| 1. |
- Froslev Nielsen, Jens Christian, 1987, et al.
(författare)
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Global analysis of biosynthetic gene clusters reveals vast potential of secondary metabolite production in Penicillium species
- 2017
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Ingår i: Nature Microbiology. - : Springer Science and Business Media LLC. - 2058-5276. ; 2:6
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Tidskriftsartikel (refereegranskat)abstract
- Filamentous fungi produce a wide range of bioactive compounds with important pharmaceutical applications, such as antibiotic penicillins and cholesterol-lowering statins. However, less attention has been paid to fungal secondary metabolites compared to those from bacteria. In this study, we sequenced the genomes of 9 Penicillium species and, together with 15 published genomes, we investigated the secondary metabolism of Penicillium and identified an immense, unexploited potential for producing secondary metabolites by this genus. A total of 1,317 putative biosynthetic gene clusters (BGCs) were identified, and polyketide synthase and non-ribosomal peptide synthetase based BGCs were grouped into gene cluster families and mapped to known pathways. The grouping of BGCs allowed us to study the evolutionary trajectory of pathways based on 6-methylsalicylic acid (6-MSA) synthases. Finally, we cross-referenced the predicted pathways with published data on the production of secondary metabolites and experimentally validated the production of antibiotic yanuthones in Penicillia and identified a previously undescribed compound from the yanuthone pathway. This study is the first genus-wide analysis of the genomic diversity of Penicillia and highlights the potential of these species as a source of new antibiotics and other pharmaceuticals.
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| 2. |
- Björnson, Elias, 1988, et al.
(författare)
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Stratification of Hepatocellular Carcinoma Patients Based on Acetate Utilization
- 2015
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Ingår i: Cell Reports. - : Elsevier BV. - 2211-1247. ; 13:9, s. 2014-2026
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Tidskriftsartikel (refereegranskat)abstract
- Hepatocellular carcinoma (HCC) is a deadly form of liver cancer that is increasingly prevalent. We analyzed global gene expression profiling of 361 HCC tumors and 49 adjacent noncancerous liver samples by means of combinatorial network-based analysis. We investigated the correlation between transcriptome and proteome of HCC and reconstructed a functional genome-scale metabolic model (GEM) for HCC. We identified fundamental metabolic processes required for cell proliferation using the network centric view provided by the GEM. Our analysis revealed tight regulation of fatty acid biosynthesis (FAB) and highly significant deregulation of fatty acid oxidation in HCC. We predicted mitochondrial acetate as an emerging substrate for FAB through upregulation of mitochondrial acetyl-CoA synthetase (ACSS1) in HCC. We analyzed heterogeneous expression of ACSS1 and ACSS2 between HCC patients stratified by high and low ACSS1 and ACSS2 expression and revealed that ACSS1 is associated with tumor growth and malignancy under hypoxic conditions in human HCC.
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| 3. |
- Bratulic, Sinisa, 1981, et al.
(författare)
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Noninvasive detection of any-stage cancer using free glycosaminoglycans.
- 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. ; 119:50
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Tidskriftsartikel (refereegranskat)abstract
- Cancer mortality is exacerbated by late-stage diagnosis. Liquid biopsies based on genomic biomarkers can noninvasively diagnose cancers. However, validation studies have reported ~10% sensitivity to detect stage I cancer in a screening population and specific types, such as brain or genitourinary tumors, remain undetectable. We investigated urine and plasma free glycosaminoglycan profiles (GAGomes) as tumor metabolism biomarkers for multi-cancer early detection (MCED) of 14 cancer types using 2,064 samples from 1,260 cancer or healthy subjects. We observed widespread cancer-specific changes in biofluidic GAGomes recapitulated in an in vivo cancer progression model. We developed three machine learning models based on urine (Nurine = 220 cancer vs. 360 healthy) and plasma (Nplasma = 517 vs. 425) GAGomes that can detect any cancer with an area under the receiver operating characteristic curve of 0.83-0.93 with up to 62% sensitivity to stage I disease at 95% specificity. Undetected patients had a 39 to 50% lower risk of death. GAGomes predicted the putative cancer location with 89% accuracy. In a validation study on a screening-like population requiring ≥ 99% specificity, combined GAGomes predicted any cancer type with poor prognosis within 18 months with 43% sensitivity (21% in stage I; N = 121 and 49 cases). Overall, GAGomes appeared to be powerful MCED metabolic biomarkers, potentially doubling the number of stage I cancers detectable using genomic biomarkers.
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| 4. |
- Cvijovic, Marija, 1977, et al.
(författare)
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Bridging the gaps in systems biology
- 2014
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Ingår i: Molecular Genetics and Genomics. - : Springer Science and Business Media LLC. - 1617-4615 .- 1617-4623. ; 289:5, s. 727-734
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Tidskriftsartikel (refereegranskat)abstract
- Systems biology aims at creating mathematical models, i.e., computational reconstructions of biological systems and processes that will result in a new level of understanding-the elucidation of the basic and presumably conserved "design" and "engineering" principles of biomolecular systems. Thus, systems biology will move biology from a phenomenological to a predictive science. Mathematical modeling of biological networks and processes has already greatly improved our understanding of many cellular processes. However, given the massive amount of qualitative and quantitative data currently produced and number of burning questions in health care and biotechnology needed to be solved is still in its early phases. The field requires novel approaches for abstraction, for modeling bioprocesses that follow different biochemical and biophysical rules, and for combining different modules into larger models that still allow realistic simulation with the computational power available today. We have identified and discussed currently most prominent problems in systems biology: (1) how to bridge different scales of modeling abstraction, (2) how to bridge the gap between topological and mechanistic modeling, and (3) how to bridge the wet and dry laboratory gap. The future success of systems biology largely depends on bridging the recognized gaps.
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| 5. |
- Fagerberg, Linn, et al.
(författare)
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Analysis of the human tissue-specific expression by genome-wide integration of transcriptomics and antibody-based proteomics
- 2014
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Ingår i: Molecular & Cellular Proteomics. - 1535-9476 .- 1535-9484. ; 13:2, s. 397-406
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Tidskriftsartikel (refereegranskat)abstract
- Global classification of the human proteins with regards to spatial expression patterns across organs and tissues is important for studies of human biology and disease. Here, we used a quantitative transcriptomics analysis (RNA-Seq) to classify the tissue-specific expression of genes across a representative set of all major human organs and tissues and combined this analysis with antibody- based profiling of the same tissues. To present the data, we launch a new version of the Human Protein Atlas that integrates RNA and protein expression data corresponding to 80% of the human protein-coding genes with access to the primary data for both the RNA and the protein analysis on an individual gene level. We present a classification of all human protein-coding genes with regards to tissue-specificity and spatial expression pattern. The integrative human expression map can be used as a starting point to explore the molecular constituents of the human body.
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| 6. |
- Ghaffari Nouran, Pouyan, 1980, et al.
(författare)
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Identifying anti-growth factors for human cancer cell lines through genome-scale metabolic modeling
- 2015
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Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 5, s. 8183-
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Tidskriftsartikel (refereegranskat)abstract
- Human cancer cell lines are used as important model systems to study molecular mechanisms associated with tumor growth, hereunder how genomic and biological heterogeneity found in primary tumors affect cellular phenotypes. We reconstructed Genome scale metabolic models (GEMs) for eleven cell lines based on RNA-Seq data and validated the functionality of these models with data from metabolite profiling. We used cell line-specific GEMs to analyze the differences in the metabolism of cancer cell lines, and to explore the heterogeneous expression of the metabolic subsystems. Furthermore, we predicted 85 antimetabolites that can inhibit growth of, or even kill, any of the cell lines, while at the same time not being toxic for 83 different healthy human cell types. 60 of these antimetabolites were found to inhibit growth in all cell lines. Finally, we experimentally validated one of the predicted antimetabolites using two cell lines with different phenotypic origins, and found that it is effective in inhibiting the growth of these cell lines. Using immunohistochemistry, we also showed high or moderate expression levels of proteins targeted by the validated antimetabolite. Identified anti-growth factors for inhibition of cell growth may provide leads for the development of efficient cancer treatment strategies.
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| 7. |
- Kampf, Caroline, et al.
(författare)
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Defining the human gallbladder proteome by transcriptomics and affinity proteomics
- 2014
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Ingår i: Proteomics. - : Wiley. - 1615-9853 .- 1615-9861. ; 14:21-22, s. 2498-2507
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Tidskriftsartikel (refereegranskat)abstract
- Global protein analysis of human gallbladder tissue is vital for identification of molecular regulators and effectors of its physiological activity. Here, we employed a genome-wide deep RNA sequencing analysis in 28 human tissues to identify the genes overrepresented in the gallbladder and complemented it with antibody-based immunohistochemistry in 48 human tissues. We characterized human gallbladder proteins and identified 140 gallbladder-specific proteins with an elevated expression in the gallbladder as compared to the other analyzed tissues. Five genes were categorized as enriched, with at least fivefold higher levels in gallbladder, 60 genes were categorized as group enriched with elevated transcript levels in gallbladder shared with at least one other tissue and 75 genes were categorized as enhanced with higher expression than the average expression in other tissues. We explored the localization of the genes within the gallbladder through cell-type specific antibody-based protein profiling and the subcellular localization of the genes through immunofluorescent-based profiling. Finally, we revealed the biological processes and metabolic functions carried out by these genes through the use of GO, KEGG Pathway, and HMR2.0 that is compilation of the human metabolic reactions. We demonstrated the results of the combined analysis of the transcriptomics and affinity proteomics.
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| 8. |
- Kampf, Caroline, et al.
(författare)
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The human liver-specific proteome defined by transcriptomics and antibody-based profiling
- 2014
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Ingår i: FASEB Journal. - : Wiley. - 1530-6860 .- 0892-6638. ; 28:7, s. 2901-2914
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Tidskriftsartikel (refereegranskat)abstract
- Human liver physiology and the genetic etiology of the liver diseases can potentially be elucidated through the identification of proteins with enriched expression in the liver. Here, we combined data from RNA sequencing (RNA-Seq) and antibody-based immunohistochemistry across all major human tissues to explore the human liver proteome with enriched expression, as well as the cell type-enriched expression in hepatocyte and bile duct cells. We identified in total 477 protein-coding genes with elevated expression in the liver: 179 genes have higher expression as compared to all the other analyzed tissues; 164 genes have elevated transcript levels in the liver shared with at least one other tissue type; and an additional 134 genes have a mild level of increased expression in the liver. We identified the precise localization of these proteins through antibody-based protein profiling and the subcellular localization of these proteins through immunofluorescent-based profiling. We also identified the biological processes and metabolic functions associated with these proteins, investigated their contribution in the occurrence of liver diseases, and identified potential targets for their treatment. Our study demonstrates the use of RNA-Seq and antibody-based immunohistochemistry for characterizing the human liver proteome, as well as the use of tissue-specific proteins in identification of novel drug targets and discovery of biomarkers.
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| 9. |
- Lindskog, Cecilia, et al.
(författare)
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The human cardiac and skeletal muscle proteomes defined by transcriptomics and antibody-based profiling
- 2015
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Ingår i: BMC Genomics. - : Springer Science and Business Media LLC. - 1471-2164. ; 16
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Tidskriftsartikel (refereegranskat)abstract
- Background: To understand cardiac and skeletal muscle function, it is important to define and explore their molecular constituents and also to identify similarities and differences in the gene expression in these two different striated muscle tissues. Here, we have investigated the genes and proteins with elevated expression in cardiac and skeletal muscle in relation to all other major human tissues and organs using a global transcriptomics analysis complemented with antibody-based profiling to localize the corresponding proteins on a single cell level. Results: Our study identified a comprehensive list of genes expressed in cardiac and skeletal muscle. The genes with elevated expression were further stratified according to their global expression pattern across the human body as well as their precise localization in the muscle tissues. The functions of the proteins encoded by the elevated genes are well in line with the physiological functions of cardiac and skeletal muscle, such as contraction, ion transport, regulation of membrane potential and actomyosin structure organization. A large fraction of the transcripts in both cardiac and skeletal muscle correspond to mitochondrial proteins involved in energy metabolism, which demonstrates the extreme specialization of these muscle tissues to provide energy for contraction. Conclusions: Our results provide a comprehensive list of genes and proteins elevated in striated muscles. A number of proteins not previously characterized in cardiac and skeletal muscle were identified and localized to specific cellular subcompartments. These proteins represent an interesting starting point for further functional analysis of their role in muscle biology and disease.
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| 10. |
- Mardinoglu, Adil, 1982, et al.
(författare)
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Defining the Human Adipose Tissue Proteome To Reveal Metabolic Alterations in Obesity
- 2014
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Ingår i: Journal of Proteome Research. - : American Chemical Society (ACS). - 1535-3893 .- 1535-3907. ; 13:11, s. 5106-5119
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Tidskriftsartikel (refereegranskat)abstract
- White adipose tissue (WAT) has a major role in the progression of obesity. Here, we combined data from RNA-Seq and antibody-based immunohistochemistry to describe the normal physiology of human WAT obtained from three female subjects and explored WAT-specific genes by comparing WAT to 26 other major human tissues. Using the protein evidence in WAT, we validated the content of a genome-scale metabolic model for adipocytes. We employed this high-quality model for the analysis of subcutaneous adipose tissue (SAT) gene expression data obtained from subjects included in the Swedish Obese Subjects Sib Pair study to reveal molecular differences between lean and obese individuals. We integrated SAT gene expression and plasma metabolomics data, investigated the contribution of the metabolic differences in the mitochondria of SAT to the occurrence of obesity, and eventually identified cytosolic branched-chain amino acid (BCAA) transaminase 1 as a potential target that can be used for drug development. We observed decreased glutaminolysis and alterations in the BCAAs metabolism in SAT of obese subjects compared to lean subjects. We also provided mechanistic explanations for the changes in the plasma level of BCAAs, glutamate, pyruvate, and alpha-ketoglutarate in obese subjects. Finally, we validated a subset of our model-based predictions in 20 SAT samples obtained from 10 lean and 10 obese male and female subjects.
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