| 1. |
- Mardinoglu, Adil, 1982, et al.
(författare)
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Systems biology in hepatology: approaches and applications
- 2018
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Ingår i: Nature Reviews Gastroenterology & Hepatology. - : Springer Science and Business Media LLC. - 1759-5045 .- 1759-5053. ; 15:6, s. 365-377
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Tidskriftsartikel (refereegranskat)abstract
- Detailed insights into the biological functions of the liver and an understanding of its crosstalk with other human tissues and the gut microbiota can be used to develop novel strategies for the prevention and treatment of liver-associated diseases, including fatty liver disease, cirrhosis, hepatocellular carcinoma and type 2 diabetes mellitus. Biological network models, including metabolic, transcriptional regulatory, protein-protein interaction, signalling and co-expression networks, can provide a scaffold for studying the biological pathways operating in the liver in connection with disease development in a systematic manner. Here, we review studies in which biological network models were used to integrate multiomics data to advance our understanding of the pathophysiological responses of complex liver diseases. We also discuss how this mechanistic approach can contribute to the discovery of potential biomarkers and novel drug targets, which might lead to the design of targeted and improved treatment strategies. Finally, we present a roadmap for the successful integration of models of the liver and other human tissues with the gut microbiota to simulate whole-body metabolic functions in health and disease.
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| 2. |
- 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
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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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| 3. |
- Ferreira, Raphael, 1990, et al.
(författare)
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Tackling Cancer with Yeast-Based Technologies
- 2019
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Ingår i: Trends in Biotechnology. - : Elsevier BV. - 0167-7799 .- 1879-3096. ; 37:6, s. 592-603
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Forskningsöversikt (refereegranskat)abstract
- The ability to precisely engineer yeast, coupled with its genetic and metabolic similarity to tumor cells, has enabled researchers to use this organism in cancer research. Here we review advances that leveraged yeast as a model organism for studying cancer biology, including the investigation of tumorigenic mechanisms, development of advanced technologies for drug discovery, production of anticancer drugs on an industrial scale, and delivering the next generation of immunotherapies.
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| 4. |
- Liu, Zhengtao, et al.
(författare)
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Pyruvate kinase L/R is a regulator of lipid metabolism and mitochondrial function
- 2019
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Ingår i: Metabolic Engineering. - : Elsevier BV. - 1096-7176 .- 1096-7184. ; 52, s. 263-272
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Tidskriftsartikel (refereegranskat)abstract
- The pathogenesis of non-alcoholic fatty liver disease (NAFLD) and hepatocellular carcinoma (HCC) has been associated with altered expression of liver-specific genes including pyruvate kinase liver and red blood cell (PKLR), patatin-like phospholipase domain containing 3 (PNPLA3) and proprotein convertase subtilisin/kexin type 9 (PCSK9). Here, we inhibited and overexpressed the expression of these three genes in HepG2 cells, generated RNA-seq data before and after perturbation and revealed the altered global biological functions with the modulation of these genes using integrated network (IN) analysis. We found that modulation of these genes effects the total triglycerides levels within the cells and viability of the cells. Next, we generated IN for HepG2 cells, identified reporter transcription factors based on IN and found that the modulation of these genes affects key metabolic pathways associated with lipid metabolism (steroid biosynthesis, PPAR signalling pathway, fatty acid synthesis and oxidation) and cancer development (DNA replication, cell cycle and p53 signalling) involved in the progression of NAFLD and HCC. Finally, we observed that inhibition of PKLR lead to decreased glucose uptake and decreased mitochondrial activity in HepG2 cells. Hence, our systems level analysis indicated that PKLR can be targeted for development efficient treatment strategy for NAFLD and HCC.
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| 5. |
- Li, Xiangyu, et al.
(författare)
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Prediction of drug candidates for clear cell renal cell carcinoma using a systems biology-based drug repositioning approach
- 2022
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Ingår i: EBioMedicine. - : Elsevier BV. - 2352-3964. ; 78, s. 103963-
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Tidskriftsartikel (refereegranskat)abstract
- SummaryBackground: The response rates of the clinical chemotherapies are still low in clear cell renal cell carcinoma (ccRCC). Computational drug repositioning is a promising strategy to discover new uses for existing drugs to treat patients who cannot get benefits from clinical drugs.Methods: We proposed a systematic approach which included the target prediction based on the co-expression network analysis of transcriptomics profiles of ccRCC patients and drug repositioning for cancer treatment based on the analysis of shRNA-and drug-perturbed signature profiles of human kidney cell line.Findings: First, based on the gene co-expression network analysis, we identified two types of gene modules in ccRCC, which significantly enriched with unfavorable and favorable signatures indicating poor and good survival outcomes of patients, respectively. Then, we selected four genes, BUB1B, RRM2, ASF1B and CCNB2, as the potential drug targets based on the topology analysis of modules. Further, we repurposed three most effective drugs for each target by applying the proposed drug repositioning approach. Finally, we evaluated the effects of repurposed drugs using an in vitro model and observed that these drugs inhibited the protein levels of their corresponding target genes and cell viability.Interpretation: These findings proved the usefulness and efficiency of our approach to improve the drug repositioning researches for cancer treatment and precision medicine.Funding: This study was funded by Knut and Alice Wallenberg Foundation and Bash Biotech Inc., San Diego, CA, USA.
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| 6. |
- Lam, S., et al.
(författare)
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Addressing the heterogeneity in liver diseases using biological networks
- 2021
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Ingår i: Briefings in Bioinformatics. - : Oxford University Press (OUP). - 1467-5463 .- 1477-4054. ; 22:2, s. 1751-1766
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Tidskriftsartikel (refereegranskat)abstract
- The abnormalities in human metabolism have been implicated in the progression of several complex human diseases, including certain cancers. Hence, deciphering the underlying molecular mechanisms associated with metabolic reprogramming in a disease state can greatly assist in elucidating the disease aetiology. An invaluable tool for establishing connections between global metabolic reprogramming and disease development is the genome-scale metabolic model (GEM). Here, we review recent work on the reconstruction of cell/tissue-type and cancer-specific GEMs and their use in identifying metabolic changes occurring in response to liver disease development, stratification of the heterogeneous disease population and discovery of novel drug targets and biomarkers. We also discuss how GEMs can be integrated with other biological networks for generating more comprehensive cell/tissue models. In addition, we review the various biological network analyses that have been employed for the development of efficient treatment strategies. Finally, we present three case studies in which independent studies converged on conclusions underlying liver disease.
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| 7. |
- 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. - 1091-6490 .- 0027-8424. ; 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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| 8. |
- Nilsson, Avlant, 1985, et al.
(författare)
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Quantitative analysis of amino acid metabolism in liver cancer links glutamate excretion to nucleotide synthesis
- 2020
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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. ; 117:19, s. 10294-10304
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Tidskriftsartikel (refereegranskat)abstract
- Many cancer cells consume glutamine at high rates; counterintuitively, they simultaneously excrete glutamate, the first intermediate in glutamine metabolism. Glutamine consumption has been linked to replenishment of tricarboxylic acid cycle (TCA) intermediates and synthesis of adenosine triphosphate (ATP), but the reason for glutamate excretion is unclear. Here, we dynamically profile the uptake and excretion fluxes of a liver cancer cell line (HepG2) and use genome-scale metabolic modeling for in-depth analysis. We find that up to 30% of the glutamine is metabolized in the cytosol, primarily for nucleotide synthesis, producing cytosolic glutamate. We hypothesize that excreting glutamate helps the cell to increase the nucleotide synthesis rate to sustain growth. Indeed, we show experimentally that partial inhibition of glutamate excretion reduces cell growth. Our integrative approach thus links glutamine addiction to glutamate excretion in cancer and points toward potential drug targets.
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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
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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. |
- 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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