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The genome sequence of the giant clam, Tridacna gigas (Linnaeus, 1758)

We present a chromosomal-level genome assembly from an individual Tridacna gigas (the giant clam; Mollusca; Bivalvia; Veneroida; Cardiidae). The genome sequence is 1,175.9 megabases in span. Most of the assembly is scaffolded into 17 chromosomal pseudomolecules. The mitochondrial genome has also been assembled and is 25.34 kilobases in length. Gene annotation of this assembly on Ensembl identified 18,177 protein coding genes.

CXCL17 is an allosteric inhibitor of CXCR4 through a mechanism of action involving glycosaminoglycans

CXCL17 is a chemokine principally expressed by mucosal tissues, where it facilitates chemotaxis of monocytes, dendritic cells, and macrophages and has antimicrobial properties. CXCL17 is also implicated in the pathology of inflammatory disorders and progression of several cancers, and its expression is increased during viral infections of the lung. However, the exact role of CXCL17 in health and disease requires further investigation, and there is a need for confirmed molecular targets mediating CXCL17 functional responses. Using a range of bioluminescence resonance energy transfer (BRET)-based assays, here we demonstrated that CXCL17 inhibited CXCR4-mediated signaling and ligand binding. Moreover, CXCL17 interacted with neuropillin-1, a VEGFR2 coreceptor. In addition, we found that CXCL17 only inhibited CXCR4 ligand binding in intact cells and demonstrated that this effect was mimicked by known glycosaminoglycan binders, surfen and protamine sulfate. Disruption of putative GAG binding domains in CXCL17 prevented CXCR4 binding. This indicated that CXCL17 inhibited CXCR4 by a mechanism of action that potentially required the presence of a glycosaminoglycan-containing accessory protein. Together, our results revealed that CXCL17 is an endogenous inhibitor of CXCR4 and represents the next step in our understanding of the function of CXCL17 and regulation of CXCR4 signaling.

Multidrug resistance plasmids commonly reprogram the expression of metabolic genes in Escherichia coli

Multidrug-resistant Escherichia coli is a leading cause of global mortality. Transfer of plasmids carrying genes encoding beta-lactamases, carbapenamases, and colistin resistance between lineages is driving the rising rates of hard-to-treat nosocomial and community infections. Multidrug resistance (MDR) plasmid acquisition commonly causes transcriptional disruption, and while a number of studies have shown strain-specific fitness and transcriptional effects of an MDR plasmid across diverse bacterial lineages, fewer studies have compared the impacts of different MDR plasmids in a common bacterial host. As such, our ability to predict which MDR plasmids are the most likely to be maintained and spread in bacterial populations is limited. Here, we introduced eight diverse MDR plasmids encoding resistances against a range of clinically important antibiotics into E. coli K-12 MG1655 and measured their fitness costs and transcriptional impacts. The scale of the transcriptional responses varied substantially between plasmids, ranging from >650 to <20 chromosomal genes being differentially expressed. However, the scale of regulatory disruption did not correlate significantly with the magnitude of the plasmid fitness cost, which also varied between plasmids. The identities of differentially expressed genes differed between transconjugants, although the expression of certain metabolic genes and functions were convergently affected by multiple plasmids, including the downregulation of genes involved in L-methionine transport and metabolism. Our data show the complexity of the interaction between host genetic background and plasmid genetic background in determining the impact of MDR plasmid acquisition on E. coli.

IMPORTANCE

The increase in infections that are resistant to multiple classes of antibiotics, including those isolates that carry carbapenamases, beta-lactamases, and colistin resistance genes, is of global concern. Many of these resistances are spread by conjugative plasmids. Understanding more about how an isolate responds to an incoming plasmid that encodes antibiotic resistance will provide information that could be used to predict the emergence of MDR lineages. Here, the identification of metabolic networks as being particularly sensitive to incoming plasmids suggests the possible targets for reducing plasmid transfer.

A novel stacking-based predictor for accurate prediction of antimicrobial peptides

Antimicrobial peptides (AMPs) are gaining acceptance and support as a chief antibiotic substitute since they boost human immunity. They retain a wide range of actions and have a low risk of developing resistance, which are critical properties to the pharmaceutical industry for drug discovery. Antibiotic sensitivity, however, is an issue that affects people all around the world and has the potential to one day lead to an epidemic. As cutting-edge therapeutic agents, AMPs are also expected to cure microbial infections. In order to produce tolerable drugs, it is crucial to understand the significance of the basic architecture of AMPs. Traditional laboratory methods are expensive and time-consuming for AMPs testing and detection. Currently, bioinformatics techniques are being successfully applied to the detection of AMPs. In this study, we have developed a novel STacking-based ensemble learning framework for AntiMicrobial Peptide (STAMP) prediction. First, we constructed 84 different baseline models by using 12 different feature encoding schemes and 7 popular machine learning algorithms. Second, these baseline models were trained and employed to create a new probabilistic feature vector. Finally, based on the feature selection strategy, we determined the optimal probabilistic feature vector, which was further utilized for the construction of our stacked model. Resultantly, the STAMP predictor achieved excellent performance during cross-validation with an accuracy and Matthew's correlation coefficient of 0.930 and 0.860, respectively. The corresponding metrics during the independent test were 0.710 and 0.464, respectively. Overall, STAMP achieved a more accurate and stable performance than the baseline models and significantly outperformed the existing predictors, demonstrating the effectiveness of our proposed hybrid framework. Furthermore, STAMP is expected to assist community-wide efforts in identifying AMPs and will contribute to the development of novel therapeutic methods and drug-design for immunity.Communicated by Ramaswamy H. Sarma.

A deep profile of gene expression across 18 human cancers

Clinically and biologically valuable information may reside untapped in large cancer gene expression data sets. Deep unsupervised learning has the potential to extract this information with unprecedented efficacy but has thus far been hampered by a lack of biological interpretability and robustness. Here, we present DeepProfile, a comprehensive framework that addresses current challenges in applying unsupervised deep learning to gene expression profiles. We use DeepProfile to learn low-dimensional latent spaces for 18 human cancers from 50,211 transcriptomes. DeepProfile outperforms existing dimensionality reduction methods with respect to biological interpretability. Using DeepProfile interpretability methods, we show that genes that are universally important in defining the latent spaces across all cancer types control immune cell activation, while cancer type-specific genes and pathways define molecular disease subtypes. By linking DeepProfile latent variables to secondary tumor characteristics, we discover that tumor mutation burden is closely associated with the expression of cell cycle-related genes. DNA mismatch repair and MHC class II antigen presentation pathway expression, on the other hand, are consistently associated with patient survival. We validate these results through Kaplan-Meier analyses and nominate tumor-associated macrophages as an important source of survival-correlated MHC class II transcripts. Our results illustrate the power of unsupervised deep learning for discovery of novel cancer biology from existing gene expression data.

Using protein language models for protein interaction hot spot prediction with limited data

BACKGROUND

Protein language models, inspired by the success of large language models in deciphering human language, have emerged as powerful tools for unraveling the intricate code of life inscribed within protein sequences. They have gained significant attention for their promising applications across various areas, including the sequence-based prediction of secondary and tertiary protein structure, the discovery of new functional protein sequences/folds, and the assessment of mutational impact on protein fitness. However, their utility in learning to predict protein residue properties based on scant datasets, such as protein-protein interaction (PPI)-hotspots whose mutations significantly impair PPIs, remained unclear. Here, we explore the feasibility of using protein language-learned representations as features for machine learning to predict PPI-hotspots using a dataset containing 414 experimentally confirmed PPI-hotspots and 504 PPI-nonhot spots.

RESULTS

Our findings showcase the capacity of unsupervised learning with protein language models in capturing critical functional attributes of protein residues derived from the evolutionary information encoded within amino acid sequences. We show that methods relying on protein language models can compete with methods employing sequence and structure-based features to predict PPI-hotspots from the free protein structure. We observed an optimal number of features for model precision, suggesting a balance between information and overfitting.

CONCLUSIONS

This study underscores the potential of transformer-based protein language models to extract critical knowledge from sparse datasets, exemplified here by the challenging realm of predicting PPI-hotspots. These models offer a cost-effective and time-efficient alternative to traditional experimental methods for predicting certain residue properties. However, the challenge of explaining why specific features are important for determining certain residue properties remains.

Comparative genomics provides insights into molecular adaptation to hypermetamorphosis and cantharidin metabolism in blister beetles (Coleoptera: Meloidae)

Blister beetles (Coleoptera: Meloidae) are currently subdivided into three subfamilies: Eleticinae (a basal group), Nemognathinae, and Meloinae. These are all characterized by the endogenous production of the defensive terpene cantharidin (CA), whereas the two most derived subfamilies show a hypermetamorphic larval development. Here, we provide novel draft genome assemblies of five species sampled across the three blister beetle subfamilies (Iselma pallidipennis, Stenodera caucasica, Zonitis immaculata, Lydus trimaculatus, and Mylabris variabilis) and performed a comparative analysis with other available Meloidae genomes and the closely-related canthariphilous species (Pyrochroa serraticornis) to disclose adaptations at a molecular level. Our results highlighted the expansion and selection of genes potentially responsible for CA production and metabolism, as well as its mobilization and vesicular compartmentalization. Furthermore, we observed adaptive selection patterns and gain of genes devoted to epigenetic regulation, development, and morphogenesis, possibly related to hypermetamorphosis. We hypothesize that most genetic adaptations occurred to support both CA biosynthesis and hypermetamorphosis, two crucial aspects of Meloidae biology that likely contributed to their evolutionary success.

GO2Sum: generating human-readable functional summary of proteins from GO terms

Understanding the biological functions of proteins is of fundamental importance in modern biology. To represent a function of proteins, Gene Ontology (GO), a controlled vocabulary, is frequently used, because it is easy to handle by computer programs avoiding open-ended text interpretation. Particularly, the majority of current protein function prediction methods rely on GO terms. However, the extensive list of GO terms that describe a protein function can pose challenges for biologists when it comes to interpretation. In response to this issue, we developed GO2Sum (Gene Ontology terms Summarizer), a model that takes a set of GO terms as input and generates a human-readable summary using the T5 large language model. GO2Sum was developed by fine-tuning T5 on GO term assignments and free-text function descriptions for UniProt entries, enabling it to recreate function descriptions by concatenating GO term descriptions. Our results demonstrated that GO2Sum significantly outperforms the original T5 model that was trained on the entire web corpus in generating Function, Subunit Structure, and Pathway paragraphs for UniProt entries.

Non-covalent Lasso Entanglements in Folded Proteins: Prevalence, Functional Implications, and Evolutionary Significance

One-third of protein domains in the CATH database contain a recently discovered tertiary topological motif: non-covalent lasso entanglements, in which a segment of the protein backbone forms a loop closed by non-covalent interactions between residues and is threaded one or more times by the N- or C-terminal backbone segment. Unknown is how frequently this structural motif appears across the proteomes of organisms. And the correlation of these motifs with various classes of protein function and biological processes have not been quantified. Here, using a combination of protein crystal structures, AlphaFold2 predictions, and Gene Ontology terms we show that in E. coli, S. cerevisiae and H. sapiens that 71%, 52% and 49% of globular proteins contain one-or-more non-covalent lasso entanglements in their native fold, and that some of these are highly complex with multiple threading events. Further, proteins containing these tertiary motifs are consistently enriched in certain functions and biological processes across these organisms and depleted in others, strongly indicating an influence of evolutionary selection pressures acting positively and negatively on the distribution of these motifs. Together, these results demonstrate that non-covalent lasso entanglements are widespread and indicate they may be extensively utilized for protein function and subcellular processes, thus impacting phenotype.

Identification of homologous protein models via 3D comparisons using predicted structures

Recent advances in protein structure prediction enable 3D homology alignment and domain annotation using tertiary structures. Here, we present a protocol to identify homologous structures and annotate protein domains through in silico comparisons using the AlphaFold database. We describe steps for downloading and installing PyMOL software, preparing the query structure, and conducting a 3D homology search. The example provided highlights the application of this protocol in reevaluating an mpox viral protein annotation. For complete details on the use and execution of this protocol, please refer to Pan et al. (2023).1.

Molecular Property Diagnostic Suite for COVID-19 (MPDSCOVID-19): an open-source disease-specific drug discovery portal

Molecular Property Diagnostic Suite (MPDS) was conceived and developed as an open-source disease-specific web portal based on Galaxy. MPDSCOVID-19 was developed for COVID-19 as a one-stop solution for drug discovery research. Galaxy platforms enable the creation of customized workflows connecting various modules in the web server. The architecture of MPDSCOVID-19 effectively employs Galaxy v22.04 features, which are ported on CentOS 7.8 and Python 3.7. MPDSCOVID-19 provides significant updates and the addition of several new tools updated after six years. Tools developed by our group in Perl/Python and open-source tools are collated and integrated into MPDSCOVID-19 using XML scripts. Our MPDS suite aims to facilitate transparent and open innovation. This approach significantly helps bring inclusiveness in the community while promoting free access and participation in software development.

Availability & Implementation

The MPDSCOVID-19 portal can be accessed at https://mpds.neist.res.in:8085/.

StackDPP: a stacking ensemble based DNA-binding protein prediction model

BACKGROUND

DNA-binding proteins (DNA-BPs) are the proteins that bind and interact with DNA. DNA-BPs regulate and affect numerous biological processes, such as, transcription and DNA replication, repair, and organization of the chromosomal DNA. Very few proteins, however, are DNA-binding in nature. Therefore, it is necessary to develop an efficient predictor for identifying DNA-BPs.

RESULT

In this work, we have proposed new benchmark datasets for the DNA-binding protein prediction problem. We discovered several quality concerns with the widely used benchmark datasets, PDB1075 (for training) and PDB186 (for independent testing), which necessitated the preparation of new benchmark datasets. Our proposed datasets UNIPROT1424 and UNIPROT356 can be used for model training and independent testing respectively. We have retrained selected state-of-the-art DNA-BP predictors in the new dataset and reported their performance results. We also trained a novel predictor using the new benchmark dataset. We extracted features from various feature categories, then used a Random Forest classifier and Recursive Feature Elimination with Cross-validation (RFECV) to select the optimal set of 452 features. We then proposed a stacking ensemble architecture as our final prediction model. Named Stacking Ensemble Model for DNA-binding Protein Prediction, or StackDPP in short, our model achieved 0.92, 0.92 and 0.93 accuracy in 10-fold cross-validation, jackknife and independent testing respectively.

CONCLUSION

StackDPP has performed very well in cross-validation testing and has outperformed all the state-of-the-art prediction models in independent testing. Its performance scores in cross-validation testing generalized very well in the independent test set. The source code of the model is publicly available at https://github.com/HasibAhmed1624/StackDPP . Therefore, we expect this generalized model can be adopted by researchers and practitioners to identify novel DNA-binding proteins.

Synthesis of biologically active Shiga toxins in cell-free systems

Shiga toxins (Stx) produced by pathogenic bacteria can cause mild to severe diseases in humans. Thus, the analysis of such toxins is of utmost importance. As an AB5 toxin, Stx consist of a catalytic A-subunit acting as a ribosome-inactivating protein (RIP) and a B-pentamer binding domain. In this study we synthesized the subunits and holotoxins from Stx and Stx2a using different cell-free systems, namely an E. coli- and CHO-based cell-free protein synthesis (CFPS) system. The functional activity of the protein toxins was analyzed in two ways. First, activity of the A-subunits was assessed using an in vitro protein inhibition assay. StxA produced in an E. coli cell-free system showed significant RIP activity at concentrations of 0.02 nM, whereas toxins synthesized in a CHO cell-free system revealed significant activity at concentrations of 0.2 nM. Cell-free synthesized StxA2a was compared to StxA2a expressed in E. coli cells. Cell-based StxA2a had to be added at concentrations of 20 to 200 nM to yield a significant RIP activity. Furthermore, holotoxin analysis on cultured HeLa cells using an O-propargyl-puromycin assay showed significant protein translation reduction at concentrations of 10 nM and 5 nM for cell-free synthesized toxins derived from E. coli and CHO systems, respectively. Overall, these results show that Stx can be synthesized using different cell-free systems while remaining functionally active. In addition, we were able to use CFPS to assess the activity of different Stx variants which can further be used for RIPs in general.

Classification of likely functional class for ligand binding sites identified from fragment screening

Fragment screening is used to identify binding sites and leads in drug discovery, but it is often unclear which binding sites are functionally important. Here, data from 37 experiments, and 1309 protein structures binding to 1601 ligands were analysed. A method to group ligands by binding sites is introduced and sites clustered according to profiles of relative solvent accessibility. This identified 293 unique ligand binding sites, grouped into four clusters (C1-4). C1 includes larger, buried, conserved, and population missense-depleted sites, enriched in known functional sites. C4 comprises smaller, accessible, divergent, missense-enriched sites, depleted in functional sites. A site in C1 is 28 times more likely to be functional than one in C4. Seventeen sites, which to the best of our knowledge are novel, in 13 proteins are identified as likely to be functionally important with examples from human tenascin and 5-aminolevulinate synthase highlighted. A multi-layer perceptron, and K-nearest neighbours model are presented to predict cluster labels for ligand binding sites with an accuracy of 96% and 100%, respectively, so allowing functional classification of sites for proteins not in this set. Our findings will be of interest to those studying protein-ligand interactions and developing new drugs or function modulators.

Bioglass/ceria nanoparticle hybrids for the treatment of seroma: a comparative long-term study in rats

Background: Seroma formation is a common postoperative complication. Fibrin-based glues are typically employed in an attempt to seal the cavity. Recently, the first nanoparticle (NP)-based treatment approaches have emerged. Nanoparticle dispersions can be used as tissue glues, capitalizing on a phenomenon known as 'nanobridging'. In this process, macromolecules such as proteins physically adsorb onto the NP surface, leading to macroscopic adhesion. Although significant early seroma reduction has been shown, little is known about long-term efficacy of NPs. The aim of this study was to assess the long-term effects of NPs in reducing seroma formation, and to understand their underlying mechanism. Methods: Seroma was surgically induced bilaterally in 20 Lewis rats. On postoperative day (POD) 7, seromas were aspirated on both sides. In 10 rats, one side was treated with NPs, while the contralateral side received only NP carrier solution. In the other 10 rats, one side was treated with fibrin glue, while the other was left untreated. Seroma fluid, blood and tissue samples were obtained at defined time points. Biochemical, histopathological and immunohistochemical assessments were made. Results: NP-treated sides showed no macroscopically visible seroma formation after application on POD 7, in stark contrast to the fibrin-treated sides, where 60% of the rats had seromas on POD 14, and 50% on POD 21. At the endpoint (POD 42), sides treated with nanoparticles (NPs) exhibited significant macroscopic differences compared to other groups, including the absence of a cavity, and increased fibrous adhesions. Histologically, there were more macrophage groupings and collagen type 1 (COL1) deposits in the superficial capsule on NP-treated sides. Conclusion: NPs not only significantly reduced early manifestations of seroma and demonstrated an anti-inflammatory response, but they also led to increased adhesion formation over the long term, suggesting a decreased risk of seroma recurrence. These findings highlight both the adhesive properties of NPs and their potential for clinical therapy.

Structural and biochemical basis of FLS2-mediated signal activation and transduction in rice

The receptor-like kinase FLAGELLIN-SENSITIVE 2 (FLS2) functions as a bacterial flagellin receptor localized on the cell membrane of plants. In Arabidopsis, the co-receptor BRI1-ASSOCIATED RECEPTOR KINASE 1 (BAK1) cooperates with FLS2 to detect the flagellin epitope flg22, resulting in formation of a signaling complex that triggers plant defense responses. However, the co-receptor responsible for recognizing and signaling the flg22 epitope in rice remains to be determined, and the precise structural mechanism underlying FLS2-mediated signal activation and transduction has not been clarified. This study presents the structural characterization of a kinase-dead mutant of the intracellular kinase domain of OsFLS2 (OsFLS2-KDD1013A) in complex with ATP or ADP, resolved at resolutions of 1.98 Å and 2.09 Å, respectively. Structural analysis revealed that OsFLS2 can adopt an active conformation in the absence of phosphorylation, although it exhibits only weak basal catalytic activity for autophosphorylation. Subsequent investigations demonstrated that OsSERK2 effectively phosphorylates OsFLS2, which reciprocally phosphorylates OsSERK2, leading to complete activation of OsSERK2 and rapid phosphorylation of the downstream substrate receptor-like cytoplasmic kinases OsRLCK176 and OsRLCK185. Through mass spectrometry experiments, we successfully identified critical autophosphorylation sites on OsSERK2, as well as sites transphosphorylated by OsFLS2. Furthermore, we demonstrated the interaction between OsSERK2 and OsFLS2, which is enhanced in the presence of flg22. Genetic evidence suggests that OsRLCK176 and OsRLCK185 may function downstream of the OsFLS2-mediated signaling pathway. Our study reveals the molecular mechanism by which OsFLS2 mediates signal transduction pathways in rice and provides a valuable example for understanding RLK-mediated signaling pathways in plants.

Analysis and visualization of quantitative proteomics data using FragPipe-Analyst

The FragPipe computational proteomics platform is gaining widespread popularity among the proteomics research community because of its fast processing speed and user-friendly graphical interface. Although FragPipe produces well-formatted output tables that are ready for analysis, there is still a need for an easy-to-use and user-friendly downstream statistical analysis and visualization tool. FragPipe-Analyst addresses this need by providing an R shiny web server to assist FragPipe users in conducting downstream analyses of the resulting quantitative proteomics data. It supports major quantification workflows including label-free quantification, tandem mass tags, and data-independent acquisition. FragPipe-Analyst offers a range of useful functionalities, such as various missing value imputation options, data quality control, unsupervised clustering, differential expression (DE) analysis using Limma, and gene ontology and pathway enrichment analysis using Enrichr. To support advanced analysis and customized visualizations, we also developed FragPipeAnalystR, an R package encompassing all FragPipe-Analyst functionalities that is extended to support site-specific analysis of post-translational modifications (PTMs). FragPipe-Analyst and FragPipeAnalystR are both open-source and freely available.

Mi-2β promotes immune evasion in melanoma by activating EZH2 methylation

Recent development of new immune checkpoint inhibitors has been particularly successfully in cancer treatment, but still the majority patients fail to benefit. Converting resistant tumors to immunotherapy sensitive will provide a significant improvement in patient outcome. Here we identify Mi-2β as a key melanoma-intrinsic effector regulating the adaptive anti-tumor immune response. Studies in genetically engineered mouse melanoma models indicate that loss of Mi-2β rescues the immune response to immunotherapy in vivo. Mechanistically, ATAC-seq analysis shows that Mi-2β controls the accessibility of IFN-γ-stimulated genes (ISGs). Mi-2β binds to EZH2 and promotes K510 methylation of EZH2, subsequently activating the trimethylation of H3K27 to inhibit the transcription of ISGs. Finally, we develop an Mi-2β-targeted inhibitor, Z36-MP5, which reduces Mi-2β ATPase activity and reactivates ISG transcription. Consequently, Z36-MP5 induces a response to immune checkpoint inhibitors in otherwise resistant melanoma models. Our work provides a potential therapeutic strategy to convert immunotherapy resistant melanomas to sensitive ones.

Polyphyllin B inhibited STAT3/NCOA4 pathway and restored gut microbiota to ameliorate lung tissue injury in cigarette smoke-induced mice

OBJECTIVE

Smoking was a major risk factor for chronic obstructive pulmonary disease (COPD). This study plan to explore the mechanism of Polyphyllin B in lung injury induced by cigarette smoke (CSE) in COPD.

METHODS

Network pharmacology and molecular docking were applied to analyze the potential binding targets for Polyphyllin B and COPD. Commercial unfiltered CSE and LPS were used to construct BEAS-2B cell injury in vitro and COPD mouse models in vivo, respectively, which were treated with Polyphyllin B or fecal microbiota transplantation (FMT). CCK8, LDH and calcein-AM were used to detect the cell proliferation, LDH level and labile iron pool. Lung histopathology, Fe3+ deposition and mitochondrial morphology were observed by hematoxylin-eosin, Prussian blue staining and transmission electron microscope, respectively. ELISA was used to measure inflammation and oxidative stress levels in cells and lung tissues. Immunohistochemistry and immunofluorescence were applied to analyze the 4-HNE, LC3 and Ferritin expression. RT-qPCR was used to detect the expression of FcRn, pIgR, STAT3 and NCOA4. Western blot was used to detect the expression of Ferritin, p-STAT3/STAT3, NCOA4, GPX4, TLR2, TLR4 and P65 proteins. 16S rRNA gene sequencing was applied to detect the gut microbiota.

RESULTS

Polyphyllin B had a good binding affinity with STAT3 protein, which as a target gene in COPD. Polyphyllin B inhibited CS-induced oxidative stress, inflammation, mitochondrial damage, and ferritinophagy in COPD mice. 16S rRNA sequencing and FMT confirmed that Akkermansia and Escherichia_Shigella might be the potential microbiota for Polyphyllin B and FMT to improve CSE and LPS-induced COPD, which were exhausted by the antibiotics in C + L and C + L + P mice. CSE and LPS induced the decrease of cell viability and the ferritin and LC3 expression, and the increase of NCOA4 and p-STAT3 expression in BEAS-2B cells, which were inhibited by Polyphyllin B. Polyphyllin B promoted ferritin and LC3II/I expression, and inhibited p-STAT3 and NCOA4 expression in CSE + LPS-induced BEAS-2B cells.

CONCLUSION

Polyphyllin B improved gut microbiota disorder and inhibited STAT3/NCOA4 pathway to ameliorate lung tissue injury in CSE and LPS-induced mice.

The genome sequence of a heart cockle, Fragum fragum (Linnaeus, 1758)

We present a genome assembly from an individual specimen of Fragum fragum (a heart cockle; Mollusca; Bivalvia; Veneroida; Cardiidae). The genome sequence is 1,153.1 megabases in span. Most of the assembly is scaffolded into 19 chromosomal pseudomolecules. The mitochondrial genome has also been assembled and is 22.36 kilobases in length. Gene annotation of this assembly on Ensembl identified 17,262 protein coding genes.

Effect of the aggregated protein dye YAT2150 on Leishmania parasite viability

The problems associated with the drugs currently used to treat leishmaniasis, including resistance, toxicity, and the high cost of some formulations, call for the urgent identification of new therapeutic agents with novel modes of action. The aggregated protein dye YAT2150 has been found to be a potent antileishmanial compound, with a half-maximal inhibitory concentration (IC50) of approximately 0.5 µM against promastigote and amastigote stages of Leishmania infantum. The encapsulation in liposomes of YAT2150 significantly improved its in vitro IC50 to 0.37 and 0.19 µM in promastigotes and amastigotes, respectively, and increased the half-maximal cytotoxic concentration in human umbilical vein endothelial cells to >50 µM. YAT2150 became strongly fluorescent when binding intracellular protein deposits in Leishmania cells. This fluorescence pattern aligns with the proposed mode of action of this drug in the malaria parasite Plasmodium falciparum, the inhibition of protein aggregation. In Leishmania major, YAT2150 rapidly reduced ATP levels, suggesting an alternative antileishmanial mechanism. To the best of our knowledge, this first-in-class compound is the only one described so far having significant activity against both Plasmodium and Leishmania, thus being a potential drug for the treatment of co-infections of both parasites.

De novo assembly and annotation of the Patagonian toothfish (Dissostichus eleginoides) genome

BACKGROUND

Patagonian toothfish (Dissostichus eleginoides) is an economically and ecologically important fish species in the family Nototheniidae. Juveniles occupy progressively deeper waters as they mature and grow, and adults have been caught as deep as 2500 m, living on or in just above the southern shelves and slopes around the sub-Antarctic islands of the Southern Ocean. As apex predators, they are a key part of the food web, feeding on a variety of prey, including krill, squid, and other fish. Despite its importance, genomic sequence data, which could be used for more accurate dating of the divergence between Patagonian and Antarctic toothfish, or establish whether it shares adaptations to temperature with fish living in more polar or equatorial climes, has so far been limited.

RESULTS

A high-quality D. eleginoides genome was generated using a combination of Illumina, PacBio and Omni-C sequencing technologies. To aid the genome annotation, the transcriptome derived from a variety of toothfish tissues was also generated using both short and long read sequencing methods. The final genome assembly was 797.8 Mb with a N50 scaffold length of 3.5 Mb. Approximately 31.7% of the genome consisted of repetitive elements. A total of 35,543 putative protein-coding regions were identified, of which 50% have been functionally annotated. Transcriptomics analysis showed that approximately 64% of the predicted genes (22,617 genes) were found to be expressed in the tissues sampled. Comparative genomics analysis revealed that the anti-freeze glycoprotein (AFGP) locus of D. eleginoides does not contain any AFGP proteins compared to the same locus in the Antarctic toothfish (Dissostichus mawsoni). This is in agreement with previously published results looking at hybridization signals and confirms that Patagonian toothfish do not possess AFGP coding sequences in their genome.

CONCLUSIONS

We have assembled and annotated the Patagonian toothfish genome, which will provide a valuable genetic resource for ecological and evolutionary studies on this and other closely related species.

Golgi α-mannosidase: opposing structures of Drosophila melanogaster and novel human model using molecular dynamics simulations and docking at different pHs

The search for Golgi α-mannosidase II (GMII) potent and specific inhibitors has been a focus of many studies for the past three decades since this enzyme is a key target for cancer treatment. α-Mannosidases, such as those from Drosophila melanogaster or Jack bean, have been used as functional models of the human Golgi α-mannosidase II (hGMII) because mammalian mannosidases are difficult to purify and characterize experimentally. Meanwhile, computational studies have been seen as privileged tools able to explore assertive solutions to specific enzymes, providing molecular details of these macromolecules, their protonation states and their interactions. Thus, modelling techniques can successfully predict hGMII 3D structure with high confidence, speeding up the development of new hits. In this study, Drosophila melanogaster Golgi mannosidase II (dGMII) and a novel human model, developed in silico and equilibrated via molecular dynamics simulations, were both opposed for docking. Our findings highlight that the design of novel inhibitors should be carried out considering the human model's characteristics and the enzyme operating pH. A reliable model is evidenced, showing a good correlation between Ki/IC50 experimental data and theoretical ΔGbinding estimations in GMII, opening the possibility of optimizing the rational drug design of new derivatives.Communicated by Ramaswamy H. Sarma.

3-styrylcoumarin scaffold-based derivatives as a new approach for leishmaniasis intervention: biological and molecular modeling studies

Seven 3-styrylcoumarins were tested for antileishmanial activity against Leishmania (Viannia) panamensis amastigotes. Cytotoxic activity was also evaluated against mammalian U-937 cells. The 3-methoxy-4-hydroxy coumarin derivative 6 was the most active with an IC50 of 40.5 µM, and did not reveal any conspicuous toxicity toward mammalian U-937 cells. Therefore, it may have potential to be considered as candidate for antileishmanial drug development. Further, among several druggable Leishmania targets, molecular docking studies revealed that compound 6 had docking preference by the N-myristoyltransferase (Lp-NMT) of Leishmania panamensis, showing a higher docking score of - 10.1 kcal mol-1 than positive controls and making this protein as a presumably druggable target for this compound. On the other hand, molecular dynamics simulations affirm the docking hypothesis, showing a conformational stability of the 6/Lp-NMT complex throughout 100 ns simulation. Moreover, the molecular mechanics/Poisson-Boltzmann surface area method also support the docking findings, revealing a total free energy of binding of - 47.26 ± 0.08 kcal mol-1, and identifying through energy decomposition analysis that those key aminoacids are contributing strongly to ligand binding. Finally, an optimal pharmacokinetic profile was also estimated for 6. Altogether, coumarin 6 could be addressed as starting point for further pharmacological studies concerning the therapeutic leishmaniasis intervention.

Graphical abstract

Multi-epitope vaccine design against leishmaniasis using IFN-γ inducing epitopes from immunodominant gp46 and gp63 proteins

There is no currently approved human vaccine against leishmaniasis. Utilization of immunogenic antigens and their epitopes capable of enhancing immune responses against leishmaniasis is a crucial step for rational in silico vaccine design. The objective of this study was to generate and evaluate a potential vaccine candidate against leishmaniasis, designed by immunodominant proteins from gp46 and gp63 of Leishmania major, which can stimulate helper T-lymphocytes (HTL) and cytotoxic T-lymphocytes (CTL). For this aim, the IFN-γ-inducing MHC-I and MHC-II binders were predicted for each examined protein (gp46 and gp63) and connected with appropriate linkers, along with an adjuvant (Mycobacterium tuberculosis L7/L12) and a histidine tag. The vaccine's stability, antigenicity, structure, and interaction with the TLR-4 receptor were evaluated in silico. The resulting chimeric vaccine was composed of 344 amino acids and had a molecular weight of 35.64 kDa. Physico-chemical properties indicated that it was thermotolerant, soluble, highly antigenic, and non-allergenic. Predictions of the secondary and tertiary structures were made, and further analyses confirmed that the vaccine construct could interact with the human TLR-4 receptor. Virtual immune simulation demonstrated strong stimulation of T-cell responses, particularly by an increase in IFN-γ, following vaccination. In summary, the in silico data indicated that the vaccine candidate showed high antigenicity in humans. It was also found to trigger significant levels of clearance mechanisms and other components of the cellular immune profile. Nevertheless, further wet experiments are required to properly assess the efficacy of this multi-epitope vaccine candidate against leishmaniasis.

Construction of an aerolysin-based multi-epitope vaccine against Aeromonas hydrophila: an in silico machine learning and artificial intelligence-supported approach

Aeromonas hydrophila, a gram-negative coccobacillus bacterium, can cause various infections in humans, including septic arthritis, diarrhea (traveler's diarrhea), gastroenteritis, skin and wound infections, meningitis, fulminating septicemia, enterocolitis, peritonitis, and endocarditis. It frequently occurs in aquatic environments and readily contacts humans, leading to high infection rates. This bacterium has exhibited resistance to numerous commercial antibiotics, and no vaccine has yet been developed. Aiming to combat the alarmingly high infection rate, this study utilizes in silico techniques to design a multi-epitope vaccine (MEV) candidate against this bacterium based on its aerolysin toxin, which is the most toxic and highly conserved virulence factor among the Aeromonas species. After retrieval, aerolysin was processed for B-cell and T-cell epitope mapping. Once filtered for toxicity, antigenicity, allergenicity, and solubility, the chosen epitopes were combined with an adjuvant and specific linkers to create a vaccine construct. These linkers and the adjuvant enhance the MEV's ability to elicit robust immune responses. Analyses of the predicted and improved vaccine structure revealed that 75.5%, 19.8%, and 1.3% of its amino acids occupy the most favored, additional allowed, and generously allowed regions, respectively, while its ERRAT score reached nearly 70%. Docking simulations showed the MEV exhibiting the highest interaction and binding energies (-1,023.4 kcal/mol, -923.2 kcal/mol, and -988.3 kcal/mol) with TLR-4, MHC-I, and MHC-II receptors. Further molecular dynamics simulations demonstrated the docked complexes' remarkable stability and maximum interactions, i.e., uniform RMSD, fluctuated RMSF, and lowest binding net energy. In silico models also predict the vaccine will stimulate a variety of immunological pathways following administration. These analyses suggest the vaccine's efficacy in inducing robust immune responses against A. hydrophila. With high solubility and no predicted allergic responses or toxicity, it appears safe for administration in both healthy and A. hydrophila-infected individuals.

Clinical application of prospective whole-exome sequencing in the diagnosis of genetic disease: Experience of a regional disease center in South Korea

INTRODUCTION

Next-generation sequencing helps clinicians diagnose patients with suspected genetic disorders. The current study aimed to investigate the diagnostic yield and clinical utility of prospective whole-exome sequencing (WES) in rare diseases.

METHODS

WES was performed in 92 patients who presented with clinical symptoms suggestive of genetic disorders. The WES data were analyzed using an in-house developed software. The patients' phenotypic characteristics were classified according to the human phenotype ontology.

RESULTS

WES detected 64 variants, 13 were classified as pathogenic, 26 as likely pathogenic, and 25 as variants of uncertain significance. In 57 patients with these variants, 30 were identified as causal variants. The diagnostic yield was higher in patients with abnormalities in joint mobility and skin morphology than in those with cerebellar hypoplasia/atrophy, epilepsy, global developmental delay, dysmorphic features/facial dysmorphisms, and chronic kidney disease/abnormal renal morphology.

CONCLUSION

In this study, a WES-based variant interpretation system was employed to provide a definitive diagnosis for 28.3% of the patients suspected of having genetic disorders. WES is particularly useful for diagnosing rare diseases with symptoms that affect more than one system, when targeted genetic panels are difficult to employ.

Developmental and epileptic encephalopathy 82 (DEE82) with novel compound heterozygous mutations of GOT2 gene

PURPOSE

Developmental and Epileptic Encephalopathies (DEEs) are rare neurological disorders characterized by early-onset medically resistant epileptic seizures, structural brain malformations, and severe developmental delays. These disorders can arise from mutations in genes involved in vital metabolic pathways, including those within the brain. Recent studies have implicated defects in the mitochondrial malate aspartate shuttle (MAS) as potential contributors to the clinical manifestation of infantile epileptic encephalopathy. Although rare, mutations in MDH1, MDH2, AGC1, or GOT2 genes have been reported in patients exhibiting neurological symptoms such as global developmental delay, epilepsy, and progressive microcephaly.

METHOD

In this study, we employed exome data analysis of a patient diagnosed with DEE, focusing on the screening of 1896 epilepsy-related genes listed in the HPO and ClinVar databases. Sanger sequencing was subsequently conducted to validate and assess the inheritance pattern of the identified variants within the family. The evolutionary conservation scores of the mutated residues were evaluated using the ConSurf Database. Furthermore, the impacts of the causative variations on protein stability were analyzed through I-Mutant and MuPro bioinformatic tools. Structural comparisons between wild-type and mutant proteins were performed using PyMOL, and the physicochemical effects of the mutations were assessed using Project Hope.

RESULTS

Exome data analysis unveiled the presence of novel compound heterozygous mutations in the GOT2 gene coding for mitochondrial glutamate aspartate transaminase. Sanger sequencing confirmed the paternal inheritance of the p.Asp257Asn mutation and the maternal inheritance of the p.Arg262Cys mutation. The affected individual exhibited plasma metabolic disturbances, including hyperhomocysteinemia, hyperlactatemia, and reduced levels of methionine and arginine. Detailed bioinformatic analysis indicated that the mutations were located within evolutionarily conserved domains of the enzyme, resulting in disruptions to protein stability and structure.

CONCLUSION

Herein, we describe a case with DEE82 (MIM: # 618721) with pathologic novel biallelic mutations in the GOT2 gene. Early genetic diagnosis of metabolic epilepsies is crucial for long-term neurodevelopmental improvements and seizure control as targeted treatments can be administered based on the affected metabolic pathways.

Identifying potential ligand-receptor interactions based on gradient boosted neural network and interpretable boosting machine for intercellular communication analysis

Cell-cell communication is essential to many key biological processes. Intercellular communication is generally mediated by ligand-receptor interactions (LRIs). Thus, building a comprehensive and high-quality LRI resource can significantly improve intercellular communication analysis. Meantime, due to lack of a "gold standard" dataset, it remains a challenge to evaluate LRI-mediated intercellular communication results. Here, we introduce CellGiQ, a high-confident LRI prediction framework for intercellular communication analysis. Highly confident LRIs are first inferred by LRI feature extraction with BioTriangle, LRI selection using LightGBM, and LRI classification based on ensemble of gradient boosted neural network and interpretable boosting machine. Subsequently, known and identified high-confident LRIs are filtered by combining single-cell RNA sequencing (scRNA-seq) data and further applied to intercellular communication inference through a quartile scoring strategy. To validation the predictions, CellGiQ exploited several evaluation strategies: using AUC and AUPR, it surpassed six competing LRI prediction models on four LRI datasets; through Venn diagrams and molecular docking, its predicted LRIs were validated by five other popular intercellular communication inference methods; based on the overlapping LRIs, it computed high Jaccard index with six other state-of-the-art intercellular communication prediction tools within human HNSCC tissues; by comparing with classical models and literature retrieve, its inferred HNSCC-related intercellular communication results was further validated. The novelty of this study is to identify high-confident LRIs based on machine learning as well as design several LRI validation ways, providing reference for computational LRI prediction. CellGiQ provides an open-source and useful tool to decompose LRI-mediated intercellular communication at single cell resolution. CellGiQ is freely available at https://github.com/plhhnu/CellGiQ.

Genome biology and evolution of mating-type loci in four cereal rust fungi

Permanent heterozygous loci, such as sex- or mating-compatibility regions, often display suppression of recombination and signals of genomic degeneration. In Basidiomycota, two distinct loci confer mating compatibility. These loci encode homeodomain (HD) transcription factors and pheromone receptor (Pra)-ligand allele pairs. To date, an analysis of genome level mating-type (MAT) loci is lacking for obligate biotrophic basidiomycetes in the Pucciniales, an order containing serious agricultural plant pathogens. Here, we focus on four species of Puccinia that infect oat and wheat, including P. coronata f. sp. avenae, P. graminis f. sp. tritici, P. triticina and P. striiformis f. sp. tritici. MAT loci are located on two separate chromosomes supporting previous hypotheses of a tetrapolar mating compatibility system in the Pucciniales. The HD genes are multiallelic in all four species while the PR locus appears biallelic, except for P. graminis f. sp. tritici, which potentially has multiple alleles. HD loci are largely conserved in their macrosynteny, both within and between species, without strong signals of recombination suppression. Regions proximal to the PR locus, however, displayed signs of recombination suppression and genomic degeneration in the three species with a biallelic PR locus. Our observations support a link between recombination suppression, genomic degeneration, and allele diversity of MAT loci that is consistent with recent mathematical modelling and simulations. Finally, we confirm that MAT genes are expressed during the asexual infection cycle, and we propose that this may support regulating nuclear maintenance and pairing during infection and spore formation. Our study provides insights into the evolution of MAT loci of key pathogenic Puccinia species. Understanding mating compatibility can help predict possible combinations of nuclear pairs, generated by sexual reproduction or somatic recombination, and the potential evolution of new virulent isolates of these important plant pathogens.

Direct prediction of intrinsically disordered protein conformational properties from sequence

Intrinsically disordered regions (IDRs) are ubiquitous across all domains of life and play a range of functional roles. While folded domains are generally well described by a stable three-dimensional structure, IDRs exist in a collection of interconverting states known as an ensemble. This structural heterogeneity means that IDRs are largely absent from the Protein Data Bank, contributing to a lack of computational approaches to predict ensemble conformational properties from sequence. Here we combine rational sequence design, large-scale molecular simulations and deep learning to develop ALBATROSS, a deep-learning model for predicting ensemble dimensions of IDRs, including the radius of gyration, end-to-end distance, polymer-scaling exponent and ensemble asphericity, directly from sequences at a proteome-wide scale. ALBATROSS is lightweight, easy to use and accessible as both a locally installable software package and a point-and-click-style interface via Google Colab notebooks. We first demonstrate the applicability of our predictors by examining the generalizability of sequence-ensemble relationships in IDRs. Then, we leverage the high-throughput nature of ALBATROSS to characterize the sequence-specific biophysical behavior of IDRs within and between proteomes.

Deletion of Sarcolemmal Membrane-Associated Protein Isoform 3 (SLMAP3) in Cardiac Progenitors Delays Embryonic Growth of Myocardium without Affecting Hippo Pathway

The slmap gene is alternatively spliced to generate many isoforms that are abundant in developing myocardium. The largest protein isoform SLMAP3 is ubiquitously expressed and has been linked to cardiomyopathy, Brugada syndrome and Hippo signaling. To examine any role in cardiogenesis, mice homozygous for floxed slmap allele were crossed with Nkx2.5-cre mice to nullify its expression in cardiac progenitors. Targeted deletion of the slmap gene resulted in the specific knockout (KO) of the SLMAP3 (~91 KDa) isoform without any changes in the expression of the SLMAP2 (~43 kDa) or the SLMAP1 (~35 kDa) isoforms which continued to accumulate to similar levels as seen in Wt embryonic hearts. The loss of SLMAP3 from cardiac progenitors resulted in decreased size of the developing embryonic hearts evident at E9.5 to E16.5 with four small chambers and significantly thinner left ventricles. The proliferative capacity assessed with the phosphorylation of histone 3 or with Ki67 in E12.5 hearts was not significantly altered due to SLMAP3 deficiency. The size of embryonic cardiomyocytes, marked with anti-Troponin C, revealed significantly smaller cells, but their hypertrophic response (AKT1 and MTOR1) was not significantly affected by the specific loss of SLMAP3 protein. Further, no changes in phosphorylation of MST1/2 or YAP were detected in SLMAP3-KO embryonic myocardium, ruling out any impact on Hippo signaling. Rat embryonic cardiomyocytes express the three SLMAP isoforms and their knockdown (KD) with sh-RNA, resulted in decreased proliferation and enhanced senescence but without any impact on Hippo signaling. Collectively, these data show that SLMAP is critical for normal cardiac development with potential for the various isoforms to serve compensatory roles. Our data imply novel mechanisms for SLMAP action in cardiac growth independent of Hippo signaling.

Antitumor Activity and Mechanism of Terpenoids in Seaweeds Based on Literature Review and Network Pharmacology

Seaweeds are a treasure trove of natural secondary metabolites. Terpenoids extracted from seaweeds are shown to possess a variety of antitumor cellular activities. However, due to the complex and diverse structures of terpenoids, their therapeutic targets and complex mechanisms of action have not been clarified. The present study summarises the research on terpenoids from seaweeds in oncological diseases over the last 20 years. Terpenoids show different degrees of inhibitory effects on different types of tumor cells, suggesting that terpenoids in seaweeds may have potential antitumor disease potential. Terpenoids with potential antitumor activity and their mechanism of action are investigated using network pharmacology. A total of 125 terpenoids and 286 targets are obtained. Proto-oncogene tyrosine-protein kinase Src(SRC), Signal transducer and activator of transcription 3 (STAT3), Mitogen-activated protein kinase (MAPK3, MAPK1), Heat shock protein HSP 90-alpha (HSP90AA1), Phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA), and RAC-alpha serine/threonine-protein kinase (AKT1) are defined as core targets. According to GO function and Kyoto encyclopedia of genes and genomes(KEGG) enrichment analysis, terpenoids may affect the Phoshatidylinositol 3'-kinase (PI3K)-Akt signaling pathway, Epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor resistance, Prostate cancer, MAPK signaling pathway, and Proteoglycans in cancer. In addition, the molecular docking results show that the selected terpenoids are all able to bind strongly to the active protein. Terpenoids may slow down the progression of cancer by controlling apoptosis, proliferation, and protein and enzyme binding.

NCoR1 limits angiogenic capacity by altering Notch signaling

Corepressors negatively regulate gene expression by chromatin compaction. Targeted regulation of gene expression could provide a means to control endothelial cell phenotype. We hypothesize that by targeting corepressor proteins, endothelial angiogenic function can be improved. To study this, the expression and function of nuclear corepressors in human umbilical vein endothelial cells (HUVEC) and in murine organ culture was studied. RNA-seq revealed that nuclear receptor corepressor 1 (NCoR1), silencing mediator of retinoid and thyroid hormone receptors (SMRT) and repressor element-1 silencing transcription factor (REST) are the highest expressed corepressors in HUVECs. Knockout and knockdown strategies demonstrated that the depletion of NCoR1 increased the angiogenic capacity of endothelial cells, whereas depletion of SMRT or REST did not. Interestingly, the effect was VEGF signaling independent. NCoR1 depletion significantly upregulated angiogenesis-associated genes, especially tip cell genes, including ESM1, DLL4 and NOTCH4, as observed by RNA- and ATAC-seq. Confrontation assays comparing cells with and without NCoR1-deficiency revealed that loss of NCoR1 promotes a tip-cell position during spheroid sprouting. Moreover, a proximity ligation assay identified NCoR1 as a direct binding partner of the Notch-signaling-related transcription factor RBPJk. Luciferase assays showed that siRNA-mediated knockdown of NCOR1 promotes RBPJk activity. Furthermore, NCoR1 depletion prompts upregulation of several elements in the Notch signaling cascade. Downregulation of NOTCH4, but not NOTCH1, prevented the positive effect of NCOR1 knockdown on spheroid outgrowth. Collectively, these data indicate that decreasing NCOR1 expression is an attractive approach to promote angiogenic function.

Machine-Learning-Assisted Nanozyme Design: Lessons from Materials and Engineered Enzymes

Nanozymes are nanomaterials that exhibit enzyme-like biomimicry. In combination with intrinsic characteristics of nanomaterials, nanozymes have broad applicability in materials science, chemical engineering, bioengineering, biochemistry, and disease theranostics. Recently, the heterogeneity of published results has highlighted the complexity and diversity of nanozymes in terms of consistency of catalytic capacity. Machine learning (ML) shows promising potential for discovering new materials, yet it remains challenging for the design of new nanozymes based on ML approaches. Alternatively, ML is employed to promote optimization of intelligent design and application of catalytic materials and engineered enzymes. Incorporation of the successful ML algorithms used in the intelligent design of catalytic materials and engineered enzymes can concomitantly facilitate the guided development of next-generation nanozymes with desirable properties. Here, recent progress in ML, its utilization in the design of catalytic materials and enzymes, and how emergent ML applications serve as promising strategies to circumvent challenges associated with time-expensive and laborious testing in nanozyme research and development are summarized. The potential applications of successful examples of ML-aided catalytic materials and engineered enzymes in nanozyme design are also highlighted, with special focus on the unified aims in enhancing design and recapitulation of substrate selectivity and catalytic activity.

The genome sequence of the wood-carving leafcutter bee, Megachile ligniseca (Kirby, 1802)

We present a genome assembly from an individual female Megachile ligniseca (the wood-carving leafcutter bee; Arthropoda; Insecta; Hymenoptera; Megachilidae). The genome sequence is 290.0 megabases in span. Most of the assembly is scaffolded into 16 chromosomal pseudomolecules. The mitochondrial genome has also been assembled and is 23.71 kilobases in length. Gene annotation of this assembly on Ensembl 11,722 protein coding genes.

Drugging the entire human proteome: Are we there yet?

Each of the ∼20,000 proteins in the human proteome is a potential target for compounds that bind to it and modify its function. The 3D structures of most of these proteins are now available. Here, we discuss the prospects for using these structures to perform proteome-wide virtual HTS (VHTS). We compare physics-based (docking) and AI VHTS approaches, some of which are now being applied with large databases of compounds to thousands of targets. Although preliminary proteome-wide screens are now within our grasp, further methodological developments are expected to improve the accuracy of the results.

A PDZ scaffolding/CaM-mediated pathway in Cryptochrome signaling

Cryptochromes are cardinal constituents of the circadian clock, which orchestrates daily physiological rhythms in living organisms. A growing body of evidence points to their participation in pathways that have not traditionally been associated with circadian clock regulation, implying that cryptochromes may be subject to modulation by multiple signaling mechanisms. In this study, we demonstrate that human CRY2 (hCRY2) forms a complex with the large, modular scaffolding protein known as Multi-PDZ Domain Protein 1 (MUPP1). This interaction is facilitated by the calcium-binding protein Calmodulin (CaM) in a calcium-dependent manner. Our findings suggest a novel cooperative mechanism for the regulation of mammalian cryptochromes, mediated by calcium ions (Ca2+ ) and CaM. We propose that this Ca2+ /CaM-mediated signaling pathway may be an evolutionarily conserved mechanism that has been maintained from Drosophila to mammals, most likely in relation to its potential role in the broader context of cryptochrome function and regulation. Further, the understanding of cryptochrome interactions with other proteins and signaling pathways could lead to a better definition of its role within the intricate network of molecular interactions that govern circadian rhythms.

Sex-Specific Effect of CARM1 in Skeletal Muscle Adaptations to Exercise

PURPOSE

The purpose of this study was to determine how the intersection of coactivator-associated arginine methyltransferase 1 (CARM1) and biological sex affects skeletal muscle adaptations to chronic physical activity.

METHODS

Twelve-week-old female (F) and male (M) wild-type (WT) and CARM1 skeletal muscle-specific knockout (mKO) mice were randomly assigned to sedentary (SED) or voluntary wheel running (VWR) experimental groups. For 8 wk, the animals in the VWR cohort had volitional access to running wheels. Subsequently, we performed whole-body functional tests, and 48 h later muscles were harvested for molecular analysis. Western blotting, enzyme activity assays, as well as confocal and transmission electron microscopy were used to examine skeletal muscle biology.

RESULTS

Our data reveal a sex-dependent reduction in VWR volume caused by muscle-specific ablation of CARM1, as F CARM1 mKO mice performed less chronic, volitional exercise than their WT counterparts. Regardless of VWR output, exercise-induced adaptations in physiological function were similar between experimental groups. A broad panel of protein arginine methyltransferase (PRMT) biology measurements, including markers of arginine methyltransferase expression and activity, were unaffected by VWR, except for CARM1 and PRMT7 protein levels, which decreased and increased with VWR, respectively. Changes in myofiber morphology and mitochondrial protein content showed similar trends among animals. However, a closer examination of transmission electron microscopy images revealed contrasting responses to VWR in CARM1 mKO mice compared with WT littermates, particularly in mitochondrial size and fractional area.

CONCLUSIONS

The present findings demonstrate that CARM1 mKO reduces daily running volume in F mice, as well as exercise-evoked skeletal muscle mitochondrial plasticity, which indicates that this enzyme plays an essential role in sex-dependent differences in exercise performance and mitochondrial health.

Toward Robust Self-Training Paradigm for Molecular Prediction Tasks

Molecular prediction tasks normally demand a series of professional experiments to label the target molecule, which suffers from the limited labeled data problem. One of the semisupervised learning paradigms, known as self-training, utilizes both labeled and unlabeled data. Specifically, a teacher model is trained using labeled data and produces pseudo labels for unlabeled data. These labeled and pseudo-labeled data are then jointly used to train a student model. However, the pseudo labels generated from the teacher model are generally not sufficiently accurate. Thus, we propose a robust self-training strategy by exploring robust loss function to handle such noisy labels in two paradigms, that is, generic and adaptive. We have conducted experiments on three molecular biology prediction tasks with four backbone models to gradually evaluate the performance of the proposed robust self-training strategy. The results demonstrate that the proposed method enhances prediction performance across all tasks, notably within molecular regression tasks, where there has been an average enhancement of 41.5%. Furthermore, the visualization analysis confirms the superiority of our method. Our proposed robust self-training is a simple yet effective strategy that efficiently improves molecular biology prediction performance. It tackles the labeled data insufficient issue in molecular biology by taking advantage of both labeled and unlabeled data. Moreover, it can be easily embedded with any prediction task, which serves as a universal approach for the bioinformatics community.

PdmIRD: missense variants pathogenicity prediction for inherited retinal diseases in a disease-specific manner

Accurate discrimination of pathogenic and nonpathogenic variation remains an enormous challenge in clinical genetic testing of inherited retinal diseases (IRDs) patients. Computational methods for predicting variant pathogenicity are the main solutions for this dilemma. The majority of the state-of-the-art variant pathogenicity prediction tools disregard the differences in characteristics among different genes and treat all types of mutations equally. Since missense variants are the most common type of variation in the coding region of the human genome, we developed a novel missense mutation pathogenicity prediction tool, named Prediction of Deleterious Missense Mutation for IRDs (PdmIRD) in this study. PdmIRD was tailored for IRDs-related genes and constructed with the conditional random forest model. Population frequencies and a newly available prediction tool were incorporated into PdmIRD to improve the performance of the model. The evaluation of PdmIRD demonstrated its superior performance over nonspecific tools (areas under the curves, 0.984 and 0.910) and an existing eye abnormalities-specific tool (areas under the curves, 0.975 and 0.891). We also demonstrated the submodel that used a smaller gene panel further slightly improved performance. Our study provides evidence that a disease-specific model can enhance the prediction of missense mutation pathogenicity, especially when new and important features are considered. Additionally, this study provides guidance for exploring the characteristics and functions of the mutated proteins in a greater number of Mendelian disorders.

Understanding the basis of thermostability for enzyme "Nanoluc" towards designing industry-competent engineered variants

As a leading contender in the study of luminescence, nanoluciferase has recently attracted attention and proven effective in a wide variety of research areas. Although numerous attempts have been made to improve activity, there has yet to be a thorough exploration of further possibilities to improve thermostability. In this study, protein engineering in tandem with molecular dynamics simulation at various temperatures (300 K, 400 K, 450 K and 500 K) was used to improve our understanding of nanoluciferase dynamics and identification of factors that could significantly enhance the thermostability. Based on these, three novel mutations have been narrowed down, which were hypothesised to improve thermostability. Root mean square deviation and root mean square fluctuation studies confirmed higher stability of mutant at high temperature. Solvent-accessible surface area and protein unfolding studies revealed a decreased tendency of mutant to unfold at higher temperatures. Further free energy landscape and principal component analysis was adapted to get deeper insights into the thermodynamic and structural behavior of these proteins at elevated temperature. Thus, this study provides a deeper insight into the dynamic factors for thermostability and introduces a novel, enhanced nanoluciferase candidate with potential use in industry.Communicated by Ramaswamy H. Sarma.

Revolutionizing and identifying novel drug targets in Citrobacter koseri via subtractive proteomics and development of a multi-epitope vaccine using reverse vaccinology and immuno-informatics

Citrobacter koseri is a gram-negative rod that has been linked to infections in people with significant comorbidities and immunocompromised immune systems. It is most commonly known to cause urinary tract infections. Thus, the development of an efficacious C. koseri vaccine is imperative, as the pathogen has acquired resistance to current antibiotics. Subtractive proteomics was employed during this research to identify potential antigenic proteins to design an effective vaccine against C. koseri. The pipeline identified two antigenic proteins as potential vaccine targets: DP-3-O-acyl-N-acetylglucosamine deacetylase and Arabinose 5-phosphate isomerase. B and T cell epitopes from the specific proteins were forecasted employing several immunoinformatic and bioinformatics resources. A vaccine was created using a combination of seven cytotoxic T cell lymphocytes (CTL), five helper T cell lymphocyte (HTL), and seven linear B cell lymphocyte (LBL) epitopes. An adjuvant (β-defensin) was added to the vaccine to enhance immunological responses. The created vaccine was stable for use in humans, highly antigenic, and non-allergenic. The vaccine's molecular and interactions binding affinity with the human immunological receptor TLR3 were studied using MMGBSA, molecular dynamics (MD) simulations, and molecular docking analyses. E. coli (strain-K12) plasmid vector pET-28a (+) was used to examine the ability of the vaccine to be expressed. The vaccine shows great promise in terms of developing protective immunity against diseases, based on the results of these computer experiments. However, in vitro and animal research are required to validate our findings.Communicated by Ramaswamy H. Sarma.

Bacteria employ lysine acetylation of transcriptional regulators to adapt gene expression to cellular metabolism

The Escherichia coli TetR-related transcriptional regulator RutR is involved in the coordination of pyrimidine and purine metabolism. Here we report that lysine acetylation modulates RutR function. Applying the genetic code expansion concept, we produced site-specifically lysine-acetylated RutR proteins. The crystal structure of lysine-acetylated RutR reveals how acetylation switches off RutR-DNA-binding. We apply the genetic code expansion concept in E. coli in vivo revealing the consequences of RutR acetylation on the transcriptional level. We propose a model in which RutR acetylation follows different kinetic profiles either reacting non-enzymatically with acetyl-phosphate or enzymatically catalysed by the lysine acetyltransferases PatZ/YfiQ and YiaC. The NAD+-dependent sirtuin deacetylase CobB reverses enzymatic and non-enzymatic acetylation of RutR playing a dual regulatory and detoxifying role. By detecting cellular acetyl-CoA, NAD+ and acetyl-phosphate, bacteria apply lysine acetylation of transcriptional regulators to sense the cellular metabolic state directly adjusting gene expression to changing environmental conditions.

Establishment and characterization of turtle liver organoids provides a potential model to decode their unique adaptations

Painted turtles are remarkable for their freeze tolerance and supercooling ability along with their associated resilience to hypoxia/anoxia and oxidative stress, rendering them an ideal biomedical model for hypoxia-induced injuries (including strokes), tissue cooling during surgeries, and organ cryopreservation. Yet, such research is hindered by their seasonal reproduction and slow maturation. Here we developed and characterized adult stem cell-derived turtle liver organoids (3D self-assembled in vitro structures) from painted, snapping, and spiny softshell turtles spanning ~175My of evolution, with a subset cryopreserved. This development is, to the best of our knowledge, a first for this vertebrate Order, and complements the only other non-avian reptile organoids from snake venom glands. Preliminary characterization, including morphological, transcriptomic, and proteomic analyses, revealed organoids enriched in cholangiocytes. Deriving organoids from distant turtles and life stages demonstrates that our techniques are broadly applicable to chelonians, permitting the development of functional genomic tools currently lacking in herpetological research. Such platform could potentially support studies including genome-to-phenome mapping, gene function, genome architecture, and adaptive responses to climate change, with implications for ecological, evolutionary, and biomedical research.

Network Pharmacology with Metabolomics Study to Reveal the Mechanisms of Bushen Huoxue Formula in Intervertebral Disc Degeneration Treatment

Background

Intervertebral disc degeneration (IVDD) is a pathophysiological process that leads to severe back pain or neurological deficits. The Bushen Huoxue Formula (BSHXF) is a traditional herbal remedy widely used to treat diseases related to IVDD. However, its pharmacological mechanism needs further exploration.

Objective

This study aimed to elucidate the mechanisms through which BSHXF treats IVDD-related diseases by integrating metabolomics with network pharmacology.

Methods

Network pharmacology was utilized to identify potential targets of BSHXF against IVDD. Additionally, an animal model of needle puncture-induced disc degeneration was established to assess the effect of BSHXF. Mice were randomly assigned to the sham group, model group, and BSHXF group. Various techniques, including PCR, CCK-8 assay, MRI, histological examinations, and immunohistochemical analyses, were employed to evaluate degenerative and oxidative stress conditions in mouse disc tissue and cultured nucleus pulposus (NP) cells. UHPLC-HRMS/MS was used to differential distinct metabolites in the disc tissue from different groups, and MetaboAnalyst 5.0 was employed to enrich the metabolic pathways.

Results

Through network pharmacology, 15 core proteins were identified through protein-protein interaction (PPI) network construction. Functional enrichment analysis highlighted the critical role of BSHXF in addressing IVDD by influencing the response to oxidative stress. Furthermore, experimental evidence demonstrated that BSHXF significantly improved the pathological progression of IVDD and increased oxidative stress markers SOD-1 and GPX1, both in the disc degeneration model and cultured NP cells. Metabolomics identified differential metabolites among the three groups, revealing 15 metabolic pathways between the sham and model groups, and 13 metabolic pathways enriched between the model and BSHXF groups.

Conclusion

This study, integrating network pharmacology and metabolomics, suggests that BSHXF can alleviate IVDD progression by modulating oxidative stress. Key metabolic pathways associated with BSHXF-mediated reduction of oxidative stress include the citrate cycle, cysteine and methionine metabolism, alanine, aspartate and glutamate metabolism, glycine, serine and threonine metabolism, D-glutamine and D-glutamate metabolism, glutathione metabolism, and tryptophan metabolism. While this research demonstrates the therapeutic potential of BSHXF in reducing oxidative stress levels in IVDD, further research is needed to thoroughly understand its underlying mechanisms.

[Efficacy of navel application of Jianpiwenyang Gel for chronic diarrhea of spleen and stomach weakness type: a randomized controlled trial and analysis of the mechanism]

OBJECTIVE

To investigate the efficacy of Jianpiwenyang Gel (SSWYG) for treating chronic diarrhea and explore its therapeutic mechanism.

METHODS

Eighty patients with chronic diarrhea of spleen and stomach weakness type were randomized into two groups for interventions with lifestyle adjustment and treatment with bifid triple viable capsules (control group, n=40) or naval application with SSWYG (treatment group, n=40) for one week, after which symptoms of chronic diarrhea were evaluated. The Chinese medicine system pharmacology analysis platform (TCMSP), GeneCards, NCBI, OMIM database and GEO database (GSE14841) were used to obtain the active ingredients and target proteins of SSWYG and chronic diarrhea-related targets. The key targets were obtained by topological analysis for Gene Ontology (GO) and KEGG analyses. The affinity and binding characteristics of SSWYG for specific targets were verified by molecular docking using AutoDock software.

RESULTS

In both groups, gastrointestinal symptom rating scale (GSRS), Bristol Scale and TCM syndrome scores significantly improved after the treatments (P < 0.05), and better effects were observed in the treatment group (P < 0.05). Sixtyeight targets of SSWYG in treating chronic diarrhea were obtained, and 33 most probable ones were screened out by topological analysis. GO and KEGG analyses identified several chronic diarrhea-related pathways including the TNF and IL-17 pathways. Molecular docking study showed good affinity of the core components of SSWYG for the key targets CASP3, JNK, IL1B, IL6, and AKT1. JUN and CASP3 had the lowest binding energy and the highest stable binding energy with multiple major active ingredients of SSWYG.

CONCLUSION

SSWYG can significantly improve clinical symptoms of chronic diarrhea possibly by regulating the TNF and IL-17 as well as other pathways via CASP3 and JUN, suggesting a complex therapeutic mechanism of SSWYG involving multiple ingredients and targets and coordinated regulation of multiple pathways.

Combination of computational new approach methodologies for enhancing evidence of biological pathway conservation across species

The ability to predict which chemicals are of concern for environmental safety is dependent, in part, on the ability to extrapolate chemical effects across many species. This work investigated the complementary use of two computational new approach methodologies to support cross-species predictions of chemical susceptibility: the US Environmental Protection Agency Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool and Unilever's recently developed Genes to Pathways - Species Conservation Analysis (G2P-SCAN) tool. These stand-alone tools rely on existing biological knowledge to help understand chemical susceptibility and biological pathway conservation across species. The utility and challenges of these combined computational approaches were demonstrated using case examples focused on chemical interactions with peroxisome proliferator activated receptor alpha (PPARα), estrogen receptor 1 (ESR1), and gamma-aminobutyric acid type A receptor subunit alpha (GABRA1). Overall, the biological pathway information enhanced the weight of evidence to support cross-species susceptibility predictions. Through comparisons of relevant molecular and functional data gleaned from adverse outcome pathways (AOPs) to mapped biological pathways, it was possible to gain a toxicological context for various chemical-protein interactions. The information gained through this computational approach could ultimately inform chemical safety assessments by enhancing cross-species predictions of chemical susceptibility. It could also help fulfill a core objective of the AOP framework by potentially expanding the biologically plausible taxonomic domain of applicability of relevant AOPs.

In Silico Insights into the Arsenic Binding Mechanism Deploying Application of Computational Biology-Based Toolsets

An assortment of environmental matrices includes arsenic (As) in its different oxidation states, which is often linked to concerns that pose a threat to public health worldwide. The current difficulty lies in addressing toxicological concerns and achieving sustained detoxification of As. Multiple conventional degradation methods are accessible; however, they are indeed labor-intensive, expensive, and reliant on prolonged laboratory evaluations. Molecular interaction and atomic level degradation mechanisms for enzyme-As exploration are, however, underexplored in those approaches. A feasible approach in this case for tackling this accompanying concern of As might be to cope with undertaking multivalent computational methodologies and tools. This work aimed to provide molecular-level insight into the enzyme-aided As degradation mechanism. AutoDock Vina, CABS-flex 2.0, and Desmond high-performance molecular dynamics simulation (MDS) were utilized in the current investigation to simulate multivalent molecular processes on two protein sets: arsenate reductase (ArsC) and laccase (LAC) corresponding arsenate (ART) and arsenite (AST), which served as model ligands to comprehend binding, conformational, and energy attributes. The structural configurations of both proteins exhibited variability in flexibility and structure framework within the range of 3.5-4.5 Å. The LAC-ART complex exhibited the lowest calculated binding affinity, measuring -5.82 ± 0.01 kcal/mol. Meanwhile, active site residues ILE-200 and HIS-206 were demonstrated to engage in H-bonding with the ART ligand. In contrast to ArsC, the ligand binding affinity of this bound complex was considerably greater. Additional validation of docked complexes was carried out by deploying Desmond MDS of 100 ns to capture protein and ligand conformation behavior. The system achieved stability during the 100 ns simulation run, as confirmed by the average P-L RMSD, which was ∼1 Å. As a preliminary test of the enzyme's ability to catalyze As species, corresponding computational insights might be advantageous for bridging gaps and regulatory consideration.

Shaping the Future of Obesity Treatment: In Silico Multi-Modeling of IP6K1 Inhibitors for Obesity and Metabolic Dysfunction

Recent research has uncovered a promising approach to addressing the growing global health concern of obesity and related disorders. The inhibition of inositol hexakisphosphate kinase 1 (IP6K1) has emerged as a potential therapeutic strategy. This study employs multiple ligand-based in silico modeling techniques to investigate the structural requirements for benzisoxazole derivatives as IP6K1 inhibitors. Firstly, we developed linear 2D Quantitative Structure-Activity Relationship (2D-QSAR) models to ensure both their mechanistic interpretability and predictive accuracy. Then, ligand-based pharmacophore modeling was performed to identify the essential features responsible for the compounds' high activity. To gain insights into the 3D requirements for enhanced potency against the IP6K1 enzyme, we employed multiple alignment techniques to set up 3D-QSAR models. Given the absence of an available X-ray crystal structure for IP6K1, a reliable homology model for the enzyme was developed and structurally validated in order to perform structure-based analyses on the selected dataset compounds. Finally, molecular dynamic simulations, using the docked poses of these compounds, provided further insights. Our findings consistently supported the mechanistic interpretations derived from both ligand-based and structure-based analyses. This study offers valuable guidance on the design of novel IP6K1 inhibitors. Importantly, our work exclusively relies on non-commercial software packages, ensuring accessibility for reproducing the reported models.