Time Series Transcriptome Analysis in Medicago truncatula Shoot and Root Tissue During Early Nodulation

Abstract

In response to colonization by rhizobia bacteria, legumes are able to form nitrogen-fixing nodules in their roots, allowing the plants to grow efficiently in nitrogen-depleted environments. Legumes utilize a complex, long-distance signaling pathway to regulate nodulation that involves signals in both roots and shoots. We measured the transcriptional response to treatment with rhizobia in both the shoots and roots of Medicago truncatula over a 72-h time course. To detect temporal shifts in gene expression, we developed GeneShift, a novel computational statistics and machine learning workflow that addresses the time series replicate the averaging issue for detecting gene expression pattern shifts under different conditions. We identified both known and novel genes that are regulated dynamically in both tissues during early nodulation including leginsulin, defensins, root transporters, nodulin-related, and circadian clock genes. We validated over 70% of the expression patterns that GeneShift discovered using an independent M. truncatula RNA-Seq study. GeneShift facilitated the discovery of condition-specific temporally differentially expressed genes in the symbiotic nodulation biological system. In principle, GeneShift should work for time-series gene expression profiling studies from other systems.

Transcriptome analysis ofMedicago truncatulaAutoregulation of Nodulation mutants reveals that disruption of the SUNN pathway causes constitutive expression changes in a small group of genes, but the overall response to rhizobia resembles wild type, including induction ofTML1andTML2

“…The root data set consists of 75 libraries, including 60 libraries from this work (three replicates of five time points each for inoculated wild type A17, sunn-4 and rdn1-2 root segments, and uninoculated sunn-4 root segments) and 15 libraries from uninoculated wild type A17 root segments generated in the same way and used in two manuscripts on new analysis algorithms (Gao et al, 2022; Poehlman et al, 2019). The shoot data set has 60 libraries (three replicates of five time points each for inoculated wild type A17, sunn-4 , and rdn1-2 and uninoculated A17), including 30 from this work for the two mutants and 30 from wild type previously reported (Gao et al, 2022). Read mapping and alignment data for each library is included in Supplemental Data Set 1.…”

A laser capture microdissection transcriptome ofM. truncatularoots responding to rhizobia reveals spatiotemporal tissue expression patterns of genes involved in nodule signaling and organogenesis

“…In a second approach we used GeneShift (Gao et al, 2022) to analyze differing patterns of genes expressed in the ICA and ICB over the entire time course without the step of defining DTEPs of genes and focused on any pattern differences during 0-24 hpi. GeneShift identifies genes with statistically similar patterns of expression, especially those that are expressed at low levels; these genes were eliminated in the first analysis because of the parameters of DeSeq2.…”

“…GeneShift identifies genes with statistically similar patterns of expression, especially those that are expressed at low levels; these genes were eliminated in the first analysis because of the parameters of DeSeq2. GeneShift outputs trajectory sets, grouping genes with a similar pattern defined by the algorithm as statistically significant (Gao et al, 2022). GeneShift identified 52 genes in 9 trajectory sets with different patterns of expression in the two tissues after inoculation (Supplemental Data Set 2E).…”

“…The same data was also processed with GeneShift (Supplemental Figures 4 and 5), a computational workflow for time-series gene expression profiling using the two tissues as conditions. GeneShift uses a six-stage process incorporating a deep learning classification model to obtain high quality expression pattern shifts between multiple conditions and timepoints and is described in (Gao et al, 2022).…”

Cell Mutagenic Autopolyploidy Enhances Salinity Stress Tolerance in Leguminous Crops

“…In shoots and roots of M . truncatula , transcriptional responses to treatment with rhizobia detected varied gene expression pattern shifts under different conditions [ 76 ].…”