Biology subjects

Sozzani, R.

Publications and source records attributed to Sozzani, R..

6 recordsLinked to original sources

bioRxiv2018

Positive feedback and feedforward loops between PERIANTHIA, WUSCHEL-RELATED HOMEOBOX5 and GRF-INTERACTING FACTOR 1 modulate gene expression and function in the Arabidopsis root

The Arabidopsis root meristem consists of populations of stem cells that surround the mitotically less active cells known as the Quiescent Center (QC). The QC maintains the stem cells in a non-cell-autonomous manner through the function of the transcription factor (TF) WUSCHEL-RELATED HOMEOBOX5 (WOX5), which is required for columella stem cell (CSC) maintenance. However, whether WOX5 has a regulatory role in any other adjacent stem cells is less understood. To this end, we identified a set of TFs downstream of WOX5 in both QC and Cortex Endodermis Initial (CEI) cells. We then utilized Gene Regulatory Network (GRN) inference to identify GRF-INTERACTING FACTOR 1 (GIF1) as a key gene involved in positive feedback and feedforward loops with WOX5 as well as another stem cell regulator, PERIANTHIA (PAN). Finally, we constructed an ordinary differential equation model based on this inferred GRN to simulate GIF1, PAN, and WOX5 expression over time, which suggests the precise temporal expression of WOX5 and GIF1 is important to sustain QC function.

plant biology↗

bioRxiv2018

Cell type-specific differences in protein complex stoichiometry and transcriptional regulation affect the timing of stem cell division

Stem cells divide and differentiate to form all the specialized cell types in a multicellular organism. In the Arabidopsis root, stem cells are maintained in an undifferentiated state by a less mitotically active population of cells called the Quiescent Center (QC). Determining how the QC regulates the surrounding stem cell initials, or what makes the QC fundamentally different from the actively dividing initials, is important for understanding how stem cell divisions are maintained. Here, we gained insight into the differences between the QC and the Cortex Endodermis Initials (CEI) by studying the mobile transcription factor SHORTROOT (SHR) and its binding partner SCARECROW (SCR). We constructed an Ordinary Differential Equation (ODE) model of SHR and SCR in the QC and CEI which incorporated the stoichiometry of the SHR-SCR complex as well as upstream transcriptional regulation of SHR and SCR. Our model prediction coupled with experimental validation showed that high levels of the SHR-SCR complex is associated with more CEI division but less QC division. Further, our model prediction allowed us to establish the timing of QC and CEI division and propose that SHR repression of QC division depends on the formation of SHR homodimer. Thus, our results support that SHR-SCR protein complex stoichiometry and regulation of SHR transcription modulate the division timing of two different specialized cell types in the root stem cell niche.

plant biology↗

bioRxiv2018

TuxNet: A simple interface to process RNA sequencing data and infer gene regulatory networks

Predicting gene regulatory networks (GRNs) from gene expression profiles has become a common approach for identifying important biological regulators. Despite the increase in the use of inference methods, existing computational approaches do not integrate RNA-sequencing data analysis, are often not automated, and are restricted to users with bioinformatics and programming backgrounds. To address these limitations, we have developed TuxNet, an integrated user-friendly platform, which, with just a few selections, allows to process raw RNA-sequencing data (using the Tuxedo pipeline) and infer GRNs from these processed data. TuxNet is implemented as a graphical user interface and, using expression data from any organism with an existing reference genome, can mine the regulations among genes either by applying a dynamic Bayesian network inference algorithm, GENIST, or a regression tree-based pipeline that uses spatiotemporal data, RTP-STAR. To illustrate the use of TuxNet while getting insight into the regulatory cascade downstream of the Arabidopsis root stem cell regulator PERIANTHIA (PAN), we obtained time course gene expression data of a PAN inducible line and inferred a GRN using GENIST. Using RTP-STAR, we then inferred the network of a PAN secondary downstream gene, ATHB13, for which we obtained wildtype and mutant expression profiles. Our case studies feature the versatility of TuxNet to infer networks using different types of gene expression data (i.e time course and steady-state data) as well as how inference networks are used to identify important regulators.\n\nSUMMARYTuxNet offers a simple interface for non-computational biologists to infer GRNs from raw RNA-seq data.

plant biology↗

bioRxiv2018

Dorsal/NF-κB exhibits a dorsal-to-ventral mobility gradient in the Drosophila embryo

Morphogen-mediated patterning is a highly dynamic developmental process. To obtain an accurate understanding of morphogen gradient formation and downstream gene expression, biophysical parameters such as protein mobilities must be quantified in vivo. The dorsal-ventral (DV) patterning of early Drosophila embryos by the NF-{kappa}B homolog Dorsal (Dl) is an excellent system for understanding morphogen gradient formation. Dl gradient formation is controlled by the inhibitor Cactus/I{kappa}B (Cact), which regulates the nuclear import and diffusion of Dl protein. However, quantitative measurements of Dl mobility and binding are currently lacking. Here, we use scanning fluorescence correlation spectroscopy to quantify the mobility of GFP-tagged Dl. We find that the DNA binding of Dl-GFP, which affects its mobility, varies along the DV axis, with highest DNA binding on the ventral side. Moreover, we also observe that the time scale for Dl-GFP to exit the nucleus is longer in the ventral and lateral regions of the embryo, which is consistent with stronger DNA binding. Using analysis of mutant alleles of dl tagged with GFP, we conclude that Dl-GFP/Cact interactions in the nuclei are responsible for the variation in Dl-GFP/DNA binding along the DV axis, which impacts our understanding of the spatial range of the Dl gradient and the robustness and precision of downstream gene expression. Thus, our results highlight the complexity of morphogen gradient dynamics and the ability of quantitative measurements of biophysical interactions to drive biological discovery.

developmental biology↗

bioRxiv2017

An Inference Approach Combines Spatial And Temporal Gene Expression Data To Predict Gene Regulatory Networks In Arabidopsis Stem Cells

Identifying the transcription factors (TFs) and associated networks involved in stem cell regulation is key for understanding the initiation and growth of plant tissues and organs. Although many TFs have been shown to have a role in the Arabidopsis root stem cells, a comprehensive view of the transcriptional signature of the stem cells is lacking. In this work, we used spatial and temporal transcriptomic data to predict interactions among the genes involved in stem cell regulation. For this, we transcriptionally profiled several stem cell populations and developed a gene regulatory network (GRN) inference algorithm that combines clustering with Dynamic Bayesian Network (DBN) inference. We leveraged the topology of our networks to infer potential key regulators. The results presented in this work show that our combination of molecular biology approaches, computational biology and mathematical modeling was key to identify candidate factors that function in the stem cells. Specifically, through experimental validation and mathematical modeling, we identified PERIANTHIA (PAN) as an important molecular regulator of quiescent center (QC) function.

plant biology↗

bioRxiv2017

Genetic Architecture and Molecular Networks Underlying Leaf Thickness in Desert-Adapted Tomato Solanum pennellii

Thicker leaves allow plants to grow in water-limited conditions. However, our understanding of the genetic underpinnings of this highly functional leaf shape trait is poor. We used a custom-built confocal profilometer to directly measure leaf thickness in a set of introgression lines (ILs) derived from the desert tomato species Solanum pennellii, and identified quantitative trait loci (QTL). We report evidence of a complex genetic architecture of this trait and roles for both genetic and environmental factors. Several ILs with thick leaves have dramatically elongated palisade mesophyll cells and, in some cases, increased leaf ploidy. We characterized thick ILs 2-5 and 4-3 in detail and found increased mesophyll cell size and leaf ploidy levels, suggesting that endoreduplication underpins leaf thickness in tomato. Next, we queried the transcriptomes and inferred Dynamic Bayesian Networks of gene expression across early leaf ontogeny in these lines to compare the molecular networks that pattern leaf thickness. We show that thick ILs share S. pennellii-like expression profiles for putative regulators of cell shape and meristem determinacy, as well as a general signature of cell cycle related gene expression. However, our network data suggest that leaf thickness in these two lines is patterned by at least partially distinct mechanisms. Consistent with this hypothesis, double homozygote lines combining introgression segments from these two ILs show additive phenotypes including thick leaves, higher ploidy levels and larger palisade mesophyll cells. Collectively, these data establish a framework of genetic, anatomical, and molecular mechanisms that pattern leaf thickness in desert-adapted tomato.

plant biology↗