Biomedical subjects

Fei Fang

Publications and source records attributed to Fei Fang.

4 recordsLinked to original sources

PubMed2026

A Draft Map of E. coli Proteoforms.

Top-down proteomics (TDP) enables direct characterization of intact proteoforms, providing protein-level insights into molecular diversity arising from post-translational modifications and sequence variations. Despite this advantage, proteome coverage in TDP remains limited relative to bottom-up proteomics (BUP). To expand coverage, we developed an integrated multidimensional approach combining sequential protein extraction, size-exclusion chromatography (SEC) fractionation, and capillary zone electrophoresis (CZE)-tandem mass spectrometry (MS/MS) and reversed-phase liquid chromatography (RPLC)-MS/MS. This approach identified 743 proteoform families and 10,613 proteoforms from E. coli cells through hundreds of MS runs. By incorporating previous E. coli TDP data sets from our group, we identified 14,932 proteoforms from 985 proteoform families, covering 43% of the E. coli proteome. The data represent the highest proteome coverage of cells by MS-based TDP, creating a draft map of E. coli proteoforms. The results offer strong evidence that MS-based TDP can reach high proteome coverage.

Escherichia coli↗

PubMed2026

Enhanced Prediction of Peripheral Artery Disease Using Plasma Proteomics Among Individuals Without Diabetes.

BACKGROUND: Although peripheral artery disease (PAD) is an important diabetes complication, a substantial proportion of cases occur among individuals without diabetes. This study aimed to assess the predictive value of plasma proteomics in the long-term risk of PAD among individuals initially free of diabetes. METHODS: Included were 46 508 participants (6046 with prediabetes) without diabetes or major cardiovascular disease at recruitment of the UK Biobank. Using multivariable Cox regression models, a total of 2923 unique plasma proteins were assessed for the associations with incident PAD. Significant proteins were subsequently processed by a trained light gradient boosting machine classifier to determine important proteins. Using receiver operating characteristic analyses, the performance of these important proteins in predicting incident PAD were evaluated, in the whole sample and by glycemic status (normoglycemia and prediabetes). RESULTS: During a median follow-up of 12.7 years, 461 participants developed PAD. There were 107 proteins associated with incident PAD, with 103 positive associations. The LGBM approach identified 9 proteins (eg, WFDC2 [WAP 4-disulfide core domain protein 2], MMP12 [macrophage metalloelastase], and GDF15 [growth differentiation factor 15]) as the top-ranked proteins based on their importance ordering. Whereas glycated hemoglobin showed very modest predictive accuracy, a panel incorporating these top proteins showed good performance in the prediction of PAD risk (area under the curve 0.820), and it significantly enhanced the prediction beyond traditional risk factors (raising area under the curve from 0.803 to 0.837, DeLong test P=5.21×10-3). These observations were consistent for participants with normoglycemia or prediabetes. CONCLUSIONS: Plasma protein biomarkers enhance the prediction of long-term risk for PAD among individuals without diabetes, regardless of glycemic status.

Humans↗

PubMed2025

Mass spectrometry-based top-down proteomics for proteoform profiling of protein coronas.

The protein corona is a layer of biomolecules-primarily proteins-that adsorbs to nanoparticle (NP) surfaces in biological fluids. If the purpose of the NP is therapeutic, this can have a profound effect on its biological activity and function in vivo. Protein corona formation can also be exploited for diagnostic purposes and to differentially enrich proteins for biomarker discovery. For all of these applications, it is useful to determine which proteins, and which specific proteoforms, bind to different types of NP. The traditional mass spectrometry (MS)-based bottom-up proteomics does not accurately identify specific proteoforms within the protein corona. This limitation impedes the nanomedicine field's ability to precisely predict the biological fate and pharmacokinetics of nanomedicines and their effectiveness in early-stage biomarker discovery and disease detection because many different proteoforms of the same gene could exist in the corona, and they have divergent biological functions. Here, we describe how to use capillary zone electrophoresis (CZE)-MS-based top-down proteomics to characterize the proteoform landscape of the protein corona. Our procedures detail the recovery of intact proteoforms from NP surfaces by using detergent-assisted proteoform elution and the measurement of these proteoforms by using CZE-tandem MS (MS/MS) and CZE-high-field asymmetric waveform ion mobility spectrometry (FAIMS)-MS/MS. The entire workflow is completed within 3-4 d. Using this protocol, hundreds of proteoforms from the protein corona of polystyrene NPs can be identified. Distinct protein corona proteoform profiles were observed from NPs with different physicochemical properties. The addition of FAIMS is beneficial for more in-depth proteoform characterization.

Proteomics↗

PubMed2025

Insights into dill (Anethum graveolens) flavor formation via integrative analysis of chromosomal-scale genome, metabolome and transcriptome.

INTRODUCTION: Dill (Anethum graveolens) is a significant medicinal herb belonging to the Apiaceae family. Owing to its high levels of volatile organic compounds (VOCs), dill is commonly utilized for essential oil extraction and medicine purpose. However, the biosynthesis of the crucial VOC in dill remains obscure. OBJECTIVES: Identify the key VOCs related to the flavor formation in dill and dissect the regulatory mechanism of their synthesis. METHODS: The dill chromosomal-level genome was constructed by PacBio HiFi, Hi-C, and BGISEQ second generation sequencing and assembly. The VOCs in dill leaves were identified through GC-MS. The potential mechanism involved in regulating the VOC accumulation in dill flavor formation was analyzed by multi-omics analysis. RESULTS: A 1.17 Gb chromosome-scale genome of dill with a contig N50 of 10.78 Mb was constructed. A total of 46,538 genes were annotated across 11 assembled chromosomes. Comparative genomics analysis suggested that transposable element insertions, especially LTR-Gypsy, have contributed to the evolution and expansion of the dill genome. The flavor formation of dill was mainly attributed to terpenoids, especially α-phellandrene, β-ocimene, and o-cymene. The contribution of expansion and replication of terpenoid synthesis pathway genes, especially terpene synthase (TPS), to the abundant terpenoid production of dill was identified. Differential gene expression patterns observed at various developmental stages and tissues provided key candidate genes for the regulation of terpenoid synthesis, as well as transcription factors. The different accumulation of esters and aromatics also affected the flavor formation of dill. The key genes implicated in the synthesis of anethole, namely AIS and AMT were further identified. CONCLUSION: This study constructed the chromosome level genome and identified the main VOCs and related key genes in flavor formation of dill, shedding lights on our understanding of terpenoid biosynthesis but also offered guidance for future genetic research on molecular breeding in Anethum graveolens.

Transcriptome↗