Enzyme annotation in UniProtKB using Rhea

EnzChemRED, a rich enzyme chemistry relation extraction dataset

Expert curation is essential to capture knowledge of enzyme functions from the scientific literature in FAIR open knowledgebases but cannot keep pace with the rate of new discoveries and new publications. In this work we present EnzChemRED, for Enzyme Chemistry Relation Extraction Dataset, a new training and benchmarking dataset to support the development of Natural Language Processing (NLP) methods such as (large) language models that can assist enzyme curation. EnzChemRED consists of 1,210 expert curated PubMed abstracts where enzymes and the chemical reactions they catalyze are annotated using identifiers from the protein knowledgebase UniProtKB and the chemical ontology ChEBI. We show that fine-tuning language models with EnzChemRED significantly boosts their ability to identify proteins and chemicals in text (86.30% F1 score) and to extract the chemical conversions (86.66% F1 score) and the enzymes that catalyze those conversions (83.79% F1 score). We apply our methods to abstracts at PubMed scale to create a draft map of enzyme functions in literature to guide curation efforts in UniProtKB and the reaction knowledgebase Rhea.

Genomics of ecological adaptation in Canary Island Descurainia (Brassicaceae) and comparisons with other Brassicaceae

Oceanic archipelagos provide striking examples of lineages that have radiated over pronounced ecological gradients. Accompanying this diversification, lineages have evolved adaptations allowing survival in extreme environments. Here, we investigate the genomic basis of ecological adaptation in Canary Island Descurainia (Brassicaceae), an island relative of Arabidopsis. The seven endemic species have diversified in situ along an elevational and ecological gradient, from low-elevation scrub to high-elevation sub-alpine desert. We first generated a reference genome for Descurainia millefolia, phylogenetic analysis of which placed it as sister to D. sophioides. Ninety-six gene families were found to be specific to D. millefolia and a further 1087 and 1469 gene families have expanded or contracted in size, respectively, along the D. millefolia branch. We then employed genome re-sequencing to sample 14 genomes across the seven species of Canary Island Descurainia and an outgroup. Phylogenomic analyses were consistent with previous reconstructions of Canary Island Descurainia in resolving low- and high-elevation clades. Using the branch-site dN/dS method, we detected positive selection for 275 genes on the branch separating the low- and high-elevation species and these positively selected genes (PSGs) were significantly enriched for functions related to reproduction and stress tolerance. Comparing PSGs to those in analyses of adaptation to elevation and/or latitude in other Brassicaceae, we found little evidence of widespread convergence and gene reuse, except for two examples, one of which was a significant overlap between Descurainia and Draba nivalis, a species restricted to high latitudes. The study of Canary Island Descurainia suggests that the transition to high-elevation environments such as that found in the high mountains of the Canary Islands involves selection on genes related to reproduction and stress tolerance but that repeated evolution across different lineages that have evolved into similar habitats is limited, indicating substantially different molecular trajectories to adaptation.

Chromosome-scale genome assemblies of Himalopsyche anomala and Eubasilissa splendida (Insecta: Trichoptera)

Trichoptera is one of the most evolutionarily successful aquatic insect lineages and is highly valued value in adaptive evolution research. This study presents the chromosome-level genome assemblies of Himalopsyche anomala and Eubasilissa splendida achieved using PacBio, Illumina, and Hi-C sequencing. For H. anomala and E. splendida, assembly sizes were 663.43 and 859.28 Mb, with scaffold N50 lengths of 28.44 and 31.17 Mb, respectively. In H. anomala and E. splendida, we anchored 24 and 29 pseudochromosomes, and identified 11,469 and 10,554 protein-coding genes, respectively. The high-quality genomes of H. anomala and E. splendida provide critical genomic resources for understanding the evolution and ecology of Trichoptera and performing comparative genomics analyses.

Two chromosome-level genomes of Smittia aterrima and Smittia pratorum (Diptera, Chironomidae)

Chironomids are one of the most abundant aquatic insects and are widely distributed in various biological communities. However, the lack of high-quality genomes has hindered our ability to study the evolution and ecology of this group. Here, we used Nanopore long reads and Hi-C data to produce two chromosome-level genomes from mixed genomic data. The genomes of Smittia aterrima (SateA) and Smittia pratorum (SateB) were assembled into three chromosomes, with sizes of 78.45 Mb and 71.56 Mb, scaffold N50 lengths of 25.73 and 23.53 Mb, and BUSCO completeness of 98.5% and 97.8% (n = 1,367), 5.68 Mb (7.24%) and 1.94 Mb (2.72%) of repetitive elements, and predicted 12,330 (97.70% BUSCO completeness) and 11,250 (97.40%) protein-coding genes, respectively. These high-quality genomes will serve as valuable resources for comprehending the evolution and environmental adaptation of chironomids.

Challenges and perspectives for naming lipids in the context of lipidomics

INTRODUCTION

Lipids are key compounds in the study of metabolism and are increasingly studied in biology projects. It is a very broad family that encompasses many compounds, and the name of the same compound may vary depending on the community where they are studied.

OBJECTIVES

In addition, their structures are varied and complex, which complicates their analysis. Indeed, the structural resolution does not always allow a complete level of annotation so the actual compound analysed will vary from study to study and should be clearly stated. For all these reasons the identification and naming of lipids is complicated and very variable from one study to another, it needs to be harmonized.

METHODS & RESULTS

In this position paper we will present and discuss the different way to name lipids (with chemoinformatic and semantic identifiers) and their importance to share lipidomic results.

CONCLUSION

Homogenising this identification and adopting the same rules is essential to be able to share data within the community and to map data on functional networks.

Integrating network pharmacology and experimental verification to explore the mechanism of Tripterygium wilfordii in ankylosing spondylitis

OBJECTIVE

This study aimed to validate the mechanism of triptolide in treating ankylosing spondylitis (AS) through network pharmacology, molecular docking, and in vitro experiments.

METHODS

We gathered AS-related genes using databases including DrugBank, OMIM, GeneCards, TTD and DisGeNET. TCMSP database was used to collect Tripterygium wilfordii (TWHF)-related data. Additionally, the potential targets of TWHF in treating AS were predicted by consulting databases such as Venny, String, Cytoscape, and Cytohubba. Subsequently, a protein-protein interaction network was created and the gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis were performed by metascape database. After selecting the most active ingredient of TWHF, molecular docking was performed to confirm the predicted results. Furthermore, we explore the potential mechanism of the most active ingredient of TWHF in the treatment of AS in vitro.

RESULT

By integrating the results of network pharmacological analysis, 62 genes were found to be strongly associated with AS, such as STAT3, TNF, MMP9, VEGFA, CXCL8, PTGS2, etc. Triptolide (TP) is one of the most active ingredients in TWHF. The enrichment analysis indicated that 292 biological processes and 132 signaling pathways were involved, with the T helper 17 cells cell differentiation pathway as the key pathway. TP was selected for molecular docking and in vitro experiments. The molecular docking results indicated that TP had excellent affinity with 6 key targets. Further, flow cytometry, cell counting assay, and ELISA demonstrated that the serum level of IL-17 was higher in AS patients compared to XXX, and 25 μg/mL TP was the optimal intervention concentration. RT-qPCR and Western blotting further verified that TP could inhibit the activation of RORγt and the JAK2/STAT3 signaling pathway.

CONCLUSION

In conclusion, based on network pharmacology, molecular docking, and experimental verification in vitro, we proposed that the TP can inhibit the activation of RORγt and the JAK2/STAT3 signaling pathway and inhibit the differentiation of T helper 17 cells cells. The article provide a theoretical basis for further development and utilization of TWHF in AS management.

Characteristic Binding Landscape of Estrogen Receptor-α36 Protein Enhances Promising Cancer Drug Design

Breast cancer (BC) remains the most common cancer among women worldwide, and estrogen receptor-α expression is a critical diagnostic factor for BC. Estrogen receptor (ER-α36) is a dominant-negative effector of ER-α66-mediated estrogen-responsive gene pathways. ER-α36 is a novel target that mediates the non-genomic estrogen signaling pathway. However, the crystallized structure of ER-α36 remains unavailable for molecular studies. ER-positive and triple-negative BC tumors aggressively resist the FDA-approved drugs; therefore, highly potent structure-based inhibitors with preeminent benefits over toxicity will preferably replace the current BC treatment. Broussoflanol B (BFB), a B. papyrifera bark compound, exhibits potent growth inhibitory activity in ER-negative BC cells by inducing cell cycle arrest. For the first time, we unravel the comparative dynamic events of the enzymes' structures and the binding mechanisms of BFB when bound to the ER-α36 and ER-α66 ligand-binding domain using an all-atom molecular dynamics simulations approach and MM/PBSA-binding-free energy calculations. The dynamic findings have revealed that ER-α36 and ER-α66 LBD undergo timescale "coiling", opening and closing conformations favoring the high-affinity BFB-bound ER-α36 (ΔG = -52.57 kcal/mol) compared to the BFB-bound ER-α66 (ΔG = -42.41 kcal/mol). Moreover, the unbound (1.260 Å) and bound ER-α36 (1.182 Å) exhibit the highest flexibilities and atomistic motions relative to the ER-α66 systems. The RMSF (Å) of the unbound ER-α36 and ER-α66 exhibit lesser stabilities than the BFB-bound systems, resulting in higher structural flexibilities and atomistic motions than the bound variants. These findings present a model that describes the mechanisms by which the BFB compound induces downregulation-accompanied cell cycle arrest at the Gap0 and Gap1 phases.

Identification of plant vacuole proteins by using graph neural network and contact maps

Plant vacuoles are essential organelles in the growth and development of plants, and accurate identification of their proteins is crucial for understanding their biological properties. In this study, we developed a novel model called GraphIdn for the identification of plant vacuole proteins. The model uses SeqVec, a deep representation learning model, to initialize the amino acid sequence. We utilized the AlphaFold2 algorithm to obtain the structural information of corresponding plant vacuole proteins, and then fed the calculated contact maps into a graph convolutional neural network. GraphIdn achieved accuracy values of 88.51% and 89.93% in independent testing and fivefold cross-validation, respectively, outperforming previous state-of-the-art predictors. As far as we know, this is the first model to use predicted protein topology structure graphs to identify plant vacuole proteins. Furthermore, we assessed the effectiveness and generalization capability of our GraphIdn model by applying it to identify and locate peroxisomal proteins, which yielded promising outcomes. The source code and datasets can be accessed at https://github.com/SJNNNN/GraphIdn .

Quantitative proteomics analysis of permethrin and temephos-resistant Ae. aegypti revealed diverse differentially expressed proteins associated with insecticide resistance from Penang Island, Malaysia

Synthetic insecticides are the primary vector control method used globally. However, the widespread use of insecticides is a major cause of insecticide-resistance in mosquitoes. Hence, this study aimed at elucidating permethrin and temephos-resistant protein expression profiles in Ae. aegypti using quantitative proteomics. In this study, we evaluated the susceptibility of Ae. aegypti from Penang Island dengue hotspot and non-hotspot against 0.75% permethrin and 31.25 mg/l temephos using WHO bioassay method. Protein extracts from the mosquitoes were then analysed using LC-ESI-MS/MS for protein identification and quantification via label-free quantitative proteomics (LFQ). Next, Perseus 1.6.14.0 statistical software was used to perform differential protein expression analysis using ANOVA and Student's t-test. The t-test selected proteins with≥2.0-fold change (FC) and ≥2 unique peptides for gene expression validation via qPCR. Finally, STRING software was used for functional ontology enrichment and protein-protein interactions (PPI). The WHO bioassay showed resistance with 28% and 53% mortalities in adult mosquitoes exposed to permethrin from the hotspot and non-hotspot areas. Meanwhile, the susceptibility of Ae. aegypti larvae revealed high resistance to temephos in hotspot and non-hotspot regions with 80% and 91% mortalities. The LFQ analyses revealed 501 and 557 (q-value <0.05) differentially expressed proteins in adults and larvae Ae. aegypti. The t-test showed 114 upregulated and 74 downregulated proteins in adult resistant versus laboratory strains exposed to permethrin. Meanwhile, 13 upregulated and 105 downregulated proteins were observed in larvae resistant versus laboratory strains exposed to temephos. The t-test revealed the upregulation of sodium/potassium-dependent ATPase β2 in adult permethrin resistant strain, H15 domain-containing protein, 60S ribosomal protein, and PB protein in larvae temephos resistant strain. The downregulation of troponin I, enolase phosphatase E1, glucosidase 2β was observed in adult permethrin resistant strain and tubulin β chain in larvae temephos resistant strain. Furthermore, the gene expression by qPCR revealed similar gene expression patterns in the above eight differentially expressed proteins. The PPI of differentially expressed proteins showed a p-value at <1.0 x 10-16 in permethrin and temephos resistant Ae. aegypti. Significantly enriched pathways in differentially expressed proteins revealed metabolic pathways, oxidative phosphorylation, carbon metabolism, biosynthesis of amino acids, glycolysis, and citrate cycle. In conclusion, this study has shown differentially expressed proteins and highlighted upregulated and downregulated proteins associated with insecticide resistance in Ae. aegypti. The validated differentially expressed proteins merit further investigation as a potential protein marker to monitor and predict insecticide resistance in field Ae. aegypti. The LC-MS/MS data were submitted into the MASSIVE database with identifier no: MSV000089259.

Genome-wide and constrained ordination-based analyses of EC code data support reclassification of the species of Massilia La Scola et al. 2000 into Telluria Bowman et al. 1993, Mokoshia gen. nov. and Zemynaea gen. nov

Based on genome-wide data, Massilia species belonging to the clade including Telluria mixta LMG 11547T should be entirely transferred to the genus Telluria owing to the nomenclatural priority of the type species Telluria mixta. This results in the transfer of 35 Massilia species to the genus Telluria. The presented data also supports the creation of two new genera since peripherally branching Massilia species are distinct from Telluria and other related genera. It is proposed that 13 Massilia species are transferred to Mokoshia gen. nov. with the type species designated Mokoshia eurypsychrophila comb. nov. The species Massilia arenosa is proposed to belong to the genus Zemynaea gen. nov. as the type species Zemynaea arenosa comb. nov. The genome-wide analysis was well supported by canonical ordination analysis of Enzyme Commission (EC) codes annotated from genomes via pannzer2. This new approach was performed to assess the conclusions of the genome-based data and reduce possible ambiguity in the taxonomic decision making. Cross-validation of EC code data compared within canonical plots validated the reclassifications and correctly visualized the expected genus-level taxonomic relationships. The approach is complementary to genome-wide methodology and could be used for testing sequence alignment based data across genetically related genera. In addition to the proposed broader reclassifications, invalidly described species 'Massilia antibiotica', 'Massilia aromaticivorans', 'Massilia cellulosiltytica' and 'Massilia humi' are described as Telluria antibiotica sp. nov., Telluria aromaticivorans sp. nov., Telluria cellulosilytica sp. nov. and Pseudoduganella humi sp. nov., respectively. In addition, Telluria chitinolytica is reclassified as Pseudoduganella chitinolytica comb. nov. The use of combined genome-wide and annotation descriptors compared using canonical ordination clarifies the taxonomy of Telluria and its sibling genera and provides another way to evaluate complex taxonomic data.

Glycoinformatics in the Artificial Intelligence Era

Artificial intelligence (AI) methods have been and are now being increasingly integrated in prediction software implemented in bioinformatics and its glycoscience branch known as glycoinformatics. AI techniques have evolved in the past decades, and their applications in glycoscience are not yet widespread. This limited use is partly explained by the peculiarities of glyco-data that are notoriously hard to produce and analyze. Nonetheless, as time goes, the accumulation of glycomics, glycoproteomics, and glycan-binding data has reached a point where even the most recent deep learning methods can provide predictors with good performance. We discuss the historical development of the application of various AI methods in the broader field of glycoinformatics. A particular focus is placed on shining a light on challenges in glyco-data handling, contextualized by lessons learnt from related disciplines. Ending on the discussion of state-of-the-art deep learning approaches in glycoinformatics, we also envision the future of glycoinformatics, including development that need to occur in order to truly unleash the capabilities of glycoscience in the systems biology era.

KinMod database: a tool for investigating metabolic regulation

The ability of current kinetic models to simulate the phenotypic behaviour of cells is limited since cell metabolism is regulated at different levels including enzyme regulation. The small molecule regulation network (SMRN) enables cells to respond rapidly to environmental fluctuations by controlling the activity of enzymes in metabolic pathways. However, SMRN is not as well studied relative to metabolic networks. The main contributor to the lack of knowledge on this regulatory system is the sparsity of experimental data and the absence of a standard framework for representing available information. In this paper, we introduce the KinMod database that encompasses more than 2 million data points on the metabolism and metabolic regulation network of 9814 organisms KinMod database employs a hierarchical data structure to: (i) signify relationships between kinetic information obtained through in-vitro experiments and proteins, with an emphasis on SMRN, (ii) provide a thorough insight into available kinetic parameters and missing experimental measurements of this regulatory network and (iii) facilitate machine learning approaches for parameter estimation and accurate kinetic model construction by providing a homogeneous list of linked omics data. The hierarchical ontology of the KinMod database allows flexible exploration of data attributes and investigation of metabolic relationships within- and cross-species. Identifying missing experimental values suggests additional experiments required for kinetic parameter estimation. Linking multi-omics data and providing data on SMRN encourages the development of novel machine learning techniques for predicting missing kinetic parameters and promotes accurate kinetic model construction of cells metabolism by providing a comprehensive list of available kinetic measurements. To illustrate the value of KinMod data, we develop six analyses to visualize associations between data classes belonging to separate sections of the metabolism. Through these analyses, we demonstrate that the KinMod database provides a unique framework for biologists and engineers to retrieve, evaluate and compare the functional metabolism of species, including the regulatory network, and discover the extent of available and missing experimental values of the metabolic regulation.

Database URL: https://lmse.utoronto.ca/kinmod/KINMOD.sql.gz

A roadmap for the functional annotation of protein families: a community perspective

Over the last 25 years, biology has entered the genomic era and is becoming a science of 'big data'. Most interpretations of genomic analyses rely on accurate functional annotations of the proteins encoded by more than 500 000 genomes sequenced to date. By different estimates, only half the predicted sequenced proteins carry an accurate functional annotation, and this percentage varies drastically between different organismal lineages. Such a large gap in knowledge hampers all aspects of biological enterprise and, thereby, is standing in the way of genomic biology reaching its full potential. A brainstorming meeting to address this issue funded by the National Science Foundation was held during 3-4 February 2022. Bringing together data scientists, biocurators, computational biologists and experimentalists within the same venue allowed for a comprehensive assessment of the current state of functional annotations of protein families. Further, major issues that were obstructing the field were identified and discussed, which ultimately allowed for the proposal of solutions on how to move forward.

Metabolite-Based Mutualistic Interaction between Two Novel Clostridial Species from Pit Mud Enhances Butyrate and Caproate Production

Pit mud microbial consortia play crucial roles in the formation of Chinese strong-flavor baijiu's key flavor-active compounds, especially butyric and caproic acids. Clostridia, one of the abundant bacterial groups in pit mud, were recognized as important butyric and caproic acid producers. Research on the interactions of the pit mud microbial community mainly depends on correlation analysis at present. Interaction between Clostridium and other microorganisms and its involvement in short/medium-chain fatty acid (S/MCFA) metabolism are still unclear. We previously found coculture of two clostridial strains isolated from pit mud, Clostridium fermenticellae JN500901 (C.901) and Novisyntrophococcus fermenticellae JN500902 (N.902), could enhance S/MCFA accumulation. Here, we investigated their underlying interaction mechanism through the combined analysis of phenotype, genome, and transcriptome. Compared to monocultures, coculture of C.901 and N.902 obviously promoted their growth, including shortening the growth lag phase and increasing biomass, and the accumulation of butyric acid and caproic acid. The slight effects of inoculation ratio and continuous passage on the growth and metabolism of coculture indicated the relative stability of their interaction. Transwell coculture and transcriptome analysis showed the interaction between C.901 and N.902 was accomplished by metabolite exchange, i.e., formic acid produced by C.901 activated the Wood-Ljungdahl pathway of N.902, thereby enhancing its production of acetic acid, which was further converted to butyric acid and caproic acid by C.901 through reverse β-oxidation. This work demonstrates the potential roles of mutually beneficial interspecies interactions in the accumulation of key flavor compounds in pit mud. IMPORTANCE Microbial interactions played crucial roles in influencing the assembly, stability, and function of the microbial community. The metabolites of pit mud microbiota are the key to flavor formation of Chinese strong-flavor baijiu. So far, researches on the interactions of the pit mud microbial community have been mainly based on the correlation analysis of sequencing data, and more work needs to be performed to unveil the complicated interaction patterns. Here, we identified a material exchange-based mutualistic interaction system involving two fatty acid-producing clostridial strains (Clostridium fermenticellae JN500901 and Novisyntrophococcus fermenticellae JN500902) isolated from pit mud and systematically elucidated their interaction mechanism for promoting the production of butyric acid and caproic acid, the key flavor-active compounds of baijiu. Our findings provide a new perspective for understanding the complicated interactions of pit mud microorganisms.

Network Pharmacology and Bioinformatics Methods Reveal the Mechanism of Berberine in the Treatment of Ischaemic Stroke

Aim

To elucidate the mechanism of action of berberine on ischaemic stroke based on network pharmacology, bioinformatics, and experimental verification.

Methods

Berberine-related long noncoding RNAs (lncRNAs) were screened from public databases. Differentially expressed lncRNAs in ischaemic stroke were retrieved from the Gene Expression Omnibus (GEO) database. GSE102541 was comprehensively analysed using GEO2R. The correlation between lncRNAs and ischaemic stroke was evaluated by the mammalian noncoding RNA-disease repository (MNDR) database. The component-target-disease network and protein-protein interaction (PPI) network of berberine in the treatment of ischaemic stroke were constructed by using network pharmacology. We then performed gene ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) enrichment analyses. Finally, according to the molecular docking analysis and the binding probability between the lncRNA and key proteins, the effectiveness of the results was further verified by in vitro experiments.

Results

After matching stroke-related lncRNAs with berberine-related lncRNAs, four genes were selected as potential targets of berberine in the treatment of ischaemic stroke. Subsequently, lncRNA H19 was identified as the potential crucial regulatory lncRNA of berberine. Here, 52 target proteins of berberine in the treatment of ischaemic stroke were identified through database mining. Through topological analysis, 20 key targets were identified which were enriched in inflammation, apoptosis, and immunity. Molecular docking results showed that MAPK8, JUN, and EGFR were central genes. Finally, in vitro experiments demonstrated that lncRNA H19, p-JNK1/JNK1, p-c-Jun/c-Jun, and EGFR expressions were significantly increased in hypoxia-treated SH-SY5Y cells and were restored by berberine treatment.

Conclusion

The potential targets and biological effects of berberine in the treatment of ischaemic stroke were predicted in this study. The lncRNA H19/EGFR/JNK1/c-Jun signalling pathway may be a key mechanism of berberine-induced neuroprotection in ischaemic stroke.

Similarity based enzymatic retrosynthesis

Enzymes synthesize complex natural products effortlessly by catalyzing chemo-, regio-, and enantio-selective transformations. Further, biocatalytic processes are increasingly replacing conventional organic synthesis steps because they use mild solvents, avoid the use of metals, and reduce overall non-biodegradable waste. Here, we present a single-step retrosynthesis search algorithm to facilitate enzymatic synthesis of natural product analogs. First, we develop a tool, RDEnzyme, capable of extracting and applying stereochemically consistent enzymatic reaction templates, i.e., subgraph patterns that describe the changes in connectivity between a product molecule and its corresponding reactant(s). Using RDEnzyme, we demonstrate that molecular similarity is an effective metric to propose retrosynthetic disconnections based on analogy to precedent enzymatic reactions in UniProt/RHEA. Using ∼5500 reactions from RHEA as a knowledge base, the recorded reactants to the product are among the top 10 proposed suggestions in 71% of ∼700 test reactions. Second, we trained a statistical model capable of discriminating between reaction pairs belonging to homologous enzymes and evolutionarily distant enzymes using ∼30 000 reaction pairs from SwissProt as a knowledge base. This model is capable of understanding patterns in enzyme promiscuity to evaluate the likelihood of experimental evolution success. By recursively applying the similarity-based single-step retrosynthesis and evolution prediction workflow, we successfully plan the enzymatic synthesis routes for both active pharmaceutical ingredients (e.g. Islatravir, Molnupiravir) and commodity chemicals (e.g. 1,4-butanediol, branched-chain higher alcohols/biofuels), in a retrospective fashion. Through the development and demonstration of the single-step enzymatic retrosynthesis strategy using natural transformations, our approach provides a first step towards solving the challenging problem of incorporating both enzyme- and organic-chemistry based transformations into a computer aided synthesis planning workflow.

Icariin: A Potential Molecule for Treatment of Knee Osteoarthritis

Background: Knee osteoarthritis (KOA) is a degenerative disease that develops over time. Icariin (ICA) has a positive effect on KOA, although the mechanism is unknown. To investigate drug-disease connections and processes, network pharmacology is commonly used. The molecular mechanisms of ICA for the treatment of KOA were investigated using network pharmacology, molecular docking and literature research approaches in this study.

Methods: We gathered KOA-related genes using the DisGeNET database, the OMIM database, and GEO microarray data. TCMSP database, Pubchem database, TTD database, SwissTargetPrediction database, and Pharmmapper database were used to gather ICA-related data. Following that, a protein-protein interaction (PPI) network was created. Using the Metascape database, we performed GO and KEGG enrichment analyses. After that, we built a targets-pathways network. Furthermore, molecular docking confirms the prediction. Finally, we looked back over the last 5 years of literature on icariin for knee osteoarthritis to see if the findings of this study were accurate.

Results: core targets relevant to KOA treatment include TNF, IGF1, MMP9, PTGS2, ESR1, MMP2 and so on. The main biological process involved regulation of inflammatory response, collagen catabolic process, extracellular matrix disassembly and so on. The most likely pathways involved were the IL-17 signaling pathway, TNF signaling pathway, Estrogen signaling pathway.

Conclusion: ICA may alleviate KOA by inhibiting inflammation, cartilage breakdown and extracellular matrix degradation. Our study reveals the molecular mechanism of ICA for the treatment of KOA, demonstrating its potential value for further research and as a new drug.

Action Mechanism Underlying Improvement Effect of Fuzi Lizhong Decoction on Nonalcoholic Fatty Liver Disease: A Study Based on Network Pharmacology and Molecular Docking

OBJECTIVE

This study aimed to decipher the bioactive compounds and potential mechanism of traditional Chinese medicine (TCM) formula Fuzi Lizhong Decoction (FLD) for nonalcoholic fatty liver disease (NAFLD) treatment via an integrative network pharmacology approach.

METHODS

The candidate compounds of FLD and its relative targets were obtained from the TCMSP and PharmMapper web server, and the intersection genes for NAFLD were discerned using OMIM, GeneCards, and DisGeNET. Then, the PPI and component-target-pathway networks were constructed. Moreover, GO enrichment and KEGG pathway analysis were performed to investigate the potential signaling pathways associated with FLD's effect on NAFLD. Eventually, molecular docking simulation was carried out to validate the binding affinity between potential core components and key targets.

RESULTS

A total of 143 candidate active compounds and 129 relative drug targets were obtained, in which 61 targets were overlapped with NAFLD. The PPI network analysis identified ALB, MAPK1, CASP3, MARK8, and AR as key targets, mainly focusing on cellular response to organic cyclic compound, steroid metabolic process, and response to steroid hormone in the biological processes. The KEGG pathway analysis demonstrated that 16 signaling pathways were closely correlated with FLD's effect on NALFD with cancer pathways, Th17 cell differentiation, and IL-17 signaling pathways as the most significant ones. In addition, the molecular docking analysis revealed that the core active compounds of FLD, such as 3'-methoxyglabridin, chrysanthemaxanthin, and Gancaonin H, had a high binding activity with such key targets as ALB, MAPK1, and CASP3.

CONCLUSIONS

This study suggested that FLD exerted its effect on NAFLD via modulating multitargets with multicompounds through multipathways. It also demonstrated that the network pharmacology-based approach might provide insights for understanding the interrelationship between complex diseases and interventions of the TCM formula.

Characterization and genomic analysis of the first Oceanospirillum phage, vB_OliS_GJ44, representing a novel siphoviral cluster

Background

Marine bacteriophages play key roles in the community structure of microorganisms, biogeochemical cycles, and the mediation of genetic diversity through horizontal gene transfer. Recently, traditional isolation methods, complemented by high-throughput sequencing metagenomics technology, have greatly increased our understanding of the diversity of bacteriophages. Oceanospirillum, within the order Oceanospirillales, are important symbiotic marine bacteria associated with hydrocarbon degradation and algal blooms, especially in polar regions. However, until now there has been no isolate of an Oceanospirillum bacteriophage, and so details of their metagenome has remained unknown.

Results

Here, we reported the first Oceanospirillum phage, vB_OliS_GJ44, which was assembled into a 33,786 bp linear dsDNA genome, which includes abundant tail-related and recombinant proteins. The recombinant module was highly adapted to the host, according to the tetranucleotides correlations. Genomic and morphological analyses identified vB_OliS_GJ44 as a siphovirus, however, due to the distant evolutionary relationship with any other known siphovirus, it is proposed that this virus could be classified as the type phage of a new Oceanospirivirus genus within the Siphoviridae family. vB_OliS_GJ44 showed synteny with six uncultured phages, which supports its representation in uncultured environmental viral contigs from metagenomics. Homologs of several vB_OliS_GJ44 genes have mostly been found in marine metagenomes, suggesting the prevalence of this phage genus in the oceans.

Conclusions

These results describe the first Oceanospirillum phage, vB_OliS_GJ44, that represents a novel viral cluster and exhibits interesting genetic features related to phage–host interactions and evolution. Thus, we propose a new viral genus Oceanospirivirus within the Siphoviridae family to reconcile this cluster, with vB_OliS_GJ44 as a representative member.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07978-4.

Efficient machine learning model for predicting drug-target interactions with case study for Covid-19

BACKGROUND

Discover possible Drug Target Interactions (DTIs) is a decisive step in the detection of the effects of drugs as well as drug repositioning. There is a strong incentive to develop effective computational methods that can effectively predict potential DTIs, as traditional DTI laboratory experiments are expensive, time-consuming, and labor-intensive. Some technologies have been developed for this purpose, however large numbers of interactions have not yet been detected, the accuracy of their prediction still low, and protein sequences and structured data are rarely used together in the prediction process.

METHODS

This paper presents DTIs prediction model that takes advantage of the special capacity of the structured form of proteins and drugs. Our model obtains features from protein amino-acid sequences using physical and chemical properties, and from drugs smiles (Simplified Molecular Input Line Entry System) strings using encoding techniques. Comparing the proposed model with different existing methods under K-fold cross validation, empirical results show that our model based on ensemble learning algorithms for DTI prediction provide more accurate results from both structures and features data.

RESULTS

The proposed model is applied on two datasets:Benchmark (feature only) datasets and DrugBank (Structure data) datasets. Experimental results obtained by Light-Boost and ExtraTree using structures and feature data results in 98 % accuracy and 0.97 f-score comparing to 94 % and 0.92 achieved by the existing methods. Moreover, our model can successfully predict more yet undiscovered interactions, and hence can be used as a practical tool to drug repositioning. A case study of applying our prediction model on the proteins that are known to be affected by Corona viruses in order to predict the possible interactions among these proteins and existing drugs is performed. Also, our model is applied on Covid-19 related drugs announced on DrugBank. The results show that some drugs like DB00691 and DB05203 are predicted with 100 % accuracy to interact with ACE2 protein. This protein is a self-membrane protein that enables Covid-19 infection. Hence, our model can be used as an effective tool in drug reposition to predict possible drug treatments for Covid-19.

In-Pero: Exploiting Deep Learning Embeddings of Protein Sequences to Predict the Localisation of Peroxisomal Proteins

Peroxisomes are ubiquitous membrane-bound organelles, and aberrant localisation of peroxisomal proteins contributes to the pathogenesis of several disorders. Many computational methods focus on assigning protein sequences to subcellular compartments, but there are no specific tools tailored for the sub-localisation (matrix vs. membrane) of peroxisome proteins. We present here In-Pero, a new method for predicting protein sub-peroxisomal cellular localisation. In-Pero combines standard machine learning approaches with recently proposed multi-dimensional deep-learning representations of the protein amino-acid sequence. It showed a classification accuracy above 0.9 in predicting peroxisomal matrix and membrane proteins. The method is trained and tested using a double cross-validation approach on a curated data set comprising 160 peroxisomal proteins with experimental evidence for sub-peroxisomal localisation. We further show that the proposed approach can be easily adapted (In-Mito) to the prediction of mitochondrial protein localisation obtaining performances for certain classes of proteins (matrix and inner-membrane) superior to existing tools.

Interpreting the lipidome: bioinformatic approaches to embrace the complexity

BACKGROUND

Improvements in mass spectrometry (MS) technologies coupled with bioinformatics developments have allowed considerable advancement in the measurement and interpretation of lipidomics data in recent years. Since research areas employing lipidomics are rapidly increasing, there is a great need for bioinformatic tools that capture and utilize the complexity of the data. Currently, the diversity and complexity within the lipidome is often concealed by summing over or averaging individual lipids up to (sub)class-based descriptors, losing valuable information about biological function and interactions with other distinct lipids molecules, proteins and/or metabolites.

AIM OF REVIEW

To address this gap in knowledge, novel bioinformatics methods are needed to improve identification, quantification, integration and interpretation of lipidomics data. The purpose of this mini-review is to summarize exemplary methods to explore the complexity of the lipidome.

KEY SCIENTIFIC CONCEPTS OF REVIEW

Here we describe six approaches that capture three core focus areas for lipidomics: (1) lipidome annotation including a resolvable database identifier, (2) interpretation via pathway- and enrichment-based methods, and (3) understanding complex interactions to emphasize specific steps in the analytical process and highlight challenges in analyses associated with the complexity of lipidome data.

A Novel GH Family 20 β-N-acetylhexosaminidase With Both Chitosanase and Chitinase Activity From Aspergillus oryzae

Aminooligosaccharides possess various biological activities and can exploit wide applications in food, pharmaceutical and cosmetic industries. Commercial aminooligosaccharides are often prepared by the hydrolysis of chitin and chitosan. In this study, a novel GH family 20 β-N-acetylhexosaminidases gene named AoNagase was cloned from Aspergillus oryzae and expressed in Pichia pastoris. The purified AoNagase had maximal activity at pH 5.5 and 65°C. It exhibited good pH stability in the range of pH 6.0–7.5 and at temperatures below 50°C. AoNagase was capable of hydrolyzing not only colloidal chitosan (508.26 U/mg) but also chitin (29.78 U/mg). The kinetic parameters (K_m and V_max) of AoNagase were 1.51 mM, 1106.02 U/mg for chitosan and 0.41 mM, 40.31 U/mg for colloidal chitin. To our knowledge, AoNagase is the first GH family 20 β-N-acetylhexosaminidase capable of hydrolyzing both chitosan and chitin. AoNagase is an endo-type β-N-acetylhexosaminidases and can potentially be used for the manufacturing of aminooligosaccharides.

IND-enzymes: a repository for hydrolytic enzymes derived from thermophilic and psychrophilic bacterial species with potential industrial usage

Biocatalysts provide many advantages over the traditional chemically assisted processes prevalent in industries. Consequently, the search for novel enzymes has increased over the years with a renewed interest in thermophilic and psychrophilic bacterial species. Enzymes or extremozymes extracted from such species have exhibited an affinity to extreme temperatures which is a prerequisite for many industrial applications. However, utilisation of these enzymes faces a major bottleneck. The distribution of sequence data associated with thermophiles and psychrophiles is overwhelming, spanning various databases and scientific literature. Based on more than 100 publications and genomes from over 300 thermophilic and psychrophilic bacterial species, we have constructed the database IND-Enzymes (indenzymes.srmist.edu.in). This database consists of over 20,120 nucleotide and protein sequences belonging to the hydrolytic enzyme class lipase, protease, esterase and amylase. Users can access over 100 published enzymes, 200 PDB structural data. Enzymes derived from genomes can be directly downloaded and users can also access the entire annotation data derived from species individually. Along with an alignment tool and python based pipelines, IND-Enzymes serves as the largest sequence repository for hydrolytic enzymes from thermophilic and psychrophilic bacterial species. This database showcases resources that are essential for protein engineering of hot-cold stable enzymes.

Network Pharmacology Analysis of ZiShenWan for Diabetic Nephropathy and Experimental Verification of Its Anti-Inflammatory Mechanism

Background

Diabetic nephropathy (DN) is the leading cause of end-stage renal disease (ESRD). The inflammatory response plays a critical role in DN. ZiShenWan (ZSW) is a classical Chinese medicinal formula with remarkable clinical therapeutic effects on DN, but its pharmacological action mechanisms remain unclear.

Aim

In this study, a network pharmacology approach was applied to investigate the pharmacological mechanisms of ZSW in DN therapy. Based on the results of network analysis, the core targets and signaling pathways related to anti-inflammatory effect were verified via experiments in vivo.

Methods

The candidate chemical ingredients of ZSW as well as its putative targets and known therapeutic targets of DN were acquired from appropriate databases. The “herb-ingredient-target” network for ZSW in DN treatment was established. The protein–protein interaction (PPI) network of potential targets was constructed to screen the core targets. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. In addition to biochemical and pathological indicators, the core targets and signaling pathways associated with inflammation were partially validated in db/db mice at molecular level.

Results

A total of 56 active ingredients in ZSW and 166 DN-related targets were selected from databases. A high proportion of core targets and top signaling pathways participate in inflammation. ZSW markedly alleviated renal injuries pathologically and regulated related biomarkers. In particular, ZSW significantly inhibited the exaggerated release of inflammatory cytokines such as interleukin (IL)-1β, IL-6, tumor necrosis factor receptor (TNF)-ɑ, and monocyte chemotactic protein (MCP)-1 as well as regulating p38 mitogen-activated protein kinases (MAPK) and phosphoinositide 3-kinase (PI3K)–protein kinase B (Akt) signaling pathways in db/db mice.

Conclusion

This study first comprehensively investigated the active ingredients, potential targets, and molecular mechanism of ZSW as a therapy for DN. ZSW achieved renoprotective effects in DN via regulation of multiple targets and signaling pathways, especially by alleviating inflammation. Results indicate that ZSW is a promising multi-target therapeutic approach for DN treatment.

A Vernalization Response in a Winter Safflower (Carthamus tinctorius) Involves the Upregulation of Homologs of FT, FUL, and MAF

Safflower (Carthamus tinctorius) is a member of the Asteraceae family that is grown in temperate climates as an oil seed crop. Most commercially grown safflower varieties can be sown in late winter or early spring and flower rapidly in the absence of overwintering. There are winter-hardy safflower accessions that can be sown in autumn and survive over-wintering. Here, we show that a winter-hardy safflower possesses a vernalization response, whereby flowering is accelerated by exposing germinating seeds to prolonged cold. The impact of vernalization was quantitative, such that increasing the duration of cold treatment accelerated flowering to a greater extent, until the response was saturated after 2 weeks exposure to low-temperatures. To investigate the molecular-basis of the vernalization-response in safflower, transcriptome activity was compared and contrasted between vernalized versus non-vernalized plants, in both 'winter hardy' and 'spring' cultivars. These genome-wide expression analyses identified a small set of transcripts that are both differentially expressed following vernalization and that also have different expression levels in the spring versus winter safflowers. Four of these transcripts were quantitatively induced by vernalization in a winter hardy safflower but show high basal levels in spring safflower. Phylogenetic analyses confidently assigned that the nucleotide sequences of the four differentially expressed transcripts are related to FLOWERING LOCUS T (FT), FRUITFUL (FUL), and two genes within the MADS-like clade genes. Gene models were built for each of these sequences by assembling an improved safflower reference genome using PacBio-based long-read sequencing, covering 85% of the genome, with N50 at 594,000 bp in 3000 contigs. Possible evolutionary relationships between the vernalization response of safflower and those of other plants are discussed.

Evaluation of female Aedes aegypti proteome via LC-ESI-MS/MS using two protein extraction methods

Background

Proteomic analyses have broadened the horizons of vector control measures by identifying proteins associated with different biological and physiological processes and give further insight into the mosquitoes’ biology, mechanism of insecticide resistance and pathogens-mosquitoes interaction. Female Ae. aegypti ingests human blood to acquire the requisite nutrients to make eggs. During blood ingestion, female mosquitoes transmit different pathogens. Therefore, this study aimed to determine the best protein extraction method for mass spectrometry analysis which will allow a better proteome profiling for female mosquitoes.

Methods

In this present study, two protein extractions methods were performed to analyze female Ae. aegyti proteome, via TCA acetone precipitation extraction method and a commercial protein extraction reagent CytoBuster^TM. Then, protein identification was performed by LC-ESI-MS/MS and followed by functional protein annotation analysis.

Results

The CytoBuster^TM reagent gave the highest protein yield with a mean of 475.90 µg compared to TCA acetone precipitation extraction showed 283.15 µg mean of protein. LC-ESI-MS/MS identified 1,290 and 890 proteins from the CytoBuster^TM reagent and TCA acetone precipitation, respectively. When comparing the protein class categories in both methods, there were three additional categories for proteins identified using CytoBuster^TM reagent. The proteins were related to scaffold/adaptor protein (PC00226), protein binding activity modulator (PC00095) and intercellular signal molecule (PC00207). In conclusion, the CytoBuster^TM protein extraction reagent showed a better performance for the extraction of proteins in term of the protein yield, proteome coverage and extraction speed.

An integrative docking and simulation-based approach towards the development of epitope-based vaccine against enterotoxigenic Escherichia coli

Enterotoxigenic E.coli is causing diarrheal illness in children as well as adults with the majority of the cases occurring in developing countries. To reduce the number of cases occurring worldwide, the development of an effectual vaccine against these bacteria can be the only prevention. This conjectural work was performed using modern bioinformatics tools for investigation of proteome of ETEC strain E24377A. Different computational vaccinology approaches were deployed to assess several parameters including antigenicity, allergenicity, stability, localization, molecular weight and toxicity of the predicted epitopes required for good vaccine candidate to elicit immune response against diarrhea. We estimated two known control antigens, epitope ¹⁴¹STLPETTVV¹⁴⁹ of Hepatitis B virus and epitope ²⁶⁵ILRGSVAHK²⁷³ of H1N1 Nucleoprotein in an attempt to corroborate our research work. Furthermore molecular docking was performed to evaluate the interaction between HLA allele and peptide, the peptide QYGGGNSAL and peptide LPYFELRWL were considered to be the most promiscuous T cell epitopes with the highest binding energy value of −2.09 kcal/mol and −1.84 kcal/mol, respectively. In addition, dynamic simulation revealed good stability of the vaccine construct as well as population coverage analysis exhibits the highest population coverage in the regions of East Asia, India, Northeast Asia, South Asia and North America. Therefore, these two epitopes can be further synthesized for wet lab analysis and could be considered as a promising vaccine against diarrhea.

Lack of Molecular Mimicry between Nonhuman Primates and Infectious Pathogens: The Possible Genetic Bases

Recently, it was found that proteomes from poliovirus, measles virus, dengue virus, and severe acute respiratory syndrome-related Coronavirus 2 (SARS-CoV-2) have high molecular mimicry at the heptapeptide level with the human proteome, while heptapeptide commonality is minimal or absent with proteomes from nonhuman primates, that is, gorilla, chimpanzee, and rhesus macaque. To acquire more data on the issue, analyses here have been expanded to Ebola virus, Francisella tularensis , human immunodeficiency virus-1 (HIV-1), Toxoplasma gondii , Variola virus, and Yersinia pestis . Results confirm that heptapeptide overlap is high between pathogens and Homo sapiens , but not between pathogens and primates. Data are discussed in light of the possible genetic bases that differently model primate phenomes, thus possibly underlying the zero/low level of molecular mimicry between infectious agents and primates. Notably, this study might help address preclinical vaccine tests that currently utilize primates as animal models, since autoimmune cross-reactions and the consequent adverse events cannot occur in absentia of shared sequences.

HAMAP as SPARQL rules-A portable annotation pipeline for genomes and proteomes

Background

Genome and proteome annotation pipelines are generally custom built and not easily reusable by other groups. This leads to duplication of effort, increased costs, and suboptimal annotation quality. One way to address these issues is to encourage the adoption of annotation standards and technological solutions that enable the sharing of biological knowledge and tools for genome and proteome annotation.

Results

Here we demonstrate one approach to generate portable genome and proteome annotation pipelines that users can run without recourse to custom software. This proof of concept uses our own rule-based annotation pipeline HAMAP, which provides functional annotation for protein sequences to the same depth and quality as UniProtKB/Swiss-Prot, and the World Wide Web Consortium (W3C) standards Resource Description Framework (RDF) and SPARQL (a recursive acronym for the SPARQL Protocol and RDF Query Language). We translate complex HAMAP rules into the W3C standard SPARQL 1.1 syntax, and then apply them to protein sequences in RDF format using freely available SPARQL engines. This approach supports the generation of annotation that is identical to that generated by our own in-house pipeline, using standard, off-the-shelf solutions, and is applicable to any genome or proteome annotation pipeline.

Conclusions

HAMAP SPARQL rules are freely available for download from the HAMAP FTP site, ftp://ftp.expasy.org/databases/hamap/sparql/, under the CC-BY-ND 4.0 license. The annotations generated by the rules are under the CC-BY 4.0 license. A tutorial and supplementary code to use HAMAP as SPARQL are available on GitHub at https://github.com/sib-swiss/HAMAP-SPARQL, and general documentation about HAMAP can be found on the HAMAP website at https://hamap.expasy.org.

The Gene Ontology resource: enriching a GOld mine

The Gene Ontology Consortium (GOC) provides the most comprehensive resource currently available for computable knowledge regarding the functions of genes and gene products. Here, we report the advances of the consortium over the past two years. The new GO-CAM annotation framework was notably improved, and we formalized the model with a computational schema to check and validate the rapidly increasing repository of 2838 GO-CAMs. In addition, we describe the impacts of several collaborations to refine GO and report a 10% increase in the number of GO annotations, a 25% increase in annotated gene products, and over 9,400 new scientific articles annotated. As the project matures, we continue our efforts to review older annotations in light of newer findings, and, to maintain consistency with other ontologies. As a result, 20 000 annotations derived from experimental data were reviewed, corresponding to 2.5% of experimental GO annotations. The website (http://geneontology.org) was redesigned for quick access to documentation, downloads and tools. To maintain an accurate resource and support traceability and reproducibility, we have made available a historical archive covering the past 15 years of GO data with a consistent format and file structure for both the ontology and annotations.

Diverse Taxonomies for Diverse Chemistries: Enhanced Representation of Natural Product Metabolism in UniProtKB

The UniProt Knowledgebase UniProtKB is a comprehensive, high-quality, and freely accessible resource of protein sequences and functional annotation that covers genomes and proteomes from tens of thousands of taxa, including a broad range of plants and microorganisms producing natural products of medical, nutritional, and agronomical interest. Here we describe work that enhances the utility of UniProtKB as a support for both the study of natural products and for their discovery. The foundation of this work is an improved representation of natural product metabolism in UniProtKB using Rhea, an expert-curated knowledgebase of biochemical reactions, that is built on the ChEBI (Chemical Entities of Biological Interest) ontology of small molecules. Knowledge of natural products and precursors is captured in ChEBI, enzyme-catalyzed reactions in Rhea, and enzymes in UniProtKB/Swiss-Prot, thereby linking chemical structure data directly to protein knowledge. We provide a practical demonstration of how users can search UniProtKB for protein knowledge relevant to natural products through interactive or programmatic queries using metabolite names and synonyms, chemical identifiers, chemical classes, and chemical structures and show how to federate UniProtKB with other data and knowledge resources and tools using semantic web technologies such as RDF and SPARQL. All UniProtKB data are freely available for download in a broad range of formats for users to further mine or exploit as an annotation source, to enrich other natural product datasets and databases.

UniProt: the universal protein knowledgebase in 2021

The aim of the UniProt Knowledgebase is to provide users with a comprehensive, high-quality and freely accessible set of protein sequences annotated with functional information. In this article, we describe significant updates that we have made over the last two years to the resource. The number of sequences in UniProtKB has risen to approximately 190 million, despite continued work to reduce sequence redundancy at the proteome level. We have adopted new methods of assessing proteome completeness and quality. We continue to extract detailed annotations from the literature to add to reviewed entries and supplement these in unreviewed entries with annotations provided by automated systems such as the newly implemented Association-Rule-Based Annotator (ARBA). We have developed a credit-based publication submission interface to allow the community to contribute publications and annotations to UniProt entries. We describe how UniProtKB responded to the COVID-19 pandemic through expert curation of relevant entries that were rapidly made available to the research community through a dedicated portal. UniProt resources are available under a CC-BY (4.0) license via the web at https://www.uniprot.org/.

UniProt: the universal protein knowledgebase in 2021

The aim of the UniProt Knowledgebase is to provide users with a comprehensive, high-quality and freely accessible set of protein sequences annotated with functional information. In this article, we describe significant updates that we have made over the last two years to the resource. The number of sequences in UniProtKB has risen to approximately 190 million, despite continued work to reduce sequence redundancy at the proteome level. We have adopted new methods of assessing proteome completeness and quality. We continue to extract detailed annotations from the literature to add to reviewed entries and supplement these in unreviewed entries with annotations provided by automated systems such as the newly implemented Association-Rule-Based Annotator (ARBA). We have developed a credit-based publication submission interface to allow the community to contribute publications and annotations to UniProt entries. We describe how UniProtKB responded to the COVID-19 pandemic through expert curation of relevant entries that were rapidly made available to the research community through a dedicated portal. UniProt resources are available under a CC-BY (4.0) license via the web at https://www.uniprot.org/.

Study on Network Pharmacological Analysis and Preliminary Validation to Understand the Mechanisms of Plantaginis Semen in Treatment of Gouty Nephropathy

Plantaginis Semen (PS) has been used to promote diuresis and clear away dampness. Recent reports have shown that PS can be used to treat gouty nephropathy (GN). However, the action and mechanism of PS have not been well defined in treating GN. The present study aimed to define the molecular mechanisms of PS as a potential therapeutic approach to treat GN. A combination of network pharmacology and validation experiments in GN is used to understand the potential mechanism. Information on pharmaceutically active compounds in PS and gene information related to GN was obtained from public databases. The compound target network and protein-protein interaction network were constructed to study the mechanism of action of PS in the treatment of GN. The mechanism of action of PS in the treatment of GN was analyzed via Gene Ontology (GO) biological process annotation and Kyoto Gene and Genomics Encyclopedia (KEGG) pathway enrichment. Validation experiments were performed to verify the core targets. The GN rat model was prepared by the method of combining yeast and adenine. Hematoxylin-eosin (HE) staining was used to observe the morphology of renal tissue in rats. ELISA was applied to detect TGF-β1, TNF-α, and IL-1β levels in renal tissue. The expressions of TGF-β1, TNF-α, and IL-1β were determined using immunohistochemistry. Through the results of network pharmacology, we obtained 9 active components, 118 predicted targets, and 149 GN targets from the public database. Based on the protein-protein interaction (PPI), 26 hub genes for interaction with PS treating for GN were screened, including MMP9, TNF, IL1β, and IL6. The enrichment analysis results showed that the treatment of GN with PS was mainly involved in the TGF-β1 signaling pathway, MAPK signaling pathway, TNF signaling pathway, NF-κB signaling pathway, and PI3K Akt signaling pathway. Validation experiment results showed that PS could reduce the content of urinary protein and UA and deregulate the expression of TGF-β1, TNF-α, and IL-1β in the treatment of GN. The molecular mechanism of PS in the treatment of GN indicated the synergistic features of multicomponent, multitarget, and multipathway of traditional Chinese medicine, which provided an essential scientific basis for further elucidating the mechanism of PS in the treatment of GN.

Bioinformatics Analysis Revealed Novel 3'UTR Variants Associated with Intellectual Disability

MicroRNAs (or miRNAs) are short nucleotide sequences (~17–22 bp long) that play important roles in gene regulation through targeting genes in the 3′untranslated regions (UTRs). Variants located in genomic regions might have different biological consequences in changing gene expression. Exonic variants (e.g., coding variant and 3′UTR variant) are often causative of diseases due to their influence on gene product. Variants harbored in the 3′UTR region where miRNAs perform their targeting function could potentially alter the binding relationships for target pairs, which could relate to disease causation. We gathered miRNA–mRNA targeting pairs from published studies and then employed the database of microRNA Target Site single nucleotide variants (SNVs) (dbMTS) to discover novel SNVs within the selected pairs. We identified a total of 183 SNVs for the 114 pairs of accurate miRNA–mRNA targeting pairs selected. Detailed bioinformatics analysis of the three genes with identified variants that were exclusively located in the 3′UTR section indicated their association with intellectual disability (ID). Our result showed an exceptionally high expression of GPR88 in brain tissues based on GTEx gene expression data, while WNT7A expression data were relatively high in brain tissues when compared to other tissues. Motif analysis for the 3′UTR region of WNT7A showed that five identified variants were well-conserved across three species (human, mouse, and rat); the motif that contains the variant identified in GPR88 is significant at the level of the 3′UTR of the human genome. Studies of pathways, protein–protein interactions, and relations to diseases further suggest potential association with intellectual disability of our discovered SNVs. Our results demonstrated that 3′UTR variants could change target interactions of miRNA–mRNA pairs in the context of their association with ID. We plan to automate the methods through developing a bioinformatics pipeline for identifying novel 3′UTR SNVs harbored by miRNA-targeted genes in the future.

Metabolomic and transcriptomic signatures of prenatal excessive methionine support nature rather than nurture in schizophrenia pathogenesis

The imbalance of prenatal micronutrients may perturb one-carbon (C1) metabolism and increase the risk for neuropsychiatric disorders. Prenatal excessive methionine (MET) produces in mice behavioral phenotypes reminiscent of human schizophrenia. Whether in-utero programming or early life caregiving mediate these effects is, however, unknown. Here, we show that the behavioral deficits of MET are independent of the early life mother-infant interaction. We also show that MET produces in early life profound changes in the brain C1 pathway components as well as glutamate transmission, mitochondrial function, and lipid metabolism. Bioinformatics analysis integrating metabolomics and transcriptomic data reveal dysregulations of glutamate transmission and lipid metabolism, and identify perturbed pathways of methylation and redox reactions. Our transcriptomics Linkage analysis of MET mice and schizophrenia subjects reveals master genes involved in inflammation and myelination. Finally, we identify potential metabolites as early biomarkers for neurodevelopmental defects and suggest therapeutic targets for schizophrenia.

Chen, Alhassen et al. show that schizophrenia-like behavioral deficits induced by excessive prenatal methionine administration are due to in-uterus aberrations rather than through early life mother-infant interaction in mice. This study identifies the brain metabolites and transcriptomic signatures, which potentially serve as early biomarkers for schizophrenia-like behaviors.

Current Knowledge on the Function of α-Methyl Acyl-CoA Racemase in Human Diseases

Branched chain fatty acids perform very important functions in human diet and drug metabolism. they cannot be metabolized in mitochondria and are instead processed and degraded in peroxisomes due to the presence of methyl groups on the carbon chains. Oxidative degradation pathways for lipids include α- and β-oxidation and several pathways. In all metabolic pathways, α-methyl acyl-CoA racemase (AMACR) plays an essential role by regulating the metabolism of lipids and drugs. AMACR regulates β-oxidation of branched chain lipids in peroxisomes and mitochondria and promotes chiral reversal of 2-methyl acids. AMACR defects cause sensory-motor neuronal and liver abnormalities in humans. These phenotypes are inherited and are caused by mutations in AMACR. In addition, AMACR has been found to be overexpressed in prostate cancer. In addition, the protein levels of AMACR have increased significantly in many types of cancer. Therefore, AMACR may be an important marker in tumors. In this review, a comprehensive overview of AMACR studies in human disease will be described.

Genome-Scale Metabolic Model Driven Design of a Defined Medium for Campylobacter jejuni M1cam

Campylobacter jejuni, the most frequent cause of food-borne bacterial gastroenteritis, is a fastidious organism when grown in the laboratory. Oxygen is required for growth, despite the presence of the metabolic mechanism for anaerobic respiration. Amino acid auxotrophies are variably reported and energy metabolism can occur through several electron donor/acceptor combinations. Overall, the picture is one of a flexible, but vulnerable metabolism. To understand Campylobacter metabolism, we have constructed a fully curated, metabolic model for the reference organism M1 (our variant is M1cam) and validated it through laboratory experiments. Our results show that M1cam is auxotrophic for methionine, niacinamide, and pantothenate. There are complete biosynthesis pathways for all amino acids except methionine and it can produce energy, but not biomass, in the absence of oxygen. M1cam will grow in DMEM/F-12 defined media but not in the previously published Campylobacter specific defined media tested. Using the model, we identified potential auxotrophies and substrates that may improve growth. With this information, we designed simple defined media containing inorganic salts, the auxotrophic substrates, L-methionine, niacinamide, and pantothenate, pyruvate and additional amino acids L-cysteine, L-serine, and L-glutamine for growth enhancement. Our defined media supports a 1.75-fold higher growth rate than Brucella broth after 48 h at 37°C and sustains the growth of other Campylobacter jejuni strains. This media can be used to design reproducible assays that can help in better understanding the adaptation, stress resistance, and the virulence mechanisms of this pathogen. We have shown that with a well-curated metabolic model it is possible to design a media to grow this fastidious organism. This has implications for the investigation of new Campylobacter species defined through metagenomics, such as C. infans.

Synthesis, In Vitro Evaluation and Molecular Docking of the 5-Acetyl-2-aryl-6-hydroxybenzo[b]furans against Multiple Targets Linked to Type 2 Diabetes

The 5-acetyl-2-aryl-6-hydroxybenzo[b]furans 2a-h have been evaluated through in vitro enzymatic assay against targets which are linked to type 2 diabetes (T2D), namely, α-glucosidase, protein tyrosine phosphatase 1B (PTP1B) and β-secretase. These compounds have also been evaluated for antioxidant activity using the 2,2-diphenyl-1-picrylhydrazyl (DPPH) free-radical scavenging method. The most active compounds against α-glucosidase and/or PTP1B, namely, 4-fluorophenyl 2c, 4-methoxyphenyl 2g and 3,5-dimethoxyphenyl substituted 2h derivatives were also evaluated for potential anti-inflammatory properties against cyclooxygenase-2 activity. The Lineweaver-Burk and Dixon plots were used to determine the type of inhibition on compounds 2c and 2h against α-glucosidase and PTP1B receptors. The interactions were investigated in modelled complexes against α-glucosidase and PTP1B via molecular docking.