UniProt: the Universal Protein Knowledgebase in 2023

PROPERMAB: an integrative framework for in silico prediction of antibody developability using machine learning

Selection of lead therapeutic molecules is often driven predominantly by pharmacological efficacy and safety. Candidate developability, such as biophysical properties that affect the formulation of the molecule into a product, is usually evaluated only toward the end of the drug development pipeline. The ability to evaluate developability properties early in the process of antibody therapeutic development could accelerate the timeline from discovery to clinic and save considerable resources. In silico predictive approaches, such as machine learning models, which map molecular features to predictions of developability properties could offer a cost-effective and high-throughput alternative to experiments for antibody developability assessment. We developed a computational framework, PROPERMAB (PROPERties of Monoclonal AntiBodies), for large-scale and efficient in silico prediction of developability properties for monoclonal antibodies, using custom molecular features and machine learning modeling. We demonstrate the power of PROPERMAB by using it to develop models to predict antibody hydrophobic interaction chromatography retention time and high-concentration viscosity. We further show that structure-derived features can be rapidly and accurately predicted directly from sequences by pre-training simple models for molecular features, thus providing the ability to scale these approaches to repertoire-scale sequence datasets.

Prediction of protein biophysical traits from limited data: a case study on nanobody thermostability through NanoMelt

In-silico prediction of protein biophysical traits is often hindered by the limited availability of experimental data and their heterogeneity. Training on limited data can lead to overfitting and poor generalizability to sequences distant from those in the training set. Additionally, inadequate use of scarce and disparate data can introduce biases during evaluation, leading to unreliable model performances being reported. Here, we present a comprehensive study exploring various approaches for protein fitness prediction from limited data, leveraging pre-trained embeddings, repeated stratified nested cross-validation, and ensemble learning to ensure an unbiased assessment of the performances. We applied our framework to introduce NanoMelt, a predictor of nanobody thermostability trained with a dataset of 640 measurements of apparent melting temperature, obtained by integrating data from the literature with 129 new measurements from this study. We find that an ensemble model stacking multiple regression using diverse sequence embeddings achieves state-of-the-art accuracy in predicting nanobody thermostability. We further demonstrate NanoMelt's potential to streamline nanobody development by guiding the selection of highly stable nanobodies. We make the curated dataset of nanobody thermostability freely available and NanoMelt accessible as a downloadable software and webserver.

Predicting purification process fit of monoclonal antibodies using machine learning

In early-stage development of therapeutic monoclonal antibodies, assessment of the viability and ease of their purification typically requires extensive experimentation. However, the work required for upstream protein expression and downstream purification development often conflicts with timeline pressures and material constraints, limiting the number of molecules and process conditions that can reasonably be assessed. Recently, high-throughput batch-binding screen data along with improved molecular descriptors have enabled development of robust quantitative structure-property relationship (QSPR) models that predict monoclonal antibody chromatographic binding behavior from the amino acid sequence. Here, we describe a QSPR strategy for in silico monoclonal antibody purification process fit assessment. Principal Component Analysis is applied to extract a one-dimensional basis for comparison of molecular chromatographic binding behavior from multi-dimensional high-throughput batch-binding screen data. Kernel Ridge Regression is used to predict the first principal component for new molecular sequences. This workflow is demonstrated with a set of 97 monoclonal antibodies for five chromatography resins in two salt types across a range of pH and salt concentrations. Model development benchmarks four descriptor sets from biophysical structural models and protein language models. The investigation illustrates the value QSPR models can provide to purification process fit assessment, and selection of resins and operating conditions from sequence alone.

Sequence and taxonomic feature evaluation facilitated the discovery of alcohol oxidases

Recent advancements in data technology offer immense opportunities for the discovery and development of new enzymes for the green synthesis of chemicals. Current protein databases predominantly prioritize overall sequence matches. The multi-scale features underpinning catalytic mechanisms and processes, which are scattered across various data sources, have not been sufficiently integrated to be effectively utilized in enzyme mining. In this study, we developed a sequence- and taxonomic-feature evaluation driven workflow to discover enzymes that can be expressed in E. coli and catalyze chemical reactions in vitro, using alcohol oxidase (AOX) for demonstration, which catalyzes the conversion of methanol to formaldehyde. A dataset of 21 reported AOXs was used to construct sequence scoring rules based on features, including sequence length, structural motifs, catalytic-related residues, binding residues, and overall structure. These scoring rules were applied to filter the results from HMM-based searches, yielding 357 candidate sequences of eukaryotic origin, which were categorized into six classes at 85 % sequence similarity. Experimental validation was conducted in two rounds on 31 selected sequences representing all classes. Among these selected sequences, 19 were expressed as soluble proteins in E. coli, and 18 of these soluble proteins exhibited AOX activity, as predicted. Notably, the most active recombinant AOX exhibited an activity of 8.65 ± 0.29 U/mg, approaching the highest activity of native eukaryotic enzymes. Compared to the established UniProt-annotation-based workflow, this feature-evaluation-based approach yielded a higher probability of highly active recombinant AOX (from 8.3 % to 19.4 %), demonstrating the efficiency and potential of this multi-dimensional feature evaluation method in accelerating the discovery of active enzymes.

Structure of the microtubule-anchoring factor NEDD1 bound to the γ-tubulin ring complex

The γ-tubulin ring complex (γ-TuRC) is an essential multiprotein assembly that provides a template for microtubule nucleation. The γ-TuRC is recruited to microtubule-organizing centers (MTOCs) by the evolutionarily conserved attachment factor NEDD1. However, the structural basis of the NEDD1-γ-TuRC interaction is not known. Here, we report cryo-EM structures of NEDD1 bound to the human γ-TuRC in the absence or presence of the activating factor CDK5RAP2. We found that the C-terminus of NEDD1 forms a tetrameric α-helical assembly that contacts the lumen of the γ-TuRC cone and orients its microtubule-binding domain away from the complex. The structure of the γ-TuRC simultaneously bound to NEDD1 and CDK5RAP2 reveals that both factors can associate with the "open" conformation of the complex. Our results show that NEDD1 does not induce substantial conformational changes in the γ-TuRC but suggest that anchoring of γ-TuRC-capped microtubules by NEDD1 would be structurally compatible with the significant conformational changes experienced by the γ-TuRC during microtubule nucleation.

Integrating network pharmacology and in vivo pharmacological validation to explore the gastroprotective mechanism of Sotetsuflavone against indomethacin-induced gastric ulcer in rats: Involvement of JAK2/STAT3 pathway

Sotetsuflavone (SF) is an antioxidant flavonoid derived from the Cycas thouarsii R.Br. plant. Although SF regulates numerous cellular pathways influencing inflammation, its antiinflammatory benefits against gastric ulcers are less well-studied. Hence, it is imperative to thoroughly understand the potential gastroprotective mechanisms of SF. This study aimed to explore the effectiveness of SF against indomethacin (IND)-induced gastric ulcers. Network analysis and molecular docking were used to identify the specific targets and pathways related to SF and stomach ulcers. To validate the in vivo pharmacological action of SF, 36 rats were divided into six groups. Ulcer index (UI), protective percentage (PP), gastric mucosal mediators, oxidant/antioxidant status, and inflammatory markers (MIF, M-CSF, and AIF-1) were assessed. Additionally, the expression of PI3K, Akt, Siah2, SOCS3, JAK2, and STAT3 was determined. Stomach histopathology and immunohistochemistry were done. Network pharmacology detected 46 overlapping targets between SF and stomach ulcers, with HIF1A as the primary target among the top hubs. The network also revealed that JAK/STAT, PI3K/Akt, and HIF-1A signaling are among the top 50 markedly enriched KEGG pathways. Furthermore, docking results confirmed that SF has a strong binding affinity towards SOCS3, JAK2, STAT3, M-CSF (CSF-1), and AIF-1. Therefore, we hypothesized that the JAK2/STAT3 pathway may be primarily responsible for SF antiinflammatory action. Through up-regulating SOCS3, SF altered the PI3K/Akt pathway, mitigating oxidative stress, blocking the outflow of inflammatory mediators, and impeding gastric ulcer development. Overall, SF, by the SOCS3-mediated JAK2/STAT3 suppression, might considerably reduce oxidative stress, inflammation, and ulceration caused by indomethacin in the stomach.

CNKSR2 interactome analysis indicates its association with the centrosome/microtubule system

JOURNAL/nrgr/04.03/01300535-202508000-00031/figure1/v/2024-09-30T120553Z/r/image-tiff The protein connector enhancer of kinase suppressor of Ras 2 (CNKSR2), present in both the postsynaptic density and cytoplasm of neurons, is a scaffolding protein with several protein-binding domains. Variants of the CNKSR2 gene have been implicated in neurodevelopmental disorders, particularly intellectual disability, although the precise mechanism involved has not yet been fully understood. Research has demonstrated that CNKSR2 plays a role in facilitating the localization of postsynaptic density protein complexes to the membrane, thereby influencing synaptic signaling and the morphogenesis of dendritic spines. However, the function of CNKSR2 in the cytoplasm remains to be elucidated. In this study, we used immunoprecipitation and high-resolution liquid chromatography-mass spectrometry to identify the interactors of CNKSR2. Through a combination of bioinformatic analysis and cytological experiments, we found that the CNKSR2 interactors were significantly enriched in the proteome of the centrosome. We also showed that CNKSR2 interacted with the microtubule protein DYNC1H1 and with the centrosome marker CEP290. Subsequent colocalization analysis confirmed the centrosomal localization of CNKSR2. When we downregulated CNKSR2 expression in mouse neuroblastoma cells (Neuro 2A), we observed significant changes in the expression of numerous centrosomal genes. This manipulation also affected centrosome-related functions, including cell size and shape, cell proliferation, and motility. Furthermore, we found that CNKSR2 interactors were highly enriched in de novo variants associated with intellectual disability and autism spectrum disorder. Our findings establish a connection between CNKSR2 and the centrosome, and offer new insights into the underlying mechanisms of neurodevelopmental disorders.

Outer membrane vesicles of carbapenem-resistant clinical Acinetobacter baumannii isolates protect both the vesicle-producing bacteria and non-resistant bacteria against carbapenems

Infections caused by carbapenem-resistant Acinetobacter baumannii (A. baumannii; CRAb) are associated with high patient morbidity and mortality. The serious threat for human health imposed by CRAb was recently underscored by identification of close-to-untouchable carbapenem- and tetracycline-resistant isolates. Since outer membrane vesicles (OMVs) of Gram-negative bacteria may contribute to antimicrobial resistance, our present study was aimed at investigating OMVs produced by the first two carbapenem- and tetracycline-resistant A. baumannii isolates in Europe. These isolates, denoted CRAb1 and CRAb2, contain large, nearly identical plasmids that specify multiple resistances. Both isolates produce OMVs that were analyzed by differential light scattering, transmission electron microscopy and proteomics. By comparison with OMVs from the plasmid-free non-carbapenem-resistant A. baumannii isolate Ab1, which is an isogenic ancestor of the CRAb1 isolate, we show that plasmid carriage by the CRAb1 and CRAb2 isolates leads to an increased OMV size that is accompanied by increased diversity of the OMV proteome. Our analyses show that OMVs from CRAb1 and CRAb2 are major reservoirs of proteins involved in antimicrobial resistance, including the plasmid-encoded carbapenemases New Delhi metallo-β-lactamase-1 (NDM-1), and carbapenem-hydrolyzing oxacillinase OXA-97 (OXA-97). Here we report that these OMV-borne carbapenemases hydrolyze imipenem and protect otherwise carbapenem-sensitive A. baumannii and Escherichia coli (E. coli) isolates against this antibiotic. In conclusion, our findings demonstrate that OMVs from highly drug-resistant CRAb confer protection against last-resort antibiotics to non-resistant bacterial pathogens.

PILOT: Deep Siamese network with hybrid attention improves prediction of mutation impact on protein stability

Evaluating the mutation impact on protein stability (ΔΔG) is essential in the study of protein engineering and understanding molecular mechanisms of disease-associated mutations. Here, we propose a novel deep learning framework, PILOT, for improved prediction of ΔΔG using a Siamese network with hybrid attention mechanism. The PILOT framework leverages multiple attention modules to effectively extract representations for amino acids, atoms, and protein sequences, respectively. This approach significantly ensures the deep fusion of structural information at both residue and atom levels, the seamless integration of structural and sequence representations, and the effective capture of both long-range and short-range dependencies among amino acids. Our extensive evaluations demonstrate that PILOT greatly outperforms other state-of-the-art methods. We also showcase that PILOT identifies exceptional patterns for different mutation types. Moreover, we illustrate the clinical applicability of PILOT in highlighting pathogenic variants from benign variants and VUS (variants of uncertain significance), and distinguishing de novo mutations in disease cases and controls. In summary, PILOT presents a robust deep learning tool that could offer significant insights into drug design, medical applications, and protein engineering studies.

Metabolic modelling of anaerobic amino acid uptake and storage by fermentative polyphosphate accumulating organisms

Existing enhanced biological phosphorus removal (EBPR) models do not fully describe the metabolism of fermentative polyphosphate accumulating organisms (fPAOs), particularly under mixed substrate conditions representative of fermentation-enhanced EBPR (F-EBPR) processes. This study presents a steady-state metabolic model integrating anaerobic amino acid (AA) fermentation and storage processes in fPAOs. The model identifies key metabolic interactions underlying fPAO metabolism, prioritising substrate accumulation over fermentation. This results in significant changes to ATP and reduction-oxidation (redox) flows as compared to when relying on previous AA fermentation models typically used to describe fPAO metabolism, with medium-chain-length (MCL) polyhydroxyalkanoate (PHA) formation and polyphosphate (polyP) consumption acting as important electron and energy management mechanisms, respectively. Succinate, rather than volatile fatty acids (VFAs), was identified as the more likely synergetic substrate between fermentative and conventional PAOs (cPAOs) under these conditions. Moreover, conditions favourable of VFA efflux by fPAOs may also favour a shift away from a polyP accumulation to a fermentation dominant metabolism. Further work is required to verify the role of MCL-PHA fractions, alongside the contribution of free intracellular AA accumulation as compared to polymers such as cyanophycin or polyglutamate on fPAO metabolism. This metabolic model provides a framework for better understanding the role of fPAOs and their interactions with cPAOs within EBPR processes, informing future modelling and optimisation of F-EBPR systems.

Higher order volatile fatty acid metabolism and atypical polyhydroxyalkanoate production in fermentation-enhanced biological phosphorus removal

Enhanced biological phosphorus removal (EBPR) is an established wastewater treatment process, but its wider implementation has been limited by factors like high temperature and low carbon availability. Fermentation-enhanced EBPR (F-EBPR) processes have shown promise in addressing these limitations, but the underlying mechanisms are not fully understood. This study investigates the metabolism of higher order (C4-5) volatile fatty acids (VFAs) in F-EBPR systems using a combination of carbon isotope labelling and shotgun metagenomic sequencing analyses. Results show that butyrate (HBu) uptake leads to the formation of both typical (C4-5) and atypical (C6+) polyhydroxyalkanoates (PHAs) through a combination ofβ-oxidation and standard condensation pathways, while the putative role of HBu oxidisers were identified relative to substrate composition in F-EBPR processes. Metagenomic analysis reveals the presence of genes required for higher order VFA metabolism in both polyphosphate accumulating organisms (PAOs) and glycogen accumulating organisms (GAOs). This study also highlights the limitations of current models in describing F-EBPR processes and emphasises the need for improved models that account for higher order VFA metabolism and microbial community dynamics.

Repurposing the amino-3,5-dicyanopyridine scaffold from adenosine receptor ligands to carbonic anhydrase activators

We are repurposing a set of imidazole-containing amino-3,5-dicyanopyridines, previously reported as adenosine receptor ligands, in the role of activators of human-expressed carbonic anhydrase isoenzymes (hCA I, II, VA, and VII) considered relevant to controlling brain functions. Our focus has been to identify new carbonic anhydrase activators (CAAs) as pharmacological tools useful to investigate the CA role in psychiatric and neurodegenerative disorders. All tested compounds were inactive at hCA II, highlighting a trend similar to that of the reference activator histamine. On the contrary, most of them showed different activation profiles at the other CAs tested. In particular, while compounds 13 and 24 had the lowest KA values at hCA VII (KA = 0.8 μM) and I (KA = 0.7 μM), respectively, derivatives 14 and 17 displayed the most effective and balanced activation profile at hCA I, VA, and VII, with KA values in the low micromolar range. The binding mode of compound 14 was investigated in silico using X-ray solved (hCA I and VII) and homology built (hCA VA) structures. Focusing our attention on drug-like compounds to find new pharmacological tools, the ADME properties of all derivatives were in silico calculated to investigate their drug-like behavior. Compound 17 emerged as a candidate, as it showed high oral availability and permeability of the gut-blood barrier, together with a good potential to cross the BBB.

Proteomic profile of multidrug-resistant Serratia marcescens under meropenem challenge

Serratia marcescens is an opportunistic bacterium implicated in the prevalence of serious nosocomial infections and increased outbreaks in Intensive Care Units (ICUs) and Neonatal Intensive Care Units (NICUs). S. marcescens strains are resistant to several antimicrobial classes and express numerous virulence factors that promote pathogenicity. In the present study, the proteomic profile of the multidrug-resistant (MDR) S. marcescens clinical isolate challenged with the antimicrobial meropenem was evaluated. The proteins obtained were analyzed using liquid chromatography coupled with tandem mass spectrometry (LC-MSE). A total of 199 induced proteins were identified revealing that multidrug-resistant S. marcescens promotes increasing of proteins related to energy metabolism and efflux pump and decreases synthesis of proteins related to oxidative stress response and cell mobility upon meropenem challenge, shedding some light on the relationship between expressed proteins and bacterial pathogenicity after antimicrobial induction.

Bioinformatics assisted construction of the link between biosynthetic gene clusters and secondary metabolites in fungi

Fungal secondary metabolites are considered as important resources for drug discovery. Despite various methods being employed to facilitate the discovery of new fungal secondary metabolites, the trend of identifying novel secondary metabolites from fungi is inevitably slowing down. Under laboratory conditions, the majority of biosynthetic gene clusters, which store information for secondary metabolites, remain inactive. Therefore, establishing the link between biosynthetic gene clusters and secondary metabolites would contribute to understanding the genetic logic underlying secondary metabolite biosynthesis and alleviating the current challenges in discovering novel natural products. Bioinformatics methods have garnered significant attention due to their powerful capabilities in data mining and analysis, playing a crucial role in various aspects. Thus, we have summarized successful cases since 2016 in which bioinformatics methods were utilized to establish the link between fungal biosynthetic gene clusters and secondary metabolites, focusing on their biosynthetic gene clusters and associated secondary metabolites, with the goal of aiding the field of natural product discovery.

Scaling metabolic model reconstruction up to the pan-genome level: A systematic review and prospective applications to photosynthetic organisms

Advances in genomics technologies have generated large data sets that provide tremendous insights into the genetic diversity of taxonomic groups. However, it remains challenging to pinpoint the effect of genetic diversity on different traits without performing resource-intensive phenotyping experiments. Pan-genome-scale metabolic models (panGEMs) extend traditional genome-scale metabolic models by considering the entire reaction repertoire that enables the prediction and comparison of metabolic capabilities within a taxonomic group. Here, we systematically review the state-of-the-art methodologies for constructing panGEMs, focusing on used tools, databases, experimental datasets, and orthology relationships. We highlight the unique advantages of panGEMs compared to single-species GEMs in predicting metabolic phenotypes and in guiding the experimental validation of genome annotations. In addition, we emphasize the disparity between the available (pan-)genomic data on photosynthetic organisms and their under-representation in current (pan)GEMs. Finally, we propose a perspective for tackling the reconstruction of panGEMs for photosynthetic eukaryotes that can help advance our understanding of the metabolic diversity in this taxonomic group.

Closing the gap: Nonviral TFAMoplex transfection boosted by bZIP domains compared to AAV-mediated transduction

The TFAMoplex is a nanoparticulate gene delivery system based on the mitochondrial transcription factor A (TFAM) protein, which can be engineered with various functional domains to enhance plasmid DNA transfection. In this study, we aimed at improving the TFAMoplex system by incorporating basic leucine zipper (bZIP) domains, derived from the cyclic AMP (cAMP)-responsive element-binding protein (CREB), which are known to bind DNA upon dimerization. Additionally, we screened bZIP domains of other proteins (i.e., transcription regulator protein BACH1, cyclic AMP-dependent transcription factor ATF-3, and basic leucine zipper transcriptional factor ATF-like BATF) under challenging transfection conditions, identifying the bZIP domain of BACH1, bZIPBACH1, as particularly effective in enhancing the TFAMoplex performance, reducing the half-maximal effective concentration by more than 2-fold. We show that bZIP domains facilitate interactions with the cell membrane as single proteins and thus increase the cell association of TFAMoplexes. Finally, we compared the optimized bZIPBACH1-TFAMoplex to adeno-associated viruses (AAVs) regarding in vitro transfection efficiency and transgene expression levels. While AAVs achieved higher transfection efficiency based on the number of transfected cells, both the original and improved TFAMoplex constructs surpassed AAVs in transgene expression per cell.

Aluminium oxide nanoparticles (Al2O3-NPs) exposure impairs cardiovascular physiology and elevates health risk - proteomic and molecular mechanistic insights

The interactions of nanoparticles with biomolecules lead to toxicopathological outcomes through various mechanisms including oxidative stress. In this regard, the interplay of oxidative stress with other molecular mechanisms of cytotoxicity during aluminium oxide nanoparticles (Al2O3-NPs) induced cardiovascular toxicity was not yet precisely explored. Initially, the human serum protein interaction and its corona composition were explored through the gel/label-free proteomics (nLC-HRMS/MS) method. In addition, endothelial cells (EC) and cardiomyoblasts (CM) cultures were employed along with various oxidative stress and cell stress assays. Further, various expression studies (RT-qPCR, western blot, and immunofluorescence), kinase signalling, and siRNA mediated gene knockout assays were performed. Alongside, the in ovo impact on antioxidant enzymes and metabolomic pathways (1H NMR) in the heart validated the role of oxidative stress during cardiotoxicity. The current outcome illustrates the dose-dependent increase of cytotoxicity and caspase (3 and 9) activation. The dose-dependent elevation and its synergy with cardiovascular stress signalling (ET-1 and Ang-II) illustrate the prominent role of oxidative stress during toxicity. In conclusion, the current study connects the role of the redox system and molecular stress pathways during Al2O3-NPs induced cardiotoxicity which extends the knowledge towards the precise health risk assessment during human exposure.

Subtle genomic differences in Klebsiella pneumoniae sensu stricto isolates indicate host adaptation

Klebsiella pneumoniae sensu stricto (KpI) is an opportunistic pathogen capable of residing as a commensal in both human and bovine intestinal tracts and can cause serious systemic infections in humans and severe clinical mastitis in dairy cattle. It is unclear what role zoonotic and anthroponotic transmission play in the dissemination of KpI. In this study, we use a comparative genomic approach to identify differences between KpI associated with disease in humans and cattle and aimed to identify any potential genetic barriers limiting transmission of KpI between these two hosts. A total of 128 KpI strains (bovine n = 65; human n = 63) were whole genome sequenced and human and bovine strains were compared based on phylogenomics, the pangenome, mobile genetic elements, and differential gene abundance. No obvious phylogenomic differentiation was observed between isolates from these hosts. However, subtle genetic differences exist between bovine and human KpI which likely reflect environmental adaptation to different host niches, including a higher representation of gene clusters encoding ferric citrate uptake transporters, as well as histidine, arginine, and lactose utilization pathways in bovine isolates. These gene clusters may be positively selected due to the unique metabolic environment of the mammary gland, where lactose, citrate-bound iron, and amino acids like histidine and arginine provide growth advantages for KpI during mastitis. Overall, our study identified no obvious genetic barriers to zoonotic transmission of KpI within the dairy environment and provides insight into the development of host-specific therapeutic options for KpI infections in humans and bovine.

Trim38 attenuates pressure overload‑induced cardiac hypertrophy by suppressing the TAK1/JNK/P38 signaling pathway

Pathological cardiac hypertrophy is a major contributor to heart failure (HF), resulting in high mortality rates worldwide; therefore, identifying key molecules in pathological cardiac hypertrophy is of critical importance for preventing or reversing HF. Tripartite motif 38 (Trim38) is an E3 ubiquitin ligase that serves a pivotal role in various diseases. The present study aimed to elucidate the regulatory role of Trim38 in pressure overload‑induced pathological cardiac hypertrophy and to explore its underlying molecular mechanisms. The expression of Trim38 was decreased in hypertrophic heart tissues from a murine model of transverse aortic constriction (TAC) and in neonatal rat cardiomyocytes (NRCMs) treated with phenylephrine (PE). Furthermore, Trim38 knockout (Trim38‑KO) aggravated cardiac hypertrophy after TAC, and Trim38 knockdown in cardiomyocytes increased cell cross section area, and upregulated the expression of atrial natriuretic peptide (ANP) and brain natriuretic peptide (BNP) following treatment with PE. Ubiquitinomics analysis revealed that the MAPK signaling pathway was regulated by Trim38. Furthermore, western blotting confirmed that Trim38‑KO activated TAK1 and JNK/P38. By contrast, Trim38 overexpression in NRCMs suppressed the JNK/P38 signaling pathway and inhibited the phosphorylation of TAK1. Furthermore, Trim38 knockdown resulted in a marked enhancement of TAK1 phosphorylation, concomitant with an augmentation of cardiomyocyte area and a significant upregulation of the hypertrophic biomarkers ANP and BNP. By contrast, infection with an adenovirus containing dominant‑negative TAK1 inhibited TAK1 activity, which attenuated Trim38 knockdown‑induced cardiomyocyte hypertrophy, confirming that TAK1 is a key molecule involved in the protective effects of Trim38 on cardiomyocytes. In conclusion, to the best of our knowledge, the present study is the first to reveal that Trim38 confers protection against pathological cardiac hypertrophy by inhibiting the TAK1/JNK/P38 signaling pathway; therefore, Trim38 may be a promising target for treating cardiac hypertrophy.

Protein identification using Cryo-EM and artificial intelligence guides improved sample purification

Protein purification is essential in protein biochemistry, structural biology, and protein design, enabling the determination of protein structures, the study of biological mechanisms, and the characterization of both natural and de novo designed proteins. However, standard purification strategies often encounter challenges, such as unintended co-purification of contaminants alongside the target protein. This issue is particularly problematic for self-assembling protein nanomaterials, where unexpected geometries may reflect novel assembly states, cross-contamination, or native proteins originating from the expression host. Here, we used an automated structure-to-sequence pipeline to first identify an unknown co-purifying protein found in several purified designed protein samples. By integrating cryo-electron microscopy (Cryo-EM), ModelAngelo's sequence-agnostic model-building, and Protein BLAST, we identified the contaminant as dihydrolipoamide succinyltransferase (DLST). This identification was validated through comparisons with DLST structures in the Protein Data Bank, AlphaFold 3 predictions based on the DLST sequence from our E. coli expression vector, and traditional biochemical methods. The identification informed subsequent modifications to our purification protocol, which successfully excluded DLST from future preparations. To explore the potential broader utility of this approach, we benchmarked four computational methods for DLST identification across varying resolution ranges. This study demonstrates the successful application of a structure-to-sequence protein identification workflow, integrating Cryo-EM, ModelAngelo, Protein BLAST, and AlphaFold 3 predictions, to identify and ultimately help guide the removal of DLST from sample purification efforts. It highlights the potential of combining Cryo-EM with AI-driven tools for accurate protein identification and addressing purification challenges across diverse contexts in protein science.

The C. elegans glutamate transporters GLT-4 and GLT-5 regulate protein expression, behavior, and lifespan

Glutamate transporters are important for regulating extracellular glutamate levels, impacting neural function and metabolic homeostasis. This study explores the behavioral, lifespan, and proteomic profiles in Caenorhabditis elegans strains with either glt-4 or glt-5 null mutations, highlighting contrasting phenotypes. Δglt-4 mutants displayed impaired mechanosensory and chemotactic responses, reduced lifespans, and decreased expression levels of ribosomal proteins and chaperonins involved in protein synthesis and folding. In contrast, Δglt-5 mutants displayed heightened chemorepulsion, extended lifespans, and upregulation of mitochondrial pyruvate carriers and cytoskeletal proteins. Proteomic profiling via mass spectrometry identified 53 differentially expressed proteins in Δglt-4 mutants and 45 in Δglt-5 mutants. Δglt-4 mutants showed disruptions in ribonucleoprotein complex organization and translational processes, including downregulation of glycogen phosphorylase and V-type ATPase subunits, while Δglt-5 mutants revealed altered metabolic protein expression, such as increased levels of mitochondrial pyruvate carriers and decreased levels of fibrillarin and ribosomal proteins. Gene ontology enrichment analysis highlighted differential regulation of protein biosynthesis and metabolic pathways between the strains. Overall, these findings underscore the distinct, tissue-specific roles of GLT-4 and GLT-5 in C. elegans, with broader implications for glutamate regulation and systemic physiology. The results also reinforce the utility of C. elegans as a model for studying glutamate transporters' impact on behavior, longevity, and proteostasis.

Evaluating sequence and structural similarity metrics for predicting shared paralog functions

Gene duplication is the primary source of new genes, resulting in most genes having identifiable paralogs. Over time, paralog pairs may diverge in some respects but many retain the ability to perform the same functional role. Protein sequence identity is often used as a proxy for functional similarity and can predict shared functions between paralogs as revealed by synthetic lethal experiments. However, the advent of alternative protein representations, including embeddings from protein language models (PLMs) and predicted structures from AlphaFold, raises the possibility that alternative similarity metrics could better capture functional similarity between paralogs. Here, using two species (budding yeast and human) and two different definitions of shared functionality (shared protein-protein interactions and synthetic lethality), we evaluated a variety of alternative similarity metrics. For some tasks, predicted structural similarity or PLM similarity outperform sequence identity, but more importantly these similarity metrics are not redundant with sequence identity, i.e. combining them with sequence identity leads to improved predictions of shared functionality. By adding contextual features, representing similarity to homologous proteins within and across species, we can significantly enhance our predictions of shared paralog functionality. Overall, our results suggest that alternative similarity metrics capture complementary aspects of functional similarity beyond sequence identity alone.

Molecular mimicry in the pathogenesis of autoimmune rheumatic diseases

Autoimmune rheumatic diseases (ARDs) are a heterogeneous group of conditions characterized by excessive and misdirected immune responses against the body's own musculoskeletal tissues. Their exact aetiology remains unclear, with genetic, demographic, behavioural and environmental factors implicated in disease onset. One prominent hypothesis for the initial breach of immune tolerance (leading to autoimmunity) is molecular mimicry, which describes structural or sequence similarities between human and microbial proteins (mimotopes). This similarity can lead to cross-reactive antibodies and T-cell receptors, resulting in an immune response against autoantigens. Both commensal microbes in the human microbiome and pathogens can trigger molecular mimicry, thereby potentially contributing to the onset of ARDs. In this review, we focus on the role of molecular mimicry in the onset of rheumatoid arthritis and systemic lupus erythematosus. Moreover, implications of molecular mimicry are also briefly discussed for ankylosing spondylitis, systemic sclerosis and myositis.

Co-expression of HIF1A with multi-drug transporters (P-GP, MRP1, and BCRP) in chemoresistant breast, colorectal, and ovarian cancer cells

Therapeutic resistance poses a significant challenge in treating most cancers and often leads to poor clinical outcomes and even treatment failure. One of the primary mechanisms that confer multidrug resistance phenotype to cancer cells is the hyperactivity of certain drug efflux transporters. P-GP, MRP1, and BCRP are the key ABC efflux pumps that collectively extrude a broad spectrum of chemotherapeutic drugs. Besides, HIF1A, a master transcription regulatory protein, is also associated with cancer development and therapeutic resistance. Thereby, this study aimed to delve into the mechanisms of drug resistance, specifically focusing on HIF1A-driven overexpression of ABC transporters. A total of 57 chemoresistant and 57 paired control tissue samples (breast, colorectal, and ovarian) from Bangladeshi cancer patients were analyzed to determine the co-expression level of ABC transporters and HIF1A. Molecular docking was also conducted to evaluate the interactions of HIF1A protein and hypoxia response element (HRE) sequences in the promoter regions transporter genes. This study revealed that HIF1A is significantly overexpressed in chemoresistant tissues, suggesting its pivotal role in chemoresistance mechanisms across malignancies and its potential as a target to overcome therapeutic resistance. The findings from this study also suggest a direct upregulation of ABCB1, ABCC1, and ABCG2 transcription by HIF1A in chemoresistant cancer cells by binding to the HRE sequence in the promoter regions. Thus, inhibition of these interactions of HIF1A appears to be a promising approach to reverse chemoresistance. The findings of this study can serve as a foundation for future research, resolving molecular intricacies to improve treatment outcomes in chemoresistant patients.

Optimized expression of human interleukin-15 in Nicotiana benthamiana and in vitro assessment of its activity on human keratinocytes

Human interleukin-15 (hIL-15) is a cytokine essential for immune modulation with therapeutic applications in cancer and chronic wound healing. Although hIL-15 is commercially available, large-scale production studies remain limited. With promising clinical trial results, demand for hIL-15 is expected to rise. Plant expression systems offer a sustainable, low-cost alternative for rapid biopharmaceutical production. In this study, we optimized hIL-15 expression in Nicotiana benthamiana and assessed its physicochemical properties and biological activity. We fused hIL-15 to the Fc domain of human IgG1 for efficient purification. Through optimization of the pre- and post-infiltration conditions, we achieved transient expression and recovery at 4 dpi, yielding 33.8 µg/g fresh weight. Peptide mapping confirmed 97 % overall sequence coverage of the primary structure. Treatment with plant-produced hIL-15-Fc effectively promoted human keratinocyte HaCaT cell proliferation and migration in vitro. These findings demonstrated the potential of plant-based platforms for producing therapeutic recombinant hIL-15 that support wound healing.

Biocontrol potential of Vibrio maritimus chitinase: Heterologous expression and insecticidal activity against Acanthoscelides obtectus

In this study, the chitinase gene from the marine bacterium Vibrio maritimus was heterologously expressed in Escherichia coli, purified via affinity chromatography and tested for its insecticidal activity against the storage pest Acanthoscelides obtectus. The recombinant VmChiA protein exhibited a molecular mass of ∼60 kDa, with optimum activity observed at pH 6.0 and 40 °C. Enzyme kinetic analysis revealed a Km value of 0.042 mM, Vmax of 17.48 μmol min-1, kcat of 1.75 min-1 and catalytic efficiency of 41.61 mM-1 min-1, respectively. Furthermore, a dose of 40 U mL-1 of recombinant VmChiA showed similar efficacy to malathion insecticide against A. obtectus, with 100 % mortality in both treatments. LC50 and LC90 values of VmChiA were 13.95 U mL-1 and 27.66 U mL-1, respectively. Furthermore, the three-dimensional structure of the catalytic site of VmChiA was modeled. Molecular dynamics simulation technique was used to explore and analyze the dynamics and interactions. A salt bridge (GLU274-ARG296) in the α + β domain was observed as a critical feature facilitating substrate (GlcNAc)2 binding and enzymatic activity. These findings demonstrate that recombinant VmChiA possesses potent insecticidal properties, highlighting its potential as a bio-based, eco-friendly alternative for managing significant agricultural pests.

The MFN2 Q367H variant reveals a novel pathomechanism connected to mtDNA-mediated inflammation

Pathogenic variants in the mitochondrial protein MFN2 are typically associated with a peripheral neuropathy phenotype, but can also cause a variety of additional pathologies including myopathy. Here, we identified an uncharacterized MFN2 variant, Q367H, in a patient diagnosed with late-onset distal myopathy, but without peripheral neuropathy. Supporting the hypothesis that this variant contributes to the patient's pathology, patient fibroblasts and transdifferentiated myoblasts showed changes consistent with impairment of several MFN2 functions. We also observed mtDNA outside of the mitochondrial network that colocalized with early endosomes, and measured activation of both TLR9 and cGAS-STING inflammation pathways that sense mtDNA. Re-expressing the Q367H variant in MFN2 KO cells also induced mtDNA release, demonstrating this phenotype is a direct result of the variant. As elevated inflammation can cause myopathy, our findings linking the Q367H MFN2 variant with elevated TLR9 and cGAS-STING signalling can explain the patient's myopathy. Thus, we characterize a novel MFN2 variant in a patient with an atypical presentation that separates peripheral neuropathy and myopathy phenotypes, and establish a potential pathomechanism connecting MFN2 dysfunction to mtDNA-mediated inflammation.

Valencene as a novel potential downregulator of THRB in NSCLC: network pharmacology, molecular docking, molecular dynamics simulation, ADMET analysis, and in vitro analysis

This study investigates the molecular targets and pathways affected by valencene in non-small cell lung cancer (NSCLC) through network pharmacology and in vitro assays. Valencene's chemical structure was sourced from PubChem, and target identification utilized the PharmMapper database, cross-referenced with UniProtKB for official gene symbols. NSCLC-associated targets were identified via GeneCards, followed by protein-protein interaction analysis using STRING. Molecular docking studies employed AutoDock Vina to assess binding interactions with key nuclear receptors (RXRA, RXRB, RARA, RARB, THRB). Molecular dynamics simulations were conducted in GROMACS over 200 ns, while ADME/T properties were evaluated using Protox. In vitro assays measured cell viability in A549 and HEL 299 cells via MTT assays, assessed apoptosis through Hoechst staining, and evaluated mitochondrial potential with JC-1. Molecular docking revealed strong binding affinities of valencene (below - 5 kcal/mol) to nuclear receptors, outperforming 5-fluorouracil (5-FU). Molecular dynamics simulations indicated robust structural stability of the THRB-valencene complex, with favorable interaction energies. Notably, valencene exhibited a selectivity index of 2.293, higher than 5-FU's 2.231, suggesting enhanced safety for normal cells (HEL 299). Fluorescence microscopy confirmed dose-dependent DNA fragmentation and decreased mitochondrial membrane potential. These findings underscore valencene's potential as an effective therapeutic agent for lung cancer, demonstrating an IC50 of 16.71 μg/ml in A549 cells compared to 5-FU's 12.7 μg/ml, warranting further investigation in preclinical models and eventual clinical trials.

Alzheimer's disease knowledge graph enhances knowledge discovery and disease prediction

OBJECTIVE

To construct an Alzheimer's Disease Knowledge Graph (ADKG) by extracting and integrating relationships among Alzheimer's disease (AD), genes, variants, chemicals, drugs, and other diseases from biomedical literature, aiming to identify existing treatments, potential targets, and diagnostic methods for AD.

METHODS

We annotated 800 PubMed abstracts (ADERC corpus) with 20,886 entities and 4935 relationships, augmented via GPT-4. A SpERT model (SciBERT-based) trained on this data extracted relations from PubMed abstracts, supported by biomedical databases and entity linking refined via abbreviation resolution/string matching. The resulting knowledge graph trained embedding models to predict novel relationships. ADKG's utility was validated by integrating it with UK Biobank data for predictive modeling.

RESULTS

The ADKG contained 3,199,276 entity mentions and 633,733 triplets, linking >5K unique entities and capturing complex AD-related interactions. Its graph embedding models produced evidence-supported predictions, enabling testable hypotheses. In UK Biobank predictive modeling, ADKG-enhanced models achieved higher AUROC of 0.928 comparing to 0.903 without ADKG enhancement.

CONCLUSION

By synthesizing literature-derived insights into a computable framework, ADKG bridges molecular mechanisms to clinical phenotypes, advancing precision medicine in Alzheimer's research. Its structured data and predictive utility underscore its potential to accelerate therapeutic discovery and risk stratification.

BCOR-rearranged sarcomas: In silico insights into altered domains and BCOR interactions

BCOR (BCL-6 corepressor) rearranged small round cell sarcoma (BRS) represents an uncommon soft tissue malignancy, frequently characterized by the BCOR::CCNB3 fusion. Other noteworthy fusions include BCOR::MAML3, BCOR::CLGN, BCOR::MAML1, ZC3H7B::BCOR, KMT2D::BCOR, CIITA::BCOR, RTL9::BCOR, and AHR::BCOR. The BCOR gene plays a pivotal role in the Polycomb Repressive Complex 1 (PRC1), essential for histone modification and gene silencing. It interfaces with the Polycomb group RING finger homolog (PCGF1). This study employed comprehensive in silico methodologies to investigate the structural and functional effects of BCOR fusion events in BRS. The analysis revealed significant alterations in the domain architecture of BCOR, which resulted in the loss of BCL6-regulated transcriptional repression. Furthermore, IUPred3 prediction indicated a significant increase in disorder in the C-terminal regions of the BCOR in the fusion proteins. A detailed analysis of the physicochemical properties by ProtParam revealed a decrease in isoelectric point, stability, and hydrophobicity. The analysis of protein structures predicted by AlphaFold3 using the PRODIGY algorithm revealed statistically significant deviations in binding affinities for BCOR-PCGF1 dimers and a non-canonical PRC1 variant tetramer compared to the wild-type BCOR. The findings provide a comprehensive summary and elucidation of the fusion proteome associated with BRS, suggesting a substantial impact on the stability and functionality of the fusion proteins, thereby contributing to the oncogenic mechanisms underlying BRS. In this study, we provide the first compilation and comparative analysis of the known BCOR fusions of BRS and introduce a new in silico approach to enhance a better understanding of the molecular basis of BRS.

Artificial intelligence prediction of carcinoembryonic antigen structure and interactions relevant for colorectal cancer

Carcinoembryonic antigen (CEA) is used as a biomarker for colorectal cancer. It is expressed during fetal development but in healthy adult cells the expression is low. Due to its size and the high degree of glycosylation, there are no structures available for mature CEA. By employing novel structure prediction methods, we aim to investigate CEA tertiary structure and interactions. Alphafold 3 server has increased the accuracy of structure predictions and allows for modelling of glycans in proteins and complexes. Models were created for a monomeric CEA, dimeric CEA and for CEA in complex with the antibody Tusamitamab. The structure of the monomeric glycosylated CEA exhibit two bends, one in the domain interface B1-A2 and one in the domain interface B2-A3. The dimer structure pairs in a parallel manner, with direct contacts in the N and the A2 domains of the two chains. The complex of CEA with Tusamitamab closely resembles the EM structure of the complex that was released after the training of Alphafold 3 was completed. Overall, the investigations give new angles to investigate for CEA. The predicted bend, primarily in the B2 and A3 domain interface, would allow for dimer formation of CEA from both the same cell as from adjacent cells and could help to explain the outstanding issue on how it can fulfil both tasks. The prediction of the antibody binding to CEA was accurate, the all-atom RMSD was 1.3 Å. This is encouraging for other antibody - protein complexes predictions as the complex structure was not part of the training set for Alphafold 3.

A quantitative comparison of the deleteriousness of missense and nonsense mutations using the structurally resolved human protein interactome

The complex genotype-to-phenotype relationships in Mendelian diseases can be elucidated by mutation-induced disturbances to the networks of molecular interactions (interactomes) in human cells. Missense and nonsense mutations cause distinct perturbations within the human protein interactome, leading to functional and phenotypic effects with varying degrees of severity. Here, we structurally resolve the human protein interactome at atomic-level resolutions and perform structural and thermodynamic calculations to assess the biophysical implications of these mutations. We focus on a specific type of missense mutation, known as "quasi-null" mutations, which destabilize proteins and cause similar functional consequences (node removal) to nonsense mutations. We propose a "fold difference" quantification of deleteriousness, which measures the ratio between the fractions of node-removal mutations in datasets of Mendelian disease-causing and non-pathogenic mutations. We estimate the fold differences of node-removal mutations to range from 3 (for quasi-null mutations with folding ΔΔG ≥2 kcal/mol) to 20 (for nonsense mutations). We observe a strong positive correlation between biophysical destabilization and phenotypic deleteriousness, demonstrating that the deleteriousness of quasi-null mutations spans a continuous spectrum, with nonsense mutations at the extreme (highly deleterious) end. Our findings substantiate the disparity in phenotypic severity between missense and nonsense mutations and suggest that mutation-induced protein destabilization is indicative of the phenotypic outcomes of missense mutations. Our analyses of node-removal mutations allow for the potential identification of proteins whose removal or destabilization lead to harmful phenotypes, enabling the development of targeted therapeutic approaches, and enhancing comprehension of the intricate mechanisms governing genotype-to-phenotype relationships in clinically relevant diseases.

Oxidation of α-hydroxy acids by D-2-hydroxyglutarate dehydrogenase enzymes

α-Hydroxy acids are naturally occurring organic molecules with various medical and industrial applications. However, some α-hydroxy acids, like D-2-hydroxyglutarate (D2HG), have been implicated in cancers and neurometabolic disorders such as D2HG aciduria. Several studies on the D2HG oxidizing enzyme D-2-hydroxyglutarate dehydrogenase (D2HGDH) from various eukaryotic and prokaryotic sources focus on the use and application of the enzyme as biosensors for detecting D2HG. A recent gene knockout study on the bacterial D2HGDH homologs from Pseudomonas stutzeri and Pseudomonas aeruginosa identified the D2HGDH to be essential for bacterial survival by driving l-serine biosynthesis. Thus, D2HGDH is a good candidate for a therapeutic target against the multidrug-resistant P. aeruginosa. However, there is no consensus on the D2HGDH catalytic mechanism, and several D2HGDH homologs have not been characterized in their structural properties, which are two crucial features for therapeutic design. P. aeruginosa D2HGDH, the most extensively studied D2HGDH homolog, is emerging as a paradigm for D2HGDH and flavoproteins with metal ions in their active site. In this review, we have explored the structures of all published D2HGDH homologs from 12 species using AlphaFold 3 and highlighted the fully conserved structure and active site topologies of all D2HGDH homologs. Additionally, evolutionary and functional studies coupled with analyses of enzymatic activities reveal that prokaryotic and eukaryotic D2HGDH homologs, diverging from two distinct ancestors, may have differentially evolved to specialize in their α-hydroxy acid catalysis. Additionally, this review identifies all D2HGDH homologs as metal and FAD-dependent enzymes that employ a metal-triggered FAD reduction in their catalysis. Elucidation of the D2HGDH mechanism will allow designing antibiotics that target these enzymes as potential therapeutics against pathogenic bacteria like P. aeruginosa in addition to the application of D2HGDH homologs as biosensors.

A FAM8A1 frameshift variant is associated with REM sleep behavior disorder, urinary retention, and mydriasis in Russian Blue cats

REM sleep behavior disorder (RBD) is a disease characterized by the loss of lower motor neuron inhibition responsible for skeletal muscle atonia during REM sleep. It has been reported in humans, dogs and cats, and can be idiopathic or secondary to a neurodegenerative disease. Five young adult Russian Blue cats from two related families were presented for progressively worsening RBD episodes frequently associated with urinary loss. Three of these cats also suffered urinary retention with overflow incontinence between RBD episodes. Neurological examination revealed a large bladder in three cats and a bilateral mydriasis with absent pupillary light reflexes in two cats; further examinations were unremarkable. Treatment attempts were unsatisfactory, with four cats being euthanized. Histopathology of the brain did not reveal any abnormalities. A disease-associated 23-bp deletion in exon 1 of FAM8A1 (NC_058372.1:g.11622168_11622190del), introducing a frameshift at codon 162 and a premature stop codon at codon 276 (XM_019831563.3:c.485_507del p.(Gln162Profs*115)), was identified by whole genome sequencing. The variant segregated in the affected families with a recessive mode of inheritance, showed an allele frequency of 1.5% in West-European Russian Blue cats (N = 68) and was not present in 276 cats belonging to 32 other breeds (including the closely related Nebelung breed). The variant FAM8A1 isoform is predicted to affect the assembly and activity of the endoplasmic reticulum-associated protein degradation pathway, which plays an important role in cell homeostasis. RBD and urinary retention syndrome is a hereditary encephalopathy affecting Russian Blue cats. A genetic test now allows diagnosis and prevention of this debilitating disease.

TBC1D30 regulates proinsulin and insulin secretion and is the target of a genomic association signal for proinsulin

AIMS/HYPOTHESIS

Components of the insulin processing and secretion pathways remain incompletely understood. Here, we examined a genome-wide association study (GWAS) signal for plasma proinsulin levels. Lead GWAS variant rs150781447-T encodes an Arg279Cys substitution in TBC1 domain family member 30 (TBC1D30), but no role for this protein in insulin processing or secretion has been established previously. This study aimed to evaluate whether TBC1D30 drives the GWAS association signal by determining whether TBC1D30 is involved in proinsulin secretion and, if so, to examine the effects of variant alleles and potential mechanisms.

METHODS

Using CRISPR/Cas9 genome editing to create double-strand breaks and prime editing to install substitutions in INS1 832/13 insulinoma cells, we generated clonal cell lines with altered TBC1D30, as well as homozygous and heterozygous lines carrying the lead GWAS variant. We characterised lines by Sanger sequencing, quantitative PCR and ELISAs to measure glucose-stimulated proinsulin and insulin secretion. We also tested the effects of TBC1D30 knockdown on proinsulin and insulin secretion in human islets. We further assessed TBC1D30's contribution to secretory pathways by examining the effects of altered gene function on intracellular proinsulin and insulin content and insulin localisation, and by identifying potential proteins that interact with TBC1D30 using affinity purification mass spectrometry.

RESULTS

Compared with mock-edited cells, cell lines with reduced TBC1D30 expression or altered Rab GTPase-activating protein (RabGAP) domain had significantly more secreted proinsulin, 1.8- and 2.6-fold more than controls, respectively. Similarly, cells expressing the variant substitution demonstrated increased proinsulin secretion. Cell lines with a partial deletion of a critical functional domain showed 1.8-fold higher expression of Tbc1d30 and at least 2.0-fold less secreted proinsulin. Cells with altered RabGAP domain sequence also demonstrated, to a lesser extent, changes in secreted insulin levels. TBC1D30 knockdown in human islets resulted in increased insulin secretion with no significant effect on proinsulin secretion. The effects of altered TBC1D30 on mislocalisation of insulin, intracellular proinsulin and insulin content and the identities of interacting proteins are consistent with a role for TBC1D30 in proinsulin and insulin secretion.

CONCLUSIONS/INTERPRETATION

These findings suggest that effects on TBC1D30 are responsible for the GWAS signal and that TBC1D30 plays a critical role in the secretion of mature insulin.

Soluble form of tumor necrosis factor-related apoptosis-inducing ligand interacts with S100P protein

Tumor Necrosis Factor (TNF)-Related Apoptosis-Inducing Ligand (TRAIL) is a therapeutically relevant protein belonging to the TNF superfamily. Both membrane-bound and soluble (sTRAIL) forms of TRAIL affect innate and adaptive immune responses. We recently showed that soluble TNF binds specific members of the S100 family of multifunctional calcium-binding proteins, leading to suppression of its cytotoxic activity (Int. J. Mol. Sci. 2022, 23(24), 15,956). To test the ability of S100 proteins to affect sTRAIL functioning, we used surface plasmon resonance spectroscopy, intrinsic fluorescence, chemical crosslinking, molecular modeling, site-directed mutagenesis, cytotoxicity assay, and bioinformatics to study interaction of human sTRAIL with human non-fused S100 proteins. Of the 21 S100 proteins examined, only S100P protein showed specific interaction with sTRAIL characterized by equilibrium dissociation constant, Kd, reaching (0.16 ± 0.07) μM. sTRAIL monomer binds dimeric S100P strictly in the presence of Ca2+, while sTRAIL trimer interacts with S100P dimer regardless of Ca2+. Site-directed mutagenesis confirmed involvement of the 'hinge' and C-terminal regions of S100P in the sTRAIL recognition, consistent with the structural modeling results. Bioinformatic analysis indicates dysregulation of TRAIL and S100P in various neoplasms. S100P lowers cytotoxicity of sTRAIL against human fibrosarcoma HT-1080 cells. The suppression of proapoptotic sTRAIL signaling by S100P protein may contribute to oncogenic effects of the latter.

3D Genome Constrains Breakpoints of Inversions That Can Act as Barriers to Gene Flow in the Stickleback

DNA within the nucleus is organised into a well-regulated three-dimensional (3D) structure. However, how such 3D genome structures influence speciation processes remains largely elusive. Recent studies have shown that 3D genome structures influence mutation rates, including the occurrence of chromosomal rearrangement. For example, breakpoints of chromosomal rearrangements tend to be located at topologically associating domain (TAD) boundaries. Here, we hypothesised that TAD structures may constrain the location of chromosomal inversions and thereby shape the genomic landscape of divergence between species with ongoing gene flow, given that inversions can act as barriers to gene flow. To test this hypothesis, we used a pair of Japanese stickleback species, Gasterosteus nipponicus (Japan Sea stickleback) and G. aculeatus (three-spined stickleback). We first constructed chromosome-scale genome assemblies of both species using high fidelity long reads and high-resolution proximity ligation data and identified several chromosomal inversions. Second, via population genomic analyses, we revealed higher genetic differentiation in inverted regions than in colinear regions and no gene flow within inversions, which contrasts with the significant gene flow in colinear regions. Third, using Hi-C data, we revealed 3D genome structures of sticklebacks, delineated by A/B compartments and TADs. Finally, we found that inversion breakpoints tend to be located at TAD boundaries. Thus, our study demonstrates that the 3D genome constrains breakpoints of inversions that can act as barriers to gene flow in the stickleback. Further integration of 3D genome analyses with population genomics could provide novel insights into how the 3D genome influences speciation.

Integrative transcriptomic, proteomic, biochemical and neutralization studies on the venom of Micrurus ephippifer

Species of the genus Micrurus belong to the family Elapidae and possess venoms of significant clinical importance. This study presents an analysis of the venom composition of Micrurus ephippifer, employing transcriptomic and proteomic methodologies. A total of 2885 venom gland transcripts were assembled, of which 42 were identified as toxins. Transcripts for three-finger toxins (3FTx) were the most abundant (80.7 %), followed by PLA2 transcripts (16.3 %). Tryptic peptide sequences obtained through bottom-up shotgun MS/MS venom analysis were assigned to 46 distinct proteins in the SwissProt/UniProt database, of which 23 belong to the 3FTx family. Peptide spectral matching against the venom gland transcriptome database identified 24 proteins, 12 of which correspond to 3FTx, and three belong to PLA2. Venom decomplexation by RP-HPLC followed by N-terminal amino acid sequencing of fractions allowed an estimation of the relative abundance of protein families, indicating that 3FTx comprise over 50 % of the venom. The identified toxic fractions displayed distinct lethality profiles in mice, with certain combinations exhibiting enhanced toxicity, very similar to what has been reported with Brownitoxin-I, with only the PLA2 sequence showing similarity. Our results emphasize the importance of integrating transcriptomic and proteomic approaches to understand venom diversity and its implications for antivenom development. SIGNIFICANCE: Mexico ranks first in the Americas in snake venom diversity. Paradoxically, very little is known about the composition of coral snake venoms, and Micrurus ephippifer is a clear example of this gap, as nothing was known about its venom composition. This type of study provides valuable information that helps fill these knowledge gaps. This study presents the second report of coral snake venoms containing a complex of phospholipase A2 and a three-finger toxin, offering important data that, with further research, will contribute to understanding venom evolution and evaluating the efficacy of antivenoms.

DeepUSPS: Deep Learning-Empowered Unconstrained-Structural Protein Sequence Design

Currently, the unconstrained-structural protein sequence design models suffer from low optimization efficiency, and their generated proteins exhibit significant similarities to natural proteins and low thermal stability. To address these challenges, we propose the Deep Learning-Empowered Unconstrained-Structural Protein Sequence Design (DeepUSPS) model. To effectively address the inadequate thermal stability problem, we employ the innovative Inverted Dense Residual Network (IDRNet). To mitigate the designed proteins similarity issue, the Sequence-Pairwise Features Extraction Synthetic Network (SPFESN) is constructed. Furthermore, we introduce the Warm Restart AngularGrad (WRA) optimizer to optimize the 3D Position-Specific Scoring Matrix (3Dpssm) for unconstrained-structural protein sequence, only involving 2100 iterations (140.36 min) updates to generate idealization (IDE) protein sequences. We obtained a total of 1000 IDE protein sequences. Then we utilized in silico experiments to evaluate them, including similarity, clarity and iterations, thermal stability, spatial distribution of similarity, and predicted local-distance difference test (pLDDT) confidence assessment. Notably, the mean lg(E-value) for IDE protein sequences reached -0.051, the mean TM-score for IDE protein structures reached 0.594, the iterations only need 2100, and the mean Tm (melting point) for thermal stability reached 74.78°C. The average pLDDT value for 3D structures reached 76. Additionally, the IDE proteins' 3D structures exhibit diverse types. These in silico results conclusively demonstrate the superior performance of DeepUSPS compared with Hallucinate.

Protein Sequence Analysis landscape: A Systematic Review of Task Types, Databases, Datasets, Word Embeddings Methods, and Language Models

Protein sequence analysis examines the order of amino acids within protein sequences to unlock diverse types of a wealth of knowledge about biological processes and genetic disorders. It helps in forecasting disease susceptibility by finding unique protein signatures, or biomarkers that are linked to particular disease states. Protein Sequence analysis through wet-lab experiments is expensive, time-consuming and error prone. To facilitate large-scale proteomics sequence analysis, the biological community is striving for utilizing AI competence for transitioning from wet-lab to computer aided applications. However, Proteomics and AI are two distinct fields and development of AI-driven protein sequence analysis applications requires knowledge of both domains. To bridge the gap between both fields, various review articles have been written. However, these articles focus revolves around few individual tasks or specific applications rather than providing a comprehensive overview about wide tasks and applications. Following the need of a comprehensive literature that presents a holistic view of wide array of tasks and applications, contributions of this manuscript are manifold: It bridges the gap between Proteomics and AI fields by presenting a comprehensive array of AI-driven applications for 63 distinct protein sequence analysis tasks. It equips AI researchers by facilitating biological foundations of 63 protein sequence analysis tasks. It enhances development of AI-driven protein sequence analysis applications by providing comprehensive details of 68 protein databases. It presents a rich data landscape, encompassing 627 benchmark datasets of 63 diverse protein sequence analysis tasks. It highlights the utilization of 25 unique word embedding methods and 13 language models in AI-driven protein sequence analysis applications. It accelerates the development of AI-driven applications by facilitating current state-of-the-art performances across 63 protein sequence analysis tasks.

Transcriptomic response of marine dinoflagellate Prorocentrum cordatum to phosphorus deficiency

Phosphorus is crucial for marine phytoplankton viability as a key biogenic element. Under phosphorus deficiency, dinoflagellates exhibit changes in their feeding regime, alterations in transporters functioning, a reduction in cell proliferation rate and, in some cases, a transition to sexual reproduction. In this study, we performed RNA-sequencing analysis to assess the transcriptomic response of the dinoflagellate Prorocentrum cordatum to phosphorus deficiency in the cultivation medium. The aim of this work was to elucidate shifts in P. cordatum life cycle under these conditions focusing on the increase in the percentage of cells with a relative nuclear DNA content of 2C and the appearance of 4C cells, which may indicate a transition to the sexual process. We identified 196 differentially expressed genes - 169 up-regulated and 27 down-regulated-in cells grown for 14 days under phosphorus-depleted conditions. Analysis revealed up-regulation of pathways for phosphate uptake and assimilation, along with activation of RNA, protein, and lipid metabolic processes. Additionally, mechanisms regulating the cell cycle and inducing meiotic division were triggered. We identified up-regulated genes encoding proteins involved in meiotic recombination, including those promoting crossover. These findings indicate that phosphorus limitation can induce shift to sexual phase in P. cordatum life cycle.

Proteomic profiling of small extracellular vesicles from bovine nucleus pulposus cells

Small extracellular vesicles (small EV) are a conserved means of communication across the domains of life and lately gained more interest in mammalian non-cancerous work as non-cellular, biological therapeutic with encouraging results in recent studies of chronic degenerative diseases. The nucleus pulposus (NP) is the avascular and aneural center of an intervertebral disc (IVD), home to unique niche conditions and affected in IVD degeneration. We investigated autologous and mesenchymal stem cell (MSC) small EVs for their potential to contribute to cell and tissue homeostasis in the NP niche via mass spectrometric proteome and functional enrichment analysis using adult and fetal donors. We compared these findings to published small EV databases and MSC small EV data. We propose several mechanisms associated with NP small EVs: Membrane receptor trafficking to modify signal responses promoting niche homeostasis; Redox and energy homeostasis via metabolic enzymes delivery; Cell homeostasis via proteasome delivery and immunomodulation beyond an association with a serum protein corona. The proteome signature of small EVs generated by NP parent cells is similar to previously published small EV data, yet with a focus on supplementing anaerobic metabolism and redox balance while contributing to the maintenance of an aneural and avascular microniche.

Deep palaeoproteomic profiling of archaeological human brains

Palaeoproteomics leverages the persistence, diversity, and biological import of ancient proteins to explore the past, and answer fundamental questions about phylogeny, environment, diet, and disease. These insights are largely gleaned from hard tissues like bone and teeth, as well-established protocols exist for extracting ancient proteins from mineralised tissues. No such method, however, exists for the soft tissues, which are underexplored in palaeoproteomics given permission for destructive analysis routinely depends on a proven methodology. Considering less than one-tenth of all human proteins are expressed in bone, compared to three-quarters in the internal organs, the amount of biological information presently inaccessible is substantial. We address this omission with an optimised LC-FAIMS-MS/MS workflow yielding the largest, most diverse palaeoproteome yet described. Using archaeological human brains, we test ten protocols with varied chemistries and find that urea lysis effectively disrupts preserved membrane regions to expose low-abundant, intracellular analytes. Further, we show that ion mobility spectrometry improves unique protein identification by as much as 40%, and represents a means of "cleaning" dirty archaeological samples. Our methodology will be useful for improving protein recovery from a range of ancient tissues and depositional environments.

GLUT1 sensitizes tumor cells to EGFR-TKIs by binding with activated EGFR and regulating its downstream signaling pathways

BACKGROUND

We have previously demonstrated that GLUT1 can interact with phosphorylated EGFR and has an oncogenic role in lung cancer. Here, we aim to investigate their binding region and its signaling pathways.

METHODS

The AlphaFold 3 prediction, Co-immunoprecipitation, and Western blot were used to uncover the interaction conditions of GLUT1 and EGFR. The RNA-seq data was analyzed to evaluate the difference in signaling pathways between wild-type EGFR and activated mutated EGFR. The xenograft tumor model was established to determine the therapy effect of the combination of GLUT1 inhibitor BAY-876 and EGFR TKI Osimertinib.

RESULTS

We found that the interaction ability of GLUT1 and EGFR depended on the activation of EGFR. GLUT1 interacted with EGFRvIII (loss 2-7 exons) but not with EGFRvI (loss 1-16 exons), so GLUT1 interacts with EGFR in the EGFR extracellular transmembrane region. GLUT1 regulated EGFR downstream signaling pathways. GLUT1 inhibitor BAY-876 can sensitize tumor cells to EGFR TKI Osimertinib.

CONCLUSIONS

GLUT1 participates in tumor progression by interacting with phosphor-EGFR, suggesting that inhibition of the GLUT1-EGFR axis may be a potential therapeutic strategy for lung cancer treatment.

Importin-alpha transports Norrin to the nucleus to promote proliferation and Notch signaling in glioblastoma stem cells

Norrin, a secreted protein encoded by NDP gene, is recognized for its established role as a paracrine canonical Frizzled-4/Wnt ligand that mediates angiogenesis and barrier function in the brain. However, emerging evidence suggests that Norrin possesses Frizzled-4-independent functions, notably impacting Notch activation and proliferation of cancer stem cells. We conducted a BioID protein-proximity screen to identify Norrin-interacting proteins. Surprisingly, a significant proportion of the proteins we identified were nuclear. Through comprehensive tagging and proximity ligation assays, we demonstrate that Norrin is transported to the nucleus through KPNA2 (member of the Importin-alpha family). Subsequently, we demonstrate that KPNA2 loss of function in patient-derived primary glioblastoma stem cells results in a nuclear to cytoplasmic shift of Norrin distribution, and a complete abrogation of its function in stimulating Notch signaling and cellular proliferation. These results indicate that Norrin is actively transported into the nucleus to regulate vital signaling pathways and cellular functions.

HCK regulates NLRP12-mediated PANoptosis

NOD-like receptors (NLRs) are a highly conserved family of cytosolic pattern recognition receptors that drive innate immune responses against pathogens, pathogen-associated molecular patterns, damage-associated molecular patterns, and homeostatic disruptions. Within the NLR family, NLRP12 was recently identified as a key regulator of PANoptosis, which is an innate immune, lytic cell death pathway initiated by innate immune sensors and driven by caspases and RIPKs through PANoptosome complexes. While NLRP12 activation is critical for maintaining homeostasis, aberrant activation has been implicated in a broad range of disorders, including cancers and metabolic, infectious, autoinflammatory, and hemolytic diseases. However, the molecular mechanisms of NLRP12 activation remain poorly understood. Here, we identified hematopoietic cell kinase (HCK) as a regulator of NLRP12-mediated PANoptosis. HCK expression was significantly upregulated in response to NLRP12-PANoptosome triggers. Moreover, Hck knockdown inhibited NLRP12-mediated PANoptosis. Computational analyses identified residues in the putative interaction interface between NLRP12 and HCK, suggesting that HCK likely binds NLRP12 in the region between its NACHT domain and pyrin domain (PYD); removal of the NLRP12 PYD abrogated this interaction in vitro. Overall, our work identifies HCK as a regulator of NLRP12-mediated PANoptosis, suggesting that it may serve as a potential therapeutic target for mitigating inflammation and pathology.

Development and validation of a carnitine cycle and transport disorders (CCD) panel: an ONT-compatible multi-gene diagnostic kit for newborn and selective screening

Carnitine transport and cycle disorders (CCD) are a group of metabolic disorders characterized by either carnitine depletion or dysfunction in the carnitine cycle, a critical process for the transport of fatty acids into the mitochondria and their subsequent β-oxidation. Clinically, CCD can manifest with a wide range of symptoms, including hypoketotic hypoglycemia, which may be accompanied by signs of liver dysfunction, hepatic steatosis, myopathy and cardiomyopathy. Biochemical diagnosis typically involves measuring carnitine and acylcarnitine levels in blood, alongside organic acid profiling in urine. However, due to phenotypic overlaps with other metabolic disorders, precise molecular diagnosis is essential for accurate disease classification and subtype determination. The present study aimed to develop and clinically validate a novel CCD panel, specifically designed for Oxford Nanopore Technologies (ONT) platform compatibility. The panel targeted four key CCD related genes (CPT-1, CPT-2, SLC22A5 and SLC25A20). An amplification-based library preparation method pooling 21 primers specific to the CCD-related genes into two tubes was optimized. The panel was then applied to screen 20 patients previously diagnosed with CCD via second-generation sequencing platform. Comparative analysis of results from both platforms revealed a 100% concordance in detecting pathogenic, likely pathogenic, and variants of unknown significance associated with CCD. In silico analysis was also performed to predict the pathogenic potential of the variants of unknown significance. Here we report the development and clinical validation of a multi-gene diagnostic panel for ONT platform. The results demonstrated the feasibility of ONT-based genetic testing for CCD and set the stage for the development of similar diagnostic panels for other genetic disorders, offering a streamlined and putatively cost-effective alternative to current sequencing methodologies.

Advancing Enzyme Optimal pH Prediction via Retrieved Embedding Data Augmentation

The optimal enzyme pH is a critical factor that directly influences the catalytic efficiency of the enzymes. Accurate computational prediction of the optimal pH can greatly advance our understanding and design of enzymes for diverse scientific and industrial applications. However, current prediction tools often fall short in terms of accuracy and robustness. In this study, we propose OpHReda, a novel method that significantly improves enzyme optimal pH prediction by leveraging a retrieved embedding data augmentation mechanism. Given an enzyme sequence, OpHReda first retrieves similar sequence embeddings from a preconstructed augmentation database. It then jointly analyzes the original and retrieved embeddings through the Multiple Embedding Alignment transformer to narrow the prediction range. Finally, the calibrator integrates residue-level information with the refined prediction range to make the final prediction. By moving beyond the limitations of single-sequence-based models, OpHReda achieves a 55% improvement in F1-score compared to that of state-of-the-art methods. Extensive ablation studies demonstrate that this enhancement arises from the synergy between our tailored architecture and the augmentation mechanism. Overall, OpHReda offers a promising advancement in enzyme optimal pH prediction and holds potential for downstream applications such as enzyme engineering and rational design.

Prognostic and therapeutic insights from lactate metabolism and tumor immune microenvironment in head and neck squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSCC) exhibits a poor prognosis, particularly in advanced stages characterized by high recurrence and metastasis rates. This study investigates the role of lactate metabolism in HNSCC, aiming to develop a prognostic model to predict immunotherapy outcomes. Genomic and clinical data from The Cancer Genome Atlas and Gene Expression Omnibus databases were analyzed, focusing on 233 lactate metabolism-related genes (LMGs). Differential expression and Cox regression analyses identified two significant prognostic genes: glycogen phosphorylase L (PYGL) and solute carrier family 16 member 3 (SLC16 A3, encoding MCT4). A lactate risk score (LRS) model constructed from these genes demonstrated robust predictive accuracy across multiple validation datasets. Multivariate analysis validated LRS as an independent prognostic factor, and a nomogram integrating LRS with clinical parameters further improved survival prediction accuracy. Immune infiltration analyses revealed distinct immune landscapes between high- and low-risk groups. Elevated levels of CD4 naïve T cells, resting NK cells, M0 macrophages, and activated mast cells characterized the high-risk group, whereas naive B cells, plasma cells, CD8 T cells, T follicular helper cells, regulatory T cells, gamma delta T cells, resting dendritic cells, resting mast cells, and eosinophils predominated in the low-risk group. Additionally, molecular docking suggested valproic acid as a potential inhibitor of MCT4. Immunohistochemical analyses showed increased PYGL and MCT4 expression correlated with advanced tumor stage, alongside decreased expression of CXCL9 and CXCL10. These findings highlight the critical role of lactate metabolism in HNSCC progression and immunotherapy resistance, identifying PYGL and MCT4 as promising therapeutic targets.

Metaproteomics in the One Health framework for unraveling microbial effectors in microbiomes

One Health seeks to integrate and balance the health of humans, animals, and environmental systems, which are intricately linked through microbiomes. These microbial communities exchange microbes and genes, influencing not only human and animal health but also key environmental, agricultural, and biotechnological processes. Preventing the emergence of pathogens as well as monitoring and controlling the composition of microbiomes through microbial effectors including virulence factors, toxins, antibiotics, non-ribosomal peptides, and viruses holds transformative potential. However, the mechanisms by which these microbial effectors shape microbiomes and their broader functional consequences for host and ecosystem health remain poorly understood. Metaproteomics offers a novel methodological framework as it provides insights into microbial dynamics by quantifying microbial biomass composition, metabolic functions, and detecting effectors like viruses, antimicrobial resistance proteins, and non-ribosomal peptides. Here, we highlight the potential of metaproteomics in elucidating microbial effectors and their impact on microbiomes and discuss their potential for modulating microbiomes to foster desired functions.