More than meets the eye: Emergent properties of transcription factors networks in Arabidopsis

Muhammad, DurreShahwar; Schmittling, Selene; Williams, Cranos M.; Long, Terri A.

doi:10.1016/j.bbagrm.2016.07.017

Cited by 7 publications

(3 citation statements)

References 89 publications

(159 reference statements)

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“…All aspects of transcription and its regulation involve dynamic interactions of TFs with chromatin; this fundamental property is known as an important modulator of biologic processes 3,4 . Given that TFs are central to the regulation of gene expression, elucidating the molecular mechanisms of their actions is a major focus of research particularly since the complete genome sequence of Arabidopsis thaliana (hereafter Arabidopsis) became available in 2000 5 …”

Section: Introductionmentioning

confidence: 99%

Elucidating the role of WRKY27 in male sterility in Arabidopsis

Mukhtar

Liu

Somssich

2017

Plant Signaling & Behavior

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The WRKY proteins belong to a superfamily of TFs that play pivotal roles in responses to a wide range of biotic, abiotic, developmental and physiologic cues. Here, we assayed the accumulation of basal WRKY27 transcripts in diverse tissue including root, shoot, leaf and flowers. We demonstrated that plants over-expressing WRKY27 transcript levels exhibit growth aberrations and fertility defects. Scanning electron microscopic data suggest that WRKY27 overexpressor plants exhibit pollen dehiscence defects. Our fluorescein diacetate hydrolysis assay showed that flowers of plants overexpressing WRKY27 display significantly decreased pollen viability. These sterility-related phenotypes were not rescued by the exogenous applications of different phytohormones. Our results indicate the involvement of WRKY27 in particular for proper plant biomass accumulation and male fertility.

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Section: Introductionmentioning

confidence: 99%

Elucidating the role of WRKY27 in male sterility in Arabidopsis

Mukhtar

Liu

Somssich

2017

Plant Signaling & Behavior

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“…Because of the ability of the plant genetics community to study variations of complex traits, understand the evolutionary processes behind these traits, and target sites for gene editing, expression prediction holds a lot of research potential. To gather the necessary resolution to explore these concepts, researchers generally focus on the systematic role of regulatory machinery that facilitates variation in gene expression over space and time [5,6]. Gene regulation requires elaborate interplay between linked (cis) and distant (trans) regulatory elements.…”

Section: Introductionmentioning

confidence: 99%

Exploring the utility of regulatory network-based machine learning for gene expression prediction in maize

Ferebee

Buckler

2023

Preprint

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Genomic selection and gene editing in crops could be enhanced by multi-species, mechanistic models predicting effects of changes in gene regulation. Current expression abundance prediction models require extensive computational resources, hard-to-measure species-specific training data, and often fail to incorporate data from multiple species. We hypothesize that gene expression prediction models that harness the regulatory network structure ofArabidopsis thalianatranscription factor-target gene interactions will improve on the present maize models. To this end, we collect 147Oryza sativaand 99Sorghum bicolorgene expression assays and assign them to maize family-based orthologous groups. Using three popular graph-based machine learning frameworks, including a shallow graph convolutional autoencoder, a deep graph convolutional autoencoder, and the inductive GraphSage strategy, we encode anArabidopsis thalianaintegrated gene regulatory network (iGRN) structure and TF gene expression values to predict gene expression both within and between species. We then evaluate the network methods against a partial least-squares baseline. We find that the baseline gives the best predictions within species, with Spearman correlations averaging between 0.74 and 0.78. The graph autoencoder methods were more variable with correlations between -0.1 and 0.65. In particular, the GraphSage and deep autoencoders performed the worst, and the shallow autoencoders performed the best. In the most challenging prediction context, where predictions were in new species and on genes that were not seen, we found that the shallow graph autoencoder framework averaged around 0.65. Unlike initial thoughts about preserved network structure improving gene expression predictions, this study shows that within-species predictions only need simple models, such as partial least squares, to capture expression variations. In cross-species predictions, the best model is often a more complex strategy utilizing regulatory network structure and other studies' expressions.

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“…As a consequence, advancing our understanding of these regulatory systems can directly impact (personalized) medicine as many traits and diseases are associated with variation of regulatory sequences or dysfunctional regulators [2]. In agriculture, targeted alteration of transcriptional regulation has been essential to improve modern crop yields [4], and its elucidation may further help increase metabolite production rates as well as resilience against environmental stresses [5,6].…”

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confidence: 99%

The Reasonable Effectiveness of Randomness in Scalable and Integrative Gene Regulatory Network Inference and Beyond

Banf¹,

Hartwig

2021

Computation

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Gene regulation is orchestrated by a vast number of molecules, including transcription factors and co-factors, chromatin regulators, as well as epigenetic mechanisms, and it has been shown that transcriptional misregulation, e.g., caused by mutations in regulatory sequences, is responsible for a plethora of diseases, including cancer, developmental or neurological disorders. As a consequence, decoding the architecture of gene regulatory networks has become one of the most important tasks in modern (computational) biology. However, to advance our understanding of the mechanisms involved in the transcriptional apparatus, we need scalable approaches that can deal with the increasing number of large-scale, high-resolution, biological datasets. In particular, such approaches need to be capable of efficiently integrating and exploiting the biological and technological heterogeneity of such datasets in order to best infer the underlying, highly dynamic regulatory networks, often in the absence of sufficient ground truth data for model training or testing. With respect to scalability, randomized approaches have proven to be a promising alternative to deterministic methods in computational biology. As an example, one of the top performing algorithms in a community challenge on gene regulatory network inference from transcriptomic data is based on a random forest regression model. In this concise survey, we aim to highlight how randomized methods may serve as a highly valuable tool, in particular, with increasing amounts of large-scale, biological experiments and datasets being collected. Given the complexity and interdisciplinary nature of the gene regulatory network inference problem, we hope our survey maybe helpful to both computational and biological scientists. It is our aim to provide a starting point for a dialogue about the concepts, benefits, and caveats of the toolbox of randomized methods, since unravelling the intricate web of highly dynamic, regulatory events will be one fundamental step in understanding the mechanisms of life and eventually developing efficient therapies to treat and cure diseases.

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More than meets the eye: Emergent properties of transcription factors networks in Arabidopsis

Cited by 7 publications

References 89 publications

Elucidating the role of WRKY27 in male sterility in Arabidopsis

Elucidating the role of WRKY27 in male sterility in Arabidopsis

Exploring the utility of regulatory network-based machine learning for gene expression prediction in maize

The Reasonable Effectiveness of Randomness in Scalable and Integrative Gene Regulatory Network Inference and Beyond

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