Whole genome sequencing based analysis of inflammation biomarkers in the Trans-Omics for Precision Medicine (TOPMed) consortium.

Inflammation biomarkers can provide valuable insight into the role of inflammatory processes in many diseases and conditions. Sequencing based analyses of such biomarkers can also serve as an exemplar of the genetic architecture of quantitative traits. To evaluate the biological insight, which can be provided by a multi-ancestry, whole-genome based association study, we performed a comprehensive analysis of 21 inflammation biomarkers from up to 38 465 individuals with whole-genome sequencing from the Trans-Omics for Precision Medicine (TOPMed) program (with varying sample size by trait, where the minimum sample size was n = 737 for MMP-1). We identified 22 distinct single-variant associations across 6 traits-E-selectin, intercellular adhesion molecule 1, interleukin-6, lipoprotein-associated phospholipase A2 activity and mass, and P-selectin-that remained significant after conditioning on previously identified associations for these inflammatory biomarkers. We further expanded upon known biomarker associations by pairing the single-variant analysis with a rare variant set-based analysis that further identified 19 significant rare variant set-based associations with 5 traits. These signals were distinct from both significant single variant association signals within TOPMed and genetic signals observed in prior studies, demonstrating the complementary value of performing both single and rare variant analyses when analyzing quantitative traits. We also confirm several previously reported signals from semi-quantitative proteomics platforms. Many of these signals demonstrate the extensive allelic heterogeneity and ancestry-differentiated variant-trait associations common for inflammation biomarkers, a characteristic we hypothesize will be increasingly observed with well-powered, large-scale analyses of complex traits.

Gut microbial interactions based on network construction and bacterial pairwise cultivation.

Association networks are widely applied for the prediction of bacterial interactions in studies of human gut microbiomes. However, the experimental validation of the predicted interactions is challenging due to the complexity of gut microbiomes and the limited number of cultivated bacteria. In this study, we addressed this challenge by integrating in vitro time series network (TSN) associations and co-cultivation of TSN taxon pairs. Fecal samples were collected and used for cultivation and enrichment of gut microbiome on YCFA agar plates for 13 days. Enriched cells were harvested for DNA extraction and metagenomic sequencing. A total of 198 metagenome-assembled genomes (MAGs) were recovered. Temporal dynamics of bacteria growing on the YCFA agar were used to infer microbial association networks. To experimentally validate the interactions of taxon pairs in networks, we selected 24 and 19 bacterial strains from this study and from the previously established human gut microbial biobank, respectively, for pairwise co-cultures. The co-culture experiments revealed that most of the interactions between taxa in networks were identified as neutralism (51.67%), followed by commensalism (21.67%), amensalism (18.33%), competition (5%) and exploitation (3.33%). Genome-centric analysis further revealed that the commensal gut bacteria (helpers and beneficiaries) might interact with each other via the exchanges of amino acids with high biosynthetic costs, short-chain fatty acids, and/or vitamins. We also validated 12 beneficiaries by adding 16 additives into the basic YCFA medium and found that the growth of 66.7% of these strains was significantly promoted. This approach provides new insights into the gut microbiome complexity and microbial interactions in association networks. Our work highlights that the positive relationships in gut microbial communities tend to be overestimated, and that amino acids, short-chain fatty acids, and vitamins are contributed to the positive relationships.

MagicalRsq-X: A cross-cohort transferable genotype imputation quality metric.

Since genotype imputation was introduced, researchers have been relying on the estimated imputation quality from imputation software to perform post-imputation quality control (QC). However, this quality estimate (denoted as Rsq) performs less well for lower-frequency variants. We recently published MagicalRsq, a machine-learning-based imputation quality calibration, which leverages additional typed markers from the same cohort and outperforms Rsq as a QC metric. In this work, we extended the original MagicalRsq to allow cross-cohort model training and named the new model MagicalRsq-X. We removed the cohort-specific estimated minor allele frequency and included linkage disequilibrium scores and recombination rates as additional features. Leveraging whole-genome sequencing data from TOPMed, specifically participants in the BioMe, JHS, WHI, and MESA studies, we performed comprehensive cross-cohort evaluations for predominantly European and African ancestral individuals based on their inferred global ancestry with the 1000 Genomes and Human Genome Diversity Project data as reference. Our results suggest MagicalRsq-X outperforms Rsq in almost every setting, with 7.3%-14.4% improvement in squared Pearson correlation with true R2, corresponding to 85-218 K variant gains. We further developed a metric to quantify the genetic distances of a target cohort relative to a reference cohort and showed that such metric largely explained the performance of MagicalRsq-X models. Finally, we found MagicalRsq-X saved up to 53 known genome-wide significant variants in one of the largest blood cell trait GWASs that would be missed using the original Rsq for QC. In conclusion, MagicalRsq-X shows superiority for post-imputation QC and benefits genetic studies by distinguishing well and poorly imputed lower-frequency variants.

Christensenella minuta interacts with multiple gut bacteria.

Introduction: Gut microbes form complex networks that significantly influence host health and disease treatment. Interventions with the probiotic bacteria on the gut microbiota have been demonstrated to improve host well-being. As a representative of next-generation probiotics, Christensenella minuta (C. minuta) plays a critical role in regulating energy balance and metabolic homeostasis in human bodies, showing potential in treating metabolic disorders and reducing inflammation. However, interactions of C. minuta with the members of the networked gut microbiota have rarely been explored. Methods: In this study, we investigated the impact of C. minuta on fecal microbiota via metagenomic sequencing, focusing on retrieving bacterial strains and coculture assays of C. minuta with associated microbial partners. Results: Our results showed that C. minuta intervention significantly reduced the diversity of fecal microorganisms, but specifically enhanced some groups of bacteria, such as Lactobacillaceae. C. minuta selectively enriched bacterial pathways that compensated for its metabolic defects on vitamin B1, B12, serine, and glutamate synthesis. Meanwhile, C. minuta cross-feeds Faecalibacterium prausnitzii and other bacteria via the production of arginine, branched-chain amino acids, fumaric acids and short-chain fatty acids (SCFAs), such as acetic. Both metagenomic data analysis and culture experiments revealed that C. minuta negatively correlated with Klebsiella pneumoniae and 14 other bacterial taxa, while positively correlated with F. prausnitzii. Our results advance our comprehension of C. minuta's in modulating the gut microbial network. Conclusions: C. minuta disrupts the composition of the fecal microbiota. This disturbance is manifested through cross-feeding, nutritional competition, and supplementation of its own metabolic deficiencies, resulting in the specific enrichment or inhibition of the growth of certain bacteria. This study will shed light on the application of C. minuta as a probiotic for effective interventions on gut microbiomes and improvement of host health.

Identification of the Major Facilitator Superfamily Efflux Pump KpsrMFS in Klebsiella pneumoniae That Is Down-Regulated in the Presence of Multi-Stress Factors.

Efflux pumps play important roles in bacterial detoxification and some of them are stress-response elements that are up-regulated when the host is treated with antibiotics. However, efflux pumps that are down-regulated by stimulations are rarely discovered. Herein, we analyzed multiple transcriptome data and discovered a special (Major Facilitator Superfamily) MFS efflux pump, KpsrMFS, from Klebsiella pneumoniae, which was down-regulated when treated with antibiotics or extra carbon sources. Interestingly, overexpression of kpsrmfs resulted in halted cell growth in normal conditions, while the viable cells were rarely affected. The function of KpsrMFS was further analyzed and this efflux pump was determined to be a proton-driven transporter that can reduce the intracellular tetracycline concentration. In normal conditions, the expression of kpsrmfs was at a low level, while artificial overexpression of it led to increased endogenous reactive oxygen species (ROS) production. Moreover, by comparing the functions of adjacent genes of kpsrmfs, we further discovered another four genes that can confer similar phenotypes, indicating a special regulon that regulates cell growth. Our work provides new insights into the roles of efflux pumps and suggests a possible regulon that may regulate cell growth and endogenous ROS levels.

Genome-wide study investigating effector genes and polygenic prediction for kidney function in persons with ancestry from Africa and the Americas.

Chronic kidney disease is a leading cause of death and disability globally and impacts individuals of African ancestry (AFR) or with ancestry in the Americas (AMS) who are under-represented in genome-wide association studies (GWASs) of kidney function. To address this bias, we conducted a large meta-analysis of GWASs of estimated glomerular filtration rate (eGFR) in 145,732 AFR and AMS individuals. We identified 41 loci at genome-wide significance (p < 5 × 10-8), of which two have not been previously reported in any ancestry group. We integrated fine-mapped loci with epigenomic and transcriptomic resources to highlight potential effector genes relevant to kidney physiology and disease, and reveal key regulatory elements and pathways involved in renal function and development. We demonstrate the varying but increased predictive power offered by a multi-ancestry polygenic score for eGFR and highlight the importance of population diversity in GWASs and multi-omics resources to enhance opportunities for clinical translation for all.

Gut commensal Christensenella minuta modulates host metabolism via acylated secondary bile acids.

A strong correlation between gut microbes and host health has been observed in numerous gut metagenomic cohort studies. However, the underlying mechanisms governing host-microbe interactions in the gut remain largely unknown. Here we report that the gut commensal Christensenella minuta modulates host metabolism by generating a previously undescribed class of secondary bile acids with 3-O-acylation substitution that inhibit the intestinal farnesoid X receptor. Administration of C. minuta alleviated features of metabolic disease in high fat diet-induced obese mice associated with a significant increase in these acylated bile acids, which we refer to as 3-O-acyl-cholic acids. Specific knockout of intestinal farnesoid X receptor in mice counteracted the beneficial effects observed in their wild-type counterparts. Finally, we showed that 3-O-acyl-CAs were prevalent in healthy humans but significantly depleted in patients with type 2 diabetes. Our findings indicate a role for C. minuta and acylated bile acids in metabolic diseases.

Primary hepatic lymphoma a case report and literature review.

RATIONALE: Primary hepatic lymphoma is a rare extranodal non-Hodgkin lymphoma that is primarily localized in the liver. It predominantly affects elderly males and presents with nonspecific laboratory findings, imaging results, and clinical symptoms, making diagnosis challenging. Histopathological examination serves as the gold standard for diagnosis, and treatment options include chemotherapy or surgical intervention combined with chemotherapy. PATIENT CONCERNS: A 50-year-old male patient came to our hospital for treatment after finding a mass in his liver. DIAGNOSES: Laboratory tests and clinical symptoms lack specificity for primary hepatic lymphoma, and imaging findings can be difficult to differentiate. Pathology is the gold standard. OUTCOMES: The patient was dead. CONCLUSION: A definitive diagnosis primarily relies on histopathological examination, and surgical resection combined with chemotherapy yields better treatment outcomes.

Whole Genome Sequencing Based Analysis of Inflammation Biomarkers in the Trans-Omics for Precision Medicine (TOPMed) Consortium.

Inflammation biomarkers can provide valuable insight into the role of inflammatory processes in many diseases and conditions. Sequencing based analyses of such biomarkers can also serve as an exemplar of the genetic architecture of quantitative traits. To evaluate the biological insight, which can be provided by a multi-ancestry, whole-genome based association study, we performed a comprehensive analysis of 21 inflammation biomarkers from up to 38,465 individuals with whole-genome sequencing from the Trans-Omics for Precision Medicine (TOPMed) program. We identified 22 distinct single-variant associations across 6 traits - E-selectin, intercellular adhesion molecule 1, interleukin-6, lipoprotein-associated phospholipase A2 activity and mass, and P-selectin - that remained significant after conditioning on previously identified associations for these inflammatory biomarkers. We further expanded upon known biomarker associations by pairing the single-variant analysis with a rare variant set-based analysis that further identified 19 significant rare variant set-based associations with 5 traits. These signals were distinct from both significant single variant association signals within TOPMed and genetic signals observed in prior studies, demonstrating the complementary value of performing both single and rare variant analyses when analyzing quantitative traits. We also confirm several previously reported signals from semi-quantitative proteomics platforms. Many of these signals demonstrate the extensive allelic heterogeneity and ancestry-differentiated variant-trait associations common for inflammation biomarkers, a characteristic we hypothesize will be increasingly observed with well-powered, large-scale analyses of complex traits.

Functional characterization of Alzheimer's disease genetic variants in microglia.

Candidate cis-regulatory elements (cCREs) in microglia demonstrate the most substantial enrichment for Alzheimer's disease (AD) heritability compared to other brain cell types. However, whether and how these genome-wide association studies (GWAS) variants contribute to AD remain elusive. Here we prioritize 308 previously unreported AD risk variants at 181 cCREs by integrating genetic information with microglia-specific 3D epigenome annotation. We further establish the link between functional variants and target genes by single-cell CRISPRi screening in microglia. In addition, we show that AD variants exhibit allelic imbalance on target gene expression. In particular, rs7922621 is the effective variant in controlling TSPAN14 expression among other nominated variants in the same cCRE and exerts multiple physiological effects including reduced cell surface ADAM10 and altered soluble TREM2 (sTREM2) shedding. Our work represents a systematic approach to prioritize and characterize AD-associated variants and provides a roadmap for advancing genetic association to experimentally validated cell-type-specific phenotypes and mechanisms.

Canonical correlation analysis for multi-omics: Application to cross-cohort analysis.

Integrative approaches that simultaneously model multi-omics data have gained increasing popularity because they provide holistic system biology views of multiple or all components in a biological system of interest. Canonical correlation analysis (CCA) is a correlation-based integrative method designed to extract latent features shared between multiple assays by finding the linear combinations of features-referred to as canonical variables (CVs)-within each assay that achieve maximal across-assay correlation. Although widely acknowledged as a powerful approach for multi-omics data, CCA has not been systematically applied to multi-omics data in large cohort studies, which has only recently become available. Here, we adapted sparse multiple CCA (SMCCA), a widely-used derivative of CCA, to proteomics and methylomics data from the Multi-Ethnic Study of Atherosclerosis (MESA) and Jackson Heart Study (JHS). To tackle challenges encountered when applying SMCCA to MESA and JHS, our adaptations include the incorporation of the Gram-Schmidt (GS) algorithm with SMCCA to improve orthogonality among CVs, and the development of Sparse Supervised Multiple CCA (SSMCCA) to allow supervised integration analysis for more than two assays. Effective application of SMCCA to the two real datasets reveals important findings. Applying our SMCCA-GS to MESA and JHS, we identified strong associations between blood cell counts and protein abundance, suggesting that adjustment of blood cell composition should be considered in protein-based association studies. Importantly, CVs obtained from two independent cohorts also demonstrate transferability across the cohorts. For example, proteomic CVs learned from JHS, when transferred to MESA, explain similar amounts of blood cell count phenotypic variance in MESA, explaining 39.0% ~ 50.0% variation in JHS and 38.9% ~ 49.1% in MESA. Similar transferability was observed for other omics-CV-trait pairs. This suggests that biologically meaningful and cohort-agnostic variation is captured by CVs. We anticipate that applying our SMCCA-GS and SSMCCA on various cohorts would help identify cohort-agnostic biologically meaningful relationships between multi-omics data and phenotypic traits.

Exogenous Interleukin-37 Alleviates Hepatitis with Reduced Dendritic Cells and Induced Regulatory T Cells in Acute Murine Cytomegalovirus Infection.

Human cytomegalovirus (HCMV) infection is globally distributed, and the liver is one of the major targeting organs. So far, the mechanisms for cell and organ damage have not fully been elucidated and the treatments for the infection are mainly at symptoms. IL-37 has shown a protective role in certain inflammatory diseases. In the present study, potential protective effect of exogenous IL-37 on murine cytomegalovirus- (MCMV-) infected hepatitis was evaluated through analyses of serum transaminases, the liver histopathology and cytokine expression, and functional state of dendritic cells (DCs) and regulatory T cells (Tregs). These analyses showed a significant decrease in serum transaminase levels and a lower Ishak histopathologic score at the early stage of MCMV-infected mice with exogenous IL-37 pretreatment. The frequencies of MHC-Ⅱ, CD40, CD80, and CD86 positive DCs in the liver and spleen were decreased significantly at 7 days postinfection (dpi) in MCMV-infected mice with IL-37 pretreatment when compared with those without the pretreatment, while the total number of DCs in the liver was reduced in IL-37-pretreated mice. The induction of Tregs in the spleen was enhanced at dpi 3 with IL-37 pretreatment in MCMV-infected mice. The mRNA expression levels of cytokines in the liver were decreased significantly (IL-1ß, IL-6, IL-10, IL-4) or to some extent (TGF-ß and TNF-α). The present study suggested that exogenous IL-37 can alleviate MCMV-infected hepatitis, likely through reduced DCs and induced Tregs with a weaker cytokine storm, demonstrating its potential value in clinical management for HCMV-infected hepatitis.

Cell composition inference and identification of layer-specific spatial transcriptional profiles with POLARIS.

Spatial transcriptomics (ST) technology, providing spatially resolved transcriptional profiles, facilitates advanced understanding of key biological processes related to health and disease. Sequencing-based ST technologies provide whole-transcriptome profiles but are limited by the non-single cell-level resolution. Lack of knowledge in the number of cells or cell type composition at each spot can lead to invalid downstream analysis, which is a critical issue recognized in ST data analysis. Methods developed, however, tend to underuse histological images, which conceptually provide important and complementary information including anatomical structure and distribution of cells. To fill in the gaps, we present POLARIS, a versatile ST analysis method that can perform cell type deconvolution, identify anatomical or functional layer-wise differentially expressed (LDE) genes, and enable cell composition inference from histology images. Applied to four tissues, POLARIS demonstrates high deconvolution accuracy, accurately predicts cell composition solely from images, and identifies LDE genes that are biologically relevant and meaningful.

Whole genome sequencing identifies structural variants contributing to hematologic traits in the NHLBI TOPMed program.

Genome-wide association studies have identified thousands of single nucleotide variants and small indels that contribute to variation in hematologic traits. While structural variants are known to cause rare blood or hematopoietic disorders, the genome-wide contribution of structural variants to quantitative blood cell trait variation is unknown. Here we utilized whole genome sequencing data in ancestrally diverse participants of the NHLBI Trans Omics for Precision Medicine program (N = 50,675) to detect structural variants associated with hematologic traits. Using single variant tests, we assessed the association of common and rare structural variants with red cell-, white cell-, and platelet-related quantitative traits and observed 21 independent signals (12 common and 9 rare) reaching genome-wide significance. The majority of these associations (N = 18) replicated in independent datasets. In genome-editing experiments, we provide evidence that a deletion associated with lower monocyte counts leads to disruption of an S1PR3 monocyte enhancer and decreased S1PR3 expression.

MagicalRsq: Machine-learning-based genotype imputation quality calibration.

Whole-genome sequencing (WGS) is the gold standard for fully characterizing genetic variation but is still prohibitively expensive for large samples. To reduce costs, many studies sequence only a subset of individuals or genomic regions, and genotype imputation is used to infer genotypes for the remaining individuals or regions without sequencing data. However, not all variants can be well imputed, and the current state-of-the-art imputation quality metric, denoted as standard Rsq, is poorly calibrated for lower-frequency variants. Here, we propose MagicalRsq, a machine-learning-based method that integrates variant-level imputation and population genetics statistics, to provide a better calibrated imputation quality metric. Leveraging WGS data from the Cystic Fibrosis Genome Project (CFGP), and whole-exome sequence data from UK BioBank (UKB), we performed comprehensive experiments to evaluate the performance of MagicalRsq compared to standard Rsq for partially sequenced studies. We found that MagicalRsq aligns better with true R2 than standard Rsq in almost every situation evaluated, for both European and African ancestry samples. For example, when applying models trained from 1,992 CFGP sequenced samples to an independent 3,103 samples with no sequencing but TOPMed imputation from array genotypes, MagicalRsq, compared to standard Rsq, achieved net gains of 1.4 million rare, 117k low-frequency, and 18k common variants, where net gains were gained numbers of correctly distinguished variants by MagicalRsq over standard Rsq. MagicalRsq can serve as an improved post-imputation quality metric and will benefit downstream analysis by better distinguishing well-imputed variants from those poorly imputed. MagicalRsq is freely available on GitHub.

Droplet microfluidics-based high-throughput bacterial cultivation for validation of taxon pairs in microbial co-occurrence networks.

Co-occurrence networks inferred from the abundance data of microbial communities are widely applied to predict microbial interactions. However, the high workloads of bacterial isolation and the complexity of the networks themselves constrained experimental demonstrations of the predicted microbial associations and interactions. Here, we integrate droplet microfluidics and bar-coding logistics for high-throughput bacterial isolation and cultivation from environmental samples, and experimentally investigate the relationships between taxon pairs inferred from microbial co-occurrence networks. We collected Potamogeton perfoliatus plants (including roots) and associated sediments from Beijing Olympic Park wetland. Droplets of series diluted homogenates of wetland samples were inoculated into 126 96-well plates containing R2A and TSB media. After 10 days of cultivation, 65 plates with > 30% wells showed microbial growth were selected for the inference of microbial co-occurrence networks. We cultivated 129 bacterial isolates belonging to 15 species that could represent the zero-level OTUs (Zotus) in the inferred co-occurrence networks. The co-cultivations of bacterial isolates corresponding to the prevalent Zotus pairs in networks were performed on agar plates and in broth. Results suggested that positively associated Zotu pairs in the co-occurrence network implied complicated relations including neutralism, competition, and mutualism, depending on bacterial isolate combination and cultivation time.

A comprehensive comparison on cell-type composition inference for spatial transcriptomics data.

Spatial transcriptomics (ST) technologies allow researchers to examine transcriptional profiles along with maintained positional information. Such spatially resolved transcriptional characterization of intact tissue samples provides an integrated view of gene expression in its natural spatial and functional context. However, high-throughput sequencing-based ST technologies cannot yet reach single cell resolution. Thus, similar to bulk RNA-seq data, gene expression data at ST spot-level reflect transcriptional profiles of multiple cells and entail the inference of cell-type composition within each ST spot for valid and powerful subsequent analyses. Realizing the critical importance of cell-type decomposition, multiple groups have developed ST deconvolution methods. The aim of this work is to review state-of-the-art methods for ST deconvolution, comparing their strengths and weaknesses. In particular, we construct ST spots from single-cell level ST data to assess the performance of 10 methods, with either ideal reference or non-ideal reference. Furthermore, we examine the performance of these methods on spot- and bead-level ST data by comparing estimated cell-type proportions to carefully matched single-cell ST data. In comparing the performance on various tissues and technological platforms, we concluded that RCTD and stereoscope achieve more robust and accurate inferences.

Bacteroides propionicigenes sp. nov., isolated from human faeces.

An anaerobic bacterial strain, designated as NSJ-90T, was isolated from the faeces of a healthy adult in China. Cells of strain NSJ-90T were Gram-stain-negative, non-motile, non-spore-forming and rod-shaped. Based on 16S rRNA gene sequence analysis, strain NSJ-90T belonged to the genus Bacteroides and was phylogenetically closely related to Bacteroides clarus YIT 12056T (16S rRNA gene identity was 97.04 %). The DNA G+C content of strain NSJ-90T was 44.85 mol% (calculated from the genome). The average nucleotide identity between strain NSJ-90T and B. clarus YIT 12056T was 87.60 %. The major cellular fatty acids (>10 %) of strain NSJ-90T were iso-C15 : 0, anteiso-C15 : 0 and iso-C17 : 0 3-OH. Menaquinone-10 was detected as the respiratory quinone. The major products of glucose fermentation were acetic, propionic and isovaleric acids. Based on its phylogenetic, phenotypic and chemotaxonomic characteristics, we propose that strain NSJ-90T represents a novel species of the genus Bacteroides, for which the name Bacteroides propionicigenes sp. nov. is proposed. The type strain is NSJ-90T (=CGMCC 1.17886T=KCTC 25305T).

A systematic evaluation of Hi-C data enhancement methods for enhancing PLAC-seq and HiChIP data.

The three-dimensional organization of chromatin plays a critical role in gene regulation. Recently developed technologies, such as HiChIP and proximity ligation-assisted ChIP-Seq (PLAC-seq) (hereafter referred to as HP for brevity), can measure chromosome spatial organization by interrogating chromatin interactions mediated by a protein of interest. While offering cost-efficiency over genome-wide unbiased high-throughput chromosome conformation capture (Hi-C) data, HP data remain sparse at kilobase (Kb) resolution with the current sequencing depth in the order of 108 reads per sample. Deep learning models, including HiCPlus, HiCNN, HiCNN2, DeepHiC and Variationally Encoded Hi-C Loss Enhancer (VEHiCLE), have been developed to enhance the sequencing depth of Hi-C data, but their performance on HP data has not been benchmarked. Here, we performed a comprehensive evaluation of HP data sequencing depth enhancement using models developed for Hi-C data. Specifically, we analyzed various HP data, including Smc1a HiChIP data of the human lymphoblastoid cell line GM12878, H3K4me3 PLAC-seq data of four human neural cell types as well as of mouse embryonic stem cells (mESC), and mESC CCCTC-binding factor (CTCF) PLAC-seq data. Our evaluations lead to the following three findings: (i) most models developed for Hi-C data achieve reasonable performance when applied to HP data (e.g. with Pearson correlation ranging 0.76-0.95 for pairs of loci within 300 Kb), and the enhanced datasets lead to improved statistical power for detecting long-range chromatin interactions, (ii) models trained on HP data outperform those trained on Hi-C data and (iii) most models are transferable across cell types. Our results provide a general guideline for HP data enhancement using existing methods designed for Hi-C data.