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Many plant transcription factors (TFs) are multifunctional and regulate growth and development in more than one tissue. These TFs can generally associate with different protein partners depending on the tissue type, thereby regulating tissue-specific target gene sets. However, how interaction specificity is ensured is still largely unclear. Here, we examine protein-protein interaction specificity using subfunctionalized co-orthologs of the FRUITFULL (FUL) subfamily of MADS-domain TFs. In Arabidopsis, FUL is multifunctional, playing important roles in flowering and fruiting, whereas these functions have partially been divided in the tomato co-orthologs FUL1 and FUL2. By linking protein sequence and function, we discovered a key amino acid motif that determines interaction specificity of MADS-domain TFs, which in Arabidopsis FUL determines the interaction with AGAMOUS and SEPALLATA proteins, linked to the regulation of a subset of targets. This insight offers great opportunities to dissect the biological functions of multifunctional MADS TFs., (© The Author(s) 2024. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Genes of the family PHOSPHATIDYLETHANOLAMINE-BINDING PROTEINS (PEBP) have been intensely studied in plants for their role in cell (re)programming and meristem differentiation. Recently, sporadic reports of the presence of a new type of PEBP in plants became available, highly similar to the YY-PEBPs of prokaryotes. A comprehensive investigation of their spread, origin, and function revealed conservation across the plant kingdom. The YY-PEBP clade in plants seems to have resulted from a single Horizontal Gene Transfer (HGT) episode from a prokaryotic organism to an ancestral streptophyte. YY-PEBPs are also present in other eukaryotes, such as certain fungi, diatoms, and rotifers, and these cases derive from independent HGT events. Reciprocally, the occurrence of the eukaryotic CETS/RKIP type PEBPs (CR-PEBPs) was noticed in bacteria of the genus Nocardia, showing that HGT has occurred as well from eukaryotes to prokaryotes. Based on these observations, we propose that the current model of the PEBP family in plants needs to be updated with the clade STEPMOTHER OF FT AND TFL1 (SMFT). SMFT genes not only share high sequence conservation but also show specific expression in homologous plant structures that serve as propagules. Functional analysis of Arabidopsis smft mutant lines pointed to a function for this gene in regulating seed germination, both concerning primary dormancy release and in response to adverse high-temperature conditions. Overall, our study reveals an increasing complexity in the evolutionary history of the PEBP gene family, unlocking new potential in understanding the evolution and functional spectrum of these important key regulatory genes., (© 2024 The Author(s). The Plant Journal published by Society for Experimental Biology and John Wiley & Sons Ltd.)

Protein engineering increasingly relies on machine learning models to computationally pre-screen promising novel candidates. Although machine learning approaches have proven effective, their performance on prospective screening data leaves room for improvement; prediction accuracy can vary greatly from one protein variant to the next. So far, it is unclear what characterizes variants that are associated with large prediction error. In order to establish whether structural characteristics influence predictability, we created a novel high-order combinatorial dataset for an enzyme spanning 3,706 variants, that can be partitioned into subsets of variants with mutations at positions exclusively belonging to a particular structural class. By training four different supervised variant effect prediction (VEP) models on structurally partitioned subsets of our data, we found that predictability strongly depended on all four structural characteristics we tested; buriedness, number of contact residues, proximity to the active site and presence of secondary structure elements. These dependencies were also found in several single mutation enzyme variant datasets, albeit with dataset specific directions. Most importantly, we found that these dependencies were similar for all four models we tested, indicating that there are specific structure and function determinants that are insufficiently accounted for by current machine learning algorithms. Overall, our findings suggest that improvements can be made to VEP models by exploring new inductive biases and by leveraging different data modalities of protein variants, and that stratified dataset design can highlight areas of improvement for machine learning guided protein engineering., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Author(s).)

Background: Polyploidy is widely recognized as a significant evolutionary force in the plant kingdom, contributing to the diversification of plants. One of the notable features of allopolyploidy is the occurrence of homoeologous exchange (HE) events between the subgenomes, causing changes in genomic composition, gene expression, and phenotypic variations. However, the role of HE in plant adaptation and domestication remains unclear., Results: Here we analyze the whole-genome resequencing data from Brassica napus accessions representing the different morphotypes and ecotypes, to investigate the role of HE in domestication. Our findings demonstrate frequent occurrence of HEs in Brassica napus, with substantial HE patterns shared across populations, indicating their potential role in promoting crop domestication. HE events are asymmetric, with the A genome more frequently replacing C genome segments. These events show a preference for specific genomic regions and vary among populations. We also identify candidate genes in HE regions specific to certain populations, which likely contribute to flowering-time diversification across diverse morphotypes and ecotypes. In addition, we assemble a new genome of a swede accession, confirming the HE signals on the genome and their potential involvement in root tuber development. By analyzing HE in another allopolyploid species, Brassica juncea, we characterize a potential broader role of HE in allopolyploid crop domestication., Conclusions: Our results provide novel insights into the domestication of polyploid Brassica species and highlight homoeologous exchange as a crucial mechanism for generating variations that are selected for crop improvement in polyploid species., (© 2024. The Author(s).)

Increasing natural resistance and resilience in plants is key for ensuring food security within a changing climate. Breeders improve these traits by crossing cultivars with their wild relatives and introgressing specific alleles through meiotic recombination. However, some genomic regions are devoid of recombination especially in crosses between divergent genomes, limiting the combinations of desirable alleles. Here, we used pooled-pollen sequencing to build a map of recombinant and non-recombinant regions between tomato and five wild relatives commonly used for introgressive tomato breeding. We detected hybrid-specific recombination coldspots that underscore the role of structural variations in modifying recombination patterns and maintaining genetic linkage in interspecific crosses. Crossover regions and coldspots show strong association with specific TE superfamilies exhibiting differentially accessible chromatin between somatic and meiotic cells. About two-thirds of the genome are conserved coldspots, located mostly in the pericentromeres and enriched with retrotransposons. The coldspots also harbor genes associated with agronomic traits and stress resistance, revealing undesired consequences of linkage drag and possible barriers to breeding. We presented examples of linkage drag that can potentially be resolved by pairing tomato with other wild species. Overall, this catalogue will help breeders better understand crossover localization and make informed decisions on generating new tomato varieties., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Fuentes et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Salinity stress constrains lateral root (LR) growth and severely affects plant growth. Auxin signaling regulates LR formation, but the molecular mechanism by which salinity affects root auxin signaling and whether salt induces other pathways that regulate LR development remains unknown. In Arabidopsis thaliana, the auxin-regulated transcription factor LATERAL ORGAN BOUNDARY DOMAIN 16 (LBD16) is an essential player in LR development under control conditions. Here, we show that under high-salt conditions, an alternative pathway regulates LBD16 expression. Salt represses auxin signaling but, in parallel, activates ZINC FINGER OF ARABIDOPSIS THALIANA 6 (ZAT6), a transcriptional activator of LBD16. ZAT6 activates LBD16 expression, thus contributing to downstream cell wall remodeling and promoting LR development under high-salt conditions. Our study thus shows that the integration of auxin-dependent repressive and salt-activated auxin-independent pathways converging on LBD16 modulates root branching under high-salt conditions., Competing Interests: Conflict of interest statement. None declared., (© The Author(s) 2023. Published by Oxford University Press on behalf of American Society of Plant Biologists.)

Phytoplasmas are pathogenic bacteria that reprogram plant host development for their own benefit. Previous studies have characterized a few different phytoplasma effector proteins that destabilize specific plant transcription factors. However, these are only a small fraction of the potential effectors used by phytoplasmas; therefore, the molecular mechanisms through which phytoplasmas modulate their hosts require further investigation. To obtain further insights into the phytoplasma infection mechanisms, we generated a protein-protein interaction network between a broad set of phytoplasma effectors and a large, unbiased collection of Arabidopsis thaliana transcription factors and transcriptional regulators. We found widespread, but specific, interactions between phytoplasma effectors and host transcription factors, especially those related to host developmental processes. In particular, many unrelated effectors target specific sets of TCP transcription factors, which regulate plant development and immunity. Comparison with other host-pathogen protein interaction networks shows that phytoplasma effectors have unusual targets, indicating that phytoplasmas have evolved a unique and unusual infection strategy. This study contributes a rich and solid data source that guides further investigations of the functions of individual effectors, as demonstrated for some herein. Moreover, the dataset provides insights into the underlying molecular mechanisms of phytoplasma infection., (© 2023 The Authors. The Plant Journal published by Society for Experimental Biology and John Wiley & Sons Ltd.)

The Arabidopsis thaliana transcription factor BRANCHED1 (BRC1) plays a pivotal role in the control of shoot branching as it integrates environmental and endogenous signals that influence axillary bud growth. Despite its remarkable activity as a growth inhibitor, the mechanisms by which BRC1 promotes bud dormancy are largely unknown. We determined the genome-wide BRC1 binding sites in vivo and combined these with transcriptomic data and gene co-expression analyses to identify bona fide BRC1 direct targets. Next, we integrated multi-omics data to infer the BRC1 gene regulatory network (GRN) and used graph theory techniques to find network motifs that control the GRN dynamics. We generated an open online tool to interrogate this network. A group of BRC1 target genes encoding transcription factors (BTFs) orchestrate this intricate transcriptional network enriched in abscisic acid-related components. Promoter::β-GLUCURONIDASE transgenic lines confirmed that BTFs are expressed in axillary buds. Transient co-expression assays and studies in planta using mutant lines validated the role of BTFs in modulating the GRN and promoting bud dormancy. This knowledge provides access to the developmental mechanisms that regulate shoot branching and helps identify candidate genes to use as tools to adapt plant architecture and crop production to ever-changing environmental conditions., (© 2023 The Authors. New Phytologist © 2023 New Phytologist Foundation.)

Polyploidy is recurrent across the tree of life and known as an evolutionary driving force in plant diversification and crop domestication. How polyploid plants adapt to various habitats has been a fundamental question that remained largely unanswered. Brassica napus is a major crop cultivated worldwide, resulting from allopolyploidy between unknown accessions of diploid B. rapa and B. oleracea. Here, we used whole-genome resequencing data of accessions representing the majority of morphotypes and ecotypes from the species B. rapa, B. oleracea, and B. napus to investigate the role of polyploidy during domestication. To do so, we first reconstructed the phylogenetic history of B. napus, which supported the hypothesis that the emergence of B. napus derived from the hybridization of European turnip of B. rapa and wild B. oleracea. These analyses also showed that morphotypes of swede and Siberian kale (used as vegetable and fodder) were domesticated before rapeseed (oil crop). We next observed that frequent interploidy introgressions from sympatric diploids were prominent throughout the domestication history of B. napus. Introgressed genomic regions were shown to increase the overall genetic diversity and tend to be localized in regions of high recombination. We detected numerous candidate adaptive introgressed regions and found evidence that some of the genes in these regions contributed to phenotypic diversification and adaptation of different morphotypes. Overall, our results shed light on the origin and domestication of B. napus and demonstrate interploidy introgression as an important mechanism that fuels rapid diversification in polyploid species., (© The Author(s) 2023. Published by Oxford University Press on behalf of Society for Molecular Biology and Evolution.)

Maize ( Zea mays ) is a major staple crop in Africa, where its yield and the livelihood of millions are compromised by the parasitic witchweed Striga . Germination of Striga is induced by strigolactones exuded from maize roots into the rhizosphere. In a maize germplasm collection, we identified two strigolactones, zealactol and zealactonoic acid, which stimulate less Striga germination than the major maize strigolactone, zealactone. We then showed that a single cytochrome P450, ZmCYP706C37, catalyzes a series of oxidative steps in the maize-strigolactone biosynthetic pathway. Reduction in activity of this enzyme and two others involved in the pathway, ZmMAX1b and ZmCLAMT1, can change strigolactone composition and reduce Striga germination and infection. These results offer prospects for breeding Striga -resistant maize.

Key Message: A highly specialized function for individual LTPs for different products from the same terpenoid biosynthesis pathway is described and the function of an LTP GPI anchor is studied. Sequiterpenes produced in glandular trichomes of the medicinal plant Tanacetum parthenium (feverfew) accumulate in the subcuticular extracellular space. Transport of these compounds over the plasma membrane is presumably by specialized membrane transporters, but it is still not clear how these hydrophobic compounds are subsequently transported over the hydrophilic cell wall. Here we identified eight so-called non-specific Lipid transfer proteins (nsLTPs) genes that are expressed in feverfew trichomes. A putative function of these eight nsLTPs in transport of the lipophilic sesquiterpene lactones produced in feverfew trichomes, was tested in an in-planta transport assay using transient expression in Nicotiana benthamiana. Of eight feverfew nsLTP candidate genes analyzed, two (TpLTP1 and TpLTP2) can specifically improve extracellular accumulation of the sesquiterpene costunolide, while one nsLTP (TpLTP3) shows high specificity towards export of parthenolide. The specificity of the nsLTPs was also tested in an assay that test for the exclusion capacity of the nsLTP for influx of extracellular substrates. In such assay, TpLTP3 was identified as most effective in blocking influx of both costunolide and parthenolide, when these substrates are infiltrated into the apoplast. The TpLTP3 is special in having a GPI-anchor domain, which is essential for the export activity of TpLTP3. However, addition of the TpLTP3 GPI-anchor domain to TpLTP1 resulted in loss of TpLTP1 export activity. These novel export and exclusion assays thus provide new means to test functionality of plant nsLTPs., (© 2022. The Author(s).)

Recent breakthroughs in protein structure prediction demarcate the start of a new era in structural bioinformatics. Combined with various advances in experimental structure determination and the uninterrupted pace at which new structures are published, this promises an age in which protein structure information is as prevalent and ubiquitous as sequence. Machine learning in protein bioinformatics has been dominated by sequence-based methods, but this is now changing to make use of the deluge of rich structural information as input. Machine learning methods making use of structures are scattered across literature and cover a number of different applications and scopes; while some try to address questions and tasks within a single protein family, others aim to capture characteristics across all available proteins. In this review, we look at the variety of structure-based machine learning approaches, how structures can be used as input, and typical applications of these approaches in protein biology. We also discuss current challenges and opportunities in this all-important and increasingly popular field., Competing Interests: We have no conflicts of interest to disclose., (© 2023 The Authors.)

Plant monoterpenes are challenging compounds, since they often act as solvents, and thus have both phytotoxic and antimicrobial properties. In this study an approach is developed to identify and characterize enzymes that can detoxify monoterpenoids, and thus would protect both plants and microbial production systems from these compounds. Plants respond to the presence of monoterpenes by expressing glycosyltransferases (UGTs), which conjugate the monoterpenoids into glycosides. By identifying these enzymes in a transcriptomics approach using Mentha × piperita, a family of UGTs was identified which is active on cyclic monoterpenoids such as menthol, and on acyclic monoterpenoids such as geranic acid. Other members of this family, from tomato, were also shown to be active on these monoterpenoids. In vitro and in vivo activity of different UGTs were tested with different substrates. We found that some glycosyltransferases significantly affect the toxicity of selected monoterpenoids in Escherichia coli, suggesting that glycosyltransferases can protect cells from monoterpenoid toxicity., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Rumyana Karlova, Florence Schempp reports financial support was provided by European Union., (Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Strigolactones (SLs) are rhizosphere signalling molecules and phytohormones. The biosynthetic pathway of SLs in tomato has been partially elucidated, but the structural diversity in tomato SLs predicts that additional biosynthetic steps are required. Here, root RNA-seq data and co-expression analysis were used for SL biosynthetic gene discovery. This strategy resulted in a candidate gene list containing several cytochrome P450s. Heterologous expression in Nicotiana benthamiana and yeast showed that one of these, CYP712G1, can catalyse the double oxidation of orobanchol, resulting in the formation of three didehydro-orobanchol (DDH) isomers. Virus-induced gene silencing and heterologous expression in yeast showed that one of these DDH isomers is converted to solanacol, one of the most abundant SLs in tomato root exudate. Protein modelling and substrate docking analysis suggest that hydroxy-orbanchol is the likely intermediate in the conversion from orobanchol to the DDH isomers. Phylogenetic analysis demonstrated the occurrence of CYP712G1 homologues in the Eudicots only, which fits with the reports on DDH isomers in that clade. Protein modelling and orobanchol docking of the putative tobacco CYP712G1 homologue suggest that it can convert orobanchol to similar DDH isomers as tomato., (© 2022 The Authors. New Phytologist © 2022 New Phytologist Foundation.)

Background: Many studies have demonstrated the utility of machine learning (ML) methods for genomic prediction (GP) of various plant traits, but a clear rationale for choosing ML over conventionally used, often simpler parametric methods, is still lacking. Predictive performance of GP models might depend on a plethora of factors including sample size, number of markers, population structure and genetic architecture. Methods: Here, we investigate which problem and dataset characteristics are related to good performance of ML methods for genomic prediction. We compare the predictive performance of two frequently used ensemble ML methods (Random Forest and Extreme Gradient Boosting) with parametric methods including genomic best linear unbiased prediction (GBLUP), reproducing kernel Hilbert space regression (RKHS), BayesA and BayesB. To explore problem characteristics, we use simulated and real plant traits under different genetic complexity levels determined by the number of Quantitative Trait Loci (QTLs), heritability ( h 2 and h 2 e ), population structure and linkage disequilibrium between causal nucleotides and other SNPs. Results: Decision tree based ensemble ML methods are a better choice for nonlinear phenotypes and are comparable to Bayesian methods for linear phenotypes in the case of large effect Quantitative Trait Nucleotides (QTNs). Furthermore, we find that ML methods are susceptible to confounding due to population structure but less sensitive to low linkage disequilibrium than linear parametric methods. Conclusions: Overall, this provides insights into the role of ML in GP as well as guidelines for practitioners., Competing Interests: No competing interests were disclosed., (Copyright: © 2023 Farooq M et al.)

Meiotic recombination is a biological process of key importance in breeding, to generate genetic diversity and develop novel or agronomically relevant haplotypes. In crop tomato, recombination is curtailed as manifested by linkage disequilibrium decay over a longer distance and reduced diversity compared with wild relatives. Here, we compared domesticated and wild populations of tomato and found an overall conserved recombination landscape, with local changes in effective recombination rate in specific genomic regions. We also studied the dynamics of recombination hotspots resulting from domestication and found that loss of such hotspots is associated with selective sweeps, most notably in the pericentromeric heterochromatin. We detected footprints of genetic changes and structural variants, among them associated with transposable elements, linked with hotspot divergence during domestication, likely causing fine-scale alterations to recombination patterns and resulting in linkage drag., (© The Author(s) 2021. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.)

Sesquiterpene synthases (STSs) catalyze the formation of a large class of plant volatiles called sesquiterpenes. While thousands of putative STS sequences from diverse plant species are available, only a small number of them have been functionally characterized. Sequence identity-based screening for desired enzymes, often used in biotechnological applications, is difficult to apply here as STS sequence similarity is strongly affected by species. This calls for more sophisticated computational methods for functionality prediction. We investigate the specificity of precursor cation formation in these elusive enzymes. By inspecting multi-product STSs, we demonstrate that STSs have a strong selectivity towards one precursor cation. We use a machine learning approach combining sequence and structure information to accurately predict precursor cation specificity for STSs across all plant species. We combine this with a co-evolutionary analysis on the wealth of uncharacterized putative STS sequences, to pinpoint residues and distant functional contacts influencing cation formation and reaction pathway selection. These structural factors can be used to predict and engineer enzymes with specific functions, as we demonstrate by predicting and characterizing two novel STSs from Citrus bergamia., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: JB and EM are currently employed by Isobionics, a company involved in using terpenes for fragrance and flavour products.

Prediction of growth-related complex traits is highly important for crop breeding. Photosynthesis efficiency and biomass are direct indicators of overall plant performance and therefore even minor improvements in these traits can result in significant breeding gains. Crop breeding for complex traits has been revolutionized by technological developments in genomics and phenomics. Capitalizing on the growing availability of genomics data, genome-wide marker-based prediction models allow for efficient selection of the best parents for the next generation without the need for phenotypic information. Until now such models mostly predict the phenotype directly from the genotype and fail to make use of relevant biological knowledge. It is an open question to what extent the use of such biological knowledge is beneficial for improving genomic prediction accuracy and reliability. In this study, we explored the use of publicly available biological information for genomic prediction of photosynthetic light use efficiency (Φ PSII ) and projected leaf area (PLA) in Arabidopsis thaliana . To explore the use of various types of knowledge, we mapped genomic polymorphisms to Gene Ontology (GO) terms and transcriptomics-based gene clusters, and applied these in a Genomic Feature Best Linear Unbiased Predictor (GFBLUP) model, which is an extension to the traditional Genomic BLUP (GBLUP) benchmark. Our results suggest that incorporation of prior biological knowledge can improve genomic prediction accuracy for both Φ PSII and PLA. The improvement achieved depends on the trait, type of knowledge and trait heritability. Moreover, transcriptomics offers complementary evidence to the Gene Ontology for improvement when used to define functional groups of genes. In conclusion, prior knowledge about trait-specific groups of genes can be directly translated into improved genomic prediction., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Farooq, van Dijk, Nijveen, Aarts, Kruijer, Nguyen, Mansoor and de Ridder.)

Cucumis melo (melon or muskmelon) is an important crop in the family of the Cucurbitaceae. Melon is cross pollinated and domesticated at several locations throughout the breeding history, resulting in highly diverse genetic structure in the germplasm. Yet, the relations among the groups and cultivars are still incomplete. We shed light on the melonbreeding history, analyzing structural variations ranging from 50 bp up to 100 kb, identified from whole genome sequences of 100 selected melon accessions and wild relatives. Phylogenetic trees based on SV types completely resolve cultivars and wild accessions into two monophyletic groups and clustering of cultivars largely correlates with their geographic origin. Taking into account morphology, we found six mis-categorized cultivars. Unique inversions are more often shared between cultivars, carrying advantageous genes and do not directly originate from wild species. Approximately 60% of the inversion breaks carry a long poly A/T motif, and following observations in other plant species, suggest that inversions in melon likely resulted from meiotic recombination events. We show that resistance genes in the linkage V region are expanded in the cultivar genomes compared to wild relatives. Furthermore, particular agronomic traits such as fruit ripening, fragrance, and stress response are specifically selected for in the melon subspecies. These results represent distinctive footprints of selective breeding that shaped today's melon. The sequences and genomic relations between land races, wild relatives, and cultivars will serve the community to identify genetic diversity, optimize experimental designs, and enhance crop development., (© The Author(s) 2020. Published by Oxford University Press on behalf of Genetics Society of America.)

Motivation: As the number of experimentally solved protein structures rises, it becomes increasingly appealing to use structural information for predictive tasks involving proteins. Due to the large variation in protein sizes, folds and topologies, an attractive approach is to embed protein structures into fixed-length vectors, which can be used in machine learning algorithms aimed at predicting and understanding functional and physical properties. Many existing embedding approaches are alignment based, which is both time-consuming and ineffective for distantly related proteins. On the other hand, library- or model-based approaches depend on a small library of fragments or require the use of a trained model, both of which may not generalize well., Results: We present Geometricus, a novel and universally applicable approach to embedding proteins in a fixed-dimensional space. The approach is fast, accurate, and interpretable. Geometricus uses a set of 3D moment invariants to discretize fragments of protein structures into shape-mers, which are then counted to describe the full structure as a vector of counts. We demonstrate the applicability of this approach in various tasks, ranging from fast structure similarity search, unsupervised clustering and structure classification across proteins from different superfamilies as well as within the same family., Availability and Implementation: Python code available at https://git.wur.nl/durai001/geometricus., (© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Motivation: Polyketide synthases (PKSs) are enzymes that generate diverse molecules of great pharmaceutical importance, including a range of clinically used antimicrobials and antitumor agents. Many polyketides are synthesized by cis-AT modular PKSs, which are organized in assembly lines, in which multiple enzymes line up in a specific order. This order is defined by specific protein-protein interactions (PPIs). The unique modular structure and catalyzing mechanism of these assembly lines makes their products predictable and also spurred combinatorial biosynthesis studies to produce novel polyketides using synthetic biology. However, predicting the interactions of PKSs, and thereby inferring the order of their assembly line, is still challenging, especially for cases in which this order is not reflected by the ordering of the PKS-encoding genes in the genome., Results: Here, we introduce PKSpop, which uses a coevolution-based PPI algorithm to infer protein order in PKS assembly lines. Our method accurately predicts protein orders (93% accuracy). Additionally, we identify new residue pairs that are key in determining interaction specificity, and show that coevolution of N- and C-terminal docking domains of PKSs is significantly more predictive for PPIs than coevolution between ketosynthase and acyl carrier protein domains., Availability and Implementation: The code is available on http://www.bif.wur.nl/ (under 'Software')., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Technological developments have revolutionized measurements on plant genotypes and phenotypes, leading to routine production of large, complex data sets. This has led to increased efforts to extract meaning from these measurements and to integrate various data sets. Concurrently, machine learning has rapidly evolved and is now widely applied in science in general and in plant genotyping and phenotyping in particular. Here, we review the application of machine learning in the context of plant science and plant breeding. We focus on analyses at different phenotype levels, from biochemical to yield, and in connecting genotypes to these. In this way, we illustrate how machine learning offers a suite of methods that enable researchers to find meaningful patterns in relevant plant data., (© 2020 The Author(s).)

Plant terpene synthases (TPSs) can mediate formation of a large variety of terpenes, and their diversification contributes to the specific chemical profiles of different plant species and chemotypes. Plant genomes often encode a number of related terpene synthases, which can produce very different terpenes. The relationship between TPS sequence and resulting terpene product is not completely understood. In this work we describe two TPSs from the Camphor tree Cinnamomum camphora (L.) Presl. One of these, CiCaMS, acts as a monoterpene synthase (monoTPS), and mediates the production of myrcene, while the other, CiCaSSy, acts as a sesquiterpene synthase (sesquiTPS), and catalyses the production of α-santalene, β-santalene and trans-α-bergamotene. Interestingly, these enzymes share 97% DNA sequence identity and differ only in 22 amino acid residues out of 553. To understand which residues are essential for the catalysis of monoterpenes resp. sesquiterpenes, a number of hybrid synthases were prepared, and supplemented by a set of single-residue variants. These were tested for their ability to produce monoterpenes and sesquiterpenes by in vivo production of sesquiterpenes in E. coli, and by in vitro enzyme assays. This analysis pinpointed three residues in the sequence which could mediate the change in product specificity from a monoterpene synthase to a sesquiterpene synthase. Another set of three residues defined the sesquiterpene product profile, including the ratios between sesquiterpene products., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

Exome sequencing is now mainstream in clinical practice. However, identification of pathogenic Mendelian variants remains time-consuming, in part, because the limited accuracy of current computational prediction methods requires manual classification by experts. Here we introduce CAPICE, a new machine-learning-based method for prioritizing pathogenic variants, including SNVs and short InDels. CAPICE outperforms the best general (CADD, GAVIN) and consequence-type-specific (REVEL, ClinPred) computational prediction methods, for both rare and ultra-rare variants. CAPICE is easily added to diagnostic pipelines as pre-computed score file or command-line software, or using online MOLGENIS web service with API. Download CAPICE for free and open-source (LGPLv3) at https://github.com/molgenis/capice .

Plants produce a large variety of highly functionalized terpenoids. Functional groups such as partially unsaturated rings and carboxyl groups provide handles to use these compounds as feedstock for biobased commodity chemicals. For instance, methylperillate, a monoterpenoid found in Salvia dorisiana, may be used for this purpose, as it carries both an unsaturated ring and a methylated carboxyl group. The biosynthetic pathway of methylperillate in plants is still unclear. In this work, we identified glandular trichomes from S. dorisiana as the location of biosynthesis and storage of methylperillate. mRNA from purified trichomes was used to identify four genes that can encode the pathway from geranyl diphosphate towards methylperillate. This pathway includes a (-)-limonene synthase (SdLS), a limonene 7-hydroxylase (SdL7H, CYP71A76), and a perillyl alcohol dehydrogenase (SdPOHDH). We also identified a terpene acid methyltransferase, perillic acid O-methyltransferase (SdPAOMT), with homology to salicylic acid OMTs. Transient expression in Nicotiana benthamiana of these four genes, in combination with a geranyl diphosphate synthase to boost precursor formation, resulted in production of methylperillate. This demonstrates the potential of these enzymes for metabolic engineering of a feedstock for biobased commodity chemicals., (© The Author(s) 2020. Published by Oxford University Press on behalf of the Society for Experimental Biology.)

Genome wide screening of pooled pollen samples from a single interspecific F1 hybrid obtained from a cross between tomato, Solanum lycopersicum and its wild relative, Solanum pimpinellifolium using linked read sequencing of the haploid nuclei, allowed profiling of the crossover (CO) and gene conversion (GC) landscape. We observed a striking overlap between cold regions of CO in the male gametes and our previously established F6 recombinant inbred lines (RILs) population. COs were overrepresented in non-coding regions in the gene promoter and 5'UTR regions of genes. Poly-A/T and AT rich motifs were found enriched in 1 kb promoter regions flanking the CO sites. Non-crossover associated allelic and ectopic GCs were detected in most chromosomes, confirming that besides CO, GC represents also a source for genetic diversity and genome plasticity in tomato. Furthermore, we identified processed break junctions pointing at the involvement of both homology directed and non-homology directed repair pathways, suggesting a recombination machinery in tomato that is more complex than currently anticipated., (© 2019 The Authors The Plant Journal © 2019 John Wiley & Sons Ltd.)

The vast number of protein structures currently available opens exciting opportunities for machine learning on proteins, aimed at predicting and understanding functional properties. In particular, in combination with homology modelling, it is now possible to not only use sequence features as input for machine learning, but also structure features. However, in order to do so, robust multiple structure alignments are imperative. Here we present Caretta, a multiple structure alignment suite meant for homologous but sequentially divergent protein families which consistently returns accurate alignments with a higher coverage than current state-of-the-art tools. Caretta is available as a GUI and command-line application and additionally outputs an aligned structure feature matrix for a given set of input structures, which can readily be used in downstream steps for supervised or unsupervised machine learning. We show Caretta's performance on two benchmark datasets, and present an example application of Caretta in predicting the conformational state of cyclin-dependent kinases., (© 2020 The Authors.)

Controlling the expression of genes is one of the key challenges of synthetic biology. Until recently fine-tuned control has been out of reach, particularly in eukaryotes owing to their complexity of gene regulation. With advances in machine learning (ML) and in particular with increasing dataset sizes, models predicting gene expression levels from regulatory sequences can now be successfully constructed. Such models form the cornerstone of algorithms that allow users to design regulatory regions to achieve a specific gene expression level. In this review we discuss strategies for data collection, data encoding, ML practices, design algorithm choices, and finally model interpretation. Ultimately, these developments will provide synthetic biologists with highly specific genetic building blocks to rationally engineer complex pathways and circuits., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Members of the Arabidopsis thaliana TCP transcription factor (TF) family affect plant growth and development. We systematically quantified the effect of mutagenizing single or multiple TCP TFs and how altered vegetative growth or branching influences final seed yield. We monitored rosette growth over time and branching patterns and seed yield characteristics at the end of the lifecycle. Subsequently, an approach was developed to disentangle vegetative growth and to determine possible effects on seed yield. Analysis of growth parameters showed all investigated tcp mutants to be affected in certain growth aspects compared with wild-type plants, highlighting the importance of TCP TFs in plant development. Furthermore, we found evidence that all class II TCPs are involved in axillary branch outgrowth, either as inhibitors (BRANCHED-like genes) or enhancers (JAW- and TCP5-like genes). Comprehensive phenotyping of plants mutant for single or multiple TCP TFs reveals that the proposed opposite functions of class I and class II TCPs in plant growth needs revision and shows complex interactions between closely related TCP genes instead of full genetic redundancy. In various instances, the alterations in vegetative growth or in branching patterns result into negative trade-off effects on seed yield that were missed in previous studies, showing the importance of comprehensive and quantitative phenotyping., (© 2019 The Authors. The Plant Journal published by John Wiley & Sons Ltd and Society for Experimental Biology.)

Motivation: Predicting residue-residue contacts between interacting proteins is an important problem in bioinformatics. The growing wealth of sequence data can be used to infer these contacts through correlated mutation analysis on multiple sequence alignments of interacting homologs of the proteins of interest. This requires correct identification of pairs of interacting proteins for many species, in order to avoid introducing noise (i.e. non-interacting sequences) in the analysis that will decrease predictive performance., Results: We have designed Ouroboros, a novel algorithm to reduce such noise in intermolecular contact prediction. Our method iterates between weighting proteins according to how likely they are to interact based on the correlated mutations signal, and predicting correlated mutations based on the weighted sequence alignment. We show that this approach accurately discriminates between protein interaction versus non-interaction and simultaneously improves the prediction of intermolecular contact residues compared to a naive application of correlated mutation analysis. This requires no training labels concerning interactions or contacts. Furthermore, the method relaxes the assumption of one-to-one interaction of previous approaches, allowing for the study of many-to-many interactions., Availability and Implementation: Source code and test data are available at www.bif.wur.nl/., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2018. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: Correct flower formation requires highly specific temporal and spatial regulation of gene expression. In Arabidopsis thaliana the majority of the master regulators that determine flower organ identity belong to the MADS-domain transcription factor family. The canonical DNA binding motif for this transcription factor family is the CArG-box, which has the consensus CC(A/T) 6 GG. However, so far, a comprehensive analysis of MADS-domain binding patterns has not yet been performed., Results: Eight publicly available ChIP-seq datasets of MADS-domain proteins that regulate the floral transition and flower formation were analyzed. Surprisingly, the preferred DNA binding motif of each protein was a CArG-box with an NAA extension. Furthermore, motifs of other transcription factors were found in the vicinity of binding sites of MADS-domain transcription factors, suggesting that interaction of MADS-domain proteins with other transcription factors is important for target gene regulation. Finally, conservation of CArG-boxes between Arabidopsis ecotypes was assessed to obtain information about their evolutionary importance. CArG-boxes that fully matched the consensus were more conserved than other CArG-boxes, suggesting that the perfect CArG-box is evolutionary more important than other CArG-box variants., Conclusion: Our analysis provides detailed insight into MADS-domain protein binding patterns. The results underline the importance of an extended version of the CArG-box and provide a first view on evolutionary conservation of MADS-domain protein binding sites in Arabidopsis ecotypes.

A better understanding of genomic features influencing the location of meiotic crossovers (COs) in plant species is both of fundamental importance and of practical relevance for plant breeding. Using CO positions with sufficiently high resolution from four plant species [Arabidopsis thaliana, Solanum lycopersicum (tomato), Zea mays (maize) and Oryza sativa (rice)] we have trained machine-learning models to predict the susceptibility to CO formation. Our results show that CO occurrence within various plant genomes can be predicted by DNA sequence and shape features. Several features related to genome content and to genomic accessibility were consistently either positively or negatively related to COs in all four species. Other features were found as predictive only in specific species. Gene annotation-related features were especially predictive for maize, whereas in tomato and Arabidopsis propeller twist and helical twist (DNA shape features) and AT/TA dinucleotides were found to be the most important. In rice, high roll (another DNA shape feature) and low CA dinucleotide frequency in particular were found to be associated with CO occurrence. The accuracy of our models was sufficient for Arabidopsis and rice (area under receiver operating characteristic curve, AUROC > 0.5), and was high for tomato and maize (AUROC ≫ 0.5), demonstrating that DNA sequence and shape are predictive for meiotic COs throughout the plant kingdom., (© 2018 The Authors The Plant Journal © 2018 John Wiley & Sons Ltd.)

Plants accumulate secondary metabolites to adapt to environmental conditions. These compounds, here exemplified by the purple-colored anthocyanins, are accumulated upon high temperatures, UV-light, drought, and nutrient deficiencies, and may contribute to tolerance to these stresses. Producing compounds is often part of a more broad response of the plant to changes in the environment. Here we investigate how a transcription-factor-mediated program for controlling anthocyanin biosynthesis also has effects on formation of specialized cell structures and changes in the plant root architecture. A systems biology approach was developed in tomato ( Solanum lycopersicum ) for coordinated induction of biosynthesis of anthocyanins, in a tissue- and development-independent manner. A transcription factor couple from Antirrhinum that is known to control anthocyanin biosynthesis was introduced in tomato under control of a dexamethasone-inducible promoter. By application of dexamethasone, anthocyanin formation was induced within 24 h in vegetative tissues and in undifferentiated cells. Profiles of metabolites and gene expression were analyzed in several tomato tissues. Changes in concentration of anthocyanins and other phenolic compounds were observed in all tested tissues, accompanied by induction of the biosynthetic pathways leading from Glc to anthocyanins. A number of pathways that are not known to be involved in anthocyanin biosynthesis were observed to be regulated. Anthocyanin-producing plants displayed profound physiological and architectural changes, depending on the tissue, including root branching, root epithelial cell morphology, seed germination, and leaf conductance. The inducible anthocyanin-production system reveals a range of phenomena that accompanies anthocyanin biosynthesis in tomato, including adaptions of the plants architecture and physiology., (© 2018 American Society of Plant Biologists. All Rights Reserved.)

Floral induction in Tulipa gesneriana and Lilium longiflorum is triggered by contrasting temperature conditions, high and low temperature, respectively. In Arabidopsis, the floral integrator FLOWERING LOCUS T (FT), a member of the PEBP (phosphatidyl ethanolamine-binding protein) gene family, is a key player in flowering time control. In this study, one PEBP gene was identified and characterized in lily (LlFT) and three PEBP genes were isolated from tulip (TgFT1, TgFT2 and TgFT3). Overexpression of these genes in Arabidopsis thaliana resulted in an early flowering phenotype for LlFT and TgFT2, but a late flowering phenotype for TgFT1 and TgFT3. Overexpression of LlFT in L. longiflorum also resulted in an early flowering phenotype, confirming its proposed role as a flowering time-controlling gene. The tulip PEBP genes TgFT2 and TgFT3 have a similar expression pattern in tulip, but show opposite effects on the timing of flowering in Arabidopsis. Therefore, the difference between these two proteins was further investigated by interchanging amino acids thought to be important for the FT function. This resulted in the conversion of phenotypes in Arabidopsis upon overexpressing the substituted TgFT2 and TgFT3 genes, revealing the importance of these interchanged amino acid residues. Based on all obtained results, we hypothesize that LlFT is involved in creating meristem competence to flowering-related cues in lily, and TgFT2 is considered to act as a florigen involved in the floral induction in tulip. The function of TgFT3 remains unclear, but, based on our observations and phylogenetic analysis, we propose a bulb-specific function for this gene., (© The Author 2017. Published by Oxford University Press on behalf of Japanese Society of Plant Physiologists. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Background: Small Auxin-Upregulated RNA (SAUR) genes encode growth regulators that induce cell elongation. Arabidopsis contains more than 70 SAUR genes, of which the growth-promoting function has been unveiled in seedlings, while their role in other tissues remained largely unknown. Here, we focus on the regulatory regions of Arabidopsis SAUR genes, to predict the processes in which they play a role, and understand the dynamics of plant growth., Results: In this study, we characterized in detail the entire SAUR10-clade: SAUR8, SAUR9, SAUR10, SAUR12, SAUR16, SAUR50, SAUR51 and SAUR54. Overexpression analysis revealed that the different proteins fulfil similar functions, while the SAUR expression patterns were highly diverse, showing expression throughout plant development in a variety of tissues. In addition, the response to application of different hormones largely varied between the different genes. These tissue-specific and hormone-specific responses could be linked to transcription factor binding sites using in silico analyses. These analyses also supported the existence of two groups of SAURs in Arabidopsis: Class I genes can be induced by combinatorial action of ARF-BZR-PIF transcription factors, while Class II genes are not regulated by auxin., Conclusions: SAUR10-clade genes generally induce cell-elongation, but exhibit diverse expression patterns and responses to hormones. Our experimental and in silico analyses suggest that transcription factors involved in plant development determine the tissue specific expression of the different SAUR genes, whereas the amplitude of this expression can often be controlled by hormone response transcription factors. This allows the plant to fine tune growth in a variety of tissues in response to internal and external signals.

Protein turnover is a key aspect of cellular homeostasis and proteome dynamics. However, there is little consensus on which properties of a protein determine its lifetime in the cell. In this work, we exploit two reliable datasets of experimental protein degradation rates to learn models and uncover determinants of protein degradation, with particular focus on properties that can be derived from the sequence. Our work shows that simple sequence features suffice to obtain predictive models of which the output correlates reasonably well with the experimentally measured values. We also show that intrinsic disorder may have a larger effect than previously reported, and that the effect of PEST regions, long thought to act as specific degradation signals, can be better explained by their disorder. We also find that determinants of protein degradation depend on the cell types or experimental conditions studied. This analysis serves as a first step towards the development of more complex, mature computational models of degradation of proteins and eventually of their full life cycle. Proteins 2017; 85:1593-1601. © 2017 Wiley Periodicals, Inc., (© 2017 Wiley Periodicals, Inc.)

Understanding of phenotypes and their genetic basis is a major focus in current plant biology. Large amounts of phenotype data are being generated, both for macroscopic phenotypes such as size or yield, and for molecular phenotypes such as expression levels and metabolite levels. More insight in the underlying genetic and molecular mechanisms that influence phenotypes will enable a better understanding of how various phenotypes are related to each other. This will be a major step forward in understanding plant biology, with immediate value for plant breeding and academic plant research. Currently the genetic basis of most phenotypes remains however to be discovered, and the relatedness of different traits is unclear. We here present a novel approach to connect phenotypes to underlying biological processes and molecular functions. These connections define similarities between different types of phenotypes. The approach starts by using Quantitative Trait Locus (QTL) data, which are abundantly available for many phenotypes of interest. Overrepresentation analysis of gene functions based on Gene Ontology term enrichment across multiple QTL regions for a given phenotype, be it macroscopic or molecular, results in a small set of biological processes and molecular functions for each phenotype. Subsequently, similarity between different phenotypes can be defined in terms of these gene functions. Using publicly available rice data as example, a close relationship with defined molecular phenotypes is demonstrated for many macroscopic phenotypes. This includes for example a link between 'leaf senescence' and 'aspartic acid', as well as between 'days to maturity' and 'choline'. Relationships between macroscopic and molecular phenotypes may result in more efficient marker-assisted breeding and are likely to direct future research aimed at a better understanding of plant phenotypes.

The appropriate timing of flowering is crucial for the reproductive success of plants. Hence, intricate genetic networks integrate various environmental and endogenous cues such as temperature or hormonal statues. These signals integrate into a network of floral pathway integrator genes. At a quantitative level, it is currently unclear how the impact of genetic variation in signaling pathways on flowering time is mediated by floral pathway integrator genes. Here, using datasets available from literature, we connect Arabidopsis thaliana flowering time in genetic backgrounds varying in upstream signalling components with the expression levels of floral pathway integrator genes in these genetic backgrounds. Our modelling results indicate that flowering time depends in a quite linear way on expression levels of floral pathway integrator genes. This gradual, proportional response of flowering time to upstream changes enables a gradual adaptation to changing environmental factors such as temperature and light., Competing Interests: The authors declare there are no competing interests.

Background: Milk is the most important food for growth and development of the neonate, because of its nutrient composition and presence of many bioactive proteins. Differences between human and bovine milk in low abundant proteins have not been extensively studied. To better understand the differences between human and bovine milk, the qualitative and quantitative differences in the milk proteome as well as their changes over lactation were compared using both label-free and labelled proteomics techniques. These datasets were analysed and compared, to better understand the role of milk proteins in development of the newborn., Methods: Human and bovine milk samples were prepared by using filter-aided sample preparation (FASP) combined with dimethyl labelling and analysed by nano LC LTQ-Orbitrap XL mass spectrometry., Results: The human and bovine milk proteome show similarities with regard to the distribution over biological functions, especially the dominant presence of enzymes, transport and immune-related proteins. At a quantitative level, the human and bovine milk proteome differed not only between species but also over lactation within species. Dominant enzymes that differed between species were those assisting in nutrient digestion, with bile salt-activated lipase being abundant in human milk and pancreatic ribonuclease being abundant in bovine milk. As lactation advances, immune-related proteins decreased slower in human milk compared to bovine milk. Notwithstanding these quantitative differences, analysis of human and bovine co-expression networks and protein-protein interaction networks indicated that a subset of milk proteins displayed highly similar interactions in each of the different networks, which may be related to the general importance of milk in nutrition and healthy development of the newborn., Conclusions: Our findings promote a better understanding of the differences and similarities in dynamics of human and bovine milk proteins, thereby also providing guidance for further improvement of infant formula.

Specific and dynamic gene expression strongly depends on transcription factor (TF) activity and most plant TFs function in a combinatorial fashion. They can bind to DNA and control the expression of the corresponding gene in an additive fashion or cooperate by physical interactions, forming larger protein complexes. The importance of protein-protein interactions between members of a particular plant TF family has long been recognised; however, a significant number of interfamily TF interactions has recently been reported. The biological implications and the molecular mechanisms involved in cross-family interactions have now started to be elucidated and the examples illustrate potential roles in the bridging of biological processes. Hence, cross-family TF interactions expand the molecular toolbox for plants with additional mechanisms to control and fine-tune robust gene expression patterns and to adapt to their continuously changing environment., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

: Gene Ontology (GO) is a hierarchical vocabulary for the description of biological functions and locations, often employed by computational methods for protein function prediction. Due to the structure of GO, function predictions can be self- contradictory. For example, a protein may be predicted to belong to a detailed functional class, but not in a broader class that, due to the vocabulary structure, includes the predicted one.We present a novel discrete optimization algorithm called Functional Annotation with Labeling CONsistency (FALCON) that resolves such contradictions. The GO is modeled as a discrete Bayesian Network. For any given input of GO term membership probabilities, the algorithm returns the most probable GO term assignments that are in accordance with the Gene Ontology structure. The optimization is done using the Differential Evolution algorithm. Performance is evaluated on simulated and also real data from Arabidopsis thaliana showing improvement compared to related approaches. We finally applied the FALCON algorithm to obtain genome-wide function predictions for six eukaryotic species based on data provided by the CAFA (Critical Assessment of Function Annotation) project.