Dampening of ISGylation of RIG-I by ADAP regulates type I interferon response of macrophages to RNA virus infection.

While macrophage is one of the major type I interferon (IFN-I) producers in multiple tissues during viral infections, it also serves as an important target cell for many RNA viruses. However, the regulatory mechanism for the IFN-I response of macrophages to respond to a viral challenge is not fully understood. Here we report ADAP, an immune adaptor protein, is indispensable for the induction of the IFN-I response of macrophages to RNA virus infections via an inhibition of the conjugation of ubiquitin-like ISG15 (ISGylation) to RIG-I. Loss of ADAP increases RNA virus replication in macrophages, accompanied with a decrease in LPS-induced IFN-ß and ISG15 mRNA expression and an impairment in the RNA virus-induced phosphorylation of IRF3 and TBK1. Moreover, using Adap-/- mice, we show ADAP deficiency strongly increases the susceptibility of macrophages to RNA-virus infection in vivo. Mechanically, ADAP selectively interacts and functionally cooperates with RIG-I but not MDA5 in the activation of IFN-ß transcription. Loss of ADAP results in an enhancement of ISGylation of RIG-I, whereas overexpression of ADAP exhibits the opposite effect in vitro, indicating ADAP is detrimental to the RNA virus-induced ISGylation of RIG-I. Together, our data demonstrate a novel antagonistic activity of ADAP in the cell-intrinsic control of RIG-I ISGylation, which is indispensable for initiating and sustaining the IFN-I response of macrophages to RNA virus infections and replication.

Contigs directed gene annotation (ConDiGA) for accurate protein sequence database construction in metaproteomics.

BACKGROUND: Microbiota are closely associated with human health and disease. Metaproteomics can provide a direct means to identify microbial proteins in microbiota for compositional and functional characterization. However, in-depth and accurate metaproteomics is still limited due to the extreme complexity and high diversity of microbiota samples. It is generally recommended to use metagenomic data from the same samples to construct the protein sequence database for metaproteomic data analysis. Although different metagenomics-based database construction strategies have been developed, an optimization of gene taxonomic annotation has not been reported, which, however, is extremely important for accurate metaproteomic analysis. RESULTS: Herein, we proposed an accurate taxonomic annotation pipeline for genes from metagenomic data, namely contigs directed gene annotation (ConDiGA), and used the method to build a protein sequence database for metaproteomic analysis. We compared our pipeline (ConDiGA or MD3) with two other popular annotation pipelines (MD1 and MD2). In MD1, genes were directly annotated against the whole bacterial genome database; in MD2, contigs were annotated against the whole bacterial genome database and the taxonomic information of contigs was assigned to the genes; in MD3, the most confident species from the contigs annotation results were taken as reference to annotate genes. Annotation tools, including BLAST, Kaiju, and Kraken2, were compared. Based on a synthetic microbial community of 12 species, it was found that Kaiju with the MD3 pipeline outperformed the others in the construction of protein sequence database from metagenomic data. Similar performance was also observed with a fecal sample, as well as in silico mixed datasets of the simulated microbial community and the fecal sample. CONCLUSIONS: Overall, we developed an optimized pipeline for gene taxonomic annotation to construct protein sequence databases. Our study can tackle the current taxonomic annotation reliability problem in metagenomics-derived protein sequence database and can promote the in-depth metaproteomic analysis of microbiome. The unique metagenomic and metaproteomic datasets of the 12 bacterial species are publicly available as a standard benchmarking sample for evaluating various analysis pipelines. The code of ConDiGA is open access at GitHub for the analysis of microbiota samples. Video Abstract.

Proteomic and metabolomic features in patients with HCC responding to lenvatinib and anti-PD1 therapy.

Combination therapy (lenvatinib/programmed death-1 inhibitor) is effective for treating unresectable hepatocellular carcinoma (uHCC). We reveal that responders have better overall and progression-free survival, as well as high tumor mutation burden and special somatic variants. We analyze the proteome and metabolome of 82 plasma samples from patients with hepatocellular carcinoma (HCC; n = 51) and normal controls (n = 15), revealing that individual differences outweigh treatment differences. Responders exhibit enhanced activity in the alternative/lectin complement pathway and higher levels of lysophosphatidylcholines (LysoPCs), predicting a favorable prognosis. Non-responders are enriched for immunoglobulins, predicting worse outcomes. Compared to normal controls, HCC plasma proteins show acute inflammatory response and platelet activation, while LysoPCs decrease. Combination therapy increases LysoPCs/phosphocholines in responders. Logistic regression/random forest models using metabolomic features achieve good performance in the prediction of responders. Proteomic analysis of cancer tissues unveils molecular features that are associated with side effects in responders receiving combination therapy. In conclusion, our analysis identifies plasma features associated with uHCC responders to combination therapy.