Bioinformatics-assisted, integrated omics studies on medicinal plants

CMAUP database update 2024: extended functional and association information of useful plants for biomedical research

Knowledge of the collective activities of individual plants together with the derived clinical effects and targeted disease associations is useful for plant-based biomedical research. To provide the information in complement to the established databases, we introduced a major update of CMAUP database, previously featured in NAR. This update includes (i) human transcriptomic changes overlapping with 1152 targets of 5765 individual plants, covering 74 diseases from 20 027 patient samples; (ii) clinical information for 185 individual plants in 691 clinical trials; (iii) drug development information for 4694 drug-producing plants with metabolites developed into approved or clinical trial drugs; (iv) plant and human disease associations (428 737 associations by target, 220 935 reversion of transcriptomic changes, 764 and 154121 associations by clinical trials of individual plants and plant ingredients); (v) the location of individual plants in the phylogenetic tree for navigating taxonomic neighbors, (vi) DNA barcodes of 3949 plants, (vii) predicted human oral bioavailability of plant ingredients by the established SwissADME and HobPre algorithm, (viii) 21-107% increase of CMAUP data over the previous version to cover 60 222 chemical ingredients, 7865 plants, 758 targets, 1399 diseases, 238 KEGG human pathways, 3013 gene ontologies and 1203 disease ontologies. CMAUP update version is freely accessible at https://bidd.group/CMAUP/index.html.

Multiomics Reveals the Key Microorganisms and Metabolites in the Resistance to Root Rot Disease of Paris polyphylla

Root rot of Paris polyphylla has received widespread attention due to its threat to yield and leads to serious economic losses. However, the relationship among the rhizosphere microbial community, metabolites and root rot disease remained largely unexplored. Herein, we used integrated 16S rRNA, ITS, RNA sequencing and UPLC-MS/MS to systematically investigate the differences between healthy and diseased P. polyphylla. We found that root rot reduced the microbial diversity in the diseased P. polyphylla compared with the healthy control. The relative abundance of the bacterial phylum Actinobacteria increased in the diseased rhizome of P. polyphylla. For the fungal community, root rot disease contributed to an increased relative abundance of Ascomycota and decreased Glomeromycota at the phylum level. The transcriptomic results showed that the differently expressed genes were significantly enriched in the "Biosynthesis of various alkaloids", "flavonoid biosynthesis" and "isoflavonoid biosynthesis" and "Phenylpropanoid biosynthesis" was dramatically enriched in healthy P. polyphylla compared with that in diseased P. polyphylla. Likewise, the metabolomic results showed that the biosynthesis of secondary metabolites and metabolic pathways was found to be significantly enriched by differential metabolites. Taken together, the study of combining metabolomics with microbiomes can help us enhance our understanding of the mechanisms of plant resistance to root rot disease, thereby discovering specific metabolites and microorganisms that can resist pathogen infection in P. polyphylla.

Phenotyping of Salvia miltiorrhiza Roots Reveals Associations between Root Traits and Bioactive Components

Plant phenomics aims to perform high-throughput, rapid, and accurate measurement of plant traits, facilitating the identification of desirable traits and optimal genotypes for crop breeding. Salvia miltiorrhiza (Danshen) roots possess remarkable therapeutic effect on cardiovascular diseases, with huge market demands. Although great advances have been made in metabolic studies of the bioactive metabolites, investigation for S. miltiorrhiza roots on other physiological aspects is poor. Here, we developed a framework that utilizes image feature extraction software for in-depth phenotyping of S. miltiorrhiza roots. By employing multiple software programs, S. miltiorrhiza roots were described from 3 aspects: agronomic traits, anatomy traits, and root system architecture. Through K-means clustering based on the diameter ranges of each root branch, all roots were categorized into 3 groups, with primary root-associated key traits. As a proof of concept, we examined the phenotypic components in a series of randomly collected S. miltiorrhiza roots, demonstrating that the total surface of root was the best parameter for the biomass prediction with high linear regression correlation (R2 = 0.8312), which was sufficient for subsequently estimating the production of bioactive metabolites without content determination. This study provides an important approach for further grading of medicinal materials and breeding practices.

Phytotherapy of abnormality of fear memory: A narrative review of mechanisms

It is generally believed that in post-traumatic stress disorder (PTSD), the high expression of fear memory is mainly determined by amygdala hyperactivity and hippocampus hypoactivity. In this review, we firstly updated the mechanisms of fear memory, and then searched the experimental evidence of phytotherapy for fear memory in the past five years. Based on the summary of those experimental studies, we further discussed the future research strategies of plant medicines, including the study of the mechanism of specific brain regions, the optimal time for the prevention and treatment of fear memory-related diseases such as PTSD, and the development of new drugs with active components of plant medicines. Accordingly, plant medicines play a clear role in improving fear memory abnormalities and have the drug development potential in the treatment of fear-related disorders.

Small molecule metabolites: discovery of biomarkers and therapeutic targets

Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks of diseases. Metabolite signatures that have close proximity to subject's phenotypic informative dimension, are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers could lead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity and selectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolic biomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeutic potentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathways changes as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists have established a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutic target exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-molecule candidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening and treatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs.

Zhachong Shisanwei Pill resists ischemic stroke by lysosome pathway based on proteomics and bioinformatics

ETHNOPHARMACOLOGICAL RELEVANCE

Zhachong Shisanwei Pill (ZSP) is a commonly used Mongolian medicine in treating cerebrovascular diseases and plays a role in the clinical treatment of ischemic stroke (IS).

AIM OF THE STUDY

Based on determining the protective effect of ZSP on cerebral ischemia, they adopted the proteomics method to explore the mechanism of ZSP against IS.

MATERIALS AND METHODS

Rats with middle cerebral artery occlusion (MCAO) model were prepared by wire embolization method, and divided into sham group, model group, ZSP high-dose group, medium-dose group, low-dose group and positive drug group. We collected the brain tissue of rats for 12 h after modeling. Neurological deficit score and cerebral infarction volume ratio evaluated pharmacodynamics, and we selected the optimal dose for subsequent experiments. Proteomics was used to screen out possible ZSP anti-IS mediated pathways and differentially expression proteins. Network pharmacology was used to verify the correlation between diseases and drugs. Hematoxylin-eosin (HE) staining and transmission electron microscope (TEM) were used to explore further the pharmacodynamic effect of ZSP against IS and its possible mechanism.

RESULTS

The cerebral infarction rate and neurological function score in rats showed that the medium-dose ZSP group had the best efficacy. Proteomics results showed that the anti-IS action of ZSP was mainly through lysosome pathway. LAMP2, AP3M1, and SCARB2 were the differentially changed proteins in this pathway. Network pharmacology verified this. HE staining and TEM results showed that ZSP could improve the pathological state of neurons in MCAO rats and reduce the number of lysosomes in MCAO rats. Western blot (WB) results showed that compared with the model group, the protein expression levels of LAMP2 and AP3M1 in the ZSP group were significantly down-regulated, and the protein expression levels of SCARB2 were significantly up-regulated.

CONCLUSION

This study confirms that ZSP regulates the lysosomal pathway, which may protect IS by down-regulating LAMP2 and AP3M1 and up-regulating SCARB2.

Light and Potassium Improve the Quality of Dendrobium officinale through Optimizing Transcriptomic and Metabolomic Alteration

Background: Dendrobium officinale is a perennial epiphytic herb in Orchidaceae. Cultivated products are the main alternative for clinical application due to the shortage of wild resources. However, the phenotype and quality of D. officinale have changed post-artificial cultivation, and environmental cues such as light, temperature, water, and nutrition supply are the major influencing factors. This study aims to unveil the mechanisms beneath the cultivation-induced variation by analyzing the changes of the metabolome and transcriptome of D. officinale seedlings treated with red- blue LED light and potassium fertilizer. Results: After light- and K-treatment, the D. officinale pseudobulbs turned purple and the anthocyanin content increased significantly. Through wide-target metabolome analysis, compared with pseudobulbs in the control group (P), the proportion of flavonoids in differentially-accumulated metabolites (DAMs) was 22.4% and 33.5% post light- and K-treatment, respectively. The gene modules coupled to flavonoids were obtained through the coexpression analysis of the light- and K-treated D. officinale transcriptome by WGCNA. The KEGG enrichment results of the key modules showed that the DEGs of the D. officinale pseudobulb were enriched in phenylpropane biosynthesis, flavonoid biosynthesis, and jasmonic acid (JA) synthesis post-light- and K-treatment. In addition, anthocyanin accumulation was the main contribution to the purple color of pseudobulbs, and the plant hormone JA induced the accumulation of anthocyanins in D. officinale. Conclusions: These results suggested that light and potassium affected the accumulation of active compounds in D. officinale, and the gene-flavone network analysis emphasizes the key functional genes and regulatory factors for quality improvement in the cultivation of this medicinal plant.

An organ-specific transcriptomic atlas of the medicinal plant Bletilla striata: Protein-coding genes, microRNAs, and regulatory networks

As one of the important species belonging to the Bletilla genus of Orchidaceae, Bletilla striata (Thunb.) Rchb. f., possesses both ornamental and medicinal values. Its dried tubers are used as a traditional Chinese medicine, and several secondary metabolites have been indicated to be the active ingredients. However, the molecular mechanisms related to the regulation of secondary metabolism have not been characterized in B. striata. In this study, integrated analysis of RNA sequencing (RNA-seq), small RNA sequencing (sRNA-seq), and degradome sequencing (degradome-seq) data from three organs (leaf, root, and tuber) of B. striata provided us with a comprehensive view of the microRNA (miRNA)-mediated regulatory network. Firstly, based on the RNA-seq data, the organ-specific expression patterns of the protein-coding genes, especially for those related to secondary metabolism, were investigated. Secondly, 342 conserved miRNA candidates were identified from B. striata. These miRNAs were assigned to 88 families, some of which were selected for expression pattern analysis. Additionally, 31 hairpin-structured precursors encoding 23 novel miRNAs were uncovered from the transcriptome assembly. Thirdly, based on the degradome signatures, 1,142 validated miRNA-target pairs (involving 167 conserved miRNAs and six novel miRNAs and 51 target genes) were included in the regulatory network. Organ-specific expression level comparison between the miRNAs and their targets revealed some interesting miRNA-target pairs. Fourthly, some valuable subnetworks were extracted for further functional studies. Additionally, some regulatory pathways were indicated to be monocot specific. Summarily, our results lay a solid basis for in-depth studies on the regulatory mechanisms underlying the production of the medicinal ingredients in B. striata.

A Walk Through the Maze of Secondary Metabolism in Orchids: A Transcriptomic Approach

Orchids have a huge reservoir of secondary metabolites making these plants of immense therapeutic importance. Their potential as curatives has been realized since times immemorial and are extensively studied for their medicinal properties. Secondary metabolism is under stringent genetic control in plants and several molecular factors are involved in regulating the production of the metabolites. However, due to the complex molecular networks, a complete understanding of the specific molecular cues is lacking. High-throughput omics technologies have the potential to fill up this lacuna. The present study deals with comparative analysis of high-throughput transcript data involving gene identification, functional annotation, and differential expression in more than 30 orchid transcriptome data sets, with a focus to elucidate the role of various factors in alkaloid and flavonoid biosynthesis. Comprehensive analysis of the mevalonate (MVA) pathway, methyl-d-erythritol 4-phosphate (MEP) pathway, and phenylpropanoid pathway provide specific insights to the potential gene targets for drug discovery. It is envisaged that a positive stimulation of these pathways through regulation of pivotal genes and alteration of specific gene expression, could facilitate the production of secondary metabolites and enable efficient tapping of the therapeutic potential of orchids. This further would lay the foundation for developing strategies for genetic and epigenetic improvement of these plants for development of therapeutic products.

Mechanisms and Efficacy of Traditional Chinese Medicine in Heart Failure

Heart failure (HF) is one of the main public health problems at present. Although some breakthroughs have been made in the treatment of HF, the mortality rate remains very high. However, we should also pay attention to improving the quality of life of patients with HF. Traditional Chinese medicine (TCM) has a long history of being used to treat HF. To demonstrate the clinical effects and mechanisms of TCM, we searched published clinical trial studies and basic studies. The search results showed that adjuvant therapy with TCM might benefit patients with HF, and its mechanism may be related to microvascular circulation, myocardial energy metabolism, oxidative stress, and inflammation.

Nextcast: a software suite to analyse and model toxicogenomics data

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Toxicogenomics is emerging as a valid approach to characterise the mechanism of action of chemicals.

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Structured pipelines for toxicogenomics increase standardisation and regulatory acceptance.

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We developed the Nextcast software suite for robust analysis and modelling of toxicogenomic data.

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Nextcast offers customisable modular pipelines to tackle multiple biological questions.

The recent advancements in toxicogenomics have led to the availability of large omics data sets, representing the starting point for studying the exposure mechanism of action and identifying candidate biomarkers for toxicity prediction. The current lack of standard methods in data generation and analysis hampers the full exploitation of toxicogenomics-based evidence in regulatory risk assessment. Moreover, the pipelines for the preprocessing and downstream analyses of toxicogenomic data sets can be quite challenging to implement. During the years, we have developed a number of software packages to address specific questions related to multiple steps of toxicogenomics data analysis and modelling. In this review we present the Nextcast software collection and discuss how its individual tools can be combined into efficient pipelines to answer specific biological questions. Nextcast components are of great support to the scientific community for analysing and interpreting large data sets for the toxicity evaluation of compounds in an unbiased, straightforward, and reliable manner. The Nextcast software suite is available at: ( https://github.com/fhaive/nextcast).

Virtual screening of phytochemicals from Indian medicinal plants against the endonuclease domain of SFTS virus L polymerase

Severe fever with thrombocytopenia syndrome virus (SFTSV) causes a highly infectious disease with reported mortality in the range 2.8% to 47%. The replication and transcription of the SFTSV genome is performed by L polymerase, which has both an RNA dependent RNA polymerase domain and an N-terminal endonuclease (endoN) domain. Due to its crucial role in the cap-snatching mechanism required for initiation of viral RNA transcription, the endoN domain is an ideal antiviral drug target. In this virtual screening study for the identification of potential inhibitors of the endoN domain of SFTSV L polymerase, we have used molecular docking and molecular dynamics (MD) simulation to explore the natural product space of 14 011 phytochemicals from Indian medicinal plants. After generating a heterogeneous ensemble of endoN domain structures reflecting conformational diversity of the corresponding active site using MD simulations, ensemble docking of the phytochemicals was performed against the endoN domain structures. Apart from the ligand binding energy from docking, our virtual screening workflow imposes additional filters such as drug-likeness, non-covalent interactions with key active site residues, toxicity and chemical similarity with other hits, to identify top 5 potential phytochemical inhibitors of endoN domain of SFTSV L polymerase. Further, the stability of the protein–ligand docked complexes for the top 5 potential inhibitors was analyzed using MD simulations. The potential phytochemical inhibitors, predicted in this study using contemporary computational methods, are expected to serve as lead molecules in future experimental studies towards development of antiviral drugs against SFTSV.

Virtual screening of a large phytochemical library from Indian medicinal plants to identify potential endonuclease inhibitors against emerging virus SFTSV.

Application of High-Throughput Sequencing on the Chinese Herbal Medicine for the Data-Mining of the Bioactive Compounds

The Chinese Herbal Medicine (CHM) has been used worldwide in clinic to treat the vast majority of human diseases, and the healing effect is remarkable. However, the functional components and the corresponding pharmacological mechanism of the herbs are unclear. As one of the main means, the high-throughput sequencing (HTS) technologies have been employed to discover and parse the active ingredients of CHM. Moreover, a tremendous amount of effort is made to uncover the pharmacodynamic genes associated with the synthesis of active substances. Here, based on the genome-assembly and the downstream bioinformatics analysis, we present a comprehensive summary of the application of HTS on CHM for the synthesis pathways of active ingredients from two aspects: active ingredient properties and disease classification, which are important for pharmacological, herb molecular breeding, and synthetic biology studies.

Transcriptome repository of North-Western Himalayan endangered medicinal herbs: a paramount approach illuminating molecular perspective of phytoactive molecules and secondary metabolism

Medicinal plants of the North-Western Himalayan region are known for their unprecedented biodiversity and valuable secondary metabolites that are unique to this dynamic geo-climatic region. From ancient times these medicinal herbs have been used traditionally for their therapeutic potentials. But from the last 2 decades increasing pharmaceutical demand, illegal and unorganized trade of these medicinal plants have accelerated the rate of over-exploitation in a non-scientific manner. In addition, climate change and anthropogenic activities also affected their natural habitat and driving most of these endemic plant species to critically endangered that foresee peril of mass extinction from this eco-region. Hence there is an urgent need for developing alternative sustainable approaches and policies to utilize this natural bioresource ensuring simultaneous conservation. Hither, arise the advent of sequencing-based transcriptomic studies significantly contributes to better understand the background of important metabolic pathways and related genes/enzymes of high-value medicinal herbs, in the absence of genomic information. The use of comparative transcriptomics in conjunction with biochemical techniques in North-Western Himalayan medicinal plants has resulted in significant advances in the identification of the molecular players involved in the production of secondary metabolic pathways over the last decade. This information could be used to further engineer metabolic pathways and breeding programs, ultimately leading to the development of in vitro systems dedicated to the production of pharmaceutically important secondary metabolites at the industrial level. Collectively, successful adoption of these approaches can certainly ensure the sustainable utilization of Himalayan bioresource by reducing the pressure on the wild population of these critically endangered medicinal herbs. This review provides novel insight as a transcriptome-based bioresource repository for the understanding of important secondary metabolic pathways genes/enzymes and metabolism of endangered high-value North-Western Himalayan medicinal herbs, so that researchers across the globe can effectively utilize this information for devising effective strategies for the production of pharmaceutically important compounds and their scale-up for sustainable usage and take a step forward in omics-based conservation genetics.

MPDB 2.0: a large scale and integrated medicinal plant database of Bangladesh

Objective

MPDB 2.0 is built to be the continuation of MPDB 1.0, to serve as a more comprehensive data repertoire for Bangladeshi medicinal plants, and to provide a user-friendly interface for researchers, health practitioners, drug developers, and students who wish to study the various medicinal & nutritive plants scattered around Bangladesh and the underlying phytochemicals contributing to their efficacy in Bangladeshi folk medicine.

Results

MPDB 2.0 database (https://www.medicinalplantbd.com/) comprises a collection of more than five hundred Bangladeshi medicinal plants, alongside a record of their corresponding scientific, family, and local names together with their utilized parts, information regarding ailments, active compounds, and PubMed ID of related publications. While medicinal plants are not limited to the borders of any country, Bangladesh and its Southeast Asian neighbors do boast a huge collection of potent medicinal plants with considerable folk-medicinal history compared to most other countries in the world. Development of MPDB 2.0 has been highly focused upon human diseases, albeit many of the plants indexed here can serve in developing biofuel (e.g.: Jatropha curcas used in biofuel) or bioremediation technologies (e.g.: Amaranthus cruentus helps to reduce cadmium level in soil) or nutritive diets (Terminalia chebula can be used in nutritive diets) or cosmetics (Aloe vera used in cosmetics), etc.

Crosstalk of Multi-Omics Platforms with Plants of Therapeutic Importance

From time immemorial, humans have exploited plants as a source of food and medicines. The World Health Organization (WHO) has recorded 21,000 plants with medicinal value out of 300,000 species available worldwide. The promising modern "multi-omics" platforms and tools have been proven as functional platforms able to endow us with comprehensive knowledge of the proteome, genome, transcriptome, and metabolome of medicinal plant systems so as to reveal the novel connected genetic (gene) pathways, proteins, regulator sequences and secondary metabolite (molecule) biosynthetic pathways of various drug and protein molecules from a variety of plants with therapeutic significance. This review paper endeavors to abridge the contemporary advancements in research areas of multi-omics and the information involved in decoding its prospective relevance to the utilization of plants with medicinal value in the present global scenario. The crosstalk of medicinal plants with genomics, transcriptomics, proteomics, and metabolomics approaches will be discussed.

Authentication of Zingiber Species Based on Analysis of Metabolite Profiles

Zingiber corallinum and Zingiber montanum, which belong to the Zingiberaceae family, are traditional Chinese folk medicinal herbs in Guizhou and Yunnan Province of China. They share great similarities in morphology, chemical constituent, and DNA barcoding sequence. The taxonomy of the two Zingiber species is controversial and discrimination of traditional Chinese medicines directly affects the pharmacological and clinical effects. In the present study, we performed a systemic analysis of "super-barcode" and untargeted metabolomics between Z. corallinum and Z. montanum using chloroplast (cp) genome sequencing and gas chromatography-mass spectrometry (GC-MS) analysis. Comparison and phylogenetic analysis of cp genomes of the two Zingiber species showed that the cp genome could not guarantee the accuracy of identification. An untargeted metabolomics strategy combining GC-MS with chemometric methods was proposed to distinguish the Zingiber samples of known variety. A total of 51 volatile compounds extracted from Z. corallinum and Z. montanum were identified, and nine compounds were selected as candidate metabolic markers to reveal the significant difference between Z. corallinum and Z. montanum. The performance of the untargeted metabolomic approach was verified with unknown Zingiber samples. Although the cp genomes could not be used to identify Zingiber species in this study, it will still provide a valuable genomics resource for population studies in the Zingiberaceae family, and the GC-MS based metabolic fingerprint is more promising for species identification and safe application of Z. corallinum and Z. montanum.