1. Transporter oligomerisation: roles in structure and function
Cristina Cecchetti, Euan Pyle, Bernadette Byrne Biochem Soc Trans. 2019 Feb 28;47(1):433-440. doi: 10.1042/BST20180316. Epub 2018 Dec 21.
Oligomerisation is a key feature of integral membrane transporters with roles in structure, function and stability. In this review, we cover some very recent advances in our understanding of how oligomerisation affects these key transporter features, with emphasis on a few groups of transporters, including the nucleobase ascorbate transporters, neurotransmitter sodium symporters and major facilitator superfamily members.
2. Current data processing strategies for cryo-electron tomography and subtomogram averaging
Euan Pyle, Giulia Zanetti Biochem J. 2021 May 28;478(10):1827-1845. doi: 10.1042/BCJ20200715.
Cryo-electron tomography (cryo-ET) can be used to reconstruct three-dimensional (3D) volumes, or tomograms, from a series of tilted two-dimensional images of biological objects in their near-native states in situ or in vitro. 3D subvolumes, or subtomograms, containing particles of interest can be extracted from tomograms, aligned, and averaged in a process called subtomogram averaging (STA). STA overcomes the low signal to noise ratio within the individual subtomograms to generate structures of the particle(s) of interest. In recent years, cryo-ET with STA has increasingly been capable of reaching subnanometer resolution due to improvements in microscope hardware and data processing strategies. There has also been an increase in the number and quality of software packages available to process cryo-ET data with STA. In this review, we describe and assess the data processing strategies available for cryo-ET data and highlight the recent software developments which have enabled the extraction of high-resolution information from cryo-ET datasets.
3. Blood RNA analysis can increase clinical diagnostic rate and resolve variants of uncertain significance
Htoo A Wai, et al. Genet Med. 2020 Jun;22(6):1005-1014. doi: 10.1038/s41436-020-0766-9. Epub 2020 Mar 3.
Purpose: Diagnosis of genetic disorders is hampered by large numbers of variants of uncertain significance (VUSs) identified through next-generation sequencing. Many such variants may disrupt normal RNA splicing. We examined effects on splicing of a large cohort of clinically identified variants and compared performance of bioinformatic splicing prediction tools commonly used in diagnostic laboratories. Methods: Two hundred fifty-seven variants (coding and noncoding) were referred for analysis across three laboratories. Blood RNA samples underwent targeted reverse transcription polymerase chain reaction (RT-PCR) analysis with Sanger sequencing of PCR products and agarose gel electrophoresis. Seventeen samples also underwent transcriptome-wide RNA sequencing with targeted splicing analysis based on Sashimi plot visualization. Bioinformatic splicing predictions were obtained using Alamut, HSF 3.1, and SpliceAI software. Results: Eighty-five variants (33%) were associated with abnormal splicing. The most frequent abnormality was upstream exon skipping (39/85 variants), which was most often associated with splice donor region variants. SpliceAI had greatest accuracy in predicting splicing abnormalities (0.91) and outperformed other tools in sensitivity and specificity. Conclusion: Splicing analysis of blood RNA identifies diagnostically important splicing abnormalities and clarifies functional effects of a significant proportion of VUSs. Bioinformatic predictions are improving but still make significant errors. RNA analysis should therefore be routinely considered in genetic disease diagnostics.
