FNICM: A New Methodology To Identify Core Metabolites Based on Significantly Perturbed Metabolic Subnetworks.

Metabolomics has emerged as a powerful tool in biomedical research to understand the pathophysiological processes and metabolic biomarkers of diseases. Nevertheless, it is a significant challenge in metabolomics to identify the reliable core metabolites that are closely associated with the occurrence or progression of diseases. Here, we proposed a new research framework by integrating detection-based metabolomics with computational network biology for function-guided and network-based identification of core metabolites, namely, FNICM. The proposed FNICM methodology is successfully utilized to uncover ulcerative colitis (UC)-related core metabolites based on the significantly perturbed metabolic subnetwork. First, seed metabolites were screened out using prior biological knowledge and targeted metabolomics. Second, by leveraging network topology, the perturbations of the detected seed metabolites were propagated to other undetected ones. Ultimately, 35 core metabolites were identified by controllability analysis and were further hierarchized into six levels based on confidence level and their potential significance. The specificity and generalizability of the discovered core metabolites, used as UC's diagnostic markers, were further validated using published data sets of UC patients. More importantly, we demonstrated the broad applicability and practicality of the FNICM framework in different contexts by applying it to multiple clinical data sets, including inflammatory bowel disease, colorectal cancer, and acute coronary syndrome. In addition, FNICM was also demonstrated as a practicality methodology to identify core metabolites correlated with the therapeutic effects of Clematis saponins. Overall, the FNICM methodology is a new framework for identifying reliable core metabolites for disease diagnosis and drug treatment from a systemic and a holistic perspective.

A novel enzyme-based functional correlation algorithm for multi-omics reveals the potential mechanisms of traditional Chinese medicines: Taking Jian-Pi-Yi-Shen formula as an example.

The integrated analysis of host metabolome and intestinal microbiome is an opportunity to explore the complex therapeutic mechanisms of traditional Chinese medicines. Currently, researchers mainly employ various statistical correlation analytical methods to investigate metabolome-microbiome correlations. However, these conventional correlation techniques often focus on statistical correlations and their biological meanings are always ignored, especially the functional relevance between them. Here, we developed a novel enzyme-based functional correlation (EBFC) algorithm to further improve the interpretability and the identified scope of microbe-metabolite correlations based on the conventional Spearman's analysis. The proposed EBFC algorithm is successfully utilized to reveal the therapeutic mechanisms of Jian-Pi-Yi-Shen (JPYS) formula on the treatment of adenine-induced chronic kidney disease (CKD) rats. JPYS, a TCM formula for treating CKD, has beneficial clinical effects. We tentatively revealed the potential mechanism of JPYS for treating CKD rats from the perspective of the serum metabolome, gut microbiome, and their interactions. Specifically, 11 metabolites and 19 bacterial genera in the CKD rats were significantly regulated to approaching normal status after JPYS treatment, suggesting that JPYS could ameliorate the pathological symptoms of CKD rats by reshaping the disturbed metabolome and gut microbiota. Further correlation analysis between the significantly perturbed metabolites, microbiota, and the related enzymes provided more strong evidence for the study of host metabolism-microbiota interactions and the therapeutic mechanism of JPYS on CKD rats. In conclusion, these findings will help us to deeply understand the pathogenesis of CKD and provide new insights into the therapeutic mechanism of JPYS.

Artificial cavernosa-like tissue based on multibubble Matrigel and a human corpus cavernous fibroblast scaffold.

Ex vivo tissue culture of the human corpus cavernosum (CC) can be used to explore the tissue structural changes and complex signaling networks. At present, artificial CC-like tissues based on acellular or three-dimensional (3D)-printed scaffolds are used to solve the scarcity of primary penis tissue samples. However, inconvenience and high costs limit the wide application of such methods. Here, we describe a simple, fast, and economical method of constructing artificial CC-like tissue. Human CC fibroblasts (FBs), endothelial cells (ECs), and smooth muscle cells (SMCs) were expanded in vitro and mixed with Matrigel in specific proportions. A large number of bubbles were formed in the mixture by vortexing combined with pipette blowing, creating a porous, spongy, and spatial structure. The CC FBs produced a variety of signaling factors, showed multidirectional differentiation potential, and grew in a 3D grid in Matrigel, which is necessary for CC-like tissue to maintain a porous structure as a cell scaffold. Within the CC-like tissue, ECs covered the surface of the lumen, and SMCs were located inside the trabeculae, similar to the structure of the primary CC. Various cell components remained stable for 3 days in vitro , but the EC content decreased on the 7 th day. Wingless/integrated (WNT) signaling activation led to lumen atrophy and increased tissue fibrosis in CC-like tissue, inducing the same changes in characteristics as in the primary CC. This study describes a preparation method for human artificial CC-like tissue that may provide an improved experimental platform for exploring the function and structure of the CC and conducting drug screening for erectile dysfunction therapy.