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

BackgroundsHepatitis 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.MethodsWe 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.ResultsWe 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.ConclusionOur 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

A Comprehensive Expression Profile of MicroRNAs in Porcine Pituitary

MicroRNAs (miRNAs) are an abundant class of small RNAs that regulate expressions of most genes. miRNAs play important roles in the pituitary, the “master” endocrine organ.However, we still don't know which role miRNAs play in the development of pituitary tissue or how much they contribute to the pituitary function. By applying a combination of microarray analysis and Solexa sequencing, we detected a total of 450 miRNAs in the porcine pituitary. Verification with RT-PCR showed a high degree of confidence for the obtained data. According to the current miRBase release17.0, the detected miRNAs included 169 known porcine miRNAs, 163 conserved miRNAs not yet identified in the pig, and 12 potentially new miRNAs not yet identified in any species, three of which were revealed using Northern blot. The pituitary might contain about 80.17% miRNA types belonging to the animal. Analysis of 10 highly expressed miRNAs with the Kyoto Encyclopedia of Genes and Genomes (KEGG) indicated that the enriched miRNAs were involved not only in the development of the organ but also in a variety of inter-cell and inner cell processes or pathways that are involved in the function of the organ

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

Summary: 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

APOBEC Alteration Contributes to Tumor Growth and Immune Escape in Pan-Cancer

The accumulating evidence demonstrates that the apolipoprotein B mRNA editing enzyme catalytic polypeptide-like (APOBEC), DNA-editing protein plays an important role in the molecular pathogenesis of cancer. In particular, the APOBEC3 family was shown to induce tumor mutations by an aberrant DNA editing mechanism. However, knowledge regarding the reconstitution of the APOBEC family genes across cancer types is still lacking. Here, we systematically analyzed the molecular alterations, immuno-oncological features, and clinical relevance of the APOBEC family in pan-cancer. We found that APOBEC genes were widely and significantly differentially expressed between normal and cancer samples in 16 cancer types, and that their expression levels are significantly correlated with the prognostic value in 17 cancer types. Moreover, two patterns of APOBEC-mediated stratification with distinct immune characteristics were identified in different cancer types, respectively. In ACC, for example, the first pattern of APOBEC-mediated stratification was closely correlated with the phenotype of immune activation, which was characterized by a high immune score, increased infiltration of CD8 T cells, and higher survival. The other pattern of APOBEC-mediated stratification was closely correlated with the low-infiltration immune phenotype, which was characterized by a low immune score, lack of effective immune infiltration, and poorer survival. Further, we found the APOBEC-mediated pattern with low-infiltration immune was also highly associated with the advanced tumor subtype and the CIMP-high tumor subtype (CpG island hypermethylation). Patients with the APOBEC-mediated pattern with immune activation were more likely to have therapeutic advantages in ICB (immunological checkpoint blockade) treatment. Overall, our results provide a valuable resource that will be useful in guiding oncologic and therapeutic analyses of the role of APOBEC family in cancer

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

DataSheet_1_Three-dimensional and single-cell sequencing of liver cancer reveals comprehensive host-virus interactions in HBV infection.docx

BackgroundsHepatitis 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.MethodsWe 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.ResultsWe 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.ConclusionOur 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.</p

OmicVerse: a framework for bridging and deepening insights across bulk and single-cell sequencing

Abstract Single-cell sequencing is frequently affected by “omission” due to limitations in sequencing throughput, yet bulk RNA-seq may contain these ostensibly “omitted” cells. Here, we introduce the single cell trajectory blending from Bulk RNA-seq (BulkTrajBlend) algorithm, a component of the OmicVerse suite that leverages a Beta-Variational AutoEncoder for data deconvolution and graph neural networks for the discovery of overlapping communities. This approach effectively interpolates and restores the continuity of “omitted” cells within single-cell RNA sequencing datasets. Furthermore, OmicVerse provides an extensive toolkit for both bulk and single cell RNA-seq analysis, offering seamless access to diverse methodologies, streamlining computational processes, fostering exquisite data visualization, and facilitating the extraction of significant biological insights to advance scientific research