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Heng Liang

Publications and source records attributed to Heng Liang.

2 recordsLinked to original sources
PubMed2026

DNA methylation landscape of cerebrospinal fluid cells in multiple sclerosis: an epigenome-wide association study.

BACKGROUND: Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system in which DNA methylation may link genetic and environmental risk factors. METHODS: We profiled genome-wide DNA methylation in cerebrospinal fluid (CSF) cells from people with MS (pwMS) and matched controls. Differentially methylated positions (DMPs) and regions (DMRs) were integrated with transcriptomic data, T-cell chromatin annotations, and pathway analyses. Protocadherin gamma (PCDHγ) expression was assessed in primary CD4+ T-cell subsets and confirmed by flow cytometry. FINDINGS: We identified 2710 DMPs and 4330 DMRs associating with genes that were enriched in immune signalling, adhesion and migration processes, and were accompanied by corresponding RNA changes. MS-associated methylation changes enriched in the cohesin chromatin-regulation pathway localised to T-cell regulatory regions, and this pathway included multiple protocadherin (PCDH) genes, which displayed consistent methylation and expression changes in CSF cells of pwMS compared to controls. PCDHγ cluster gene expression was detected in CD4+ T-cell subsets, and flow cytometry confirmed PCDHγ protein expression in peripheral blood T cells. Moreover, co-expression analysis suggests a role of PCDH genes in aryl hydrocarbon receptor (AHR) signalling. Protein-level validation showed fewer PCDHγ-positive CD4+ T cells in pwMS and activation-induced PCDHγ upregulation after T-cell stimulation. INTERPRETATION: DNA methylation changes in CSF resident cells reflect dysregulated T cell activation and migration in pwMS and suggest involvement of protocadherin molecules in MS pathogenesis. FUNDING: European Research Council, Swedish Research Council, Swedish Brain Foundation, Swedish MS Foundation, Knut and Alice Wallenberg Foundation, European Union and others.

Humans↗
PubMed2026

Integrating metagenomic next-generation sequencing into a multimodal diagnostic framework for spinal infection: enhancing etiological identification and clinical prediction.

BACKGROUND: Spinal infection (SI) remains diagnostically challenging because of heterogeneous etiologies, nonspecific clinical manifestations, and the limited sensitivity of conventional microbiological approaches, particularly following empirical antimicrobial exposure. Although metagenomic next-generation sequencing (mNGS) enables unbiased pathogen detection, its incremental clinical value beyond pathogen identification and its role within integrated diagnostic strategies remain incompletely established. METHODS: We retrospectively analyzed 208 consecutive patients with suspected SI between August 2022 and August 2025. Final diagnoses were established using a multidisciplinary-adjudicated composite reference standard incorporating clinical, radiological, microbiological, and histopathological evidence. The diagnostic performance of mNGS was compared with conventional culture and histopathology. Furthermore, multimodal predictive models integrating clinical variables and microbiological information were developed using L1-regularized logistic regression. RESULTS: In the comparative cohort, mNGS achieved a significantly higher diagnostic yield than culture (66.5% vs. 27.41%, P < 0.001). Among confirmed SI cases, mNGS demonstrated higher sensitivity than conventional culture (91.67% vs. 40.15%, P < 0.001). mNGS identified a substantially broader pathogen spectrum, ranging from fastidious organisms such as Mycobacterium tuberculosis and Brucella to rare pathogens including Talaromyces marneffei and Coxiella burnetii, and maintained robust sensitivity (98.2%) despite prior antibiotic exposure. While an integrated clinical model achieved an AUC of 0.916, mNGS as a standalone modality provided superior discriminative power (AUC = 0.889) compared to histopathology (AUC = 0.836), the Conventional Biomarker Model (AUC = 0.742), and culture (AUC = 0.693). CONCLUSIONS: mNGS is a high-yield diagnostic tool for spinal infection, particularly in culture-negative and antibiotic-pretreated scenarios. Integrating mNGS into a multimodal clinical framework facilitates etiological clarity and precision antimicrobial therapy.

Humans↗