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.

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.

An in-house database-driven untargeted identification strategy for deep profiling of chemicalome in Chinese medicinal formula.

Deep profiling of chemicalome in Chinese medicinal formulas is vital for disclosing the secret underlying their effectiveness. To address this issue, an in-house database-driven untargeted identification strategy was proposed with the use of ultra-performance liquid chromatography coupled to quadrupole time of flight mass spectrometry. Firstly, an in-house mass spectral database for the analyzed herbs was constructed, and database querying was performed for rapid recognition of known compounds. Secondly, a chemical diagnostic characteristics algorithm was originally developed for deep mining unrecorded ions, and thus expanding coverage of components beyond the database. Additionally, we proposed evaluation criteria for the untargeted identification of compounds with different confidence levels. As a case study, the integrated strategy was applied to comprehensively characterize complex multi-type components in Gegen-Qinlian Decoction. A total of 381 compounds were characterized and annotated with four different confidence levels, and 88.40% of these annotated compounds were successfully re-identified in triplicate analyses with a different instrument. The integrated strategy was demonstrated powerful in deep profiling of chemicalome in Chinese medicinal formulas with higher throughput, analytical sharpness, and lower omission ratios.

Least absolute shrinkage and selection operator-based prediction of collision cross section values for ion mobility mass spectrometric analysis of lipids.

Collision cross section (CCS) values generated from ion mobility mass spectrometry (IM-MS) have commonly been employed to facilitate lipid identification. However, this is hindered by the limited available lipid standards. Recently, CCS values were predicted by means of computational calculations, though the prediction precision was generally not good and the predicted CCS values of the lipid isomers were almost identical. To address this challenge, a least absolute shrinkage and selection operator (LASSO)-based prediction method was developed for the prediction of lipids' CCS values in this study. In this method, an array of molecular descriptors were screened and optimized to reflect the subtle differences in structures among the different lipid isomers. The use of molecular descriptors together with a wealth of standard CCS values for the lipids (365 in total) significantly improved the accuracy and precision of the LASSO model. Its accuracy was externally validated with median relative errors (MREs) of <1.1% using an independent data set. This approach was demonstrated to allow differentiation of cis/trans and sn-positional isomers. The results also indicated that the LASSO-based prediction method could practically reduce false-positive identifications in IM-MS-based lipidomics.

In silico production of relative correction factor for the quantitative analysis of multi-components by single marker method.

INTRODUCTION: Traditional methods to derive experimentally-generated relative correction factors (RCFs) for the quantitative analysis of herbal multi-components by single marker (QAMS) method require reference standards and multiple validations with different instruments and columns, which hampers high throughput implementation. OBJECTIVES: To effectively reduce the application amounts of raw material and provide higher and more stable accuracy, this study aimed to develop a method to computationally generate RCFs of herbal components. MATERIALS AND METHODS: This strategy included the published data collection, calibration curves screening, computer algorithm-based RCFs generation and accuracy validation. RESULTS: Using the in silico approach, we have successfully produced 133 RCFs for the multi-component quantitative analysis of 63 widely used herbs. CONCLUSION: Compared with conventional RCFs, this in silico method would be a low cost and highly efficient way to produce practical RCFs for the QAMS method.

Ammonium fluoride-induced stabilization for anion attachment mass spectrometry: Facilitating the pseudotargeted profiling of bile acids submetabolome.

Mass spectrometry-based approaches enable us to capture changes in the metabolome in biological systems with high sensitivity and resolution. But global MS-based profiling of the bile acids (BAs) submetabolome is still a challenging task. Particularly for unconjugated BAs, the collision-induced dissociation (CID) fragment ions showed low ion intensities which were insufficient for analysis. This study is aimed at the development of an anion attachment MS-based approach for pseudotargeted profiling of the BAs submetabolome. We demonstrated that anion attachment MS with the combination use of ammonia fluoride (NH4F) and formate could provide stable anionic adduct ([M + HCOO]-) with good MS responses for unconjugated BAs. A mechanistic study revealed that the underlying rationale is due to the NH4F-induced approximate matching of attractions between BAs and anion for the 24-carboxyl hydrogen. This 24-carboxyl hydrogen regioselectivity is useful to screen for potential unconjugated BAs from the biological matrix. The stability and regioselectivity of anion attachment allowed the establishment of SRM transitions for unconjugated BAs for the first time. To profile conjugated BAs that come from the conjugation of glycine or taurine at 24-carboxyl hydrogen, specific precursor/fragment ion transitions were used for the detection. Finally, SRM-based UPLC-MS/MS method was developed for the pseudotargeted profiling of the BAs submetabolome with good linearity (r2 > 0.995) and high sensitivity (0.20-1.37 ng mL-1 for LLOQ). With this method, a total of 83 BAs, covering 45 unconjugated BAs and 38 conjugated BAs, were successfully determined in different biosamples from experimental colitis mice. The BAs metabolism homeostasis was disrupted by colitis, characterized by the decreased BAs levels in serum and excessive BAs accumuation in the gall bladder and colon. Overall, the present anion attachment MS-based approach is sufficiently sensitive and robust to comprehensively measure various BAs.

Development and validation of a systematic platform for broad-scale profiling of microbial metabolites.

Liquid chromatography-mass spectrometry based profiling of microbial metabolites has been a challenging task due to their diverse physicochemical properties and wide concentration ranges. This study is aimed to develop a systematic platform for the broad-scale profiling of microbial metabolites by integrating aqueous-lipophilic biphasic extractions and chemical derivatizations with a data-dependent automatable metabolite annotation algorithm. This complementary strategy of detection will not only largely expand the metabolite coverage, but also facilitate the drawing out of interested submetabolome using designed chemical derivatizations. Then, the data-dependent metabolite annotation algorithm is able to automatically match the raw MS/MS data with those of compounds in the self-collected databases. The performance of this platform is illustrated through the analysis of two representative bacteria (Escherichia coli and Pseudomonas aeruginosa) and intestinal contents samples from experimental colitis mice. As a result, 292 metabolites corresponding to 875 annotated features distributing over 25 chemical families were putatively annotated in a short time. Of these metabolites, 197 and 218 are respectively from the bacteria and intestinal contents, and 107 are identified in all three biological samples. This systematic platform could be used to accomplete high-coverage detection and high-quality data processing of microbial metabolites. At the same time, chemical derivatization design and the establishment of self-collected databases will facilitate self-driven untargeted analysis.

Integrating chemical similarity and bioequivalence: A pilot study on quality consistency evaluation of dispensing granule and traditional decoction of Scutellariae Radix by a totality-of-the-evidence approach.

There is an increasing focus on the quality consistency evaluation of dispensing granule in traditional Chinese medicines (TCMs). According to the guideline from Chinese Pharmacopoeia Commission, the substantial equivalence of dispensing granule and traditional decoction should be determined, and the chromatographic fingerprint has been recommended as a comprehensive qualitative approach to assess the quality consistency between dispensing granule and traditional decoction. However, a high-degree chemical similarity does not equal a bioequivalence. Attempting to realize the quality evaluation by integrating chemical consistency and bioequivalence, we herein proposed a totality-of-the-evidence approach based on clustering analysis and equivalence evaluation taking the dispensing granule and traditional decoction of Scutellariae Radix (SR) as a typical case. Chemical fingerprints were developed by high performance liquid chromatography coupled with photodiode array detector and quadrupole time-of-flight mass spectrometry (HPLC-PDA/QTOF-MS). Subsequently, a feature selection strategy, integrated linear and nonlinear correlation analysis, was carried out to assess the correlation between chemical profiles and biological activities. Finally, quality consistency between the dispensing granule and the traditional decoction was determined by bioactive marker-guided hierarchical clustering analysis (HCA), k-means clustering method and bioequivalence evaluation. The available evidence suggested that not all the dispensing granule of SR were sufficiently similar to the traditional decoction. This study provides an applicable methodology for quality consistency evaluation of dispensing granule and traditional decoction in TCMs.

Comparison of three officinal species of Callicarpa based on a biochemome profiling strategy with UHPLC-IT-MS and chemometrics analysis.

Traditional Chinese medicine (TCM) materials with closely related species are frequently fungible in clinical use. Therefore, holistic comparison of the composition in bioactive compounds is essential to evaluate whether they are equivalent in efficacy. Taking three officinal species of Callicarpa as a case, we proposed and validated a standardized strategy for the discrimination of closely related TCM materials, which focused on the extraction, profiling and multivariate statistical analysis of their biochemome. Firstly, serial liquid-liquid extractions were utilized to prepare different batches of Callicarpa biochemome, and the preparation yields were utilized for the normalization of sampling quantity prior to UHPLC-IT-MS analysis. Secondly, 34 compounds, including 19 phenylethanoid glycosides, 10 flavonoids and 5 terpenoids, were identified based on an untargeted UHPLC-IT-MS method. Thirdly, method validation of linearity, precision and stability showed that the UHPLC-IT-MS system was qualified (R2>0.995, RSD<15%) for subsequent biochemome profiling. After PCA and PLS-DA analysis, 30 marker compounds were screened and demonstrated to be of good predictability using genetic algorithm optimized support vector machines. Finally, a heatmap visualization was employed for clarifying the distribution of marker compounds, which could be helpful to determine whether the three Callicarpa species are, in fact, equivalent substitutes. This study provides a standardized biochemome profiling strategy for systemic comparison analysis of closely related TCM materials, which shows promising perspectives in tracking the supply chain of pharmaceutical suppliers.

Rapid identification of herbal compounds derived metabolites using zebrafish larvae as the biotransformation system.

Metabolites derived from herbal compounds are becoming promising sources for discovering new drugs. However, the rapid identification of metabolites from biological matrixes is limited by massive endogenous interference and low abundance of metabolites. Thus, by using zebrafish larvae as the biotransformation system, we herein proposed and validated an integrated strategy for rapid identification of metabolites derived from herbal compounds. Two pivotal steps involved in this strategy are to differentiate metabolites from herbal compounds and match metabolites with their parent compounds. The differentiation step was achieved by cross orthogonal partial least-squares discriminant analysis. Automatic matching analysis was performed on R Project based on a self-developed program, of which the number of matched ionic clusters and its corresponding percentage between metabolite and parent compound were taken into account to assess their similarity. Using this strategy, 46 metabolites screened from incubation water samples of zebrafish treated with total Epimedium flavonoids (EFs) could be matched with their corresponding parent compounds, 37 of them were identified and validated by the known metabolic pathways and fragmentation patterns. Finally, 75% of the identified EFs metabolites were successfully detected in urine samples of rats treated with EFs. These experimental results indicate that the proposed strategy using zebrafish larvae as the biotransformation system will facilitate the rapid identification of metabolites derived from herbal compounds, which shows promising perspectives in providing additional resources for pharmaceutical developments from natural products.

A strategy to identify and quantify closely related adulterant herbal materials by mass spectrometry-based partial least squares regression.

In this study, a new strategy combining mass spectrometric (MS) techniques with partial least squares regression (PLSR) was proposed to identify and quantify closely related adulterant herbal materials. This strategy involved preparation of adulterated samples, data acquisition and establishment of PLSR model. The approach was accurate, sensitive, durable and universal, and validation of the model was done by detecting the presence of Fritillaria Ussuriensis Bulbus in the adulteration of the bulbs of Fritillaria unibracteata. Herein, three different MS techniques, namely wooden-tip electrospray ionization mass spectrometry (wooden-tip ESI/MS), ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-QTOF/MS) and UPLC-triple quadrupole tandem mass spectrometry (UPLC-TQ/MS), were applied to obtain MS profiles for establishing PLSR models. All three models afforded good linearity and good accuracy of prediction, with correlation coefficient of prediction (rp2) of 0.9072, 0.9922 and 0.9904, respectively, and root mean square error of prediction (RMSEP) of 0.1004, 0.0290 and 0.0323, respectively. Thus, this strategy is very promising in tracking the supply chain of herb-based pharmaceutical industry, especially for identifying adulteration of medicinal materials from their closely related herbal species.