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.

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.

Abstract Ca2+ signaling is central to plant development and acclimation. While Ca2+-responsive proteins have been investigated intensely in plants, only a few Ca2+-permeable channels have been identified, and our understanding of how intracellular Ca2+ fluxes is facilitated remains limited. Arabidopsis thaliana homologs of the mammalian channel-forming mitochondrial calcium uniporter (MCU) protein showed Ca2+ transport activity in vitro. Yet, the evolutionary complexity of MCU proteins, as well as reports about alternative systems and unperturbed mitochondrial Ca2+ uptake in knockout lines of MCU genes, leave critical questions about the in vivo functions of the MCU protein family in plants unanswered. Here, we demonstrate that MCU proteins mediate mitochondrial Ca2+ transport in planta and that this mechanism is the major route for fast Ca2+ uptake. Guided by the subcellular localization, expression, and conservation of MCU proteins, we generated an mcu triple knockout line. Using Ca2+ imaging in living root tips and the stimulation of Ca2+ transients of different amplitudes, we demonstrated that mitochondrial Ca2+ uptake became limiting in the triple mutant. The drastic cell physiological phenotype of impaired subcellular Ca2+ transport coincided with deregulated jasmonic acid-related signaling and thigmomorphogenesis. Our findings establish MCUs as a major mitochondrial Ca2+ entry route in planta and link mitochondrial Ca2+ transport with phytohormone signaling.

Research data is an essential part of research and almost every publication in chemistry. The data itself can be valuable for reuse if sustainably deposited, annotated and archived. Thus, it is important to publish data following the FAIR principles, to make it findable, accessible, interoperable and reusable not only for humans but also in machine-readable form. This also improves transparency and reproducibility of research findings and fosters analytical work with scientific data to generate new insights, being only accessible with manifold and diverse datasets. Research data requires complete and informative metadata and use of open data formats to obtain interoperable data. Generic data formats like AnIML and JCAMP-DX have been used for many applications. Special formats for some analytical methods are already accepted, like mzML for mass spectrometry or nmrML and NMReDATA for NMR spectroscopy data. Other methods still lack common standards for data. Only a joint effort of chemists, instrument and software vendors, publishers and infrastructure maintainers can make sure that the analytical data will be of value in the future. In this review, we describe existing data formats in analytical chemistry and introduce guidelines for the development and use of standardized and open data formats.