Sex-biased genome-editing effects of CRISPR-Cas9 across cancer cells dependent on p53 status.

The CRISPR-Cas9 system has emerged as the dominant technology for gene editing 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.

ChArmTelo Enables Large-Scale Chromosome Arm-Level Telomere Analysis across Human Populations and in Cancer Patients.

Telomeres are structures protecting chromosome ends. However, a scalable and cost-effective method to investigate chromosome arm-level (ChArm) telomeres (Telos) in large-scale projects is still lacking, hindering intensive investigation of high-resolution telomeres across cancers and other diseases. Here, ChArmTelo, the first computational toolbox to analyze telomeres at chromosome arm level in human and other animal species, using 10X linked-read and similar technologies, is presented. ChArmTelo currently consists of two algorithms, TeloEM and TeloKnow, for arm-level telomere length (TL) analysis. The algorithms are demonstrated by comprehensive analysis of chromosome arm-level telomere lengths (chArmTLs) in nearly 400 whole genome sequencing samples (WGS) from human populations and animals, including healthy and cancer samples. Notably, considerable performance improvement contributed by using the latest complete telomere-to-telomere reference genome (CHM13v2), compared to hg38, is shown. ChArmTelo reveals population-specific chArmTL differences and liver cancer signatures of chArmTLs and that DNA replication origin disruption may contribute to cancer by affecting TLs. Importantly, ChArmTelo can be readily applied to tens of thousands of cancer and healthy samples with published WGS data.

Multiomics analyses reveal pathological mechanisms of HBV infection and integration in liver cancer.

Hepatitis B virus (HBV) infection and integration are important for hepatocellular carcinoma (HCC) initiation and progression, while disease mechanisms are still largely elusive. Here, we combined bulk and single-cell sequencing technologies to tackle the disease mechanisms of HBV-related HCC. We observed high HBV mutation rate and diversity only in tumors without HBV integration. We identified human somatic risk loci for HBV integration (VIMs). Transcription factors (TFs) enriched in VIMs were involved in DNA repair and androgen receptor (AR) signaling. Aberration of AR signaling was further observed by single-cell regulon analysis in HBV-infected hepatocytes, which showed remarkable interactions between AR and the complement system that, together with the X-linked ZXDB regulon that contains albumin (ALB), probably contribute to HCC male predominance. Complement system dysregulation caused by HBV infection was further confirmed by analyses of single-cell copy numbers and cell-cell communications. Finally, HBV infection-associated immune cells presented critical defects, including TXNIP in T cells, TYROBP in NK cells, and the X-linked TIMP1 in monocytes. We further experimentally validated our findings in multiple independent patient cohorts. Collectively, our work shed light on the pathogenesis of HBV-related HCC and other liver diseases that affect billions of people worldwide.

Histone mRNA polyadenylation-mediated inflammation underlies various virus infections and cancers.

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.

Three-dimensional and single-cell sequencing of liver cancer reveals comprehensive host-virus interactions in HBV infection.

Backgrounds: Hepatitis B virus (HBV) infection is a major risk factor for chronic liver diseases and liver cancer (mainly hepatocellular carcinoma, HCC), while the underlying mechanisms and host-virus interactions are still largely elusive. Methods: We applied HiC sequencing to HepG2 (HBV-) and HepG2-2.2.15 (HBV+) cell lines and combined them with public HCC single-cell RNA-seq data, HCC bulk RNA-seq data, and both genomic and epigenomic ChIP-seq data to reveal potential disease mechanisms of HBV infection and host-virus interactions reflected by 3D genome organization. Results: We found that HBV enhanced overall proximal chromatin interactions (CIs) of liver cells and primarily affected regional CIs on chromosomes 13, 14, 17, and 22. Interestingly, HBV altered the boundaries of many topologically associating domains (TADs), and genes nearby these boundaries showed functional enrichment in cell adhesion which may promote cancer metastasis. Moreover, A/B compartment analysis revealed dramatic changes on chromosomes 9, 13 and 21, with more B compartments (inactive or closed) shifting to A compartments (active or open). The A-to-B regions (closing) harbored enhancers enriched in the regulation of inflammatory responses, whereas B-to-A regions (opening) were enriched for transposable elements (TE). Furthermore, we identified large HBV-induced structural variations (SVs) that disrupted tumor suppressors, NLGN4Y and PROS1. Finally, we examined differentially expressed genes and TEs in single hepatocytes with or without HBV infection, by using single-cell RNA-seq data. Consistent with our HiC sequencing findings, two upregulated genes that promote HBV replication, HNF4A and NR5A2, were located in regions with HBV-enhanced CIs, and five TEs were located in HBV-activated regions. Therefore, HBV may promote liver diseases by affecting the human 3D genome structure. Conclusion: Our work promotes mechanistic understanding of HBV infection and host-virus interactions related to liver diseases that affect billions of people worldwide. Our findings may also have implications for novel immunotherapeutic strategies targeting HBV infection.

Deep-learning model AIBISI predicts bacterial infection across cancer types based on pathological images.

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.

Pan-cancer investigation of C-to-U editing reveals its important role in cancer development and new targets for cancer treatment.

RNA editing is prevalent in the transcriptome and is important for multiple cellular processes. C-to-U RNA editing sites (RES) are relatively rare and understudied in humans, compared to A-to-I editing. However, the functional impact of C-to-U editing in human cancers also remains elusive. Here, we conducted the first comprehensive survey of pan-cancer C-to-U RESs. Surprisingly, we found that the same subset of RESs were associated with multiple features, including patient survival, cancer stemness, tumor mutation burden (TMB), and tumor-infiltrated immune cell compositions (ICC), suggesting an RES-mediated close relationship between these features. For example, editing sites for GALM or IFI6 that led to higher expression were linked to lower survival and more cancer stemness. Also, TMB was found to be lower in prostate cancer cases with ICC-associated RESs in CAVIN1 or VWA8 or higher in prostate cancer cases with thymoma. With experimental support, we also found RESs in CST3, TPI1, or TNC that are linked to immune checkpoint blockade by anti-PD1. We also confirmed through experiments that two C-to-U RESs in CSNK2B or RPS14 had different effects on colon cancer cells. Patients with CSNK2B editing, which increased the expression of the oncogene CLDN18, had a lower response to drugs. On the other hand, drugs worked better on people who had RPS14 editing, which greatly increased ribosome production. In summary, our study demonstrated the important roles of C-to-U RESs across cancers and shed light on personalized cancer therapy.

Distinct dosage compensations of ploidy-sensitive and -insensitive X chromosome genes during development and in diseases.

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.

Enhancer RNA-based modeling of adverse events and objective responses of cancer immunotherapy reveals associated key enhancers and target genes.

Immune checkpoint inhibitors (ICI) targeting PD-1/PD-L1 or CTLA-4 are emerging and effective immunotherapy strategies. However, ICI-treated patients present heterogeneous responses and adverse events, thus demanding effective ways to assess benefit over risk before treatment. Here, by integrating pan-cancer clinical and molecular data, we tried to predict immune-related adverse events (irAEs, risk) and objective response rates (ORRs, benefit) based on enhancer RNAs (eRNAs) expression among patients receiving anti-PD-1/PD-L1 therapies. We built two tri-variate (eRNAs) regression models, one (with ENSR00000326714, ENSR00000148786, and ENSR00000005553) explaining 71% variance (R=0.84) of irAEs and the other (with ENSR00000164478, ENSR00000035913, and ENSR00000167231) explaining 79% (R=0.89) of ORRs. Interestingly, target genes of irAE-related enhancers, including upstream regulators of MYC, were involved in metabolism, inflammation, and immune activation, while ORR-related enhancers target PAK2 and DLG1 which participate in T cell activation. More importantly, we found that ENSR00000148786 probably enhanced TMEM43/LUMA expression mainly in B cells to induce irAEs in ICI-treated patients. Our study provides references for the identification of immunotherapy-related biomarkers and potential therapeutic targets during immunotherapy.

Comprehensive Analysis of Large-Scale Transcriptomes from Multiple Cancer Types.

Various abnormalities of transcriptional regulation revealed by RNA sequencing (RNA-seq) have been reported in cancers. However, strategies to integrate multi-modal information from RNA-seq, which would help uncover more disease mechanisms, are still limited. Here, we present PipeOne, a cross-platform one-stop analysis workflow for large-scale transcriptome data. It was developed based on Nextflow, a reproducible workflow management system. PipeOne is composed of three modules, data processing and feature matrices construction, disease feature prioritization, and disease subtyping. It first integrates eight different tools to extract different information from RNA-seq data, and then used random forest algorithm to study and stratify patients according to evidences from multiple-modal information. Its application in five cancers (colon, liver, kidney, stomach, or thyroid; total samples n = 2024) identified various dysregulated key features (such as PVT1 expression and ABI3BP alternative splicing) and pathways (especially liver and kidney dysfunction) shared by multiple cancers. Furthermore, we demonstrated clinically-relevant patient subtypes in four of five cancers, with most subtypes characterized by distinct driver somatic mutations, such as TP53, TTN, BRAF, HRAS, MET, KMT2D, and KMT2C mutations. Importantly, these subtyping results were frequently contributed by dysregulated biological processes, such as ribosome biogenesis, RNA binding, and mitochondria functions. PipeOne is efficient and accurate in studying different cancer types to reveal the specificity and cross-cancer contributing factors of each cancer.It could be easily applied to other diseases and is available at GitHub.

Identification of 38 novel loci for systemic lupus erythematosus and genetic heterogeneity between ancestral groups.

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.

The landscape of long noncoding RNA-involved and tumor-specific fusions across various cancers.

The majority of the human genome encodes long noncoding RNA (lncRNA) genes, critical regulators of various cellular processes, which largely outnumber protein-coding genes. However, lncRNA-involved fusions have not been surveyed and characterized yet. Here, we present a systematic study of the lncRNA fusion landscape across cancer types and identify >30 000 high-confidence tumor-specific lncRNA fusions (using 8284 tumor and 6946 normal samples). Fusions positively correlated with DNA damage and cancer stemness and were specifically low in microsatellite instable (MSI)-High or virus-infected tumors. Moreover, fusions distribute differently among cancer molecular subtypes, but with shared enrichment in tumors that are microsatellite stable (MSS), with high somatic copy number alterations (SCNA), and with poor survival. Importantly, we find a potentially new mechanism, mediated by enhancer RNAs (eRNA), which generates secondary fusions that form densely connected fusion networks with many fusion hubs targeted by FDA-approved drugs. Finally, we experimentally validate functions of two tumor-promoting chimeric proteins derived from mRNA-lncRNA fusions, KDM4B-G039927 and EPS15L1-lncOR7C2-1. The EPS15L1 fusion protein may regulate (Gasdermin E) GSDME, critical in pyroptosis and anti-tumor immunity. Our study completes the fusion landscape in cancers, sheds light on fusion mechanisms, and enriches lncRNA functions in tumorigenesis and cancer progression.

Identification of Regulatory Modules That Stratify Lupus Disease Mechanism through Integrating Multi-Omics Data.

Although recent advances in genetic studies have shed light on systemic lupus erythematosus (SLE), its detailed mechanisms remain elusive. In this study, using datasets on SLE transcriptomic profiles, we identified 750 differentially expressed genes (DEGs) in T and B lymphocytes and peripheral blood cells. Using transcription factor (TF) binding data derived from chromatin immunoprecipitation sequencing (ChIP-seq) experiments from the Encyclopedia of DNA Elements (ENCODE) project, we inferred networks of co-regulated genes (NcRGs) based on binding profiles of the upregulated DEGs by significantly enriched TFs. Modularization analysis of NcRGs identified co-regulatory modules among the DEGs and master TFs vital for each module. Remarkably, the co-regulatory modules stratified the common SLE interferon (IFN) signature and revealed SLE pathogenesis pathways, including the complement cascade, cell cycle regulation, NETosis, and epigenetic regulation. By integrative analyses of disease-associated genes (DAGs), DEGs, and enriched TFs, as well as proteins interacting with them, we identified a hierarchical regulatory cascade with TFs regulated by DAGs, which in turn regulates gene expression. Integrative analysis of multi-omics data provided valuable molecular insights into the molecular mechanisms of SLE.

HLA-IMPUTER: an easy to use web application for HLA imputation and association analysis using population-specific reference panels.

SUMMARY: HLA allele imputation from SNP genotypes has become increasingly useful, but its accuracy is heavily dependent on the reference panels used. HLA-IMPUTER implements HIBAG algorithm for HLA imputation with different population specific reference panels, including a new Han Chinese reference panel derived from 10 689 samples. We provide a convenient platform for researchers to impute HLA alleles and perform association analysis. AVAILABILITY AND IMPLEMENTATION: http://wyanglab.org: 3838/RefPanelWebsite/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Simulation of non-inherited maternal antigens acceptable HLA mismatches to increase the chance of matched cord blood units: Hong Kong's experience.

In Cord blood transplantation (CBT), the non-inherited maternal antigen (NIMA) virtual six HLA matched CB is found to have similar outcomes to six HLA inherited matched CB. Such virtual HLA matched CB units can be generated by substituting the inherited alleles with one to three NIMAs. In Hong Kong Cord Blood Bank, CB units have no NIMA defined. 100 CB samples were collected with NIMA defined. Retrospective searches of Hong Kong patients (n = 520) were matched against the inherited and virtual HLA phenotypes of NIMA CB file. One to three NIMA matches was analyzed, virtual six HLA matches were identified for 31.7% patients, 29.4% from CB units with 5/6 HLA match with 1 NIMA match and 1.7% CB units with a 4/6 HLA match and 2 NIMA matches. However, searches in the 167,201 Bone Marrow Donors Worldwide CB units with defined NIMA did not yield similar increases, possibly due to the ethnicity differences between populations. The match performance rises from 26% to 60% after including the NIMA match. Comparing the match performance of 32% in a previous Dutch study, we calculated with 60% matching in this smaller size study. This provides a solid ground to considering NIMA in stem cell donor selection which was adopted in some centers, to be extended to Asian and local CB registries to increase the chance for matches and also to improve patient outcomes, increase the utilization of CB units, enhance clinical flexibility and signify economic intelligence.

Estimation of optimal donor number in Bone Marrow Donor Registry: Hong Kong's experience.

Better outcome for hematopoietic stem cell transplantation (HSCT) requires optimal matching between donor and recipient at the HLA-A, -B, -C, and -DRB1 loci. This study estimates the likelihood of identifying HLA matched donors in Hong Kong. 7595 volunteer unrelated Chinese donors at the Hong Kong Bone Marrow Donor Registry were typed with HLA-A, -B, -C and -DRB1 genotypes. The matching probabilities for 8/8 and 7/8 HLA match via the matching models were determined. Based on current 100,000 donors in the HKBMDR, the matching probabilities are 45% at 8/8 HLA match and 65% at 7/8 match. By increasing the registry to 200,000, the likelihoods of match become 54% and 73% at 8/8 and 7/8 match stringencies respectively. Our findings may be helpful in planning future donor recruitment and HLA typing. A cost-effective Bone Marrow Donor Registry with a larger pool of donors could increase chance of matching and the success of HSCT.

A simplified method of calculating cPRA for kidney allocation application in Hong Kong: a retrospective study.

Calculated panel reactive antibody (cPRA) represents possibility of encountering an incompatible donor for organ transplant candidates and has gradually replaced traditional PRA as a measurement of sensitization level. We tested two cPRA calculation methods on a cohort of renal candidate (n = 613). HLA typing of 563 Chinese deceased renal donors was used to estimate allele and haplotype frequencies of Hong Kong donor pool. The OPTN formula was adopted to generate cPRA (cPRA (freq)). We also incorporated a computer script to compare unacceptable antigens of patients against HLA phenotype of donors. The cPRA based on historical donor filtering was the percentage of filter out count over total number of donors (cPRA (filter)). Values of cPRA (freq) and cPRA (filter) showed almost perfect agreement with Lin's correlation coefficient equal to 1.000. SD of bias was 0.6 cPRA point. Limit of agreement was 0.9 to -1.5 points difference. Furthermore, the poor agreement between our in-house cPRA and values from other online calculators indicated the necessity to use local population data for accurate cPRA calculation. Built-in donor filtering method was more practicable for Hong Kong due to factors such as cost and flexibility. An on-going donor pool can reflect population allele frequencies and permits efficient periodic update of cPRA.

HLA-A, -B and -DRB1 genotyping and haplotype frequencies of 3892 cord blood units in the Hong Kong Chinese Cord Blood Registry.

HLA-A, -B and -DRB1 gene and haplotype frequencies have been calculated from 3892 southern Chinese unrelated cord blood units in a Hong Kong Cord Blood Registry. This is the first large-scale paper to report the distribution of A-B-DRB1 alleles in Hong Kong Chinese Cord Blood Units. This information is important for estimating the optimal and economically cost-effective donor size and likelihood of obtaining appropriately matched cord blood units for Chinese patients awaiting haematopoietic stem cell transplantation. The data are available in the Allele Frequencies Net Database under the population name ''Hong Kong Chinese Cord Blood Registry'' and the identifier (AFND003358).

HLA-A, -B, -C, and -DRB1 genotyping and haplotype frequencies for a Hong Kong Chinese population of 7595 individuals.

HLA-A, -B, -C and -DRB1 gene and haplotype frequencies have been calculated from 7595 southern Chinese unrelated donors in a Hong Kong Bone Marrow Donor Registry. This is the first large-scale paper to report the distribution of A-C-B-DRB1 alleles in Hong Kong Chinese. This information is important for phylogenetic, comparative studies and estimating the optimal and cost-effective donor size and likelihood of obtaining appropriately matched donors for Chinese patients awaiting haematopoietic stem cell transplantation. The allele and haplotype data are available in the Allele Frequencies Net Database under the population name ''Hong Kong Chinese BMDR'' and the identifier (AFND003357).

Gene-based meta-analysis of genome-wide association study data identifies independent single-nucleotide polymorphisms in ANXA6 as being associated with systemic lupus erythematosus in Asian populations.

OBJECTIVE: Previous genome-wide association studies (GWAS), which were mainly based on single-variant analysis, have identified many systemic lupus erythematosus (SLE) susceptibility loci. However, the genetic architecture of this complex disease is far from being understood. The aim of this study was to investigate whether using a gene-based analysis may help to identify novel loci, by considering global evidence of association from a gene or a genomic region rather than focusing on evidence for individual variants. METHODS: Based on the results of a meta-analysis of 2 GWAS of SLE conducted in 2 Asian cohorts, we performed an in-depth gene-based analysis followed by replication in a total of 4,626 patients and 7,466 control subjects of Asian ancestry. Differential allelic expression was measured by pyrosequencing. RESULTS: More than one-half of the reported SLE susceptibility loci showed evidence of independent effects, and this finding is important for understanding the mechanisms of association and explaining disease heritability. ANXA6 was detected as a novel SLE susceptibility gene, with several single-nucleotide polymorphisms (SNPs) contributing independently to the association with disease. The risk allele of rs11960458 correlated significantly with increased expression of ANXA6 in peripheral blood mononuclear cells from heterozygous healthy control subjects. Several other associated SNPs may also regulate ANXA6 expression, according to data obtained from public databases. Higher expression of ANXA6 in patients with SLE was also reported previously. CONCLUSION: Our study demonstrated the merit of using gene-based analysis to identify novel susceptibility loci, especially those with independent effects, and also demonstrated the widespread presence of loci with independent effects in SLE susceptibility genes.