UniProt: the Universal Protein Knowledgebase in 2023

CrypToth: Cryptic Pocket Detection through Mixed-Solvent Molecular Dynamics Simulations-Based Topological Data Analysis

Some functional proteins undergo conformational changes to expose hidden binding sites when a binding molecule approaches their surface. Such binding sites are called cryptic sites and are important targets for drug discovery. However, it is still difficult to correctly predict cryptic sites. Therefore, we introduce an advanced method, CrypToth, for the precise identification of cryptic sites utilizing the topological data analysis such as persistent homology method. This method integrates topological data analysis and mixed-solvent molecular dynamics (MSMD) simulations. To identify hotspots corresponding to cryptic sites, we conducted MSMD simulations using six probes with different chemical properties: dimethyl ether, benzene, phenol, methyl imidazole, acetonitrile, and ethylene glycol. Subsequently, we applied our topological data analysis method to rank hotspots based on the possibility of harboring cryptic sites. Evaluation of CrypToth using nine target proteins containing well-defined cryptic sites revealed its superior performance compared with recent machine-learning methods. As a result, in seven of nine cases, hotspots associated with cryptic sites were ranked the highest. CrypToth can explore hotspots on the protein surface favorable to ligand binding using MSMD simulations with six different probes and then identify hotspots corresponding to cryptic sites by assessing the protein's conformational variability using the topological data analysis. This synergistic approach facilitates the prediction of cryptic sites with a high accuracy.

The proteomic differences and expression of fatty acid-binding protein 6 (FABP6) associated with gastrointestinal injury in horses with oral administration of a clinical dose of phenylbutazone

BACKGROUND

Phenylbutazone (PBZ) can potentially induce gastrointestinal ulceration, and early detection of PBZ-induced gastroenteropathy will be useful for the diagnosis, treatment, and prevention of PBZ toxicity.

OBJECTIVES

To identify putative proteins associated with equine gastric ulcer syndrome after clinical dose (4.4 mg/kg) administration of PBZ by proteomic study.

STUDY DESIGN

In vivo experiments.

METHODS

Proteomic analysis using LC-MS/MS compared protein expression in serum and faeces of seven PBZ-treated horses with seven placebo-treated controls, and a novel putative biomarker was validated via enzyme-linked immunosorbent assay.

RESULTS

Differentially expressed proteins (DEPs) analysis on 5298 serum annotated proteins and 3538 faecal annotated proteins using the DESeq2 were performed between the control and treatment of EGUS groups. The results showed a list of 226 and 181 significant proteins in serum and faecal samples, respectively with a p adjust value <0.05. The proteomic serum and faeces samples were integrated into STITCH to illustrate PBZ interaction with bile acid homeostasis. FABP6 was significantly increased in PBZ-treated horses. The serum FABP6 concentration in the treatment group on Day 8 (1.80 ± 0.37 ng/mL) was higher than on Day 0 (1.15 ± 0.33 ng/mL, p = 0.01, 95% CI [-1.07, -0.25]). On Day 8, the serum FABP6 concentration in the treatment group was also higher than the control group (1.20 ± 0.48 ng/mL; p = 0.02, 95% CI [-1.10, -0.11]).

MAIN LIMITATIONS

Validation of all expressed proteins is a main limitation.

CONCLUSIONS

Administration of PBZ at a clinical dose of 4.4 mg/kg twice daily for 7 days may cause gastric mucosal damage. PBZ treatment increased the expression of SLC10A1 and FABP6, suggesting that early gastric mucosal injury may be linked to the bile acid pathway. Bile acids could potentially exacerbate PBZ-induced EGUS.

Genome-scale metabolic model of Staphylococcus epidermidis ATCC 12228 matches in vitro conditions

Staphylococcus epidermidis, a commensal bacterium inhabiting collagen-rich areas like human skin, has gained significance due to its probiotic potential in the nasal microbiome and as a leading cause of nosocomial infections. While infrequently leading to severe illnesses, S. epidermidis exerts a significant influence, particularly in its close association with implant-related infections and its role as a classic opportunistic biofilm former. Understanding its opportunistic nature is crucial for developing novel therapeutic strategies, addressing both its beneficial and pathogenic aspects, and alleviating the burdens it imposes on patients and healthcare systems. Here, we employ genome-scale metabolic modeling as a powerful tool to elucidate the metabolic capabilities of S. epidermidis. We created a comprehensive computational resource for understanding the organism's growth conditions within diverse habitats by reconstructing and analyzing a manually curated and experimentally validated metabolic model. The final network, iSep23, incorporates 1,415 reactions, 1,051 metabolites, and 705 genes, adhering to established community standards and modeling guidelines. Benchmarking with the Metabolic Model Testing suite yields a high score, indicating the model's remarkable semantic quality. Following the findable, accessible, interoperable, and reusable (FAIR) data principles, iSep23 becomes a valuable and publicly accessible asset for subsequent studies. Growth simulations and carbon source utilization predictions align with experimental results, showcasing the model's predictive power. Ultimately, this work provides a robust foundation for future research aimed at both exploiting the probiotic potential and mitigating the pathogenic risks posed by S. epidermidis.

IMPORTANCE

Staphylococcus epidermidis, a bacterium commonly found on human skin, has shown probiotic effects in the nasal microbiome and is a notable causative agent of hospital-acquired infections. While these infections are typically non-life-threatening, their economic impact is considerable, with annual costs reaching billions of dollars in the United States. To better understand its opportunistic nature, we employed genome-scale metabolic modeling to construct a detailed network of S. epidermidis's metabolic capabilities. This model, comprising over a thousand reactions, metabolites, and genes, adheres to established standards and demonstrates solid benchmarking performance. Following the findable, accessible, interoperable, and reusable (FAIR) data principles, the model provides a valuable resource for future research. Growth simulations and predictions closely match experimental data, underscoring the model's predictive accuracy. Overall, this work lays a solid foundation for future studies aimed at leveraging the beneficial properties of S. epidermidis while mitigating its pathogenic potential.

SMVSNN: An Intelligent Framework for Anticancer Drug-Drug Interaction Prediction Utilizing Spiking Multi-view Siamese Neural Networks

The study of synergistic drug combinations is vital in cancer treatment, enhancing efficacy, reducing resistance, and minimizing side effects through complementary drug actions. Drug-drug interaction (DDI) analysis offers essential theoretical support, and with the rise of data science, intelligent algorithms are increasingly replacing traditional in vitro screening for predicting potential DDIs. Considering the limitations of previous computational methods, such as the application of a single view, overly direct concatenation of drug pair features, and existing data encoding that is difficult to handle, this paper proposes a novel DDI analysis and prediction framework, called the Spiking Multi-View Siamese Neural Network-based (SMVSNN) framework. First, the data of two drugs in each view are processed into fused features using a Siamese spiking convolutional network and a spiking neural perceptron. Second, the processed features from multiple views are integrated into a unified representation through a self-learning attention weight module. Finally, this unified representation is fed into a spiking multilayer perceptron network to obtain the prediction results. Compared to traditional intelligent algorithms, the spiking neurons and the siamese network in SMVSNN can more effectively extract and integrate latent information from drug pair data. Real anticancer drug data, including 904 drugs, 7730 DDI records, and 19 drug interactions, were extracted from authoritative public databases to assess the effectiveness of our framework. The 5-fold cross-validation indicates that SMVSNN outperforms previous models on the majority of metrics. SMVSNN is poised to be an effective method for inferring potential synergistic drug combinations in anticancer therapy.

The α/β3 complex of human voltage-gated sodium channel hNav1.7 to study mechanistic differences in presence and absence of auxiliary subunit β3

CONTEXT

In the context of structural interactomics, we generated a 3D model between α and β3 subunits for the hitherto unknown human voltage-gated sodium channel complex (hNa 1.7α/β3). We embedded our 3D model in a membrane lipid bilayer for molecular dynamics (MD) simulations of the sodium cation passage from the outer vestibule through the inner pore segment of our hNa 1.7 complex in presence and absence of auxiliary subunit β3 with remarkable changes close to electrophysiological study results. A complete passage could not be expected due to because the inactivated state of the underlying 3D template. A complete sodium ion passage would require an open state of the channel. The computed observations concerning side chain rearrangements for favorable cooperativity under evolutionary neighborhood conditions, favorable and unfavorable amino acid interactions, proline kink, loop, and helix displacements were all found in excellent keeping with the extant literature without any exception nor contradiction. Complex-stabilizing pairs of interacting amino acids with evolutionary neighborhood complementary were identified.

METHODS

The following tools were used: sequence search and alignment by FASTA and Clustal Omega; 3D model visualization and homology modeling by Vega ZZ, SPDBV, Chimera and Modeller, respectively; missing sections (loops) by Alphafold; geometry optimization prior to MD runs by GROMACS 2021.4 under the CHARMM 36 force field; local healing of bad contacts by SPDBV based on its Ramachandran plots; protein-protein docking by HDOCK 2.4; membrane insertion assisted by OPM; Berendsen V-rescaling for NVT; Parrinello-Rahman and Nose-Hoover for MPT; MD analyses by VMD and XMGRACE.

Computational discovery and systematic analysis of protein entangling motifs in nature: from algorithm to database

Nontrivial protein topology has the potential to revolutionize protein engineering by enabling the manipulation of proteins' stability and dynamics. However, the rarity of topological proteins in nature poses a challenge for their design, synthesis and application, primarily due to the limited number of available entangling motifs as synthetic templates. Discovering these motifs is particularly difficult, as entanglement is a subtle structural feature that is not readily discernible from protein sequences. In this study, we developed a streamlined workflow enabling efficient and accurate identification of structurally reliable and applicable entangling motifs from protein sequences. Through this workflow, we automatically curated a database of 1115 entangling protein motifs from over 100 thousand sequences in the UniProt Knowledgebase. In our database, 73.3% of C2 entangling motifs and 80.1% of C3 entangling motifs exhibited low structural similarity to known protein structures. The entangled structures in the database were categorized into different groups and their functional and biological significance were analyzed. The results were summarized in an online database accessible through a user-friendly web platform, providing researchers with an expanded toolbox of entangling motifs. This resource is poised to significantly advance the field of protein topology engineering and inspire new research directions in protein design and application.

Characterization of 2-phenanthroyl-CoA reductase, an ATP-independent type III aryl-CoA reductase involved in anaerobic phenanthrene degradation

Anaerobic degradation of polycyclic aromatic hydrocarbons (PAHs) with three or more aromatic rings is extremely slow because the compounds are very poorly soluble in water and chemically stable. Phenanthrene is the only three-ring PAH where the anaerobic degradation has been partially elucidated. Phenanthrene is first activated via carboxylation producing 2-phenanthroate, which is further converted to 2-phenanthroyl-coenzyme A (CoA) via the enzyme 2-phenanthroate:CoA ligase. In this study, we elucidated the next degradation step, the reduction of 2-phenanthroyl-CoA to dihydro-2-phenanthroyl-CoA. We cloned the putative gene from the genome of culture TRIP_1 and heterologously expressed and purified the 2-phenanthroyl-CoA reductase enzyme from Escherichia coli. The identified monomeric flavo-enzyme belongs to the novel group of type III aryl-CoA reductases in the old-yellow enzyme family and has a molecular mass of 72 kDa. 2-Phenanthroyl-CoA reductase contains one FMN, one FAD, and one [4Fe-4S] iron-sulfur cluster as cofactors. The enzyme has a specific activity of 17.6 ± 0.4 nmol/min/mg, a Km value of 1.8 µM, and a Vmax of 7.9 µmol/min/mg at pH 7.5, when reduced methyl viologen was used as electron donor. 2-Phenanthroyl-CoA reductase catalyzed a two-electron reduction step producing one of five possible isomers. Quantum mechanical calculations and nuclear magnetic resonance analysis of the reaction product suggested 9,10-dihydro-2-phenanthroyl-CoA as the most stable isomer. However, our experimental evidence suggests 7,8-dihydro-2-phenanthroyl-CoA (International Union of Pure and Applied Chemistry [IUPAC]: 1,2-dihydro-7-phenanthroyl-CoA) or 5,6-dihydro-2-phenanthroyl-CoA (IUPAC: 3,4-dihydro-7-phenanthroyl-CoA) as the most likely reduced product with a saturated bond in ring 3 of the substrate 2-phenanthroyl-CoA, before undergoing isomerization changes to reach the more stable structure of 9,10-dihydro-2-phenanthroyl-CoA.IMPORTANCEPAHs are a group of highly toxic and persistent environmental pollutants. The anaerobic degradation of three-ring PAHs like phenanthrene is still poorly understood. Phenanthrene degradation starts with a carboxylation reaction to form 2-phenanthroic acid followed by a CoA-thioesterification reaction catalyzed by 2-phenanthroate:CoA ligase to produce 2-phenanthroyl-CoA. The next degradation step is the reduction of 2-phenanthroyl-CoA to dihydro-2-phenanthroyl-CoA to overcome the resonance energy of the aromatic ring system. Herein, we elucidated that the reduction reaction is catalyzed by the enzyme 2-phenanthroyl-CoA reductase. Furthermore, we provided biochemical and structural properties of the heterologously expressed and purified 2-phenanthroyl-CoA reductase, which confirmed that the enzyme belongs to the novel group of type III aryl-CoA reductases in the old-yellow enzyme family.

Chromosome-scale whole genome assembly and annotation of the Jamaican field cricket Gryllus assimilis

Gryllus assimilis, commonly known as Jamaican field cricket, is an edible insect with significant economic value in sustainable food production. Despite its importance, a high-quality reference genome of G. assimilis has not yet been published. Here, we report a chromosome-level reference genome of G. assimilis based on Oxford Nanopore Technologies (ONT) sequencing, Illumina sequencing, and Hi-C technologies. The assembled genome has a total length of 1.60 Gbp with a scaffold N50 of 102 Mbp, and 96.80% of the nucleotides was assigned to 15 chromosome-scale scaffolds. The assembly completeness was validated using BUSCO, achieving 99.5% completeness against the arthropoda database. We predicted 27,645 protein-coding genes, and 825 Mb repetitive elements were annotated in the reference genome. This reference genome of G. assimilis can provide a basis for the subsequent development of genomic resources, offering insights for future functional genomic studies, comparative genomics, and DNA-informed breeding of this species.

An ultra-rare missense variant in the KIF1B gene linked to autoinflammatory Menière's disease

Menière's disease (MD) is an inner ear disorder characterised by episodes of vertigo, sensorineural hearing loss and tinnitus linked to autoinflammation and/or type 2 immune response. We hypothesise that rare variation in immune response genes could drive the autoinflammatory phenotype in MD. We retrieved differentially expressed genes (DEG) from single-cell RNAseq and epigenomic datasets to search for rare variants in the MD exome (N = 454) and genome (N = 511) sequencing datasets. The variant chr1:10374335 C > T in the KIF1B gene was found in three MD unrelated individuals and was predicted to be likely pathogenic. According to differential transcript usage, transcript ENST00000622724.3 was found in MD samples, but absent in controls. Furthermore, this variant may influence splicing through the generation of exonic enhancers and silencers, potentially changing transcription factor binding at the promoter. These findings support that this KIF1B gene rare variant is associated with the MD autoinflammatory phenotype and may up-regulate its expression in monocytes.

miRNA-200a suppresses GNAI1 and PLCB4 to modulate skin pigmentation in cashmere goats

Coat colour formation in mammals is influenced by melanogenesis and pigmentation processes regulated by miRNAs, including miRNA-200a. Although miRNA-200a is differentially expressed in the skin of cashmere goats with varying coat colours, its regulatory mechanism remains unclear. In this study, miRNA-200a target genes were predicted using miRBase and TargetScan, identifying GNAI1 and PLCB4 as the target genes through GO and KEGG analyses. Dual-luciferase assays using wild-type and mutant plasmids confirmed a direct interaction between miRNA-200a and the 3'UTR regions of these genes. RT-qPCR and Western blot analyses demonstrated that the expression levels of miRNA-200a and its target genes differed significantly between black and white goat skin. In HaCaT cells, transfection with miRNA-200a mimics or inhibitors altered GNAI1 and PLCB4 expression at both mRNA and protein levels. To validate these findings in vivo, subcutaneous injection of antagomiR-200a into BALB/c mice significantly reduced melanin content (P < 0.01) and increased the expression of GNAI1 and PLCB4. These results indicate that miRNA-200a modulates skin pigmentation by suppressing GNAI1 and PLCB4, thereby influencing coat colour in cashmere goats. This study provides a foundational understanding for leveraging genetic regulation to enhance coat colour diversity and develop naturally pigmented breeds.

Optimization of Extraction Methods for the Quantification of Proteins in Mammalian Tissues

With the development of new vaccine technologies, such as mRNA vaccines, tissue studies are becoming increasingly important. Knowledge of the antigen expression amounts and where the antigen accumulates in the body is essential for designing safe and effective vaccines. Mammalian tissues present challenges in the detection and accurate quantification of target proteins because of their complexity and the lack of protocols that efficiently extract proteins with minimal sample loss. Here, we describe a protocol for the detection and accurate quantification of protein targets in commercially available snap-frozen lung, liver, kidney, and spleen of European domestic ferrets (Mustela putorius furo) by isotope dilution mass spectrometry (IDMS). Housekeeping proteins were chosen that range in abundance to account for different masses of tissue slices of the same organ. Target peptides used for IDMS quantification were conserved across several of the common animal model systems, including baby hamster kidney, mouse, and ferret. Hemagglutinin, the primary antigen of an influenza vaccine, was added at various concentrations to demonstrate the recovery of low-abundance proteins from the complex tissue homogenate. By using housekeeping proteins and a preparation protocol that minimizes sample loss, this study shows that IDMS can accurately quantify proteins in mammalian tissues with unmatched sensitivity and specificity.

Haplotype-resolved chromosome-level genome sequence of Elsholtzia splendens (Nakai ex F.Maek.)

Elsholtzia splendens, a perennial herb native to East Asia, is valued for its ornamental and medicinal uses, particularly in treating inflammatory and febrile conditions. Recent studies have highlighted its antibacterial, anti-inflammatory, antidepressant, antithrombotic, and lipid-lowering properties of its compounds. Additionally, E. splendens shows potential for phytoremediation owing to its ability to hyperaccumulate copper (Cu), lead (Pb), zinc (Zn), and cadmium (Cd). However, its role in remediation conflicts with its medicinal use because of the risk of heavy metal accumulation. Genome sequencing will be key to boosting beneficial compound production and reducing heavy metal risks. In this study, we generated a high-resolution, haplotype-resolved, chromosome-scale genome sequence of E. splendens using PacBio Revio long-read, Illumina short-read, and Hi-C sequencing technologies. The haplotype genome assemblies, spanned 275.4 and 265.0 Mbp with a scaffold N50 of 33.9 and 33.8 Mbp for haplotype 1 and 2, respectively. This assembly provides valuable insights into medicinal compound biosynthesis and supports genetic conservation efforts, facilitating future genetic and biotechnological applications of E. splendens for medicinal and ecological uses.

Characterization of a Highly Solvent-Tolerant SGNH Hydrolase Superfamily Lipolytic Enzyme from Sphaerobacter thermophilus

Thermophilic microbial lipases that retain activity under harsh conditions are a highly desirable tool for catalysis in numerous biosynthetic and biotechnological applications. In this study, a putative SGNH lipase gene, from Sphaerobacter thermophilus (StSGNH1), was overexpressed using a pMCSG7 plasmid in BL21(DE3) cells. The polyhistidine-tagged enzyme was expressed as inclusion bodies that were readily solubilized using Empigen BB detergent, and the protein was purified to homogeneity using immobilized metal affinity chromatography. The classification of StSGNH1 as a thermophilic and alkaliphilic lipase was supported by its ability to optimally catalyze the hydrolysis of medium-length p-nitrophenol esters at elevated temperature (55 °C) and pH (8-11). Evaluation of the StSGNH1 structure generated by AlphaFold indicated that the catalytic domain was composed of a three-layered α/β/α fold, and molecular docking studies yielded insight into which residues proximal to the active site assist in stabilizing the ligand-enzyme interaction and substrate selectivity. Notably, StSGNH1 was able to carry out ester hydrolysis in the presence of elevated concentrations of detergents, chaotropic reagents, and organic solvents, indicating that it would be suitable for employment in industrial reactions. Tryptophan fluorescence measurements in the presence of guanidine hydrochloride were employed to estimate the free energy of folding for StSGNH1 along a reversible folding pathway. The properties of StSGNH1 would be highly desirable for biotechnological applications.

Heterogeneity in recombination rates and accessory gene co-occurrence distinguish Pseudomonas aeruginosa phylogroups

Pseudomonas aeruginosa (class Gammaproteobacteria) is a ubiquitous, ecologically widespread, and metabolically versatile species. It is also an opportunistic pathogen that causes a variety of chronic and acute infections in humans. Its ability to thrive in diverse environments and exhibit a wide range of phenotypes lies in part on its large gene pool, but the processes that govern inter-strain genomic variation remain unclear. Here, we aim to characterize the recombination features and accessory genome structure of P. aeruginosa using 840 globally distributed genome sequences. The species can be subdivided into five phylogenetic sequence clusters (corresponding to known phylogroups), two of which are most prominent. Notable epidemic clones are found in the two phylogroups: ST17, ST111, ST146, ST274, and ST395 in phylogroup 1, and ST235 and ST253 in phylogroup 2. The two phylogroups differ in the frequency and characteristics of homologous recombination in their core genomes, including the specific genes that most frequently recombine and the impact of recombination on sequence diversity. Each phylogroup's accessory genome is characterized by a unique gene pool, co-occurrence networks of shared genes, and anti-phage defense systems. Different pools of antimicrobial resistance and virulence genes exist in the two phylogroups and display dissimilar patterns of co-occurrence. Altogether, our results indicate that each phylogroup displays distinct histories and patterns of acquiring exogenous DNA, which may contribute in part to their predominance in the global population. Our study has important implications for understanding the genome dynamics, within-species heterogeneity, and clinically relevant traits of P. aeruginosa.

IMPORTANCE

The consummate opportunist Pseudomonas aeruginosa inhabits many nosocomial and non-clinical environments, posing a major health burden worldwide. Our study reveals phylogroup-specific differences in recombination features and co-occurrence networks of accessory genes within the species. This genomic variation partly explains its remarkable ability to exhibit diverse ecological and phenotypic traits, and thus contribute to circumventing clinical and public health intervention strategies to contain it. Our results may help inform efforts to control and prevent P. aeruginosa diseases, including managing transmission, therapeutic efforts, and pathogen circulation in non-clinical environmental reservoirs.

Data Exploration for Target Predictions Using Proprietary and Publicly Available Data Sets

When applying machine learning (ML) approaches for the prediction of bioactivity, it is common to collect data from different assays or sources and combine them into single data sets. However, depending on the data domains and sources from which these data are retrieved, bioactivity data for the same macromolecular target may show a high variance of values (looking at a single compound) and cover very different parts of the chemical space as well as the bioactivity range (looking at the whole data set). The effectiveness and applicability domain of the resulting prediction models may be strongly influenced by the sources from which their training data were retrieved. Therefore, we investigated the chemical space and active/inactive distribution of proprietary pharmaceutical data from Bayer AG and the publicly available ChEMBL database, and their impact when applied as training data for classification models. For this end, we applied two different sets of descriptors in combination with different ML algorithms. The results show substantial differences in chemical space between the two different data sources, leading to suboptimal prediction performance when models are applied to domains other than their training data. MCC values between -0.34 and 0.37 among all targets were retrieved, indicating suboptimal model performance when models trained on Bayer AG data were tested on ChEMBL data and vice versa. The mean Tanimoto similarity of the nearest neighbors between these two data sources indicated similarities for 31 targets equal to or less than 0.3. Interestingly, all applied methods to assess overlap of chemical space of the two data sources to predict the applicability of models beyond their training data sets did not correlate with observed performances. Finally, we applied different strategies for creating mixed training data sets based on both public and proprietary sources, using assay format (cell-based and cell-free) information and Tanimoto similarities.

Biophysical characterization of a putative antimicrobial peptide-binding protein of Escherichia coli highlights its dual functionality

Antimicrobial peptides (AMPs) disrupt the integrity of the bacterial membrane, ultimately leading to their death. In counter-defense, pathogens are reported to have developed systems such as the sensitivity to antimicrobial peptides (Sap) transport system that evade the action of AMPs and sequester essential micronutrients. However, recent contrasting reports cloud the functional prospects of the Sap system. Hence, this study aimed to characterize the Escherichia coli Sap (EcSap) transport system using biophysical techniques. The results obtained from various approaches suggested the binding of heme to the substrate-binding component (EcSapA) of the EcSap system. Further, this study suggests the interaction of EcSapA with the AMP protamine. In summary, the findings of this study suggest the dual ligand-binding ability of EcSapA. Impact statement The present study reports the functional prospects of the enigmatic substrate-binding protein SapA of E. coli. This analysis highlights the essentiality of the intra-protein disulfide bonds in maintaining the structural integrity of EcSapA. Further, biophysical studies of EcSapA highlight its dual ligand binding propensity, earmarking it as a drug target.

Biocompatibility and wound-healing prospect of KAPs-depleted residual hair biomaterial

This work is an in-depth investigation of the in vitro and in vivo biocompatibility of processed and treated residual human hair samples with intact cuticle layers. The specimens included oxidized hair with minimal melanin (BLH) and hair with medium- (M-KAP) and low- (L-KAP) amounts of keratin associated proteins (KAPs), confirmed through gel electrophoresis, electron microscopy, trichrome histological staining, and tensile biomechanics, in comparison to the untreated regular hair (REG) control. All hair groups, high KAPs (H-KAPs: REG and BLH), M-KAP, and L-KAP, are non-cytotoxic in the adipose fibroblast's response to their extracts based on the ISO 10993-5 medical device biomaterial testing standard. In vivo mouse subcutaneous implantation (ISO 10993-6, local effects) at 2 weeks showed a foreign body response (FBR) with thin fibrous encapsulation at 28% relative skin dermis thickness; but the L-KAP implant mitigated a significant decrease in FBR area compared to H-KAPs and a lower number of immune cells of mostly macrophages and mast cells on the biomaterial's surface. In the bulk of the capsules, blood vessels and collagen extracellular matrix densities were similar among groups. These findings suggest that small globular KAPs diffuse out of the cortex to the host-biomaterial interface which induce a slightly-elevated FBR but limited to the implant's surface vicinity. For translatability, we evaluated the effectiveness of the residual hair with the most depleted KAPs (L-KAP) in a 10 mm-diameter, splinted, and full-thickness mouse skin excision wound. Treatment with the L-KAP mesh exhibited an 8% healing improvement per day compared to the untreated control: significantly reducing the projected complete healing time by 30%. On-going research focuses on purer keratin-based and macromolecularly organized residual hair biomaterials for drug-delivery as they are deemed the most biocompatible.

Unveiling the influence of fastest nobel prize winner discovery: alphafold's algorithmic intelligence in medical sciences

CONTEXT

AlphaFold's advanced AI technology has transformed protein structure interpretation. By predicting three-dimensional protein structures from amino acid sequences, AlphaFold has solved the complex protein-folding problem, previously challenging for experimental methods due to numerous possible conformations. Since its inception, AlphaFold has introduced several versions, including AlphaFold2, AlphaFold DB, AlphaFold Multimer, Alpha Missense, and AlphaFold3, each further enhancing protein structure prediction. Remarkably, AlphaFold is recognized as the fastest Nobel Prize winner in science history. This technology has extensive applications, potentially transforming treatment and diagnosis in medical sciences by reducing drug design costs and time, while elucidating structural pathways of human body systems. Numerous studies have demonstrated how AlphaFold aids in understanding health conditions by providing critical information about protein mutations, abnormal protein-protein interactions, and changes in protein dynamics. Researchers have also developed new technologies and pipelines using different versions of AlphaFold to amplify its potential. However, addressing existing limitations is crucial to maximizing AlphaFold's capacity to redefine medical research. This article reviews AlphaFold's impact on five key aspects of medical sciences: protein mutation, protein-protein interaction, molecular dynamics, drug design, and immunotherapy.

METHODS

This review examines the contributions of various AlphaFold versions AlphaFold2, AlphaFold DB, AlphaFold Multimer, Alpha Missense, and AlphaFold3 to protein structure prediction. The methods include an extensive analysis of computational techniques and software used in interpreting and predicting protein structures, emphasizing advances in AI technology and its applications in medical research.

The fast-evolving FIKK kinase family of Plasmodium falciparum can be inhibited by a single compound

Of 250 Plasmodium species, 6 infect humans, with P. falciparum causing over 95% of 600,000 annual malaria-related deaths. Its pathology arises from host cell remodelling driven by over 400 exported parasite proteins, including the FIKK kinase family. About one million years ago, a bird-infecting Plasmodium species crossed into great apes and a single non-exported FIKK kinase gained an export element. This led to a rapid expansion into 15-21 atypical, exported Ser/Thr effector kinases. Here, using genomic and proteomic analyses, we demonstrate FIKK differentiation via changes in subcellular localization, expression timing and substrate motifs, which supports an individual important role in host-pathogen interactions. Structural data and AlphaFold2 predictions reveal fast-evolving loops in the kinase domain that probably enabled rapid functional diversification for substrate preferences. One FIKK evolved exclusive tyrosine phosphorylation, previously thought absent in Plasmodium. Despite divergence of substrate preferences, the atypical ATP binding pocket is conserved and we identified a single compound that inhibits all FIKKs. A pan-specific inhibitor could reduce resistance development and improve malaria control strategies.

Optimized Legionella expression strain for affinity purification of His-tagged membrane proteins eliminates major multimeric contaminant

Polyhistidine tags are frequently used for isolating proteins through nickel-nitrilotriacetic acid (Ni-NTA) affinity purification. However, proteins rich in histidine can also bind to the Ni-NTA resin, leading to contamination of the purification with undesired proteins. While attempting to purify the Legionella pneumophila Dot/Icm type IVB secretion system complex for single particle analysis, we encountered an unknown contaminant protein that bound to the Ni-NTA resin and formed uniform particles visible in negative stain electron microscopy (EM). Mass spectrometry identified this protein, which is encoded by the Legionella gene lpg1596 as a homolog of enoyl-CoA hydratase. Modeling of Lpg1596 revealed surface-exposed histidine clusters, which likely explains its ability to bind to the Ni-NTA resin. Moreover, since enoyl-CoA hydratase homologs are known to multimerize, multimers of Lpg1596 would be large enough to be visible by negative stain EM. To address the problematic issue of Lpg1596 binding to the Ni-NTA resin, we constructed and analyzed a L. pneumophila ∆lpg1596 mutant strain. Notably, Ni-NTA affinity purification of lysates from the ∆lpg1596 strain did not contain the contaminant protein or generate observable particles. Since the ∆lpg1596 mutant strain exhibited replication capabilities similar to the wild-type L. pneumophila in macrophages, its deletion will likely not affect pathogenesis studies. To facilitate the deletion of lpg1596 in other Legionella strains, we developed a set of natural transformation vectors with various antibiotic resistance markers. In summary, we present a strategy for removing a common Ni-NTA resin binding protein contaminant in L. pneumophila, which improves single particle analysis outcomes.IMPORTANCENi-NTA purifications are a common method for isolating proteins with a His-tag, but they have a drawback: they often enrich unwanted proteins that are rich in histidines, which can contaminate the sample. We identified one such contaminant in Legionella pneumophila, a protein with homology to enoyl-CoA hydratases (Lpg1596). This protein binds to the Ni-NTA resin and forms particles that are observable in electron microscope (EM) images, interfering with the analysis. By removing the gene responsible for making this protein (lpg1596), the problem was solved, and no unwanted particles appeared in the EM images. The ∆lpg1596 mutant strain is the first optimized strain for purifying His-tagged membrane proteins from Legionella, which can be used for further studies like single particle analysis.

Competing signaling pathways controls electrotaxis

Understanding how cells follow exogenous cues is a key question for biology, medicine, and bioengineering. Growing evidence shows that electric fields represent a precise and programmable method to control cell migration. Most data suggest that the polarization of membrane proteins and the following downstream signaling are central to electrotaxis. Unfortunately, how these multiple mechanisms coordinate with the motile machinery of the cell is still poorly understood. Here, we develop a mechanistic model that explains electrotaxis across different cell types. Using the zebrafish proteome, we identify membrane proteins directly related to migration signaling pathways that polarize anodally and cathodally. Further, we show that the simultaneous and asymmetric distribution of these membrane receptors establish multiple cooperative and competing stimuli for directing the anodal and cathodal migration of the cell. Using electric fields, we enhance, cancel, or switch directed cell migration, with clear implications in promoting tissue regeneration or arresting tumor progression.

LIGYSIS-web: a resource for the analysis of protein-ligand binding sites

LIGYSIS-web is a free website accessible to all users without any login requirement for the analysis of protein-ligand binding sites. LIGYSIS-web hosts a database of 65,000 protein-ligand binding sites across 25,000 proteins. LIGYSIS sites are defined by aggregating unique relevant protein-ligand interfaces across different biological assemblies of the same protein deposited on the PDBe. Additionally, users can upload their own structures in PDB or mmCIF format for analysis and subsequent visualisation and download. Ligand sites are characterised using evolutionary divergence from a multiple sequence alignment, human missense genetic variation from gnomAD and relative solvent accessibility to obtain accessibility-based cluster labels and scores indicating likelihood of function. These results are displayed in the LIGYSIS web server, a Python Flask web application with a JavaScript frontend employing Jinja and jQuery to link the 3Dmol.js structure viewer with dynamic tables and Chart.js graphs in an interactive manner. LIGYSIS-web is available at https://www.compbio.dundee.ac.uk/ligysis/, whilst the source code for the analysis pipelines and web application can be accessed at https://github.com/bartongroup/LIGYSIS, https://github.com/bartongroup/LIGYSIS-custom and https://github.com/bartongroup/LIGYSIS-web, respectively.

Compositional profiling of protein hydrolysates by high resolution liquid chromatography-mass spectrometry and chemometric analysis

Protein hydrolysates have attracted growing research and commercial attention due to their numerous nutritional, functional, and biological activities. However, only a limited range of proximate properties are determined routinely due to their substantial structural complexity and compositional variability. From both a manufacturing and functional perspective, it is of critical importance to monitor the compositional variations and identify potential similar or disparate features between different protein hydrolysates. In the current study, a single-approached method employing reverse phase ultra-high performance liquid chromatography coupled to high resolution electrospray ionization tandem mass spectrometry (RP-UHPLC-HR-ESI-MS/MS) was developed, optimized, and cross-validated for comprehensive structural and compositional profiling of a range of protein hydrolysates of varying raw materials, including soy, cotton, wheat, rice, and meat. Untargeted chemometric analysis and feature-based molecular network demonstrated potential for large-scale compositional assessment of protein hydrolysates without the need of prior component annotation. Signature features were identified to differentiate soy hydrolysates prepared from different batches of raw material and by different manufacturing processes. A hybrid approach combining de novo sequencing and target-decoy database homology search for peptide annotation is also described. Short peptides of 2 to 5 amino acids represented the most abundant components in soy protein hydrolysates (SPHs). A simple yet reliable integrated workflow for comprehensive structural and compositional profiling of protein hydrolysates was developed to enable an eventual correlation between their structure and function.

Proteomic Analysis of Invasive Breast Cancer Cells Treated with CBD Reveals Proteins Associated with the Reversal of Their Epithelial-Mesenchymal Transition Induced by IL-1β

Cannabidiol (CBD) has shown promise in treating cancers with an inflammatory microenvironment. Although it has been demonstrated that IL-1β induces epithelial-mesenchymal transition (EMT) of MCF-7 cells and CBD reverts this process, in restoring the epithelial non-invasive phenotype, there is limited understanding of how this cannabinoid regulates these processes. In this work, MCF-7 cells were induced to adopt an aggressive phenotype (6D cells), which was reversed by CBD. Then, protein expression was analyzed by mass spectrometry to compare 6D vs. MCF-7 cells and 6D+CBD vs. 6D cells proteomes. Novel proteins associated with EMT and CBD signaling were identified. Twenty-four of them were oppositely regulated by IL-1β and CBD, suggesting new points of crosstalk between the IL-1β and CBD signaling pathways. From the data, two protein networks were constructed: one related to EMT with 58 up-regulated proteins and another with 21 related to CBD signaling. The first one showed the proteins BRCA1, MSN, and CORO1A as the key axis that contributes to the establishment of a mesenchymal phenotype. In the CBD signaling, the key axis was formed by SUPT16H, SETD2, and H2BC12, which suggests epigenetic regulation by CBD in the restoration of an epithelial phenotype of breast cancer cells, providing new targets for anticancer therapy.

USP18 deubiquitinates and stabilizes SOX9 to promote the stemness and malignant progression of glioblastoma

Glioblastoma (GBM), the most common and aggressive primary brain tumour, is associated with poor prognosis, primarily due to its stem-like subpopulation, glioblastoma stem cells (GSCs). The deubiquitinase (DUB) family has attracted an increasing amount of attention due to its roles in GSC biology and tumour aggressiveness. In this study, we focused on ubiquitin-specific peptidase 18 (USP18), a member of the DUB family whose role in GBM is poorly understood. Through integrated bioinformatics analyses and experimental investigations using patient-derived samples, cell models, and animal models, we elucidated the role of USP18 in enhancing GSC stemness and promoting malignant behaviours. Our findings revealed that USP18 expression is significantly elevated in GBM and is correlated with a poor prognosis. Mechanistically, USP18 interacts with SRY-box transcription factor 9 (SOX9), stabilising its protein levels by cleaving K48-linked polyubiquitin chains. Additionally, we identified YY1 as a transcriptional regulator of USP18, increasing its expression in GBM cells. These findings reveal that USP18 is a potential therapeutic target and highlight the novel YY1/USP18/SOX9 signalling axis implicated in GBM progression.

Whole genome sequencing and annotation of Pseudocercospora abelmoschi, a causal agent of black leaf mould of okra

Pseudocercospora abelmoschi causes black mould on the leaves of okra. The disease is prevalent post-rainy season when high moisture and warm temperatures prevail. Severe defoliation is observed during favourable environments, leading to a significant loss in productivity. Based on the importance of the pathogen agriculturally, the P. abelmoschi isolate Cer 86 - 18 (MCC:9491) was selected for genome sequencing. The genome assembly of P. abelmoschi resulted in a genome of 31.90 Mb with an overall GC content of 54.26%. Quantitative genome assessment using BUSCO (Benchmarking Universal Single-Copy Orthologs) identified 1,664 (97.53%) complete BUSCOs, reflecting a high representation of conserved genes with minimal duplication and strong orthologous uniqueness. Gene prediction analysis identified 11,325 protein-coding genes, of which 3,857 were annotated using the KEGG database. As per analyses, 410 genes were predicted to encode carbohydrate-active enzymes, whereas 369 genes were predicted to encode peptidases. Eighteen gene clusters involved in secondary metabolite biosynthesis were also identified. A total of 143 proteins were predicted to be effectors using the in-silico pipeline. This is the first report on the genome organisation of P. abelmoschi. This study was designed to address this gap by enhancing our understanding of the genome organisation of P. abelmoschi and gene annotation, thereby paving the way for functional genomics studies, such as identifying virulence genes to aid in resistance breeding. Also, this genome could be another addition to the available genomic resources of the genus Pseudocercospora and can provide valuable insights into host-pathogen interactions and evolutionary relationships.

Allosteric modulation of Plasmodium falciparum Isoleucyl tRNA synthetase by South African natural compounds

Targeting Plasmodium falciparum (Pf) aminoacyl tRNA synthetases is a viable strategy to overcome malaria parasite multi-drug resistance. Here, we focused on Pf Isoleucyl tRNA synthetase (PfIleRS) to identify potential allosteric inhibitors from 1019 South African Natural Compounds (SANC). Eleven potential hits, which passed ADMET and PAINS, were selected based on their docking binding affinity which was higher for PfIleRS than for human IleRS. Molecular dynamics simulations revealed that the compounds, particularly SANC456, commonly induced considerable changes in the global conformation and dynamics of PfIleRS, suggesting potential allosteric modulatory effects. Importantly, all 11 SANC hits reduced the binding affinity of the nucleotide AMP molecule by at least 25%. Some SANC ligand-bound systems (SANC456, SANC1095, and SANC1104) significantly increased the distance between the AMP and Ile ligands. Possible explanations for these changes were explored using three dynamic residue network centrality metrics. Betweenness centrality identified a major allosteric pathway in holo PfIleRS spanning the entire protein length. In contrast, SANC382, SANC456, SANC522, SANC806 and SANC1095 ligand-bound systems exhibited delta BC pathways (SANC-protein minus holo-protein), induced by the ligands, extending from their respective pockets into the active site. Additionally, eigenvector centrality revealed two important residue clusters either side of the holo active site which became altered in the ligand-bound systems, indicating possible allosteric activity. Lastly, many SANC systems showed decreased closeness centrality of zinc finger and active site residues, including the HYGH and KMSKR motifs. We believe that the compounds identified in this study as potential allosteric inhibitors have strong translational potential and warrant further investigation through in vitro and in vivo experiments. Overall, they hold promise as starting points for the development of new and effective antimalarial therapies, particularly against multidrug-resistant Plasmodium parasites.

Genomic structure of yellow lupin (Lupinus luteus): genome organization, evolution, gene family expansion, metabolites and protein synthesis

Yellow lupin (Lupinus luteus) gives valuable high-quality protein and has good sustainability due to its ability in nitrogen fixation and exudation of organic acids, which reduces the need for chemical-based phosphate fertilization in acid soils. However, the crop needs further improvements to contribute in a major way to sustainable agriculture and food security.In this study, we present the first chromosome-level genome assembly of L. luteus. The results provide insights into its genomic organization, evolution, and functional attributes. Using integrated genomic approaches, we unveil the genetic bases governing its adaptive responses to environmental stress, delineating the intricate interplay among alkaloid biosynthesis, mechanisms of pathogen resistance, and secondary metabolite transporters. Our comparative genomic analysis of closely related species highlights recent speciation events within the Lupinus genus, exposing extensive synteny preservation alongside notable structural alterations, particularly chromosome translocations. Remarkable expansions of gene families implicated in terpene metabolism, stress responses, and conglutin proteins were identified, elucidating the genetic basis of L. luteus' superior nutritional profile and defensive capabilities. Additionally, a diverse array of disease resistance-related (R) genes was uncovered, alongside the characterization of pivotal enzymes governing quinolizidine alkaloid biosynthesis, thus shedding light on the molecular mechanisms underlying "bitterness" in lupin seeds.This comprehensive genomic analysis serves as a valuable resource to improve this species in terms of resilience, yield, and seed protein levels to contribute to food and feed to face the worldwide challenge of sustainable agriculture and food security.

Biochemical mapping reveals a conserved heme transport mechanism via CcmCD in System I bacterial cytochrome c biogenesis

Heme is a redox-active cofactor for essential processes across all domains of life. Heme's redox capabilities are responsible for its biological significance but also make it highly cytotoxic, requiring tight intracellular regulation. Thus, the mechanisms of heme trafficking are still not well understood. To address this, the bacterial cytochrome c biogenesis pathways are being developed into model systems to elucidate mechanisms of heme trafficking. These pathways function to attach heme to apocytochrome c, which requires the transport of heme from inside to outside of the cell. Here, we focus on the System I pathway (CcmABCDEFGH) which is proposed to function in two steps: CcmABCD transports heme across the membrane and attaches it to CcmE. HoloCcmE then transports heme to the holocytochrome c synthase, CcmFH, for attachment to apocytochrome c. To interrogate heme transport across the membrane, we focus on CcmCD, which can form holoCcmE independent of CcmAB, leading to the hypothesis that CcmCD is a heme transporter. A structure-function analysis via cysteine/heme crosslinking identified a heme acceptance domain and heme transport channel in CcmCD. Bioinformatic analysis and structural predictions across prokaryotic organisms determined that the heme acceptance domains are structurally variable, potentially to interact with diverse heme delivery proteins. In contrast, the CcmC transmembrane heme channel is structurally conserved, indicating a common mechanism for transmembrane heme transport. We provide direct biochemical evidence mapping the CcmCD heme channel and providing insights into general mechanisms of heme trafficking by other putative heme transporters.

IMPORTANCE

Heme is a biologically important cofactor for proteins involved with essential cellular functions, such as oxygen transport and energy production. Heme can also be toxic to cells and thus requires tight regulation and specific trafficking pathways. As a result, much effort has been devoted to understanding how this important, yet cytotoxic, molecule is transported. While several heme transporters/importers/exporters have been identified, the biochemical mechanisms of transport are not well understood, representing a major knowledge gap. Here, the bacterial cytochrome c biogenesis pathway, System I (CcmABCDEFGH), is used to elucidate the transmembrane transport of heme via CcmCD. We utilize a cysteine/heme crosslinking approach, which can trap endogenous heme in specific domains, to biochemically map the heme transport channel in CcmCD, demonstrating that CcmCD is a heme transporter. These results suggest a model for heme trafficking by other heme transporters in both prokaryotes and eukaryotes.

Synphilin-1 regulates mechanotransduction in rigidity sensing through interaction with zyxin

BACKGROUND

Synphilin-1 has been studied extensively in the context of Parkinson's disease pathology. However, the biophysical functions of synphilin-1 remain unexplored. To investigate its novel functionalities herein, cellular traction force and rigidity sensing ability are analyzed based on synphilin-1 overexpression using elastomeric pillar arrays and substrates of varying stiffness. Molecular changes are analyzed using RNA sequencing-based transcriptomic and liquid chromatography-tandem mass spectrometry-based proteomic analyses.

RESULTS

Synphilin-1 overexpression reduces cell area, with a decline of local contraction on elastomeric pillar arrays. Cells overexpressing synphilin-1 exhibit an impaired ability to respond to substrate rigidity; however, synphilin-1 knockdown restores rigidity sensing abilities. Integrated omics analysis and in silico prediction corroborate the phenotypic alterations induced by synphilin-1 overexpression at a biophysical level. Zyxin emerges as a novel synphilin-1 binding protein, and synphilin-1 overexpression reduces the nuclear translocation of yes-associated protein.

CONCLUSION

These findings provide novel insights into the biophysical functions of synphilin-1, suggesting a potential protective role to the altered extracellular matrix, which may be relevant to neurodegenerative conditions such as Parkinson's disease.

Machine Learning in nanoarchitectonics

Perhaps no so visible and even difficult to notice at a quick glance, the links between nanoarchitectonics and machine learning are strong and profound both historically and thematically. From ancient times through middle-ages to modern digital world, mathematics has played an important role and made an impact on many areas, including what has emerged now as nanoscience and nanoarchitectonics. In this review, we analyze artificial intelligence, machine learning and deep learning for discovery, prediction, optimization, characterization and imaging in nanoarchitectonics. Although three more general parts are highlighted: (1) atomic and molecular sciences; (2) nanotechnology for colloids and nanofilms; (3) micro- and macro- technologies, application of machine learning in nanotechnology for colloids and nanofilms (2) is particularly relevant and important, because nanofabricated structures do not coincide with projected nano-designs. In machine learning, eXplainable Artificial Intelligence (XAI) is becoming an important area helping humans to understand why a machine would make such a decision - here, it is scrutinized through analyzing interpretability, time, accuracy, parameters (ITAP) matrix. Eventually, optimization of materials design and fabrication is linked with autonomous synthesis which is discussed in perspectives finalized with conclusions, which provides the summary and inherent links between these fields.

Prediction of protein subcellular localization in single cells

The subcellular localization of a protein is important for its function, and its mislocalization is linked to numerous diseases. Existing datasets capture limited pairs of proteins and cell lines, and existing protein localization prediction models either miss cell-type specificity or cannot generalize to unseen proteins. Here we present a method for Prediction of Unseen Proteins' Subcellular localization (PUPS). PUPS combines a protein language model and an image inpainting model to utilize both protein sequence and cellular images. We demonstrate that the protein sequence input enables generalization to unseen proteins, and the cellular image input captures single-cell variability, enabling cell-type-specific predictions. Experimental validation shows that PUPS can predict protein localization in newly performed experiments outside the Human Protein Atlas used for training. Collectively, PUPS provides a framework for predicting differential protein localization across cell lines and single cells within a cell line, including changes in protein localization driven by mutations.

GEDG: gene expression database of grape

BACKGROUND

Grapes are one of the oldest cultivated fruit species in the world, known for their high nutritional value. Grapes and grape products, including wine, are significant to many economies worldwide. Both abiotic and biotic stresses significantly impact the growth and reproduction of grapevines. RNA-Seq is a key tool for studying gene expression patterns under various conditions, such as stress responses, growth and developmental stages. In order to optimise the utilisation of these data for comprehensive research and analysis, and to enhance accessibility for users, we have developed a comprehensive database that provides gene expression profiles and traits in grapes.

DESCRIPTION

We have developed the gene expression database for grapes (GEDG), an online resource that encompasses data on 9 types of abiotic (including heat, cold, hormones, light, climate, nitrogen, frozen) and biotic (disease and pests) stresses and different developmental stages. A total of 32 datasets were related to stress, 20 to growth and development, and one to chromosomal ploidy. The primary objective of GEDG is to expand and utilize transcriptome data to better understand stress responses in grapes. GEDG is the comprehensive database to integrate transcriptome and traits on grape.

CONCLUSION

The Gene Expression Database of Grape (GEDG) was created to serve as a free resource platform for researchers, offering a comprehensive collection of genetic expression profiles for grape varieties based on RNA-seq data. The database is available at https://www.gedg-vitis.cn/index.html .

A nod to paratuberculosis: NOD1 and NOD2 expression in the pathological spectrum of Mycobacterium avium subsp. paratuberculosis infection in cattle

Mycobacterium avium subsp. paratuberculosis causes various types of granulomatous lesions in cattle, ranging from focal lesions associated with latency to diffuse lesions observed in animals with clinical disease. While the exact determining factors are unknown, recent evidence highlights the key role of innate immunity in the outcome of the infection. NOD-like receptors, which are innate immune proteins, play a significant role in recognizing intracellular pathogens, including mycobacteria. This study aimed to evaluate the expression of NOD1 and NOD2 in intestinal samples from cattle with different types of lesions associated with paratuberculosis: focal, diffuse paucibacillary, and multibacillary forms. The expression of NOD1 and NOD2 was assessed according to the number of immunolabeled cells, and only those cells consistent with macrophages were considered. A significant increase in the number of NOD1+ and NOD2+ macrophages was observed in cattle with diffuse multibacillary forms compared to the other groups. No expression of NOD1 or NOD2 was detected in the focal and diffuse paucibacillary lesions, while a strong expression of NOD2 and occasional NOD1 was observed in the multibacillary granulomas. These findings suggest that NOD1 and NOD2 are involved in the pathogenesis of bovine paratuberculosis.

Data- and knowledge-derived functional landscape of human solute carriers

The human solute carrier (SLC) superfamily of ~460 membrane transporters remains the largest understudied protein family despite its therapeutic potential. To advance SLC research, we developed a comprehensive knowledgebase that integrates systematic multi-omics data sets with selected curated information from public sources. We annotated SLC substrates through literature curation, compiled SLC disease associations using data mining techniques, and determined the subcellular localization of SLCs by combining annotations from public databases with an immunofluorescence imaging approach. This SLC-centric knowledge is made accessible to the scientific community via a web portal featuring interactive dashboards and visualization tools. Utilizing this systematically collected and curated resource, we computationally derived an integrated functional landscape for the entire human SLC superfamily. We identified clusters with distinct properties and established functional distances between transporters. Based on all available data sets and their integration, we assigned biochemical/biological functions to each SLC, making this study one of the largest systematic annotations of human gene function and a potential blueprint for future research endeavors.

AI-driven de novo enzyme design: Strategies, applications, and future prospects

Enzymes are indispensable for biological processes and diverse applications across industries. While top-down modification strategies, such as directed evolution, have achieved remarkable success in optimizing existing enzymes, bottom-up de novo enzyme design has emerged as a transformative approach for engineering novel enzymes with customized catalytic functions, independent of natural templates. Recent advancements in artificial intelligence (AI) and computational power have significantly accelerated this field, enabling breakthroughs in enzyme engineering. These technologies facilitate the rapid generation of enzyme structures and amino acid sequences optimized for specific functions, thereby enhancing design efficiency. They also support functional validation and activity optimization, improving the catalytic performance, stability, and robustness of de novo designed enzymes. This review highlights recent advancements in AI-driven de novo enzyme design, discusses strategies for validation and optimization, and examines the challenges and future prospects of integrating these technologies into enzyme development.

Designer artificial environments for membrane protein synthesis

Protein synthesis in natural cells involves intricate interactions between chemical environments, protein-protein interactions, and protein machinery. Replicating such interactions in artificial and cell-free environments can control the precision of protein synthesis, elucidate complex cellular mechanisms, create synthetic cells, and discover new therapeutics. Yet, creating artificial synthesis environments, particularly for membrane proteins, is challenging due to the poorly defined chemical-protein-lipid interactions. Here, we introduce MEMPLEX (Membrane Protein Learning and Expression), which utilizes machine learning and a fluorescent reporter to rapidly design artificial synthesis environments of membrane proteins. MEMPLEX generates over 20,000 different artificial chemical-protein environments spanning 28 membrane proteins. It captures the interdependent impact of lipid types, chemical environments, chaperone proteins, and protein structures on membrane protein synthesis. As a result, MEMPLEX creates new artificial environments that successfully synthesize membrane proteins of broad interest but previously intractable. In addition, we identify a quantitative metric, based on the hydrophobicity of the membrane-contacting amino acids, that predicts membrane protein synthesis in artificial environments. Our work allows others to rapidly study and resolve the "dark" proteome using predictive generation of artificial chemical-protein environments. Furthermore, the results represent a new frontier in artificial intelligence-guided approaches to creating synthetic environments for protein synthesis.

SARS-CoV-2 damages cardiomyocyte mitochondria and implicates long COVID-associated cardiovascular manifestations

INTRODUCTION

With the COVID-19 pandemic becoming endemic, vigilance for Long COVID-related cardiovascular issues remains essential, though their specific pathophysiology is largely unexplored.

OBJECTIVES

Our study investigates the persistent cardiovascular symptoms observed in individuals long after contracting SARS-CoV-2, a condition commonly referred to as "Long COVID", which has significantly affected millions globally.

METHODS

We meticulously describe the cardiovascular outcomes in five patients, encompassing a range of severe conditions such as sudden cardiac death during exercise, coronary atherosclerotic heart disease, palpitation, chest tightness, and acute myocarditis.

RESULTS

All five patients were diagnosed with myocarditis, confirmed through endomyocardial biopsy and histochemical staining, which identified inflammatory cell infiltration in their heart tissue. Crucially, electron microscopy revealed widespread mitochondrial vacuolations and the presence of myofilament degradation within the cardiomyocytes of these patients. These findings were mirrored in SARS-CoV-2-infected mice, suggesting a potential underlying cellular mechanism for the cardiac effects associated with Long COVID.

CONCLUSION

Our findings demonstrate a profound impact of SARS-CoV-2 on mitochondrial integrity, shedding light on the cardiovascular implications of Long COVID.

Assessing the toxicological effects and mechanism of plasticizer exposure on inflammatory bowel disease based on network toxicology and molecular docking

Phthalates (PAEs) are one of the most commonly used plasticizers. Due to their good performance, they are widely used in daily production, such as food packaging, paints, adhesives, children's toys, lubricants and building materials. However, PAEs usually have weak interactions with polymers, which can easily cause environmental pollution in use. These plasticizers have been linked to various health conditions, including inflammatory disorders. They are less intensively studied in the occurrence of inflammation, especially inflammatory bowel disease (IBD), and the necessity to evaluate their pathogenic molecular toxicity is particularly urgent. In this study, network toxicology and molecular docking methods were used to study the toxicological mechanism of IBD induced by four common plasticizers (DBP, DEHP, DEP, DNOP). Potential related targets were predicted using the PharmMapper, SwissStargetPrediction, GeneCards, DisGeNET, OMIM and TTD databases, and 286 related targets were identified. Using Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, binding protein-Protein Interaction (PPI) networks and cytoHubba plug-ins, ten relevant signaling pathways (PI3K-Akt signaling pathway, lipid and atherosclerosis, AGE-RAGE signaling pathway in diabetic complications, Proteoglycans in cancer, and so on.) and ten hub genes were identified. Four plasticizers (DBP, DEHP, DEP, DNOP) and the top 10 selected Hub gene targets (SRC, KRAS, PIK3CA, PIK3R1, JAK2, PTPN11, PIK3CD, HRAS, PIK3CG, EGFR) were analyzed by molecular docking. This study provides valuable insights into the molecular mechanisms of plasticizer-induced IBD and highlights the practicality of network toxicology in assessing the toxicity of emerging environmental pollutants. It enhances our understanding of the health risks posed by plasticizers and offers new strategies for mitigating their impact on inflammatory diseases.

Proteome trade-off between primary and secondary metabolism shapes acid stress induced bacterial exopolysaccharide production

Bacterial exopolysaccharide (EPS), as a high-value probiotic product, is known to be biosynthesized by a secondary metabolic pathway to mediate acid stress in lactic acid bacteria. However, a quantitative understanding of cellular resource coordination underlying acid stress-induced EPS production remains lacking. Systematically investigating Lactiplantibacillus plantarum HMX2, a well acknowledged EPS-producer, this study measured growth phenotypes, metabolomics, and proteomics of the target strain cultured at different pH values. Multi-omics analysis demonstrated that the EPS biosynthetic pathway was significantly up-regulated under acid stress, and pinpointed Fur as the most probable controlling transcriptional factor. Furthermore, the experimentally observed proteome re-allocation between primary metabolism and EPS biosynthesis was effectively captured by the regulatory proteome constrained flux balance analysis (RPCFBA) model via incorporating an activation function for secondary metabolism. This work, fusing the power of multi-omics analysis and genome-scale metabolic modeling, quantitatively elucidated the proteome trade-off between cellular growth and stress resistance underlying EPS production in lactic acid bacteria and shed light on the control principle of microbial secondary metabolism.

Phosphorylation of SNX17 impedes activation of Retriever-mediated sorting

Sorting nexin 17 (SNX17) functions as cargo receptor on endosomal membranes that enables the recycling of numerous membrane cargo proteins by binding to the Retriever complex. Yet, little is known how SNX17 activity or its membrane recruitment is regulated. Here, we report that phosphorylation of SNX17 at serine 38 (Ser38) within the phox (PX) domain serves as a critical regulatory switch governing its endosomal localization and function. A mutant form mimicking the phosphorylated state disrupts SNX17's ability to bind phosphatidylinositol-3-phosphate (PI3P), which in turn impairs its association with early endosomal membranes and inactivates SNX17-dependent cargo-recycling in cells. Furthermore, our results demonstrate that Ser38 is part of an autoinhibitory mechanism to regulate SNX17 cargo binding. Collectively, these findings provide new insights into the dynamic regulation of SNX17 activity and Retriever-mediated sorting processes. It also highlights SNX17 Ser38 phosphorylation as a critical regulatory mechanism that controls SNX17's endosomal localization and cargo recycling function.

Keratin 6A promotes skin inflammation through JAK1-STAT3 activation in keratinocytes

BACKGROUND

Skin barrier dysfunction and immune activation are hallmarks of inflammatory skin diseases such as rosacea and psoriasis, suggesting shared pathogenic mechanisms. While barrier disruption may trigger or exacerbate skin inflammation, the precise underlying mechanisms remain unclear. Notably, epidermal barrier compromise leads to a marked increase in barrier alarmin expression. Among these, keratin 6A (KRT6A) plays a role in maintaining skin barrier integrity.

METHODS

We treated mouse skin and human keratinocytes, with and without KRT6A expression, with LL37/TNF-α and assessed the severity of inflammation. The specific mechanism by which KRT6A promotes skin inflammation was investigated using mass spectrometry and immunoprecipitation assays.

RESULTS

KRT6A expression was elevated in lesional skin from patients and mouse models of rosacea and psoriasis. In mice with LL37-induced rosacea-like and imiquimod (IMQ)-induced psoriasis-like skin inflammation, KRT6A knockdown alleviated inflammation, whereas KRT6A overexpression exacerbated inflammatory responses. Mechanistically, KRT6A activated signal transducer and activator of transcription 3 (STAT3) and enhanced proinflammatory cytokine expression in keratinocytes by reducing Janus kinase 1 (JAK1) ubiquitination. This occurred through inhibition of ring finger protein 41 (RNF41)-mediated JAK1 binding.

CONCLUSIONS

Our findings indicate that KRT6A expression increases following epidermal barrier disruption and contributes to exacerbated skin inflammation in disease conditions. Targeting KRT6A may represent a novel therapeutic approach for inflammatory skin diseases associated with epidermal dysfunction.

DUX4 activates common and context-specific intergenic transcripts and isoforms

DUX4 regulates the expression of genic and nongenic elements and modulates chromatin accessibility during zygotic genome activation in cleavage stage embryos. Its misexpression in skeletal muscle causes facioscapulohumeral dystrophy (FSHD). By leveraging full-length RNA isoform sequencing with short-read RNA sequencing of DUX4-inducible myoblasts, we elucidate an isoform-resolved transcriptome featuring numerous unannotated isoforms from known loci and novel intergenic loci. While DUX4 activates similar programs in early embryos and FSHD muscle, the isoform usage of known DUX4 targets is notably distinct between the two contexts. DUX4 also activates hundreds of previously unannotated intergenic loci dominated by repetitive elements. The transcriptional and epigenetic profiles of these loci in myogenic and embryonic contexts indicate that the usage of DUX4-binding sites at these intergenic loci is influenced by the cellular environment. These findings demonstrate that DUX4 induces context-specific transcriptomic programs, enriching our understanding of DUX4-induced muscle pathology.

Sequence-based genome-wide association study and fine-mapping in German Holstein reveal new quantitative trait loci for health traits

We conducted a large GWAS of 11 health traits belonging to 3 trait complexes: (1) metabolic diseases, (2) infectious and noninfectious feet and claw disorders, and (3) udder-related traits in 100,809 to 180,217 German Holstein cows to investigate the genetic architecture and underlying biological mechanisms behind these complex traits. The GWAS identified 12,306 genome-wide significant variants across 10 traits. The new association signals were inspected with a Bayesian fine-mapping approach, leading to the discovery of 159 novel variants with high potential for causality. Variants were in known and novel regions for the traits studied, leading to a list of 53 novel candidate genes. Our study represents the largest whole-genome sequence GWAS for health traits so far, hence ensuring the power to detect meaningful variants, especially when enhanced with fine-mapping.

Evidence of focusing the MHC class I immunopeptidome by tapasin

Major Histocompatibility Complex class I (MHC-I) molecules bind and present peptides to cytotoxic T cells, protecting against pathogens and cancer. MHC-I is highly polymorphic and each allotype is promiscuous, and capable of binding a unique and diverse repertoire of peptide ligands. The peptide editing chaperone tapasin optimizes this allotype specific repertoire of peptides, resulting in the selection of high affinity peptides. MHC-I allotypes differ in the extent they engage tapasin. This suggests that tapasin-dependent MHC-I allotypes should present a less diverse repertoire that is enriched in higher-affinity peptides, and which are present in higher abundance, than tapasin independent MHC-I allotypes, which should present a broader repertoire containing peptides with a lower average affinity. Experimental verification of this hypothesis has been confounded by the different peptide binding specificities of MHC-I allotypes. Here, we independently investigated the peptide focusing function of tapasin by introducing a point mutation into a tapasin independent MHC-I allotype that dramatically increased its tapasin dependence without substantially altering its peptide binding specificity. This allowed us to demonstrate ligand focusing by tapasin at both the repertoire level in cellulo, and by using an in vitro system in which tapasin was artificially tethered to MHC-I, at the individual peptide level. We found that tapasin had a greater influence on tapasin dependent MHC-I molecules, and that tapasin modulated peptide selection according to peptide-MHC-I complex stability, disfavoring short-lived peptide-MHC-I complexes. Thus, tapasin dependent MHC-I molecules experience greater tapasin filtering, resulting in less diverse MHC-I immunopeptidomes that are enriched in high affinity peptide-MHC-I complexes.

Draft genome of the endemic alpine ground beetle Carabus (Platycarabus) depressus (Coleoptera: Carabidae) from long-read sequencing of a frozen archived specimen

The rapid advancement of genomic technologies has enabled the production of highly contiguous reference genomes for nonmodel organisms. However, these methods often require exceptionally fresh material containing unfragmented high-molecular-weight nucleic acids. Researchers who preserve field-collected specimens in ethanol at ambient temperatures, prior to transferring them to long-term frozen archives, face challenges in applying advanced genomic approaches due to DNA and RNA fragmentation under suboptimal preservation conditions. To explore the potential of such preserved specimens as sources of reference genomes, we utilized Nanopore MinION technology to generate genomic data from a frozen archived specimen of the endemic alpine ground beetle Carabus (Platycarabus) depressus. Using a rapid in-house protocol for high-molecular-weight DNA extraction, followed by sequencing on a single flow cell, we produced 8.75 million raw reads with an N50 of 2.8 kb. The resulting assembly achieved remarkable completeness, recovering up to 98% of Benchmarking Universal Single-Copy Orthologs genes, despite a moderate N50 of 945 kb. This genome is only the second available for the taxonomically diverse genus Carabus, demonstrating the feasibility of using short-to-long-read sequencing on frozen archived specimens commonly housed in natural history collections. These findings open new avenues for advancing nonmodel organism genomics and its downstream applications.

Derivatives of the Clinically Used HIF Prolyl Hydroxylase Inhibitor Desidustat Are Efficient Inhibitors of Human γ-Butyrobetaine Hydroxylase

The 2-oxoglutarate (2OG)/Fe(II)-dependent γ-butyrobetaine hydroxylase (BBOX) catalyzes the final step in l-carnitine biosynthesis, i.e., stereoselective C-3 oxidation of γ-butyrobetaine (GBB). BBOX inhibition is a validated clinical strategy to modulate l-carnitine levels and to enhance cardiovascular efficiency. Reported BBOX inhibitors, including the clinically used cardioprotective agent Mildronate, manifest moderate inhibitory activity in vitro, limited selectivity, and/or unfavorable physicochemical properties, indicating a need for improved BBOX inhibitors. We report that the clinically used hypoxia-inducible factor-α prolyl residue hydroxylase (PHD) inhibitors Desidustat, Enarodustat, and Vadadustat efficiently inhibit isolated recombinant BBOX, suggesting that BBOX inhibition by clinically used PHD inhibitors should be considered as a possible off-target effect. Structure-activity relationship studies on the Desidustat scaffold enabled development of potent BBOX inhibitors that manifest high levels of selectivity for BBOX inhibition over representative human 2OG oxygenases, including PHD2. The Desidustat derivatives will help to enable investigations into the biological roles of l-carnitine and the therapeutic potential of BBOX inhibition.

Integrated multi-omics analysis of metabolome and transcriptome profiles during bovine adipocyte differentiation reveals functional divergence of FADS2 isoforms in lipid metabolism regulation

BACKGROUND

Fat metabolism plays an important role in animal health and economic benefits. However, the changes in gene expression and metabolites during fat metabolism have not been systematically studied in bovine.

RESULTS

This study integrates transcriptomic and metabolomic strategies to delineate the metabolic and gene expression profiles during the adipogenesis of bovine preadipocytes in four different stages. Totally, we identified 328 differentially expressed metabolites (DEMs) and 5257 differentially expressed genes (DEGs) during adipogenesis. Functional enrichment of both DEMs and DEGs highlighted the important roles of fatty acid metabolic pathways. By integrating transcriptomic and metabolomic data, we identified key genes potentially regulating fatty acid metabolism, including FADS2, ACOT7 and ACOT2. We further applied comparison for the functional differences between two FADS2 isoforms (FADS2-T0 and FADS2-T2). The results proved that the lipid metabolism regulated by FADS2-2 has changed due to the loss of 46 amino acids with a transmembrane domain, which finally altering its promoting effect on bovine fat deposition.

CONCLUSIONS

In summary, our research provides important resources and key candidate genes for a systematic understanding of the changes in gene expression and lipid metabolism during the process of fat deposition.

Sensitivity analysis on protein-protein interaction networks through deep graph networks

BACKGROUND

Protein-protein interaction networks (PPINs) provide a comprehensive view of the intricate biochemical processes that take place in living organisms. In recent years, the size and information content of PPINs have grown thanks to techniques that allow for the functional association of proteins. However, PPINs are static objects that cannot fully describe the dynamics of the protein interactions; these dynamics are usually studied from external sources and can only be added to the PPIN as annotations. In contrast, the time-dependent characteristics of cellular processes are described in Biochemical Pathways (BP), which frame complex networks of chemical reactions as dynamical systems. Their analysis with numerical simulations allows for the study of different dynamical properties. Unfortunately, available BPs cover only a small portion of the interactome, and simulations are often hampered by the unavailability of kinetic parameters or by their computational cost. In this study, we explore the possibility of enriching PPINs with dynamical properties computed from BPs. We focus on the global dynamical property of sensitivity, which measures how a change in the concentration of an input molecular species influences the concentration of an output molecular species at the steady state of the dynamical system.

RESULTS

We started with the analysis of BPs via ODE simulations, which enabled us to compute the sensitivity associated with multiple pairs of chemical species. The sensitivity information was then injected into a PPIN, using public ontologies (BioGRID, UniPROT) to map entities at the BP level with nodes at the PPIN level. The resulting annotated PPIN, termed the DyPPIN (Dynamics of PPIN) dataset, was used to train a DGN to predict the sensitivity relationships among PPIN proteins. Our experimental results show that this model can predict these relationships effectively under different use case scenarios. Furthermore, we show that the PPIN structure (i.e., the way the PPIN is "wired") is essential to infer the sensitivity, and that further annotating the PPIN nodes with protein sequence embeddings improves the predictive accuracy.

CONCLUSION

To the best of our knowledge, the model proposed in this study is the first that allows performing sensitivity analysis directly on PPINs. Our findings suggest that, despite the high level of abstraction, the structure of the PPIN holds enough information to infer dynamic properties without needing an exact model of the underlying processes. In addition, the designed pipeline is flexible and can be easily integrated into drug design, repurposing, and personalized medicine processes.

Mechanistic decoding of octyl methoxycinnamate-induced breast toxicity via network toxicology, mendelian randomization, and molecular simulations

Octyl methoxycinnamate (OMC), a widely used UV filter, has raised concerns due to its potential reproductive toxicity and association with endocrine disruption. This study systematically identified OMC-induced breast toxicity targets and elucidated underlying molecular mechanisms by integrating network toxicology, differential gene expression analysis, Mendelian randomization (MR), molecular docking, and molecular dynamics (MD) simulations. Using SwissTargetPrediction, OMIM, GeneCards and DisGeNET databases, 185 potential targets linked to OMC exposure and breast injury were identified. STRING and Cytoscape analyses highlighted 31 hub targets. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment revealed significant associations with immune responses, cell proliferation, and signaling pathways. Analysis of GEO datasets identified overlapping differentially expressed genes (DEGs) between core targets and breast cancer (BC). MR analysis demonstrated a causal relationship between PTGS2 and BC risk. Molecular docking indicated strong binding affinities between OMC and core targets, particularly MMP9. MD simulations further confirmed stable OMC-PTGS2 interactions, supporting PTGS2 as a key mediator of OMC-induced breast toxicity. This work provides a theoretical foundation for understanding OMC's breast toxicity mechanisms and lays groundwork for preventing or managing breast disorders in populations exposed to OMC-containing environments.