UniProt: the Universal Protein Knowledgebase in 2023

Influence of different sample preparation approaches on proteoform identification by top-down proteomics

Top-down proteomics using mass spectrometry facilitates the identification of intact proteoforms, that is, all molecular forms of proteins. Multiple past advances have lead to the development of numerous sample preparation workflows. Here we systematically investigated the influence of different sample preparation steps on proteoform and protein identifications, including cell lysis, reduction and alkylation, proteoform enrichment, purification and fractionation. We found that all steps in sample preparation influence the subset of proteoforms identified (for example, their number, confidence, physicochemical properties and artificially generated modifications). The various sample preparation strategies resulted in complementary identifications, substantially increasing the proteome coverage. Overall, we identified 13,975 proteoforms from 2,720 proteins of human Caco-2 cells. The results presented can serve as suggestions for designing and adapting top-down proteomics sample preparation strategies to particular research questions. Moreover, we expect that the sampling bias and modifications identified at the intact protein level will also be useful in improving bottom-up proteomics approaches.

Validation and quantification of peptide antigens presented on MHCs using SureQuant

Vaccines and immunotherapies that target peptide-major histocompatibility complexes (peptide-MHCs) have the potential to address multiple unmet medical needs in cancer and infectious disease. Designing vaccines and immunotherapies to target peptide-MHCs requires accurate identification of target peptides in infected or cancerous cells or tissue, and may require absolute or relative quantification to identify abundant targets and measure changes in presentation under different treatment conditions. Internal standard parallel reaction monitoring (also known as 'SureQuant') can be used to validate and/or quantify MHC peptides previously identified by using untargeted methods such as data-dependent acquisition. SureQuant MHC has three main use cases: (i) conclusive confirmation of the identities of putative MHC peptides via comparison with an internal synthetic stable isotope labeled (SIL) peptide standard; (ii) accurate relative quantification by using pre-formed heavy isotope-labeled peptide-MHC complexes (hipMHCs) containing SIL peptides as internal controls for technical variation; and (iii) absolute quantification of each target peptide by using different amounts of hipMHCs loaded with synthetic peptides containing one, two or three SIL amino acids to provide an internal standard curve. Absolute quantification can help determine whether the abundance of a peptide-MHC is sufficient for certain therapeutic modalities. SureQuant MHC therefore provides unique advantages for immunologists seeking to confidently validate antigenic targets and understand the dynamics of the MHC repertoire. After synthetic standards are ordered (3-4 weeks), this protocol can be carried out in 3-4 days and is suitable for individuals with mass spectrometry experience who are comfortable with customizing instrument methods.

Chem(Pro)2: the atlas of chemoproteomic probes labelling human proteins

Chemoproteomic probes (CPPs) have been widely considered as powerful molecular biological tools that enable the highly efficient discovery of both binding proteins and modes of action for the studied compounds. They have been successfully used to validate targets and identify binders. The design of CPP has been considered extremely challenging, which asks for the generalization using a large number of probe data. However, none of the existing databases gives such valuable data of CPPs. Herein, a database entitled 'Chem(Pro)2' was therefore developed to systematically describe the atlas of diverse types of CPPs labelling human protein in living cell/lysate. With the booming application of chemoproteomic technique and artificial intelligence in current chemical biology study, Chem(Pro)2 was expected to facilitate the AI-based learning of interacting pattern among molecules for discovering innovative targets and new drugs. Till now, Chem(Pro)2 has been open to all users without any login requirement at: https://idrblab.org/chemprosquare/.

Comparative Analysis of Extracellular Vesicles from Cytotoxic CD8+ αβ T Cells and γδ T Cells

BACKGROUND

Although belonging to different branches of the immune system, cytotoxic CD8+ αβ T cells and γδ T cells utilize common cytolytic effectors including FasL, granzymes, perforin and granulysin. The effector proteins are stored in different subsets of lysosome-related effector vesicles (LREVs) and released to the immunological synapse upon target cell encounter. Notably, in activated cells, LREVs and potentially other vesicles are continuously produced and released as extracellular vesicles (EVs). Presumably, EVs serve as mediators of intercellular communication in the local microenvironment or at distant sites.

METHODS

EVs of activated and expanded cytotoxic CD8+ αβ T cells or γδ T cells were enriched from culture supernatants by differential and ultracentrifugation and characterized by nanoparticle tracking analyses and Western blotting. For a comparative proteomic profiling, EV preparations from both cell types were isobaric labeled with tandem mass tags (TMT10plex) and subjected to mass spectrometry analysis.

RESULTS

686 proteins were quantified in EV preparations of cytotoxic CD8+ αβ T cells and γδ T cells. Both populations shared a major set of similarly abundant proteins, while much fewer proteins presented higher abundance levels in either CD8+ αβ T cells or γδ T cells. To our knowledge, we provide the first comparative analysis of EVs from cytotoxic CD8+ αβ T cells and γδ T cells.

HaloClass: Salt-Tolerant Protein Classification with Protein Language Models

Salt-tolerant proteins, also known as halophilic proteins, have unique adaptations to function in high-salinity environments. These proteins have naturally evolved in extremophilic organisms, and more recently, are being increasingly applied as enzymes in industrial processes. Due to an abundance of salt-tolerant sequences and a simultaneous lack of experimental structures, most computational methods to predict stability are sequence-based only. These approaches, however, are hindered by a lack of structural understanding of these proteins. Here, we present HaloClass, an SVM classifier that leverages ESM-2 protein language model embeddings to accurately identify salt-tolerant proteins. On a newer and larger test dataset, HaloClass outperforms existing approaches when predicting the stability of never-before-seen proteins that are distal to its training set. Finally, on a mutation study that evaluated changes in salt tolerance based on single- and multiple-point mutants, HaloClass outperforms existing approaches, suggesting applications in the guided design of salt-tolerant enzymes.

DIONYSUS: a database of protein-carbohydrate interfaces

Protein-carbohydrate interactions govern a wide variety of biological processes and play an essential role in the development of different diseases. Here, we present DIONYSUS, the first database of protein-carbohydrate interfaces annotated according to structural, chemical and functional properties of both proteins and carbohydrates. We provide exhaustive information on the nature of interactions, binding site composition, biological function and specific additional information retrieved from existing databases. The user can easily search the database using protein sequence and structure information or by carbohydrate binding site properties. Moreover, for a given interaction site, the user can perform its comparison with a representative subset of non-covalent protein-carbohydrate interactions to retrieve information on its potential function or specificity. Therefore, DIONYSUS is a source of valuable information both for a deeper understanding of general protein-carbohydrate interaction patterns, for annotation of the previously unannotated proteins and for such applications as carbohydrate-based drug design. DIONYSUS is freely available at www.dsimb.inserm.fr/DIONYSUS/.

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.

Virtual screening and network pharmacology-based synergistic coagulation mechanism identification of multiple components contained in compound Kushen Injection against hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is a primary liver malignancy commonly encountered in the setting of chronic liver disease and cirrhosis. Compound Kushen Injection (CKI) has been widely used in HCC, however, the underlying mechanisms are scarce.

OBJECTIVE

To explore the molecular mechanisms of CKI for HCC.To explore the molecular mechanisms of CKI for HCC.

MATERIALS AND METHODS

The chemical ingredients of CKI were reviewed from published articles and the potential targets were got from Herbal Ingredients' Targets Platform. Coagulation-related targets were from Kyoto Encyclopedia of Genes and Genomes and HCC-related targets were from Therapeutic Target Database, Gene Expression Omnibus, and The Cancer Genome Atlas. Then the CKI-Herb-Target and CKI-Herb-Target-HCC networks were built. The shared targets between CKI and HCC were used for functional enrichment through Metascape and the shared coagulation-related target was used for molecular docking and survival analysis.

RESULTS

A total of 23 chemical ingredients and 41 potential targets shared between CKI and HCC were obtained. The results of functional enrichment indicated that several canonical pathways of CKI mostly participated in the treatment of HCC. Furthermore, a chemical ingredient of CKI formed a stable hydrogen bond link with the ASN-189 on PLG, with a best binding energy of -4.7 kcal/mol. Finally, PLG was confirmed as the shared coagulation-related target and interrelated with the prognosis of HCC.

CONCLUSION

CKI probably improves HCC prognosis through PLG. Our research undoubtedly deepened the understanding of the molecular mechanism of CKI anti-HCC.

Ligand-induced conformational changes in the β1-adrenergic receptor revealed by hydrogen-deuterium exchange mass spectrometry

G Protein Coupled Receptors (GPCRs) constitute the largest family of signalling proteins responsible for translating extracellular stimuli into intracellular functions. They play crucial roles in numerous physiological processes and are major targets for drug discovery. Dysregulation of GPCRs is implicated in various diseases, making understanding their structural dynamics critical for therapeutic development. Here, we use Hydrogen Deuterium Exchange Mass Spectrometry (HDX-MS) to explore the structural dynamics of the turkey β1-adrenergic receptor (tβ1AR) bound with nine different ligands, including agonists, partial agonists, and antagonists. We find that these ligands induce distinct dynamic patterns across the receptor, which can be grouped by compound modality. Notably, full agonist binding destabilises the intracellular loop 1 (ICL1), while antagonist binding stabilises it, highlighting ICL1's role in G protein recruitment. Our findings indicate that the conserved L72 residue in ICL1 is crucial for maintaining receptor structural integrity and stabilising the GDP-bound state. Overall, our results provide a platform for determining drug modality and highlight how HDX-MS can be used to dissect receptor ligand interaction properties and GPCR mechanism.

Fundamentals and Exceptions of the LysR-type Transcriptional Regulators

LysR-type transcriptional regulators (LTTRs) are emerging as a promising group of macromolecules for the field of biosensors. As the largest family of bacterial transcription factors, the LTTRs represent a vast and mostly untapped repertoire of sensor proteins. To fully harness these regulators for transcription factor-based biosensor development, it is crucial to understand their underlying mechanisms and functionalities. In the first part, this Review discusses the established model and features of LTTRs. As dual-function regulators, these inducible transcription factors exude precise control over their regulatory targets. In the second part of this Review, an overview is given of the exceptions to the "classic" LTTR model. While a general regulatory mechanism has helped elucidate the intricate regulation performed by LTTRs, it is essential to recognize the variations within the family. By combining this knowledge, characterization of new regulators can be done more efficiently and accurately, accelerating the expansion of transcriptional sensors for biosensor development. Unlocking the pool of LTTRs would significantly expand the currently limited range of detectable molecules and regulatory functions available for the implementation of novel synthetic genetic circuitry.

Charting the Dynamic Trophoblast Plasma Membrane Identifies LYN As a Functional Regulator of Syncytialization

The differentiation of placental cytotrophoblasts (CTBs) into the syncytiotrophoblast (STB) layer results in a significant remodeling of the plasma membrane proteome. Here, we use a peroxidase-catalyzed proximity labeling strategy to map the dynamic plasma membrane proteomes of CTBs and STBs. Coupled with mass-spectrometry-based proteomics, we identify hundreds of plasma membrane proteins and observe relative changes in protein abundance throughout differentiation, including the upregulation of the plasma-membrane-localized nonreceptor tyrosine kinase LYN. We show that both siRNA-mediated knockdown and small molecule inhibition of LYN kinase function impairs CTB fusion and reduces the expression of syncytialization markers, presenting a function for LYN outside of its canonical role in immunological signaling. Our results demonstrate the use of the proximity labeling platform to discover functional regulators within the plasma membrane and provide new avenues to regulate trophoblast differentiation.

Expanding families: a pilot study on preconception expanded carrier screening in Bahrain

BACKGROUND

Preconception expanded carrier screening (ECS) is a genetic test that enables the identification of at-risk carriers of recessive disorders by screening for up to hundreds of genes. Next-generation sequencing (NGS) development has paved the way for its integration into ECS. This study aims to identify the carrier genetic status of couples experiencing or anticipating conception challenges through NGS-based ECS and to gain an overview of the rare genetic disorders in a population with increased consanguinity.

METHODS

Thirty couples who presented to the Genetic Disease Clinic between 2015 and 2024 with failed reproductive outcomes or with a positive personal or family history of genetic disorders and underwent ECS were included and retrospectively analyzed.

RESULTS

Fifty-four individuals (90.00%) were found to carry at least one variant of 95 identified genes, totaling 174 variants. Six individuals (10.00%) tested negative for any variant. Seven individuals had one variant (11.67%), 13 had two variants (21.67%), and 34 had 3 or more variants (56.67%). The most common variants identified were of HBA, HBB, CYP21A2, and G6PD genes. Most of the detected variants were unknown or unexpected (n = 143, 82.18%). Eight couples carried two or more variants in common. Consanguinity was reported in 14 couples (46.67%).

CONCLUSIONS

Preconception ECS is crucial for reproductive planning, permitting couples to evaluate their combined genetic risks and make informed decisions, reducing the chance of having children with genetic disorders.

Behavioral screening reveals a conserved residue in Y-Box RNA-binding protein required for associative learning and memory in C. elegans

RNA-binding proteins (RBPs) regulate translation and plasticity which are required for memory. RBP dysfunction has been linked to a range of neurological disorders where cognitive impairments are a key symptom. However, of the 2,000 RBPs in the human genome, many are uncharacterized with regards to neurological phenotypes. To address this, we used the model organism C. elegans to assess the role of 20 conserved RBPs in memory. We identified eight previously uncharacterized memory regulators, three of which are in the C. elegans Y-Box (CEY) RBP family. Of these, we determined that cey-1 is the closest ortholog to the mammalian Y-Box (YBX) RBPs. We found that CEY-1 is both necessary in the nervous system for memory ability and sufficient to promote memory. Leveraging human datasets, we found both copy number variation losses and single nucleotide variants in YBX1 and YBX3 in individuals with neurological symptoms. We identified one predicted deleterious YBX3 variant of unknown significance, p.Asn127Tyr, in two individuals with neurological symptoms. Introducing this variant into endogenous cey-1 locus caused memory deficits in the worm. We further generated two humanized worm lines expressing human YBX3 or YBX1 at the cey-1 locus to test evolutionary conservation of YBXs in memory and the potential functional significance of the p.Asn127Tyr variant. Both YBX1/3 can functionally replace cey-1, and introduction of p.Asn127Tyr into the humanized YBX3 locus caused memory deficits. Our study highlights the worm as a model to reveal memory regulators and identifies YBX dysfunction as a potential new source of rare neurological disease.

Disruption of Erythritol Catabolism via the Deletion of Fructose-Bisphosphate Aldolase (Fba) and Transaldolase (Tal) as a Strategy to Improve the Brucella Rev1 Vaccine

Brucellosis is a bacterial zoonosis caused by the genus Brucella, which mainly affects domestic animals. In these natural hosts, brucellae display a tropism towards the reproductive organs, such as the placenta, replicating in high numbers and leading to placentitis and abortion, an ability also exerted by the B. melitensis live-attenuated Rev1 strain, the only vaccine available for ovine brucellosis. It is broadly accepted that this tropism is mediated, at least in part, by the presence of certain preferred nutrients in the placenta, particularly erythritol, a polyol that is ultimately incorporated into the Brucella central carbon metabolism via two reactions dependent on transaldolase (Tal) or fructose-bisphosphate aldolase (Fba). In the light of these remarks, we propose that blocking the incorporation of erythritol into the central carbon metabolism of Rev1 by deleting the genes encoding Tal and Fba may impair the ability of the vaccine to proliferate massively in the placenta. Therefore, a Rev1ΔfbaΔtal double mutant was generated and confirmed to be unable to use erythritol. This mutant exhibited a reduced intracellular fitness both in BeWo trophoblasts and THP-1 macrophages. In the murine model, Rev1ΔfbaΔtal provided comparable protection to the Rev1 reference vaccine while inducing fewer adverse reproductive events in pregnant animals. Altogether, these results postulate the Rev1ΔfbaΔtal mutant as a reproductively safer Rev1-derived vaccine candidate to be studied in the natural host.

The anti-tubercular callyaerins target the Mycobacterium tuberculosis-specific non-essential membrane protein Rv2113

Spread of antimicrobial resistances urges a need for new drugs against Mycobacterium tuberculosis (Mtb) with mechanisms differing from current antibiotics. Previously, callyaerins were identified as promising anti-tubercular agents, representing a class of hydrophobic cyclopeptides with an unusual (Z)-2,3-di-aminoacrylamide unit. Here, we investigated the molecular mechanisms underlying their antimycobacterial properties. Structure-activity relationship studies enabled the identification of structural determinants relevant for antibacterial activity. Callyaerins are bacteriostatics selectively active against Mtb, including extensively drug-resistant strains, with minimal cytotoxicity against human cells and promising intracellular activity. By combining mutant screens and various chemical proteomics approaches, we showed that callyaerins target the non-essential, Mtb-specific membrane protein Rv2113, triggering a complex dysregulation of the proteome, characterized by global downregulation of lipid biosynthesis, cell division, DNA repair, and replication. Our study thus identifies Rv2113 as a previously undescribed Mtb-specific drug target and demonstrates that also non-essential proteins may represent efficacious targets for antimycobacterial drugs.

Potent efficacy of an IgG-specific endoglycosidase against IgG-mediated pathologies

Endo-β-N-acetylglucosaminidases (ENGases) that specifically hydrolyze the Asn297-linked glycan on immunoglobulin G (IgG) antibodies, the major molecular determinant of fragment crystallizable (Fc) γ receptor (FcγR) binding, are exceedingly rare. All previously characterized IgG-specific ENGases are multi-domain proteins secreted as an immune evasion strategy by Streptococcus pyogenes strains. Here, using in silico analysis and mass spectrometry techniques, we identified a family of single-domain ENGases secreted by pathogenic corynebacterial species that exhibit strict specificity for IgG antibodies. By X-ray crystallographic and surface plasmon resonance analyses, we found that the most catalytically efficient IgG-specific ENGase family member recognizes both protein and glycan components of IgG. Employing in vivo models, we demonstrated the remarkable efficacy of this IgG-specific ENGase in mitigating numerous pathologies that rely on FcγR-mediated effector functions, including T and B lymphocyte depletion, autoimmune hemolytic anemia, and antibody-dependent enhancement of dengue disease, revealing its potential for treating and/or preventing a wide range of IgG-mediated diseases in humans.

Coarse-Grained Simulations of Adeno-Associated Virus and Its Receptor Reveal Influences on Membrane Lipid Organization and Curvature

Adeno-associated virus (AAV) is a well-known gene delivery tool with a wide range of applications, including as a vector for gene therapies. However, the molecular mechanism of its cell entry remains unknown. Here, we performed coarse-grained molecular dynamics simulations of the AAV serotype 2 (AAV2) capsid and the universal AAV receptor (AAVR) in a model plasma membrane environment. Our simulations show that binding of the AAV2 capsid to the membrane induces membrane curvature, along with the recruitment and clustering of GM3 lipids around the AAV2 capsid. We also found that the AAVR binds to the AAV2 capsid at the VR-I loops using its PKD2 and PKD3 domains, whose binding poses differs from previous structural studies. These first molecular-level insights into AAV2 membrane interactions suggest a complex process during the initial phase of AAV2 capsid internalization.

Click, Compute, Create: A Review of Web-based Tools for Enzyme Engineering

Enzyme engineering, though pivotal across various biotechnological domains, is often plagued by its time-consuming and labor-intensive nature. This review aims to offer an overview of supportive in silico methodologies for this demanding endeavor. Starting from methods to predict protein structures, to classification of their activity and even the discovery of new enzymes we continue with describing tools used to increase thermostability and production yields of selected targets. Subsequently, we discuss computational methods to modulate both, the activity as well as selectivity of enzymes. Last, we present recent approaches based on cutting-edge machine learning methods to redesign enzymes. With exception of the last chapter, there is a strong focus on methods easily accessible via web-interfaces or simple Python-scripts, therefore readily useable for a diverse and broad community.

SYNBIP 2.0: epitopes mapping, sequence expansion and scaffolds discovery for synthetic binding protein innovation

Synthetic binding proteins (SBPs) represent a pivotal class of artificially engineered proteins, meticulously crafted to exhibit targeted binding properties and specific functions. Here, the SYNBIP database, a comprehensive resource for SBPs, has been significantly updated. These enhancements include (i) featuring 3D structures of 899 SBP-target complexes to illustrate the binding epitopes of SBPs, (ii) using the structures of SBPs in the monomer or complex forms with target proteins, their sequence space has been expanded five times to 12 025 by integrating a structure-based protein generation framework and a protein property prediction tool, (iii) offering detailed information on 78 473 newly identified SBP-like scaffolds from the RCSB Protein Data Bank, and an additional 16 401 555 ones from the AlphaFold Protein Structure Database, and (iv) the database is regularly updated, incorporating 153 new SBPs. Furthermore, the structural models of all SBPs have been enhanced through the application of the AlphaFold2, with their clinical statuses concurrently refreshed. Additionally, the design methods employed for each SBP are now prominently featured in the database. In sum, SYNBIP 2.0 is designed to provide researchers with essential SBP data, facilitating their innovation in research, diagnosis and therapy. SYNBIP 2.0 is now freely accessible at https://idrblab.org/synbip/.

Unveiling MurM inhibitors in Enterococcus faecalis V583: a promising approach to tackle antibiotic resistance

Enterococcus faecalis is commonly found in the GI tract of humans and animals. It causes various infections, especially in hospital environments, and shows growing antibiotic resistance. This study utilized a subtractive proteomics approach to find out the potential drug targets in E. faecalis. Unique metabolic pathways were analysed and compared to the host to minimize adverse effects. Among twenty nine pathogenic specific and seventy three host-pathogen common pathways identified using the KEGG database, sixty seven essential proteins were found through the DEG BLAST search. PSORTB predicted that forty cytoplasmic proteins could be suitable as druggable targets. Further analysis identified fourteen proteins with virulence properties using the VFDB BLAST. Among these, seven proteins with more than ten antigenic sites were subjected to DrugBank BLAST, identifying three novel and four existing drug targets. One of the crucial drug targets, MurM, was selected due to its critical role in peptidoglycan biosynthesis. The reason for selecting MurM is crucial for addressing antibiotic resistance, disrupting bacterial cell wall synthesis, and attaining selective antimicrobial activity. MurM belongs to the mixed αβ class with two functional domains. The possible binding site residues of MurM are Trp31, Lys35, Trp38, Arg215, and Tyr219. Virtual screening identified potential lead candidates for MurM, and four were selected based on their physiochemical, pharmacokinetic, and structural properties. This study provides valuable insights into identifying and analysing a potential drug target, the MurM protein, and its inhibitors in E. faecalis V583.

The Toxoplasma gondii mitochondrial transporter ABCB7L is essential for the biogenesis of cytosolic and nuclear iron-sulfur cluster proteins and cytosolic translation

Iron-sulfur (Fe-S) clusters are ubiquitous inorganic cofactors required for numerous essential cellular pathways. Since they cannot be scavenged from the environment, Fe-S clusters are synthesized de novo in cellular compartments such as the apicoplast, mitochondrion, and cytosol. The cytosolic Fe-S cluster biosynthesis pathway relies on the transport of an intermediate from the mitochondrial pathway. An ATP-binding cassette (ABC) transporter called ABCB7 is responsible for this role in numerous commonly studied organisms, but its role in the medically important apicomplexan parasites has not yet been studied. Here we identify and characterize a Toxoplasma gondii ABCB7 homolog, which we name ABCB7-like (ABCB7L). Genetic depletion shows that it is essential for parasite growth and that its disruption triggers partial stage conversion. Characterization of the knock-down line highlights a defect in the biogenesis of cytosolic and nuclear Fe-S proteins leading to defects in protein translation and other pathways including DNA and RNA replication and metabolism. Our work provides support for a broad conservation of the connection between mitochondrial and cytosolic pathways in Fe-S cluster biosynthesis and reveals its importance for parasite survival.

IMPORTANCE

Iron-sulfur (Fe-S) clusters are inorganic cofactors of proteins that play key roles in numerous essential biological processes, for example, respiration and DNA replication. Cells possess dedicated biosynthetic pathways to assemble Fe-S clusters, including a pathway in the mitochondrion and cytosol. A single transporter, called ABCB7, connects these two pathways, allowing an essential intermediate generated by the mitochondrial pathway to be used in the cytosolic pathway. Cytosolic and nuclear Fe-S proteins are dependent on the mitochondrial pathway, mediated by ABCB7, in numerous organisms studied to date. Here, we study the role of a homolog of ABCB7, which we name ABCB7-like (ABCB7L), in the ubiquitous unicellular apicomplexan parasite Toxoplasma gondii. We generated a depletion mutant of Toxoplasma ABCB7L and showed its importance for parasite fitness. Using comparative quantitative proteomic analysis and experimental validation of the mutants, we show that ABCB7L is required for cytosolic and nuclear, but not mitochondrial, Fe-S protein biogenesis. Our study supports the conservation of a protein homologous to ABCB7 and which has a similar function in apicomplexan parasites and provides insight into an understudied aspect of parasite metabolism.

Cell shape and division septa positioning in filamentous Streptomyces require a functional cell wall glycopolymer ligase CglA

The cell wall of monoderm bacteria consists of peptidoglycan and glycopolymers in roughly equal proportions and is crucial for cellular integrity, cell shape, and bacterial vitality. Despite the immense value of Streptomyces in biotechnology and medicine as antibiotic producers, we know very little about their cell wall biogenesis, composition, and functions. Here, we have identified the LCP-LytR_C domain protein CglA (Vnz_13690) as a key glycopolymer ligase, which specifically localizes in zones of cell wall biosynthesis in S. venezuelae. Reduced amount of glycopolymers in the cglA mutant results in enlarged vegetative hyphae and failures in FtsZ-rings formation and positioning. Consequently, division septa are misplaced leading to the formation of aberrant cell compartments, misshaped spores, and reduced cell vitality. In addition, we report our discovery that c-di-AMP signaling and decoration of the cell wall with glycopolymers are physiologically linked in Streptomyces since the deletion of cglA restores growth of the S. venezuelae disA mutant at high salt. Altogether, we have identified and characterized CglA as a novel component of cell wall biogenesis in Streptomyces, which is required for cell shape maintenance and cellular vitality in filamentous, multicellular bacteria.IMPORTANCEStreptomyces are our key producers of antibitiotics and other bioactive molecules and are, therefore, of high value for medicine and biotechnology. They proliferate by apical extension and branching of hyphae and undergo complex cell differentiation from filaments to spores during their life cycle. For both, growth and sporulation, coordinated cell wall biogenesis is crucial. However, our knowledge about cell wall biosynthesis, functions, and architecture in Streptomyces and in other Actinomycetota is still very limited. Here, we identify CglA as the key enzyme needed for the attachment of glycopolymers to the cell wall of S. venezuelae. We demonstrate that defects in the cell wall glycopolymer content result in loss of cell shape in these filamentous bacteria and show that division-competent FtsZ-rings cannot assemble properly and fail to be positioned correctly. As a consequence, cell septa placement is disturbed leading to the formation of misshaped spores with reduced viability.

Physiological role and complex regulation of O2-reducing enzymes in the obligate anaerobe Clostridioides difficile

Clostridioides difficile, the major cause of antibiotic-associated diarrhea, is a strict anaerobic, sporulating Firmicutes. However, during its infectious cycle, this anaerobe is exposed to low oxygen (O2) tensions, with a longitudinal decreasing gradient along the gastrointestinal tract and a second lateral gradient with higher O2 tensions in the vicinity of the cells. A plethora of enzymes involved in oxidative stress detoxication has been identified in C. difficile, including four O2-reducing enzymes: two flavodiiron proteins (FdpA and FdpF) and two reverse rubrerythrins (revRbr1 and revRbr2). Here, we investigated the role of the four O2-reducing enzymes in the tolerance to increasing physiological O2 tensions and air. The four enzymes have different, yet overlapping, spectra of activity. revRbr2 is specific to low O2 tensions (<0.4%), FdpA to low and intermediate O2 tensions (0.4%-1%), revRbr1 has a wider spectrum of activity (0.1%-4%), and finally FdpF is more specific to tensions > 4% and air. These different O2 ranges of action partly arise from differences in regulation of expression of the genes encoding those enzymes. Indeed, we showed that revrbr2 is under the dual control of σA and σB. We also identified a regulator of the Spx family that plays a role in the induction of fdp and revrbr genes upon O2 exposure. Finally, fdpF is regulated by Rex, a regulator sensing the NADH/NAD+ ratio. Our results demonstrate that the multiplicity of O2-reducing enzymes of C. difficile is associated with different roles depending on the environmental conditions, stemming from a complex multi-leveled network of regulation.

IMPORTANCE

The gastrointestinal tract is a hypoxic environment, with the existence of two gradients of O2 along the gut, one longitudinal anteroposterior decreasing gradient and one proximodistal increasing from the lumen to the epithelial cells. O2 is a major source of stress for an obligate anaerobe such as the enteropathogen C. difficile. This bacterium possesses a plethora of enzymes capable of scavenging O2 and reducing it to H2O. In this work, we identified the role of the four O2-reducing enzymes in the tolerance to the physiological O2 tensions faced by C. difficile during its infectious cycle. These four enzymes have different spectra of action and protect the vegetative cells over a large range of O2 tensions. These differences are associated with a distinct regulation of each gene encoding those enzymes. The complex network of regulation is crucial for C. difficile to adapt to the various O2 tensions encountered during infection.

Molecular dynamics simulations involving different β-propeller mutations reported in Swiss and French patients correlate with their disease phenotypes

Integrin αIIbβ3 is the predominant receptor for fibrinogen which mediates platelet aggregation, an important step in hemostasis and thrombosis. Several mutations have been reported in the genes encoding αIIb and β3 subunits among patients with Glanzmann thrombasthenia, of which 177 are in the β-propeller domain. The two subunits form a heterodimer at the interface between β-propeller and β-I domains of αIIb and β3, respectively with their stability critical for intracellular trafficking, surface expression, and ligand binding. Our study was aimed at retrieving the β-propeller mutations from various databases and studying structural variations due to select mutations upon interaction with fibrinogen using molecular docking and molecular dynamics. Mutations were studied for their impact on phenotypic severity, structural stability, and evolutionary conservation. Molecular docking analysis and molecular dynamics simulations were carried out for αIIb-β3 complexes as well as αIIbβ3-fibrinogen complexes; in particular, E355K structure had more deviations, fluctuations, and other changes which compromised its structural stability and binding affinity when compared to both wild-type and G401C structures. Our comprehensive in silico analysis clearly reiterates that mutations in the β-propeller are not only responsible for structural changes in this domain but also have implications on the overall structure and function of integrin αIIbβ3.

Investigation of RNA-binding protein NOVA1 in silico: Comparison of the modern human V197 with the archaic I197 variant present in Neanderthals

By comparing the high-coverage archaic genome sequences to those of modern humans, specific genetic differences have been identified. For example, a human-specific substitution has been found in neuro-oncological ventral antigen 1 (NOVA1) - an RNA-binding protein that regulates the alternative splicing of neuronal pre-mRNA. The amino acid substitution results in an isoleucine-to-valine change at position 197 in NOVA1 (archaic: I197, modern human: V197). Previous studies have utilised gene editing technology to compare the archaic and modern human forms of NOVA1 in cortical organoids, however, the structural and molecular details require further investigation. Using an in silico approach, the modern human (WT) and archaic (V197I) structures of NOVA1 were generated. Moreover, the structure of NOVA1 containing a glycine-to-valine substitution at position 68 (G68V), which occurs at the RNA-binding interface, was examined for comparison. Protein-RNA docking was subsequently performed to model the interaction of NOVA1 variants with RNA and the complexes were evaluated further using classical molecular dynamics (MD) simulations. Based on the MM-PBSA analysis, the binding free energies were similar between the WT (-956.8 ± 32.6 kcal/mol), V197I (-975.4 ± 65.6 kcal/mol), and G68V (-946.7 ± 34.3 kcal/mol) complexes. The findings highlight the binding and stability of protein-RNA complexes with only modest structural changes observed in the archaic and G68V variants compared to the WT NOVA1 protein. Further clarification is required to enhance our understanding of the impact of NOVA1 mutations on alternative splicing and disease development. In particular, delineating the effect of multiple mutations in the NOVA1 gene is of importance.

Limitations of Current Machine-Learning Models in Predicting Enzymatic Functions for Uncharacterized Proteins

Thirty to seventy percent of proteins in any given genome have no assigned function and have been labeled as the protein "unknome". This large knowledge gap prevents the biological community from fully leveraging the plethora of genomic data that is now available. Machine-learning approaches are showing some promise in propagating functional knowledge from experimentally characterized proteins to the correct set of isofunctional orthologs. However, they largely fail to predict enzymatic functions unseen in the training set, as shown by dissecting the predictions made for over 450 enzymes of unknown function from the model bacteria Escherichia coli uxgsing the DeepECTransformer platform. Lessons from these failures can help the community develop machine-learning methods that assist domain experts in making testable functional predictions for more members of the uncharacterized proteome.

Article Summary

Many proteins in any genome, ranging from 30 to 70%, lack an assigned function. This knowledge gap limits the full use of the vast available genomic data. Machine learning has shown promise in transferring functional knowledge from proteins of known functions to similar ones, but largely fails to predict novel functions not seen in its training data. Understanding these failures can guide the development of better machine-learning methods to help experts make accurate functional predictions for uncharacterized proteins.

SHARK enables sensitive detection of evolutionary homologs and functional analogs in unalignable and disordered sequences

Intrinsically disordered regions (IDRs) are structurally flexible protein segments with regulatory functions in multiple contexts, such as in the assembly of biomolecular condensates. Since IDRs undergo more rapid evolution than ordered regions, identifying homology of such poorly conserved regions remains challenging for state-of-the-art alignment-based methods that rely on position-specific conservation of residues. Thus, systematic functional annotation and evolutionary analysis of IDRs have been limited, despite them comprising ~21% of proteins. To accurately assess homology between unalignable sequences, we developed an alignment-free sequence comparison algorithm, SHARK (Similarity/Homology Assessment by Relating K-mers). We trained SHARK-dive, a machine learning homology classifier, which achieved superior performance to standard alignment-based approaches in assessing evolutionary homology in unalignable sequences. Furthermore, it correctly identified dissimilar but functionally analogous IDRs in IDR-replacement experiments reported in the literature, whereas alignment-based tools were incapable of detecting such functional relationships. SHARK-dive not only predicts functionally similar IDRs at a proteome-wide scale but also identifies cryptic sequence properties and motifs that drive remote homology and analogy, thereby providing interpretable and experimentally verifiable hypotheses of the sequence determinants that underlie such relationships. SHARK-dive acts as an alternative to alignment to facilitate systematic analysis and functional annotation of the unalignable protein universe.

The genome sequence of the Large Red Damselfly Pyrrhosoma nymphula (Sulzer, 1776)

We present a genome assembly from an individual male Pyrrhosoma nymphula (the Large Red Damselfly; Arthropoda; Insecta; Odonata; Coenagrionidae). The genome sequence is 2,117.2 megabases in span. Most of the assembly is scaffolded into 14 chromosomal pseudomolecules, including the X sex chromosome. The mitochondrial genome has also been assembled and is 16.78 kilobases in length.

Intrinsically Disordered Compositional Bias in Proteins: Sequence Traits, Region Clustering, and Generation of Hypothetical Functional Associations

Compositionally biased regions (CBRs), ie, tracts that are dominated by a subset of residue types, are common features of eukaryotic proteins. These are often found bounded within or almost coterminous with intrinsically disordered or 'natively unfolded' parts. Here, it is investigated how the function of such intrinsically disordered compositionally biased regions (ID-CBRs) is directly linked to their compositional traits, focusing on the well-characterized yeast (Saccharomyces cerevisiae) proteome as a test case. The ID-CBRs that are clustered together using compositional distance are discovered to have clear functional linkages at various levels of diversity. The specific case of the Sup35p and Rnq1p proteins that underlie causally linked prion phenomena ([PSI+] and [RNQ+]) is highlighted. Their prion-forming ID-CBRs are typically clustered very close together indicating some compositional engendering for [RNQ+] seeding of [PSI+] prions. Delving further, ID-CBRs with distinct types of residue patterning such as 'blocking' or relative segregation of residues into homopeptides are found to have significant functional trends. Specific examples of such ID-CBR functional linkages that are discussed are: Q/N-rich ID-CBRs linked to transcriptional coactivation, S-rich to transcription-factor binding, R-rich to DNA-binding, S/E-rich to protein localization, and D-rich linked to chromatin remodelling. These data may be useful in informing experimental hypotheses for proteins containing such regions.

Adaptive laboratory evolution recruits the promiscuity of succinate semialdehyde dehydrogenase to repair different metabolic deficiencies

Promiscuous enzymes often serve as the starting point for the evolution of novel functions. Yet, the extent to which the promiscuity of an individual enzyme can be harnessed several times independently for different purposes during evolution is poorly reported. Here, we present a case study illustrating how NAD(P)+-dependent succinate semialdehyde dehydrogenase of Escherichia coli (Sad) is independently recruited through various evolutionary mechanisms for distinct metabolic demands, in particular vitamin biosynthesis and central carbon metabolism. Using adaptive laboratory evolution (ALE), we show that Sad can substitute for the roles of erythrose 4-phosphate dehydrogenase in pyridoxal 5'-phosphate (PLP) biosynthesis and glyceraldehyde 3-phosphate dehydrogenase in glycolysis. To recruit Sad for PLP biosynthesis and glycolysis, ALE employs various mechanisms, including active site mutation, copy number amplification, and (de)regulation of gene expression. Our study traces down these different evolutionary trajectories, reports on the surprising active site plasticity of Sad, identifies regulatory links in amino acid metabolism, and highlights the potential of an ordinary enzyme as innovation reservoir for evolution.

The proteomic signature of circulating extracellular vesicles following intracerebral hemorrhage: Novel insights into mechanisms underlying recovery

Circulating extracellular vesicles (EVs) can participate in innate repair processes triggered after intracerebral hemorrhage (ICH). We aimed to describe changes in the proteomic profile of circulating EVs between the acute and subacute phases of ICH and to compare the findings depending on outcomes, as an approach to unraveling such repair mechanisms. This was a prospective observational study including patients with non-traumatic supratentorial ICH. Exclusion criteria were previous disability, signs of herniation on baseline computed tomography, or limited life expectancy. EVs were isolated from blood samples at 24 h and 7 days after symptom onset. After 6-months' follow-up, patients were dichotomized into poor and good outcomes, defining good as an improvement of >10 points or > 50 % on the National Institutes of Health Stroke Scale and a modified Rankin Scale of 0-2. The protein cargo was analyzed by quantitative mass spectrometry and compared according to outcomes. Forty-four patients completed follow-up, 16 (35.5 %) having good outcomes. We identified 1321 proteins in EVs, 37 with differential abundance. In patients with good outcomes, proteins related to stress response (DERA, VNN2, TOMM34) and angiogenesis (RHG01) had increased abundance at 7 days. EVs from patients with poor outcomes showed higher levels of acute-phase reactants (CRP, SAA2) at 7 days compared with 24 h. In conclusion, the protein content of circulating EVs in patients with ICH changes over time, the changes varying depending on the clinical outcome, with greater abundance of proteins potentially involved in the repair processes of patients with good outcomes.

Research Progresses and Applications of Knowledge Graph Embedding Technique in Chemistry

A knowledge graph (KG) is a technique for modeling entities and their interrelations. Knowledge graph embedding (KGE) translates these entities and relationships into a continuous vector space to facilitate dense and efficient representations. In the domain of chemistry, applying KG and KGE techniques integrates heterogeneous chemical information into a coherent and user-friendly framework, enhances the representation of chemical data features, and is beneficial for downstream tasks, such as chemical property prediction. This paper begins with a comprehensive review of classical and contemporary KGE methodologies, including distance-based models, semantic matching models, and neural network-based approaches. We then catalogue the primary databases employed in chemistry and biochemistry that furnish the KGs with essential chemical data. Subsequently, we explore the latest applications of KG and KGE in chemistry, focusing on risk assessment, property prediction, and drug discovery. Finally, we discuss the current challenges to KG and KGE techniques and provide a perspective on their potential future developments.

NPCoronaPredict: A Computational Pipeline for the Prediction of the Nanoparticle-Biomolecule Corona

The corona of a nanoparticle immersed in a biological fluid is of key importance to its eventual fate and bioactivity in the environment or inside live tissues. It is critical to have insight into both the underlying bionano interactions and the corona composition to ensure biocompatibility of novel engineered nanomaterials. A prediction of these properties in silico requires the successful spanning of multiple orders of magnitude of both time and physical dimensions to produce results in a reasonable amount of time, necessitating the development of a multiscale modeling approach. Here, we present the NPCoronaPredict open-source software package: a suite of software tools to enable this prediction for complex multicomponent nanomaterials in essentially arbitrary biological fluids, or more generally any medium containing organic molecules. The package integrates several recent physics-based computational models and a library of both physics-based and data-driven parametrizations for nanomaterials and organic molecules. We describe the underlying theoretical background and the package functionality from the design of multicomponent NPs through to the evaluation of the corona.

ProEnd: a comprehensive database for identifying HbYX motif-containing proteins across the tree of life

The proteasome plays a crucial role in cellular homeostasis by degrading misfolded, damaged, or unnecessary proteins. Understanding the regulatory mechanisms of proteasome activity is vital, particularly the interaction with activators containing the hydrophobic-tyrosine-any amino acid (HbYX) motif. Here, we present ProEnd, a comprehensive database designed to identify and catalog HbYX motif-containing proteins across the tree of life. Using a simple bioinformatics pipeline, we analyzed approximately 73 million proteins from 22,000 reference proteomes in the UniProt/SwissProt database. Our findings reveal the widespread presence of HbYX motifs in diverse organisms, highlighting their evolutionary conservation and functional significance. Notably, we observed an interesting prevalence of these motifs in viral proteomes, suggesting strategic interactions with the host proteasome. As validation two novel HbYX proteins found in this database were experimentally tested by pulldowns, confirming that they directly interact with the proteasome, with one of them directly activating it. ProEnd's extensive dataset and user-friendly interface enable researchers to explore the potential proteasomal regulator landscape, generating new hypotheses to advance proteasome biology. This resource is set to facilitate the discovery of novel therapeutic targets, enhancing our approach to treating diseases such as neurodegenerative disorders and cancer.

Malaria parasites require a divergent heme oxygenase for apicoplast gene expression and biogenesis

Malaria parasites have evolved unusual metabolic adaptations that specialize them for growth within heme-rich human erythrocytes. During blood-stage infection, Plasmodium falciparum parasites internalize and digest abundant host hemoglobin within the digestive vacuole. This massive catabolic process generates copious free heme, most of which is biomineralized into inert hemozoin. Parasites also express a divergent heme oxygenase (HO)-like protein (PfHO) that lacks key active-site residues and has lost canonical HO activity. The cellular role of this unusual protein that underpins its retention by parasites has been unknown. To unravel PfHO function, we first determined a 2.8 Å-resolution X-ray structure that revealed a highly α-helical fold indicative of distant HO homology. Localization studies unveiled PfHO targeting to the apicoplast organelle, where it is imported and undergoes N-terminal processing but retains most of the electropositive transit peptide. We observed that conditional knockdown of PfHO was lethal to parasites, which died from defective apicoplast biogenesis and impaired isoprenoid-precursor synthesis. Complementation and molecular-interaction studies revealed an essential role for the electropositive N-terminus of PfHO, which selectively associates with the apicoplast genome and enzymes involved in nucleic acid metabolism and gene expression. PfHO knockdown resulted in a specific deficiency in levels of apicoplast-encoded RNA but not DNA. These studies reveal an essential function for PfHO in apicoplast maintenance and suggest that Plasmodium repurposed the conserved HO scaffold from its canonical heme-degrading function in the ancestral chloroplast to fulfill a critical adaptive role in organelle gene expression.

Seeing the unseen in characterizing RNA editome during rice endosperm development

Rice (Oryza sativa L.) endosperm is essential to provide nutrients for seed germination and determine grain yield. RNA editing, a post-transcriptional modification essential for plant development, unfortunately, is not fully characterized during rice endosperm development. Here, we perform systematic analyses to characterize RNA editome during rice endosperm development. We find that most editing sites are C-to-U CDS-recoding in mitochondria, leading to increased hydrophobic amino acids and changed structures of mitochondrial proteins. Comparative analysis of RNA editome reveals that CDS-recoding sites present higher editing frequencies with lower variabilities and their resultant recoded amino acids tend to exhibit stronger evolutionary conservation across many land plants. Furthermore, we classify mitochondrial genes into three groups, presenting distinct patterns in terms of CDS-recoding events. Besides, we conduct genome-wide screening to detect pentatricopeptide repeat (PPR) proteins and construct PPR-RNA binding profiles, yielding candidate PPR editing factors related to rice endosperm development. Taken together, our findings provide valuable insights for deciphering fundamental mechanisms of rice endosperm development underlying RNA editing machinery.

StopKB: a comprehensive knowledgebase for nonsense suppression therapies

Nonsense variations, characterized by premature termination codons, play a major role in human genetic diseases as well as in cancer susceptibility. Despite their high prevalence, effective therapeutic strategies targeting premature termination codons remain a challenge. To understand and explore the intricate mechanisms involved, we developed StopKB, a comprehensive knowledgebase aggregating data from multiple sources on nonsense variations, associated genes, diseases, and phenotypes. StopKB identifies 637 317 unique nonsense variations, distributed across 18 022 human genes and linked to 3206 diseases and 7765 phenotypes. Notably, ∼32% of these variations are classified as nonsense-mediated mRNA decay-insensitive, potentially representing suitable targets for nonsense suppression therapies. We also provide an interactive web interface to facilitate efficient and intuitive data exploration, enabling researchers and clinicians to navigate the complex landscape of nonsense variations. StopKB represents a valuable resource for advancing research in precision medicine and more specifically, the development of targeted therapeutic interventions for genetic diseases associated with nonsense variations. Database URL: https://lbgi.fr/stopkb/.

SERBP1 interacts with PARP1 and is present in PARylation-dependent protein complexes regulating splicing, cell division, and ribosome biogenesis

RNA binding proteins (RBPs) containing intrinsically disordered regions (IDRs) are present in diverse molecular complexes where they function as dynamic regulators. Their characteristics promote liquid-liquid phase separation (LLPS) and the formation of membraneless organelles such as stress granules and nucleoli. IDR-RBPs are particularly relevant in the nervous system and their dysfunction is associated with neurodegenerative diseases and brain tumor development. Serpine1 mRNA-binding protein 1 (SERBP1) is a unique member of this group, being mostly disordered and lacking canonical RNA-binding domains. We defined SERBP1's interactome, uncovered novel roles in splicing, cell division and ribosomal biogenesis, and showed its participation in pathological stress granules and Tau aggregates in Alzheimer's brains. SERBP1 preferentially interacts with other G-quadruplex (G4) binders, implicated in different stages of gene expression, suggesting that G4 binding is a critical component of SERBP1 function in different settings. Similarly, we identified important associations between SERBP1 and PARP1/polyADP-ribosylation (PARylation). SERBP1 interacts with PARP1 and its associated factors and influences PARylation. Moreover, protein complexes in which SERBP1 participates contain mostly PARylated proteins and PAR binders. Based on these results, we propose a feedback regulatory model in which SERBP1 influences PARP1 function and PARylation, while PARylation modulates SERBP1 functions and participation in regulatory complexes.

Extensive longevity and DNA virus-driven adaptation in nearctic Myotis bats

The rich species diversity of bats encompasses extraordinary adaptations, including extreme longevity and tolerance to infectious disease. While traditional approaches using genetic screens in model organisms have uncovered some fundamental processes underlying these traits, model organisms do not possess the variation required to understand the evolution of traits with complex genetic architectures. In contrast, the advent of genomics at tree-of-life scales enables us to study the genetic interactions underlying these processes by leveraging millions of years of evolutionary trial-and-error. Here, we use the rich species diversity of the genus Myotis - one of the longest-living clades of mammals - to study the evolution of longevity-associated traits and infectious disease using functional evolutionary genomics. We generated reference genome assemblies and cell lines for 8 closely-related (∼11 MYA) species of Myotis rich in phenotypic and life history diversity. Using genome-wide screens of positive selection, analysis of structural variation and copy number variation, and functional experiments in primary cell lines, we identify new patterns of adaptation in longevity, cancer resistance, and viral interactions both within Myotis and across bats. We find that the rapid evolution of lifespan in Myotis has some of the most significant variations in cancer risk across mammals, and demonstrate a unique DNA damage response in the long-lived M. lucifugus using primary cell culture models. Furthermore, we find evidence of abundant adaptation in response to DNA viruses, but not RNA viruses, in Myotis and other bats. This is in contrast to these patterns of adaptation in humans, which might contribute to the importance of bats as a reservoir of zoonotic viruses. Together, our results demonstrate the utility of leveraging natural variation to understand the genomics of traits with implications for human health and suggest important pleiotropic relationships between infectious disease tolerance and cancer resistance.

Chromosomal integrons are genetically and functionally isolated units of genomes

Integrons are genetic elements that increase the evolvability of bacteria by capturing new genes and stockpiling them in arrays. Sedentary chromosomal integrons (SCIs) can be massive and highly stabilized structures encoding hundreds of genes, whose function remains generally unknown. SCIs have co-evolved with the host for aeons and are highly intertwined with their physiology from a mechanistic point of view. But, paradoxically, other aspects, like their variable content and location within the genome, suggest a high genetic and functional independence. In this work, we have explored the connection of SCIs to their host genome using as a model the Superintegron (SI), a 179-cassette long SCI in the genome of Vibrio cholerae N16961. We have relocated and deleted the SI using SeqDelTA, a novel method that allows to counteract the strong stabilization conferred by toxin-antitoxin systems within the array. We have characterized in depth the impact in V. cholerae's physiology, measuring fitness, chromosome replication dynamics, persistence, transcriptomics, phenomics, natural competence, virulence and resistance against protist grazing. The deletion of the SI did not produce detectable effects in any condition, proving that-despite millions of years of co-evolution-SCIs are genetically and functionally isolated units of genomes.

Machine learning-assisted amidase-catalytic enantioselectivity prediction and rational design of variants for improving enantioselectivity

Biocatalysis is an attractive approach for the synthesis of chiral pharmaceuticals and fine chemicals, but assessing and/or improving the enantioselectivity of biocatalyst towards target substrates is often time and resource intensive. Although machine learning has been used to reveal the underlying relationship between protein sequences and biocatalytic enantioselectivity, the establishment of substrate fitness space is usually disregarded by chemists and is still a challenge. Using 240 datasets collected in our previous works, we adopt chemistry and geometry descriptors and build random forest classification models for predicting the enantioselectivity of amidase towards new substrates. We further propose a heuristic strategy based on these models, by which the rational protein engineering can be efficiently performed to synthesize chiral compounds with higher ee values, and the optimized variant results in a 53-fold higher E-value comparing to the wild-type amidase. This data-driven methodology is expected to broaden the application of machine learning in biocatalysis research.

Curcumin is an efficacious therapeutic agent against Chilodonella uncinata via interaction with tubulin alpha chain as protein target

Chilodonella, a parasitic ciliate that infects both cold water and warm water fish, can impede the growth of juvenile fish and cause considerable economic losses globally to freshwater aquaculture. In this study, the parasite was collected from both the gills and zygotes of largemouth bass (Micropterus salmoides). Isolated from diseased fish, the parasites were identified as Chilodonella uncinata based on morphological features and genetical diagnostic characterization using the partial small subunit ribosomal RNA gene. To develop an effective approach to treat chilodonellosis caused by C. uncinata in largemouth bass farming, we first developed an in vivo culture model for propagating C. uncinate and thus could use for morphological characterization, molecular analyses and antiparasitic drug screening. Curcumin was successfully identified as an efficacious anti-C. uncinata agent from 26 phytochemical compounds. When administered at a concentration of 6 mg/L, curcumin not only completely cured infected largemouth bass but also shielded uninfected fish from C. uncinata infections. The 24 h median effective concentration (EC50) of curcumin against C. uncinata was 3.098 mg/L. Remarkably, the 96 h median lethal concentration (LC50) of curcumin against largemouth bass was determined to be 17.143 mg/L, approximately 5.533 times higher than EC50. The mechanism of action of curcumin was investigated by the cellular thermal shift assay, demonstrating that tubulin alpha chain was the binding target for curcumin. Moreover, SEM investigations further provided morphological evidence suggesting that curcumin induces parasite demise by disrupting the parasite's body surface and subsequently infiltrating its interior. These findings collectively emphasize the potential of curcumin as a safe and effective therapeutic agent for controlling C. uncinata in aquaculture.

Assessing Darkness of the Human Kinome from a Medicinal Chemistry Perspective

In drug discovery, human protein kinases (PKs) represent one of the major target classes due to their central role in cellular signaling, implication in various diseases as a consequence of deregulated signaling, and notable druggability. Individual PKs and their disease biology have been explored to different degrees, giving rise to heterogeneous functional knowledge and disease associations across the human kinome. The U.S. National Institutes of Health previously designated 162 understudied ("dark") human PKs and lipid kinases due to the lack of functional annotations and high-quality molecular probes for functional investigations. Given the large volumes of available PK inhibitors (PKIs) and activity data, we have systematically analyzed the distribution of PKIs and associated data at different confidence levels across the human kinome and distinguished between chemically explored, underexplored, and unexplored PKs. The analysis provides a medicinal chemistry-centric view of PK exploration and further extends prior assessment of the dark kinome.

Intracellular diffusion in the cytoplasm increases with cell size in fission yeast

Diffusion in the cytoplasm can greatly impact cellular processes, yet regulation of macromolecular diffusion remains poorly understood. There is increasing evidence that cell size affects the density and macromolecular composition of the cytoplasm. Here, we studied whether cell size affects diffusion at the scale of macromolecules tens of microns in diameter. We analyzed the diffusive motions of intracellular genetically-encoded multimeric 40 nm nanoparticles (cytGEMs) in the cytoplasm of the fission yeast Schizosaccharomyces pombe . Using cell size mutants, we showed that cytGEMs diffusion coefficients decreased in smaller cells and increased in larger cells. This increase in diffusion in large cells may be due to a decrease in the DNA-to-Cytoplasm ratio, as diffusion was not affected in large multinucleate cytokinesis mutants. In investigating the underlying causes of altered cytGEMs diffusion, we found that the proteomes of large and small cells exhibited size-specific changes, including the sub-scaling of ribosomal proteins in large cells. Comparison with a similar dataset from human cells revealed that features of size-dependent proteome remodeling were conserved. These studies demonstrate that cell size is an important parameter in determining the biophysical properties and the composition of the cytoplasm.

NIAGADS: A Comprehensive National Data Repository for Alzheimer's Disease and Related Dementia Genetics and Genomics Research

NIAGADS is the National Institute on Aging (NIA) designated national data repository for human genetics research on Alzheimer's Disease and related dementia (ADRD). NIAGADS maintains a high-quality data collection for ADRD genetic/genomic research and supports genetics data production and analysis. NIAGADS hosts whole genome and exome sequence data from the Alzheimer's Disease Sequencing Project (ADSP) and other genotype/phenotype data, encompassing 209,000 samples. NIAGADS shares these data with hundreds of research groups around the world via the Data Sharing Service, a FISMA moderate compliant cloud-based platform that fully supports the NIH Genome Data Sharing Policy. NIAGADS Open Access consists of multiple knowledge bases with genome-wide association summary statistics and rich annotations on the biological significance of genetic variants and genes across the human genome. NIAGADS stands as a keystone in promoting collaborations to advance the understanding and treatment of Alzheimer's disease.

Skin-permeable gold nanoparticles with modifications azelamide monoethanolamine ameliorate inflammatory skin diseases

BACKGROUND

Traditional topical drug delivery for treating inflammatory skin diseases suffers from poor skin penetration and long-term side effects. Metal nanoparticles show promising application in topical drug delivery for inflammatory skin diseases.

METHODS

Here, we synthesized a new type of nanoparticles, azelamide monoethanolamine-functionalized gold nanoparticles (Au-MEA NPs), based on citrate-capped gold nanoparticles (Au-CA NPs) via the ligand exchange method. The physical and chemical properties of Au-CA NPs and Au-MEA NPs were characterized. In vivo studies were performed using imiquimod-induced psoriasis and LL37-induced rosacea animal models, respectively. For in vitro studies, a model of cellular inflammation was established using HaCaT cells stimulated with TNF-α. In addition, proteomics, gelatin zymography, and other techniques were used to investigate the possible therapeutic mechanisms of the Au-MEA NPs.

RESULTS

We found that Au-MEA NPs exhibited better stability and permeation properties compared to conventional Au-CA NPs. Transcutaneously administered Au-MEA NPs exerted potent therapeutic efficacy against both rosacea-like and psoriasiform skin dermatitis in vivo without overt signs of toxicity. Mechanistically, Au-MEA NPs reduced the production of pro-inflammatory mediators in keratinocytes by promoting SOD activity and inhibiting the activity of MMP9.

CONCLUSION

Au-MEA NPs have the potential to be a topical nanomedicine for the effective and safe treatment of inflammatory skin diseases.

Comparative modeling reveals the molecular determinants of aneuploidy fitness cost in a wild yeast model

Although implicated as deleterious in many organisms, aneuploidy can underlie rapid phenotypic evolution. However, aneuploidy will be maintained only if the benefit outweighs the cost, which remains incompletely understood. To quantify this cost and the molecular determinants behind it, we generated a panel of chromosome duplications in Saccharomyces cerevisiae and applied comparative modeling and molecular validation to understand aneuploidy toxicity. We show that 74%-94% of the variance in aneuploid strains' growth rates is explained by the cumulative cost of genes on each chromosome, measured for single-gene duplications using a genomic library, along with the deleterious contribution of small nucleolar RNAs (snoRNAs) and beneficial effects of tRNAs. Machine learning to identify properties of detrimental gene duplicates provided no support for the balance hypothesis of aneuploidy toxicity and instead identified gene length as the best predictor of toxicity. Our results present a generalized framework for the cost of aneuploidy with implications for disease biology and evolution.

The Evolution of Extreme Genetic Variability in a Parasite-Resistance Complex

Genomic regions that play a role in parasite defense are often found to be highly variable, with the major histocompatibility complex serving as an iconic example. Single nucleotide polymorphisms may represent only a small portion of this variability, with Indel polymorphisms and copy number variation further contributing. In extreme cases, haplotypes may no longer be recognized as orthologous. Understanding the evolution of such highly divergent regions is challenging because the most extreme variation is not visible using reference-assisted genomic approaches. Here we analyze the case of the Pasteuria Resistance Complex in the crustacean Daphnia magna, a defense complex in the host against the common and virulent bacterium Pasteuria ramosa. Two haplotypes of this region have been previously described, with parts of it being nonhomologous, and the region has been shown to be under balancing selection. Using pan-genome analysis and tree reconciliation methods to explore the evolution of the Pasteuria Resistance Complex and its characteristics within and between species of Daphnia and other Cladoceran species, our analysis revealed a remarkable diversity in this region even among host species, with many nonhomologous hyper-divergent haplotypes. The Pasteuria Resistance Complex is characterized by extensive duplication and losses of Fucosyltransferase (FuT) and Galactosyltransferase (GalT) genes that are believed to play a role in parasite defense. The Pasteuria Resistance Complex region can be traced back to common ancestors over 250 million years. The unique combination of an ancient resistance complex and a dynamic, hyper-divergent genomic environment presents a fascinating opportunity to investigate the role of such regions in the evolution and long-term maintenance of resistance polymorphisms. Our findings offer valuable insights into the evolutionary forces shaping disease resistance and adaptation, not only in the genus Daphnia, but potentially across the entire Cladocera class.

Diverse anti-defence systems are encoded in the leading region of plasmids

Plasmids are major drivers of gene mobilization by means of horizontal gene transfer and play a key role in spreading antimicrobial resistance among pathogens1,2. Despite various bacterial defence mechanisms such as CRISPR-Cas, restriction-modification systems and SOS-response genes that prevent the invasion of mobile genetic elements3, plasmids robustly transfer within bacterial populations through conjugation4,5. Here we show that the leading region of plasmids, the first to enter recipient cells, is a hotspot for an extensive repertoire of anti-defence systems, encoding anti-CRISPR, anti-restriction, anti-SOS and other counter-defence proteins. We further identified in the leading region a prevalence of promoters known to allow expression from single-stranded DNA6, potentially facilitating rapid protection against bacterial immunity during the early stages of plasmid establishment. We demonstrated experimentally the importance of anti-defence gene localization in the leading region for efficient conjugation. These results indicate that focusing on the leading region of plasmids could lead to the discovery of diverse anti-defence genes. Combined, our findings show a new facet of plasmid dissemination and provide theoretical foundations for developing efficient conjugative delivery systems for natural microbial communities.

Hierarchical Biclustering of Mouse Pancreas Mass Spectrometry Imaging Data Using Recursive Rank-2 Non-negative Matrix Factorization

One of the main challenges in mass spectrometry imaging data analysis remains the analysis of m/z-spectra displaying a low signal-to-noise ratio caused by their low abundance, sample preparation, matrix effects, fragmentation, and other artifacts. Additionally, we observe that molecules with a high abundance suppress those with lower intensities and misdirect classical tools for MSI data analysis, such as principal component analysis. As a result, the observed significance of a molecule may not always be directly related to its abundance. In this work, we present a recursive rank-2 non-negative matrix factorization (rr2-NMF) algorithm that automatically returns spectral and spatial visualization of colocalized molecules, both highly and lowly abundant. Using this hierarchical decomposition, our method finds spatial and spectral correlations on different levels of abundances. The quality of the analysis is evaluated on MALDI-TOF data of healthy mouse pancreatic tissue for the annotation of molecules of interest in the lower abundances. The results show interesting findings regarding the functioning and colocalization of certain molecules.