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- Hendrikse, Natalie M., et al.
(författare)
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Exploring the therapeutic potential of modern and ancestral phenylalanine/tyrosine ammonia-lyases as supplementary treatment of hereditary tyrosinemia
- 2020
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Ingår i: Scientific Reports. - : Nature Research. - 2045-2322. ; 10:1
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Tidskriftsartikel (refereegranskat)abstract
- Phenylalanine/tyrosine ammonia-lyases (PAL/TALs) have been approved by the FDA for treatment of phenylketonuria and may harbour potential for complementary treatment of hereditary tyrosinemia Type I. Herein, we explore ancestral sequence reconstruction as an enzyme engineering tool to enhance the therapeutic potential of PAL/TALs. We reconstructed putative ancestors from fungi and compared their catalytic activity and stability to two modern fungal PAL/TALs. Surprisingly, most putative ancestors could be expressed as functional tetramers in Escherichia coli and thus retained their ability to oligomerize. All ancestral enzymes displayed increased thermostability compared to both modern enzymes, however, the increase in thermostability was accompanied by a loss in catalytic turnover. One reconstructed ancestral enzyme in particular could be interesting for further drug development, as its ratio of specific activities is more favourable towards tyrosine and it is more thermostable than both modern enzymes. Moreover, long-term stability assessment showed that this variant retained substantially more activity after prolonged incubation at 25 °C and 37 °C, as well as an increased resistance to incubation at 60 °C. Both of these factors are indicative of an extended shelf-life of biopharmaceuticals. We believe that ancestral sequence reconstruction has potential for enhancing the properties of enzyme therapeutics, especially with respect to stability. This work further illustrates that resurrection of putative ancestral oligomeric proteins is feasible and provides insight into the extent of conservation of a functional oligomerization surface area from ancestor to modern enzyme.
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- Morgan, Daniel, et al.
(författare)
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Perturbation-based gene regulatory network inference to unravel oncogenic mechanisms
- 2020
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Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 10:1
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Tidskriftsartikel (refereegranskat)abstract
- The gene regulatory network (GRN) of human cells encodes mechanisms to ensure proper functioning. However, if this GRN is dysregulated, the cell may enter into a disease state such as cancer. Understanding the GRN as a system can therefore help identify novel mechanisms underlying disease, which can lead to new therapies. To deduce regulatory interactions relevant to cancer, we applied a recent computational inference framework to data from perturbation experiments in squamous carcinoma cell line A431. GRNs were inferred using several methods, and the false discovery rate was controlled by the NestBoot framework. We developed a novel approach to assess the predictiveness of inferred GRNs against validation data, despite the lack of a gold standard. The best GRN was significantly more predictive than the null model, both in cross-validated benchmarks and for an independent dataset of the same genes under a different perturbation design. The inferred GRN captures many known regulatory interactions central to cancer-relevant processes in addition to predicting many novel interactions, some of which were experimentally validated, thus providing mechanistic insights that are useful for future cancer research.
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- Seçilmiş, Deniz, et al.
(författare)
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Uncovering cancer gene regulation by accurate regulatory network inference from uninformative data
- 2020
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Ingår i: npj Systems Biology and Applications. - : Springer Science and Business Media LLC. - 2056-7189. ; 6:1
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Tidskriftsartikel (refereegranskat)abstract
- The interactions among the components of a living cell that constitute the gene regulatory network (GRN) can be inferred from perturbation-based gene expression data. Such networks are useful for providing mechanistic insights of a biological system. In order to explore the feasibility and quality of GRN inference at a large scale, we used the L1000 data where similar to 1000 genes have been perturbed and their expression levels have been quantified in 9 cancer cell lines. We found that these datasets have a very low signal-to-noise ratio (SNR) level causing them to be too uninformative to infer accurate GRNs. We developed a gene reduction pipeline in which we eliminate uninformative genes from the system using a selection criterion based on SNR, until reaching an informative subset. The results show that our pipeline can identify an informative subset in an overall uninformative dataset, allowing inference of accurate subset GRNs. The accurate GRNs were functionally characterized and potential novel cancer-related regulatory interactions were identified.
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