In silico prediction of immune-escaping hot spots for future COVID-19 vaccine design.

The COVID-19 pandemic has had a widespread impact on a global scale, and the evolution of considerable dominants has already taken place. Some variants contained certain key mutations located on the receptor binding domain (RBD) of spike protein, such as E484K and N501Y. It is increasingly worrying that these variants could impair the efficacy of current vaccines or therapies. Therefore, analyzing and predicting the high-risk mutations of SARS-CoV-2 spike glycoprotein is crucial to design future vaccines against the different variants. In this work, we proposed an in silico approach, immune-escaping score (IES), to predict high-risk immune-escaping hot spots on the receptor-binding domain (RBD), implemented through integrated delta binding free energy measured by computational mutagenesis of spike-antibody complexes and mutation frequency calculated from viral genome sequencing data. We identified 23 potentially immune-escaping mutations on the RBD by using IES, nine of which occurred in omicron variants (R346K, K417N, N440K, L452Q, L452R, S477N, T478K, F490S, and N501Y), despite our dataset being curated before the omicron first appeared. The highest immune-escaping score (IES = 1) was found for E484K, which agrees with recent studies stating that the mutation significantly reduced the efficacy of neutralization antibodies. Furthermore, our predicted delta binding free energy and IES show a high correlation with high-throughput deep mutational scanning data (Pearson's r = 0.70) and experimentally measured neutralization titers data (mean Pearson's r = -0.80). In summary, our work presents a new method to identify the potentially immune-escaping mutations on the RBD and provides valuable insights into future COVID-19 vaccine design.

Silmitasertib, a casein kinase 2 inhibitor, induces massive lipid droplet accumulation and nonapoptotic cell death in head and neck cancer cells.

BACKGROUND: Accumulating evidence shows that high expression of casein kinase 2 (CK2) and phosphorylated acetyl CoA carboxylase (pACC) in patients with squamous cell carcinoma of the head and neck (SCCHN) correlates with decreased survival rates. Computational analysis has shown that ACC is a potential substrate for CK2, and its inhibition can suppress ACC phosphorylation in vitro. CX-4945, also known as silmitasertib, is an orally administered, highly specific, ATP-competitive inhibitor of CK2 and is under clinical investigation as a treatment for malignancies. We hypothesize that inhibition of CK2 by CX-4945 can reduce CK2-downstream phosphorylation of ACC as a therapeutic strategy against SCCHN. METHODS: Three aggressive SCCHN cell lines (OSC-19, FaDu and HN31) were cultured to investigate the anticancer mechanism of the CK2 inhibitor, CX-4945. Cell cycle analysis, Annexin V/PI staining, and cleavage of PARP were performed to detect apoptosis. Western blot, electron microscopy and analysis of acidic vesicular organelle development were used to detect autophagy. Interference with cellular metabolism by CX-4945 treatment was determined by Seahorse XF24 Extracellular Flux Analyzer and mass spectrometry. RESULTS: Cellular metabolism was impeded by CX-4945 in aggressive SCCHN cells by Seahorse XF24 Extracellular Flux Analyzer and mass spectrometry, and consequently time- and dose-dependent lipid droplet accumulation and non-apoptotic cell death were observed. The lipogenic enzyme ACC was demonstrated to be associated with CK2, and its repressive phosphorylation could be removed by the CK2 inhibitor CX-4945. Overexpression of ACC resulted in impaired cell survival following transient transfection. CONCLUSION: The findings demonstrate that CK2 inhibition impairs normal cellular energy metabolism and may be an attractive therapy for treating aggressive SCCHN.

TargetVue: Visual Analysis of Anomalous User Behaviors in Online Communication Systems.

Users with anomalous behaviors in online communication systems (e.g. email and social medial platforms) are potential threats to society. Automated anomaly detection based on advanced machine learning techniques has been developed to combat this issue; challenges remain, though, due to the difficulty of obtaining proper ground truth for model training and evaluation. Therefore, substantial human judgment on the automated analysis results is often required to better adjust the performance of anomaly detection. Unfortunately, techniques that allow users to understand the analysis results more efficiently, to make a confident judgment about anomalies, and to explore data in their context, are still lacking. In this paper, we propose a novel visual analysis system, TargetVue, which detects anomalous users via an unsupervised learning model and visualizes the behaviors of suspicious users in behavior-rich context through novel visualization designs and multiple coordinated contextual views. Particularly, TargetVue incorporates three new ego-centric glyphs to visually summarize a user's behaviors which effectively present the user's communication activities, features, and social interactions. An efficient layout method is proposed to place these glyphs on a triangle grid, which captures similarities among users and facilitates comparisons of behaviors of different users. We demonstrate the power of TargetVue through its application in a social bot detection challenge using Twitter data, a case study based on email records, and an interview with expert users. Our evaluation shows that TargetVue is beneficial to the detection of users with anomalous communication behaviors.