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- Crowe-McAuliffe, Caillan, et al.
(författare)
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Structural basis for PoxtA-mediated resistance to phenicol and oxazolidinone antibiotics
- 2022
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Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 13
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Tidskriftsartikel (refereegranskat)abstract
- PoxtA and OptrA are ATP binding cassette (ABC) proteins of the F subtype (ABCF). They confer resistance to oxazolidinone and phenicol antibiotics, such as linezolid and chloramphenicol, which stall translating ribosomes when certain amino acids are present at a defined position in the nascent polypeptide chain. These proteins are often encoded on mobile genetic elements, facilitating their rapid spread amongst Gram-positive bacteria, and are thought to confer resistance by binding to the ribosome and dislodging the bound antibiotic. However, the mechanistic basis of this resistance remains unclear. Here we refine the PoxtA spectrum of action, demonstrate alleviation of linezolid-induced context-dependent translational stalling, and present cryo-electron microscopy structures of PoxtA in complex with the Enterococcus faecalis 70S ribosome. PoxtA perturbs the CCA-end of the P-site tRNA, causing it to shift by ∼4 Å out of the ribosome, corresponding to a register shift of approximately one amino acid for an attached nascent polypeptide chain. We postulate that the perturbation of the P-site tRNA by PoxtA thereby alters the conformation of the attached nascent chain to disrupt the drug binding site.
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| 2. |
- Stenbeck, Linnea, 1992-
(författare)
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Deconvolution of Spatial Gene Expression in Cancer
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Cancer is the second leading cause of death in the world, claiming nearly 10 million lives in 2020 alone. One of the main issues in anti-cancer treatment is the heterogeneity of the tumor microenvironment (TME). The TME consists of different cells that are critical for cancer development. Understanding the interactions and identity of these cells is vital to discovering the mechanisms for tumorigenesis. To fundamentally understand the development and mechanisms of the disease will help us in designing novel treatments moving forward. To study the TME, we need methods that both provide extensive information about the cellular profiles and their spatial location, in order to understand how they interact with each other. Single-cell RNA-seq (scRNA-seq) has provided extensive insights into the cellular composition of tumors. However, it requires dissociation of the cells and thus does not retain spatial information. There are several methods to study spatially resolved gene expression in tissues, but one that allows for untargeted and whole-transcriptome wide analysis is the in situ capturing method, Spatial transcriptomics (ST). Although this method allows us to know the location of the gene expression, the resolution is too low for single-cell analysis. With an initial capturing area of 100 μm, 3-30 cells are captured in each spot resulting in a mixture of cells giving rise to the gene expression. At this resolution, it is challenging to confidentially profile the cells, thus making it difficult to explore the cellular interactions fully. To fundamentally explore the TME, improvements need to be made.In Paper I, we aimed to bridge the gap between ST and scRNA-seq by designing a new array with a capturing area of 2 μm. This new design increased the number of capture areas from 1007 to over 1.4 million and with over a 4000-fold improved resolution. We managed to get spatially resolved gene expression from mouse olfactory bulb (MOB) and breast tumor tissue at a sub-cellular resolution with this new design. Despite a low capture efficiency of around 1.3% per bead, we were able to identify differently expressed (DE) signatures specific to morphological layers, profile specific cell types and explore sub-cellular features. Paper II focuses on the information obtained from the widely available histological images. By integrating the spatial gene expression data from 23 different breast cancer patients with their morphological images via deep learning, we could predict gene expression on different samples solely from their histological images. This was further validated on external samples to ensure that it was applicable to other clinical data. In Paper III, we explored the biology of HER2-positive breast tumors by combining scRNA-seq with ST data from eight different HER2-positive patients. With this combinatorial approach, we studied the interactions of tumor-associated cell types and found tertiary lymphoid (TL)-like structures which have been shown to hold certain predictive power in treatment outcome. From this, we constructed a predictive model that could infer the presence of these TL-like structures across different tissue types and technical platforms. This was validated on external samples from breast cancer, rheumatoid arthritis and melanoma. Lastly, in Paper IV, we sought to improve upon the reproducibility and robustness of the method by automating the 10x Visium protocol on a robotic platform. To benchmark the protocol, we compared identical samples prepared both manually and with the automated approach and achieved high correlation scores of 0.995 and 0.990. By adapting the protocol on a Bravo Liquid Handling Platform, we were able to increase the throughput and robustness of the method and reduce hands-on time by over 80%.
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