Despite of important functions of strigolactones (SLs) and karrikins (KARs) in plant development, plant–parasite and plant–fungi interactions, their roles in soybean–rhizobia interaction remain elusive. SL/KAR signaling genes
Gene regulatory networks associated with lateral root and nodule development in soybean
Abstract Legume plants such as soybean produce two major types of root lateral organs, lateral roots and root nodules. A robust computational framework was developed to predict potential gene regulatory networks (GRNs) associated with root lateral organ development in soybean. A genome-scale expression data set was obtained from soybean root nodules and lateral roots and subjected to biclustering using QUBIC (QUalitative BIClustering algorithm). Biclusters and transcription factor (TF) genes with enriched expression in lateral root tissues were converged using different network inference algorithms to predict high-confidence regulatory modules that were repeatedly retrieved in different methods. The ranked combination of results from all different network inference algorithms into one ensemble solution identified 21 GRN modules of 182 co-regulated genes networks, potentially involved in root lateral organ development stages in soybean. The workflow correctly predicted previously known nodule- and lateral root-associated TFs including the expected hierarchical relationships. The results revealed distinct high-confidence GRN modules associated with early nodule development involving AP2, GRF5 and C3H family TFs, and those associated with nodule maturation involving GRAS, LBD41 and ARR18 family TFs. Knowledge from this work supported by experimental validation in the future is expected to help determine key gene targets for biotechnological strategies to optimize nodule formation and enhance nitrogen fixation.
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- Award ID(s):
- 1849206
- NSF-PAR ID:
- 10165171
- Date Published:
- Journal Name:
- in silico Plants
- Volume:
- 2
- Issue:
- 1
- ISSN:
- 2517-5025
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Summary GmMAX2a, GmD14s, andGmKAIs are activated by rhizobia infection. GmMAX2a restoredatmax2 root hair defects and soybean root hairs were changed inGmMAX2a overexpression (GmMAX2a ‐OE ) or knockdown (GmMAX2a ‐KD ) mutants.GmMAX2a ‐KD gave fewer, whereasGmMAX2a ‐OE produced more nodules than GUS hairy roots. Mutation ofGmMAX2a in itsKD orOE transgenic hairy roots affected the rhizobia infection‐induced increases in early nodulation gene expression. Both mutant hairy roots also displayed the altered auxin, jasmonate and abscisic acid levels, as further verified by transcriptomic analyses of their synthetic genes. Overexpression of an auxin synthetic geneGmYUC2a also affected SL and KAR signaling genes. GmMAX2a physically interacted with SL/KAR receptors GmD14s, GmKAIs, and GmD14Ls with different binding affinities, depending on variations in the critical amino acids, forming active D14/KAI‐SCFMAX2complexes. The knockdown mutant roots of the nodule‐specifically expressingGmKAI s andGmD14L s gave fewer nodules, with altered expression of several early nodulation genes. The expression levels ofGmKAI s, andGmD14L s were markedly changed inGmMAX2a mutant roots, so did their target repressor genesGmD53 s andGmSMAX1 s. Thus, SL and KAR signaling were involved in soybean–rhizobia interaction and nodulation partly through interactions with hormones, and this may explain the different effects of MXA2 orthologs on legume determinate and indeterminate nodulation. The study provides fresh insights into the roles of GmMAX2‐mediated SL/KAR signaling in soybean root hair and nodule formation. -
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Abstract The Soybean Gene Atlas project provides a comprehensive map for understanding gene expression patterns in major soybean tissues from flower, root, leaf, nodule, seed, and shoot and stem. The RNA‐Seq data generated in the project serve as a valuable resource for discovering tissue‐specific transcriptome behavior of soybean genes in different tissues. We developed a computational pipeline for Soybean context‐specific network (SoyCSN) inference with a suite of prediction tools to analyze, annotate, retrieve, and visualize soybean context‐specific networks at both transcriptome and interactome levels. BicMix and Cross‐Conditions Cluster Detection algorithms were applied to detect modules based on co‐expression relationships across all the tissues. Soybean context‐specific interactomes were predicted by combining soybean tissue gene expression and protein–protein interaction data. Functional analyses of these predicted networks provide insights into soybean tissue specificities. For example, under symbiotic, nitrogen‐fixing conditions, the constructed soybean leaf network highlights the connection between the photosynthesis function and rhizobium–legume symbiosis. SoyCSN data and all its results are publicly available via an interactive web service within the Soybean Knowledge Base (SoyKB) at
http://soykb.org/SoyCSN . SoyCSN provides a useful web‐based access for exploring context specificities systematically in gene regulatory mechanisms and gene relationships for soybean researchers and molecular breeders. -
Nodule organogenesis in legumes is regulated temporally and spatially through gene networks. Genome-wide transcriptome, proteomic, and metabolomic analyses have been used previously to define the functional role of various plant genes in the nodulation process. However, while significant progress has been made, most of these studies have suffered from tissue dilution since only a few cells/root regions respond to rhizobial infection, with much of the root non-responsive. To partially overcome this issue, we adopted translating ribosome affinity purification (TRAP) to specifically monitor the response of the root cortex to rhizobial inoculation using a cortex-specific promoter. While previous studies have largely focused on the plant response within the root epidermis (e.g., root hairs) or within developing nodules, much less is known about the early responses within the root cortex, such as in relation to the development of the nodule primordium or growth of the infection thread. We focused on identifying genes specifically regulated during early nodule organogenesis using roots inoculated with Bradyrhizobium japonicum . A number of novel nodulation gene candidates were discovered, as well as soybean orthologs of nodulation genes previously reported in other legumes. The differential cortex expression of several genes was confirmed using a promoter-GUS analysis, and RNAi was used to investigate gene function. Notably, a number of differentially regulated genes involved in phytohormone signaling, including auxin, cytokinin, and gibberellic acid (GA), were also discovered, providing deep insight into phytohormone signaling during early nodule development.more » « less
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Newman, Stuart A (Ed.)Cellular differentiation during hematopoiesis is guided by gene regulatory networks (GRNs) comprising transcription factors (TFs) and the effectors of cytokine signaling. Based largely on analyses conducted at steady state, these GRNs are thought to be organized as a hierarchy of bistable switches, with antagonism between Gata1 and PU.1 driving red- and white-blood cell differentiation. Here, we utilize transient gene expression patterns to infer the genetic architecture—the type and strength of regulatory interconnections—and dynamics of a twelve-gene GRN including key TFs and cytokine receptors. We trained gene circuits, dynamical models that learn genetic architecture, on high temporal-resolution gene-expression data from the differentiation of an inducible cell line into erythrocytes and neutrophils. The model is able to predict the consequences of gene knockout, knockdown, and overexpression experiments and the inferred interconnections are largely consistent with prior empirical evidence. The inferred genetic architecture is densely interconnected rather than hierarchical, featuring extensive cross-antagonism between genes from alternative lineages and positive feedback from cytokine receptors. The analysis of the dynamics of gene regulation in the model reveals that PU.1 is one of the last genes to be upregulated in neutrophil conditions and that the upregulation of PU.1 and other neutrophil genes is driven by Cebpa and Gfi1 instead. This model inference is confirmed in an independent single-cell RNA-Seq dataset from mouse bone marrow in which Cebpa and Gfi1 expression precedes the neutrophil-specific upregulation of PU.1 during differentiation. These results demonstrate that full PU.1 upregulation during neutrophil development involves regulatory influences extrinsic to the Gata1-PU.1 bistable switch. Furthermore, although there is extensive cross-antagonism between erythroid and neutrophil genes, it does not have a hierarchical structure. More generally, we show that the combination of high-resolution time series data and data-driven dynamical modeling can uncover the dynamics and causality of developmental events that might otherwise be obscured.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1093/insilicoplants/diaa002
