Four new phenolic glycosides from Baoyuan decoction

AbstractFour new phenolic glycosides, including two flavonoid glycosides (1 and 2) and two lignan glycosides (3 and 4), were isolated from the traditional Chinese medicine formula, Baoyuan decoction. Their structures were established by detailed analysis of the NMR and HR-ESI-MS spectroscopic data and their absolute configurations were determined by the experimental electronic circular dichroism data as well as chemical methods. Furthermore, the sources of the four new compounds were determined by the UPLC-Q-trap-MS method, which proved that 1 and 2 are originated from Glycyrrhiza uralensis, and 3 and 4 are from Cinnamomum cassia

Non-homology-based prediction of gene functions in maize (\u3ci\u3eZea mays\u3c/i\u3e ssp. \u3ci\u3emays\u3c/i\u3e)

Advances in genome sequencing and annotation have eased the difficulty of identifying new gene sequences. Predicting the functions of these newly identified genes remains challenging. Genes descended from a common ancestral sequence are likely to have common functions.As a result, homology is widely used for gene function prediction. This means functional annotation errors also propagate from one species to another. Several approaches based on machine learning classification algorithms were evaluated for their ability to accurately predict gene function from non-homology gene features. Among the eight supervised classification algorithms evaluated, random forest-based prediction consistently provided the most accurate gene function prediction. Non-homology-based functional annotation provides complementary strengths to homology-based annotation, with higher average performance in Biological Process GO terms, the domain where homology-based functional annotation performs the worst, and weaker performance in Molecular Function GO terms, the domain where the accuracy of homology-based functional annotation is highest. GO prediction models trained with homology-based annotations were able to successfully predict annotations from a manually curated “gold standard” GO annotation set. Non-homology-based functional annotation based on machine learning may ultimately prove useful both as a method to assign predicted functions to orphan genes which lack functionally characterized homologs, and to identify and correct functional annotation errors which were propagated through homology-based functional annotations

Characterization of ovarian clear cell carcinoma using target drug-based molecular biomarkers: implications for personalized cancer therapy

Information of antibodies used in immunohistochemistry. Table S2A. Relationship with clinicopathological factors-HGSC. Table S2B. Relationship with clinicopathological factors-CCC. Table S3 Association molecular biomarkers expression and platinum-based chemotherapeutic response. Table S4. Comparison of molecular biomarkers between recurrent and disease-free patients. (DOCX 42 kb

Common Core Genes Play Vital Roles in Gastric Cancer With Different Stages

Background: Owing to complex molecular mechanisms in gastric cancer (GC) oncogenesis and progression, existing biomarkers and therapeutic targets could not significantly improve diagnosis and prognosis. This study aims to identify the key genes and signaling pathways related to GC oncogenesis and progression using bioinformatics and meta-analysis methods.Methods: Eligible microarray datasets were downloaded and integrated using the meta-analysis method. According to the tumor stage, GC gene chips were classified into three groups. Thereafter, the three groups’ differentially expressed genes (DEGs) were identified by comparing the gene data of the tumor groups with those of matched normal specimens. Enrichment analyses were conducted based on common DEGs among the three groups. Then protein–protein interaction (PPI) networks were constructed to identify relevant hub genes and subnetworks. The effects of significant DEGs and hub genes were verified and explored in other datasets. In addition, the analysis of mutated genes was also conducted using gene data from The Cancer Genome Atlas database.Results: After integration of six microarray datasets, 1,229 common DEGs consisting of 1,065 upregulated and 164 downregulated genes were identified. Alpha-2 collagen type I (COL1A2), tissue inhibitor matrix metalloproteinase 1 (TIMP1), thymus cell antigen 1 (THY1), and biglycan (BGN) were selected as significant DEGs throughout GC development. The low expression of ghrelin (GHRL) is associated with a high lymph node ratio (LNR) and poor survival outcomes. Thereafter, we constructed a PPI network of all identified DEGs and gained 39 subnetworks and the top 20 hub genes. Enrichment analyses were performed for common DEGs, the most related subnetwork, and the top 20 hub genes. We also selected 61 metabolic DEGs to construct PPI networks and acquired the relevant hub genes. Centrosomal protein 55 (CEP55) and POLR1A were identified as hub genes associated with survival outcomes.Conclusion: The DEGs, hub genes, and enrichment analysis for GC with different stages were comprehensively investigated, which contribute to exploring the new biomarkers and therapeutic targets

Integration of Metabolomics With Pharmacodynamics to Elucidate the Anti-myocardial Ischemia Effects of Combination of Notoginseng Total Saponins and Safflower Total Flavonoids

Notoginseng (Sanqi), the roots and rhizomes of Panax notoginseng and safflower, the flowers of Carthamus tinctorius, are widely used traditional Chinese medicines (TCMs) for the treatment of cardiovascular diseases. Positive evidences have fueled growing acceptance for cardioprotective effects of the combination of the notoginseng total saponins and safflower total flavonoids (CNS) against myocardial ischemia (MI). However, the underlying cardioprotective mechanisms of CNS are still obscured. Metabolomics is a comprehensive tool for investigating biological mechanisms of disease, monitoring therapeutic outcomes, and advancing drug discovery and development. Herein, we investigated the cardioprotective effects of CNS on the isoproterenol (ISO)-induced MI rats by using plasma and urine metabolomics based on ultra-performance liquid chromatography coupled with quadrupole-time of flight mass spectrometry (UPLC-Q-TOF/MS) and multiple pharmacodynamics approaches. The results showed that pretreatment with CNS could attenuate the cardiac injury resulting from ISO, as evidenced by decreasing the myocardial infarct size, converting the echocardiographic, histopathological, and plasma biochemical abnormalities, and reversing the perturbations of plasma and urine metabolic profiles, particularly for the 55.0 mg/kg dosage group. In addition, 44 metabolites were identified as the potential MI biomarkers, mainly including a range of free fatty acids (FFAs), sphingolipids, and glycerophospholipids. CNS pretreatment group may robustly ameliorate these potential MI-related biomarkers. The accumulation of LysoPCs and FFAs, caused by PLA2, may activate NF-κB pathway and increase proinflammatory cytokines. However, our results showed that CNS at 55.0 mg/kg dosage could maximally attenuate the NF-κB signaling pathway, depress the expressions of TNF-α, IL-6, IL-1β, and PLA2. The results suggested that the anti-inflammatory property of CNS may contribute to its cardioprotection against MI. Our results demonstrate that the integrating of metabolomics with pharmacodynamics provides a reasonable approach for understanding the therapeutic effects of TCMs and CNS provide a potential candidate for prevention and treatment of MI

The effective on intradermal acupuncture based on changes in biological specificity of acupoints for major depressive disorder: study protocol of a prospective, multicenter, randomized, controlled trial

BackgroundAntidepressants still have some side effects in treating major depressive disorder (MDD), and acupuncture therapy is a complementary therapy of research interest for MDD. Acupoints are sensitive sites for disease response and stimulation points for acupuncture treatment. Prior studies suggest that the biological specificity of acupoints is altered in physiological and pathological situations. Therefore, we hypothesize that the biological specificity of acupoints is associated with the diagnosis of MDD and that stimulating acupoints with significant biological specificity can achieve a better therapeutic effect than clinical common acupoints. This study aims to investigate the efficacy and safety of intradermal acupuncture (IA) treatment for MDD based on changes in the biological specificity of acupoints.MethodsThe first part of the study will enroll 30 MDD patients and 30 healthy control (HC) participants to assess pain sensitivity and thermal specificity of MDD-related acupoints using a pressure pain threshold gauge (PTG) and infrared thermography (IRT). The potentially superior acupoints for treating MDD will be selected based on the results of PTG and IRT tests and referred to as pressure pain threshold strong response acupoints (PSA) and temperature strong response acupoints (TSA).The second part of the study will enroll 120 eligible MDD patients randomly assigned to waiting list (WL) group, clinical common acupoint (CCA) group, TSA group, and PSA group in a 1:1:1:1 ratio. The change in the Patient Health Questionnaire-9 Items (PHQ-9), the MOS item short-form health survey (SF-36), pressure pain threshold, temperature of acupoints, and adverse effects will be observed. The outcomes of PHQ-9 and SF-36 measures will be assessed before intervention, at 3 and 6 weeks after intervention, and at a 4-week follow-up. The biological specificity of acupoint measures will be assessed before intervention and at 6 weeks after intervention. All adverse effects will be assessed.DiscussionThis study will evaluate the therapeutic effect and safety of IA for MDD based on changes in the biological specificity of acupoints. It will investigate whether there is a correlation between the biological specificity of MDD-related acupoints and the diagnosis of MDD and whether stimulating strong response acupoints is superior to clinical common acupoints in the treatment of MDD. The study’s results may provide insights into the biological mechanisms of acupuncture and its potential as a complementary therapy for MDD.Clinical Trial RegistrationClinicalTrials.gov, identifier: NCT05524519

ICTD: A semi-supervised cell type identification and deconvolution method for multi-omics data

We developed a novel deconvolution method, namely Inference of Cell Types and Deconvolution (ICTD) that addresses the fundamental issue of identifiability and robustness in current tissue data deconvolution problem. ICTD provides substantially new capabilities for omics data based characterization of a tissue microenvironment, including (1) maximizing the resolution in identifying resident cell and sub types that truly exists in a tissue, (2) identifying the most reliable marker genes for each cell type, which are tissue and data set specific, (3) handling the stability problem with co-linear cell types, (4) co-deconvoluting with available matched multi-omics data, and (5) inferring functional variations specific to one or several cell types. ICTD is empowered by (i) rigorously derived mathematical conditions of identifiable cell type and cell type specific functions in tissue transcriptomics data and (ii) a semi supervised approach to maximize the knowledge transfer of cell type and functional marker genes identified in single cell or bulk cell data in the analysis of tissue data, and (iii) a novel unsupervised approach to minimize the bias brought by training data. Application of ICTD on real and single cell simulated tissue data validated that the method has consistently good performance for tissue data coming from different species, tissue microenvironments, and experimental platforms. Other than the new capabilities, ICTD outperformed other state-of-the-art devolution methods on prediction accuracy, the resolution of identifiable cell, detection of unknown sub cell types, and assessment of cell type specific functions. The premise of ICTD also lies in characterizing cell-cell interactions and discovering cell types and prognostic markers that are predictive of clinical outcomes

Global diversity and biogeography of bacterial communities in wastewater treatment plants

Microorganisms in wastewater treatment plants (WWTPs) are essential for water purification to protect public and environmental health. However, the diversity of microorganisms and the factors that control it are poorly understood. Using a systematic global-sampling effort, we analysed the 16S ribosomal RNA gene sequences from ~1,200 activated sludge samples taken from 269 WWTPs in 23 countries on 6 continents. Our analyses revealed that the global activated sludge bacterial communities contain ~1 billion bacterial phylotypes with a Poisson lognormal diversity distribution. Despite this high diversity, activated sludge has a small, global core bacterial community (n = 28 operational taxonomic units) that is strongly linked to activated sludge performance. Meta-analyses with global datasets associate the activated sludge microbiomes most closely to freshwater populations. In contrast to macroorganism diversity, activated sludge bacterial communities show no latitudinal gradient. Furthermore, their spatial turnover is scale-dependent and appears to be largely driven by stochastic processes (dispersal and drift), although deterministic factors (temperature and organic input) are also important. Our findings enhance our mechanistic understanding of the global diversity and biogeography of activated sludge bacterial communities within a theoretical ecology framework and have important implications for microbial ecology and wastewater treatment processes