Evolutionary insights into a non-coding deletion of SARS-CoV-2 B.1.1.7

Three prevalent SARS-CoV-2 Variants of Concern (VOCs) were emerged and caused epidemic waves. It is essential to uncover the key genetic changes that cause the high transmissibility of VOCs. However, different viral mutations are generally tightly linked so traditional population genetic methods may not reliably detect beneficial mutation. In this study, we proposed a new pandemic-scale phylogenomic approach to detect mutations crucial to transmissibility. We analyzed 3,646,973 high-quality SARS-CoV-2 genomic sequences and the epidemiology metadata. Based on the sequential occurrence order of mutations and the instantaneously accelerated furcation rate, the analysis revealed that two non-coding mutations at the position of 28271 (g.a28271-/t) might be crucial for the high transmissibility of Alpha, Delta and Omicron VOCs. Both two mutations cause an A-to-T change at the core Kozak site of the N gene. The analysis also revealed that the non-coding mutations (g.a28271-/t) alone are unlikely to cause high viral transmissibility, indicating epistasis or multilocus interaction in viral transmissibility. A convergent evolutionary analysis revealed that g.a28271-/t, S:P681H/R and N:R203K/M occur independently in the three-VOC lineages, suggesting a potential interaction among these mutations. Therefore, this study unveils that non-synonymous and non-coding mutations could affect the transmissibility synergistically.

An interactive viral genome evolution network analysis system enabling rapid large-scale molecular tracing of SARS-CoV-2

Comprehensive analyses of viral genomes can provide a global picture on SARS-CoV-2 transmission and help to predict the oncoming trends of pandemic. This molecular tracing is mainly conducted through extensive phylogenetic network analyses. However, the rapid accumulation of SARS-CoV-2 genomes presents an unprecedented data size and complexity that has exceeded the capacity of existing methods in constructing evolution network through virus genotyping. Here we report a Viral genome Evolution Network Analysis System (VENAS), which uses Hamming distances adjusted by the minor allele frequency to construct viral genome evolution network. The resulting network was topologically clustered and divided using community detection algorithm, and potential evolution paths were further inferred with a network disassortativity trimming algorithm. We also employed parallel computing technology to achieve rapid processing and interactive visualization of >10,000 viral genomes, enabling accurate detection and subtyping of the viral mutations through different stages of Covid-19 pandemic. In particular, several core viral mutations can be independently identified and linked to early transmission events in Covid-19 pandemic. As a general platform for comprehensive viral genome analysis, VENAS serves as a useful computational tool in the current and future pandemics.

Reliability and validity of depression scales of Chinese version: a systematic review / 中华流行病学杂志

Objective Through systematically reviewing the reliability and validity of depression scales of Chinese version in adults in China to evaluate the psychometric properties of depression scales for different groups.Methods Eligible studies published before 6 May 2016 were retrieved from the following database:CNKI,Wanfang,PubMed and Embase.The HSROC model of the diagnostic test accuracy (DTA) for Meta-analysis was used to calculate the pooled sensitivity and specificity of the PHQ-9.Results A total of 44 papers evaluating the performance of depression scales were included.Results showed that the reliability and validity of the common depression scales were eligible,including the Beck depression inventory (BDI),the Hamilton depression scale (HAMD),the center epidemiological studies depression scale (CES-D),the patient health questionnaire (PHQ) and the Geriatric depression scale (GDS).The Cronbach's coefficient of most tools were larger than 0.8,while the test-retest reliability and split-half reliability were larger than 0.7,indicating good internal consistency and stability.The criterion validity,convergent validity,discrimination validity and screening validity were acceptable though different cut-off points were recommended by different studies.The pooled sensitivity of the 11 studies evaluating PHQ-9 was 0.88 (95％CI:0.85-0.91) while the pooled specificity was 0.89 (95％CI:0.82-0.94),which demonstrated the applicability of PHQ-9 in screening depression.Conclusion The reliability and validity of different depression scales of Chinese version are acceptable.The characteristics of different tools and study population should be taken into consideration when choosing a specific scale.

Reliability and validity of depression scales of Chinese version: a systematic review / 中华流行病学杂志

Objective Through systematically reviewing the reliability and validity of depression scales of Chinese version in adults in China to evaluate the psychometric properties of depression scales for different groups.Methods Eligible studies published before 6 May 2016 were retrieved from the following database:CNKI,Wanfang,PubMed and Embase.The HSROC model of the diagnostic test accuracy (DTA) for Meta-analysis was used to calculate the pooled sensitivity and specificity of the PHQ-9.Results A total of 44 papers evaluating the performance of depression scales were included.Results showed that the reliability and validity of the common depression scales were eligible,including the Beck depression inventory (BDI),the Hamilton depression scale (HAMD),the center epidemiological studies depression scale (CES-D),the patient health questionnaire (PHQ) and the Geriatric depression scale (GDS).The Cronbach's coefficient of most tools were larger than 0.8,while the test-retest reliability and split-half reliability were larger than 0.7,indicating good internal consistency and stability.The criterion validity,convergent validity,discrimination validity and screening validity were acceptable though different cut-off points were recommended by different studies.The pooled sensitivity of the 11 studies evaluating PHQ-9 was 0.88 (95％CI:0.85-0.91) while the pooled specificity was 0.89 (95％CI:0.82-0.94),which demonstrated the applicability of PHQ-9 in screening depression.Conclusion The reliability and validity of different depression scales of Chinese version are acceptable.The characteristics of different tools and study population should be taken into consideration when choosing a specific scale.

SySAP: a system-level predictor of deleterious single amino acid polymorphisms

Single amino acid polymorphisms (SAPs), also known as non-synonymous single nucleotide polymorphisms (nsSNPs), are responsible for most of human genetic diseases. Discriminate the deleterious SAPs from neutral ones can help identify the disease genes and understand the mechanism of diseases. In this work, a method of deleterious SAP prediction at system level was established. Unlike most existing methods, our method not only considers the sequence and structure information, but also the network information. The integration of network information can improve the performance of deleterious SAP prediction. To make our method available to the public, we developed SySAP (a System-level predictor of deleterious Single Amino acid Polymorphisms), an easy-to-use and high accurate web server. SySAP is freely available at http://www.biosino.org/ SySAP/ and http://lifecenter.sgst.cn/SySAP/.

Study of drug function based on similarity of pathway fingerprint

Drugs sharing similar therapeutic function may not bind to the same group of targets. However, their targets may be involved in similar pathway profiles which are associated with certain pathological process. In this study, pathway fingerprint was introduced to indicate the profile of significant pathways being influenced by the targets of drugs. Then drug-drug network was further constructed based on significant similarity of pathway fingerprints. In this way, the functions of a drug may be hinted by the enriched therapeutic functions of its neighboring drugs. In the test of 911 FDA approved drugs with more than one known target, 471 drugs could be connected into networks. 760 significant associations of drug-therapeutic function were generated, among which around 60% of them were supported by scientific literatures or ATC codes of drug functional classification. Therefore, pathway fingerprints may be useful to further study on the potential function of known drugs, or the unknown function of new drugs.

Prediction of functional phosphorylation sites by incorporating evolutionary information

Protein phosphorylation is a ubiquitous protein post-translational modification, which plays an important role in cellular signaling systems underlying various physiological and pathological processes. Current in silico methods mainly focused on the prediction of phosphorylation sites, but rare methods considered whether a phosphorylation site is functional or not. Since functional phosphorylation sites are more valuable for further experimental research and a proportion of phosphorylation sites have no direct functional effects, the prediction of functional phosphorylation sites is quite necessary for this research area. Previous studies have shown that functional phosphorylation sites are more conserved than non-functional phosphorylation sites in evolution. Thus, in our method, we developed a web server by integrating existing phosphorylation site prediction methods, as well as both absolute and relative evolutionary conservation scores to predict the most likely functional phosphorylation sites. Using our method, we predicted the most likely functional sites of the human, rat and mouse proteomes and built a database for the predicted sites. By the analysis of overall prediction results, we demonstrated that protein phosphorylation plays an important role in all the enriched KEGG pathways. By the analysis of protein-specific prediction results, we demonstrated the usefulness of our method for individual protein studies. Our method would help to characterize the most likely functional phosphorylation sites for further studies in this research area.

Cell type specificity of signaling: view from membrane receptors distribution and their downstream transduction networks

Studies on cell signaling pay more attention to spatial dynamics and how such diverse organization can relate to high order of cellular capabilities. To overview the specificity of cell signaling, we integrated human receptome data with proteome spatial expression profiles to systematically investigate the specificity of receptors and receptor-triggered transduction networks across 62 normal cell types and 14 cancer types. Six percent receptors showed cell-type-specific expression, and 4% signaling networks presented enriched cell-specific proteins induced by the receptors. We introduced a concept of "response context" to annotate the cell-type dependent signaling networks. We found that most cells respond similarly to the same stimulus, as the "response contexts" presented high functional similarity. Despite this, the subtle spatial diversity can be observed from the difference in network architectures. The architecture of the signaling networks in nerve cells displayed less completeness than that in glandular cells, which indicated cellular-context dependent signaling patterns are elaborately spatially organized. Likewise, in cancer cells most signaling networks were generally dysfunctional and less complete than that in normal cells. However, glioma emerged hyper-activated transduction mechanism in malignant state. Receptor ATP6AP2 and TNFRSF21 induced rennin-angiotensin and apoptosis signaling were found likely to explain the glioma-specific mechanism. This work represents an effort to decipher context-specific signaling network from spatial dimension. Our results indicated that although a majority of cells engage general signaling response with subtle differences, the spatial dynamics of cell signaling can not only deepen our insights into different signaling mechanisms, but also help understand cell signaling in disease.