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Peng Gao

Publications and source records attributed to Peng Gao.

5 recordsLinked to original sources
PubMed2026

Isolation and characterization of two novel species Neorhizobium fuzhouense sp. nov. and Neotabrizicola paludis sp. nov.

Two novel aerobic bacterial strains, designated SGZ-38T and sgz301269T, were isolated from the root of Pennisetum sp. and paddy soil, respectively. Strain SGZ-38T grew at 10-40 ℃ (optimum 30 °C) and pH 5.0-12.0 (optimum 6.5) and tolerated up to 1.0% NaCl (w/v), whereas strain sgz301269T grew at 15-37 °C (optimum 30 °C), pH 5.0-9.5 (optimum 7.0) and 0-2% NaCl (optimum 0%). Phylogenetic trees based on the 16S rRNA gene and genomes placed both strains into distinct lineages, forming separated clades from their closest relatives. Strain SGZ-381T exhibited the highest 16S rRNA gene similarities to "Neorhizobium deserti" ACCC 61627T (97.4%), and strain sgz301269T had the highest 16S rRNA gene sequence similarity to Neotabrizicola shimadae N10T (97.6%). The respiratory quinone in both strains was ubiquinone-10. The main fatty acids of SGZ-381T were Summed feature 8, Summed feature 2 and C16:0, whereas strain sgz301269T included C10:0 3OH, C18:0 3OH and Summed feature 8. The DNA G+C content of SGZ-381T and sgz301269T was 62.1% and 65.5%, respectively. The average nucleotide identity and digital DNA-DNA hybridization values between each strain and their respective closest species were 74.6% and 20.1%, 75.3% and 17.4% respectively, below the thresholds for species delineation. Based on the comprehensive chemotaxonomic, phylogenetic, and phenotypic evidence, proposed names of the novel strains are Neorhizobium fuzhouense sp. nov. (type strain SGZ-381T=GDMCC1.4207T=JCM 36770T), Neotabrizicola paludis sp. nov. (type strain sgz301269T=MCCC 1K09178T=KCTC 8856T).

Bacterial Typing Techniques↗
PubMed2026

ProMeta: a meta-learning framework for robust disease diagnosis and prediction from plasma proteomics.

MOTIVATION: The plasma proteome offers a dynamic window of human health, capturing the real-time intersections between genetics and physiology. However, the application of deep learning to proteomics is currently hindered by a reliance on large-scale labeled datasets, rendering standard models ineffective for rare or novel diseases where patient samples are inherently scarce. RESULTS: Here, we present ProMeta, a meta-learning framework designed to enable robust disease modeling under extreme data restrictions. By integrating knowledge-guided pathway encoding with bi-level meta-optimization, ProMeta projects unstructured proteomic profiles into biologically interpretable functional tokens. This architecture allows the model to learn a global initialization containing transferable biological priors from biobank-scale data, facilitating rapid adaptation to novel tasks. Through comprehensive benchmark experiments, ProMeta consistently outperformed transfer learning and traditional machine learning baselines in both disease diagnosis and prediction tasks. In the most challenging 4-shot scenarios (utilizing only 2 cases and 2 controls), the model achieved robust generalization with an average AUROC of ∼0.69, representing a 24.6% relative improvement over the best-performing baseline methods. Mechanistic investigation revealed that ProMeta disentangles cases from controls in the latent space prior to task-specific adaptation, confirming the acquisition of universal biological rules rather than rote memorization. Furthermore, gradient-based interpretation identified disease-specific protein biomarkers and functional pathways consistent with known pathophysiology. Collectively, ProMeta overcomes the data-scarcity bottleneck in precision medicine, providing a scalable, interpretable framework for characterizing the full spectrum of human diseases, particularly for rare conditions lacking extensive clinical cohorts. AVAILABILITY AND IMPLEMENTATION: The source code of ProMeta is available at GitHub (https://github.com/lihan97/ProMeta).

Proteomics↗
PubMed2026

Description of two nitrogen-fixing bacteria, Azospirillum mesophilum sp. nov. and Azospirillum terrae sp. nov., isolated from paddy soils.

Two novel aerobic, rod-shaped, motile bacterial strains, designated as sgz302134T and sgz301742T, were isolated from paddy soil in Fujian Province. Strains sgz302134T and sgz301742T shared the highest 16S rRNA gene sequence similarities with the type strains Azospirillum isscasi C340-1T (98.2%) and Azospirillum thiophilum DSM 21654T (97.4%), respectively. The phylogenetic tree based on 16S rRNA gene sequences showed that two strains clustered with members of the genus Azospirillum. Growth of strains sgz302134T and sgz301742T was observed at 10-45 °C, pH 5.0-9.5 and 0-0.5% (w/v) NaCl and 15-37 °C, pH 6.0-9.0 and 0-1.0% (w/v) NaCl, respectively. Strains sgz302134T and sgz301742T contained Q-10 as the main quinone. The main fatty acids (>10%) of both strains were summed feature 2 (C12 : 0 aldehyde), summed feature 3 (C16 : 1 ω7c and/or C16 : 1 ω6c), summed feature 8 (C18 : 1 ω7c and/or C18 : 1 ω6c) and C16 : 0. The genomic DNA G+C content of strains sgz302134T and sgz301742T was 68.4 and 68.3%, respectively. The digital DNA-DNA hybridization and average nucleotide identity values between the two strains and their related reference strains were 27.8 and 87.4% and 22.0 and 84.3%, respectively. Both strains possessed nif genes nifBDEHKN. Based on the above results, these two strains represent two novel species of the genus Azospirillum, for which the names Azospirillum mesophilum sp. nov. and Azospirillum terrae sp. nov. are proposed. The type strains are sgz302134T (=MCCC 1K09520T=KCTC 8840T) and sgz301742T (=MCCC 1K09804T=KCTC 18149T), respectively.

Soil Microbiology↗
PubMed2025

Enhancing and accelerating cell type deconvolution of large-scale spatial transcriptomics slices with dual network model.

MOTIVATION: Cell type deconvolution deciphers spatial distribution of mRNA transcripts at single cell level by integrating single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics data to infer mixture of cell types of spots in slices. Current algorithms are criticized for neglecting connection between scRNA-seq and spatial transcriptomics data, as well as time-consuming, hampering their application to large-scale datasets. RESULTS: In this study, we propose a joint learning nonnegative matrix factorization algorithm for fast cell type deconvolution (aka jMF2D), which integrates scRNA-seq and spatial transcriptomics data with network models. To bridge scRNA-seq and spatial transcriptomics data, jMF2D jointly learns cell type similarity network to enhance quality of signatures of cell types, thereby promoting accuracy and efficiency of deconvolution. Experiments demonstrate that jMF2D outperforms state-of-the-art baselines in terms of accuracy by saving about 90% running time on various datasets generated by different platforms. Furthermore, it can also facilitates the identification of spatial domains and bio-marker genes, providing an efficient and effective model for analyzing spatial transcriptomics data. AVAILABILITY AND IMPLEMENTATION: The software is coded using python, and is free available for academic https://github.com/xkmaxidian/jMF2D.

Algorithms↗