Predicting potential microbe-disease associations based on auto-encoder and graph convolution network.

The increasing body of research has consistently demonstrated the intricate correlation between the human microbiome and human well-being. Microbes can impact the efficacy and toxicity of drugs through various pathways, as well as influence the occurrence and metastasis of tumors. In clinical practice, it is crucial to elucidate the association between microbes and diseases. Although traditional biological experiments accurately identify this association, they are time-consuming, expensive, and susceptible to experimental conditions. Consequently, conducting extensive biological experiments to screen potential microbe-disease associations becomes challenging. The computational methods can solve the above problems well, but the previous computational methods still have the problems of low utilization of node features and the prediction accuracy needs to be improved. To address this issue, we propose the DAEGCNDF model predicting potential associations between microbes and diseases. Our model calculates four similar features for each microbe and disease. These features are fused to obtain a comprehensive feature matrix representing microbes and diseases. Our model first uses the graph convolutional network module to extract low-rank features with graph information of microbes and diseases, and then uses a deep sparse Auto-Encoder to extract high-rank features of microbe-disease pairs, after which the low-rank and high-rank features are spliced to improve the utilization of node features. Finally, Deep Forest was used for microbe-disease potential relationship prediction. The experimental results show that combining low-rank and high-rank features helps to improve the model performance and Deep Forest has better classification performance than the baseline model.

Accurate and Convenient Lung Cancer Diagnosis through Detection of Extracellular Vesicle Membrane Proteins via Förster Resonance Energy Transfer.

Tumor-derived extracellular vesicles (EVs) are promising to monitor early stage cancer. Unfortunately, isolating and analyzing EVs from a patient's liquid biopsy are challenging. For this, we devised an EV membrane proteins detection system (EV-MPDS) based on Förster resonance energy transfer (FRET) signals between aptamer quantum dots and AIEgen dye, which eliminated the EV extraction and purification to conveniently diagnose lung cancer. In a cohort of 80 clinical samples, this system showed enhanced accuracy (100% versus 65%) and sensitivity (100% versus 55%) in cancer diagnosis as compared to the ELISA detection method. Improved accuracy of early screening (from 96.4% to 100%) was achieved by comprehensively profiling five biomarkers using a machine learning analysis system. FRET-based tumor EV-MPDS is thus an isolation-free, low-volume (1 µL), and highly accurate approach, providing the potential to aid lung cancer diagnosis and early screening.

Schumannella soli sp. nov., a novel actinomycete isolated from mangrove soil by in situ cultivation.

A novel actinobacterial strain, designated 10F1D-1T, was isolated from soil sample collected from Futian mangrove nature reserve, China using of the in situ cultivation technique. Preliminary analysis based on the 16S rRNA gene sequence revealed that strain 10F1D-1T was the member of genus Schumannella with sharing highest sequence similarity (99.7%) to Schumannella luteola DSM 23141T. Phylogenetic analyses based on 16S rRNA gene sequences and core proteome consistently exhibited that strain 10F1D-1T formed a monophyletic clade with Schumannella luteola DSM 23141T. Comparative genomic analyses clearly separated strain 10F1D-1T from the only species of the genus Schumannella based on average nucleotide identity (ANI), average amino acid identity (AAI) and digital DNA-DNA hybridization (dDDH) values below the thresholds for species delineation. The genome of strain 10F1D-1T contains the biosynthetic gene clusters for osmoprotectants to adapt to the salt environment of mangrove. Strain 10F1D-1T also contains the biosynthetic gene clusters for bioactive compounds as secondary metabolites. On the basis of the polyphasic analysis, strain 10F1D-1T is considered to represent a novel species of the genus Schumannella, for which the name Schumannella soli sp. nov. (type strain 10F1D-1T = CGMCC1.16699T = JCM 33146T) is proposed.

Labedella phragmitis sp. nov. and Labedella populi sp. nov., two endophytic actinobacteria isolated from plants in the Taklamakan Desert and emended description of the genus Labedella.

Two novel strains, designated 11W25H-1T and 8H24J-4-2T, were isolated from surface-sterilized plant tissues collected from the Taklamakan Desert in the Xinjiang Uygur Autonomous Region, China. The strains were characterized by a polyphasic approach in order to clarify their taxonomic positions. They were Gram-stain positive, aerobic, non-motile, non-spore-forming and rod-shaped. The 16S rRNA gene sequences of the strains showed highest similarities with Labedella gwakjiensis KCTC 19176T (99.2% and 98.9%, respectively) and Labedella endophytica CPCC 203961T (98.9% and 99.0%, respectively). The sequence similarity between strains 11W25H-1T and 8H24J-4-2T was 99.4%. Phylogenetic analyses based on 16S rRNA gene sequences and single-copy phylogenetic marker genes (pMGs) showed that the two strains belonged to the genus Labedella and formed a separate cluster from the closest species L. gwakjiensis KCTC 19176T and L. endophytica CPCC 203961T. Genomic analyses, including average nucleotide identity (ANI) and digital DNA-DNA hybridization (dDDH), clearly separated the strains from each other and from the other species of the genus Labedella with values below the thresholds for species delineation. The two strains showed chemotaxonomic characteristics and phenotypic properties in agreement with the description of the genus Labedella and also confirmed the differentiation from the closest species. The data demonstrated that strains 11W25H-1T and 8H24J-4-2T represented two novel species of the genus Labedella, for which the names Labedella phragmitis sp. nov. (type strain 11W25H-1T=JCM 33144T=CGMCC 1.16700T) and Labedella populi sp. nov. (type strain 8H24J-4-2T=JCM 33143T=CGMCC 1.16697T) are proposed.

Mangrovicella endophytica gen. nov., sp. nov., a new member of the family Aurantimonadaceae isolated from Aegiceras corniculatum.

A Gram-negative, aerobic, motile and short-rod-shaped bacterium, designated strain 5T4P-12-1T, was isolated from a piece of surface-sterilized bark of Aegiceras corniculatum collected from Cotai Ecological Zones in Macao, China and tested by a polyphasic approach to clarify its taxonomic position. Strain 5T4P-12-1T grew optimally with 0-1 % (w/v) NaCl at 30 °C and at pH 7.0-8.0. The 16S rRNA gene sequence of strain 5T4P-12-1T had the highest similarity (96.7 %) to Aureimonas altamirensis DSM 21988T. Phylogenic analysis based on 16S rRNA gene sequences and coding sequences of 98 protein clusters showed that the strain represented a novel genus of the family Aurantimonadaceae. The predominant quinone system of strain 5T4P-12-1T was ubiquinone 10. The polar lipid profile contained diphosphatidylglycerol, phosphatidylglycerol, phosphatidylcholine, phosphatidylethanolamine, phosphatidylmethylethanolamine, an unidentified aminophospholipid, three unidentified aminolipids, three unidentified phospholipids and three unidentified lipids. The major fatty acids (>10 % of total fatty acids) were C18 : 1ω7c (55.4 %) and C18 : 1 2-OH (15.6 %). The DNA G+C content of strain 5T4P-12-1T was 66.5 mol%. Based on the phylogenic, phenotypic and chemotaxonomic features, strain 5T4P-12-1T is considered to represent a novel species of a new genus in the family Aurantimonadaceae, for which the name Mangrovicella endophytica gen. nov., sp. nov. is proposed. The type strain is 5T4P-12-1T (=KCTC 62053T=CGMCC 1.16279 T).