A structural analysis of the nsp9 protein from the coronavirus MERS CoV reveals a conserved RNA binding interface.

Middle East respiratory syndrome coronavirus (MERS CoV) and severe acute respiratory syndrome coronavirus 2 (SARS CoV-2) are RNA viruses from the Betacoronavirus family that cause serious respiratory illness in humans. One of the conserved non-structural proteins encoded for by the coronavirus family is non-structural protein 9 (nsp9). Nsp9 plays an important role in the RNA capping process of the viral genome, where it is covalently linked to viral RNA (known as RNAylation) by the conserved viral polymerase, nsp12. Nsp9 also directly binds to RNA; we have recently revealed a distinct RNA recognition interface in the SARS CoV-2 nsp9 by using a combination of nuclear magnetic resonance spectroscopy and biolayer interferometry. In this study, we have utilized a similar methodology to determine a structural model of RNA binding of the related MERS CoV. Based on these data, we uncover important similarities and differences to SARS CoV-2 nsp9 and other coronavirus nsp9 proteins. Our findings that replacing key RNA binding residues in MERS CoV nsp9 affects RNAylation efficiency indicate that recognition of RNA may play a role in the capping process of the virus.

Erratum: Gene replacement therapy in a schwannoma mouse model of neurofibromatosis type 2.

[This corrects the article DOI: 10.1016/j.omtm.2022.06.012.].

Gene replacement therapy in a schwannoma mouse model of neurofibromatosis type 2.

Loss of function of the neurofibromatosis type 2 (NF2) tumor suppressor gene leads to the formation of schwannomas, meningiomas, and ependymomas, comprising ∼50% of all sporadic cases of primary nervous system tumors. NF2 syndrome is an autosomal dominant condition, with bi-allelic inactivation of germline and somatic alleles resulting in loss of function of the encoded protein merlin and activation of mammalian target of rapamycin (mTOR) pathway signaling in NF2-deficient cells. Here we describe a gene replacement approach through direct intratumoral injection of an adeno-associated virus vector expressing merlin in a novel human schwannoma model in nude mice. In culture, the introduction of an AAV1 vector encoding merlin into CRISPR-modified human NF2-null arachnoidal cells (ACs) or Schwann cells (SCs) was associated with decreased size and mTORC1 pathway activation consistent with restored merlin activity. In vivo, a single injection of AAV1-merlin directly into human NF2-null SC-derived tumors growing in the sciatic nerve of nude mice led to regression of tumors over a 10-week period, associated with a decrease in dividing cells and an increase in apoptosis, in comparison with vehicle. These studies establish that merlin re-expression via gene replacement in NF2-null schwannomas is sufficient to cause tumor regression, thereby potentially providing an effective treatment for NF2.

Knee Implant Identification by Fine-Tuning Deep Learning Models.

BACKGROUND: Identification of implant model from primary knee arthroplasty in pre-op planning of revision surgery is a challenging task with added delay. The direct impact of this inability to identify the implants in time leads to the increase in complexity in surgery. Deep learning in the medical field for diagnosis has shown promising results in getting better with every iteration. This study aims to find an optimal solution for the problem of identification of make and model of knee arthroplasty prosthesis using automated deep learning models. METHODS: Deep learning algorithms were used to classify knee arthroplasty implant models. The training, validation and test comprised of 1078 radiographs with a total of 6 knee arthroplasty implant models with anterior-posterior (AP) and lateral views. The performance of the model was calculated using accuracy, sensitivity, and area under the receiver-operating characteristic curve (AUC), which were compared against multiple models trained for comparative in-depth analysis with saliency maps for visualization. RESULTS: After training for a total of 30 epochs on all 6 models, the model performing the best obtained an accuracy of 96.38%, the sensitivity of 97.2% and AUC of 0.985 on an external testing dataset consisting of 162 radiographs. The best performing model correctly and uniquely identified the implants which could be visualized using saliency maps. CONCLUSION: Deep learning models can be used to differentiate between 6 knee arthroplasty implant models. Saliency maps give us a better understanding of which regions the model is focusing on while predicting the results.