Biomedical subjects

Zhao Li

Publications and source records attributed to Zhao Li.

3 recordsLinked to original sources

PubMed2026

Identification of a novel and a recurrent CDC45 variant in a Chinese family with Meier-Gorlin syndrome 7 and a literature review.

INTRODUCTION: Meier-Gorlin syndrome 7 (MGORS7) is a rare autosomal recessive disorder characterized by primordial dwarfism, craniosynostosis, and patellar aplasia, caused by pathogenic variants of CDC45. Here, we report a Chinese patient presenting with classic hallmarks of MGORS7 alongside atypical clinical features, including hearing and visual impairments. METHODS: Clinical and radiological data were collected. Whole-genome sequencing and Sanger sequencing were performed to identify and validate the causative variants. Their functional effects were investigated using an exon-trapping assay, and a literature review of previously reported MGORS7 cases was conducted. RESULTS: Genetic analysis identified two compound heterozygous CDC45 variants: c.1416C>T (p.H472=) and c.1559+2T>A, which are a recurrent variant in the East Asian population and a novel variant, respectively. Our exon-trapping assay indicated that c.1559+2T>A induced aberrant splicing, generating transcripts predicted to undergo nonsense-mediated mRNA decay. Additionally, growth hormone therapy was initiated in our patient, with a noted improvement in growth parameters in the initial assessment and without immediate complications. The literature review identified a total of 32 CDC45 variants in 29 patients with MGORS7, who showed high heterogeneity in clinical phenotypes. DISCUSSION: Our study further expanded the mutational spectrum of CDC45 and provided a preliminary clinical observation suggesting that growth hormone therapy may be beneficial for growth retardation in patients with MGORS7.

CDC45↗

PubMed2026

Hidden proteins encoded by non-canonical open reading frames: A review.

There is increasing evidence that translation is not limited to annotated protein-coding genes. Ribosome profiling sequencing, mass spectrometry-based proteomics, and immunopeptidomics have identified the productive translation of non-canonical open reading frames (ORFs). This suggests that the functional proteome includes not only conserved proteins but also proteins hidden in non-coding RNAs and de novo proteins. Some of these translated products are functional peptides, while others may be non-functional, potentially arising from evolutionary events. Several non-canonical ORF-encoded peptides have been found to regulate multiple physiological and pathological functions, particularly in cancer, immunity, and inflammation, indicating that they have potential as biomarkers and novel therapeutic targets. To better understand the diversity of functional peptides and translated non-canonical ORFs based on existing data, we summarize their classification according to transcriptional features and supporting evidence, including non-canonical ORFs located in ncRNAs and canonical mRNAs. This review provides a concise summary of the origin, discovery methods, and classification of non-canonical ORFs. It offers insights into the origins and functions of non-canonical ORF-encoded peptides from an evolutionary perspective, while also exploring the biological functions and regulatory mechanisms of these non-canonical ORF-encoded hidden proteins in tumorigenesis and progression.

Open Reading Frames↗

PubMed2026

CeLLTra: aligning cell names with gene expression via a pathway-informed transformer.

MOTIVATION: Single-cell RNA sequencing (scRNA-Seq) technology enables detailed exploration of gene expression at the individual cell level, crucial for annotating cell types and understanding cellular diversity. Traditional methods for cell type annotation often rely on marker genes and manual labeling, posing challenges due to low data quality and incomplete reference datasets. RESULTS: We developed CeLLTra, a novel contrastive learning framework that leverages a Transformer-based model integrating biological pathway information to group genes into super tokens, effectively capturing comprehensive gene expression from scRNA-Seq data. By combining this pathway-informed Transformer with a pretrained domain-specific language model, CeLLTra accurately aligns cell-type annotations with gene expression profiles. Evaluations on a large-scale human scRNA-Seq dataset showed that CeLLTra significantly outperformed state-of-the-art methods in supervised and zero-shot cell-type prediction. Additionally, CeLLTra generalized well to external datasets, improving clustering performance and enabling better characterization of cancerous cell states in tumor-infiltrating myeloid cells from non-small cell lung cancer patients. AVAILABILITY AND IMPLEMENTATION: CeLLTra is freely available on GitHub (https://github.com/WJZheng-group/CeLLTra) and Zenodo (https://doi.org/10.5281/zenodo.17666735). The datasets underlying this article are the following: GSE201333 and GSE127465. All these datasets are publicly available and can be freely accessed on the Gene Expression Omnibus repository.

Humans↗