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1. Systematic Discovery of Archaeal Transcription Factor Functions in Regulatory Networks through Quantitative Phenotyping Analysis

   https://doi.org/10.1128/mSystems.00032-17  

   Darnell, Cynthia L.; Tonner, Peter D.; Gulli, Jordan G.; Schmidler, Scott C.; Schmid, Amy K.; Shank, Elizabeth A. (September 2017, mSystems)

   ABSTRACT Gene regulatory networks (GRNs) are critical for dynamic transcriptional responses to environmental stress. However, the mechanisms by which GRN regulation adjusts physiology to enable stress survival remain unclear. Here we investigate the functions of transcription factors (TFs) within the global GRN of the stress-tolerant archaeal microorganism Halobacterium salinarum . We measured growth phenotypes of a panel of TF deletion mutants in high temporal resolution under heat shock, oxidative stress, and low-salinity conditions. To quantitate the noncanonical functional forms of the growth trajectories observed for these mutants, we developed a novel modeling framework based on Gaussian process regression and functional analysis of variance (FANOVA). We employ unique statistical tests to determine the significance of differential growth relative to the growth of the control strain. This analysis recapitulated known TF functions, revealed novel functions, and identified surprising secondary functions for characterized TFs. Strikingly, we observed that the majority of the TFs studied were required for growth under multiple stress conditions, pinpointing regulatory connections between the conditions tested. Correlations between quantitative phenotype trajectories of mutants are predictive of TF-TF connections within the GRN. These phenotypes are strongly concordant with predictions from statistical GRN models inferred from gene expression data alone. With genome-wide and targeted data sets, we provide detailed functional validation of novel TFs required for extreme oxidative stress and heat shock survival. Together, results presented in this study suggest that many TFs function under multiple conditions, thereby revealing high interconnectivity within the GRN and identifying the specific TFs required for communication between networks responding to disparate stressors. IMPORTANCE To ensure survival in the face of stress, microorganisms employ inducible damage repair pathways regulated by extensive and complex gene networks. Many archaea, microorganisms of the third domain of life, persist under extremes of temperature, salinity, and pH and under other conditions. In order to understand the cause-effect relationships between the dynamic function of the stress network and ultimate physiological consequences, this study characterized the physiological role of nearly one-third of all regulatory proteins known as transcription factors (TFs) in an archaeal organism. Using a unique quantitative phenotyping approach, we discovered functions for many novel TFs and revealed important secondary functions for known TFs. Surprisingly, many TFs are required for resisting multiple stressors, suggesting cross-regulation of stress responses. Through extensive validation experiments, we map the physiological roles of these novel TFs in stress response back to their position in the regulatory network wiring. This study advances understanding of the mechanisms underlying how microorganisms resist extreme stress. Given the generality of the methods employed, we expect that this study will enable future studies on how regulatory networks adjust cellular physiology in a diversity of organisms. 

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2. Validation of a high-confidence regulatory network for gene-to-NUE phenotype in field-grown rice

   https://doi.org/10.3389/fpls.2022.1006044  

   Shanks, Carly M.; Huang, Ji; Cheng, Chia-Yi; Shih, Hung-Jui S.; Brooks, Matthew D.; Alvarez, José M.; Araus, Viviana; Swift, Joseph; Henry, Amelia; Coruzzi, Gloria M. (November 2022, Frontiers in Plant Science)

   Nitrogen (N) and Water (W) - two resources critical for crop productivity – are becoming increasingly limited in soils globally. To address this issue, we aim to uncover the gene regulatory networks (GRNs) that regulate nitrogen use efficiency (NUE) - as a function of water availability - in Oryza sativa, a staple for 3.5 billion people. In this study, we infer and validate GRNs that correlate with rice NUE phenotypes affected by N-by-W availability in the field. We did this by exploiting RNA-seq and crop phenotype data from 19 rice varieties grown in a 2x2 N-by-W matrix in the field. First, to identify gene-to-NUE field phenotypes, we analyzed these datasets using weighted gene co-expression network analysis (WGCNA). This identified two network modules ("skyblue" & "grey60") highly correlated with NUE grain yield (NUEg). Next, we focused on 90 TFs contained in these two NUEg modules and predicted their genome-wide targets using the N-and/or-W response datasets using a random forest network inference approach (GENIE3). Next, to validate the GENIE3 TF→target gene predictions, we performed Precision/Recall Analysis (AUPR) using nine datasets for three TFs validated in planta . This analysis sets a precision threshold of 0.31, used to "prune" the GENIE3 network for high-confidence TF→target gene edges, comprising 88 TFs and 5,716 N-and/or-W response genes. Next, we ranked these 88 TFs based on their significant influence on NUEg target genes responsive to N and/or W signaling. This resulted in a list of 18 prioritized TFs that regulate 551 NUEg target genes responsive to N and/or W signals. We validated the direct regulated targets of two of these candidate NUEg TFs in a plant cell-based TF assay called TARGET, for which we also had in planta data for comparison. Gene ontology analysis revealed that 6/18 NUEg TFs - OsbZIP23 (LOC_Os02g52780), Oshox22 (LOC_Os04g45810), LOB39 (LOC_Os03g41330), Oshox13 (LOC_Os03g08960), LOC_Os11g38870, and LOC_Os06g14670 - regulate genes annotated for N and/or W signaling. Our results show that OsbZIP23 and Oshox22, known regulators of drought tolerance, also coordinate W-responses with NUEg. This validated network can aid in developing/breeding rice with improved yield on marginal, low N-input, drought-prone soils. 

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3. Rapid rewiring of an archaeal transcription factor function via flexible cis–trans interactions

   https://doi.org/10.1091/mbc.E24-11-0505  

   Pastor, Mar Martinez; Darnell, Cynthia L; Vreugdenhil, Angie; Schmid, Amy K (July 2025, Molecular Biology of the Cell)

   Garner, Ethan (Ed.)

   For microbial cells, an appropriate response to changing environmental conditions is critical for viability. Transcription regulatory proteins, or transcription factors (TF) sense environmental signals to change gene expression. However, it remains unclear how TFs and their corresponding gene regulatory networks are selected over evolutionary time scales. The function of TFs and how they evolve are particularly understudied in archaeal organisms. Here, we identified, characterized, and compared the function of the RosR TF across three related hypersaline-adapted archaeal model species. RosR was previously characterized as a global regulator of gene expression during oxidative stress in the species Halobacterium salinarum ( hsRosR). Here, we use functional genomics and quantitative phenotyping to demonstrate that, despite strong sequence conservation of RosR across species, its function diverges substantially. Surprisingly, RosR in Haloferax volcanii ( hvRosR) and Haloferax mediterranei ( hmRosR) regulates genes whose products function in motility and the membrane, leading to significant defects in motility when RosR is deleted. Given weak conservation and degeneration in cis-regulatory sequences recognized by the RosR TF across species, we hypothesize that the RosR regulatory network is readily rewired during evolution across related species of archaea. 

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4. ConnecTF: A platform to integrate transcription factor–gene interactions and validate regulatory networks

   https://doi.org/10.1093/plphys/kiaa012  

   Brooks, Matthew D; Juang, Che-Lun; Katari, Manpreet Singh; Alvarez, José M; Pasquino, Angelo; Shih, Hung-Jui; Huang, Ji; Shanks, Carly; Cirrone, Jacopo; Coruzzi, Gloria M (November 2020, Plant Physiology)

   null (Ed.)

   Abstract Deciphering gene regulatory networks (GRNs) is both a promise and challenge of systems biology. The promise lies in identifying key transcription factors (TFs) that enable an organism to react to changes in its environment. The challenge lies in validating GRNs that involve hundreds of TFs with hundreds of thousands of interactions with their genome-wide targets experimentally determined by high-throughput sequencing. To address this challenge, we developed ConnecTF, a species-independent, web-based platform that integrates genome-wide studies of TF–target binding, TF–target regulation, and other TF-centric omic datasets and uses these to build and refine validated or inferred GRNs. We demonstrate the functionality of ConnecTF by showing how integration within and across TF–target datasets uncovers biological insights. Case study 1 uses integration of TF–target gene regulation and binding datasets to uncover TF mode-of-action and identify potential TF partners for 14 TFs in abscisic acid signaling. Case study 2 demonstrates how genome-wide TF–target data and automated functions in ConnecTF are used in precision/recall analysis and pruning of an inferred GRN for nitrogen signaling. Case study 3 uses ConnecTF to chart a network path from NLP7, a master TF in nitrogen signaling, to direct secondary TF2s and to its indirect targets in a Network Walking approach. The public version of ConnecTF (https://ConnecTF.org) contains 3,738,278 TF–target interactions for 423 TFs in Arabidopsis, 839,210 TF–target interactions for 139 TFs in maize (Zea mays), and 293,094 TF–target interactions for 26 TFs in rice (Oryza sativa). The database and tools in ConnecTF will advance the exploration of GRNs in plant systems biology applications for model and crop species. 

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5. Gene regulatory networks associated with lateral root and nodule development in soybean

   https://doi.org/10.1093/insilicoplants/diaa002  

   Smita, Shuchi; Kiehne, Jason; Adhikari, Sajag; Zeng, Erliang; Ma, Qin; Subramanian, Senthil (January 2020, in silico Plants)

   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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