Summary Predicting gene regulatory networks (GRNs) from expression profiles is a common approach for identifying important biological regulators. Despite the increased use of inference methods, existing computational approaches often do not integrate RNA‐sequencing data analysis, are not automated or are restricted to users with bioinformatics backgrounds. To address these limitations, we developed tuxnet, a user‐friendly platform that can process raw RNA‐sequencing data from any organism with an existing reference genome using a modified tuxedo pipeline (hisat 2 + cufflinks package) and infer GRNs from these processed data. tuxnet is implemented as a graphical user interface and can mine gene regulations, either by applying a dynamic Bayesian network (DBN) inference algorithm, genist, or a regression tree‐based pipeline, rtp‐star. We obtained time‐course expression data of a PERIANTHIA (PAN) inducible line and inferred a GRN using genist to illustrate the use of tuxnet while gaining insight into the regulations downstream of the Arabidopsis root stem cell regulator PAN. Using rtp‐star, we inferred the network of ATHB13, a downstream gene of PAN, for which we obtained wild‐type and mutant expression profiles. Additionally, we generated two networks using temporal data from developmental leaf data and spatial data from root cell‐type data to highlight the use of tuxnet to form new testable hypotheses from previously explored data. Our case studies feature the versatility of tuxnet when using different types of gene expression data to infer networks and its accessibility as a pipeline for non‐bioinformaticians to analyze transcriptome data, predict causal regulations, assess network topology and identify key regulators.

Grafting has been adopted for a wide range of crops to enhance productivity and resilience; for example, grafting of Solanaceous crops couples disease-resistant rootstocks with scions that produce high-quality fruit. However, incompatibility severely limits the application of grafting and graft incompatibility remains poorly understood. In grafts, immediate incompatibility results in rapid death, but delayed incompatibility can take months or even years to manifest, creating a significant economic burden for perennial crop production. To gain insight into the genetic mechanisms underlying this phenomenon, we developed a model system using heterografting of tomato (Solanum lycopersicum) and pepper (Capsicum annuum). These grafted plants express signs of anatomical junction failure within the first week of grafting. By generating a detailed timeline for junction formation, we were able to pinpoint the cellular basis for this delayed incompatibility. Furthermore, we inferred gene regulatory networks for compatible self-grafts and incompatible heterografts based on these key anatomical events, which predict core regulators for grafting. Finally, we examined the role of vascular development in graft formation and uncovered SlWOX4 as a potential regulator of graft compatibility. Following this predicted regulator up with functional analysis, we show that Slwox4 homografts fail to form xylem bridges across the junction, demonstrating that indeed, SlWOX4 is essential for vascular reconnection during grafting, and may function as an early indicator of graft failure.

Capturing cell-to-cell signals in a three-dimensional (3D) environment is key to studying cellular functions. A major challenge in the current culturing methods is the lack of accurately capturing multicellular 3D environments. In this study, we established a framework for 3D bioprinting plant cells to study cell viability, cell division, and cell identity. We established long-term cell viability for bioprinted Arabidopsis and soybean cells. To analyze the generated large image datasets, we developed a high-throughput image analysis pipeline. Furthermore, we showed the cell cycle reentry of bioprinted cells for which the timing coincides with the induction of core cell cycle genes and regeneration-related genes, ultimately leading to microcallus formation. Last, the identity of bioprinted Arabidopsis root cells expressing endodermal markers was maintained for longer periods. The framework established here paves the way for a general use of 3D bioprinting for studying cellular reprogramming and cell cycle reentry toward tissue regeneration.

Graft incompatibility is a poorly understood phenomenon that presents a serious agricultural challenge. Unlike immediate incompatibility that results in rapid death, delayed incompatibility can take months or even years to manifest, creating a significant economic burden for perennial crop production. To gain insight into the genetic mechanisms underlying this phenomenon, we developed a model system with Solanum lycopersicum 'tomato' and Capsicum annuum 'pepper' heterografting, which expresses signs of anatomical junction failure within the first week of grafting. By generating a detailed timeline for junction formation we were able to pinpoint the cellular basis for this delayed incompatibility. Furthermore, we infer gene regulatory networks for compatible self-grafts versus incompatible heterografts based on these key anatomical events, which predict core regulators for grafting. Finally, we delve into the role of vascular development in graft formation and validate SlWOX4 as a regulator for grafting in tomato. Notably, SlWOX4 is the first gene to be functionally implicated in vegetable crop grafting.

14 performed the experimental work; R.S. designed and supervised the experiments, and complemented the writing. 15 16 SUMMARY 17 TuxNet offers a simple interface for non-computational biologists to infer GRNs from raw RNA-18 seq data.19 20 ABSTRACT 21 22Predicting gene regulatory networks (GRNs) from gene expression profiles has become a 23 common approach for identifying important biological regulators. Despite the increase in the use 24 of inference methods, existing computational approaches do not integrate RNA-sequencing 25 data analysis, are often not automated, and are restricted to users with bioinformatics and 26 programming backgrounds. To address these limitations, we have developed TuxNet, an 27 integrated user-friendly platform, which, with just a few selections, allows to process raw RNA-28 sequencing data (using the Tuxedo pipeline) and infer GRNs from these processed data. 29TuxNet is implemented as a graphical user interface and, using expression data from any 30 organism with an existing reference genome, can mine the regulations among genes either by 31 applying a dynamic Bayesian network inference algorithm, GENIST, or a regression tree-based 32 pipeline that uses spatiotemporal data, RTP-STAR. To illustrate the use of TuxNet while getting 33 insight into the regulatory cascade downstream of the Arabidopsis root stem cell regulator 34 PERIANTHIA (PAN), we obtained time course gene expression data of a PAN inducible line and 35 inferred a GRN using GENIST. Using RTP-STAR, we then inferred the network of a PAN 36 secondary downstream gene, ATHB13, for which we obtained wildtype and mutant expression 37 profiles. Our case studies feature the versatility of TuxNet to infer networks using different types 38 of gene expression data (i.e time course and steady-state data) as well as how inference 39 networks are used to identify important regulators. 40 41 42 81 gene, ATHB13, were used to infer a GRN using RTP-STAR. Importantly, through our examples, 82 we guide the user on how to select the few parameters needed to run the GUI, which we 83 provide as an open source package (https://github.com/madeluis/TuxNet). 84 85 86 87 3 RESULTS 89Overview of the three tabs of TuxNet 90TuxNet provides a user-friendly environment where users can automatically process gene 91 expression data (RNA-seq data), identify differentially expressed genes (DEGs), and infer gene 92 regulatory networks (GRNs) from the processed data. TuxNet is developed as a graphical user 93 interface (GUI) divided in three tabs, TUX, GENIST, and RTP-STAR ( Fig. 1, Fig. 2, and Fig. 3). 94The three TuxNet tabs have consistent file formatting to ensure that analyses, when expanding 95 more than one tab, do not require data manipulation or coding by the user. 97The TUX tab allows users to process RNA-seq data in the form of fastq files, as usually returned 98 by an Illumina sequencer. Accordingly, the TUX tab, which implements fastq-mcf (Aronesty 99 2011; Aronesty 2013) and the Tuxedo pipeline (TopHat + Cufflinks + Cuffdiff) (Trapnell et al. 100...