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Cai, K.-W.

Publications and source records attributed to Cai, K.-W..

2 recordsLinked to original sources

Exploring multi-level microbial interactions from individual 3D genomes to community networks

Microbial communities interact with their hosts through complex genomic networks that influence ecosystem stability and disease progression. Here, we present FindMeta3D, a computational framework that simultaneously identifies microbial three-dimensional (3D) genome structures and cross-domain interaction networks. Applying this approach to 528 Hi-C samples, we resolved 3D genome structures of 344 microbial species, revealing five evolutionarily conserved chromatin folding patterns linked to intrinsic sequence features. Further analyses demonstrated distinct microbial-host interaction preferences and identified functional interaction hotspots that are critical for infection. Experimental deletion of such hotspots in the EBV genome resulted in significant infection defects, demonstrating their essential role in viral infectivity. Additionally, we constructed the first Cross-domain Microbial Interaction Network (CMIN), which uncovered pathogen-specific subnetworks and demonstrate dramatic restructuring of gut microbial communities in neutropenic patients, including enhanced Klebsiella-phage interactions. Subnetwork analysis identified potential phage therapy targets, such as Klebsiella phage ST16-OXA48phi5.4. These findings provide fundamental insights into microbial 3D genomics and establish FindMeta3D as a powerful platform for studying microbial genome structure and communities and developing antimicrobial strategies.

microbiology↗

Hi-Compass resolves cell-type chromatin interactions by single-cell and spatial ATAC-seq data across biological scales

Computational prediction of three-dimensional (3D) genome organization provides an alternative approach to overcome the cost and technical limitations of Hi-C experiments. However, current Hi-C prediction models are constrained by their narrow applicability to studying the impact of genetic variation on genome folding in specific cell lines, significantly restricting their biological utility. We present Hi-Compass, a generalizable deep learning model that accurately predicts chromatin organization across diverse biological contexts, from bulk to single-cell samples. Hi-Compass outperforms existing methods in prediction accuracy and is generalizable to unseen cell types through chromatin accessibility data, enabling broad applications in single cell omics. Hi-Compass successfully resolves cell-type-specific 3D genome architectures in complex biological scenarios, including immune cell states, organ heterogeneity, and tissue spatial organization. Furthermore, Hi-Compass enables integrative analysis of single-cell multiome data, linking chromatin interaction dynamics to gene expression changes across cell clusters, and mapping disease variants to pathogenic genes. Hi-Compass also extends to spatial multi-omics data, generating spatially resolved Hi-C maps that reveal domain-specific chromatin interactions linked to spatial gene expression patterns.

bioinformatics↗