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Graphical abstract, Specific anti-coronaviral drugs complementing available vaccines are urgently needed to fight the COVID-19 pandemic. Given its high conservation across the betacoronavirus genus and dissimilarity to human proteases, the SARS-CoV-2 main protease (Mpro) is an attractive drug target. SARS-CoV-2 Mpro inhibitors have been developed at unprecedented speed, most of them being substrate-derived peptidomimetics with cysteine-modifying warheads. In this study, Mpro has proven resistant towards the identification of high-affinity short substrate-derived peptides and peptidomimetics without warheads. 20 cyclic and linear substrate analogues bearing natural and unnatural residues, which were predicted by computational modelling to bind with high affinity and designed to establish structure-activity relationships, displayed no inhibitory activity at concentrations as high as 100 μM. Only a long linear peptide covering residues P6 to P5’ displayed moderate inhibition (Ki = 57 µM). Our detailed findings will inform current and future drug discovery campaigns targeting Mpro.

Previous phylogenetic analyses have led to incongruent evolutionary relationships between tree shrews and other suborders of Euarchontoglires. What caused the incongruence remains elusive. In this study, we identified 6845 orthologous genes between seventeen placental mammals. Tree shrews and Primates were monophyletic in the phylogenetic trees derived from the first or/and second codon positions whereas tree shrews and Glires formed a monophyly in the trees derived from the third or all codon positions. The same topology was obtained in the phylogeny inference using the slowly and fast evolving genes, respectively. This incongruence was likely attributed to the fast substitution rate in tree shrews and Glires. Notably, sequence GC content only was not informative to resolve the controversial phylogenetic relationships between tree shrews, Glires, and Primates. Finally, estimation in the confidence of the tree selection strongly supported the phylogenetic affiliation of tree shrews to Primates as a monophyly.

Ferroptosis is an outcome of metabolic disorders and closely linked to liver cancer. However, the mechanism underlying the fine regulation of ferroptosis in liver cancer remains unclear. Here, we have identified two categories of genes: ferroptosis up-regulated factors (FUF) and ferroptosis down-regulated factors (FDF), which stimulate and suppress ferroptosis by affecting the synthesis of GSH. Furthermore, FUF are controlled by one transcription factor HIC1, while FDF controlled by another transcription factor HNF4A. Occurrence of ferroptosis might depend on the histone acetyltransferase KAT2B. Upon stimulation of ferroptosis, dissociation of KAT2B prevents HNF4A from binding to the FDF promoter. This effect happens prior to the recruitment of KAT2B to the FUF promoter, which facilitates HIC1 binding to transcribe FUF. Clinically, HIC1 and HNF4A conversely correlate with tumor stage in liver cancer. Patients with lower HIC1 and higher HNF4A exhibit poorer prognostic outcomes. Disrupting the balance between HIC1 and HNF4A might be helpful in treating liver cancer. Keywords: HIC1, HNF4A, GSH, Acetyltransferase, Promoter, Metabolism

There are two main mechanisms of miRNA-mediated gene silencing: either mRNA degradation or translational repression. However, the precise mechanism of target mRNAs regulated by miRNA remains unclear. As a complementary approach to experiment, a computational method was proposed to recognize the mechanism of miRNA-mediated gene silencing in human. We have analyzed extensive features correlated with miRNA-mediated silencing mechanism of mRNA. It is found that, the duplex structure, the number of binding sites and the structural accessibility of target site region are effective factors in determining whether a target mRNA is cleaved or only translationally inhibited. An SVM-based classifier was constructed to predict the regulation mode of miRNA based on these informative features. The results indicated that the approach proposed is effective in distinguishing whether a target mRNA is cleaved or translationally inhibited in human. Furthermore, the web server microDoR (http://reprod.njmu.edu.cn/microdor) has been developed and is freely available for users.

MicroRNAs (miRNAs) are a class of small non-coding RNAs discovered in recent years, which are found to play important regulatory roles in various organisms. As the number of experimentally validated miRNAs is rapidly increasing, systematic analysis on the characteristics of these known miRNAs is necessary and indispensable, especially for computational prediction of new miRNAs. We extensively analyzed precursor sequences for all experimentally validated mature miRNAs in metazoan species, focusing on the characteristics at the level of primary sequences and secondary structures. An observation over the secondary structures of 2729 miRNA precursors (pre-miRNAs) reveals that these hairpin structures can be approximately classified into two types: one with a hairpin loop, and the other with multiple loops. Interestingly, the two types of pre-miRNAs show significant differences in both sequence and structure characteristics, and our study indicates that separate consideration on each type of pre-miRNAs is more reasonable, especially in computational prediction. Besides, we develop a new criterion called mAMFE which shows robust discriminative power in distinguishing pre-miRNAs against other RNAs, thus can potentially serve as a discriminative feature in prediction of new pre-miRNAs.

Missing values in microarray data can significantly affect subsequent analysis, thus it is important to estimate these missing values accurately. In this paper, a sequential local least squares imputation (SLLSimpute) method is proposed to solve this problem. It estimates missing values sequentially from the gene containing the fewest missing values and partially utilizes these estimated values. In addition, an automatic parameter selection algorithm, which can generate an appropriate number of neighboring genes for each target gene, is presented for parameter estimation. Experimental results confirmed that SLLSimpute method exhibited better estimation ability compared with other currently used imputation methods.