LC-MS-Based Direct Quantification of MicroRNAs in Rat Blood.

MicroRNA (miRNA) has recently garnered significant research attention, owing to its potential as a diagnostic biomarker and therapeutic target. Liquid chromatography-mass spectrometry (LC-MS) offers accurate quantification, multiplexing capacity, and high compatibility with various matrices. These advantages establish it as a preferred technique for detecting miRNA in biological samples. In this study, we presented an LC-MS method for directly quantifying seven miRNAs (rno-miR-150, 146a, 21, 155, 223, 181a, and 125a) associated with immune and inflammatory responses in rat whole blood. To ensure miRNA stability in the samples and efficiently purify target analytes, we compared Trizol- and proteinase K-based extraction methods, and the Trizol extraction proved to be superior in terms of analytical sensitivity and convenience. Chromatographic separation was carried out using an oligonucleotide C18 column with a mobile phase composed of N-butyldimethylamine, 1,1,1,3,3,3-hexafluoro-2-propanol, and methanol. For MS detection, we performed high-resolution full scan analysis using an orbitrap mass analyzer with negative electrospray ionization. The established method was validated by assessing its selectivity, linearity, limit of quantification, accuracy, precision, recovery, matrix effect, carry-over, and stability. The proposed assay was then applied to simultaneously monitor target miRNAs in lipopolysaccharide-treated rats. Although potentially less sensitive than conventional methods, such as qPCR and microarray, this direct-detection-based LC-MS method can accurately and precisely quantify miRNA. Given these promising results, this method could be effectively deployed in various miRNA-related applications.

SUMO-specific protease 2 regulates lipid droplet size through ERRα-mediated CIDEA expression in adipocytes.

Lipid droplets are not only lipid storage sites but also are closely related to lipid metabolism. Lipid droplet growth increases lipid storage capacity and suppresses lipolysis via lipase associated with the lipid droplet surface. The cell death-inducing DFF45-like effector (CIDE) family of proteins mediates lipid droplet fusion, which mainly contributes to lipid droplet growth. We previously demonstrated small ubiquitin-like modifier (SUMO)-specific protease 2 (SENP2) plays important roles in lipid metabolism and induction/maintenance of adipogenesis. In this study, we determined whether SENP2 regulates lipid droplet size in adipocytes. Overexpression of SENP2 increased lipid droplet size in differentiated 3T3-L1 adipocytes and facilitated CIDEA transcription. We found SENP2 increased CIDEA expression mainly through desumoylation of estrogen-related receptor α (ERRα), which acted in coordination with peroxisome proliferator-activated receptor γ-coactivator α. In addition, palmitate treatment increased SENP2 and CIDEA mRNA levels. Specific small interfering RNA-mediated knockdown of SENP2, as well as ERRα knockdown, eliminated palmitate-induced CIDEA expression. These results suggest SENP2 enhances CIDEA expression by modulating ERRα when SENP2 is upregulated, such as after palmitate treatment, to increase lipid droplet size in adipocytes.

The dehiscence process in Panax ginseng seeds and the stigmasterol biosynthesis pathway in terms of metabolomics.

Background: Ginseng, officially known as Panax ginseng Meyer, has been traditionally used as a medicinal herb, particularly in Asia. Ginseng is propagated from seeds; however, seed germination is challenging, especially in its natural environment on farms. The seeds typically exhibit morphophysiological dormancy and require release from both morphological and physiological dormancy before germination. Although some studies have proposed methods for increasing seed germination rates, the underlying mechanisms of its dormancy release process remain unclear. Here, we investigated metabolic alterations during dehiscence in P. ginseng to determine their potential roles in dormancy release. Methods: We compared the ginseng seed metabolome before and after dehiscence and the ginsenoside and phytosterol compositions of the seeds in both periods in the presence of related enzymes. Results: After seed dehiscence, the sugar, amino acid, and squalene concentrations were significantly altered, phytosterols associated with the stigmasterol biosynthesis pathway were increased, while ginsenoside and brassinosteroid levels were not significantly altered. In addition, squalene epoxidase, cycloartenol synthase, 24-methylenesterol C-methyltransferase, and the stigmasterol biosynthesis pathway were activated. Conclusion: Overall, our findings suggest that morphological activities that facilitate ginseng seed growth are the primary phenomena occurring during the dehiscence process. This study improves the understanding of P. ginseng germination processes and promotes further research of its germination and cultivation.

Isolation and Metabolic Assessment of Cancer Cell Mitochondria.

Mitochondrial metabolism plays an essential role in various biological processes of cancer cells. Herein, we established an experimental procedure for the metabolic assessment of mitochondria in cancer cells. We examined procedures for mitochondrial isolation coupled with various mitochondrial extraction buffers in three major cancer cell lines (PANC1, A549, and MDA-MB-231) and identified a potentially optimal and generalized approach. The purity of the mitochondrial fraction isolated by the selected protocol was verified using specific protein markers of cellular components, and the ultrastructure of the isolated mitochondria was also analyzed by transmission electron microscopy. The isolation procedure, involving a bead beater for cell lysis, a modified sucrose buffer, and differential centrifugation, appeared to be a suitable method for the extraction of mitochondria from cancer cells. Electron micrographs indicated an intact two-layer membrane and inner structures of mitochondria isolated by this procedure. Metabolomic and lipidomic analyses were conducted to examine the metabolic phenotypes of the mitochondria-enriched fractions and associated bulk cancer cells. A total of 44 metabolites, including malate and succinate, occurred at significantly higher levels in the mitochondrial fractions, whereas 51 metabolites, including citrate, oxaloacetate, and fumarate of the Krebs cycle and the oncometabolites glutamine and glutamate, were reduced in mitochondria compared to that in the corresponding bulk cells of PANC1. Similar patterns were observed in mitochondria and bulk cells of MDA-MB-231 and A549 cell lines. A clear difference between the lipid profiles of bulk PANC1, MDA-MB-231, and A549 and corresponding mitochondrial fractions of these cell lines was detected by principal component analysis. In conclusion, we developed an experimental procedure for a large-scale metabolic assessment for suborganelle metabolic profiling and multiple omics data integration in cancer cells with broad applications.

Biotherapeutic Products, Cellular Factories, and Multiomics Integration in Metabolic Engineering.

Since the introduction of the first monoclonal antibody, biopharmaceuticals and biotherapeutic products manufactured in cellular factories are on the rise in modern medicine and therapeutics. Dynamic and real-time innovation strategies for operational implementation of biotherapeutic production are rapidly emerging. The advances in related fields such as genome editing technology, systems biology, and machine learning/artificial intelligence are expected to introduce innovative solutions in every aspect of the mammalian cell culture-based biotherapeutic production. This conceptual review offers a synthesis of the prospects and challenges of integration of multiomics technologies, and an integrative biology vision to cellular factories and biotherapeutic innovation.

Comprehensive phenotyping and multi-omic profiling in the toxicity assessment of nanopolystyrene with different surface properties.

There is a growing concern regarding the toxic effects of terrestrial nanoplastic contaminants. However, an all-encompassing phenotyping- and omics-based strategy for the toxicity assessment of nanoplastics with different surface properties on soil living organisms remains to be established. Herein, we devised a comprehensive phenotyping and multi-omic profiling method to examine the molecular disturbance of nanopolystyrene (PS)-exposed Caenorhabditis elegans. The exposure time was 24 h with either 1 µg/mL or 10 µg/mL of PS. We found that PS considerably affected the reproduction and locomotion, as well as increased the oxidative stress of worms regardless of their surface properties. Nevertheless, each type of PS affected the metabolome and lipidome of the nematodes differently. Uncharged PS (PS-N) triggered significant metabolic disturbances, whereas the metabolic influences from PS-NH2 and PS-COOH were subtle. The dysregulated transcriptome profiles of PS-N were strongly associated with the metabolic pathways. Besides, the altered expression of several genes associated with autophagy and longevity was observed. Collectively, we demonstrated that comprehensive phenotyping and omics-based profiling establish a practical framework that allows us to gain deeper insights into the maladaptive consequences of PS in nematodes. It can be utilized for the evaluation of other environmental contaminants in the terrestrial ecosystem.

Research Quality-Based Multivariate Modeling for Comparison of the Pharmacological Effects of Black and Red Ginseng.

Black ginseng has various pharmacological activities, but only few studies have compared its pharmacological effects with those of red ginseng. We conducted an integrative systematic literature evaluation and developed a non-inferiority test based on the multivariate modeling approach to compare the pharmacological effects of red ginseng and black ginseng. We searched reported studies on the pharmaceutical effects and composition of ginsenosides and assigned numeric scores using nonlinear principal component analysis, based on discretization measures for the included publications. Downstream weighted linear regression models were constructed to study the eight major biological activities that are generally known to be exhibited by red ginseng. Our statistical model, based on available ordinal information gathered from previous literature, helped in comparing the overlapping effects of black ginseng. Black ginseng showed antioxidant effects comparable to those of red ginseng; however, this variant was inferior to red ginseng in enhancing immunity, relieving fatigue, alleviating depression/anxiety, decreasing body fat, and reducing blood pressure. We have showed a cost-efficient method to indirectly evaluate the biological effects of ginseng products using data from published articles. This method can also be used to compare the nutritional and medicinal value of herbal medicines that share similar compositions of bioactive compounds.

Ginger on Human Health: A Comprehensive Systematic Review of 109 Randomized Controlled Trials.

Clinical applications of ginger with an expectation of clinical benefits are receiving significant attention. This systematic review aims to provide a comprehensive discussion in terms of the clinical effects of ginger in all reported areas. Following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guideline, randomized controlled trials on the effects of ginger were investigated. Accordingly, 109 eligible papers were fully extracted in terms of study design, population characteristics, evaluation systems, adverse effects, and main outcomes. The reporting quality of the included studies was assessed based on the Cochrane Collaboration's tool for assessing the risk of bias in randomized trials and integrated together with studies that investigated the same subjects. The included studies that examined the improvement of nausea and vomiting in pregnancy, inflammation, metabolic syndromes, digestive function, and colorectal cancer's markers were consistently supported, whereas other expected functions were relatively controversial. Nevertheless, only 43 clinical trials (39.4%) met the criterion of having a 'high quality of evidence.' In addition to the quality assessment result, small populations and unstandardized evaluation systems were the observed shortcomings in ginger clinical trials. Further studies with adequate designs are warranted to validate the reported clinical functions of ginger.

Comprehensive Multi-Omics Analysis Reveals Aberrant Metabolism of Epstein-Barr-Virus-Associated Gastric Carcinoma.

The metabolic landscape of Epstein-Barr-virus-associated gastric cancer (EBVaGC) remains to be elucidated. In this study, we used transcriptomics, metabolomics, and lipidomics to comprehensively investigate aberrant metabolism in EBVaGC. Specifically, we conducted gene expression analyses using microarray-based data from gastric adenocarcinoma epithelial cell lines and tissue samples from patients with clinically advanced gastric carcinoma. We also conducted complementary metabolomics and lipidomics using various mass spectrometry platforms. We found a significant downregulation of genes related to metabolic pathways, especially the metabolism of amino acids, lipids, and carbohydrates. The effect of dysregulated metabolic genes was confirmed in a survival analysis of 3951 gastric cancer patients. We found 57 upregulated metabolites and 31 metabolites that were downregulated in EBVaGC compared with EBV-negative gastric cancer. Sixty-nine lipids, mainly ether-linked phospholipids and triacylglycerols, were downregulated, whereas 45 lipids, mainly phospholipids, were upregulated. In total, 15 metabolisms related to polar metabolites and 15 lipid-associated pathways were involved in alteration of metabolites by EBV in gastric cancer. In this work, we have described the metabolic landscape of EBVaGC at the multi-omics level. These findings could help elucidate the mechanism of EBVaGC oncogenesis.

Steroidomics for the Prevention, Assessment, and Management of Cancers: A Systematic Review and Functional Analysis.

Steroidomics, an analytical technique for steroid biomarker mining, has received much attention in recent years. This systematic review and functional analysis, following the PRISMA statement, aims to provide a comprehensive review and an appraisal of the developments and fundamental issues in steroid high-throughput analysis, with a focus on cancer research. We also discuss potential pitfalls and proposed recommendations for steroidomics-based clinical research. Forty-five studies met our inclusion criteria, with a focus on 12 types of cancer. Most studies focused on cancer risk prediction, followed by diagnosis, prognosis, and therapy monitoring. Prostate cancer was the most frequently studied cancer. Estradiol, dehydroepiandrosterone, and cortisol were mostly reported and altered in at least four types of cancer. Estrogen and estrogen metabolites were highly reported to associate with women-related cancers. Pathway enrichment analysis revealed that steroidogenesis; androgen and estrogen metabolism; and androstenedione metabolism were significantly altered in cancers. Our findings indicated that estradiol, dehydroepiandrosterone, cortisol, and estrogen metabolites, among others, could be considered oncosteroids. Despite noble achievements, significant shortcomings among the investigated studies were small sample sizes, cross-sectional designs, potential confounding factors, and problematic statistical approaches. More efforts are required to establish standardized procedures regarding study design, analytical procedures, and statistical inference.

Integrated metabolomics signature for assessing the longevity of Panax ginseng seeds.

BACKGROUND: Panax ginseng seeds have strong dormancy and a prolonged germination period in comparison to other seeds; thus, it is a great challenge to propagate ginseng. Seed longevity is closely associated with germination rate and viability, so we assumed that if a seed loses its viability, specific metabolic alterations regarding plant growth factors might occur. In this study, we divided ginseng seeds into normal and accelerated-aging groups. Both groups were treated with gibberellic acid, which is one of the most important plant-growth regulators. Afterward, gas chromatography-mass spectrometry (GC-MS) was used to analyze the samples, to identify the metabolic alterations between the two groups. RESULTS: Forty-four endogenous metabolites in normal and accelerated aging groups were putatively identified. To determine the differential significance of these metabolites, t-tests and fold-change analysis were conducted followed by principal component analysis and partial least-squares discriminant analysis to determine the metabolites that showed distinct responses between the groups. Among the differentially expressed metabolites (P value < 0.05 and FDR < 0.1), nine metabolites were selected as potential biomarker candidates for the prediction of seed longevity. CONCLUSION: Nine metabolites related to ginseng seed longevity were identified by comparing metabolomes. Our findings suggest that ginseng propagation can be facilitated by the regulation of these distinctive metabolic features of the seeds. © 2019 Society of Chemical Industry.

An Integrative Data Mining and Omics-Based Translational Model for the Identification and Validation of Oncogenic Biomarkers of Pancreatic Cancer.

Substantial alterations at the multi-omics level of pancreatic cancer (PC) impede the possibility to diagnose and treat patients in early stages. Herein, we conducted an integrative omics-based translational analysis, utilizing next-generation sequencing, transcriptome meta-analysis, and immunohistochemistry, combined with statistical learning, to validate multiplex biomarker candidates for the diagnosis, prognosis, and management of PC. Experiment-based validation was conducted and supportive evidence for the essentiality of the candidates in PC were found at gene expression or protein level by practical biochemical methods. Remarkably, the random forests (RF) model exhibited an excellent diagnostic performance and LAMC2, ANXA2, ADAM9, and APLP2 greatly influenced its decisions. An explanation approach for the RF model was successfully constructed. Moreover, protein expression of LAMC2, ANXA2, ADAM9, and APLP2 was found correlated and significantly higher in PC patients in independent cohorts. Survival analysis revealed that patients with high expression of ADAM9 (Hazard ratio (HR)OS = 2.2, p-value < 0.001), ANXA2 (HROS = 2.1, p-value < 0.001), and LAMC2 (HRDFS = 1.8, p-value = 0.012) exhibited poorer survival rates. In conclusion, we successfully explore hidden biological insights from large-scale omics data and suggest that LAMC2, ANXA2, ADAM9, and APLP2 are robust biomarkers for early diagnosis, prognosis, and management for PC.

Efficacy of Integrating a Novel 16-Gene Biomarker Panel and Intelligence Classifiers for Differential Diagnosis of Rheumatoid Arthritis and Osteoarthritis.

Introducing novel biomarkers for accurately detecting and differentiating rheumatoid arthritis (RA) and osteoarthritis (OA) using clinical samples is essential. In the current study, we searched for a novel data-driven gene signature of synovial tissues to differentiate RA from OA patients. Fifty-three RA, 41 OA, and 25 normal microarray-based transcriptome samples were utilized. The area under the curve random forests (RF) variable importance measurement was applied to seek the most influential differential genes between RA and OA. Five algorithms including RF, k-nearest neighbors (kNN), support vector machines (SVM), naïve-Bayes, and a tree-based method were employed for the classification. We found a 16-gene signature that could effectively differentiate RA from OA, including TMOD1, POP7, SGCA, KLRD1, ALOX5, RAB22A, ANK3, PTPN3, GZMK, CLU, GZMB, FBXL7, TNFRSF4, IL32, MXRA7, and CD8A. The externally validated accuracy of the RF model was 0.96 (sensitivity = 1.00, specificity = 0.90). Likewise, the accuracy of kNN, SVM, naïve-Bayes, and decision tree was 0.96, 0.96, 0.96, and 0.91, respectively. Functional meta-analysis exhibited the differential pathological processes of RA and OA; suggested promising targets for further mechanistic and therapeutic studies. In conclusion, the proposed genetic signature combined with sophisticated classification methods may improve the diagnosis and management of RA patients.

Multi-platform metabolomics and a genetic approach support the authentication of agarwood produced by Aquilaria crassna and Aquilaria malaccensis.

Agarwood, the resinous heartwood produced by some Aquilaria species such as Aquilaria crassna, Aquilaria malaccensis and Aquilaria sinensis, has been traditionally and widely used in medicine, incenses and especially perfumes. However, up to now, the authentication of agarwood has been largely based on morphological characteristics, a method which is prone to errors and lacks reproducibility. Hence, in this study, we applied metabolomics and a genetic approach to the authentication of two common agarwood chips, those produced by Aquilaria crassna and Aquilaria malaccensis. Primary metabolites, secondary metabolites and DNA markers of agarwood were authenticated by 1H NMR metabolomics, GC-MS metabolomics and DNA-based techniques, respectively. The results indicated that agarwood chips could be classified accurately by all the methods illustrated in this study. Additionally, the pros and cons of each method are also discussed. To the best of our knowledge, our research is the first study detailing all the differences in the primary and secondary metabolites, as well as the DNA markers between the agarwood produced by these two species.

Systematic assessment of cervical cancer initiation and progression uncovers genetic panels for deep learning-based early diagnosis and proposes novel diagnostic and prognostic biomarkers.

Although many outstanding achievements in the management of cervical cancer (CxCa) have obtained, it still imposes a major burden which has prompted scientists to discover and validate new CxCa biomarkers to improve the diagnostic and prognostic assessment of CxCa. In this study, eight different gene expression data sets containing 202 cancer, 115 cervical intraepithelial neoplasia (CIN), and 105 normal samples were utilized for an integrative systems biology assessment in a multi-stage carcinogenesis manner. Deep learning-based diagnostic models were established based on the genetic panels of intrinsic genes of cervical carcinogenesis as well as on the unbiased variable selection approach. Survival analysis was also conducted to explore the potential biomarker candidates for prognostic assessment. Our results showed that cell cycle, RNA transport, mRNA surveillance, and one carbon pool by folate were the key regulatory mechanisms involved in the initiation, progression, and metastasis of CxCa. Various genetic panels combined with machine learning algorithms successfully differentiated CxCa from CIN and normalcy in cross-study normalized data sets. In particular, the 168-gene deep learning model for the differentiation of cancer from normalcy achieved an externally validated accuracy of 97.96% (99.01% sensitivity and 95.65% specificity). Survival analysis revealed that ZNF281 and EPHB6 were the two most promising prognostic genetic markers for CxCa among others. Our findings open new opportunities to enhance current understanding of the characteristics of CxCa pathobiology. In addition, the combination of transcriptomics-based signatures and deep learning classification may become an important approach to improve CxCa diagnosis and management in clinical practice.

A 1H NMR-based metabolomics approach to evaluate the geographical authenticity of herbal medicine and its application in building a model effectively assessing the mixing proportion of intentional admixtures: A case study of Panax ginseng: Metabolomics for the authenticity of herbal medicine.

Ginseng, the root of Panax ginseng has long been the subject of adulteration, especially regarding its origins. Here, 60 ginseng samples from Korea and China initially displayed similar genetic makeup when investigated by DNA-based technique with 23 chloroplast intergenic space regions. Hence, (1)H NMR-based metabolomics with orthogonal projections on the latent structure-discrimination analysis (OPLS-DA) were applied and successfully distinguished between samples from two countries using seven primary metabolites as discrimination markers. Furthermore, to recreate adulteration in reality, 21 mixed samples of numerous Korea/China ratios were tested with the newly built OPLS-DA model. The results showed satisfactory separation according to the proportion of mixing. Finally, a procedure for assessing mixing proportion of intentionally blended samples that achieved good predictability (adjusted R(2)=0.8343) was constructed, thus verifying its promising application to quality control of herbal foods by pointing out the possible mixing ratio of falsified samples.

Ca2+ signaling induced by sphingosine 1-phosphate and lysophosphatidic acid in mouse B cells.

Lysophospholipids (LPLs) such as lysophosphatidic acid (LPA) and sphingosine 1-phosphate (S1P) are chemotactic for lymphocytes, and increases of in cytosolic [Ca(2+)] signal the regulation of lymphocyte activation and migration. Here, the authors investigated the effects of LPA and S1P on [Ca(2+)](c) in mouse B cell lines (WEHI-231 and Bal-17) and primary B cells isolated from mouse spleen and bone marrow, and focused on the modulation of store-operated Ca(2+) entry (SOCE) by LPLs. In Bal-17 (a mature B cell line) both LPA and S1P induced a transient [Ca(2+)](c) increase via a phospholipase C pathway. In addition, pretreatment with LPLs was found to augment thapsigargin-induced SOCE in Bal-17 cells. However, in WEHI-231 (an immature B cell line) LPLs had no significant effect on [Ca(2+)](c) or SOCE. Furthermore, in freshly isolated splenic B cells (SBCs) and bone marrow B cells (BMBCs), LPLs induced only a small increase in [Ca(2+)](c). Interestingly, however, pretreatment with LPLs markedly increased SOCE in primary B cells, and this augmentation was more prominent in BMBCs than SBCs. The unidirectional influx of Ca(2+) was measured using Ba(2+) as a surrogate ion. Similarly, Ba(2+) influx was also found to be markedly increased by LPLs in SBCs and BMBCs. Summarizing, LPLs were found to strongly augment SOCE-mediated Ca(2+)-signaling in mouse B cells. However, unlike the mature Bal-17 cell line, PLC-dependent Ca(2+) release was insignificant in primary B cells and inWEHI-231.