Sequence Diversity Research Articles

MEGA-GO: Functions prediction of diverse protein sequence length using Multi-scalE Graph Adaptive neural network.

The increasing accessibility of large-scale protein sequences through advanced sequencing technologies has necessitated the development of efficient and accurate methods for predicting protein function. Computational prediction models have emerged as a promising solution to expedite the annotation process. However, despite making significant progress in protein research, graph neural networks face challenges in capturing long-range structural correlations and identifying critical residues in protein graphs. Furthermore, existing models have limitations in effectively predicting the function of newly sequenced proteins that are not included in protein interaction networks. This highlights the need for novel approaches integrating protein structure and sequence data. We introduce MEGA-GO, highlighting the capability of capturing diverse protein sequence length features from multiple scales. The unique graph adaptive neural network architecture of MEGA-GO enables a more nuanced extraction of graph structure features, effectively capturing intricate relationships within biological data. Experimental results demonstrate that MEGA-GO outperforms mainstream protein function prediction models in the accuracy of Gene Ontology (GO) term classification, yielding 33.4%, 68.9%, and 44.6% of Area Under the Precision-Recall Curve (AUPR) on Biological Process (BP), Molecular Function (MF), and Cellular Component (CC) domains respectively. The rest of the experimental results reveal that our model consistently surpasses the state-of-the-art methods. The source code and implementation of MEGA-GO are available at https://github.com/Cheliosoops/MEGA-GO. The supplementary file can be found at Supplementary.

PRA-MutPred: Predicting the Effect of Point Mutations in Protein-RNA Complexes Using Structural Features.

Interactions between proteins and RNAs are essential for the proper functioning of cells, and mutations in these molecules may lead to diseases. These protein mutations alter the strength of interactions between the protein and RNA, generally described as binding affinity (ΔG). Hence, the affinity change upon mutation (ΔΔG) is an important parameter for understanding the effect of mutations in protein-RNA complexes. In this work, we developed a machine-learning model to predict ΔΔG values upon mutations in protein-RNA complexes. We collected experimentally determined ΔΔG values of 710 mutations in 134 protein-RNA complexes. Diverse sequence and structural features were generated from both wild-type and modeled mutant complexes, which include conservation scores, residue-based, network-based, and interface features. Further, we developed a support vector regressor model with a correlation of 0.75 and a mean absolute error of 0.84 kcal/mol in the jack-knife test. We observed that the performance of the model is dictated by structural features, such as contact potentials, atom contacts in the interface of protein-RNA complexes, and the solvent accessibility of the mutated residue. We also developed a Web server, PRA-MutPred, predicting the protein-RNA binding affinity change upon mutation, which is available in the link https://web.iitm.ac.in/bioinfo2/pramutpred/.

Toll/interleukin-1 receptor-only genes contribute to immune responses in maize.

Proteins with Toll/interleukin-1 receptor (TIR) domains are widely distributed in both prokaryotes and eukaryotes, serving as essential components of immune signaling. Although monocots lack the major TIR-nucleotide-binding (NB)-leucine-rich repeat (LRR)-type (TNL) immune receptors, they possess a small number of TIR-only proteins, the function of which remains largely unknown. In the monocot maize (Zea mays), there are three conserved TIR-only genes in the reference genome, namely ZmTIR1 to ZmTIR3. A genome-wide scan for TIR genes and comparative analysis revealed that these genes exhibit low sequence diversity and do not show copy number variation among 26 diverse inbred lines. ZmTIR1 and ZmTIR3, but not ZmTIR2, specifically trigger cell death and defense gene expression when overexpressed in Nicotiana benthamiana leaves. These responses depend on the critical glutamic acid and cysteine residues predicted to be essential for TIR-mediated NADase and 2',3'-cAMP/cGMP synthetase activity, respectively, as well as the key TIR downstream regulator Enhanced Disease Susceptibility 1 (EDS1). Overexpression of ZmTIR3 in N. benthamiana produces signaling molecules, including 2'cADPR, 2',3'-cAMP and 2',3'-cGMP, a process that requires the enzymatic glutamic acid and cysteine residues of ZmTIR3. ZmTIR expression in maize is barely detectable under normal conditions, but is substantially induced by different pathogens. Importantly, the maize Zmtir3 knockout mutant exhibits enhanced susceptibility to the fungal pathogen Cochliobolus heterostrophus, highlighting the role of ZmTIR3 in maize immunity. Overall, our results unveil the function of the maize ZmTIRs. We propose that the pathogen-inducible ZmTIRs play an important role in maize immunity, likely through their enzymatic activity and via EDS1-mediated signaling.

Major F plasmid clusters are linked with ColV and pUTI89-like marker genes in bloodstream isolates of Escherichia coli

BackgroundF plasmids are abundant in E. coli, carrying a variety of genetic cargo involved in fitness, pathogenicity, and antimicrobial resistance. ColV and pUTI89-like plasmids have drawn attention for their potential roles in various forms of extra-intestinal pathogenicity. However, the rates of their carriage and the overall diversity of F plasmids in E. coli bloodstream infections (BSI E. coli) remain unknown.MethodsWe performed a t-SNE-based cluster analysis of predicted F plasmids from a collection of 4711 BSI E. coli draft genomes to describe their diversity and abundance. We also screened them for markers of ColV and pUTI89-like plasmids, F plasmid replicon sequence types (RST) and E. coli sequence types (ST) to understand how genetic features were related to plasmid clusters.ResultsPredicted F plasmids in BSI E. coli draft genomes were embedded within five major clusters based on a model of complete F plasmid sequences. Nearly half of the clustered sequences belonged to two major clusters, which were associated with ColV and pUTI89-like marker genes, respectively. Genomes from the ColV cluster featured F2:A-:B1 and F24:A-B1 RSTs in association with ST95, ST58 and ST88, whilst the pUTI89-like cluster was mostly F29:A-:B10 linked to ST73, ST69, ST95 and ST131. Plasmids associated with different lineages of ST131 formed additional major clusters, whilst F51:A-:B10 plasmids in ST73 were also common.ConclusionsColV and pUTI89-like plasmid markers are predominant in BSI E. coli that carry F plasmids. These markers are associated with distinct clusters of plasmids across diverse sequence types of E. coli. We hypothesise that their abundance in BSI E. coli is partially driven by carriage of backbone genes previously shown to contribute to virulence in models of bloodstream infection. Their carriage by pandemic E. coli STs suggests clonal expansion also plays a role in their success in BSI. Ecological pathways via which these plasmids evolve, and spread are likely to be distinct as other studies show ColV is strongly associated with poultry and food animal production, whereas pUTI89-like plasmids appear to be mostly human-restricted.

Comprehensive analysis of 111 Pleuronectiformes mitochondrial genomes: insights into structure, conservation, variation and evolution

BackgroundPleuronectiformes, also known as flatfish, are important model and economic animals. However, a comprehensive genome survey of their important organelles, mitochondria, has been limited. Therefore, we aim to analyze the genomic structure, codon preference, nucleotide diversity, selective pressure and repeat sequences, as well as reconstruct the phylogenetic relationship using the mitochondrial genomes of 111 flatfish species.ResultsOur analysis revealed a conserved gene content of protein-coding genes and rRNA genes, but varying numbers of tRNA genes and control regions across species. Various gene rearrangements were found in flatfish species, especially for the rearrangement of nad5-nad6-cytb block in Samaridae family, the swapping rearrangement of nad6 and cytb gene in Bothidae family, as well as the control region translocation and tRNA-Gln gene inversion in the subfamily Cynoglossinae, suggesting their unique evolutionary history and/or functional benefit. Codon usage showed obvious biases, with adenine being the most frequent nucleotide at the third codon position. Nucleotide diversity and selective pressure analysis suggested that different protein-coding genes underwent varying degrees of evolutionary pressure, with cytb and cox genes being the most conserved ones. Phylogenetic analysis using both whole mitogenome information and concatenated independently aligned protein-coding genes largely mirrored the taxonomic classification of the species, but showed different phylogeny. The identification of simple sequence repeats and various long repetitive sequences provided additional complexity of genome organization and offered markers for evolutionary studies and breeding practices.ConclusionsThis study represents a significant step forward in our comprehension of the flatfish mitochondrial genomes, providing valuable insights into the structure, conservation and variation within flatfish mitogenomes, with implications for understanding their evolutionary history, functional genomics and fisheries management. Future research can delve deeper into conservation biology, evolutionary biology and functional usages of variations.

Analysis of DNA cox1 barcoding revealed novel haplotype in Schistosoma haematobium isolated from Western Sudan

Schistosomiasis poses a significant global health threat, particularly in tropical and subtropical regions like Sudan. Although numerous epidemiological studies have examined schistosomiasis in Sudan, the genetic diversity of Schistosoma haematobium populations, specifically through analysis of the mtcox1 gene, remains unexplored. This study aimed to investigate the risk factors associated with urogenital schistosomiasis among school pupils in El-Fasher, Western Sudan, as well as the mtcox1 genetic diversity of human S. haematobium in this region. A cross-sectional study was conducted among school pupils aged 4 to 19 years. In total, 196 urine samples and 196 fecal samples were collected from participants across schools, health centers, and refugee camps in El-Fasher. Samples were examined using simple centrifugation/sedimentation technique and formol-ether concentration method to detect S. haematobium and S. mansoni eggs, respectively. S. haematobium mtcox1 partial gene was amplified and sequenced by the Sanger technique. A neighbor-joining phylogenetic tree was generated by MEGA software, and a haplotype network was constructed using PopART v.1.7 with the median-joining network method. In this study, S. haematobium was detected in 6.1% (12/196) of the participants while no S. mansoni ova were observed in fecal samples. The infection was more common among those who relied on indirect water supply like tankers (6, 50%). No infection was observed among residents of refugee camps. Only eight samples were PCR-positive, which were successfully sequenced, and included in the genetic diversity analysis. A unique haplotype (Hap_1) with no sequence diversity was found among cox1 sequences from El-Fasher strains. Both El-Fasher S. haematobium haplotype (Hap_1) and Gezira haplotype (Hap_31) fall within the mainland Africa group (group 1). In conclusion, this study identified a novel S. haematobium strain and provides insights into the evolutionary history and phylogeography of S. haematobium in Sudan, particularly in the western region. This genetic data could help in the control and monitoring of urogenital schistosomiasis in this region. For the first time, we utilized the DNA mtcox1 barcoding to investigate S. haematobium haplotypes in Western Sudan.

Unveiling the Role of GhP5CS1 in Cotton Salt Stress Tolerance: A Comprehensive Genomic and Functional Analysis of P5CS Genes.

Proline, a critical osmoregulatory compound, is integral to various plant stress responses. The P5CS gene, which encodes the rate-limiting enzyme in proline biosynthesis, known as ∆1-pyrroline-5-carboxylate synthetase, is fundamental to these stress response pathways. While the functions of P5CS genes in plants have been extensively documented, their specific roles in cotton remain inadequately characterized. In this study, we identified 40 P5CS genes across four cotton species with diverse sequence lengths and molecular weights. Phylogenetic analysis of 100 P5CS genes from nine species revealed three subgroups, with Gossypium hirsutum closely related to Gossypium barbadense. Collinearity analysis highlighted significant differences in collinear gene pairs, indicating evolutionary divergence among P5CS genes in tetraploid and diploid cotton. Exon-intron structures and conserved motifs correlated with phylogenetic relationships, suggesting functional differentiation. Stress-responsive elements in P5CS promoters suggest involvement in abiotic stress. Expression analysis under salt stress revealed differential expressions of GhP5CS genes, with GhP5CS1 emerging as a potential key regulator. Virus-induced gene silencing confirmed the pivotal role of GhP5CS1 in cotton's salt stress response, as evidenced by increased salt sensitivity in the silenced plants. This study enhances our understanding of the functional diversity and roles of P5CS genes in cotton under stress conditions.

Decomposition-Aware Framework for Probabilistic and Flexible Time Series Forecasting in Aerospace Electronic Systems

Degradation prediction for aerospace electronic systems plays a crucial role in maintenance work. This paper proposes a concise and efficient framework for multivariate time series forecasting that is capable of handling diverse sequence representations through a Channel-Independent (CI) strategy. This framework integrates a decomposition-aware layer to effectively separate and fuse global trends and local variations and a temporal attention module to capture temporal dependencies dynamically. This design enables the model to process multiple distinct sequences independently while maintaining the flexibility to learn shared patterns across channels. Additionally, the framework incorporates probabilistic distribution forecasting using likelihood functions, addressing the dynamic variations and uncertainty in time series data. The experimental results on multiple real-world datasets validate the framework’s effectiveness, demonstrating its robustness and adaptability in handling diverse sequences across various application scenarios.

Differences in gut microbial diversity and composition between growth phenotypes of farmed juvenile sandfish, Holothuria scabra

BackgroundThe observed growth variability of different aquaculture species in captivity hinders its large-scale production. For the sandfish Holothuria scabra, a tropical sea cucumber species, there is a scarcity of information on its intestinal microbiota in relation to host growth, which could provide insights into the processes that affect growth and identify microorganisms with probiotic or biochemical potential that could improve current production strategies. To address this gap, this study used 16 S rRNA amplicon sequencing to characterize differences in gut and fecal microbiota among large and small juveniles reared in floating ocean nurseries.ResultsWe recovered 5915 amplicon sequence variants and diversity indices revealed significant differences between large and small juveniles (p < 0.05). Gut microbiota of large juveniles had lower bacterial diversity than its smaller counterparts. The genus cluster Burkholderia-Caballeronia-Paraburkholderia (BCP) is the most common and abundant taxa found in the gut for both size categories but less abundant in fecal samples. Small juveniles had a higher abundance of members from the Roseobacter clade (Rhodobacteriaceae) such as Ruegeria, Shimia, Psuedoruegeria and Marivita among others while the genus Schlegelella (Caldimonas) and Bosea were primarily found in larger juveniles. Predicted physiological functions identified signatures of metabolism, biosynthesis, and biodegradation pathways unique for each size category. Significant differences in diversity and composition were also exhibited between the pooled fecal and gut sample types.ConclusionsThe bacterial composition in the intestinal tract of the sandfish H. scabra is an important factor in the observed growth variability in aquaculture. The results show differences in diversity, composition and predicted physiological functions between the size groups, despite being from the same cohort and environment. It was also evident that the fecal microbiota differs from the gut and does not correspond to size category, warranting caution in using the fecal matter as a proxy to infer microbial composition and interactions in the gastrointestinal tract. Understanding the roles that these microorganisms play in sandfish growth could support the development of strategies to manage size variation in captive-bred sea cucumbers, or for the promotion and selection for faster-growing individuals.

Protein CREATE enables closed-loop design of de novo synthetic protein binders.

Proteins have proven to be useful agents in a variety of fields, from serving as potent therapeutics to enabling complex catalysis for chemical manufacture. However, they remain difficult to design and are instead typically selected for using extensive screens or directed evolution. Recent developments in protein large language models have enabled fast generation of diverse protein sequences in unexplored regions of protein space predicted to fold into varied structures, bind relevant targets, and catalyze novel reactions. Nevertheless, we lack methods to characterize these proteins experimentally at scale and update generative models based on those results. We describe Protein CREATE (Computational Redesign via an Experiment-Augmented Training Engine), an integrated computational and experimental pipeline that incorporates an experimental workflow leveraging next generation sequencing and phage display with single-molecule readouts to collect vast amounts of quantitative binding data for updating protein large language models. We use Protein CREATE to generate and assay thousands of designed binders to IL-7 receptor α and insulin receptor with parallel positive and negative selections to identify on-target binders. We discover not only individual novel binders but also features of ligand-receptor binding, including preservation of the IL7Rα - ligand hydrophobic interface specifically and existence of multiple approaches to contact the insulin receptor. We also demonstrate the importance of structural features, such as the lack of unpaired cysteine residues, toward design fidelity and find computational pre-screening metrics, such as interchain predicted TM scoring (iPTM), while useful, are imperfect predictors as they neither guarantee experimental binding nor rule it out. We use the data collected from Protein CREATE to score designs from the initial generative models. Globally, Protein CREATE will power future closed-loop design-build-test cycles to enable fine-grained design of protein binders.

Resistify: A Novel NLR Classifier That Reveals Helitron-Associated NLR Expansion in Solanaceae.

Nucleotide-binding domain leucine-rich repeat (NLR) proteins are a key component of the plant innate immune system. In plant genomes, NLRs exhibit considerable presence/absence variation and sequence diversity. Recent advances in sequencing technologies have made the generation of high-quality novel plant genome assemblies considerably more straightforward. Accurately identifying NLRs from these genomes is a prerequisite for improving our understanding of NLRs and identifying novel sources of disease resistance. While several tools have been developed to predict NLRs, they are hampered by low accuracy, speed, and availability. Here, the NLR annotation tool Resistify is presented. Resistify is an easy-to-use, rapid, and accurate tool to identify and classify NLRs from protein sequences. Applying Resistify to the RefPlantNLR database demonstrates that it can correctly identify NLRs from a diverse range of species. Applying Resistify in combination with tools to identify transposable elements to a panel of Solanaceae genomes reveals a previously undescribed association between NLRs and Helitron transposable elements.

Viroporins: discovery, methods of study, and mechanisms of host-membrane permeabilization.

The 'Viroporin' family comprises a number of mostly small-sized, integral membrane proteins encoded by animal and plant viruses. Despite their sequence and structural diversity, viroporins share a common functional trend: their capacity to assemble transmembrane channels during the replication cycle of the virus. Their selectivity spectrum ranges from low-pH-activated, unidirectional proton transporters, to size-limited permeating pores allowing passive diffusion of metabolites. Through mechanisms not fully understood, expression of viroporins facilitates virion assembly/release from infected cells, and subverts the cell physiology, contributing to cytopathogenicity. Compounds that interact with viroporins and interfere with their membrane-permeabilizing activity in vitro, are known to inhibit virus production. Moreover, viroporin-defective viruses comprise a source of live attenuated vaccines that prevent infection by notorious human and livestock pathogens. This review dives into the origin and evolution of the viroporin concept, summarizes some of the methodologies used to characterize the structure-function relationships of these important virulence factors, and attempts to classify them on biophysical grounds attending to their mechanisms of ion/solute transport across membranes.

GUT, VAGINAL AND URINARY MICROBIOTA AS POTENTIAL BIOMARKERS OF SENSITIZATION IN WOMEN WITH CHRONIC PELVIC PAIN.

A subgroup of patients with chronic pelvic pain (CPP) exhibit organ sensitization, whose origin and mechanism remains largely unknown. Changes in microbiota composition in pelvic organs have been found to be associated with various pelvic pathological conditions. Therefore, a comprehensive analysis of the gut and genito-urinary microbiota composition and interactions in women with CPP may be key to understanding their involvement in the sensitization processes. To identify pelvic organ microbiota signatures that are associated with organ hypersensitivity in CPP patients. This study involved women with high (S-CPP, n=14) and low (NS-CPP, n=14) pelvic sensitization scores according to the Convergences PP criteria. Pelvic organ sensitivity was assessed by rectal barostat, and noninvasive bladder, muscular and vulvar sensory tests. Quality of life, pelvic symptoms and psychological state were assessed. Using 16S rRNA gene sequencing, the gut, vaginal and urinary microbiota diversity and composition were analyzed and compared between S-CPP and NS-CPP women. Differentially abundant bacterial amplicon sequence variants (ASVs) between groups were associated with clinical characteristics and organ sensitivity. System biology approaches using Weighted Gene Correlation Network Analysis (WGCNA) were used to identify bacterial ASV modules associated with functional and clinical parameters. Pain pressure thresholds were significantly decreased in S-CPP women for the vulva and for the rectum, the bladder and the perineal muscles as compared to NS-CPP. However, pain intensity felt at rectal, muscular and bladder pain thresholds was significantly increased in S-CPP women. After stimulation, S-CPP women presented increased and prolonged pain in perineal muscles and bladder compared to NS-CPP women. Alpha and β-diversities were significantly increased in S-CPP women in vaginal and urinary but not in gut microbiota. Using, differential abundance analysis, we showed that 13 ASVs in the gut, 6 in the vagina and 2 in the bladder were differentially expressed between S-CPP and NS-CPP patients. More specifically, in vaginal microbiota, a significant increase of Streptococcus and Prevotella genera was observed in S-CPP as compared to NS-CPP. A significant increase in Clostridium sensu stricto 1 ASV was observed in urinary microbiota of S-CPP as compared to NS-CPP patients. Next, we found that 4 gut microbiota ASVs (belonging to Akkermansia, Desulfovibrio, Faecalibacterium and CAG-352) were correlated with pain intensity at maximal rectal distension threshold. We also identified an ASV (Blautia) which is increased in NS-CPP as being inversely correlated to several gut sensitization markers. In the vaginal microbiota, the Lactobacillus jensenii ASV was associated with less dysmenorrhea and an increased bladder capacity. Furthermore, we identified two vaginal ASVs belonging to Prevotella which are increased in S-CPP as being associated with dysmenorrhea. Finally, using WGCNA analysis methods, we identified vaginal and urinary tract ASVs modules driven by Peptostreptococcales-Tissierellales Peptoniphilus which were strongly correlated with rectal, muscular and bladder poststimulation pain. We also identified a gut ASVs module driven by Christensenellaceae_R-7 that significantly associated with anxiety, gastro-intestinal symptoms and rectal sensitivity. This work identified specific bacterial signatures of specific pelvic organs and their association with organ sensitization, which are potential therapeutic targets in CPP women.

Two-step computational redesign of Bacillus subtilis cellulase and β-glucanase for enhanced thermostability and activity

The growing demand for biocatalysts in biomass processing highlights the necessity of enhancing the thermostability of glycoside hydrolases. However, improving both thermostability and activity is often hindered by trade-offs between backbone rigidity and the flexibility of substrate-binding regions. In this study, Bacillus subtilis cellulase and β-glucanase were engineered using a two-step process incorporating the computational tools Pythia and ESM-2, which were found complementary in improving stability and activity. The engineered cellulase and β-glucanase exhibited increases in their apparent melting temperatures (5.8 °C and 8.4 °C), accompanied by up to a 1.5-fold increase in initial activities. At 50 °C, while the wild-type cellulase lost 60% of its activity after 24 h and wild-type β-glucanase lost activity completely in 2 h, the engineered cellulase-M5 retained its initial activity, and β-glucanase-M7 displayed a 2.2-fold increase in its half-life. Structural analysis indicated that Pythia-identified mutations likely enhanced backbone robustness through refined polar and hydrophobic interactions, while beneficial mutations from ESM-2 appeared to affect polysaccharide-binding regions. This two-step computational redesign offers a promising approach for optimizing both thermostability and activity in glycoside hydrolases and other enzyme families with extensive sequence diversity.

AlphaFold-guided molecular replacement for solving challenging crystal structures.

Molecular replacement (MR) is highly effective for biomolecular crystal structure determination, increasingly so as the database of known structures has increased. For candidates without recognizable similarity to known structures, however, crystal structure analyses have nearly always required experiments for de novo phase evaluation. Now, with the unprecedented accuracy of AlphaFold predictions of protein structures from amino-acid sequences, an appreciable expansion of the reach of MR for proteins is realized. Here, we sought to automate an AlphaFold-guided MR procedure that tailors predictions to the MR problem at hand. We first optimized the reliability cutoff parameters for residue inclusion as tested in application to a previously MR-intractable problem. We then examined cases where AlphaFold by default predicts a conformation alternative to that of the candidate structure, devising tests for MR solution either from domain-specific predictions or from predictions based on diverse sequence subclusters. We tested subclustering procedures on an enzyme system that entails multiple MR-challenging conformations. The overall process as implemented in Phenix automatically surveys a succession of trials of increasing computational complexity until an MR solution is found or the options are exhausted. Validated MR solutions were found for 92% of one set of 158 challenging problems from the PDB and 93% of those from a second set of 215 challenges. Thus, many crystal structure analyses that previously required experimental phase evaluation can now be solved by AlphaFold-guided MR. In effect, this and related MR approaches are de novo phasing methods.

Effect of konjac glucomannanaerogel-immobilized Chlorella vulgaris LH-1 on oil-contaminated seawater remediation and endogenous bacterial community diversity.

Ocean oil spills can severely impact ecosystems and disrupt marine biodiversity and habitats. Microbial remediation is an effective method for removing thin oil slick contamination. In this study, the adsorption and degradation of low-concentration oil spills by Chlorella vulgaris LH-1 immobilized in konjac glucomannan (KGM) aerogel were investigated. The effect of the KGM aerogel-immobilized C. vulgaris on the bacterial community structure in seawater environments was analyzed through bacterial diversity sequencing. In seawater containing 0.01 and 1.00 g/L of crude oil, after 14 days of remediation with the KGM aerogel-immobilized C. vulgaris, crude oil removal rates of 98.73% and 95.13% were achieved, respectively. The FDA hydrolytic enzyme activity curve indicated that the microbial growth activity in the immobilized C. vulgaris group was significantly higher than that in other groups. After remediation, the top three dominant bacterial genera in the seawater were found to be Vitellibacter, Roseitalea, and Methylophaga. Vitellibacter, a genus known for its ability to degrade polycyclic aromatic hydrocarbons (PAHs) in marine environments, showed increased abundance in seawater treated with the KGM aerogel-immobilized C. vulgaris, suggesting enhanced PAH degradation capability in the presence of the immobilized C. vulgaris. Functional prediction using PICRUSt indicated that the oil metabolism capability of bacteria was promoted by the KGM aerogel-immobilized C. vulgaris. PRACTITIONER POINTS: High degradation efficiency across various oil concentrations is exhibited by KGM-immobilized microalgae. KGM aerogels effectively confine C.vulgaris, reducing loss in marine systems. The impact of KGM aerogel-immobilized C. vulgaris on bacterial community structure in marine environments was analyzed. Immobilized C. vulgaris enhanced the growth of polycyclic aromatic hydrocarbon-degrading bacteria, such as Vitellibacter, in seawater.

AVP-GPT2: A Transformer-Powered Platform for De Novo Generation, Screening, and Explanation of Antiviral Peptides.

Human respiratory syncytial virus (RSV) remains a significant global health threat, particularly for vulnerable populations. Despite extensive research, effective antiviral therapies are still limited. To address this urgent need, we present AVP-GPT2, a deep-learning model that significantly outperforms its predecessor, AVP-GPT, in designing and screening antiviral peptides. Trained on a significantly expanded dataset, AVP-GPT2 employs a transformer-based architecture to generate diverse peptide sequences. A multi-modal screening approach, incorporating Star-Transformer and Vision Transformer, enables accurate prediction of antiviral activity and toxicity, leading to the identification of potent and safe candidates. SHAP analysis further enhances interpretability by explaining the underlying mechanisms of peptide activity. Our in vitro experiments confirmed the antiviral efficacy of peptides generated by AVP-GPT2, with some exhibiting EC50 values as low as 0.01 μM and CC50 values > 30 μM. This represents a substantial improvement over AVP-GPT and traditional methods. AVP-GPT2 has the potential to significantly impact antiviral drug discovery by accelerating the identification of novel therapeutic agents. Future research will explore its application to other viral targets and its integration into existing drug development pipelines.

Evolutionary and functional divergence of Sfx, a plasmid-encoded H-NS homolog, underlies the regulation of IncX plasmid conjugation.

Conjugative plasmids are widespread among prokaryotes, highlighting their evolutionary success. Conjugation systems on most natural plasmids are repressed by default. The negative regulation of F-plasmid conjugation is partially mediated by the chromosomal nucleoid-structuring protein (H-NS). Recent bioinformatic analyses have revealed that plasmid-encoded H-NS homologs are widespread and exhibit high sequence diversity. However, the functional roles of most of these homologs and the selective forces driving their phylogenetic diversification remain unclear. In this study, we characterized the functionality and evolution of Sfx, a H-NS homolog encoded by the model IncX2 plasmid R6K. We demonstrate that Sfx, but not chromosomal H-NS, can repress R6K conjugation. Notably, we find evidence of positive selection acting on the ancestral Sfx lineage. Positively selected sites are located in the dimerization, oligomerization, and DNA-binding interfaces, many of which contribute to R6K repression activity-indicating that adaptive evolution drove the functional divergence of Sfx. We additionally show that Sfx can physically interact with various chromosomally encoded proteins, including H-NS, StpA, and Hha. Hha enhances the ability of Sfx to regulate R6K conjugation, suggesting that Sfx retained functionally important interactions with chromosomal silencing proteins. Surprisingly, the loss of Sfx does not negatively affect the stability or dissemination of R6K in laboratory conditions, reflecting the complexity of selective pressures favoring conjugation repression. Overall, our study sheds light on the functional and evolutionary divergence of a plasmid-borne H-NS-like protein, highlighting how these loosely specific DNA-binding proteins evolved to specifically regulate different plasmid functions.IMPORTANCEConjugative plasmids play a crucial role in spreading antimicrobial resistance and virulence genes. Most natural conjugative plasmids conjugate only under specific conditions. Therefore, studying the molecular mechanisms underlying conjugation regulation is essential for understanding antimicrobial resistance and pathogen evolution. In this study, we characterized the conjugation regulation of the model IncX plasmid R6K. We discovered that Sfx, a H-NS homolog carried by the plasmid, represses conjugation. Molecular evolutionary analyses combined with gain-of-function experiments indicate that positive selection underlies the conjugation repression activity of Sfx. Additionally, we demonstrate that the loss of Sfx does not adversely affect R6K maintenance under laboratory conditions, suggesting additional selective forces favoring Sfx carriage. Overall, this work underscores the impact of protein diversification on plasmid biology, enhancing our understanding of how molecular evolution affects broader plasmid ecology.

Kinetic Analysis of Cyclization by the Substrate-Tolerant Lanthipeptide Synthetase ProcM.

Lanthipeptides are ribosomally synthesized and post-translationally modified peptides (RiPPs) characterized by the presence of thioether cross-links called lanthionine and methyllanthionine, formed by dehydration of Ser/Thr residues and Michael-type addition of Cys side chains onto the resulting dehydroamino acids. Class II lanthipeptide synthetases are bifunctional enzymes responsible for both steps, thus generating macrocyclic natural products. ProcM is part of a group of class II lanthipeptide synthetases that are known for their remarkable substrate tolerance, having large numbers of natural substrates with highly diverse peptide sequences. They install multiple (methyl)lanthionine rings with high accuracy, attributes that have been used to make large libraries of polycyclic peptides. Previous studies suggested that the final ring pattern of the lanthipeptide product may be determined by the substrate sequence rather than by ProcM. The current investigation on the ProcM-catalyzed modification of one of its 30 natural substrates (ProcA3.3) and its sequence variants utilizes kinetic assays to understand the factors that determine the ring pattern. The data show that changes in the substrate sequence result in changes to the reaction rates of ring formation that in some cases lead to a change in the order of the modifications and thereby bring about different ring patterns. These observations provide further support that the substrate sequence determines to a large degree the final ring pattern. The data also show that similar to a previous study on another substrate (ProcA2.8), the reaction rates of successive reactions slow down as the peptide is matured; rate constants observed for the reactions of these two substrates are similar, suggesting that they reflect the intrinsic activity of the enzyme with its 30 natural substrates. We also investigated whether rates of formation of single isolated rings can predict the final ring pattern of polycyclic products, an important question for the products of genome mining exercises, as well as library generation. Collectively, the findings in this study indicate that the rates of isolated modifications can be used for predicting the final ProcM-produced ring pattern, but they also revealed limitations. One unexpected observation was that even changing Ser to Thr and vice versa, a common means to convert lanthionine to methyllanthionine and vice versa, can result in a change in the ring pattern.

Genetic Characterization of Plasmodium falciparum Histidine-Rich Protein 2 Deletions and Their Impact on Malaria Interventions in Odisha, India.

Diagnostic escape via Plasmodium falciparum (P. falciparum) histidine-rich protein 2 (pfhrp2) gene deletions is a major potential hurdle for global malaria elimination efforts. We investigated the prevalence of pfhrp2 gene deletions in 15 malaria-endemic villages in the state of Odisha, India, and modeled their impact on an ongoing in-country malaria intervention program. We found that 61.6% of subpatent P. falciparum infections (i.e., rapid diagnostic test [RDT]-negative and positive by polymerase chain reaction [PCR]) had pfhrp2 gene deletions, which were predominantly located in the exon 2 region (96.2%) and largely identified in samples from febrile individuals (82.6%). DNA sequencing and protein diversity features were characterized in a subset of samples from individuals with subpatent infections carrying intact pfhrp2 exon 2 loci. Our analyses revealed novel amino acid repeat motifs (231-293 amino acids), and these variant repeat sequences differed from those of RDT+/PCR+ samples. We also evaluated the state-sponsored mass screening and treatment intervention in the context of pfhrp2 gene deletions. We found that mass screening and treatment conducted alongside additional interventions (e.g., long-lasting insecticidal net distribution, indoor residual spraying) reduced the relative risk of infection for both P. falciparum parasites harboring a pfhrp2 deletion (adjusted relative risk ratio [aRRR] = 0.3; 95% CI = 0.1-1.0) and P. falciparum parasites with intact pfhrp2 genes (aRRR = 0.4; 95% CI = 0.2-1.1) when compared with the use of mass screening and treatment by RDT alone. Combined, our findings highlight the need for alternative diagnostic targets and tools as India moves toward its goal of malaria elimination by 2030.