hc-OTU: A Fast and Accurate Method for Clustering Operational Taxonomic Units Based on Homopolymer Compaction

Changes in the gut microbiota of patients with Graves' orbitopathy according to severity grade

BACKGROUND

To explore the changes of gut microbiota in Graves' orbitopathy (GO) patients of different severity grades and to identify the pathogenic bacteria of GO and the associated mechanism.

METHODS

A total of 18 healthy controls and 62 GO patients were recruited. The baseline information and faecal samples of all subjects were collected for gut microbiota analysis and metabolic function prediction analysis. 16SrDNA sequencing was used for microbial diversity detection. The operational taxonomic unit (OTU) was divided using the Mothur software, and the dominant microbiota was analysed. OTU number, Chao1 index, ACE index, and Shannon index of microbiota in faecal samples were analysed using the QIIME1.9.0 software. The relative abundance of microbiota in faecal samples was analysed through principal component analysis (PCA) using the Canoco Software 5.0. The metabolic function of microbiota in faecal samples was predicted using PICRUSt 2.0.

RESULTS

There was no remarkable difference in gut microbiota diversity between groups; however, the gut microbial community and dominant microbiota significantly differed among groups. Klebsiella_pneumoniae was deemed the potentially pathogenic bacteria of GO, and its abundance was positively correlated with disease severity. The metabolic prediction results revealed that inorganic nutrition metabolism, fatty acid and lipid degradation, electron transfer, aromatic compound degradation, and alcohol degradation were notably different between groups with high and low abundance of Klebsiella_pneumoniae and among groups with different GO severity grades, thereby showing a positive correlation with GO clinical risks.

CONCLUSIONS

Klebsiella_pneumoniae was a potential GO-related pathogen, which may regulate the metabolic pathways to affect GO progression.

Visual-Tactile Fused Graph Learning for Object Clustering

Object clustering has received considerable research attention most recently. However, 1) most existing object clustering methods utilize visual information while ignoring important tactile modality, which would inevitably lead to model performance degradation and 2) simply concatenating visual and tactile information via multiview clustering method can make complementary information to not be fully explored, since there are many differences between vision and touch. To address these issues, we put forward a graph-based visual-tactile fused object clustering framework with two modules: 1) a modality-specific representation learning module M_R and 2) a unified affinity graph learning module M_U . Specifically, M_R focuses on learning modality-specific representations for visual-tactile data, where deep non-negative matrix factorization (NMF) is adopted to extract the hidden information behind each modality. Meanwhile, we employ an autoencoder-like structure to enhance the robustness of the learned representations, and two graphs to improve its compactness. Furthermore, M_U highlights how to mitigate the differences between vision and touch, and further maximize the mutual information, which adopts a minimizing disagreement scheme to guide the modality-specific representations toward a unified affinity graph. To achieve ideal clustering performance, a Laplacian rank constraint is imposed to regularize the learned graph with ideal connected components, where noises that caused wrong connections are removed and clustering labels can be obtained directly. Finally, we propose an efficient alternating iterative minimization updating strategy, followed by a theoretical proof to prove framework convergence. Comprehensive experiments on five public datasets demonstrate the superiority of the proposed framework.

An Introduction to Next Generation Sequencing Bioinformatic Analysis in Gut Microbiome Studies

The gut microbiome is a microbial ecosystem which expresses 100 times more genes than the human host and plays an essential role in human health and disease pathogenesis. Since most intestinal microbial species are difficult to culture, next generation sequencing technologies have been widely applied to study the gut microbiome, including 16S rRNA, 18S rRNA, internal transcribed spacer (ITS) sequencing, shotgun metagenomic sequencing, metatranscriptomic sequencing and viromic sequencing. Various software tools were developed to analyze different sequencing data. In this review, we summarize commonly used computational tools for gut microbiome data analysis, which extended our understanding of the gut microbiome in health and diseases.

Unambiguous identification of fungi: where do we stand and how accurate and precise is fungal DNA barcoding?

True fungi (Fungi) and fungus-like organisms (e.g. Mycetozoa, Oomycota) constitute the second largest group of organisms based on global richness estimates, with around 3 million predicted species. Compared to plants and animals, fungi have simple body plans with often morphologically and ecologically obscure structures. This poses challenges for accurate and precise identifications. Here we provide a conceptual framework for the identification of fungi, encouraging the approach of integrative (polyphasic) taxonomy for species delimitation, i.e. the combination of genealogy (phylogeny), phenotype (including autecology), and reproductive biology (when feasible). This allows objective evaluation of diagnostic characters, either phenotypic or molecular or both. Verification of identifications is crucial but often neglected. Because of clade-specific evolutionary histories, there is currently no single tool for the identification of fungi, although DNA barcoding using the internal transcribed spacer (ITS) remains a first diagnosis, particularly in metabarcoding studies. Secondary DNA barcodes are increasingly implemented for groups where ITS does not provide sufficient precision. Issues of pairwise sequence similarity-based identifications and OTU clustering are discussed, and multiple sequence alignment-based phylogenetic approaches with subsequent verification are recommended as more accurate alternatives. In metabarcoding approaches, the trade-off between speed and accuracy and precision of molecular identifications must be carefully considered. Intragenomic variation of the ITS and other barcoding markers should be properly documented, as phylotype diversity is not necessarily a proxy of species richness. Important strategies to improve molecular identification of fungi are: (1) broadly document intraspecific and intragenomic variation of barcoding markers; (2) substantially expand sequence repositories, focusing on undersampled clades and missing taxa; (3) improve curation of sequence labels in primary repositories and substantially increase the number of sequences based on verified material; (4) link sequence data to digital information of voucher specimens including imagery. In parallel, technological improvements to genome sequencing offer promising alternatives to DNA barcoding in the future. Despite the prevalence of DNA-based fungal taxonomy, phenotype-based approaches remain an important strategy to catalog the global diversity of fungi and establish initial species hypotheses.