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.

Introduction Frankincense (Boswellia sp.) gum resins have been employed as an incense in cultural and religious ceremonies for many years. Frankincense resin has over the years been employed to treat depression, inflammation, and cancer in traditional medicines. Areas coveredThis inclusive review focuses on the significance of frankincense diterpenoids, and in particular, incensole derivatives for establishment future treatments of depression, neurological disorders, and cancer. The authors survey the available literature and furnish an overview of future perspectives of these intriguing molecules. Expert opinion Numerous diterpenoids including cembrane, prenylaromadendrane, and the verticillane-type have been isolated from various Boswellia resins. Cembrane-type diterpenoids occupy a crucial position in pharmaceutical chemistry and related industries because of their intriguing biological and encouraging pharmacological potentials. Several cembranes have been reported to possess anti-Alzheimer, anti-inflammatory, hepatoprotective, and antimalarial effects along with a good possibility to treat anxiety and depression. Although some slight drawbacks of these compounds have been noted, including the selectivity of these diterpenoids, there is a great need to address these in future research endeavors. Moreover, it is vitally important for medicinal chemists to prepare libraries of incensole-heterocyclic analogs as well as hybrid compounds between incensole or its acetate and anti-depressant or anti-inflammatory drugs.

Novel unimolecular bivalent glycoconjugates were assembled combining several functionalized capsular polysaccharides of Streptococcus pneumoniae and Neisseria meningitidis to a carrier protein by using an effective strategy based on the Ugi 4-component reaction. The development of multivalent glycoconjugates opens new opportunities in the field of vaccine design, but their high structural complexity involves new analytical challenges. Nuclear Magnetic Resonance has found wide applications in the characterization and impurity profiling of carbohydrate-based vaccines. Eight bivalent conjugates were studied by quantitative NMR analyzing the structural identity, the content of each capsular polysaccharide, the ratios between polysaccharides, the polysaccharide to protein ratios and undesirable contaminants. The qNMR technique involves experiments with several modified parameters for obtaining spectra with quantifiable signals. In addition, the achieved NMR results were combined with the results of colorimetric assay and Size Exclusion HPLC for assessing the protein content and free protein percentage, respectively. The application of quantitative NMR showed to be efficient to clear up the new structural complexities while allowing the quantitative assessment of the components.

4-Hydroxyphenylacetate 3-hydroxylase (4HPA3H), a flavin-dependent monooxygenase from E. coli that catalyzes the hydroxylation of monophenols to catechols, was modified by rational re-design to convert also more bulky substrates, especially phenolic natural products like phenylpropanoids, flavones or coumarins. Selected amino acid positions in the binding pocket of 4HPA3H were exchanged by residues from the homologous protein from Pseudomonas aeruginosa, yielding variants with improved conversion of spacious substrates such as the flavonoid naringenin or the alkaloid mimetic 2-hydroxycarbazole. Reactions were followed by an adapted Fe(III)-catechol chromogenic assay selective for the products. Especially substitution of the residue Y301 facilitated modulation of substrate specificity: introduction of non-aromatic but hydrophobic (iso)leucine resulted in the preference of the substrate ferulic acid (having a guaiacyl (guajacyl) moiety, part of the vanilloid motif) over unsubstituted monophenols. The in vivo (whole-cell biocatalysts) and in vitro (three-enzyme cascade) transformations of substrates by 4HPA3H and its optimized variants was strictly regiospecific and proceeded without generation of by-products.

Research data management (RDM) is needed to assist experimental advances and data collection in the chemical sciences. Many funders require RDM because experiments are often paid for by taxpayers and the resulting data should be deposited sustainably for posterity. However, paper notebooks are still common in laboratories and research data is often stored in proprietary and/or dead-end file formats without experimental context. Data must mature beyond a mere supplement to a research paper. Electronic lab note-books (ELN) and laboratory information managementsystems (LIMS) allow researchers to manage data better and they simplify research and publication. Thus, an agreement is needed on minimum information standards for data handling to support structured approaches to data reporting. As digitalization becomes part of curricular teaching, future generations of digital native chemists will embrace RDM and ELN as an organic part of their research.

The impact of cocoa lipid content on chocolate quality has been extensively described. Nevertheless, few studies have elucidated the cocoa lipid composition and their bioactive properties, focusing only on specific lipids. In the present study the lipidome of fine-flavor cocoa fermentation was analyzed using LC-MS-QTOF and a Machine Learning model to assess potential bioactivity was developed. Our results revealed that the cocoa lipidome, comprised mainly of fatty acyls and glycerophospholipids, remains stable during fine-flavor cocoa fermentations. Also, several Machine Learning algorithms were trained to explore potential biological activity among the identified lipids. We found that K-Nearest Neighbors had the best performance. This model was used to classify the identified lipids as bioactive or non-bioactive, nominating 28 molecules as potential bioactive lipids. None of these compounds have been previously reported as bioactive. Our work is the first untargeted lipidomic study and systematic effort to investigate potential bioactivity in fine-flavor cocoa lipids.