MutRank: an R shiny web-application for exploratory targeted mutual rank-based coexpression analyses integrated with user-provided supporting information

OsCYP706C2 diverts rice strigolactone biosynthesis to a noncanonical pathway branch

Strigolactones exhibit dual functionality as regulators of plant architecture and signaling molecules in the rhizosphere. The important model crop rice exudes a blend of different strigolactones from its roots. Here, we identify the inaugural noncanonical strigolactone, 4-oxo-methyl carlactonoate (4-oxo-MeCLA), in rice root exudate. Comprehensive, cross-species coexpression analysis allowed us to identify a cytochrome P450, OsCYP706C2, and two methyl transferases as candidate enzymes for this noncanonical rice strigolactone biosynthetic pathway. Heterologous expression in yeast and Nicotiana benthamiana indeed demonstrated the role of these enzymes in the biosynthesis of 4-oxo-MeCLA, which, expectedly, is derived from carlactone as substrate. The oscyp706c2 mutants do not exhibit a tillering phenotype but do have delayed mycorrhizal colonization and altered root phenotype. This work sheds light onto the intricate complexity of strigolactone biosynthesis in rice and delineates its role in symbiosis and development.

Polyploidy and the evolution of phenotypic integration: Network analysis reveals relationships among anatomy, morphology, and physiology

Premise

Most traits are polygenic and most genes are pleiotropic, resulting in complex, integrated phenotypes. Polyploidy presents an excellent opportunity to explore the evolution of phenotypic integration as entire genomes are duplicated, allowing for new associations among traits and potentially leading to enhanced or reduced phenotypic integration. Despite the multivariate nature of phenotypic evolution, studies often rely on simplistic bivariate correlations that cannot accurately represent complex phenotypes or data reduction techniques that can obscure specific trait relationships.

Methods

We apply network modeling, a common gene co-expression analysis, to the study of phenotypic integration to identify multivariate patterns of phenotypic evolution, including anatomy and morphology (structural) and physiology (functional) traits in response to whole genome duplication in the genus Brassica.

Results

We identify four key structural traits that are overrepresented in the evolution of phenotypic integration. Seeding networks with key traits allowed us to identify structure-function relationships not apparent from bivariate analyses. In general, allopolyploids exhibited larger, more robust networks indicative of increased phenotypic integration compared to diploids.

Discussion

Phenotypic network analysis may provide important insights into the effects of selection on non-target traits, even when they lack direct correlations with the target traits. Network analysis may allow for more nuanced predictions of both natural and artificial selection.

Harnessing the predicted maize pan-interactome for putative gene function prediction and prioritization of candidate genes for important traits

The recent assembly and annotation of the 26 maize nested association mapping population founder inbreds have enabled large-scale pan-genomic comparative studies. These studies have expanded our understanding of agronomically important traits by integrating pan-transcriptomic data with trait-specific gene candidates from previous association mapping results. In contrast to the availability of pan-transcriptomic data, obtaining reliable protein-protein interaction (PPI) data has remained a challenge due to its high cost and complexity. We generated predicted PPI networks for each of the 26 genomes using the established STRING database. The individual genome-interactomes were then integrated to generate core- and pan-interactomes. We deployed the PPI clustering algorithm ClusterONE to identify numerous PPI clusters that were functionally annotated using gene ontology (GO) functional enrichment, demonstrating a diverse range of enriched GO terms across different clusters. Additional cluster annotations were generated by integrating gene coexpression data and gene description annotations, providing additional useful information. We show that the functionally annotated PPI clusters establish a useful framework for protein function prediction and prioritization of candidate genes of interest. Our study not only provides a comprehensive resource of predicted PPI networks for 26 maize genomes but also offers annotated interactome clusters for predicting protein functions and prioritizing gene candidates. The source code for the Python implementation of the analysis workflow and a standalone web application for accessing the analysis results are available at https://github.com/eporetsky/PanPPI.

Stressing the importance of plant specialized metabolites: omics-based approaches for discovering specialized metabolism in plant stress responses

Plants produce a diverse range of specialized metabolites that play pivotal roles in mediating environmental interactions and stress adaptation. These unique chemical compounds also hold significant agricultural, medicinal, and industrial values. Despite the expanding knowledge of their functions in plant stress interactions, understanding the intricate biosynthetic pathways of these natural products remains challenging due to gene and pathway redundancy, multifunctionality of proteins, and the activity of enzymes with broad substrate specificity. In the past decade, substantial progress in genomics, transcriptomics, metabolomics, and proteomics has made the exploration of plant specialized metabolism more feasible than ever before. Notably, recent advances in integrative multi-omics and computational approaches, along with other technologies, are accelerating the discovery of plant specialized metabolism. In this review, we present a summary of the recent progress in the discovery of plant stress-related specialized metabolites. Emphasis is placed on the application of advanced omics-based approaches and other techniques in studying plant stress-related specialized metabolism. Additionally, we discuss the high-throughput methods for gene functional characterization. These advances hold great promise for harnessing the potential of specialized metabolites to enhance plant stress resilience in the future.

A Novel ypTLM Staging System Based on LODDS for Gastric Cancer After Neoadjuvant Therapy: Multicenter and Large-Sample Retrospective Study

BACKGROUND

The accuracy of the eighth AJCC ypTNM staging system on the prognosis of gastric cancer (GC) patients after neoadjuvant therapy (NAT) is controversial. This study aimed to develop and validate a novel staging system using the log odds of positive lymph nodes scheme (LODDS).

METHODS

A retrospective analysis of 606 GC patients who underwent radical gastrectomy after neoadjuvant therapy was conducted as the development cohort. (Fujian Medical University Affiliated Union Hospital (n = 183), Qinghai University Affiliated Hospital (n = 169), Mayo Clinic (n = 236), Lanzhou University First Hospital (n = 18)). The validation cohort came from the SEER database (n = 1701). A novel ypTLoddsS (ypTLM) staging system was established using the 3-year overall survival. The predictive performance of two systems was compared.

RESULTS

Two-step multivariate Cox regression analysis in both cohorts showed that ypTLM was an independent predictor of overall survival of GC patients after neoadjuvant therapy (HR: 1.57, 95% CI: 1.30-1.88, p < 0.001). In the development cohort, ypTLM had better discrimination ability than ypTNM (C-index: 0.663 vs 0.633, p < 0.001), better prediction homogeneity (LR: 97.7 vs. 70.9), and better prediction accuracy (BIC: 3067.01 vs 3093.82; NRI: 0.36). In the validation cohort, ypTLM had a better prognostic predictive ability (C-index: 0.614 vs 0.588, p < 0.001; LR: 11,909.05 vs. 11,975.75; BIC: 13,263.71 vs 13,328.24; NRI: 0.22). The time-dependent ROC curve shows that the predictive performance of ypTLM is better than ypTNM, and the analysis of the decision curve shows that ypTLM achieved better net benefits.

CONCLUSION

A LODDS-based ypTLM staging system based on multicenter data was established and validated. The predictive performance was superior to the eighth AJCC ypTNM staging system.

Maize Terpene Synthase 8 (ZmTPS8) Contributes to a Complex Blend of Fungal-Elicited Antibiotics

In maize (Zea mays), fungal-elicited immune responses include the accumulation of terpene synthase (TPS) and cytochrome P450 monooxygenases (CYP) enzymes resulting in complex antibiotic arrays of sesquiterpenoids and diterpenoids, including α/β-selinene derivatives, zealexins, kauralexins and dolabralexins. To uncover additional antibiotic families, we conducted metabolic profiling of elicited stem tissues in mapping populations, which included B73 × M162W recombinant inbred lines and the Goodman diversity panel. Five candidate sesquiterpenoids associated with a chromosome 1 locus spanning the location of ZmTPS27 and ZmTPS8. Heterologous enzyme co-expression studies of ZmTPS27 in Nicotiana benthamiana resulted in geraniol production while ZmTPS8 yielded α-copaene, δ-cadinene and sesquiterpene alcohols consistent with epi-cubebol, cubebol, copan-3-ol and copaborneol matching the association mapping efforts. ZmTPS8 is an established multiproduct α-copaene synthase; however, ZmTPS8-derived sesquiterpene alcohols are rarely encountered in maize tissues. A genome wide association study further linked an unknown sesquiterpene acid to ZmTPS8 and combined ZmTPS8-ZmCYP71Z19 heterologous enzyme co-expression studies yielded the same product. To consider defensive roles for ZmTPS8, in vitro bioassays with cubebol demonstrated significant antifungal activity against both Fusarium graminearum and Aspergillus parasiticus. As a genetically variable biochemical trait, ZmTPS8 contributes to the cocktail of terpenoid antibiotics present following complex interactions between wounding and fungal elicitation.

Maize resistance to witchweed through changes in strigolactone biosynthesis

Maize (Zea mays) is a major staple crop in Africa, where its yield and the livelihood of millions are compromised by the parasitic witchweed Striga. Germination of Striga is induced by strigolactones exuded from maize roots into the rhizosphere. In a maize germplasm collection, we identified two strigolactones, zealactol and zealactonoic acid, which stimulate less Striga germination than the major maize strigolactone, zealactone. We then showed that a single cytochrome P450, ZmCYP706C37, catalyzes a series of oxidative steps in the maize-strigolactone biosynthetic pathway. Reduction in activity of this enzyme and two others involved in the pathway, ZmMAX1b and ZmCLAMT1, can change strigolactone composition and reduce Striga germination and infection. These results offer prospects for breeding Striga-resistant maize.

Development and validation of metabolic scoring to individually predict prognosis and monitor recurrence early in gastric cancer: A large-sample analysis

PURPOSE

To develop and validate a simple metabolic score (Metabolic score, MS) for use in evaluating the prognosis of gastric cancer (GC) patients and dynamically monitor for early recurrence.

METHODS

We retrospectively collected general clinicopathological data of patients who underwent radical gastrectomy for GC between September 2012 and December 2017 in the Department of Gastric Surgery of the Fujian Medical University Union Hospital. Using a random forest algorithm to screen preoperative blood indicators into the Least absolute shrinkage and selection operator (LASSO) model, we developed a novel MS to predict prognosis.

RESULTS

Data of 1974 patients were used to develop and validate the model. Total cholesterol (TCHO), bilirubin (TBIL), direct bilirubin (DBIL), and 15 other metabolic indicators had significant predictive value for the prognosis using the random forest algorithm. In the overall population, 533 patients (27.0%) had high and 1441 (73%) had low MS status. High MS status was related to tumor progression. The KM curves of 3-year OS and RFS for training set patients showed low MS had a better prognosis than high MS (OS: 79.4% vs 59.7%, P < 0.001; RFS: 76.0% vs 56.2%, P < 0.001).

CONCLUSIONS

We have developed and validated MS to predict the long-term survival of GC patients and allow early monitoring of recurrence. This will provide physicians with simple, economical, and dynamic tumor monitoring information.

Comprehensive Analysis of SRO Gene Family in Sesamum indicum (L.) Reveals Its Association with Abiotic Stress Responses

SIMILAR TO RCD-ONEs (SROs) comprise a small plant-specific gene family which play important roles in regulating numerous growth and developmental processes and responses to environmental stresses. However, knowledge of SROs in sesame (Sesamum indicum L.) is limited. In this study, four SRO genes were identified in the sesame genome. Phylogenetic analysis showed that 64 SROs from 10 plant species were divided into two groups (Group I and II). Transcriptome data revealed different expression patterns of SiSROs over various tissues. Expression analysis showed that Group II SROs, especially SiSRO2b, exhibited a stronger response to various abiotic stresses and phytohormones than those in Group I, implying their crucial roles in response to environmental stimulus and hormone signals. In addition, the co-expression network and protein-protein interaction network indicated that SiSROs are associated with a wide range of stress responses. Moreover, transgenic yeast harboring SiSRO2b showed improved tolerance to salt, osmotic and oxidative stress, indicating SiSRO2b could confer multiple tolerances to transgenic yeast. Taken together, this study not only lays a foundation for further functional dissection of the SiSRO gene family, but also provides valuable gene candidates for genetic improvement of abiotic stress tolerance in sesame.

Getting back to the grass roots: harnessing specialized metabolites for improved crop stress resilience

Roots remain an understudied site of complex and important biological interactions mediating plant productivity. In grain and bioenergy crops, grass root specialized metabolites (GRSM) are central to key interactions, yet our basic knowledge of the chemical language remains fragmentary. Continued improvements in plant genome assembly and metabolomics are enabling large-scale advances in the discovery of specialized metabolic pathways as a means of regulating root-biotic interactions. Metabolomics, transcript coexpression analyses, forward genetic studies, gene synthesis and heterologous expression assays drive efficient pathway discoveries. Functional genetic variants identified through genome wide analyses, targeted CRISPR/Cas9 approaches, and both native and non-native overexpression studies critically inform novel strategies for bioengineering metabolic pathways to improve plant traits.