Biology subjects

Yuan, C.

Publications and source records attributed to Yuan, C..

5 recordsLinked to original sources

bioRxiv2018

Microbiota-metabolites interactions in non-human primate gastrointestinal tract

BackgroundThe microbiota has been recognised as an important part for maintaining human health. Perturbation to its structure has been implicated in many diseases, such as obesity and cancers. The microbiota is highly metabolically active and fills in many niche metabolic pathways absent from the human host. Diseases such as obesity, cardiovascular disease and colorectal cancer has been linked to altered microbiota metabolism. However, there is a gap in the current knowledge on how mucosal-associated microbiota and colon mucosa interact. Here we performed an integrated analysis between the mucosal-associated microbiota and the mucosal tissue metabolites in healthy non-human primates.\n\nResultsWe found that the overall microbiota composition is influenced by both the tissue location as well as the host. We also identified bacteria signatures for different intestinal locations. The distal colon bacterial signature includes Ruminococcaceae, Bacteroidales, Christensenellaceae, Clostridiales, Sphaerochaeta, Victivallaceae, GMD14H09, CF231, ML615J-28, RF39 and R4-45B taxa. In the cecum, the signatures include Prevotella, Anaerovibrio, Roseburia, and Anaerostipes. Desulfovibrionaceae family is the only taxon that may be a signature for the duodenum. We also found an intricate global relationship between the microbiota and the host tissue metabolome that is mainly driven by the distal colon. Most importantly, we found microbial-centric tissue metabolites clusters that may have potential implications to studying host-microbiota metabolic interactions.

genomics↗

bioRxiv2018

OMCD: OncoMir Cancer Database

MicroRNAs (miRNAs) are crucially important in the development of cancer. Their dysregulation, commonly observed in various types of cancer, is largely cancer-dependent. Thus, to understand the tumor biology and to develop accurate and sensitive biomarkers, we need to understand pan-cancer miRNA expression. At the University of Minnesota, we developed the OncoMir Cancer Database (OMCD), hosted on a web server, which allows easy and systematic comparative genomic analyses of miRNA sequencing data derived from more than 9,500 cancer patients tissue samples available in the Cancer Genome Atlas (TCGA). OMCD includes associated clinical information and is searchable by organ-specific terms common to the TCGA. Freely available to all users (www.oncomir.umn.edu/omcd/), OMCD enables (1) simple visualization of TCGA miRNA sequencing data, (2) statistical analysis of differentially expressed miRNAs for each cancer type, and (3) exploration of miRNA clusters across cancer types.\n\nDatabase URLwww.oncomir.umn.edu/omcd

bioinformatics↗

bioRxiv2017

Analysis of LRRC45 indicates cooperative functions of distal appendages at early steps of ciliogenesis

Cilia perform essential signalling functions during development and tissue homeostasis. Ciliary malfunction causes a variety of diseases, named ciliopathies. The key role that the mother centriole plays in cilia formation can be attributed to appendage proteins that associate exclusively with the mother centriole. The distal appendages form a platform that docks early ciliary vesicles and removes CP110/Cep97 inhibitory complexes from the mother centriole. Here, we analysed the role played by LRRC45 in appendage formation and ciliogenesis. We show that the core appendage proteins Cep83 and SCLT1 recruit LRRC45 to the mother centriole. Once there LRRC45 recruits FBF1. The association of LRRC45 with the basal body of primary and motile cilia in differentiated and stem cells reveals a broad function in ciliogenesis. In contrast to the appendage components Cep164 and Cep123, LRRC45 was neither essential for docking of early ciliary vesicles nor for removal of CP110. Rather, LRRC45 promotes cilia biogenesis in CP110-uncapped centrioles by organising centriolar satellites and promoting the docking of Rab8 GTPase-positive vesicles. We propose that, instead of acting solely as a platform to recruit early vesicles, centriole appendages form discrete scaffolds of cooperating proteins that execute specific functions that promote the initial steps of ciliogenesis.

cell biology↗

bioRxiv2017

Interaction Between Host MicroRNAs and the Gut Microbiota in Colorectal Cancer

BackgroundAlthough variation in gut microbiome composition has been linked with colorectal cancer (CRC), the factors that mediate the interactions between CRC tumors and the microbiome are poorly understood. MicroRNAs (miRNAs) are known to regulate CRC progression and patient survival outcomes. In addition, recent studies suggested that host miRNAs can also regulate bacterial growth and influence the composition of the gut microbiome. Here, we investigated the association between miRNAs expression in human CRC tumor and normal tissues and the microbiome composition associated with these same tissues.\n\nMethodWe sequenced the small RNAs from patient-matched tumor and normal tissue samples collected from 44 human CRC patients performed an integrated analysis with microbiome taxonomic composition data from these same samples. We then interrogated the functions of the bacteria correlated with miRNAs that were differentially expressed (DE) between tumor and matched normal tissues, as well as the functions of miRNAs correlated with bacterial taxa that have been previously associated with CRC, including Fusobacterium, Providencia, Bacteroides, Akkermansia, Roseburia, Porphyromonas, and Peptostreptococcus.\n\nResultsWe identified 76 miRNAs as DE between CRC and normal tissue, including known oncogenic miRNAs miR-182, miR-503, and miR-17[~]92. These DE miRNAs were correlated with the relative abundance of several bacterial taxa, including Firmicutes, Bacteroidetes, and Proteobacteria. Bacteria correlated with DE miRNAs were enriched with distinct predicted metabolic categories. Additionally, we found that miRNAs correlated with CRC-associated bacteria are predicted to regulate targets that are relevant for host-microbiome interactions, and highlight a possible role for miRNA-driven glycan production in the recruitment of pathogenic microbial taxa.\n\nConclusionsOur work characterized a global relationship between microbial community composition and miRNA expression in human CRC tissues. Our results support a role for miRNAs in mediating a bi-directional host-microbiome interaction in CRC. In addition, we highlight sets of potentially interacting microbes and host miRNAs, suggesting several pathways that can be targeted via future therapies.

systems biology↗

bioRxiv2017

A mechanistic perspective of ecological networks highlights the contribution of alternative interaction strategies

O_LISpecies traits mediate ecological interaction outcomes and community structure. It is important, therefore, to identify the minimum number of traits required to characterise observed networks, i.e. the minimum dimensionality. Existing methods for estimating minimum dimensionality often lack three features commonly associated with a higher numbers of traits: a mechanistic description of observed interactions, alternative interaction modes (e.g. different feeding strategies such as active vs sit-and-wait feeding), and trait-mediated forbidden links. Omitting these features can lead to underestimation of the trait numbers involved, and therefore, minimum dimensionality. Here, we develop a minimum mechanistic dimensionality measure, accounting for these three features. C_LIO_LIThe only input required by our method is the observed network of interaction outcomes. We then assume how traits are mechanistically involved in alternative interaction modes. These unidentified traits are contrasted using pairwise performance inequalities between the interacting species. E.g. if a predator feeds upon a prey species via a typical predation mode, in each step of the predation sequence the predators performance must be greater than the preys. We construct a system of inequalities from all observed outcomes, which we attempt to solve with mixed integer linear programming. The minimum number of traits required for a feasible system of inequalities provides our dimensionality estimate. C_LIO_LIWe applied our method to 658 published empirical ecological networks including animal dominance, predator-prey, primary consumption, pollination, parasitism and seed dispersal networks, to compare with minimum dimensionality estimates when the three focal features are missing. Minimum dimensionality was typically higher when including alternative interaction modes (54% of empirical networks), forbidden interactions as trait-mediated interaction outcomes (92%), or a mechanistic perspective (81%), compared to network dimensionality estimates missing these features. C_LIO_LIOur method can reduce the risk of omitting essential traits that are involved mechanistically, in different interaction modes, or in failure outcomes. More accurate estimates will allow us to parameterise models to generate theoretical networks with a more realistic structure at the interaction outcome level. Thus, we hope our method can improve predictions of community structure and structure-dependent dynamics. C_LI

ecology↗