Systemic lupus erythematosus (SLE), a worldwide autoimmune disease with high heritability, shows differences in prevalence, severity and age of onset among different ancestral groups. Previous genetic studies have focused more on European populations, which appear to be the least affected. Consequently, the genetic variations that underlie the commonalities, differences and treatment options in SLE among ancestral groups have not been well elucidated. To address this, we undertake a genome-wide association study, increasing the sample size of Chinese populations to the level of existing European studies. Thirty-eight novel SLE-associated loci and incomplete sharing of genetic architecture are identified. In addition to the human leukocyte antigen (HLA) region, nine disease loci show clear ancestral differences and implicate antibody production as a potential mechanism for differences in disease manifestation. Polygenic risk scores perform significantly better when trained on ancestry-matched data sets. These analyses help to reveal the genetic basis for disparities in SLE among ancestral groups.

Microorganisms play an important role in many physiological functions. Many studies have found that bacteria also regulate cancer susceptibility and tumor progression by affecting some metabolic or immune system signaling pathways. However, current bacterial detection methods are inaccurate or inefficient. Thus, we constructed a deep neural network (AIBISI) based on hematoxylin and eosin (H&E)-stained pathology slides to predict and visualize bacterial infection. Our model performance achieved as high as 0.81 of AUC (area under the ROC curve) within cancer type. We also built a pan-cancer model to predict bacterial infection across cancer types. To facilitate clinical usage, AIBISI visualized image areas affected by possible infection. Importantly, we successfully validated our model (AUC = 0.755) in pathological images from an independent patient cohort of stomach cancer (n = 32). To our best knowledge, this is the first artificial intelligence (AI)-based model to investigate bacterial infection in pathology images and has the potential to enable fast clinical decision related to pathogens in tumors.

The active X chromosome in mammals is upregulated to balance its dosage to autosomes during evolution. However, it is elusive why the known dosage compensation machinery showed uneven and small influence on X genes. Here, based on >20,000 transcriptomes, we identified two X gene groups (ploidy-sensitive [PSX] and ploidy-insensitive [PIX]), showing distinct but evolutionarily conserved dosage compensations (termed XAR). We demonstrated that XAR-PIX was downregulated whereas XAR-PSX upregulated at both RNA and protein levels across cancer types, in contrast with their trends during stem cell differentiation. XAR-PIX, but not XAR-PSX, was lower and correlated with autoantibodies and inflammation in patients of lupus, suggesting that insufficient dosage of PIX genes contribute to lupus pathogenesis. We further identified and experimentally validated two XAR regulators, TP53 and ATRX. Collectively, we provided insights into X dosage compensation in mammals and demonstrated different regulation of PSX and PIX and their pathophysiological roles in human diseases.

The CRISPR-Cas9 system has emerged as the dominant technology for gene edit-ing and clinical applications. One major concern is its off-target effect after the introduction of exogenous CRISPR-Cas9 into cells. Several previous studies have investigated either Cas9 alone or CRISPR-Cas9 interactions with p53. Here, we reanalyzed previously reported data of p53-associated Cas9 activities and observed large significant sex differences between p53-wildtype and p53 -mutant cells. To expand the impact of this finding, we further examined all protein-coding genes for sex-specific dependencies in a large-scale CRISPR-Cas9 screening dataset from the DepMap project. We highlighted the p53 -dependent sex bias of gene knockouts (including MYC, PIK3CA, KAT2B, KDM4E, SUV39H1, FANCB, TLR7, and APC2) across cancer types and potential mechanisms (mediated by transcriptional factors, including SOX9, FOXO4, LEF1, and RYBP) underlying this phenomenon. Our results suggest that the p53-dependent sex bias may need to be considered in future clinical applications of CRISPR-Cas9, especially in cancer.

The mechanistic understanding of virus infection and inflammation in many diseases is incomplete. Normally, messenger RNA (mRNA) tails of replication‐dependent histones (RDH) that safeguard naked nuclear DNAs are protected by a specialized stem‐loop instead of polyadenylation. Here, we showed that infection by various RNA viruses (including severe acute respiratory syndrome coronavirus 2) induced aberrant polyadenylation of RDH mRNAs (pARDH) that resulted in inflammation or cellular senescence, based on which we constructed a pARDH inflammation score (pARIS). We further investigated pARIS elevation in various disease conditions, including different types of virus infection, cancer, and cellular senescence. Notably, we found that pARIS was positively correlated with coronavirus disease 2019 severity in specific immune cell types. We also detected a subset of HIV‐1 elite controllers characterized by pARDH “flipping” potentially mediated by HuR. Importantly, pARIS was positively associated with transcription of endogenous retrovirus but negatively associated with most immune cell infiltration in tumors of various cancer types. Finally, we identified and experimentally verified two pARIS regulators, ADAR1 and ZKSCAN1, which was first linked to inflammation. The ZKSCAN1 was known as a transcription factor but instead was shown to regulate pARIS as a novel RNA binding protein. Both regulators were upregulated under most infection and inflammation conditions. In conclusion, we unraveled a potential antiviral mechanism underlying various types of virus infections and cancers.

Whole exome and targeted sequencing have been playing a major role in diagnoses of Mendelian diseases, but analysis of these data involves using many complicated tools and comprehensive understanding of the analysis results is difficult.Here, we report RETA, an R package to provide a one-stop analysis of these data and a comprehensive, interactive and easy-to-understand report with many advanced visualization features. It facilitates clinicians and scientists alike to better analyze and interpret this type of sequencing data for disease diagnoses. Availability and implementation: https://github.com/reta-s/reta/releases Contact: yangwl@hku.hk