoietin production in HEK293 cells
- 2022
-
Ingår i: Cell Reports. - : Elsevier BV. - 2211-1247. ; 39:11
-
Tidskriftsartikel (refereegranskat)abstract
- Recombinant protein production can cause severe stress on cellular metabolism, resulting in limited titer and product quality. To investigate cellular and metabolic characteristics associated with these limitations, we compare HEK293 clones producing either erythropoietin (EPO) (secretory) or GFP (non-secretory) protein at different rates. Transcriptomic and functional analyses indicate significantly higher metabolism and oxidative phosphorylation in EPO producers compared with parental and GFP cells. In addition, ribosomal genes exhibit specific expression patterns depending on the recombinant protein and the production rate. In a clone displaying a dramatically increased EPO secretion, we detect higher gene expression related to negative regulation of endoplasmic reticulum (ER) stress, including upregulation of ATF6B, which aids EPO production in a subset of clones by overexpression or small interfering RNA (siRNA) knockdown. Our results offer potential target pathways and genes for further development of the secretory power in mammalian cell factories.
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| 24. |
- Saghaleyni, Rasool, 1987, et al.
(författare)
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Machine learning-based investigation of the cancer protein secretory pathway
- 2021
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Ingår i: PLoS Computational Biology. - : Public Library of Science (PLoS). - 1553-734X .- 1553-7358. ; 17:4
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Tidskriftsartikel (refereegranskat)abstract
- Deregulation of the protein secretory pathway (PSP) is linked to many hallmarks of cancer, such as promoting tissue invasion and modulating cell-cell signaling. The collection of secreted proteins processed by the PSP, known as the secretome, is often studied due to its potential as a reservoir of tumor biomarkers. However, there has been less focus on the protein components of the secretory machinery itself. We therefore investigated the expression changes in secretory pathway components across many different cancer types. Specifically, we implemented a dual approach involving differential expression analysis and machine learning to identify PSP genes whose expression was associated with key tumor characteristics: mutation of p53, cancer status, and tumor stage. Eight different machine learning algorithms were included in the analysis to enable comparison between methods and to focus on signals that were robust to algorithm type. The machine learning approach was validated by identifying PSP genes known to be regulated by p53, and even outperformed the differential expression analysis approach. Among the different analysis methods and cancer types, the kinesin family members KIF20A and KIF23 were consistently among the top genes associated with malignant transformation or tumor stage. However, unlike most cancer types which exhibited elevated KIF20A expression that remained relatively constant across tumor stages, renal carcinomas displayed a more gradual increase that continued with increasing disease severity. Collectively, our study demonstrates the complementary nature of a combined differential expression and machine learning approach for analyzing gene expression data, and highlights key PSP components relevant to features of tumor pathophysiology that may constitute potential therapeutic targets.
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| 25. |
- van Daalen, Kim R., et al.
(författare)
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The 2024 Europe report of the lancet countdown on health and climate change : unprecedented warming demands unprecedented action
- 2024
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Ingår i: The Lancet Public Health. - : Elsevier. - 2468-2667. ; 9:7, s. e495-e522
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Forskningsöversikt (refereegranskat)abstract
- Record-breaking temperatures were recorded across the globe in 2023. Without climate action, adverse climate-related health impacts are expected to worsen worldwide, affecting billions of people. Temperatures in Europe are warming at twice the rate of the global average, threatening the health of populations across the continent and leading to unnecessary loss of life. The Lancet Countdown in Europe was established in 2021, to assess the health profile of climate change aiming to stimulate European social and political will to implement rapid health-responsive climate mitigation and adaptation actions. In 2022, the collaboration published its indicator report, tracking progress on health and climate change via 33 indicators and across five domains.This new report tracks 42 indicators highlighting the negative impacts of climate change on human health, the delayed climate action of European countries, and the missed opportunities to protect or improve health with health-responsive climate action. The methods behind indicators presented in the 2022 report have been improved, and nine new indicators have been added, covering leishmaniasis, ticks, food security, health-care emissions, production and consumption-based emissions, clean energy investment, and scientific, political, and media engagement with climate and health. Considering that negative climate-related health impacts and the responsibility for climate change are not equal at the regional and global levels, this report also endeavours to reflect on aspects of inequality and justice by highlighting at-risk groups within Europe and Europe's responsibility for the climate crisis.
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