A prior-based integrative framework for functional transcriptional regulatory network inference.

Transcriptional regulatory networks specify regulatory proteins controlling the context-specific expression levels of genes. Inference of genome-wide regulatory networks is central to understanding gene regulation, but remains an open challenge. Expression-based network inference is among the most popular methods to infer regulatory networks, however, networks inferred from such methods have low overlap with experimentally derived (e.g. ChIP-chip and transcription factor (TF) knockouts) networks. Currently we have a limited understanding of this discrepancy. To address this gap, we first develop a regulatory network inference algorithm, based on probabilistic graphical models, to integrate expression with auxiliary datasets supporting a regulatory edge. Second, we comprehensively analyze our and other state-of-the-art methods on different expression perturbation datasets. Networks inferred by integrating sequence-specific motifs with expression have substantially greater agreement with experimentally derived networks, while remaining more predictive of expression than motif-based networks. Our analysis suggests natural genetic variation as the most informative perturbation for network inference, and, identifies core TFs whose targets are predictable from expression. Multiple reasons make the identification of targets of other TFs difficult, including network architecture and insufficient variation of TF mRNA level. Finally, we demonstrate the utility of our inference algorithm to infer stress-specific regulatory networks and for regulator prioritization.

A proteomic atlas of the legume Medicago truncatula and its nitrogen-fixing endosymbiont Sinorhizobium meliloti.

Legumes are essential components of agricultural systems because they enrich the soil in nitrogen and require little environmentally deleterious fertilizers. A complex symbiotic association between legumes and nitrogen-fixing soil bacteria called rhizobia culminates in the development of root nodules, where rhizobia fix atmospheric nitrogen and transfer it to their plant host. Here we describe a quantitative proteomic atlas of the model legume Medicago truncatula and its rhizobial symbiont Sinorhizobium meliloti, which includes more than 23,000 proteins, 20,000 phosphorylation sites, and 700 lysine acetylation sites. Our analysis provides insight into mechanisms regulating symbiosis. We identify a calmodulin-binding protein as a key regulator in the host and assign putative roles and targets to host factors (bioactive peptides) that control gene expression in the symbiont. Further mining of this proteomic resource may enable engineering of crops and their microbial partners to increase agricultural productivity and sustainability.

Deep Sequencing of the Medicago truncatula Root Transcriptome Reveals a Massive and Early Interaction between Nodulation Factor and Ethylene Signals.

The legume-rhizobium symbiosis is initiated through the activation of the Nodulation (Nod) factor-signaling cascade, leading to a rapid reprogramming of host cell developmental pathways. In this work, we combine transcriptome sequencing with molecular genetics and network analysis to quantify and categorize the transcriptional changes occurring in roots of Medicago truncatula from minutes to days after inoculation with Sinorhizobium medicae. To identify the nature of the inductive and regulatory cues, we employed mutants with absent or decreased Nod factor sensitivities (i.e. Nodulation factor perception and Lysine motif domain-containing receptor-like kinase3, respectively) and an ethylene (ET)-insensitive, Nod factor-hypersensitive mutant (sickle). This unique data set encompasses nine time points, allowing observation of the symbiotic regulation of diverse biological processes with high temporal resolution. Among the many outputs of the study is the early Nod factor-induced, ET-regulated expression of ET signaling and biosynthesis genes. Coupled with the observation of massive transcriptional derepression in the ET-insensitive background, these results suggest that Nod factor signaling activates ET production to attenuate its own signal. Promoter:ß-glucuronidase fusions report ET biosynthesis both in root hairs responding to rhizobium as well as in meristematic tissue during nodule organogenesis and growth, indicating that ET signaling functions at multiple developmental stages during symbiosis. In addition, we identified thousands of novel candidate genes undergoing Nod factor-dependent, ET-regulated expression. We leveraged the power of this large data set to model Nod factor- and ET-regulated signaling networks using MERLIN, a regulatory network inference algorithm. These analyses predict key nodes regulating the biological process impacted by Nod factor perception. We have made these results available to the research community through a searchable online resource.