Flex Meta-Storms elucidates the microbiome local beta-diversity under specific phenotypes

Zhang, Mingqian; Zhang, Wenke; Chen, Yuzhu; Jin-yuan, Zhao; Wu, Shunyao; Su, Xiaoquan

doi:10.1093/bioinformatics/btad148

Cited by 2 publications

(1 citation statement)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These critical but limited members may not significantly impact the aggregate dissimilarity measures, leading to their potential oversight in comprehensive comparisons. Additionally, patterns of alpha-and beta-diversity may be distorted by the inherent sparsity of data, particularly when the analysis is restricted to less prevalent microbial species [19,20]. Therefore, alterations in the mycobiome composition of rats afflicted with PAH were further investigated across various taxonomic ranks.…”

Section: Discussionmentioning

confidence: 99%

Gut Fungal Microbiota Alterations in Pulmonary Arterial Hypertensive Rats

Chen,

Meng,

Yuan

et al. 2024

Biomedicines

View full text Add to dashboard Cite

The gut microbiome’s imbalance has been implicated in the pathogenesis of pulmonary arterial hypertension (PAH), yet the contribution of the gut mycobiome remains largely unclear. This study delineates the gut mycobiome profile in PAH and examines its interplay with the bacterial microbiome alterations. Fecal samples from monocrotaline-induced PAH rats and matched controls were subjected to internal transcribed spacer 1 (ITS1) sequencing for fungal community assessment and 16S ribosomal RNA (rRNA) gene sequencing for bacterial community characterization. Comparative analysis revealed no significant disparities in the overall mycobiome diversity between the PAH and control groups. However, taxonomic profiling identified differential mycobiome compositions, with the PAH group exhibiting a significant enrichment of genera such as Wallemia, unidentified_Branch02, Postia, Malassezia, Epicoccum, Cercospora, and Alternaria. Conversely, genera Xeromyces, unidentified_Plectosphaerellaceae, and Monilia were more abundant in the controls. Correlations of Malassezia and Wallemia abundance with hemodynamic parameters were observed. Indications of bidirectional fungal–bacterial community interactions were also noted. This investigation reveals distinct gut mycobiome alterations in PAH, which are intricately associated with concurrent bacterial microbiome changes, suggesting a possible contributory role of gut fungi in PAH pathophysiology. These findings underscore the potential for novel gut mycobiome-targeted therapeutic interventions in PAH management.

show abstract

Section: Discussionmentioning

confidence: 99%

Gut Fungal Microbiota Alterations in Pulmonary Arterial Hypertensive Rats

Chen,

Meng,

Yuan

et al. 2024

Biomedicines

View full text Add to dashboard Cite

show abstract

PM-CNN: microbiome status recognition and disease detection model based on phylogeny and multi-path neural network

Wang,

Fan,

et al. 2024

Bioinformatics Advances

View full text Add to dashboard Cite

Motivation The human microbiome, found throughout various body parts, plays a crucial role in health dynamics and disease development. Recent research has highlighted microbiome disparities between patients with different diseases and healthy individuals, suggesting the microbiome's potential in recognizing health states. Traditionally, microbiome-based status classification relies on pre-trained machine learning (ML) models. However, most ML methods overlook microbial relationships, limiting model performance. Results To address this gap, we propose PM-CNN (Phylogenetic Multi-path Convolutional Neural Network), a novel phylogeny-based neural network model for multi-status classification and disease detection using microbiome data. PM-CNN organizes microbes based on their phylogenetic relationships and extracts features using a multi-path convolutional neural network. An ensemble learning method then fuses these features to make accurate classification decisions. We applied PM-CNN to human microbiome data for status and disease detection, demonstrating its significant superiority over existing ML models. These results provide a robust foundation for microbiome-based state recognition and disease prediction in future research and applications. Availability and Implementation PM-CNN software is available at https://github.com/qdu-bioinfo/PM_CNN

show abstract

Flex Meta-Storms elucidates the microbiome local beta-diversity under specific phenotypes

Cited by 2 publications

References 42 publications

Gut Fungal Microbiota Alterations in Pulmonary Arterial Hypertensive Rats

Gut Fungal Microbiota Alterations in Pulmonary Arterial Hypertensive Rats

PM-CNN: microbiome status recognition and disease detection model based on phylogeny and multi-path neural network

Contact Info

Product

Resources

About