New insights into diosgenin biosynthesis pathway and its regulation in Trigonella foenum-graecum L.

INTRODUCTION: Throughout history, thousands of medicinal and aromatic plants have been widely utilised by people worldwide. Owing to them possessing of valuable compounds with little side effects in comparison with chemical drugs, herbs have been of interest to humans for a number of purposes. Diosgenin, driven from fenugreek, Trigonella foenum-graecum L., has extensively drawn scientist's attention owing to having curable properties and being a precursor of steroid hormones synthesis. Nonetheless, complete knowledge about the biosynthesis pathway of this metabolite is still elusive. OBJECTIVE: In the present research, we isolated the full-length CDS of 14 genes involving in diosgenin formation and measured their expression rate in various genotypes, which had illustrated different amount of diosgenin. METHODOLOGY: The genes were successfully isolated, and functional motifs were also assessed using in silico approaches. RESULTS: Moreover, combining transcript and metabolite analysis revealed that there are many genes playing the role in diosgenin formation, some of which are highly influential. Among them, ∆24 -reductase, which converts cycloartenol to cycloartanol, is the first-committed and rate-limiting enzyme in this pathway. Additionally, no transcripts indicating to the presence or expression of lanosterol synthase were detected, contradicting the previous hypothesis about the biosynthetic pathway of diosgenin in fenugreek. CONCLUSION: Considering all these, therefore, we propose the most possible pathway of diosgenin. This knowledge will then pave the way toward cloning the genes as well as engineering the diosgenin biosynthesis pathway.

Co-regulation analysis of co-expressed modules under cold and pathogen stress conditions in tomato.

A primary mechanism for controlling the development of multicellular organisms is transcriptional regulation, which carried out by transcription factors (TFs) that recognize and bind to their binding sites on promoter region. The distance from translation start site, order, orientation, and spacing between cis elements are key factors in the concentration of active nuclear TFs and transcriptional regulation of target genes. In this study, overrepresented motifs in cold and pathogenesis responsive genes were scanned via Gibbs sampling method, this method is based on detection of overrepresented motifs by means of a stochastic optimization strategy that searches for all possible sets of short DNA segments. Then, identified motifs were checked by TRANSFAC, PLACE and Soft Berry databases in order to identify putative TFs which, interact to the motifs. Several cis/trans regulatory elements were found using these databases. Moreover, cross-talk between cold and pathogenesis responsive genes were confirmed. Statistical analysis was used to determine distribution of identified motifs on promoter region. In addition, co-regulation analysis results, illustrated genes in pathogenesis responsive module are divided into two main groups. Also, promoter region was crunched to six subareas in order to draw the pattern of distribution of motifs in promoter subareas. The result showed the majority of motifs are concentrated on 700 nucleotides upstream of the translational start site (ATG). In contrast, this result isn't true in another group. In other words, there was no difference between total and compartmentalized regions in cold responsive genes.

A strategy for differential abundance analysis of sparse microbiome data with group-wise structured zeros.

Comparing the abundance of microbial communities between different groups or obtained under different experimental conditions using count sequence data is a challenging task due to various issues such as inflated zero counts, overdispersion, and non-normality. Several methods and procedures based on counts, their transformation and compositionality have been proposed in the literature to detect differentially abundant species in datasets containing hundreds to thousands of microbial species. Despite efforts to address the large numbers of zeros present in microbiome datasets, even after careful data preprocessing, the performance of existing methods is impaired by the presence of inflated zero counts and group-wise structured zeros (i.e. all zero counts in a group). We propose and validate using extensive simulations an approach combining two differential abundance testing methods, namely DESeq2-ZINBWaVE and DESeq2, to address the issues of zero-inflation and group-wise structured zeros, respectively. This combined approach was subsequently successfully applied to two plant microbiome datasets that revealed a number of taxa as interesting candidates for further experimental validation.

A Combination of Metabolomics and Machine Learning Results in the Identification of a New Cyst Nematode Hatching Factor.

Potato Cyst Nematodes (PCNs) are an economically important pest for potato growers. A crucial event in the life cycle of the nematode is hatching, after which the juvenile will move toward the host root and infect it. The hatching of PCNs is induced by known and unknown compounds in the root exudates of host plant species, called hatching factors (HFs, induce hatching independently), such as solanoeclepin A (solA), or hatching stimulants (HSs, enhance hatching activity of HFs). Unraveling the identity of unknown HSs and HFs and their natural variation is important for the selection of cultivars that produce low amounts of HFs and HSs, thus contributing to more sustainable agriculture. In this study, we used a new approach aimed at the identification of new HFs and HSs for PCNs in potato. Hereto, root exudates of a series of different potato cultivars were analyzed for their PCN hatch-inducing activity and their solA content. The exudates were also analyzed using untargeted metabolomics, and subsequently the data were integrated using machine learning, specifically random forest feature selection, and Pearson's correlation testing. As expected, solA highly correlates with hatching. Furthermore, this resulted in the discovery of a number of metabolite features present in the root exudate that correlate with hatching and solA content, and one of these is a compound of m/z 526.18 that predicts hatching even better than solA with both data methods. This compound's involvement in hatch stimulation was confirmed by the fractionation of three representative root exudates and hatching assays with the resulting fractions. Moreover, the compound shares mass fragmentation similarity with solA, and we therefore assume it has a similar structure. With this work, we show that potato likely produces a solA analogue, and we contribute to unraveling the hatch-inducing cocktail exuded by plant roots.

Metabolic interactions in beneficial microbe recruitment by plants.

During millions of years of evolution, land plants and microorganisms have established elaborate partnerships. Microbes play essential roles in plant fitness and help plants cope with environmental challenges. Vice versa, plants provide the microbes with a niche and food. In the soil, a complex network of interactions mediated by metabolic signals drives the relationship between plants and microbes. Here, we review the roles of metabolic signaling in the plant-microbiome interaction. We discuss how plant-produced small molecules are involved in the recruitment of the microbiome. Also the microbial partners in this relationship use small molecules, such as quorum sensing molecules and volatiles for intra-species and inter-species communication. We give an overview of the regulation of the biosynthesis, secretion and perception of both plant and microbial small molecules and discuss the examples of biotechnological approaches to engineer the plant-microbiome interaction by targeting these metabolic dialogues. Ultimately, an improved understanding of the plant-microbiome interaction and engineering possibilities will pave the way to a more sustainable agriculture.

The effects of promoter variations of the N-Methylcanadine 1-Hydroxylase (CYP82Y1) gene on the noscapine production in opium poppy.

Noscapine is an antitumor alkaloid produced in opium poppy (Papaver somniferum) and some members of the Papaveraceae family. It has been primarily used for its antitussive effects; more recently, its anticancer properties were shown. Herein, we detected an SSR embedded in the promoter region of the CYP82Y1 gene, which was found to be the first committed-step enzyme in the noscapine biosynthesis pathway, using the MISA program. Some collected ecotypes of P. somniferum were investigated for understanding of SSRs role in the regulation of gene expression and metabolite content. Quantitative PCR showed that a variation in the motif repeat number (either a decrease or increase) down-regulated the expression of the CYP82Y1 gene. Furthermore, the analysis of noscapine content suggested that a variation in the promoter region influence noscapine amount. Moreover, P. bracteatum was analyzed in both transcript and metabolite levels, and illustrated much less expression and metabolite level in comparison to P. somniferum. By exploiting the transcriptome data from the eight genera of the Papaveraceae family, we found that noscapine biosynthesis genes are present in P. bracteatum and are not shared in other genera of the Papaveraceae family. This results may explain production of a confined metabolite within a genus.