Flavor is the main factor driving consumers acceptance of food products. However, tracking the biochemistry of flavor is a formidable challenge due to the complexity of food composition. Current methodologies for linking individual molecules to flavor in foods and beverages are expensive and time-consuming. Predictive models based on machine learning (ML) are emerging as an alternative to speed up this process. Nonetheless, the optimal approach to predict flavor features of molecules remains elusive. In this work we present FlavorMiner, an ML-based multilabel flavor predictor. FlavorMiner seamlessly integrates different combinations of algorithms and mathematical representations, augmented with class balance strategies to address the inherent class of the input dataset. Notably, Random Forest and K-Nearest Neighbors combined with Extended Connectivity Fingerprint and RDKit molecular descriptors consistently outperform other combinations in most cases. Resampling strategies surpass weight balance methods in mitigating bias associated with class imbalance. FlavorMiner exhibits remarkable accuracy, with an average ROC AUC score of 0.88. This algorithm was used to analyze cocoa metabolomics data, unveiling its profound potential to help extract valuable insights from intricate food metabolomics data. FlavorMiner can be used for flavor mining in any food product, drawing from a diverse training dataset that spans over 934 distinct food products.Scientific Contribution FlavorMiner is an advanced machine learning (ML)-based tool designed to predict molecular flavor features with high accuracy and efficiency, addressing the complexity of food metabolomics. By leveraging robust algorithmic combinations paired with mathematical representations FlavorMiner achieves high predictive performance. Applied to cocoa metabolomics, FlavorMiner demonstrated its capacity to extract meaningful insights, showcasing its versatility for flavor analysis across diverse food products. This study underscores the transformative potential of ML in accelerating flavor biochemistry research, offering a scalable solution for the food and beverage industry.

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 (https://github.com/zmahnoor14/MAW). The performance of MAW is evaluated on two case studies. 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.

The exporter of the auxin precursor indole-3-butyric acid (IBA), ABCG36/PDR8/PEN3, from the model plant Arabidopsis has recently been proposed to also function in the transport of the phytoalexin camalexin. Based on these bonafide substrates, it has been suggested that ABCG36 functions at the interface between growth and defense. Here, we provide evidence that ABCG36 catalyzes the direct, ATP-dependent export of camalexin across the plasma membrane. We identify the leucine-rich repeat receptor kinase, QIAN SHOU KINASE1 (QSK1), as a functional kinase that physically interacts with and phosphorylates ABCG36. Phosphorylation of ABCG36 by QSK1 unilaterally represses IBA export, allowing camalexin export by ABCG36 conferring pathogen resistance. As a consequence, phospho-dead mutants of ABCG36, as well as qsk1 and abcg36 alleles, are hypersensitive to infection with the root pathogen Fusarium oxysporum, caused by elevated fungal progression. Our findings indicate a direct regulatory circuit between a receptor kinase and an ABC transporter that functions to control transporter substrate preference during plant growth and defense balance decisions.

Access to inorganic phosphate (Pi), a principal intermediate of energy and nucleotide metabolism, profoundly affects cellular activities and plant performance. In most soils, antagonistic Pi-metal interactions restrict Pi bioavailability, which guides local root development to maximize Pi interception. Growing root tips scout the essential but immobile mineral nutrient; however, the mechanisms monitoring external Pi sta-tus are unknown. Here, we show that Arabidopsis LOW PHOSPHATE ROOT 1 (LPR1), one key determinant of Fe-dependent Pi sensing in root meristems, encodes a novel ferroxidase of high substrate specificity and affinity (apparent KM ∼2 μmM Fe2+). LPR1 typifies an ancient, Fe-oxidizing multicopper protein family that evolved early upon bacterial land colonization. The ancestor of streptophyte algae and embryophytes (land plants) acquired LPR1-type ferroxidase from soil bacteria via horizontal gene transfer, a hypothesis supported by phylogenomics, homology modeling, and biochemistry. Our molecular and kinetic data on LPR1 regulation indicate that Pi-dependent Fe substrate availability determines LPR1 activity and function. Guided by the metabolic lifestyle of extant sister bacterial genera, we propose that Arabidopsis LPR1 monitors subtle concentration differentials of external Fe availability as a Pi-dependent cue to adjust root meristem maintenance via Fe redox signaling and cell wall modification. We further hypothesize that the acquisition of bacterial LPR1-type ferroxidase by embryophyte progenitors facilitated the evolution of local Pi sensing and acquisition during plant terrestrialization.

Wound healing is a fundamental property of plants and animals that requires recognition of cellular damage to initiate regeneration. In plants, wounding activates a defense response via the production of jasmonic acid and a regeneration response via the hormone auxin and several ethylene response factor (ERF) and NAC domain-containing protein (ANAC) transcription factors. To better understand how plants recognize damage and initiate healing, we searched for factors upregulated during the horticulturally relevant process of plant grafting and found four related DNA binding with one finger (DOF) transcription factors, HIGH CAMBIAL ACTIVITY2 (HCA2), TARGET OF MONOPTEROS6 (TMO6), DOF2.1, and DOF6, whose expression rapidly activated at the Arabidopsis graft junction. Grafting or wounding a quadruple hca2, tmo6, dof2.1, dof6 mutant inhibited vascular and cell-wall-related gene expression. Furthermore, the quadruple dof mutant reduced callus formation, tissue attachment, vascular regeneration, and pectin methylesterification in response to wounding. Wcalluscallus found that activation of DOF gene expression after wounding required auxin, but hormone treatment alone was insufficient for their induction. However, modifying cell walls by enzymatic digestion of cellulose or pectin greatly enhanced TMO6 and HCA2 expression, whereas genetic modifications to the pectin or cellulose matrix using the PECTIN METHYLESTERASE INHIBITOR5 overexpression line or korrigan1 mutant altered TMO6 and HCA2 expression. Changes to the cellulose or pectin matrix were also sufficient to activate the wound-associated ERF115 and ANAC096 transcription factors, suggesting that cell-wall damage represents a common mechanism for wound perception and the promotion of tissue regeneration.Graphical abstract

In plants, the cortical endoplasmic reticulum (ER) network is connected to the plasma membrane (PM) through the ER-PM contact sites (EPCSs), whose structures are maintained by EPCS resident proteins and the cytoskeleton.1-7 Strong co-alignment between EPCSs and the cytoskeleton is observed in plants,1,8 but little is known of how the cytoskeleton is maintained and regulated at the EPCS. Here, we have used a yeast-two-hybrid screen and subsequent in vivo interaction studies in plants by fluorescence resonance energy transfer (FRET)-fluorescence lifetime imaging microscopy (FLIM) analysis to identify two microtubule binding proteins, KLCR1 (kinesin-light-chain-related protein 1) and IQD2 (IQ67-domain 2), that interact with the actin binding protein NET3C and form a component of plant EPCS that mediates the link between the actin and microtubule networks. The NET3C-KLCR1-IQD2 module, acting as an actin-microtubule bridging complex, has a direct influence on ER morphology and EPCS structure. Their loss-of-function mutants, net3a/NET3C RNAi, klcr1, or iqd2, exhibit defects in pavement cell morphology, which we suggest is linked to the disorganization of both actin filaments and microtubules. In conclusion, our results reveal a novel cytoskeletal-associated complex, which is essential for the maintenance and organization of cytoskeletal structure and ER morphology at the EPCS and for normal plant cell morphogenesis.

Premitotic control of cell division orientation is critical for plant development, as cell walls prevent extensive cell remodeling or migration. While many divisions are proliferative and add cells to existing tissues, some divisions are formative and generate new tissue layers or growth axes. Such formative divisions are often asymmetric in nature, producing daughters with different fates. We have previously shown that, in the Arabidopsis thaliana embryo, developmental asymmetry is correlated with geometric asymmetry, creating daughter cells of unequal volume. Such divisions are generated by division planes that deviate from a default “minimal surface area” rule. Inhibition of auxin response leads to reversal to this default, yet the mechanisms underlying division plane choice in the embryo have been unclear. Here, we show that auxin-dependent division plane control involves alterations in cell geometry, but not in cell polarity axis or nuclear position. Through transcriptome profiling, we find that auxin regulates genes controlling cell wall and cytoskeleton properties. We confirm the involvement of microtubule (MT)-binding proteins in embryo division control. Organization of both MT and actin cytoskeleton depends on auxin response, and genetically controlled MT or actin depolymerization in embryos leads to disruption of asymmetric divisions, including reversion to the default. Our work shows how auxin-dependent control of MT and actin cytoskeleton properties interacts with cell geometry to generate asymmetric divisions during the earliest steps in plant development.Graphical abstract

Compound (or chemical) databases are an invaluable resource for many scientific disciplines. Exposomics researchers need to find and identify relevant chemicals that cover the entirety of potential (chemical and other) exposures over entire lifetimes. This daunting task, with over 100 million chemicals in the largest chemical databases, coupled with broadly acknowledged knowledge gaps in these resources, leaves researchers faced with too much—yet not enough—information at the same time to perform comprehensive exposomics research. Furthermore, the improvements in analytical technologies and computational mass spectrometry workflows coupled with the rapid growth in databases and increasing demand for high throughput “big data” services from the research community present significant challenges for both data hosts and workflow developers. This article explores how to reduce candidate search spaces in non-target small molecule identification workflows, while increasing content usability in the context of environmental and exposomics analyses, so as to profit from the increasing size and information content of large compound databases, while increasing efficiency at the same time. In this article, these methods are explored using PubChem, the NORMAN Network Suspect List Exchange and the in silico fragmentation approach MetFrag. A subset of the PubChem database relevant for exposomics, PubChemLite, is presented as a database resource that can be (and has been) integrated into current workflows for high resolution mass spectrometry. Benchmarking datasets from earlier publications are used to show how experimental knowledge and existing datasets can be used to detect and fill gaps in compound databases to progressively improve large resources such as PubChem, and topic-specific subsets such as PubChemLite. PubChemLite is a living collection, updating as annotation content in PubChem is updated, and exported to allow direct integration into existing workflows such as MetFrag. The source code and files necessary to recreate or adjust this are jointly hosted between the research parties (see data availability statement). This effort shows that enhancing the FAIRness (Findability, Accessibility, Interoperability and Reusability) of open resources can mutually enhance several resources for whole community benefit. The authors explicitly welcome additional community input on ideas for future developments.

AbstractWe report the major conclusions of the online open-access workshop “Computational Applications in Secondary Metabolite Discovery (CAiSMD)” that took place from 08 to 10 March 2021. Invited speakers from academia and industry and about 200 registered participants from five continents (Africa, Asia, Europe, South America, and North America) took part in the workshop. The workshop highlighted the potential applications of computational methodologies in the search for secondary metabolites (SMs) or natural products (NPs) as potential drugs and drug leads. During 3 days, the participants of this online workshop received an overview of modern computer-based approaches for exploring NP discovery in the “omics” age. The invited experts gave keynote lectures, trained participants in hands-on sessions, and held round table discussions. This was followed by oral presentations with much interaction between the speakers and the audience. Selected applicants (early-career scientists) were offered the opportunity to give oral presentations (15 min) and present posters in the form of flash presentations (5 min) upon submission of an abstract. The final program available on the workshop website (https://caismd.indiayouth.info/) comprised of 4 keynote lectures (KLs), 12 oral presentations (OPs), 2 round table discussions (RTDs), and 5 hands-on sessions (HSs). This meeting report also references internet resources for computational biology in the area of secondary metabolites that are of use outside of the workshop areas and will constitute a long-term valuable source for the community. The workshop concluded with an online survey form to be completed by speakers and participants for the goal of improving any subsequent editions.

Flower organ abscission in Arabidopsis is regulated by a peptide hormone that is released from its precursor by a network of redundant subtilases. An exciting new study describes how drought-induced flower abscission in tomato is regulated similarly, but distinctly via a single, different subtilase that releases a very different peptide hormone.