Mapping the chemical space of compounds to chemical structures remains a challenge in metabolomics. Despite the advancements in untargeted liquid chromatography-mass spectrometry (LC-MS) to achieve a high-throughput profile of metabolites from complex biological resources, only a small fraction of these metabolites can be annotated with confidence. Many novel computational methods and tools have been developed to enable chemical structure annotation to known and unknown compounds such as in silico generated spectra and molecular networking. Here, we present an automated and reproducible Metabolome Annotation Workflow (MAW) for untargeted metabolomics data to further facilitate and automate the complex annotation by combining tandem mass spectrometry (MS2) input data pre-processing, spectral and compound database matching with computational classification, and in silico annotation. MAW takes the LC-MS2 spectra as input and generates a list of putative candidates from spectral and compound databases. The databases are integrated via the R package Spectra and the metabolite annotation tool SIRIUS as part of the R segment of the workflow (MAW-R). The final candidate selection is performed using the cheminformatics tool RDKit in the Python segment (MAW-Py). Furthermore, each feature is assigned a chemical structure and can be imported to a chemical structure similarity network. MAW is following the FAIR (Findable, Accessible, Interoperable, Reusable) principles and has been made available as the docker images, maw-r and maw-py. The source code and documentation are available on GitHub. The performance of MAW is evaluated on two case studies. We found that MAW can improve candidate ranking by integrating spectral databases with annotation tools like SIRIUS which contributes to an efficient candidate selection procedure. The results from MAW are also reproducible and traceable, compliant with the FAIR guidelines. Taken together, MAW could greatly facilitate automated metabolite characterization in diverse fields such as clinical metabolomics and natural product discovery.

DNA double strand breaks (DSBs) are lethal threats that need to be repaired. Although many of the proteins involved in the early steps of DSB repair have been characterized, recent reports indicate that damage induced long and small RNAs also play an important role in DSB repair. Here, using a Nicotiana benthamiana transgenic line originally designed as a reporter for targeted knock-ins, we show that DSBs generated by Cas9 induce the transcription of long stable RNAs (damage-induced long RNAs - dilRNAs) that are translated into proteins. Using an array of single guide RNAs we show that the initiation of transcription takes place in the vicinity of the DSB. Single strand DNA nicks are not able to induce transcription, showing that cis DNA damage-induced transcription is specific for DSBs. Our results support a model in which a default and early event in the processing of DSBs is transcription into RNA which, depending on the genomic and genic context, can undergo distinct fates, including translation into protein, degradation or production of small RNAs. Our results have general implications for understanding the role of transcription in the repair of DSBs and, reciprocally, reveal DSBs as yet another way to regulate gene expression.

Protein engineering through directed evolution and (semi-)rational approaches has been applied successfully to optimize protein properties for broad applications in molecular biology, biotechnology, and biomedicine. The potential of protein engineering is not yet fully realized due to the limited screening throughput hampering the efficient exploration of the vast protein sequence space. Data-driven strategies have emerged as a powerful tool to leverage protein engineering by providing a model of the sequence-fitness landscape that can exhaustively be explored in silico and capitalize on the high diversity potential offered by nature However, as both the quality and quantity of the inputted data determine the success of such approaches, the applicability of data-driven strategies is often limited due to sparse data. Here, we present a hybrid model that combines direct coupling analysis and machine learning techniques to enable data-driven protein engineering when only few labeled sequences are available. Our method achieves high performance in predicting a protein’s fitness based on its sequence regardless of the number of sequences-fitness pairs in the training dataset. Besides reducing the computational effort compared to state-of-the-art methods, it outperforms them for sparse data situations, i.e., 50 − 250 labeled sequences available for training. In essence, the developed method is auspicious for data-driven protein engineering, especially for protein engineers who have only access to a limited amount of data for sequence-fitness landscape modeling.

The shape of tomato fruits is closely correlated to microtubule organization and the activity of microtubule associated proteins (MAP), but insights into the mechanism from a cell biology perspective are still largely elusive. Analysis of tissue expression profiles of different microtubule regulators revealed that functionally distinct classes of MAPs are highly expressed during fruit development. Among these, several members of the plant-specific MAP70 family are preferably expressed at the initiation stage of fruit development. Transgenic tomato lines overexpressing SlMAP70 produced elongated fruits that show reduced cell circularity and microtubule anisotropy, while SlMAP70 loss-of-function mutant showed an opposite effect with flatter fruits. Microtubule anisotropy of fruit endodermis cells exhibited dramatic rearrangement during tomato fruit development, and SlMAP70-1 is likely implicated in cortical microtubule organization and fruit elongation throughout this stage by interacting with SUN10/SlIQD21a. The expression of SlMAP70 (or co-expression of SlMAP70 and SUN10/SlIQD21a) induces microtubule stabilization and prevents its dynamic rearrangement, both activities are essential for fruit shape establishment after anthesis. Together, our results identify SlMAP70 as a novel regulator of fruit elongation, and demonstrate that manipulating microtubule stability and organization at the early fruit developmental stage has a strong impact on fruit shape.

Arabidopsis seeds release large capsules of mucilaginous polysaccharides, which are shaped by an intricate network of cellulosic microfibrils. Cellulose synthase complexes are guided by the microtubule cytoskeleton, but it is unclear which proteins mediate this process in the seed coat epidermis. Using reverse genetics, we identified IQ67 DOMAIN 9 (IQD9) and KINESIN LIGHT CHAIN-RELATED 1 (KLCR1) as two highly expressed genes during seed development and comprehensively characterized their roles in cell wall polysaccharide biosynthesis. Mutations in IQD9 as well as in KLCR1 lead to compact mucilage capsules with aberrant cellulose distribution, which can be rescued by transgene complementation. IQD9 physically interacts with KLCR1 and localizes to cortical MTs to maintain their organization in SCE cells. IQD9 as well as a previously identified TONNEAU1 (TON1) RECRUITING MOTIF 4 (TRM4) protein act to maintain cellulose synthase velocity. Our results demonstrate that IQD9, KLCR1 and TRM4 are MT-associated proteins that are required for seed mucilage architecture. This study provides the first direct evidence that members of the IQD, KLCR and TRM families have overlapping roles in cell wall biosynthesis. Therefore, SCE cells provide an attractive system to further decipher the complex genetic regulation of polarized cellulose deposition.

Engineering proteins and enzymes with the desired functionality has broad applications in molecular biology, biotechnology, biomedical sciences, health, and medicine. The vastness of protein sequence space and all the possible proteins it represents can pose a considerable barrier for enzyme engineering campaigns through directed evolution and rational design. The nonlinear effects of coevolution between amino acids in protein sequences complicate this further. Data-driven models increasingly provide scientists with the computational tools to navigate through the largely undiscovered forest of protein variants and catch a glimpse of the rules and effects underlying the topology of sequence space. In this review, we outline a complete theoretical journey through the processes of protein engineering methods such as directed evolution and rational design and reflect on these strategies and data-driven hybrid strategies in the context of sequence space. We discuss crucial phenomena of residue coevolution, such as epistasis, and review the history of models created over the past decade, aiming to infer rules of protein evolution from data and use this knowledge to improve the prediction of the structure− function relationship of proteins. Data-driven models based on deep learning algorithms are among the most promising methods that can account for the nonlinear phenomena of sequence space to some degree. We also critically discuss the available models to predict evolutionary coupling and epistatic effects (classical and deep learning) in terms of their capabilities and limitations. Finally, we present our perspective on possible future directions for developing data-driven approaches and provide key orientation points and necessities for the future of the fast-evolving field of enzyme engineering.

1. Plants produce thousands of compounds, collectively called the metabolome, which mediate interactions with other organisms. The metabolome of an individual plant may change according to the number and nature of these interactions. We tested the hypothesis that tree diversity level affects the metabolome of four subtropical tree species in a biodiversity–ecosystem functioning experiment, BEF-China. We postulated that the chemical diversity of leaves, roots and root exudates increases with tree diversity. We expected that the strength of this diversity effect differs among leaf, root and root exudates samples. Considering their role in plant competition, we expected to find the strongest effects in root exudates. 2. Roots, root exudates and leaves of four tree species (Cinnamomum camphora, Cyclobalanopsis glauca, Daphniphyllum oldhamii and Schima superba) were sampled from selected plots in BEF-China. The exudate metabolomes were normalized over their non-purgeable organic carbon level. Multivariate analyses were applied to identify the effect of both neighbouring (local) trees and plot diversity on tree metabolomes. The species- and sample-specific metabolites were assigned to major compound classes using the ClassyFire tool, whereas potential metabolites related to diversity effects were annotated manually. 3. Individual tree species showed distinct leaf, root and root exudate metabolomes. The main compound class in leaves was the flavonoids, whereas carboxylic acids, prenol lipids and specific alkaloids were most prominent in root exudates and roots. Overall, plot diversity had a stronger effect on metabolome profiles than the local diversity. Leaf metabolomes responded more often to tree diversity level than exudates, whereas root metabolomes varied the least. We found no uniform or general pattern of alterations in metabolite richness or diversity in response to variation in tree diversity. The response differed among species and tissues. 4. Synthesis. Classification of metabolites supported initial ecological interpretation of differences among species and organs. Particularly, the metabolomes of leaves and root exudates respond to differences in tree diversity. These responses were neither linear nor uniform and individual metabolites showed different dynamics. More controlled interaction experiments are needed to dissect the causes and consequences of the observed shifts in plant metabolomes.

Terpene synthase-mediated biotransformation of eleven synthetic sulfur- or oxygen-containing non-natural prenyl diphosphates resulted in the formation of five novel terpenoids and analogues. Uniquely, they trap intermediate steps and form heterocycles or compounds with alkyne side chains. Computational modelling differentiates convertible from inconvertible substrates and thereby provides an understanding of the detailed molecular mechanism of terpene cyclases. Two terpene cyclases were used as biocatalytic tool, namely, limonene synthase from Cannabis sativa (CLS) and 5-epi-aristolochene synthase (TEAS) from Nicotiana tabacum. They showed significant substrate flexibility towards non-natural prenyl diphosphates to form novel terpenoids, including core oxa- and thia-heterocycles and alkyne-modified terpenoids. We elucidated the structures of five novel monoterpene-analogues and a known sesquiterpene-analogue. These results reflected the terpene synthases′ ability and promiscuity to broaden the pool of terpenoids with structurally complex analogues. Docking studies highlight an on-off conversion of the unnatural substrates.

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1. A major aim of ecology is to upscale attributes of individuals to understand processes at population, community and ecosystem scales. Such attributes are typically described using functional traits, that is, standardised characteristics that impact fitness via effects on survival, growth and/or reproduction. However, commonly used functional traits (e.g. wood density, SLA) are becoming increas-ingly criticised for not being truly mechanistic and for being questionable pre-dictors of ecological processes.2. This Special Feature reviews and studies how the metabolome (i.e. the thousands of unique metabolites that underpin physiology) can enhance trait-based ecology and our understanding of plant and ecosystem functioning.3. In this Editorial, we explore how the metabolome relates to plant functional traits, with reference to life-history trade-offs governing fitness between generations and plasticity shaping fitness within generations. We also identify solutions to challenges of acquiring, interpreting and contextualising metabolome data, and propose a roadmap for integrating the metabolome into ecology. 4. We next summarise the seven studies composing the Special Feature, which use the metabolome to examine mechanisms behind plant community assembly, plant-organismal interactions and effects of plants and soil micro-organisms on ecosystem processes. 5. Synthesis. We demonstrate the potential of the metabolome to improve mechanistic and predictive power in ecology by providing a high-resolution coupling between physiology and fitness. However, applying metabolomics to ecological questions is currently limited by a lack of conceptual, technical and data frameworks, which needs to be overcome to realise the full potential of the metabolome for ecology.