SARS-CoV-2 infection causes complicated clinical manifestations with variable multi-organ injuries, however, the underlying mechanism, in particular immune responses in different organs, remains elusive. In this study, comprehensive transcriptomic alterations of 14 tissues from rhesus macaque infected with SARS-CoV-2 were analyzed. Compared to normal controls, SARS-CoV-2 infection resulted in dysregulation of genes involving diverse functions in various examined tissues/organs, with drastic transcriptomic changes in cerebral cortex and right ventricle. Intriguingly, cerebral cortex exhibited a hyperinflammatory state evidenced by significant upregulation of inflammation response-related genes. Meanwhile, expressions of coagulation, angiogenesis and fibrosis factors were also up-regulated in cerebral cortex. Based on our findings, neuropilin 1 (NRP1), a receptor of SARS-CoV-2, was significantly elevated in cerebral cortex post infection, accompanied by active immune response releasing inflammatory factors and signal transmission among tissues, which enhanced infection of the central nervous system (CNS) in a positive feedback way, leading to viral encephalitis. Overall, our study depicts a multi-tissue/organ transcriptomic landscapes of rhesus macaque with early infection of SARS-CoV-2, and provides important insights into the mechanistic basis for COVID-19-associated clinical complications.

Metformin is currently a strong candidate anti-tumor agent in multiple cancers. However, its anti-tumor effectiveness varies among different cancers or subpopulations, potentially due to tumor heterogeneity. It thus remains unclear which hepatocellular carcinoma (HCC) patient subpopulation(s) can benefit from metformin treatment. Here, through a genome-wide CRISPR-Cas9-based knockout screen, we find that DOCK1 levels determine the anti-tumor effects of metformin and that DOCK1 is a synthetic lethal target of metformin in HCC. Mechanistically, metformin promotes DOCK1 phosphorylation, which activates RAC1 to facilitate cell survival, leading to metformin resistance. The DOCK1-selective inhibitor, TBOPP, potentiates anti-tumor activity by metformin in vitro in liver cancer cell lines and patient-derived HCC organoids, and in vivo in xenografted liver cancer cells and immunocompetent mouse liver cancer models. Notably, metformin improves overall survival of HCC patients with low DOCK1 levels but not among patients with high DOCK1 expression. This study shows that metformin effectiveness depends on DOCK1 levels and that combining metformin with DOCK1 inhibition may provide a promising personalized therapeutic strategy for metformin-resistant HCC patients.

He was one of the leaders of the synthetic crystalline bovine insulin project in China and a person of outstanding talent in the teaching of organic chemistry. On the occasion of 110th anniversary of the establishment of the chemistry department at Peking University, it is also Professor Xing's 110th birthday, and we pay tribute to his lifelong commitment to the development of organic chemistry, especially protein synthesis, and his teaching style.

A fundamental challenge that arises in biomedicine is the need to characterize compounds in a relevant cellular context in order to reveal potential on-target or off-target effects. Recently, the fast accumulation of gene transcriptional profiling data provides us an unprecedented opportunity to explore the protein targets of chemical compounds from the perspective of cell transcriptomics and RNA biology. Here, we propose a novel Siamese spectral-based graph convolutional network (SSGCN) model for inferring the protein targets of chemical compounds from gene transcriptional profiles. Although the gene signature of a compound perturbation only provides indirect clues of the interacting targets, and the biological networks under different experiment conditions further complicate the situation, the SSGCN model was successfully trained to learn from known compound-target pairs by uncovering the hidden correlations between compound perturbation profiles and gene knockdown profiles. On a benchmark set and a large time-split validation dataset, the model achieved higher target inference accuracy as compared to previous methods such as Connectivity Map. Further experimental validations of prediction results highlight the practical usefulness of SSGCN in either inferring the interacting targets of compound, or reversely, in finding novel inhibitors of a given target of interest.

Bingwen Zou, a renowed agronomist and educator, is one of the founders of agricultural higher education in China (Fig. He is committed to set up modern agriculture education in China and supervised many students who later became well-known experts in Agriculture, including Shanbao

Sicien H. Chen (陈世骧

Sperm cooperation has been observed in multiple species (Pizzari and Foster, 2008), yet its existence and benefit for reproductive success in mammals remains underexplored. Here, combining tissue-clearing with deep three-dimensional (3D) imaging, we demonstrate that postcopulatory mouse sperm congregate into unidirectional sperm cooperative clusters at the utero-tubal junction (UTJ), a key physical barrier for passage into the oviduct. Reducing sperm number in male mice by unilateral vasoligation (Uni-Vas) or busulfan-treatment impairs sperm cluster formation and oviduct entry. Interestingly, Tex101−/− male mouse produce normal sperm number, motility and morphology, yet their sperm cannot form clusters and fail to pass through the UTJ, which is at least in part due to the altered tail beating pattern of the Tex101 null sperm. Moreover, Tex101 null sperm’s defect in oviduct entry cannot be rescued by the presence of wild-type (WT) sperm in the same uteri by sequential mating, suggesting sperm cooperative cluster as an essential behavior contributing to male fertility.

In the original publication the display of Fig. 1 is incorrect. The correct figure is available in this correction.