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Droughts cause severe crop losses worldwide and climate change is projected to increase their prevalence in the future. Similar to the situation for many crops, the reference plant Arabidopsis thaliana (Ath) is considered drought-sensitive, whereas, as we demonstrate, its close relatives Arabidopsis lyrata (Aly) and Eutrema salsugineum (Esa) are drought-resistant. To understand the molecular basis for this plasticity we conducted a deep phenotypic, biochemical and transcriptomic comparison using developmentally matched plants. We demonstrate that Aly responds most sensitively to decreasing water availability with early growth reduction, metabolic adaptations and signaling network rewiring. By contrast, Esa is in a constantly prepared mode as evidenced by high basal proline levels, ABA signaling transcripts and late growth responses. The stress-sensitive Ath responds later than Aly and earlier than Esa, although its responses tend to be more extreme. All species detect water scarcity with similar sensitivity; response differences are encoded in downstream signaling and response networks. Moreover, several signaling genes expressed at higher basal levels in both Aly and Esa have been shown to increase water-use efficiency and drought resistance when overexpressed in Ath. Our data demonstrate contrasting strategies of closely related Brassicaceae to achieve drought resistance.

The plant vacuole is a central organelle that is involved in various biological processes throughout the plant life cycle. Elucidating the mechanism of vacuole biogenesis and maintenance is thus the basis for our understanding of these processes. Proper formation of the vacuole has been shown to depend on the intracellular membrane trafficking pathway. Although several mutants with altered vacuole morphology have been characterized in the past, the molecular basis for plant vacuole biogenesis has yet to be fully elucidated.With the aim to identify key factors that are essential for vacuole biogenesis, we performed a forward genetics screen in Arabidopsis (Arabidopsis thaliana) and isolated mutants with altered vacuole morphology. Thevacuolar fusion defective1(vfd1) mutant shows seedling lethality and defects in central vacuole formation.VFD1encodes a Fab1, YOTB, Vac1, and EEA1 (FYVE) domain-containing protein, FYVE1, that has been implicated in intracellular trafficking. FYVE1 localizes on late endosomes and interacts with Src homology-3 domain-containing proteins. Mutants ofFYVE1are defective in ubiquitin-mediated protein degradation, vacuolar transport, and autophagy. Altogether, our results show that FYVE1 is essential for plant growth and development and place FYVE1 as a key regulator of intracellular trafficking and vacuole biogenesis.

Plants are essential for life and are extremely diverse organisms with unique molecular capabilities 1 . Here we present a quantitative atlas of the transcriptomes, proteomes and phosphoproteomes of 30 tissues of the model plant Arabidopsis thaliana . Our analysis provides initial answers to how many genes exist as proteins (more than 18,000), where they are expressed, in which approximate quantities (a dynamic range of more than six orders of magnitude) and to what extent they are phosphorylated (over 43,000 sites). We present examples of how the data may be used, such as to discover proteins that are translated from short open-reading frames, to uncover sequence motifs that are involved in the regulation of protein production, and to identify tissue-specific protein complexes or phosphorylation-mediated signalling events. Interactive access to this resource for the plant community is provided by the ProteomicsDB and ATHENA databases, which include powerful bioinformatics tools to explore and characterize Arabidopsis proteins, their modifications and interactions. A quantitative atlas of the transcriptomes, proteomes and phosphoproteomes of 30 tissues of the model plant Arabidopsis thaliana provides a valuable resource for plant research.

Current yeast interactome network maps contain several hundred molecular complexes with limited and somewhat controversial representation of direct binary interactions. We carried out a comparative quality assessment of current yeast interactome data sets, demonstrating that high-throughput yeast two-hybrid (Y2H) screening provides high-quality binary interaction information. Because a large fraction of the yeast binary interactome remains to be mapped, we developed an empirically controlled mapping framework to produce a \"second-generation\" high-quality, high-throughput Y2H data set covering ~20% of all yeast binary interactions. Both Y2H and affinity purification followed by mass spectrometry (AP/MS) data are of equally high quality but of a fundamentally different and complementary nature, resulting in networks with different topological and biological properties. Compared to co-complex interactome models, this binary map is enriched for transient signaling interactions and intercomplex connections with a highly significant clustering between essential proteins. Rather than correlating with essentiality, protein connectivity correlates with genetic pleiotropy.

Understanding phenotype-to-genotype relationships is a grand challenge of 21st century biology with translational implications. The recently proposed “omnigenic” model postulates that effects of genetic variation on traits are mediated by core- genes and -proteins whose activities mechanistically influence the phenotype, whereas peripheral genes encode a regulatory network that indirectly affects phenotypes via core gene products. Here, we develop a positive-unlabeled graph representation-learning ensemble-approach based on a nested cross-validation to predict core-like genes for diverse diseases using Mendelian disorder genes for training. Employing mouse knockout phenotypes for external validations, we demonstrate that core-like genes display several key properties of core genes: Mouse knockouts of genes corresponding to our most confident predictions give rise to relevant mouse phenotypes at rates on par with the Mendelian disorder genes, and all candidates exhibit core gene properties like transcriptional deregulation in disease and loss-of-function intolerance. Moreover, as predicted for core genes, our candidates are enriched for drug targets and druggable proteins. In contrast to Mendelian disorder genes the new core-like genes are enriched for druggable yet untargeted gene products, which are therefore attractive targets for drug development. Interpretation of the underlying deep learning model suggests plausible explanations for our core gene predictions in form of molecular mechanisms and physical interactions. Our results demonstrate the potential of graph representation learning for the interpretation of biological complexity and pave the way for studying core gene properties and future drug development. Understanding phenotype-genotype relationships is a grand challenge of current biological research. Here, the authors use graph representation learning to identify human genes which display key characteristics of core genes for five complex diseases.

The regulation of protein abundance of receptors and transporters at the plasma membrane is important for proper signaling in many biological pathways. The removal of plasma membrane proteins can occur via the endocytic protein degradation pathway, in which posttranslational modification by ubiquitin plays a key role. The activity of ubiquitinating and deubiquitinating enzymes can determine the ubiquitination status of a given target protein, and it has been shown that both classes of enzymes have important physiological roles. However, how these enzymes themselves are regulated at the molecular level has not yet been completely understood. In this study, we report a possible mechanism by which the deubiquitinating enzyme AMSH3 is regulated by its interacting protein, apoptosis-linked gene-2 interacting protein X (ALIX), in Arabidopsis . Ubiquitination is a signal for various cellular processes, including for endocytic degradation of plasma membrane cargos. Ubiquitinating as well as deubiquitinating enzymes (DUBs) can regulate these processes by modifying the ubiquitination status of target protein. Although accumulating evidence points to the important regulatory role of DUBs, the molecular basis of their regulation is still not well understood. Associated molecule with the SH3 domain of signal transduction adaptor molecule (STAM) (AMSH) is a conserved metalloprotease DUB in eukaryotes. AMSH proteins interact with components of the endosomal sorting complex required for transport (ESCRT) and are implicated in intracellular trafficking. To investigate how the function of AMSH is regulated at the cellular level, we carried out an interaction screen for the Arabidopsis AMSH proteins and identified the Arabidopsis homolog of apoptosis-linked gene-2 interacting protein X (ALIX) as a protein interacting with AMSH3 in vitro and in vivo. Analysis of alix knockout mutants in Arabidopsis showed that ALIX is essential for plant growth and development and that ALIX is important for the biogenesis of the vacuole and multivesicular bodies (MVBs). Cell biological analysis revealed that ALIX and AMSH3 colocalize on late endosomes. Although ALIX did not stimulate AMSH3 activity in vitro, in the absence of ALIX, AMSH3 localization on endosomes was abolished. Taken together, our data indicate that ALIX could function as an important regulator for AMSH3 function at the late endosomes.

Background High-throughput live-cell imaging is a powerful tool to study dynamic cellular processes in single cells but creates a bottleneck at the stage of data analysis, due to the large amount of data generated and limitations of analytical pipelines. Recent progress on deep learning dramatically improved cell segmentation and tracking. Nevertheless, manual data validation and correction is typically still required and tools spanning the complete range of image analysis are still needed. Results We present Cell-ACDC, an open-source user-friendly GUI-based framework written in Python, for segmentation, tracking and cell cycle annotations. We included state-of-the-art deep learning models for single-cell segmentation of mammalian and yeast cells alongside cell tracking methods and an intuitive, semi-automated workflow for cell cycle annotation of single cells. Using Cell-ACDC, we found that mTOR activity in hematopoietic stem cells is largely independent of cell volume. By contrast, smaller cells exhibit higher p38 activity, consistent with a role of p38 in regulation of cell size. Additionally, we show that, in S. cerevisiae , histone Htb1 concentrations decrease with replicative age. Conclusions Cell-ACDC provides a framework for the application of state-of-the-art deep learning models to the analysis of live cell imaging data without programming knowledge. Furthermore, it allows for visualization and correction of segmentation and tracking errors as well as annotation of cell cycle stages. We embedded several smart algorithms that make the correction and annotation process fast and intuitive. Finally, the open-source and modularized nature of Cell-ACDC will enable simple and fast integration of new deep learning-based and traditional methods for cell segmentation, tracking, and downstream image analysis. Source code: https://github.com/SchmollerLab/Cell_ACDC

Background Although transcript abundance is often used as a proxy for protein abundance, it is an unreliable predictor. As proteins execute biological functions and their expression levels influence phenotypic outcomes, we developed a convolutional neural network (CNN) to predict protein abundances from mRNA abundances, protein sequence, and mRNA sequence in Homo sapiens (H. sapiens) and the reference plant Arabidopsis thaliana (A. thaliana) . Results After hyperparameter optimization and initial data exploration, we implemented distinct training modules for value-based and sequence-based data. By analyzing the learned weights, we revealed common and organism-specific sequence features that influence protein-to-mRNA ratios (PTRs), including known and putative sequence motifs. Adding condition-specific protein interaction information identified genes correlated with many PTRs but did not improve predictions, likely due to insufficient data. The integrated model predicted protein abundance on unseen genes with a coefficient of determination (r 2 ) of 0.30 in H. sapiens and 0.32 in A. thaliana. Conclusions For H. sapiens, our model improves prediction performance by nearly 50% compared to previous sequence-based approaches, and for A. thaliana it represents the first model of its kind. The model’s learned motifs recapitulate known regulatory elements, supporting its utility in systems-level and hypothesis-driven research approaches related to protein regulation.

Protein microarrays enable investigation of diverse biochemical properties for thousands of proteins in a single experiment, an unparalleled capacity. Using a high-density system called HaloTag nucleic acid programmable protein array (HaloTag-NAPPA), we created high-density protein arrays comprising 12,000 Arabidopsis ORFs. We used these arrays to query protein–protein interactions for a set of 38 transcription factors and transcriptional regulators (TFs) that function in diverse plant hormone regulatory pathways. The resulting transcription factor interactome network, TF-NAPPA, contains thousands of novel interactions. Validation in a benchmarked in vitro pull-down assay revealed that a random subset of TF-NAPPA validated at the same rate of 64% as a positive reference set of literature-curated interactions. Moreover, using a bimolecular fluorescence complementation (BiFC) assay, we confirmed in planta several interactions of biological interest and determined the interaction localizations for seven pairs. The application of HaloTag-NAPPA technology to plant hormone signaling pathways allowed the identification of many novel transcription factor–protein interactions and led to the development of a proteome-wide plant hormone TF interactome network.