Three-dimensional chromatin analysis reveals Sp1 as a mediator to program and reprogram HPV-host epigenetic architecture in cervical cancer.

Human papillomavirus (HPV) is predominantly associated with HPV-related cancers, however, the precise mechanisms underlying the HPV-host epigenetic architectures in HPV carcinogenesis remain elusive. Here, we employed high-throughput chromosome conformation capture (Hi-C) to comprehensively map HPV16/18-host chromatin interactions. Our study identified the transcription factor Sp1 as a pivotal mediator in programming HPV-host interactions. By targeting Sp1, the active histone modifications (H3K27ac, H3K4me1, and H3K4me3) and the HPV-host chromatin interactions are reprogrammed, which leads to the downregulation of oncogenes located near the integration sites in both HPV (E6/E7) and the host genome (KLF5/MYC). Additionally, Sp1 inhibition led to the upregulation of immune checkpoint genes by reprogramming histone modifications in host cells. Notably, humanized patient-derived xenograft (PDX-HuHSC-NSG) models demonstrated that Sp1 inhibition promoted anti-PD-1 immunotherapy via remodeling the tumor immune microenvironment in cervical cancer. Moreover, single-cell transcriptomic analysis validated the enrichment of transcription factor Sp1 in epithelial cells of cervical cancer. In summary, our findings elucidate Sp1 as a key mediator involved in the programming and reprogramming of HPV-host epigenetic architecture. Inhibiting Sp1 with plicamycin may represent a promising therapeutic option for HPV-related carcinoma.

Constructing telomere-to-telomere diploid genome by polishing haploid nanopore-based assembly.

Draft genomes generated from Oxford Nanopore Technologies (ONT) long reads are known to have a higher error rate. Although existing genome polishers can enhance their quality, the error rate (including mismatches, indels and switching errors between paternal and maternal haplotypes) can be significant. Here, we develop two polishers, hypo-short and hypo-hybrid to address this issue. Hypo-short utilizes Illumina short reads to polish an ONT-based draft assembly, resulting in a high-quality assembly with low error rates and switching errors. Expanding on this, hypo-hybrid incorporates ONT long reads to further refine the assembly into a diploid representation. Leveraging on hypo-hybrid, we have created a diploid genome assembly pipeline called hypo-assembler. Hypo-assembler automates the generation of highly accurate, contiguous and nearly complete diploid assemblies using ONT long reads, Illumina short reads and optionally Hi-C reads. Notably, our solution even allows for the production of telomere-to-telomere diploid genomes with additional manual steps. As a proof of concept, we successfully assembled a fully phased telomere-to-telomere diploid genome of HG00733, achieving a quality value exceeding 50.

Integrated 3D genome, epigenome and transcriptome analyses reveal transcriptional coordination of circadian rhythm in rice.

Photoperiods integrate with the circadian clock to coordinate gene expression rhythms and thus ensure plant fitness to the environment. Genome-wide characterization and comparison of rhythmic genes under different light conditions revealed delayed phase under constant darkness (DD) and reduced amplitude under constant light (LL) in rice. Interestingly, ChIP-seq and RNA-seq profiling of rhythmic genes exhibit synchronous circadian oscillation in H3K9ac modifications at their loci and long non-coding RNAs (lncRNAs) expression at proximal loci. To investigate how gene expression rhythm is regulated in rice, we profiled the open chromatin regions and transcription factor (TF) footprints by time-series ATAC-seq. Although open chromatin regions did not show circadian change, a significant number of TFs were identified to rhythmically associate with chromatin and drive gene expression in a time-dependent manner. Further transcriptional regulatory networks mapping uncovered significant correlation between core clock genes and transcription factors involved in light/temperature signaling. In situ Hi-C of ZT8-specific expressed genes displayed highly connected chromatin association at the same time, whereas this ZT8 chromatin connection network dissociates at ZT20, suggesting the circadian control of gene expression by dynamic spatial chromatin conformation. These findings together implicate the existence of a synchronization mechanism between circadian H3K9ac modifications, chromatin association of TF and gene expression, and provides insights into circadian dynamics of spatial chromatin conformation that associate with gene expression rhythms.

INSurVeyor: improving insertion calling from short read sequencing data.

Insertions are one of the major types of structural variations and are defined as the addition of 50 nucleotides or more into a DNA sequence. Several methods exist to detect insertions from next-generation sequencing short read data, but they generally have low sensitivity. Our contribution is two-fold. First, we introduce INSurVeyor, a fast, sensitive and precise method that detects insertions from next-generation sequencing paired-end data. Using publicly available benchmark datasets (both human and non-human), we show that INSurVeyor is not only more sensitive than any individual caller we tested, but also more sensitive than all of them combined. Furthermore, for most types of insertions, INSurVeyor is almost as sensitive as long reads callers. Second, we provide state-of-the-art catalogues of insertions for 1047 Arabidopsis Thaliana genomes from the 1001 Genomes Project and 3202 human genomes from the 1000 Genomes Project, both generated with INSurVeyor. We show that they are more complete and precise than existing resources, and important insertions are missed by existing methods.

SVsearcher: A more accurate structural variation detection method in long read data.

Structural variations (SVs) represent genomic rearrangements (such as deletions, insertions, and inversions) whose sizes are larger than 50bp. They play important roles in genetic diseases and evolution mechanism. Due to the advance of long-read sequencing (i.e. PacBio long-read sequencing and Oxford Nanopore (ONT) long-read sequencing), we can call SVs accurately. However, for ONT long reads, we observe that existing long read SV callers miss a lot of true SVs and call a lot of false SVs in repetitive regions and in regions with multi-allelic SVs. Those errors are caused by messy alignments of ONT reads due to their high error rate. Hence, we propose a novel method, SVsearcher, to solve these issues. We run SVsearcher and other callers in three real datasets and find that SVsearcher improves the F1 score by approximately 10% for high coverage (50×) datasets and more than 25% for low coverage (10×) datasets. More importantly, SVsearcher can identify 81.7%-91.8% multi-allelic SVs while existing methods only identify 13.2% (Sniffles)-54.0% (nanoSV) of them. SVsearcher is available at https://github.com/kensung-lab/SVsearcher.

Distribution based MIL pooling filters: Experiments on a lymph node metastases dataset.

Histopathology is a crucial diagnostic tool in cancer and involves the analysis of gigapixel slides. Multiple instance learning (MIL) promises success in digital histopathology thanks to its ability to handle gigapixel slides and work with weak labels. MIL is a machine learning paradigm that learns the mapping between bags of instances and bag labels. It represents a slide as a bag of patches and uses the slide's weak label as the bag's label. This paper introduces distribution-based pooling filters that obtain a bag-level representation by estimating marginal distributions of instance features. We formally prove that the distribution-based pooling filters are more expressive than the classical point estimate-based counterparts, like 'max' and 'mean' pooling, in terms of the amount of information captured while obtaining bag-level representations. Moreover, we empirically show that models with distribution-based pooling filters perform equal to or better than those with point estimate-based pooling filters on distinct real-world MIL tasks defined on the CAMELYON16 lymph node metastases dataset. Our model with a distribution pooling filter achieves an area under the receiver operating characteristics curve value of 0.9325 (95% confidence interval: 0.8798 - 0.9743) in the tumor vs. normal slide classification task.

An AI-assisted tool for efficient prostate cancer diagnosis in low-grade and low-volume cases.

Pathologists diagnose prostate cancer by core needle biopsy. In low-grade and low-volume cases, they look for a few malignant glands out of hundreds within a core. They may miss a few malignant glands, resulting in repeat biopsies or missed therapeutic opportunities. This study developed a multi-resolution deep-learning pipeline to assist pathologists in detecting malignant glands in core needle biopsies of low-grade and low-volume cases. Analyzing a gland at multiple resolutions, our model exploited morphology and neighborhood information, which were crucial in prostate gland classification. We developed and tested our pipeline on the slides of a local cohort of 99 patients in Singapore. Besides, we made the images publicly available, becoming the first digital histopathology dataset of patients of Asian ancestry with prostatic carcinoma. Our multi-resolution classification model achieved an area under the receiver operating characteristic curve (AUROC) value of 0.992 (95% confidence interval [CI]: 0.985-0.997) in the external validation study, showing the generalizability of our multi-resolution approach.

Drug repositioning for esophageal squamous cell carcinoma.

Esophageal cancer (EC) remains a significant challenge globally, having the 8th highest incidence and 6th highest mortality worldwide. Esophageal squamous cell carcinoma (ESCC) is the most common form of EC in Asia. Crucially, more than 90% of EC cases in China are ESCC. The high mortality rate of EC is likely due to the limited number of effective therapeutic options. To increase patient survival, novel therapeutic strategies for EC patients must be devised. Unfortunately, the development of novel drugs also presents its own significant challenges as most novel drugs do not make it to market due to lack of efficacy or safety concerns. A more time and cost-effective strategy is to identify existing drugs, that have already been approved for treatment of other diseases, which can be repurposed to treat EC patients, with drug repositioning. This can be achieved by comparing the gene expression profiles of disease-states with the effect on gene-expression by a given drug. In our analysis, we used previously published microarray data and identified 167 differentially expressed genes (DEGs). Using weighted key driver analysis, 39 key driver genes were then identified. These driver genes were then used in Overlap Analysis and Network Analysis in Pharmomics. By extracting drugs common to both analyses, 24 drugs are predicted to demonstrate therapeutic effect in EC patients. Several of which have already been shown to demonstrate a therapeutic effect in EC, most notably Doxorubicin, which is commonly used to treat EC patients, and Ixazomib, which was recently shown to induce apoptosis and supress growth of EC cell lines. Additionally, our analysis predicts multiple psychiatric drugs, including Venlafaxine, as repositioned drugs. This is in line with recent research which suggests that psychiatric drugs should be investigated for use in gastrointestinal cancers such as EC. Our study shows that a drug repositioning approach is a feasible strategy for identifying novel ESCC therapies and can also improve the understanding of the mechanisms underlying the drug targets.

Parafibromin governs cell polarity and centrosome assembly in Drosophila neural stem cells.

Neural stem cells (NSCs) divide asymmetrically to balance their self-renewal and differentiation, an imbalance in which can lead to NSC overgrowth and tumor formation. The functions of Parafibromin, a conserved tumor suppressor, in the nervous system are not established. Here, we demonstrate that Drosophila Parafibromin/Hyrax (Hyx) inhibits ectopic NSC formation by governing cell polarity. Hyx is essential for the asymmetric distribution and/or maintenance of polarity proteins. hyx depletion results in the symmetric division of NSCs, leading to the formation of supernumerary NSCs in the larval brain. Importantly, we show that human Parafibromin rescues the ectopic NSC phenotype in Drosophila hyx mutant brains. We have also discovered that Hyx is required for the proper formation of interphase microtubule-organizing center and mitotic spindles in NSCs. Moreover, Hyx is required for the proper localization of 2 key centrosomal proteins, Polo and AurA, and the microtubule-binding proteins Msps and D-TACC in dividing NSCs. Furthermore, Hyx directly regulates the polo and aurA expression in vitro. Finally, overexpression of polo and aurA could significantly suppress ectopic NSC formation and NSC polarity defects caused by hyx depletion. Our data support a model in which Hyx promotes the expression of polo and aurA in NSCs and, in turn, regulates cell polarity and centrosome/microtubule assembly. This new paradigm may be relevant to future studies on Parafibromin/HRPT2-associated cancers.

Genome assembly and chemogenomic profiling of National Flower of Singapore Papilionanthe Miss Joaquim 'Agnes' reveals metabolic pathways regulating floral traits.

Singapore's National Flower, Papilionanthe (Ple.) Miss Joaquim 'Agnes' (PMJ) is highly prized as a horticultural flower from the Orchidaceae family. A combination of short-read sequencing, single-molecule long-read sequencing and chromatin contact mapping was used to assemble the PMJ genome, spanning 2.5 Gb and 19 pseudo-chromosomal scaffolds. Genomic resources and chemical profiling provided insights towards identifying, understanding and elucidating various classes of secondary metabolite compounds synthesized by the flower. For example, presence of the anthocyanin pigments detected by chemical profiling coincides with the expression of ANTHOCYANIN SYNTHASE (ANS), an enzyme responsible for the synthesis of the former. Similarly, the presence of vandaterosides (a unique class of glycosylated organic acids with the potential to slow skin aging) discovered using chemical profiling revealed the involvement of glycosyltransferase family enzymes candidates in vandateroside biosynthesis. Interestingly, despite the unnoticeable scent of the flower, genes involved in the biosynthesis of volatile compounds and chemical profiling revealed the combination of oxygenated hydrocarbons, including traces of linalool, beta-ionone and vanillin, forming the scent profile of PMJ. In summary, by combining genomics and biochemistry, the findings expands the known biodiversity repertoire of the Orchidaceae family and insights into the genome and secondary metabolite processes of PMJ.

Deep Learning-Based Segmentation of Peach Diseases Using Convolutional Neural Network.

Peach diseases seriously affect peach yield and people's health. The precise identification of peach diseases and the segmentation of the diseased areas can provide the basis for disease control and treatment. However, the complex background and imbalanced samples bring certain challenges to the segmentation and recognition of lesion area, and the hard samples and imbalance samples can lead to a decline in classification of foreground class and background class. In this paper we applied deep network models (Mask R-CNN and Mask Scoring R-CNN) for segmentation and recognition of peach diseases. Mask R-CNN and Mask Scoring R-CNN are classic instance segmentation models. Using instance segmentation model can obtain the disease names, disease location and disease segmentation, and the foreground area is the basic feature for next segmentation. Focal Loss can solve the problems caused by difficult samples and imbalance samples, and was used for this dataset to improve segmentation accuracy. Experimental results show that Mask Scoring R-CNN with Focal Loss function can improve recognition rate and segmentation accuracy comparing to Mask Scoring R-CNN with CE loss or comparing to Mask R-CNN. When ResNet50 is used as the backbone network based on Mask R-CNN, the segmentation accuracy of segm_mAP_50 increased from 0.236 to 0.254. When ResNetx101 is used as the backbone network, the segmentation accuracy of segm_mAP_50 increased from 0.452 to 0.463. In summary, this paper used Focal Loss on Mask R-CNN and Mask Scoring R-CNN to generate better mAP of segmentation and output more detailed information about peach diseases.

Obtaining spatially resolved tumor purity maps using deep multiple instance learning in a pan-cancer study.

Tumor purity is the percentage of cancer cells within a tissue section. Pathologists estimate tumor purity to select samples for genomic analysis by manually reading hematoxylin-eosin (H&E)-stained slides, which is tedious, time consuming, and prone to inter-observer variability. Besides, pathologists' estimates do not correlate well with genomic tumor purity values, which are inferred from genomic data and accepted as accurate for downstream analysis. We developed a deep multiple instance learning model predicting tumor purity from H&E-stained digital histopathology slides. Our model successfully predicted tumor purity in eight The Cancer Genome Atlas (TCGA) cohorts and a local Singapore cohort. The predictions were highly consistent with genomic tumor purity values. Thus, our model can be utilized to select samples for genomic analysis, which will help reduce pathologists' workload and decrease inter-observer variability. Furthermore, our model provided tumor purity maps showing the spatial variation within sections. They can help better understand the tumor microenvironment.

Oner, Sung, and Lee: Researchers in digital pathology for the future of modern medicine.

Oner, an early-career researcher, and Lee and Sung, group leaders, have developed a deep learning model for accurate prediction of the proportion of cancer cells within tumor tissue. This is a necessary step for precision oncology and target therapy in cancer. They talk about their view of data science and the evolution of pathology in the coming years.

ASMdb: a comprehensive database for allele-specific DNA methylation in diverse organisms.

DNA methylation is known to be the most stable epigenetic modification and has been extensively studied in relation to cell differentiation, development, X chromosome inactivation and disease. Allele-specific DNA methylation (ASM) is a well-established mechanism for genomic imprinting and regulates imprinted gene expression. Previous studies have confirmed that certain special regions with ASM are susceptible and closely related to human carcinogenesis and plant development. In addition, recent studies have proven ASM to be an effective tumour marker. However, research on the functions of ASM in diseases and development is still extremely scarce. Here, we collected 4400 BS-Seq datasets and 1598 corresponding RNA-Seq datasets from 47 species, including human and mouse, to establish a comprehensive ASM database. We obtained the data on DNA methylation level, ASM and allele-specific expressed genes (ASEGs) and further analysed the ASM/ASEG distribution patterns of these species. In-depth ASM distribution analysis and differential methylation analysis conducted in nine cancer types showed results consistent with the reported changes in ASM in key tumour genes and revealed several potential ASM tumour-related genes. Finally, integrating these results, we constructed the first well-resourced and comprehensive ASM database for 47 species (ASMdb, www.dna-asmdb.com).

Single-cell analysis reveals androgen receptor regulates the ER-to-Golgi trafficking pathway with CREB3L2 to drive prostate cancer progression.

Androgen receptor (AR) plays a central role in driving prostate cancer (PCa) progression. How AR promotes this process is still not completely clear. Herein, we used single-cell transcriptome analysis to reconstruct the transcriptional network of AR in PCa. Our work shows AR directly regulates a set of signature genes in the ER-to-Golgi protein vesicle-mediated transport pathway. The expression of these genes is required for maximum androgen-dependent ER-to-Golgi trafficking, cell growth, and survival. Our analyses also reveal the signature genes are associated with PCa progression and prognosis. Moreover, we find inhibition of the ER-to-Golgi transport process with a small molecule enhanced antiandrogen-mediated tumor suppression of hormone-sensitive and insensitive PCa. Finally, we demonstrate AR collaborates with CREB3L2 in mediating ER-to-Golgi trafficking in PCa. In summary, our findings uncover a critical role for dysregulation of ER-to-Golgi trafficking expression and function in PCa progression, provide detailed mechanistic insights for how AR tightly controls this process, and highlight the prospect of targeting the ER-to-Golgi pathway as a therapeutic strategy for advanced PCa.

Calling large indels in 1047 Arabidopsis with IndelEnsembler.

Large indels greatly impact the observable phenotypes in different organisms including plants and human. Hence, extracting large indels with high precision and sensitivity is important. Here, we developed IndelEnsembler to detect large indels in 1047 Arabidopsis whole-genome sequencing data. IndelEnsembler identified 34 093 deletions, 12 913 tandem duplications and 9773 insertions. Our large indel dataset was more comprehensive and accurate compared with the previous dataset of AthCNV (1). We captured nearly twice of the ground truth deletions and on average 27% more ground truth duplications compared with AthCNV, though our dataset has less number of large indels compared with AthCNV. Our large indels were positively correlated with transposon elements across the Arabidopsis genome. The non-homologous recombination events were the major formation mechanism of deletions in Arabidopsis genome. The Neighbor joining (NJ) tree constructed based on IndelEnsembler's deletions clearly divided the geographic subgroups of 1047 Arabidopsis. More importantly, our large indels represent a previously unassessed source of genetic variation. Approximately 49% of the deletions have low linkage disequilibrium (LD) with surrounding single nucleotide polymorphisms. Some of them could affect trait performance. For instance, using deletion-based genome-wide association study (DEL-GWAS), the accessions containing a 182-bp deletion in AT1G11520 had delayed flowering time and all accessions in north Sweden had the 182-bp deletion. We also found the accessions with 65-bp deletion in the first exon of AT4G00650 (FRI) flowered earlier than those without it. These two deletions cannot be detected in AthCNV and, interestingly, they do not co-occur in any Arabidopsis thaliana accession. By SNP-GWAS, surrounding SNPs of these two deletions do not correlate with flowering time. This example demonstrated that existing large indel datasets miss phenotypic variations and our large indel dataset filled in the gap.

A chromosome-level genome assembly of Cairina moschata and comparative genomic analyses.

BACKGROUND: The Muscovy duck (Cairina moschata) is an economically important duck species, with favourable growth and carcass composition parameters in comparison to other ducks. However, limited genomic resources for Muscovy duck hinder our understanding of its evolution and genetic diversity. RESULTS: We combined linked-reads sequencing technology and reference-guided methods for de novo genome assembly. The final draft assembly was 1.12 Gbp with 29 autosomes, one sex chromosome and 4,583 unlocalized scaffolds with an N50 size of 77.35 Mb. Based on universal single-copy orthologues (BUSCO), the draft genome assembly completeness was estimated to be 93.30 %. Genome annotation identified 15,580 genes, with 15,537 (99.72 %) genes annotated in public databases. We conducted comparative genomic analyses and found that species-specific and rapidly expanding gene families (compared to other birds) in Muscovy duck are mainly involved in Calcium signaling, Adrenergic signaling in cardiomyocytes, and GnRH signaling pathways. In comparison to the common domestic duck (Anas platyrhynchos), we identified 104 genes exhibiting strong signals of adaptive evolution (Ka/Ks > 1). Most of these genes were associated with immune defence pathways (e.g. IFNAR1 and TLR5). This is indicative of the existence of differences in the immune responses between the two species. Additionally, we combined divergence and polymorphism data to demonstrate the "faster-Z effect" of chromosome evolution. CONCLUSIONS: The chromosome-level genome assembly of Muscovy duck and comparative genomic analyses provide valuable resources for future molecular ecology studies, as well as the evolutionary arms race between the host and influenza viruses.

Analysis of whole-genome re-sequencing data of ducks reveals a diverse demographic history and extensive gene flow between Southeast/South Asian and Chinese populations.

BACKGROUND: The most prolific duck genetic resource in the world is located in Southeast/South Asia but little is known about the domestication and complex histories of these duck populations. RESULTS: Based on whole-genome resequencing data of 78 ducks (Anas platyrhynchos) and 31 published whole-genome duck sequences, we detected three geographic distinct genetic groups, including local Chinese, wild, and local Southeast/South Asian populations. We inferred the demographic history of these duck populations with different geographical distributions and found that the Chinese and Southeast/South Asian ducks shared similar demographic features. The Chinese domestic ducks experienced the strongest population bottleneck caused by domestication and the last glacial maximum (LGM) period, whereas the Chinese wild ducks experienced a relatively weak bottleneck caused by domestication only. Furthermore, the bottleneck was more severe in the local Southeast/South Asian populations than in the local Chinese populations, which resulted in a smaller effective population size for the former (7100-11,900). We show that extensive gene flow has occurred between the Southeast/South Asian and Chinese populations, and between the Southeast Asian and South Asian populations. Prolonged gene flow was detected between the Guangxi population from China and its neighboring Southeast/South Asian populations. In addition, based on multiple statistical approaches, we identified a genomic region that included three genes (PNPLA8, THAP5, and DNAJB9) on duck chromosome 1 with a high probability of gene flow between the Guangxi and Southeast/South Asian populations. Finally, we detected strong signatures of selection in genes that are involved in signaling pathways of the nervous system development (e.g., ADCYAP1R1 and PDC) and in genes that are associated with morphological traits such as cell growth (e.g., IGF1R). CONCLUSIONS: Our findings provide valuable information for a better understanding of the domestication and demographic history of the duck, and of the gene flow between local duck populations from Southeast/South Asia and China.