UPLC-MS(E) application in disease biomarker discovery: the discoveries in proteomics to metabolomics

Untargeted Metabolic Phenotyping by LC-MS

Untargeted analysis by LC-MS is a valuable tool for metabolic profiling (metabonomics/metabolomics), and applications of this technology have grown rapidly over the past decade. LC-MS offers advantages of speed, sensitivity, relative ease of sample preparation, and large dynamic range compared to other platforms in this role. However, like any analytical approach, there are still drawbacks and challenges that have to be overcome, some of which are being addressed by advances in both column chemistries and instrumentation. In particular, the combination of LC-MS with ion mobility offers many new possibilities for improved analyte separation, detection, and structural identification. There are many untargeted LC-MS approaches which can be applied to metabolic phenotyping, and these usually need to be optimized for the type of sample, the nature of the study, or the biological question. Some of the main LC-MS approaches for untargeted metabolic phenotyping are described in detail in the following protocol.

ESMSec: Prediction of Secreted Proteins in Human Body Fluids Using Protein Language Models and Attention

The secreted proteins of human body fluid have the potential to be used as biomarkers for diseases. These biomarkers can be used for early diagnosis and risk prediction of diseases, so the study of secreted proteins of human body fluid has great application value. In recent years, the deep-learning-based transformer language model has transferred from the field of natural language processing (NLP) to the field of proteomics, leading to the development of protein language models (PLMs) for protein sequence representation. Here, we propose a deep learning framework called ESM Predict Secreted Proteins (ESMSec) to predict three types of proteins secreted in human body fluid. The ESMSec is based on the ESM2 model and attention architecture. Specifically, the protein sequence data are firstly put into the ESM2 model to extract the feature information from the last hidden layer, and all the input proteins are encoded into a fixed 1000 × 480 matrix. Secondly, multi-head attention with a fully connected neural network is employed as the classifier to perform binary classification according to whether they are secreted into each body fluid. Our experiment utilized three human body fluids that are important and ubiquitous markers. Experimental results show that ESMSec achieved average accuracy of 0.8486, 0.8358, and 0.8325 on the testing datasets for plasma, cerebrospinal fluid (CSF), and seminal fluid, which on average outperform the state-of-the-art (SOTA) methods. The outstanding performance results of ESMSec demonstrate that the ESM can improve the prediction performance of the model and has great potential to screen the secretion information of human body fluid proteins.

UPLC-QTOF-MS based metabolomics unravels the modulatory effect of ginseng water extracts on rats with Qi-deficiency

Ginseng is commonly used as a nutritional supplement and daily wellness product due to its ability to invigorate qi. As a result, individuals with Qi-deficiency often use ginseng as a health supplement. Ginsenosides and polysaccharides are the primary components of ginseng. However, the therapeutic effects and mechanisms of action of these components in Qi-deficiency remain unclear. This study aimed to determine the modulatory effects and mechanisms of ginseng water extract, ginsenosides, and ginseng polysaccharides in a rat model of Qi-deficiency using metabolomics and network analysis. The rat model of Qi-deficiency was established via swimming fatigue and a restricted diet. Oral administration of different ginseng water extracts for 30 days primarily alleviated oxidative stress and disrupted energy metabolism and immune response dysfunction caused by Qi-deficiency in rats. Ultra-high-performance liquid chromatography combined with quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) was used for untargeted serum metabolomic analysis. Based on the analysis results, the active constituents of ginseng significantly reversed the changes in serum biomarkers related to Qi-deficiency in rats, particularly energy, amino acid, and unsaturated fatty acid metabolism. Furthermore, analysis of the metabolite-gene network suggested that the anti-Qi-deficiency effects of the ginseng components were mainly associated with toll-like receptor (TLR) signaling and inflammatory response. Additional verification revealed that treatment with the ginseng components effectively reduced the inflammatory response and activation of the myocardial TLR4/NF-κB pathway induced by Qi-deficiency, especially the ginseng water extracts. Therefore, ginseng could be an effective preventive measure against the progression of Qi-deficiency by regulating metabolic and inflammatory responses.

Biomarkers identification in follicular fluid in relation to live birth in in vitro fertilization of women with polycystic ovary syndrome in different subtypes by using UPLC-MS method

BACKGROUND

Polycystic ovary syndrome (PCOS) is a common infertility disorder which affects reproductive-aged women. However, metabolic change profiles of follicular fluid (FF) in lean and obese women diagnosed with and without PCOS remains unclear.

METHODS

95 infertile women were divided into four subgroups: LC (lean control), OC (overweight control), LP (lean PCOS), and OP (overweight PCOS). The FF samples were collected during oocyte retrieval and assayed by ultra-performance liquid chromatography coupled with mass spectrometry (UPLC-MS) metabolomics.

RESULTS

A total of 236 metabolites were identified by metabolic analysis. The pathway enrichment analysis revealed that the glycerophospholipid metabolism (impact = 0.11182), ether lipid metabolism (impact = 0.14458), and primary bile acid biosynthesis (impact = 0.03267) were related to metabolic pathway between PCOS and control. Correlation analyses showed that epitestosterone sulfate was found positively correlated with fertilization rate in PCOS, while falcarindione, lucidone C. and notoginsenoside I was found to be negatively correlated. The combined four biomarkers including lucidone C, epitestosterone sulfate, falcarindione, and notoginsenoside I was better in predicting live birth rate, with AUC of 0.779.

CONCLUSION

The follicular fluid of women with PCOS showed unique metabolic characteristics. Our study provides better identification of PCOS follicular fluid metabolic dynamics, which may serve as potential biomarkers of live birth.

Neuroprotective mechanisms of defatted walnut powder against scopolamine-induced Alzheimer's disease in mice revealed through metabolomics and proteomics analyses

ETHNOPHARMACOLOGICAL RELEVANCE

Defatted walnut powder (DWP), the byproduct remaining after extracting oil from the walnut kernel, has the actions of nourishing liver and the kidney, replenishing blood, and calming the nerves, which is believed to be a brain-nourishing in Chinese medicine. DWP is rich in phenolic substances with demonstrated anti-inflammatory, antioxidant, lipid-lowering and neuroprotective effects. Despite these promising properties of DWP, its effectiveness in treating Alzheimer's disease (AD) remains unclear, and further research is needed to understand the mechanism of action.

AIM OF THE STUDY

This study aimed to investigate the potential mechanism of DWP on AD by constructing the overall metabolic profile of mice with an anti-scopolamine AD model and verification of the highly correlated pathway.

MATERIALS AND METHODS

The neuroprotective efficacy of DWP in a mouse model of AD established by scopolamine injection was examined. Spatial memory performance in the Morris water maze (MWM), markers of cholinergic function in hippocampus and cortex, and neuropathological changes were compared among control, model, and DWP-consuming model group mice. In addition, combined metabolomic and proteomic analyses were conducted to investigate changes in metabolite and protein expression profiles in AD model mice induced by DWP consumption. Differentially expressed proteins and metabolites were then analyzed for KEGG pathway enrichment and results confirmed through targeted amino acid metabolomics.

RESULTS

The results showed that consumption of DWP improved spatial learning and memory in the MWM, enhanced cholinergic function, and reduced histopathological damage in the cortex and hippocampus of AD model mice. Based on differentially abundant metabolites and proteins, 43 metabolic pathways modulated by DWP were identified, mainly involving in amino acid metabolic pathways strongly associated with cellular energetics and antioxidant capacity, and targeted amino acid metabolomics confirmed that DWPE significantly elevated the levels of Arginine (Arg), Histidine (His), Proline (Pro), Serine (Ser), and Tyrosine (Tyr), while reducing the levels of Glutamate (Glu). This ultimately resulted in an improvement in the progression of AD.

CONCLUSION

This study identified numerous metabolic networks modulated by DWP that can mitigate scopolamine-induced AD neuropathology and cognitive dysfunction. DWP is a promising resource to identify AD-related pathogenic pathways and therapeutic strategies.

Precision Medicine in Depression: The Role of Proteomics and Metabolomics in Personalized Treatment Approaches

Depression, or major depressive disorder (MDD), is a widespread mental health condition marked by enduring feelings of sorrow and loss of interest. Treatment of depression frequently combines psychotherapy, medication, and lifestyle modifications. However, the occurrence of treatment resistance in certain individuals makes it difficult for physicians to effectively manage this disorder, calling for the implementation of alternative therapeutic strategies. Recently, precision medicine has gained increased attention in the field of mental health, paving the way for more personalized and effective therapeutic interventions in depression. Also known as personalized medicine, this approach relies on genetic composition, molecular profiles, and environmental variables to customize therapies to individual patients. In particular, precision medicine has offered novel viewpoints on depression through two specific domains: proteomics and metabolomics. On one hand, proteomics is the thorough study of proteins in a biological system, while metabolomics focuses on analyzing the complete set of metabolites in a living being. In the past few years, progress in research has led to the identification of numerous depression-related biomarkers using proteomics and metabolomics techniques, allowing for early identification, precise diagnosis, and improved clinical outcome. However, despite significant progress in these techniques, further efforts are required for advancing precision medicine in the diagnosis and treatment of depression. The overarching goal of this chapter is to provide the current state of knowledge regarding the use of proteomics and metabolomics in identifying biomarkers related to depression. It also highlights the potential of proteomics and metabolomics in elucidating the intricate processes underlying depression, opening the door for tailored therapies that could eventually enhance clinical outcome in depressed patients. This chapter finally discusses the main challenges in the use of proteomics and metabolomics and discusses potential future research directions.

A Critical Analysis of the FDA's Omics-Driven Pharmacodynamic Biomarkers to Establish Biosimilarity

Demonstrating biosimilarity entails comprehensive analytical assessment, clinical pharmacology profiling, and efficacy testing in patients for at least one medical indication, as required by the U.S. Biologics Price Competition and Innovation Act (BPCIA). The efficacy testing can be waived if the drug has known pharmacodynamic (PD) markers, leaving most therapeutic proteins out of this concession. To overcome this, the FDA suggests that biosimilar developers discover PD biomarkers using omics technologies such as proteomics, glycomics, transcriptomics, genomics, epigenomics, and metabolomics. This approach is redundant since the mode-action-action biomarkers of approved therapeutic proteins are already available, as compiled in this paper for the first time. Other potential biomarkers are receptor binding and pharmacokinetic profiling, which can be made more relevant to ensure biosimilarity without requiring biosimilar developers to conduct extensive research, for which they are rarely qualified.

Kaempferol and Biomodified Kaempferol from Sophora japonica Extract as Potential Sources of Anti-Cancer Polyphenolics against High Grade Glioma Cell Lines

The enzymatic hydrolysis of the extract of Sophora japonica by two glycosyl hydrolases (hesperidinase and galactosidase) was performed in order to obtain kaempferol (KPF)-enriched extract with an enhanced anticancer activity. The current study examined the effectiveness of both Sophora japonica extracts (before (KPF-BBR) and after (KPF-ABR) bioconversion reactions) in reducing cell viability and inducing apoptosis in human high-degree gliomas in vitro. Cytotoxicity was determined using an MTT assay. The effects of both compounds on the proliferation of glioma cell lines were measured using trypan blue exclusion, flow cytometry for cell cycle, wound healing (WH), and neurosphere formation assays. Cellular apoptosis was detected by DNA fragmentation and phosphatidylserine exposure. qPCR and luciferase assays evaluated NF-kB pathway inhibition. The survival rate of NG-97 and U-251 cells significantly decreased in a time- and dose-dependent manner after the addition of KPF-BBR or KPF-ABR. Thus, a 50% reduction was observed in NG-97 cells at 800 µM (KPF-BBR) and 600 µM (KPF-ABR) after 72 h. Both compounds presented an IC50 of 1800 µM for U251 after 72 h. The above IC50 values were used in all of the following analyses. Neither of the KPF presented significant inhibitory effects on the non-tumoral cells (HDFa). However, after 24 h, both extracts (KPF-BBR and KPF-ABR) significantly inhibited the migration and proliferation of NG-97 and U-251 cells. In addition, MMP-9 was downregulated in glioma cells stimulated by 12-O-tetradecanoylphorbol-13-acetate (TPA) plus KPF-BBR and TPA+KPF-ABR compared with the TPA-treated cells. Both KPF-BBR and KPF-ABR significantly inhibited the proliferation of glioma stem cells (neurospheres) after 24 h. DNA fragmentation assays demonstrated that the apoptotic ratio of KPF-ABR-treated cell lines was significantly higher than in the control groups, especially NG-97, which is not TMZ resistant. In fact, the flow cytometric analysis indicated that KPF-BBR and KPF-ABR induced significant apoptosis in both glioma cells. In addition, both KPF induced S and G2/M cell cycle arrest in the U251 cells. The qPCR and luciferase assays showed that both KPFs downregulated TRAF6, IRAK2, IL-1β, and TNF-α, indicating an inhibitory effect on the NF-kB pathway. Our findings suggest that both KPF-BBR and KPF-ABR can confer anti-tumoral effects on human cell glioma cells by inhibiting proliferation and inducing apoptosis, which is related to the NF-κB-mediated pathway. The KPF-enriched extract (KPF-ABR) showed an increased inhibitory effect on the cell migration and invasion, characterizing it as the best antitumor candidate.

Overall metabolic network analysis of urine in hyperlipidemic rats treated with Bidens bipinnata L

Hyperlipidemia has been highlighted as one of the most prominent and global chronic conditions nowadays. Bidens bipinnata L. (BBL), a folk medicine in contemporary China, has efficacy in the treatment of hyperlipidemia (HLP) in China. Although some physiological and pathological function parameters of hyperlipidemia have been investigated, little information about the changes in small metabolites in biofluids has been reported. In the present study, global metabolic profiling with high-performance liquid chromatography-linear ion trap/Orbitrap high-resolution mass spectrometry (HPLC-LTQ/Orbitrap MS) combined with a pattern recognition method was performed to discover the underlying lipid-regulating mechanisms of BBL on hyperlipidemic rats induced by high-fat diet (HFD). The total of four metabolites, up- or down-regulated (p < 0.05 or 0.01), were identified and contributed to the progression of hyperlipidemia. These promising identified biomarkers underpin the metabolic pathway, including glyoxylate and dicarboxylate metabolism, the TCA cycle, sphingolipid metabolism and purine metabolism. They are disturbed in hyperlipidemic rats, and are identified using pathway analysis with MetPA. The altered metabolite indices could be regulated closer to normal levels after BBL intervention. The results demonstrated that urinary metabolomics is a powerful tool in the clinical diagnosis and treatment of hyperlipidemia to provide information on changes in metabolite pathways.

Metabolome profiling in the study of aging processes

Aging of a living organism is closely related to systemic metabolic changes. But due to the multilevel and network nature of metabolic pathways, it is difficult to understand these connections. Today, this problem is solved using one of the main approaches of metabolomics — untargeted metabolome profiling. The purpose of this publication is to systematize the results of metabolomic studies based on such profiling, both in animal models and in humans.

Membranous nephropathy: Systems biology-based novel mechanism and traditional Chinese medicine therapy

Membranous nephropathy (MN) is a renal-limited non-inflammatory autoimmune disease in the glomerulus, which is the second or third main cause of end-stage kidney diseases in patients with primary glomerulonephritis. Substantial achievements have increased our understanding of the aetiology and pathogenesis of murine and human MN. The identification of nephritogenic autoantibodies against neutral endopeptidase, phospholipase A2 receptor (PLA2R) and thrombospondin type-1 domain-containing 7A (THSD7A) antigens provide more specific concept-driven intervention strategies for treatments by specific B cell-targeting monoclonal antibodies to inhibit antibody production and antibody-antigen immune complex deposition. Furthermore, additional antibody specificities for antigens have been discovered, but their pathogenic effects are uncertain. Although anti-PLA2R and anti-THSD7A antibodies as a diagnostic marker is widely used in MN patients, many questions including autoimmune response development, antigenic epitopes, and podocyte damage signalling pathways remain unresolved. This review describes the current available evidence regarding both established and novel molecular mechanisms based on systems biology approaches (gut microbiota, long non-coding RNAs, metabolite biomarkers and DNA methylation) in MN, with an emphasis on clinical findings. This review further summarizes the applications of traditional Chinese medicines such as Tripterygium wilfordii and Astragalus membranaceus for MN treatment. Lastly, this review considers how the identification of novel antibodies/antigens and unresolved questions and future challenges reveal the pathogenesis of MN.

DenSec: Secreted Protein Prediction in Cerebrospinal Fluid Based on DenseNet and Transformer

Cerebrospinal fluid (CSF) exists in the surrounding spaces of mammalian central nervous systems (CNS); therefore, there are numerous potential protein biomarkers associated with CNS disease in CSF. Currently, approximately 4300 proteins have been identified in CSF by protein profiling. However, due to the diverse modifications, as well as the existing technical limits, large-scale protein identification in CSF is still considered a challenge. Inspired by computational methods, this paper proposes a deep learning framework, named DenSec, for secreted protein prediction in CSF. In the first phase of DenSec, all input proteins are encoded as a matrix with a fixed size of 1000 × 20 by calculating a position-specific score matrix (PSSM) of protein sequences. In the second phase, a dense convolutional network (DenseNet) is adopted to extract the feature from these PSSMs automatically. After that, Transformer with a fully connected dense layer acts as classifier to perform a binary classification in terms of secretion into CSF or not. According to the experiment results, DenSec achieves a mean accuracy of 86.00% in the test dataset and outperforms the state-of-the-art methods.

Mass Spectrometry as a Crucial Analytical Basis for Omics Sciences

This review is devoted to the consideration of mass spectrometric platforms as applied to omics sciences. The most significant attention is paid to omics related to life sciences (genomics, proteomics, meta-bolomics, lipidomics, glycomics, plantomics, etc.). Mass spectrometric approaches to solving the problems of petroleomics, polymeromics, foodomics, humeomics, and exosomics, related to inorganic sciences, are also discussed. The review comparatively presents the advantages of various principles of separation and mass spectral techniques, complementary derivatization, used to obtain large arrays of various structural and quantitative information in the mentioned omics sciences.

DeepSec: a deep learning framework for secreted protein discovery in human body fluids

MOTIVATION

Human proteins that are secreted into different body fluids from various cells and tissues can be promising disease indicators. Modern proteomics research empowered by both qualitative and quantitative profiling techniques has made great progress in protein discovery in various human fluids. However, due to the large number of proteins and diverse modifications present in the fluids, as well as the existing technical limits of major proteomics platforms (e.g. mass spectrometry), large discrepancies are often generated from different experimental studies. As a result, a comprehensive proteomics landscape across major human fluids are not well determined.

RESULTS

To bridge this gap, we have developed a deep learning framework, named DeepSec, to identify secreted proteins in 12 types of human body fluids. DeepSec adopts an end-to-end sequence-based approach, where a Convolutional Neural Network is built to learn the abstract sequence features followed by a Bidirectional Gated Recurrent Unit with fully connected layer for protein classification. DeepSec has demonstrated promising performances with average area under the ROC curves of 0.85-0.94 on testing datasets in each type of fluids, which outperforms existing state-of-the-art methods available mostly on blood proteins. As an illustration of how to apply DeepSec in biomarker discovery research, we conducted a case study on kidney cancer by using genomics data from the cancer genome atlas and have identified 104 possible marker proteins.

AVAILABILITY

DeepSec is available at https://bmbl.bmi.osumc.edu/deepsec/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Serum metabolomics of end-stage renal disease patients with depression: potential biomarkers for diagnosis

Background

End-stage renal disease (ESRD) is the final stage during the development of renal failure. Depression is the most common psychiatric disorder in patients with ESRD, which in turn aggravates the progression of renal failure, however, its underlying mechanism remains unclear. This study aimed to reveal the pathogenesis and to discover novel peripheral biomarkers for ESRD patients with depression through metabolomic analysis.

Methods

Ultra-high-performance liquid chromatography coupled with mass spectrometry (UPLC-MS) was used to explore changes of serum metabolites among healthy controls, ESRD patients with or without depression. The differential metabolites between groups were subjected to clustering analysis, pathway analysis, receiver operating characteristic (ROC) curve analysis.

Results

A total of 57 significant serum differential metabolites were identified between ESRD patients with or without depression, which were involved in 19 metabolic pathways, such as energy metabolism, glycerolipid metabolism, and glutamate-centered metabolism. Moreover, the area under the ROC curve of gentisic acid, uric acid, 5-hydroxytryptamine, 2-phosphoglyceric acid, leucyl-phenylalanine, propenyl carnitine, naloxone, pregnenolone, 6-thioxanthene 5'-monophosphate, hydroxyl ansoprazole, zileuton O-glucuronide, cabergoline, PA(34:2), PG(36:1), probucol and their combination was greater than 0.90.

Conclusions

Inflammation, oxidative stress and energy metabolism abnormalities, glycerolipid metabolism, and glutamate-centered metabolism are associated with the pathogenesis of ESRD with depression, which may be promising targets for therapy. Furthermore, the identified differential metabolites may serve as biomarkers for the diagnosis of ESRD patients with depression.

Integrated metabolomics coupled with pattern recognition and pathway analysis to reveal molecular mechanism of cadmium-induced diabetic nephropathy

Diabetic nephropathy (DN) is becoming a worldwide public health problem and its pathophysiological mechanism is not well understood. Emerging evidences indicated that cadmium (Cd), an industrial material but also an environmental toxin, may be involved in the development and progression of diabetes and diabetes-related kidney disease. However, the underlying mechanism is still unclear. Herein, a DN animal model was constructed by exposing to Cd, the metabolomic profiling of DN mice were obtained by using ultra performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS), pattern recognition and pathway analysis were performed to screen potential biomarker. Moreover, western blotting was employed to verify the possible mechanism involved in the occurrence of Cd-induced DN. A total of 66 metabolites in serum have been screened out and identified as biomarkers, including free fatty acids, phospholipids, sphingomyelins, glycerides, and others. Significant differences were demonstrated between the metabolic profiles, including decreased levels of phospholipid and increased content of triglyceride, diacylglycerols, ceramide, lysophosphatidylcholine in Cd-induced DN mice compared with control. Protein expression level of p38 MAPK and Wnt/β-catenin were significantly increased. UPLC-Q-TOF/MS-based serum metabolomics coupled with pattern recognition methods and pathway analysis provide a powerful approach to identify potential biomarkers and is a new strategy to predict the underlying mechanism of disease caused by environmental toxicant.

Changes in the gut microbiome and metabolome in a rat model of pulmonary arterial hypertension

The gut microbiota is widely considered to be involved in several diseases, including atherosclerosis, obesity, chronic obstructive pulmonary disease (COPD) and pulmonary arterial hypertension (PAH). This study aimed to determine if changes in the gut microbiome and metabolome play a major role in the early pathogenesis of PAH. Male Wistar rats were injected with monocrotaline (MCT) (55 mg/kg) at day 1 and injected with calcium-sensing receptor (CaSR) antagonist NPS2143 (4.5 mg/kg/d) from days 1 to 21. Fecal samples were obtained. The gut microbiota and metabolome were analyzed by 16S rRNA gene sequencing and mass spectrometry-based analysis to investigate the effect of PAH in this rat model. MCT injection had a marked effect on the composition of the gut microbiota. This finding was further confirmed by metabolomic analysis with identification of several metabolites relevant to the gut microflora. However, NPS2143 partially abrogated this intestinal flora disorder and reversed fecal metabolite abnormalities. In conclusion, our study shows correlations between changes in the gut microbiome and the metabolome in PAH, which are affected by NPS2143.

Serum metabolomics in chickens infected with Cryptosporidium baileyi

Background

Cryptosporidium baileyi is an economically important zoonotic pathogen that causes serious respiratory symptoms in chickens for which no effective control measures are currently available. An accumulating body of evidence indicates the potential and usefulness of metabolomics to further our understanding of the interaction between pathogens and hosts, and to search for new diagnostic or pharmacological biomarkers of complex microorganisms. The aim of this study was to identify the impact of C. baileyi infection on the serum metabolism of chickens and to assess several metabolites as potential diagnostic biomarkers for C. baileyi infection.

Methods

Ultraperformance liquid chromatography-mass spectrometry (UPLC-MS) and subsequent multivariate statistical analysis were applied to investigate metabolomics profiles in the serum samples of chickens infected with C. baileyi, and to identify potential metabolites that can be used to distinguish chickens infected with C. baileyi from non-infected birds.

Results

Multivariate statistical analysis identified 138 differential serum metabolites between mock- and C. baileyi-infected chickens at 5 days post-infection (dpi), including 115 upregulated and 23 downregulated compounds. These metabolites were significantly enriched into six pathways, of which two pathways associated with energy and lipid metabolism, namely glycerophospholipid metabolism and sphingolipid metabolism, respectively, were the most enriched. Interestingly, some important immune-related pathways were also significantly enriched, including the intestinal immune network for IgA production, autophagy and cellular senescence. Nine potential C. baileyi-responsive metabolites were identified, including choline, sirolimus, all-trans retinoic acid, PC(14:0/22:1(13Z)), PC(15:0/22:6(4Z,7Z,10Z,13Z,16Z,19Z)), PE(16:1(9Z)/24:1(15Z)), phosphocholine, SM(d18:0/16:1(9Z)(OH)) and sphinganine.

Conclusions

This is the first report on serum metabolic profiling of chickens with early-stage C. baileyi infection. The results provide novel insights into the pathophysiological mechanisms of C. baileyi in chickens.

Graphic abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s13071-021-04834-y.

Untargeted serum metabolomics and tryptophan metabolism profiling in type 2 diabetic patients with diabetic glomerulopathy

Diabetic glomerulopathy (DG) remains the prevalent microvascular complication and leading cause of shortened lifespan in type-2 diabetes mellitus (T2DM) despite improvement in hyperglycemia control. Considering the pivotal role of kidney in metabolism, using untargeted metabolomic techniques to globally delineate the serum metabolite profiles will help advance understanding pathogenetic underpinnings of renal biopsy-confirmed DG from the perspective of metabolism specifically. Fourteen pathologically diagnosed DG patients secondary to T2DM and 14 age- and gender-matched healthy controls (HCs) were recruited for study. We employed mass spectrometry-based untargeted metabolomic methods to reveal the metabolite profiles of serum samples collected from all included subjects. We identified a total of 334 and 397 metabolites in positive and negative ion mode respectively. One hundred and eighty-two important differential metabolites whose variable importance in projection (VIP) > 1 and p value <0.05 were selected and annotated to metabolic pathways. KEGG pathway enrichment analysis revealed tryptophan metabolism enriched most significantly. Among the tryptophan derivatives, L-tryptophan (L-Trp) and serotonin were relatively accumulated in DGs compared with HCs, while 5-hydroxyindoleacetic acid (5-HIAA) and indole-3-acetamide were depleted. Correlation analysis showed serotonin and L-Trp are negatively correlated with 24 h urine protein and glycosylated hemoglobin (Ghb). To exclude the interference of preexisting T2DM on DG exacerbation, we selected 5-HIAA and 3-(3-hydroxyphenyl) propionic acid (3-OHPPA) which are not correlated with Ghb and analyzed their correlation relationship with crucial renal indices. We found 3-OHPPA is positively correlated with urine total protein and creatinine ratio (T/Cr) and 24 h urine protein, 5-HIAA is positively correlated with serum creatinine and urea.

Human body-fluid proteome: quantitative profiling and computational prediction

Empowered by the advancement of high-throughput bio technologies, recent research on body-fluid proteomes has led to the discoveries of numerous novel disease biomarkers and therapeutic drugs. In the meantime, a tremendous progress in disclosing the body-fluid proteomes was made, resulting in a collection of over 15 000 different proteins detected in major human body fluids. However, common challenges remain with current proteomics technologies about how to effectively handle the large variety of protein modifications in those fluids. To this end, computational effort utilizing statistical and machine-learning approaches has shown early successes in identifying biomarker proteins in specific human diseases. In this article, we first summarized the experimental progresses using a combination of conventional and high-throughput technologies, along with the major discoveries, and focused on current research status of 16 types of body-fluid proteins. Next, the emerging computational work on protein prediction based on support vector machine, ranking algorithm, and protein-protein interaction network were also surveyed, followed by algorithm and application discussion. At last, we discuss additional critical concerns about these topics and close the review by providing future perspectives especially toward the realization of clinical disease biomarker discovery.

Untargeted Metabolomic Analysis Combined With Multivariate Statistics Reveal Distinct Metabolic Changes in GPR40 Agonist-Treated Animals Related to Bile Acid Metabolism

Metabolomics has been increasingly applied to biomarker discovery, as untargeted metabolic profiling represents a powerful exploratory tool for identifying causal links between biomarkers and disease phenotypes. In the present work, we used untargeted metabolomics to investigate plasma specimens of rats, dogs, and mice treated with small-molecule drugs designed for improved glycemic control of type 2 diabetes mellitus patients via activation of GPR40. The in vivo pharmacology of GPR40 is not yet fully understood. Compounds targeting this receptor have been found to induce drug-induced liver injury (DILI). Metabolomic analysis facilitating an integrated UPLC-TWIMS-HRMS platform was used to detect metabolic differences between treated and non-treated animals within two 4-week toxicity studies in rat and dog, and one 2-week toxicity study in mouse. Multivariate statistics of untargeted metabolomics data subsequently revealed the presence of several significantly upregulated endogenous compounds in the treated animals whose plasma level is known to be affected during DILI. A specific bile acid metabolite useful as endogenous probe for drug-drug interaction studies was identified (chenodeoxycholic acid-24 glucuronide), as well as a metabolic precursor indicative of acidic bile acid biosynthesis (7α-hydroxy-3-oxo-4-cholestenoic acid). These results correlate with typical liver toxicity parameters on the individual level.

Systems Biology Approaches for Therapeutics Development Against COVID-19

Understanding the systems biology approaches for promoting the development of new therapeutic drugs is attaining importance nowadays. The threat of COVID-19 outbreak needs to be vanished for global welfare, and every section of research is focusing on it. There is an opportunity for finding new, quick, and accurate tools for developing treatment options, including the vaccine against COVID-19. The review at this moment covers various aspects of pathogenesis and host factors for exploring the virus target and developing suitable therapeutic solutions through systems biology tools. Furthermore, this review also covers the extensive details of multiomics tools i.e., transcriptomics, proteomics, genomics, lipidomics, immunomics, and in silico computational modeling aiming towards the study of host-virus interactions in search of therapeutic targets against the COVID-19.

Rapid qualitative profiling of metabolites present in Fusarium solani, a rhizospheric fungus derived from Senna spectabilis, using GC/MS and UPLC-QTOF/MSE techniques assisted by UNIFI information system

Fungi are an important source of natural products found in a variety of plant species. A wide range of methods for the detection of metabolites present in fungi have been reported in the literature. The search for methodologies that allow the rapid detection of compounds present in crude extracts is crucial to enable the metabolite annotation doing a qualitative analysis of the complex matrix. Mass spectrometry is an important ally when it comes to in silico detection of previously reported metabolites. In this work, the ethyl acetate extract of Fusarium solani was analyzed by gas chromatography coupled to mass spectrometry (GC/MS) after derivatization process. The ethyl acetate extract was also investigated by liquid chromatography coupled with high-resolution tandem mass spectrometry assisted by the UNIFI software system. A library containing previously reported metabolites from the Fusarium genus was added to the UNIFI platform. Simultaneously, the extract was analyzed through anticholinesterase and antifungal assays. The analysis of the derivatized extract by GC/MS led to the putative identification of five metabolites, and the investigation using Ultra-High Performance Liquid Chromatography - Quadrupole Time-of-Flight Mass Spectrometry (UPLC-QTOF) analysis in data-independent acquisition mode (mass spectrometry) led to the annotation of 15 compounds present in the built-in Fusarium library added to the UNIFI system. The Fusarium solani extract showed potential anticholinesterase and in vitro antifungal activity supported by the detection of bioactive metabolites.

Multi-omics reveals the mechanisms of antidepressant-like effects of the low polarity fraction of Bupleuri Radix

ETHNOPHARMACOLOGICAL RELEVANCE

Radix Bupleuri (Bupleurum chinense DC.) is a traditional Chinese medicine that has the effect of soothing the liver and relieving depression, and widely used in the field of antidepression.

AIM OF THE STUDY

The low polarity fraction of Bupleuri Radix (PBR) has proved to be effective for the treatment of depression based on the results of our previous study. However, mechanisms of definite antidepressant-like effects remained unclear. The purpose of this study is to reveal mechanisms of antidepressant-like effects of PBR with multi-dimensional omics.

MATERIALS AND METHODS

LC-MS metabolomics combined with 16S rRNA gene sequencing were used to investigate the effects of PBR on gut microbiota and metabolites in CUMS-induced depression, and Pearson correlation analysis was carried out on gut microbiota and metabolites.

RESULTS

PBR significantly improved depression-like behaviors in the CUMS model rats. Moreover, PBR significantly increased the levels of BDNF in the hippocampus. Cecum contents metabolomics revealed that 16 biomarkers associated with PBR antidepressant effect were screened, which were involved 3 metabolic pathways including primary bile acid biosynthesis, taurine and hypotaurine metabolism, glyoxylate and dicarboxylate metabolism. Gut microbiota further analysis demonstrated that PBR increased the diversity of gut microbiota, and significantly inhibited the growth of [Prevotella] and Ochrobactrum. Furthermore, Pearson analysis revealed there was a strong correlation between cecum contents of metabolites and gut microbiota.

CONCLUSIONS

PBR improved depression-like behavior by regulating metabolic profiles and gut microbiota, and contributing to further understand the entailed antidepressant-like mechanisms of PBR.

Plasma Lipid Profile Reveals Plasmalogens as Potential Biomarkers for Colon Cancer Screening

In this era of precision medicine, there is an increasingly urgent need for highly sensitive tests for detecting tumors such as colon cancer (CC), a silent disease where the first symptoms may take 10–15 years to appear. Mass spectrometry-based lipidomics is an emerging tool for such clinical diagnosis. We used ultra-performance liquid chromatography coupled to electrospray ionization quadrupole time-of-flight mass spectrometry operating in high energy collision spectral acquisition mode (MS^E) mode (UPLC-QTOF-MS^E) and gas chromatography (GC) to investigate differences between the plasmatic lipidic composition of CC patients and control (CTR) subjects. Key enzymes in lipidic metabolism were investigated using immuno-based detection assays. Our partial least squares discriminant analysis (PLS-DA) resulted in a suitable discrimination between CTR and CC plasma samples. Forty-two statistically significant discriminating lipids were putatively identified. Ether lipids showed a prominent presence and accordingly, a decrease in glyceronephosphate O-acyltransferase (GNPAT) enzyme activity was found. A receiver operating characteristic (ROC) curve built for three plasmalogens of phosphatidylserine (PS), named PS(P-36:1), PS(P-38:3) and PS(P-40:5), presented an area under the curve (AUC) of 0.998, and sensitivity and specificity of 100 and 85.7% respectively. These results show significant differences in CC patients’ plasma lipid composition that may be useful in discriminating them from CTR individuals with a special role for plasmalogens.

Identification of bilobetin metabolites, in vivo and in vitro, based on an efficient ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry strategy

Bilobetin, a natural compound extracted from Ginkgo biloba, has various pharmacological activities such as antioxidation, anticancer, antibacterial, antifungal, anti-inflammatory, antiviral, and promoting osteoblast differentiation. However, few studies have been conducted and there are no reports on its metabolites owing to its low content in nature. In addition, it has been reported to have potential liver and kidney toxicity. Therefore, this study aimed to identify the metabolites of bilobetin in vitro and in vivo. Bilobetin was incubated with liver microsomes to determine metabolites in vitro, and faeces and urine were collected after oral administration to rats to determine metabolites in vivo. After the samples were processed, they were measured using ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry. As a result, a total of 21 and 9 metabolites were detected in vivo and in vitro, respectively. Demethylation, demethylation and loss of water, demethylation and hydrogenation, demethylation and glycine conjugation, oxidation, methylation, oxidation and methylation, and hydrogenation were the main metabolic pathways. This study is the first to identify the metabolites of bilobetin and provides a theoretical foundation for the safe use of bilobetin in clinical application and the development of new drugs.

Novel biomarkers for lysosomal storage disorders: Metabolomic and proteomic approaches

Lysosomal storage disorders (LSDs) are characterized by the accumulation of specific disease substrates inside the lysosomes of various cells, eventually leading to the deterioration of cellular function and multisystem organ damage. With the continuous discovery and validation of novel and advanced therapies for most LSDs, there is an urgent need to discover more versatile and clinically relevant biomarkers. The utility of these biomarkers should ideally extend beyond the screening and diagnosis of LSDs to the evaluation of disease severity and monitoring of therapy. Metabolomic and proteomic approaches provide the means to the discovery and validation of such novel biomarkers. This is achieved mainly through the application of various mass spectrometric techniques to common and easily accessible biological samples, such as plasma, urine and dried blood spots. In this review, we tried to summarize the complexity of the lysosomal disorders phenotypes, their current diagnostic and therapeutic approaches, the various techniques supporting metabolomic and proteomic studies and finally we tried to explore the newly discovered biomarkers for most LSDs and their reported clinical values.

A comprehensive analysis of metabolomics and transcriptomics in non-small cell lung cancer

Non-small cell lung cancer (NSCLC) remains a leading cause of cancer death globally. More accurate and reliable diagnostic methods/biomarkers are urgently needed. Joint application of metabolomics and transcriptomics technologies possesses the high efficiency of identifying key metabolic pathways and functional genes in lung cancer patients. In this study, we performed an untargeted metabolomics analysis of 142 NSCLC patients and 159 healthy controls; 35 identified metabolites were significantly different between NSCLC patients and healthy controls, of which 6 metabolites (hypoxanthine, inosine, L-tryptophan, indoleacrylic acid, acyl-carnitine C10:1, and lysoPC(18:2)) were chosen as combinational potential biomarkers for NSCLC. The area under the curve (AUC) value, sensitivity (SE), and specificity (SP) of these six biomarkers were 0.99, 0.98, and 0.99, respectively. Potential diagnostic implications of the metabolic characteristics in NSCLC was studied. The metabolomics results were further verified by transcriptomics analysis of 1027 NSCLC patients and 108 adjacent peritumoral tissues from TCGA database. This analysis identified 2202 genes with significantly different expressions in cancer cells compared to normal controls, which in turn defined pathways implicated in the metabolism of the compounds revealed by metabolomics analysis. We built a fully connected network of metabolites and genes, which shows a good correspondence between the transcriptome analysis and the metabolites selected for diagnosis. In conclusion, this work provides evidence that the metabolic biomarkers identified may be used for NSCLC diagnosis and screening. Comprehensive analysis of metabolomics and transcriptomics data offered a validated and comprehensive understanding of metabolism in NSCLC.

Metabolomics approach based on utra-performance liquid chromatography coupled to mass spectrometry with chemometrics methods for high-throughput analysis of metabolite biomarkers to explore the abnormal metabolic pathways associated with myocardial dysfunction

Ultra-performance liquid chromatography/mass spectrometry-based metabolomics can been used for discovery of metabolite biomarkers to explore the metabolic pathway of diseases. Identification of metabolic pathways is key to understanding the pathogenesis and mechanism of disease. Myocardial dysfunction induced by sepsis (SMD) is a severe complication of septic shock and represents major causes of death in intensive care units; however its pathological mechanism is still not clear. In this study, ultrahigh-pressure liquid chromatography with mass spectrometry-based metabolomics with chemometrics anaylsis and multivariate pattern recognition analysis were used to detect urinary metabolic profile changes in a lipopolysaccharide-induced SMD mouse model. Multivariate statistical analysis including principal component analysis and orthogonapartial least squares discriminant analysis for the discrimination of SMD was conducted to identify potential biomarkers. A total of 19 differential metabolites were discovered by high-resolution mass spectrometry-based urinary metabolomics strategy. The altered biochemical pathways based on these metabolites showed that tyrosine metabolism, phenylalanine metabolism, ubiquinone biosynthesis and vitamin B6 metabolism were closely connected to the pathological processes of SMD. Consequently, integrated chemometric analyses of these metabolic pathways are necessary to extract information for the discovery of novel insights into the pathogenesis of disease.

Proteomic analysis of rat kidney under maleic acid treatment by SWATH-MS technology

RATIONALE

Maleic acid is an industrial-grade chemical that is often used in adhesives, stabilizers, and preservatives. It is unknown whether long-term consumption of maleic acid modified starch is harmful to humans. However, many studies have indicated that maleic acid causes renal tubular damage in animal models, even as the associated pathways remain unclear. Sequential window acquisition of all theoretical fragment ion spectra (SWATH) is the most innovative of the label-free quantitative technologies which have better quantification performance. Therefore, SWATH technology was used to investigate the effect of maleic acid on the rat kidney proteome in this study.

METHODS

Sprague-Dawley(SD) rats were treated with 0 mg/kg (control), 6 mg/kg (low-dose), 10 mg/kg (medium-dose), and 60 mg/kg (high-dose) of maleic acid. After kidney protein extraction, 28% SDS-PAGE was used, followed by in-gel digestion and desalting. Next, the samples were analyzed with ultra-performance liquid chromatography (UPLC) coupled with quadrupole time-of-flight mass spectrometry (Q-TOF MS), and data-dependent acquisition (DDA) and SWATH technology were also used. The gene ontology and pathway analysis were accomplished. Ultimately, these protein biomarkers were validated by using scheduled high-resolution multiple reaction monitoring (sMRM^HR ).

RESULTS

Comparisons of the control group with the other three groups revealed that 95, 130, and 103 proteins were expressed at significantly different levels in the control group and in the low-dose, medium-dose, and high-dose groups, respectively. According to the gene ontology analysis, the major processes that these proteins were involved in were metabolic processes, biological regulation, cellular processes, and responses to stimuli; the major functions that these proteins were involved in were binding, hydrolase activity, catalytic activity, and oxidoreductase activity; and the major cellular components hat they were involved in were the cytoplasm, extracellular region, membrane, and mitochondria. According to the KEGG pathway analysis, these proteins were involved in 35 pathways, five of which, the carbohydrate metabolism, folate biosynthesis, renal tubular resorption, amino acid metabolism, and Ras signaling pathways, are discussed in this study. Ultimately, 19 proteins involved in 12 important pathways were validated by sMRM^HR .

CONCLUSIONS

It was demonstrated that maleic acid caused insufficient energy production, which might lead to a decrease in the activity of the sodium-potassium ATP pump and hydrogen ion ATP pump, which could in turn have caused renal tubular resorption and hydrogen ion regulation to be blocked, thus leading to the accumulation of hydrogen ions in the renal tubules, which would then result in renal tubular acidification followed finally by Fanconi syndrome.

UPLC-HDMS-based on serum metabolomics reveals the toxicity of arecae semen

ETHNOPHARMACOLOGICAL RELEVANCE

Arecae semen has been used as vermifuge and digestant in traditional Chinese medicine (TCM) for more than one thousand years. However, the toxicity effect of areca semen and its underlying mechanism are still unclear.

THE AIM OF THE STUDY

This study was aimed to investigate the toxicity of arecae semen and to explore its mechanisms by serum metabolomics.

MATERIALS AND METHODS

The male Wistar rats were divided into the control group and treated group (n = 6 in each group), which were given by gavage with distill water or arecae semen aqueous extract (ASAE) once a day for 30 days, respectively. Serum samples were collected from all the rats after treatment of 7-day, 14-day and 30-day for metabolomics analysis. Moreover, biochemistry analysis and histopathological examination were performed at the end of study.

RESULTS

The phenomenon of diarrhea, less physical activity, tremors and body curl up were observed in the treated group. Additionally, the body weights of treated rats were significantly decreased compared with control rats from the 8th day after oral administration. Except the level of creatinekinase (CK) in the treated group significantly increased compared with the control group, there were no differences on biochemistry parameters and histopathological test in the two groups. Combined with the methods of principal component analysis (PCA), orthogonal projection to latent structure-discrimination analysis (OPLS-DA) and available databases, the treated and control rats were clearly distinguished from each other and 19 metabolites were identified as the potential biomarkers in the arecae semen treated rats. The identified biomarkers indicated that there were perturbations of the phospholipid metabolism, amino acid metabolism and fat acid metabolism in the treated group.

CONCLUSIONS

This indicated that arecae semen possessed certain cardiotoxicity and inhibited the normal growth in Wistar male rats. In addition, the metabolomics approach is a useful tool to study the toxicity in TCM.

Metabolic and transcriptional changes in seminal plasma of asthenozoospermia patients

This study was designed to investigate the metabolic and transcriptional alterations in seminal fluid caused by asthenozoospermia (AS). To address these issues, a method of metabonomics based on ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) and real-time quantitative PCR (RT-qPCR) was performed to identify some crucial biomarkers and transcription levels of the enzymes in seminal fluid. Seminal fluid samples were collected from 87 AS patients and 73 healthy males with normozoospermia. The quantitative analysis by UPLC-MS/MS showed that 19 metabolites in seminal plasma were associated with AS, and they were involved in several metabolic pathways, such as energy metabolism, purine metabolism, methionine cycle, and branched chain amino acid metabolism. Among these metabolites, the levels of citric acid, malic acid, succinic acid, and pyruvic acid, which are related to energy metabolism, were collectively reduced in the AS group, whereas the lactic acid level was enhanced. These results indicated that lesser energy source (adenosine triphosphate) was produced through the anaerobic glycolysis pathway rather than via aerobic catabolism of suger and tricarboxylic acid cycle, resulting in reduced power of sperms. Meanwhile, partial least squares discriminant analysis showed significant differences in metabolic profiles between the AS and control groups. In addition, RT-qPCR results revealed that the expression levels of four genes encoding fructokinase citrate synthase, succinate dehydrogenase, and spermine synthase, which were related to energy metabolism, were decreased in the AS group. The 23 descriptors with differential expression in AS may be valuable for the diagnosis and sequential study on AS. These results will help highlight the role of sperm inactivity in AS pathogenesis.

Differential Proteome Analysis of Hybrid Bamboo (Bambusa pervariabilis × Dendrocalamopsis grandis) Under Fungal Stress (Arthrinium phaeospermum)

In this study, TMT (tandem mass tag)-labeled quantitative protein technology combined with LC–MS/MS (liquid chromatography-mass spectrometry/mass spectrometry) was used to isolate and identify the proteins of the hybrid bamboo (Bambusa pervariabilis × Dendrocalamopsis grandis) and the bamboo inoculated with the pathogenic fungi Arthrinium phaeospermum. A total of 3320 unique peptide fragments were identified after inoculation with either A. phaeospermum or sterile water, and 1791 proteins were quantified. A total of 102 differentially expressed proteins were obtained, of which 66 differential proteins were upregulated and 36 downregulated in the treatment group. Annotation and enrichment analysis of these peptides and proteins using the GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) databases with bioinformatics software showed that the differentially expressed protein functional annotation items were mainly concentrated on biological processes and cell components. The LC–PRM/MS (liquid chromatography-parallel reaction monitoring/mass spectrometry) quantitative analysis technique was used to quantitatively analyze 11 differential candidate proteins obtained by TMT combined with LC–MS/MS. The up–down trend of 10 differential proteins in the PRM results was consistent with that of the TMT quantitative analysis. The coincidence rate of the two results was 91%, which confirmed the reliability of the proteomic results. Therefore, the differentially expressed proteins and signaling pathways discovered here may be the further concern for the bamboo-pathogen interaction studies.

Metabolite profiling of traditional Chinese medicine XIAOPI formula: An integrated strategy based on UPLC-Q-Orbitrap MS combined with network pharmacology analysis

XIAOPI formula has been approved for mammary hyperplasia treatment by National Medical Products Administration in China. However, the absorbed substances of XIAOPI formula and their influences on metabolic pathways are largely remained unknown. Liquid chromatography coupled with mass spectrometry was used to identify the substances existing in the serum. Network pharmacology was utilized to explore the underlying metabolic targets and pathways involved in. Western blotting and immunofluorescence assays were carried out for target validation. The exogenous results demonstrated 196 compounds were filtered as absorbed substances, among which 63 constituents or metabolites were tentatively identified in rat serum, and the metabolites of tanshinone II and tanshinone I were found to act as the major metabolic pathways. Subsequently, the endogenous results revealed that XIAOPI formula could significantly regulate serum biochemical indices and the bile acid secretion signaling ranks as top1 among all the involved pathways. The levels of intermediates including cholic acid, glycocholic acid, taurochenodeoxycholic acid and taurocholic acid were significantly upregulated following XIAOPI treatment, accompanied by increased expression of key enzyme CYP7A1, indicating that XIAOPI formula could accelerate the bile acid metabolism pathway. Our study presented a comprehensive metabolic profile of XIAOPI formula in vivo for the first time, and bile acid synthesis pathway might be one of the key mechanisms contributing to the pharmacological function of the formula.

Therapeutic Effect and Mechanism Study of Rhodiola wallichiana var. cholaensis Injection to Acute Blood Stasis Using Metabolomics Based on UPLC-Q/TOF-MS

In traditional Chinese medicine theory, blood stasis syndrome (BSS), characterized by blood flow retardation and blood stagnation, is one of the main pathologic mechanisms and clinical syndromes of cardiovascular diseases (CVDs). Rhodiola wallichiana var. cholaensis injection (RWCI) is made from dry roots and stems of RWC via the processes of decoction, alcohol precipitation, filtration, and dilution. Studies indicated the extracts of RWC could alleviate CVDs; however, the mechanism had not been illustrated. In the present study, the acute blood stasis rat model was established to investigate the pathogenesis of BSS and the therapeutic mechanism of RWCI against BSS. Hemorheological parameters (whole blood viscosity and plasma viscosity) and inflammatory factors (TNF-α and IL-6) were used to evaluate the success of the BSS rat model and RWCI efficacy. 14 and 33 differential metabolites were identified from plasma and urine samples using the metabolomics approach based on ultrahigh-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry. The results of multivariate analysis displayed that there were significant separations among model, control, and treatment groups, but the high-dose RWCI treatment group was closer to the control group. 9 perturbed metabolic pathways were related to BSS's development and RWCI intervention. 5 metabolic pathways (arachidonic acid metabolism, linoleic acid metabolism, alpha-linolenic acid metabolism, retinol metabolism, and steroid hormone biosynthesis) showed apparent correlations. These differential metabolites and perturbed metabolic pathways might provide a novel view to understand the pathogenesis of BSS and the pharmacological mechanism of RWCI.

Aryl hydrocarbon receptor activation mediates kidney disease and renal cell carcinoma

The aryl hydrocarbon receptor (AhR) is a well-known ligand-activated cytoplasmic transcription factor that contributes to cellular responses against environmental toxins and carcinogens. AhR is activated by a range of structurally diverse compounds from the environment, microbiome, natural products, and host metabolism, suggesting that AhR possesses a rather promiscuous ligand binding site. Increasing studies have indicated that AhR can be activated by a variety of endogenous ligands and induce the expression of a battery of genes. AhR regulates a variety of physiopathological events, including cell proliferation, differentiation, apoptosis, adhesion and migration. These new roles have expanded our understanding of the AhR signalling pathways and endogenous metabolites interacting with AhR under homeostatic and pathological conditions. Recent studies have demonstrated that AhR is linked to cardiovascular disease (CVD), chronic kidney disease (CKD) and renal cell carcinoma (RCC). In this review, we summarize gut microbiota-derived ligands inducing AhR activity in patients with CKD, CVD, diabetic nephropathy and RCC that may provide a new diagnostic and prognostic approach for complex renal damage. We further highlight polyphenols from natural products as AhR agonists or antagonists that regulate AhR activity. A better understanding of structurally diverse polyphenols and AhR biological activities would allow us to illuminate their molecular mechanism and discover potential therapeutic strategies targeting AhR activation.

Discrimination of Different Parts of Saffron by Metabolomic-Based Ultra-Performance Liquid Chromatography Coupled with High-Definition Mass Spectrometry

In this study, the metabolite profiling of three different parts of Crocus sativus L. was measured by using ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-QTof-MS/MS). Multivariate statistical analysis was used to distinguish among the samples from different parts. A total of 54 compounds were identified in tepals, stigmas and stamens by UPLC-QTof-MS/MS. The results stated that chemical characteristics of saffron were obviously diverse in terms of the parts of flower. Through analysis, coniferin and crocin-2 were special components in stigmas when compared to tepals and stamens. The content of flavonoids was high in tepals when compared with the stigmas. The tepal of saffron may processed as a source of flavonoids in the future. The research provided the basis for the theory that only the stigma can be used as medicine.

Metabolomics analysis of Pulsatilla decoction on treatment of wetness-heat-induced diarrhea in rats based on UPLC-Q/TOF-MS/MS

Pulsatilla decoction (PD) is a classical prescription in traditional Chinese medicine that has therapeutic effects on wetness-heat-induced diarrhea (WHD). To investigate the therapeutic effects of PD in the treatment of WHD and elucidate the potential mechanism, we used a metabolomics strategy on the base of ultraperformance liquid chromatography coupled with quadrupole time-of-flight/mass spectrometry (UPLC-Q/TOF-MS/MS) and analyzed the serum samples of 32 rats to identify differential metabolites and pathways associated with the PD treatment of WHD. With variable importance for projection >1.0 in the Orthogonal partial least-squares discriminant analysis (OPLS-DA ) models and FC ≥1.2 or ≤0.8, 67 differential metabolites in the model and control groups and 33 differential metabolites in the model and PD groups were screened. A total of 23 differential metabolites were selected based on Venny analysis. Functional analysis showed that the differential metabolites identified were primarily involved in pentose and glucuronate interconversions, glycerophospholipid metabolism, tryptophan metabolism, starch and sucrose metabolism, and glycerolipid metabolism. This study suggested that PD exerts inhibitory effects on WHD. In particular, the significant roles of PD for treating WHD lie in regulating perturbed energy metabolism, glycerophospholipid metabolism and glycerolipid metabolism, and promoting lysoPC production restoring the function of intestinal tract.

An update of urine and blood metabolomics in chronic kidney disease

Chronic kidney disease is considered as a serious obstacle in global health, with increasing incidence and prevalence. In spite of numerous attempts by using recent omics technologies, specially metabolomics, for understanding pathophysiology, molecular mechanism and identification reliable consensus biomarkers for diagnosis and prognosis of this complex disease, the current biomarkers are still insensitive and many questions about its pathomechanism are still to be unanswered. This review is focused on recent findings about urine and serum/plasma metabolite biomarkers and changes in the pathways that occurs in the disease conditions. The urine and blood metabolome content in the normal and disease state is investigated based on the current metabolomics studies and well known metabolite candidate biomarkers for chronic kidney disease are discussed.

Application of Proteomics to Inflammatory Bowel Disease Research: Current Status and Future Perspectives

Inflammatory bowel disease (IBD) is a chronic relapsing/remitting inflammatory illness of the gastrointestinal tract of unknown aetiology. Despite recent advances in decoding the pathophysiology of IBD, many questions regarding disease pathogenesis remain. Genome-wide association studies (GWAS) and knockout mouse models have significantly advanced our understanding of genetic susceptibility loci and inflammatory pathways involved in IBD pathogenesis. Despite their important contribution to a better delineation of the disease process in IBD, these genetic findings have had little clinical impact to date. This is because the presence of a given gene mutation does not automatically correspond to changes in its expression or final metabolic or structural effect(s). Furthermore, the existence of these gene susceptibility loci in the normal population suggests other driving prerequisites for the disease manifestation. Proteins can be considered the main functional units as almost all intracellular physiological functions as well as intercellular interactions are dependent on them. Proteomics provides methods for the large-scale study of the proteins encoded by the genome of an organism or a cell, to directly investigate the proteins and pathways involved. Understanding the proteome composition and alterations yields insights into IBD pathogenesis as well as identifying potential biomarkers of disease activity, mucosal healing, and cancer progression. This review describes the state of the art in the field with respect to the study of IBD and the potential for translation from biomarker discovery to clinical application.

Microbiome-metabolome reveals the contribution of gut-kidney axis on kidney disease

Dysbiosis represents changes in composition and structure of the gut microbiome community (microbiome), which may dictate the physiological phenotype (health or disease). Recent technological advances and efforts in metagenomic and metabolomic analyses have led to a dramatical growth in our understanding of microbiome, but still, the mechanisms underlying gut microbiome-host interactions in healthy or diseased state remain elusive and their elucidation is in infancy. Disruption of the normal gut microbiota may lead to intestinal dysbiosis, intestinal barrier dysfunction, and bacterial translocation. Excessive uremic toxins are produced as a result of gut microbiota alteration, including indoxyl sulphate, p-cresyl sulphate, and trimethylamine-N-oxide, all implicated in the variant processes of kidney diseases development. This review focuses on the pathogenic association between gut microbiota and kidney diseases (the gut-kidney axis), covering CKD, IgA nephropathy, nephrolithiasis, hypertension, acute kidney injury, hemodialysis and peritoneal dialysis in clinic. Targeted interventions including probiotic, prebiotic and symbiotic measures are discussed for their potential of re-establishing symbiosis, and more effective strategies for the treatment of kidney diseases patients are suggested. The novel insights into the dysbiosis of the gut microbiota in kidney diseases are helpful to develop novel therapeutic strategies for preventing or attenuating kidney diseases and complications.

Middle-down approach: a choice to sequence and characterize proteins/proteomes by mass spectrometry

Owing to rapid growth in the elucidation of genome sequences of various organisms, deducing proteome sequences has become imperative, in order to have an improved understanding of biological processes. Since the traditional Edman method was unsuitable for high-throughput sequencing and also for N-terminus modified proteins, mass spectrometry (MS) based methods, mainly based on soft ionization modes: electrospray ionization and matrix-assisted laser desorption/ionization, began to gain significance. MS based methods were adaptable for high-throughput studies and applicable for sequencing N-terminus blocked proteins/peptides too. Consequently, over the last decade a new discipline called 'proteomics' has emerged, which encompasses the attributes necessary for high-throughput identification of proteins. 'Proteomics' may also be regarded as an offshoot of the classic field, 'biochemistry'. Many protein sequencing and proteomic investigations were successfully accomplished through MS dependent sequence elucidation of 'short proteolytic peptides (typically: 7-20 amino acid residues), which is called the 'shotgun' or 'bottom-up (BU)' approach. While the BU approach continues as a workhorse for proteomics/protein sequencing, attempts to sequence intact proteins without proteolysis, called the 'top-down (TD)' approach started, due to ambiguities in the BU approach, e.g., protein inference problem, identification of proteoforms and the discovery of posttranslational modifications (PTMs). The high-throughput TD approach (TD proteomics) is yet in its infancy. Nevertheless, TD characterization of purified intact proteins has been useful for detecting PTMs. With the hope to overcome the pitfalls of BU and TD strategies, another concept called the 'middle-down (MD)' approach was put forward. Similar to BU, the MD approach also involves proteolysis, but in a restricted manner, to produce 'longer' proteolytic peptides than the ones usually obtained in BU studies, thereby providing better sequence coverage. In this regard, special proteases (OmpT, Sap9, IdeS) have been used, which can cleave proteins to produce longer proteolytic peptides. By reviewing ample evidences currently existing in the literature that is predominantly on PTM characterization of histones and antibodies, herein we highlight salient features of the MD approach. Consequently, we are inclined to claim that the MD concept might have widespread applications in future for various research areas, such as clinical, biopharmaceuticals (including PTM analysis) and even for general/routine characterization of proteins including therapeutic proteins, but not just limited to analysis of histones or antibodies.

Natural products for the prevention and treatment of kidney disease

BACKGROUND

Chronic kidney disease (CKD) is one of the common causes resulting in a high morbidity and mortality. Renal fibrosis is the main pathological features of CKD. Natural products have begun to gain widely popularity worldwide for promoting healthcare and preventing CKD, and have been used as a conventional or complementary therapy for CKD treatment.

PURPOSE

The present paper reviewed the therapeutic effects of natural products on CKD and revealed the molecular mechanisms of their anti-fibrosis.

METHODS

All the available information on natural products against renal fibrosis was collected via a library and electronic search (using Web of Science, Pubmed, ScienceDirect, Splinker, etc.).

RESULTS

Accumulated evidence demonstrated that natural products exhibited the beneficial effects for CKD treatment and against renal fibrosis. This review presents an overview of the molecular mechanism of CKD and natural products against renal fibrosis, followed by an in-depth discussion of their molecular mechanism of natural products including isolated compounds and crude extracts against renal fibrosis in vitro and in vivo. A number of isolated compounds have been confirmed to retard renal fibrosis.

CONCLUSION

The review provides comprehensive insights into pathophysiological mechanisms of CKD and natural products against renal fibrosis. Particular challenges are presented and placed within the context of future applications of natural products against renal fibrosis.

Rapid discrimination between red and white ginseng based on unique mass-spectrometric features

Red ginseng (RG) and white ginseng (WG), two processed products of Panax ginseng C. A. Meyer, are in high demand due to their unique features. In this study, some of these unique features were identified and confirmed as biomarkers of RG by using ultra-high-performance liquid chromatography-mass spectrometry, data mining, support vector machine, and artificial neural network. Principal component analysis showed clear separation between the RG and WG extracts, indicating the presence of potential discriminators. In addition, 20 features that are dominant in RG were found by data mining. Samples of Panax quinquefolium (PQ) and Panax notoginseng (PN), close relatives of Panax ginseng C.A.Meyer, were investigated and it was found that 17 features which were absent in PQ and PN samples, were present in RG and WG. Five of these markers were identified as nitrogen-containing compounds that have not been previously reported. Finally, we found that RG can be identified among different ginseng medicinal herbs including RG, WG, PQ, and PN samples, by loading four feature markers corresponding to nitrogen-containing compounds into a discriminating model, based on a support vector machine or an artificial neural network. Thus, this study provides an efficient tool to identify RG during pharmacological research.