Biology subjects

Husbands, A. Y.

Publications and source records attributed to Husbands, A. Y..

8 recordsLinked to original sources

bioRxiv2025

A cryptic START domain regulates deeply conserved transcription factors

Transcription factors (TFs) integrate a diverse array of inputs to achieve the exquisite control of gene expression necessary for life. In plants, this is exemplified by the deeply conserved CLASS III HOMEODOMAIN LEUCINE ZIPPER (HD-ZIPIII) family of TFs. HD-ZIPIII activity is controlled by inputs at transcriptional, post-transcriptional, and post-translational levels. As part of their multidomain architecture, HD-ZIPIII TFs contain a StAR-related lipid transfer (START) domain, a ubiquitously distributed evolutionary module that binds various types of lipophilic ligands. Here, we show that HD-ZIPIII and HD-ZIPIV proteins contain a cryptic, deeply conserved START domain which we term the disorder-containing START domain (dSTART). The dSTART domain is required for HD-ZIPIII developmental function, controlling their subcellular localization and DNA-binding properties. The dSTART domain also helps discriminate responsive from non-responsive binding sites across the HD-ZIPIII shared genetic network. Finally, we identify candidate ligands of the dSTART domain including several species of phosphatidylglycerol and phosphatidic acid. The identification and functional characterization of a cryptic START domain provides new mechanistic insights into a deeply conserved family of TFs with roles in nearly all aspects of plant development.

plant biology↗

bioRxiv2024

START domains generate paralog-specific regulons from a single network architecture

Functional divergence of transcription factors (TFs) has driven cellular and organismal complexity throughout evolution, but its mechanistic drivers remain poorly understood. Here we test for new mechanisms using CORONA (CNA) and PHABULOSA (PHB), two functionally diverged paralogs in the CLASS III HOMEODOMAIN LEUCINE ZIPPER (HD-ZIPIII) family of TFs. We show that virtually all genes bound by PHB ([~]99%) are also bound by CNA, ruling out occupation of distinct sets of genes as a mechanism of functional divergence. Further, genes bound and regulated by both paralogs are almost always regulated in the same direction, ruling out opposite regulation of shared targets as a mechanistic driver. Functional divergence of CNA and PHB instead results from differential usage of shared binding sites, with hundreds of uniquely regulated genes emerging from a commonly bound genetic network. Regulation of a given gene by CNA or PHB is thus a function of whether a bound site is considered responsive versus non-responsive by each paralog. Discrimination between responsive and non-responsive sites is controlled, at least in part, by their lipid binding START domain. This suggests a model in which HD-ZIPIII TFs use information integrated by their START domain to generate paralog-specific transcriptional outcomes from a shared network architecture. Taken together, our study identifies a new mechanism of HD-ZIPIII TF paralog divergence and proposes the ubiquitously distributed START evolutionary module as a driver of functional divergence.

genomics↗

bioRxiv2023

A data-driven evaluation of Arabidopsis-centric research and the model species concept

The selection of Arabidopsis as a model organism played a pivotal role in advancing genomic science, firmly establishing the cornerstone of today s plant molecular biology. Competing frameworks to select an agricultural- or ecological-based model species, or to decentralize plant science and study a multitude of diverse species, were selected against in favor of building core knowledge in a species that would facilitate genome-enabled research that could assumedly be transferred to other plants. Here, we examine the ability of models based on Arabidopsis gene expression data to predict tissue identity in other flowering plant species. Comparing different machine learning algorithms, models trained and tested on Arabidopsis data achieved near perfect precision and recall values using the K-Nearest Neighbor method, whereas when tissue identity is predicted across the flowering plants using models trained on Arabidopsis data, precision values range from 0.69 to 0.74 and recall from 0.54 to 0.64, depending on the algorithm used. Below-ground tissue is more predictable than other tissue types, and the ability to predict tissue identity is not correlated with phylogenetic distance from Arabidopsis. This suggests that gene expression signatures rather than marker genes are more valuable to create models for tissue and cell type prediction in plants. Our data-driven results highlight that, in hindsight, the assertion that knowledge from Arabidopsis is translatable to other plants is not always true. Considering the current landscape of abundant sequencing data and computational resources, it may be prudent to reevaluate the scientific emphasis on Arabidopsis and to prioritize the exploration of plant diversity.

plant biology↗

bioRxiv2023

Topological data analysis reveals a core heteroblastic program embedded in leaves of grapevine and maracuya

Leaves have a single shape. However, embedded in that shape are a multitude of latent shapes arising from evolutionary, developmental, environmental, and other effects. These confounded effects manifest at distinct developmental time points and evolve at different tempos. Here, revisiting datasets comprised of thousands of leaves of vining grapevine (Vitaceae) and maracuya (Passifloraceae) species, we apply a technique from the mathematical field of topological data analysis to comparatively visualize the structure of heteroblastic effects on leaf shape in each group. Consistent with a morphologically closer relationship, members of the grapevine dataset possess a strong core heteroblasty program with little deviation between species. Remarkably, we found that most members of the maracuya family also share a core heteroblasty program despite dramatic species-to-species leaf shape differences. This conservation was not detected using traditional analyses. We also identify two morphotypes of maracuya that deviate from the core structure, suggesting the evolution of new heteroblastic properties in this phylogenetically related sub-group. Our findings illustrate how topological data analysis can be used to disentangle previously confounded developmental and evolutionary effects to visualize latent shapes and hidden relationships, even ones embedded in complex, high-dimensional datasets.

plant biology↗

bioRxiv2022

The START domain potentiates HD-ZIPIII transcriptional activity

HD-ZIPIII transcription factors (TFs) were repeatedly deployed over 725 million years of evolution to regulate central developmental innovations. The START domain of this pivotal class of developmental regulators was recognized over twenty years ago, but its putative ligands and functional contributions remain unknown. Here, we demonstrate that the START domain promotes HD-ZIPIII TF homodimerization and increases transcriptional potency. Effects on transcriptional output can be ported onto heterologous TFs, consistent with principles of evolution via domain capture. We also show the START domain binds several species of phospholipids, and that mutations in conserved residues predicted to affect either ligand binding, or its downstream readout, abolish HD-ZIPIII DNA-binding competence. Our data present a model in which the START domain potentiates transcriptional activity and uses ligand-induced conformational change to render HD-ZIPIII dimers competent to bind DNA. These findings resolve a long-standing mystery in plant development and highlight the flexible and diverse regulatory potential coded within this widely distributed evolutionary module.

plant biology↗

bioRxiv2022

The topological shape of gene expression across the evolution of flowering plants

Since they emerged ~125 million years ago, flowering plants have evolved to dominate the terrestrial landscape and survive in the most inhospitable environments on earth. At their core, these adaptations have been shaped by changes in numerous, interconnected pathways and genes that collectively give rise to emergent biological phenomena. Linking gene expression to morphological outcomes remains a grand challenge in biology, and new approaches are needed to begin to address this gap. Here, we implemented topological data analysis (TDA) to summarize the high dimensionality and noisiness of gene expression data using lens functions that delineate plant tissue and stress responses. Using this framework, we created a topological representation of the shape of gene expression across plant evolution, development, and environment for the phylogenetically diverse flowering plants. The TDA-based Mapper graphs form a well-defined gradient of tissues from leaves to seeds, or from healthy to stressed samples, depending on the lens function. This suggests there are distinct and conserved expression patterns across angiosperms that delineate different tissue types or responses to biotic and abiotic stresses. Genes that correlate with the tissue lens function are enriched in central processes such as photosynthetic, growth and development, housekeeping, or stress responses. Together, our results highlight the power of TDA for analyzing complex biological data and reveal a core expression backbone that defines plant form and function. Significance statementA grand challenge in biology is to link gene expression to phenotypes across evolution, development, and the environment, but efforts have been hindered by biological complexity and dataset heterogeneity. Here, we implemented topological data analysis across thousands of gene expression datasets in phylogenetically diverse flowering plants. We created a topological representation of gene expression across plants and observed well-defined gradients of tissues from leaves to seeds, or from healthy to environmentally stressed. Using this framework, we identified a core and deeply conserved expression backbone that defines plant form and function, with key patterns that delineate plant tissues, abiotic, and biotic stresses. Our results highlight the power of topological approaches for analyzing complex biological datasets.

plant biology↗

bioRxiv2021

An siRNA-guided Argonaute protein directs RNA Polymerase V to initiate DNA methylation

In mammals and plants, cytosine DNA methylation is essential for the epigenetic repression of transposable elements and foreign DNA. In plants, DNA methylation is guided by small interfering RNAs (siRNAs) in a self-reinforcing cycle termed RNA-directed DNA methylation (RdDM). RdDM requires the specialized RNA Polymerase V (Pol V), and the key unanswered question is how Pol V is first recruited to new target sites without preexisting DNA methylation. We find that Pol V follows and is dependent upon the recruitment of an AGO4-clade ARGONAUTE protein, and any siRNA can guide the ARGONAUTE protein to the new target locus independent of preexisting DNA methylation. These findings reject long-standing models of RdDM initiation and instead demonstrate that siRNA-guided ARGONAUTE targeting is necessary, sufficient and first to target Pol V recruitment and trigger the cycle of RdDM at a transcribed target locus, thereby establishing epigenetic silencing.

plant biology↗

bioRxiv2020

Identifying Cancer-Relevant Mutations in the DLC START Domain using Evolutionary and Structure-Function Analyses

Rho GTPase signaling promotes proliferation, invasion, and metastasis in a broad spectrum of cancers. Rho GTPase activity is regulated by the Deleted in Liver Cancer (DLC) family of bonafide tumor suppressors which directly inactivate Rho GTPases by stimulating GTP hydrolysis. In addition to a RhoGAP domain, DLC proteins contain a StAR-related lipid transfer (START) domain. START domains in other organisms bind hydrophobic small molecules and can regulate interacting partners or co-occurring domains through a variety of mechanisms. In the case of DLC proteins, their START domain appears to contribute to tumor suppressive activity. However, the nature of this START-directed mechanism, as well as the identities of relevant functional residues, remain virtually unknown. Using the Catalogue of Somatic Mutations in Cancer (COSMIC) dataset, and evolutionary and structure-function analyses, we identify several conserved residues likely to be required for START-directed regulation of DLC-1 and DLC-2 tumor suppressive capability. This pan-cancer analysis shows that conserved residues of both START domains are highly-overrepresented in cancer cells from a wide range tissues. Interestingly, in DLC-1 and DLC-2, three of these residues form multiple interactions at the tertiary structural level. Further, mutation of any of these residues is predicted to disrupt interactions and thus destabilize the START domain. As such, these mutations would not have emerged from traditional hotspot scans of COSMIC. We propose that evolutionary and structure-function analyses are an underutilized strategy which could be used to unmask cancer-relevant mutations within COSMIC. Our data also suggest DLC-1 and DLC-2 as high- priority candidates for development of novel therapeutics targeting their START domain. Simple SummaryDeleted in Liver Cancer (DLC) proteins are tumor suppressors that contain a StAR-related lipid transfer (START) domain. Little is known about the DLC START domain including the residues that mediate its activation. Using the Catalogue of Somatic Mutations in Cancer (COSMIC) dataset, evolutionary, and structure-function analyses, we identify key functional residues of DLC START domains. Mutations in these residues are significantly overrepresented in numerous cancers in multiple tissue types. In other contexts, START domains bind hydrophobic small molecules and stimulate regulatory outputs of interacting partners or co-occurring domains. The identification of functional residues in the DLC START domain may thus have implications for targeted manipulation of DLC tumor suppressive activity. Critically, these residues would not have been identified using traditional queries of COSMIC. Thus, we propose tandem evolutionary and structure-function approaches are an underutilized strategy to unmask cancer-relevant mutations within COSMIC.

cancer biology↗