Gapped BLAST and PSI-BLAST: a new generation of protein database search programs

Comparative genomics of Elusimicrobiaceae (phylum Elusimicrobiota) and description of the isolates Elusimicrobium simillimum sp. nov., Elusimicrobium posterum sp. nov., and Parelusimicrobium proximum gen. nov. sp. nov

The tree of life comprises many deep-branching lineages with no or only very few cultured representatives. One such lineage is the phylum Elusimicrobiota, which contains only two described species and whose biology has been only poorly explored. We isolated three new species from this phylum from the intestinal tracts of cockroaches. Like their closest relative, Elusimicrobium minutum, the only member of the family Elusimicrobiaceae described to date, they are small, pleomorphic gram-negative rods characterized by a distinct cell cycle, and like all ultramicrobacteria, they pass through a 0.22-μm filter membrane. Physiological characterization of all isolates revealed that they are obligately anaerobic fermenters that lack catalase and cytochrome c oxidase activities but can remove oxygen from their environment in a non-respiratory manner. Their substrate range is limited to a few hexoses, such as d-glucose, d-galactose, and N-acetyl-d-glucosamine, which are fermented to lactate, acetate, ethanol, and hydrogen as major products. Comparative genome analysis, which included more than 100 MAGs of uncultured lineages of Elusimicrobiaceae, revealed the underlying metabolic pathways and outlined a new phylogenomic framework of the family. Based on phylogenomic, physiological, and morphological evidence, we describe the new isolates as Parelusimicrobium proximum gen. nov., sp. nov., Elusimicrobium posterum sp. nov., and Elusimicrobium simillimum sp. nov. under the rules of ICNP. Based on high-quality genomes of all uncultured representatives, we propose a comprehensive taxonomy of all lineages in the family under the rules of SeqCode, including the new genera Avelusimicrobium, Proelusimicrobium, and the candidate genus "Pseudelusimicrobium".

Natronomicrosphaera hydrolytica, gen. nov., sp. nov., a first representative of the phylum Planctomycetota from soda lakes

Despite intensive microbiological characterization of soda lake microbial communities, no culturable representatives from the phylum Planctomycetota have been isolated from these haloalkaline habitats. In the context of studying polysaccharide utilization by soda lake microbial communities, we used polysaccharide hyaluronic acid as enrichment substrate at aerobic, moderate haloalkaline conditions (1 M total Na+, pH 9.5). This resulted in a selective enrichment and isolation in pure culture of a bacterial strain AB-hyl4 belonging to Planctomycetota. The cells are tiny motile cocci growing in large aggregates, with the Gram-negative type of ultrastructure and producing a yellow pigment. This obligate aerobic saccharolytic heterotroph has an extremely narrow growth substrate range including, besides hyaluronic acid, melezitose and glycerol. The membrane lipids consist of phosphatidylcholine and two types of neutral lipids, including hopanoids and monounsaturated C17 and C19 hydrocarbons. Phylogenomic analysis placed the isolate into the family Phycisphaeraceae, class Phycisphaerae, as a new genus-level lineage. Its genome contained a gene encoding a polysaccharide lyase from the PL8 family which is probably responsible for the degradation of hyaluronic acid to a dimer, followed by its transport and hydrolysis into monomers in periplasm and final glycolytic degradation in cytoplasm. On the basis of distinct phenotypic and genomic properties, strain AB-hyl4T (DSM 117794 = UQM 41914) is proposed to be classified as Natronomicrosphaera hydrolytica gen. nov., sp. nov.

The crystal structure of an uncharacterized domain of P113 from Plasmodium falciparum

The surface protein P113 serves as a membrane-anchored protein that tethers the Plasmodium falciparum RH5 complex, including its associated partners CyRPA and RIPR, to the parasite surface. This anchoring mechanism ensures the proper localization and stabilization of RH5, facilitating its critical interaction with the host erythrocyte receptor basigin during erythrocyte invasion. Here, the helical-rich domain of P113 (residues 311-679) from a Plasmodium species was expressed, purified and crystallized to elucidate its structural and functional characteristics. The recombinant protein, with a molecular weight of approximately 44 kDa, was confirmed to be monomeric in solution. Crystallization in 0.5 mM MES pH 6.0, 22% PEG 3350 yielded high-quality crystals, enabling the determination of the structure of the apo form at 1.7 Å resolution. The structure revealed a predominant α-helical composition, with two distinct left-handed orthogonal four-helix bundles formed by helices α1-α4 and α6-α9 connected by a disordered region. Sequence analysis demonstrated high conservation of P113 across all human-infecting Plasmodium species, including P. vivax, P. malariae, P. falciparum and P. ovale, as well as in Plasmodium species infecting primates and rodents. Protein-protein interaction analysis using the STRING tool identified P113 as a hub protein that interacts with ten proteins, including small nuclear ribonucleoprotein, DNA polymerase delta small subunit and RIPR, which is part of the RH5-CyRPA-RIPR complex. AlphaFold predictions further elucidated the interaction patterns, revealing moderate to strong interaction scores (0.39-0.74) with key partners. Notably, the helical-rich domain of P113 was identified as the critical binding region for PF3D7_0308000, with key interaction sites mapped to residues Asp475, Arg381, Lys386, Asn390, Asp392 and Lys533. These findings provide critical insights into the structural and functional roles of P113 and its interaction network, advancing our understanding of its molecular mechanisms in Plasmodium biology.

Phylogenomic studies and molecular markers clarifying the evolutionary relationships and classification of Pseudalkalibacillus species: proposal for the family Guptibacillaceae fam. nov. harbouring the genera Guptibacillus gen. nov. and Exobacillus gen. nov

The genus Pseudalkalibacillus, created by the reclassification of specific deep-branching Alkalihalobacillus species, exhibits polyphyletic branching. The Genome Taxonomy Database (GTDB) also assigns Pseudalkalibacillus species into two families and three genera. To clarify the evolutionary relationships and classification of Pseudalkalibacillus species, we report detailed investigations using phylogenomic and molecular signature-based approaches. In phylogenomic trees, Pseudalkalibacillus species are distributed within two family-level lineages. One of these clades, containing the type species of Pseudalkalibacillus (viz. Pseudalkalibacillus decolorationis), represents the genus Pseudalkalibacillus, groups within the family Fictibacillaceae. Ten novel conserved signature indels (CSIs) identified in this study are specific for this clade, providing a robust means for the differentiation of the emended genus Pseudalkalibacillus. The remaining Pseudalkalibacillus species form a separate family-level clade, designated as f_HBI72195 in the GTDB. Within this clade, all species except Pseudalkalibacillus caeni form a robust clade designated as Pseudalkalibacillus clade -2 in our work and g_Anaerobacillus_A in the GTDB. We have also identified 15 novel CSIs specific to this clade. As the Pseudalkalibacillus clade -2 is distinct from Pseudalkalibacillus, we propose transferring species from this clade into a new genus, Guptibacillus gen. nov. The species P. caeni branches distinctly from other Pseudalkalibacillus species, and the GTDB considers it a novel genus (g_Bacillus_BR). Six newly identified CSIs are specific to this species, and we are proposing the transfer of this species into a new genus, Exobacillus gen. nov. Two additional identified CSIs are shared by members of the novel family-level taxon (f_HBI72195) comprising the proposed genera Guptibacillus and Exobacillus, for which we are proposing the name Guptibacillaceae fam. nov. Lastly, the results presented here also show that 'Pseudalkalibacillus hemicentroti' and 'Pseudalkalibacillus macyae' are later heterotypic synonyms of Guptibacillus hwajinpoensis. These changes, which reliably depict the evolutionary relationships among Pseudalkalibacillus species, should be helpful in future studies of these organisms.

Effect of temperature on circadian clock functioning of trees in the context of global warming

Plant survival in a warmer world requires the timely adjustment of biological processes to cyclical changes in the new environment. Circadian oscillators have been proposed to contribute to thermal adaptation and plasticity. However, the influence of temperature on circadian clock performance and its impact on plant behaviour in natural ecosystems are not well-understood. We combined bioinformatics, molecular biology and ecophysiology to investigate the effects of increasing temperatures on the functioning of the circadian clock in two closely related tree species from Patagonian forests that constitute examples of adaptation to different thermal environments based on their altitudinal profiles. Nothofagus pumilio, the species from colder environments, showed a major rearrangement of its transcriptome and reduced ability to maintain rhythmicity at high temperatures compared with Nothofagus obliqua, which inhabits warmer zones. In altitude-swap experiments, N. pumilio, but not N. obliqua, showed limited oscillator function in warmer zones of the forest, and reduced survival and growth. Our findings show that interspecific differences in the influence of temperature on circadian clock performance are associated with preferred thermal niches, and to thermal plasticity of seedlings in natural environments, highlighting the potential role of a resonating oscillator in ecological adaptation to a warming environment.

Genome-Wide Identification and Functional Analysis of the Norcoclaurine Synthase Gene Family in Aristolochia contorta

Aristolochia contorta Bunge has been widely used as traditional Chinese medicine materials. However, its utility faces a great challenge due to the presence of aristolochic acids (AAs), a class of benzylisoquinoline alkaloid (BIA) derivatives. The first step in BIA skeleton formation is catalysis by norcoclaurine synthase (NCS). To gain knowledge of BIA and AA biosynthesis in A. contorta, genome-wide characterizations of NCS genes were carried out. This resulted in the identification of 15 A. contorta NCSs, namely, AcNCS1-AcNCS15. The AcNCS1-AcNCS8 proteins contained one catalytic domain, whereas the AcNCS9-AcNCS15 proteins had two. Phylogenetic analysis shows that AcNCS proteins can be classified into two clades. Gene expression analysis shows that five AcNCSs, including AcNCS2, AcNCS4, AcNCS5, AcNCS14, and AcNCS15, exhibited relatively high expression in roots and flowers, where norcoclaurine accumulated. An enzyme catalytic activity assay shows that all five of the AcNCSs can catalyze norcoclaurine formation with AcNCS14 and AcNCS15, exhibiting higher catalytic efficiency. Precolumn derivatization analysis shows that the formed norcoclaurine included (S)- and (R)-norcoclaurine, with more (S)-configuration. The results provide useful information for further understanding BIA and AA biosynthesis in A. contorta and for AA elimination and bioactive compound improvement in AA-containing medicinal materials.

A New Structure Feature Introduced to Predict Protein-Protein Interaction Sites

Interaction between proteins often depends on the sequence features and structure features of proteins. Both of these features are helpful for machine learning methods to predict (protein-protein interaction) PPI sites. In this study, we introduced a new structure feature: concave-convex feature on the protein surface, which was computed by the structural data of proteins in Protein Data Bank database. And then, a prediction model combining protein sequence features and structure features was constructed, named SSPPI_Ensemble (Sequence and Structure geometric feature-based PPI site prediction). Three sequence features, i.e., PSSMs (Position-Specific Scoring Matrices), HMM (Hidden Markov Models) and raw protein sequence, were used. The Dictionary of Secondary Structure in Proteins and the concave-convex feature were used as the structure feature. Compared with the other prediction methods, our method has achieved better performance or showed the obvious advantages on the same test datasets, confirming the proposed concave-convex feature is useful in predicting PPI sites.

A novel endo-1,3-fucanase in glycoside hydrolase family 187 provided a biotechnological tool for preparing sulfated fucan oligosaccharides

Sulfated fucan, an important marine polysaccharide frequently presented in echinoderms and brown algae, has gained growing attention owing to its various biological activities. Fucanases are essential tools for degrading sulfated fucan to produce corresponding oligosaccharides. In this context, an endo-1,3-fucanase (Fun187Al) belonging to the GH187 family was successfully expressed in Escherichia coli. Fun187Al showed the highest activity at 30-40 °C and pH 7.5. It hydrolyzed sulfated fucan in a random endo-acting pattern, and displayed a substrate specificity different from the endo-1,3-fucanases of other glycoside hydrolase family. Analyses of ultra-performance liquid chromatography coupled with high-resolution mass spectrometry revealed that tetrasaccharide with two sulfate groups (Fuc4S2), Fuc4S3, and Fuc4S4 were respectively the major components in the end products of Fun187Al against sulfated fucans from Acaudina molpadioides, Thelonota ananas, and Holothuria tubulosa. The capability of Fun187Al to produce oligosaccharides with different degrees of polymerization and sulfation patterns demonstrated that it could be regarded as a favorable tool for establishing the structure-activity relationships of sulfated fucan and its oligosaccharides.

The coevolutionary landscape of drug resistance in epidermal growth factor receptor: A cancer perspective

Epidermal growth factor receptor (EGFR), the first receptor tyrosine kinase, plays a critical role in neoplastic metastasis, angiogenesis, tumor invasion, and apoptosis, making it a prime target for treating non-small cell lung cancer (NSCLC). Although tyrosine kinase inhibitors (TKIs) have shown high efficacy and promise for cancer patients, resistance to these drugs often develops within a year due to alterations. The present study investigates the compensatory alterations in EGFR to understand the evolutionary process behind drug resistance. Our findings reveal that coevolutionary alterations expand the drug-binding pocket; leading to reduced drug efficacy and suggested that such changes significantly influence the structural adaptation of the EGFR against these drugs. Analysis such as root mean square deviation (RMSD), root mean square fluctuation (RMSF), solvent accessible surface area (SASA), principal component analysis (PCA), and free energy landscape (FEL) demonstrated that structures of wild EGFR docked with gefitinib are more stable which suggests its susceptibility towards drug than coevolution dependent double mutant. The findings were supported by MM-GBSA binding affinity analysis. The insights from this study highlighted the evolution-induced structural changes which contributes to drug resistance in EGFR and may certainly aid in designing more effective drugs.

Predicting C- and S-linked Glycosylation sites from protein sequences using protein language models

Among various post-translational modifications (PTMs), predicting C-linked and S-linked glycosites is an essential task, yet experimental techniques such as Capillary Electrophoresis (CE), Enzymatic Deglycosylation, and Mass Spectrometry (MS) are expensive. Therefore, computational techniques are required to predict these glycosites. Here, different language model embeddings and sequential features were explored. Two separate feature selection methods: Recursive Feature Elimination (RFE) and Particle Swarm Optimization (PSO) were employed and utilized for identifying the optimal feature set. Cross-validation results were generated for choosing the final models. Three sampling strategies to handle imbalanced datasets were examined: Random undersampling, Synthetic Minority Over-sampling Technique (SMOTE) and Adaptive Synthetic Sampling Approach for Imbalanced Learning (ADASYN). In this study, two models: DeepCSEmbed-C and DeepCSEmbed-S are proposed for C-linked and S-linked glycosylation prediction respectively. DeepCSEmbed-C is a dual-branch deep learning model comprising a Feedforward Neural Network (FNN) branch and an Inception branch, coupled with a Random undersampling strategy. DeepCSEmbed-S is a Categorical Boosting (CAT) model with the SMOTE oversampling strategy. DeepCSEmbed-C outperformed available state-of-the-art (SOTA) methods, achieving 92.9% sensitivity, 95.1% F1-score and 90.6% MCC on the Independent dataset. Datasets and python scripts for training and testing the models are provided and made freely accessible at https://github.com/nafcoder/DeepCSEmbed.

The Insect Vector Diaphorina citri Exhibits a Lower Level of Fatty Acids upon Infection with the Phytopathogenic Bacterium 'Candidatus Liberibacter asiaticus'

Diaphorina citri (Hemiptera: Liviidae) is the main vector for the bacterium 'Candidatus Liberibacter asiaticus', which is associated with citrus greening, also known as Huanglongbing. D. citri transmits 'Ca. L. asiaticus' during its feeding on citrus phloem sap. Transmission occurs in a circulative, propagative, and persistent manner. 'Ca. L. asiaticus' has a small genome (1.2 Mb). Therefore, it acquires most of its nutrients and energetic nucleotides from its hosts. The objective of this study was to assess the effect of 'Ca. L. asiaticus' infection on the level of the free fatty acids in its vector. The fatty acids were extracted from adult D. citri using ethyl acetate, derivatized with boron trifluoride-methanol, and analyzed using gas chromatography-mass spectrometry. Nine fatty acids were identified in the D. citri extracts. Oleic acid was the most predominant fatty acid, followed by stearic and palmitic acid, whereas the rest of the fatty acids were present in low amounts. In general, the levels of the detected fatty acids in 'Ca. L. asiaticus'-infected D. citri were lower than those found in healthy psyllids. Our findings showed that the reduction of fatty acids in 'Ca. L. asiaticus'-infected psyllids resulted from the higher activity of β-oxidation to generate acetyl-coenzyme A, which causes more production of ATP. Our results indicated that 'Ca. L. asiaticus' may enhance the β-oxidation of fatty acids in its vector insect to fulfill its nutrient and energetic nucleotide requirements.

Complete Mitochondrial Genomes of Ancyromonads Provide Clues for the Gene Content and Genome Structures of Ancestral Mitochondria

Mitochondria of eukaryotic cells are direct descendants of an endosymbiotic bacterium related to Alphaproteobacteria. These organelles retain their own genomes, which are highly reduced and divergent when compared to those of their bacterial relatives. To better understand the trajectory of mitochondrial genome evolution from the last eukaryotic common ancestor (LECA) to extant species, mitochondrial genome sequences from phylogenetically diverse lineages of eukaryotes-particularly protists-are essential. For this reason, we focused on the mitochondrial genomes of Ancyromonadida, an independent and understudied protist lineage in the eukaryote tree of life. Here we report the mitochondrial genomes from three Ancyromonadida: Ancyromonas sigmoides, Nutomonas longa, and Fabomonas tropica. Our analyses reveal that these mitochondrial genomes are circularly mapping molecules with inverted repeats that carry genes. This inverted repeat structure has been observed in other mitochondrial genomes but is patchily distributed over the tree of eukaryotes. Ancyromonad mitochondrial genomes possess several protein-coding genes, which have not been detected from any other mitochondrial genomes of eukaryotes sequenced to date, thereby extending the known mitochondrial gene repertoire of ancestral eukaryotes, including LECA. These findings significantly expand our understanding of mitochondrial genome diversity across eukaryotes, shedding light on the early phases of mitochondrial genome evolution.

KMT2A degradation is observed in decitabine-responsive acute lymphoblastic leukemia cells

Hypermethylation of tumor suppressor genes is a hallmark of leukemia. The hypomethylating agent decitabine covalently binds, and degrades DNA (cytosine-5)-methyltransferase 1 (DNMT1). Structural similarities within DNA-binding domains of DNMT1, and the leukemic driver histone-lysine N-methyltransferase 2A (KMT2A) suggest that decitabine might also affect the latter. In acute lymphoblastic leukemia (ALL) cell lines, and xenograft models, we observed increased DNMT1, and KMT2A expression in response to decitabine-induced demethylation. Strikingly, KMT2A protein expression was diminished in all cell lines that experienced DNMT1 degradation. Moreover, only cells with reduced KMT2A protein levels showed biological effects following decitabine treatment. KMT2A wild-type, and rearranged cells were locked in G2 and G1 cell cycle phases, respectively, likely due to p27/p16 activation. Primary sample gene expression profiling confirmed different patterns between KMT2A wild-type, and translocated cells. This newly discovered decitabine mode of action via KMT2A degradation evokes anti-leukemic activity in adult ALL cells, and can act synergistically with menin inhibition. Following the successful clinical implementation of decitabine for acute myeloid leukemia, the drug should be considered a potential promising addition to the therapeutic portfolio for ALL as well.

ProFun-SOM: Protein Function Prediction for Specific Ontology Based on Multiple Sequence Alignment Reconstruction

Protein function prediction is crucial for understanding species evolution, including viral mutations. Gene ontology (GO) is a standardized representation framework for describing protein functions with annotated terms. Each ontology is a specific functional category containing multiple child ontologies, and the relationships of parent and child ontologies create a directed acyclic graph. Protein functions are categorized using GO, which divides them into three main groups: cellular component ontology, molecular function ontology, and biological process ontology. Therefore, the GO annotation of protein is a hierarchical multilabel classification problem. This hierarchical relationship introduces complexities such as mixed ontology problem, leading to performance bottlenecks in existing computational methods due to label dependency and data sparsity. To overcome bottleneck issues brought by mixed ontology problem, we propose ProFun-SOM, an innovative multilabel classifier that utilizes multiple sequence alignments (MSAs) to accurately annotate gene ontologies. ProFun-SOM enhances the initial MSAs through a reconstruction process and integrates them into a deep learning architecture. It then predicts annotations within the cellular component, molecular function, biological process, and mixed ontologies. Our evaluation results on three datasets (CAFA3, SwissProt, and NetGO2) demonstrate that ProFun-SOM surpasses state-of-the-art methods. This study confirmed that utilizing MSAs of proteins can effectively overcome the two main bottlenecks issues, label dependency and data sparsity, thereby alleviating the root problem, mixed ontology. A freely accessible web server is available at http://bliulab.net/ ProFun-SOM/.

Phytochemistry, Antibacterial and Antioxidant Activities of Grewia lasiocarpa E. Mey. Ex Harv. Fungal Endophytes: A Computational and Experimental Validation Study

The genus Grewia are well-known for their medicinal properties and are widely used in traditional remedies due to their rich phytochemical composition and potential health benefits. This study isolated and characterized five endophytic fungi from Grewia lasiocarpa E. Mey. Ex Harv. and evaluated their in vitro antibacterial and antioxidant activities. Five [Aspergillus fumigatus (MK243397.1), A. fumigatus (MK243451.1), Penicillium raistrickii (MK243492.1), P. spinulosum (MK243479.1), Meyerozyma guilliermondii (MK243634.1)] of the 22 isolated endophytic fungi had inhibitory activity (62.5-1000 µg/mL) against methicillin-resistant Staphylococcus aureus (MRSA). The antioxidant activities were 66.5% and 98.4% for 2,2-diphenyl-1-picrylhydrazyl (DPPH) and ferric ion reducing antioxidant power (FRAP), respectively. In silico evaluation of the phytochemicals of the extract (containing majorly n-hexadecanoic acid) was performed against penicillin-binding protein 2a (PBP2a) implicated in the broad clinical resistance of MRSA to conventional beta-lactams. Molecular docking and molecular dynamic simulation analyses revealed that the phytosterol constituents of the extract, especially dehydroergosterol (-46.28 kcal/mol), had good stability (4.35 Å) and compactness (35.08 Å) with PBP2a relative to the unbound PBP2a and amoxicillin-PBP2a complex during the 100 ns simulation period, reinforcing them as putative leads that may be developed as viable alternatives to beta-lactams against infections caused by MRSA. However, the prediction that dehydroergosterol lacks oral bioavailability with poor water solubility suggests that it could benefit from structural optimization for improved druggability. Hence, isolating and derivatizing dehydroergosterol for subsequent evaluation against PBP2a in vitro and in vivo is highly recommended.

Copepod phylogenomics supports Canuelloida as a valid order separate from Harpacticoida

Copepods are small crustaceans that are ubiquitous in aquatic environments. They are particularly abundant in marine and freshwater plankton, marine sediments, and as parasites or commensals of other aquatic organisms. Despite their abundance and importance, phylogenetic relationships among copepods are poorly resolved. The validity of higher-level taxa, including several orders, has continued to be controversial throughout the 21st century. This study has two main goals: first, to use phylogenomic data to assess relationships among the four major copepod orders: Calanoida, Cyclopoida, Harpacticoida, and Siphonostomatoida, which together include more than 98 % of copepod species diversity, and second, to test the validity of the recently proposed order Canuelloida. Towards these goals, we sampled 28 copepod transcriptomes and genomes spanning 20 families and 5 orders, including the first transcriptome of a representative of Canuelloida. We identified 2,527 single copy protein coding genes comprising 939,460 amino acid (aa) positions and 530,269 informative sites. All phylogenetic analyses support a monophyletic Podoplea (i.e., the superorder comprising all copepod orders except for Calanoida and Platycopioida) with Calanoida as its sister taxon. We find robust support across all methods for Canuelloida as a distinct order separate from the traditionally recognized Harpacticoida (Oligoarthra). Contrary to several recent studies of smaller sets of nuclear genes or mitochondrial genomes, we recover Cyclopoida and Harpacticoida as sister taxa and find that gene tree discordance analysis rejects the alternative topologies. Transcriptomic data are promising for resolving the backbone of the copepod phylogeny but collecting and sequencing the nearly 15,000 species of copepods, many of which are infrequently encountered and less than 1 mm in size, remains a major hurdle.

Transcriptional Reprogramming Deploys a Compartmentalized 'Timebomb' in Catharanthus roseus to Fend Off Chewing Herbivores

The evolutionary arms race between plants and insects has led to key adaptive innovations that drive diversification. Alkaloids are well-documented anti-herbivory compounds in plant chemical defences, but how these specialized metabolites are allocated to cope with both biotic and abiotic stresses concomitantly is largely unknown. To examine how plants prioritize their metabolic resources responding to herbivory and cold, we integrated dietary toxicity bioassay in insects with co-expression analysis, hierarchical clustering, promoter assay, and protein-protein interaction in plants. Catharanthus roseus, a medicinal plant known for its insecticidal property against chewing herbivores, produces two terpenoid indole alkaloid monomers, vindoline and catharanthine. Individually, they exhibited negligible toxicity against Manduca sexta, a chewing herbivore; their condensed product, anhydrovinblastine; however, was highly toxic. Such a unique insecticidal mode of action demonstrates that terpenoid indole alkaloid 'timebomb' can only be activated when the two spatially isolated monomeric precursors are dimerized by herbivory. Without initial selection pressure and apparent fitness costs, this adaptive chemical defence against herbivory is innovative and sustainable. The biosynthesis of insecticidal terpenoid indole alkaloids is induced by herbivory but suppressed by cold. Here, we identified a transcription factor, herbivore-induced vindoline-gene Expression (HIVE), that coordinates the production of terpenoid indole alkaloids in response to herbivory and cold stress. The HIVE-mediated transcriptional reprogramming allows this herbaceous perennial to allocate its metabolic resources for chemical defence at a normal temperature when herbivory pressure is high, but switches to cold tolerance under a cooler temperature when insect infestation is secondary.

SUPERMAGO: Protein Function Prediction Based on Transformer Embeddings

Recent technological advancements have enabled the experimental determination of amino acid sequences for numerous proteins. However, analyzing protein functions, which is essential for understanding their roles within cells, remains a challenging task due to the associated costs and time constraints. To address this challenge, various computational approaches have been proposed to aid in the categorization of protein functions, mainly utilizing amino acid sequences. In this study, we introduce SUPERMAGO, a method that leverages amino acid sequences to predict protein functions. Our approach employs Transformer architectures, pre-trained on protein data, to extract features from the sequences. We use multilayer perceptrons for classification and a stacking neural network to aggregate the predictions, which significantly enhances the performance of our method. We also present SUPERMAGO+, an ensemble of SUPERMAGO and DIAMOND, based on neural networks that assign different weights to each term, offering a novel weighting mechanism compared with existing methods in the literature. Additionally, we introduce SUPERMAGO+Web, a web server-compatible version of SUPERMAGO+ designed to operate with reduced computational resources. Both SUPERMAGO and SUPERMAGO+ consistently outperformed state-of-the-art approaches in our evaluations, establishing them as leading methods for this task when considering only amino acid sequence information.

Digestive System Development and Posterior Hox/Parahox Gene Expression During Larval Life and Metamorphosis of the Phoronid Phoronopsis harmeri

Phoronida is a small group of marine animals, most of which are characterized by a long larval period and complex metamorphosis. As a result of metamorphosis, their body changes so much that their true anterior and posterior ends are very close to each other, and the intestine becomes long and U-shaped. Using histology and electron microscopy, we have shown that the elongation and change in shape of the digestive tract that occurs during metamorphosis in Phoronopsis harmeri larvae is accompanied by the formation of new parts and changes in ultrastructure. At the same time, our in situ hybridization data suggest that the posterior markers Cdx and Post2 are expressed in posterior tissues at larval stages, during metamorphosis, and in juveniles, and that changes in their expression correlate with remodeling of the posterior parts of the digestive tract. Our data may shed light on the evolution of body patterning in animals undergoing complex metamorphosis.

The archaeal class Nitrososphaeria is a key component of the reproductive microbiome in sponges during gametogenesis

Sponge-associated microbes play fundamental roles in regulating their hosts' physiology, yet their contribution to sexual reproduction has been largely overlooked. Most studies have concentrated on the proportion of the microbiome transmitted from parents to offspring, providing little evidence of the putative microbial role during gametogenesis in sponges. Here, we use 16S rRNA gene analysis to assess whether the microbial composition of five gonochoristic sponge species differs between reproductive and non-reproductive individuals and correlate these changes with their gametogenic stages. In sponges with mature oocytes, reproductive status did not influence either beta or alpha microbial diversity. However, in two of the studied species, Geodia macandrewii and Petrosia ficiformis, which presented oocytes at the previtellogenic stage, significant microbial composition changes were detected between reproductive and non-reproductive individuals. These disparities were primarily driven by differentially abundant taxa affiliated with the Nitrososphaeria archaeal class in both species. We speculate that the previtellogenic stages are more energetically demanding, leading to microbial changes due to the phagocytosis of microbes to meet nutritional demands during this period. Supporting our hypothesis, we observed significant transcriptomic differences in G. macandrewii, mainly associated with the immune system, indicating potential changes in the sponge's recognition system. Overall, we provide new insights into the possible roles of sponge microbiomes during reproductive periods, potentially uncovering critical interactions that support reproductive success.

IMPORTANCE

Our research explores the fascinating relationship between sponges and their resident microbes, focusing specifically on how these microbes might influence sponge reproduction. Sponges are marine animals known for their complex and beneficial partnerships with various microbes. While previous studies have mainly looked at how these microbes are passed from parent sponges to their offspring, our study is among the first to examine how microbial communities change during the different stages of sponge reproduction. By analyzing the microbial composition in five sponge species, we discovered that significant changes occur in species with premature oocytes, suggesting that microbes may play a crucial role in providing the necessary nutrients during early egg development. This work not only enhances our understanding of sponge biology but also opens up new avenues for studying how microbes support the reproductive success of their hosts in marine environments.

Genome-wide identification and expression analysis of the TCP transcription factor family and its response to abiotic stress in rapeseed (Brassica napus L.)

The study used 80 BnTCP genes (Brassica napus TCP genes) in rapeseed, which were identified and designated with nomenclature based on their chromosomal locations. A systematic analysis encompassed the evolutionary relationships, classifications, gene structures, motif compositions, chromosome localization, and gene replication events within these BnTCP genes. These 80 BnTCP proteins were categorized into three subfamilies, with the PCF subfamily showing significant expansion during evolution. Segmental duplications were identified as a major driver of TCP family amplification. To comprehensively assess the evolutionary relationships of the TCP family across diverse plant species, nine comparative genomic maps were constructed, elucidating homologous genes between B. napus and representative monocotyledonous and dicotyledonous plants. In the final phase of the study, the gene expression response characteristics of 15 selected BnTCP genes across various biological processes and stress responses were examined. Noteworthy candidates, including BnTCP28, BnTCP30, and BnTCP76, were identified as potentially crucial in tissue development and environmental stress responses.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-025-04273-x.

Precise metabolic modeling in post-omics era: accomplishments and perspectives

Microbes have been extensively utilized for their sustainable and scalable properties in synthesizing desired bio-products. However, insufficient knowledge about intracellular metabolism has impeded further microbial applications. The genome-scale metabolic models (GEMs) play a pivotal role in facilitating a global understanding of cellular metabolic mechanisms. These models enable rational modification by exploring metabolic pathways and predicting potential targets in microorganisms, enabling precise cell regulation without experimental costs. Nonetheless, simplified GEM only considers genome information and network stoichiometry while neglecting other important bio-information, such as enzyme functions, thermodynamic properties, and kinetic parameters. Consequently, uncertainties persist particularly when predicting microbial behaviors in complex and fluctuant systems. The advent of the omics era with its massive quantification of genes, proteins, and metabolites under various conditions has led to the flourishing of multi-constrained models and updated algorithms with improved predicting power and broadened dimension. Meanwhile, machine learning (ML) has demonstrated exceptional analytical and predictive capacities when applied to training sets of biological big data. Incorporating the discriminant strength of ML with GEM facilitates mechanistic modeling efficiency and improves predictive accuracy. This paper provides an overview of research innovations in the GEM, including multi-constrained modeling, analytical approaches, and the latest applications of ML, which may contribute comprehensive knowledge toward genetic refinement, strain development, and yield enhancement for a broad range of biomolecules.

Characterization of antibiotic resistance genes and virulence factors in organic managed tea plantation soils in southwestern China by metagenomics

Sustainable organic management practices have gained significant attentions for its potential health and environmental benefits. However, the spread of antibiotic resistance genes (ARGs) and virulence factors (VFs) in soils, plants, and agricultural products has severely limited the development of organic managements on agriculture. At present, the distribution and assembly of ARGs and VFs in organic managed tea plantation systems remains largely unknown. Here, we used metagenomic analysis to explore soil microbial taxa, ARGs and VFs in 20 years of conventional managed (CM) and organic managed (OM) tea plantation soils. Results showed that total abundance of ARGs in OM was 16.9% (p < 0.001) higher than that in CM, and the increased ARGs were rpoB2, evgS, MuxB, TaeA, and efrA. As for VFs, OM significantly increased the abundance of adherence, stress protein and actin-based motility compared to CM. Moreover, OM increased the relative abundance of soil microbial taxa harboring ARGs and VFs, which were Streptomyces, Pseudomonas, and Terrabacter, compared to CM. Network analysis suggested that OM increased the positive interactions of microbial taxa-ARGs, microbial taxa-VFs and ARGs-VFs compared to CM. Impact of stochastic process on the assembly of soil microbial taxa, ARGs and VFs in OM was stronger than that in CM. Overall, these findings provide a basis for integrating ARGs, VFs and pathogen hosts to assess the ecological and health risks in long-term organic managed soils, and increased efforts need to be done in reducing ARGs, VFs and bacterial pathogens in fertilizers for organic managements on agriculture.

Redistribution of Ancestral Functions Underlies the Evolution of Venom Production in Marine Predatory Snails

Venom-secreting glands are highly specialized organs evolved throughout the animal kingdom to synthetize and secrete toxins for predation and defense. Venom is extensively studied for its toxin components and application potential; yet, how animals become venomous remains poorly understood. Venom systems therefore offer a unique opportunity to understand the molecular mechanisms underlying functional innovation. Here, we conducted a multispecies multi-tissue comparative transcriptomics analysis of 12 marine predatory gastropod species, including species with venom glands and species with homologous non-venom-producing glands, to examine how specialized functions evolve through gene expression changes. We found that while the venom gland specialized for the mass production of toxins, its homologous glands retained the ancestral digestive functions. The functional divergence and specialization of the venom gland were achieved through a redistribution of its ancestral digestive functions to other organs, specifically the esophagus. This entailed concerted expression changes and accelerated transcriptome evolution across the entire digestive system. The increase in venom gland secretory capacity was achieved through the modulation of an ancient secretory machinery, particularly genes involved in endoplasmic reticulum stress and unfolded protein response. This study shifts the focus from the well-explored evolution of toxins to the lesser-known evolution of the organ and mechanisms responsible for venom production. As such, it contributes to elucidating the molecular mechanisms underlying organ evolution at a fine evolutionary scale, highlighting the specific events that lead to functional divergence.

Wastewater Metavirome Diversity: Exploring Replicate Inconsistencies and Bioinformatic Tool Disparities

This study investigates viral composition in wastewater through metagenomic analysis, evaluating the performance of four bioinformatic tools-Genome Detective, CZ.ID, INSaFLU-TELEVIR and Trimmomatic + Kraken2-on samples collected from four sites in each of two wastewater treatment plants (WWTPs) in Lisbon, Portugal in April 2019. From each site, we collected and processed separately three replicates and one pool of nucleic acids extracted from the replicates. A total of 32 samples were processed using sequence-independent single-primer amplification (SISPA) and sequenced on an Illumina MiSeq platform. Across the 128 sample-tool combinations, viral read counts varied widely, from 3 to 288,464. There was a lack of consistency between replicates and their pools in terms of viral abundance and diversity, revealing the heterogeneity of the wastewater matrix and the variability in sequencing effort. There was also a difference between software tools highlighting the impact of tool selection on community profiling. A positive correlation between crAssphage and human pathogens was found, supporting crAssphage as a proxy for public health surveillance. A custom Python pipeline automated viral identification report processing, taxonomic assignments and diversity calculations, streamlining analysis and ensuring reproducibility. These findings emphasize the importance of sequencing depth, software tool selection and standardized pipelines in advancing wastewater-based epidemiology.

Retrospective analysis of clustered neuroinflammatory and neurodegenerative diseases in captive lions in the early 1970s

Rustrela virus (RusV), a recently discovered pathogen for domestic and wildlife animals, was identified as the causative agent of meningoencephalomyelitis in domestic cats and various zoo animals including lions. To analyze a past outbreak of increased mortality in lions and to reveal its possible etiological relationship with an RusV infection, this retrospective study re-evaluates 20 cases of lions originating from a zoo in Western Germany using archived formalin-fixed paraffin-embedded (FFPE) tissues. Animals with different neurologic signs were submitted for necropsy between December 1970 and April 1971. Eight lions (40%) suffered from non-suppurative meningoencephalomyelitis with RusV RNA and antigen detectable in the central nervous system (CNS). Twelve animals (60%) were negative for RusV. Eleven animals had an etiologically undetermined degenerative encephalomyelopathy characterized by dilated myelin sheaths, myelinophages, and spheroids. Eight of these 12 lions suffered from an erosive, lymphohistiocytic enteritis with nuclear inclusion bodies in enterocytes associated with parvoviral antigen and nucleic acid in the intestines, lymph nodes, and spleen, but not in the CNS. Five of the parvovirus-infected animals had a granulomatous inflammation in mesenteric lymph nodes that was also the only detectable lesion in one other lion. Acid-fast bacilli and Mycobacterium bovis DNA confirmed the diagnosis of tuberculosis. In summary, this study provides convincing evidence of the usefulness of long-term stored FFPE material for further investigations using immunohistochemistry, reverse transcription quantitative polymerase chain reaction, and in situ hybridization for resolving past disease outbreaks. It provides further insights into the epidemiology of infectious agents like RusV and parvovirus.

Phylogenetic analysis of pathogenic algae reveals lineage-dependent patterns of phagocytosis

Prototheca is an unusual genus of algae that lack chlorophyll and are obligate heterotrophs. To date, six paraphyletic pathogenic species have been identified in the context of vertebrates, principally in cattle-associated and human-associated infections. Together with the genus Auxenochlorella and Helicosporidium, rDNA sequence analysis currently favors grouping Prototheca under a clade known as the Auxenochlorella, Helicosporidium and Prototheca (AHP) lineage. Most studies so far have focused only on Prototheca bovis and Prototheca ciferrii as cattle-associated species and on Prototheca wickerhamii as a human-associated species. However, such studies remain limited in scope as they focus on only three species of Prototheca, which is not representative of the total number of species within the AHP lineage. In this study, we employ a phylogenetics approach based on five new organelle-encoded genes to delineate higher-level relationships within the AHP lineage. We use the resultant data to then guide a live-cell imaging-based investigation of aspects of the mammalian innate immune response to 11 Prototheca species and four Auxenochlorella species. Our data reveal varying patterns of phagocytosis dynamics that are both host cell type- and algal species-dependent. Together, these findings reveal the interaction between pathogen phylogeny and host immune response, revealing ways to identify new therapeutic targets in the future.

IMPORTANCE

Protothecosis is a rare algal infection caused by members of the genus Prototheca, which is comprised of unusual non-photosynthetic algae. Six pathogenic species have been identified so far that can cause infection in vertebrates, primarily cattle and humans. The phylogeny of this genus remains obscure and has been revised multiple times recently. However, this phylogeny has largely been based on only three species of Prototheca. To resolve this phylogenetic conundrum, here, we employ a phylogenetics approach based on five new organelle-encoded genes. We then use these data to perform live-cell imaging of a selected range of Prototheca species co-cultured with mammalian immune cells. Visualizing these phagocytic interactions in this context helps delineate both host cell-type- and species-dependent differences in phagocytic uptake, thereby providing novel insight into lineage-based differences.

Skin mucus proteomic provides insights into the alkaline tolerance of grass carp (Ctenopharyngodon idella)

Fish skin mucus is a dynamic mucosal layer located between the epidermis and the environment, serving as the first line of defense against external environmental stresses. Studying changes in fish skin mucus under high alkaline conditions can help identify potential biomarkers of alkaline tolerance, thereby providing a basis for developing non-invasive detection methods. In this study, grass carp (Ctenopharyngodon idella) were subjected to a 7-day stress test at three different alkalinity levels: low alkalinity (10 mmol/L sodium bicarbonate), medium alkalinity (30 mmol/L sodium bicarbonate), and high alkalinity (50 mmol/L sodium bicarbonate). Fish skin mucus was collected and analyzed using liquid chromatography-tandem mass spectrometry (LC-MS/MS) in data-independent acquisition (DIA) mode. A total of 9656 proteins were identified across 12 samples from four groups. The low and medium alkalinity groups shared more common functional enrichments, while the high alkalinity group showed significant enrichments in pathways related to oxygen binding, the immune system, and myocarditis. More nodes and pairwise interactions were founded in the high alkalinity group. Multiple proteins related to osmoregulation, including Prostaglandin G/H Synthase 1 (PTGS1) and Group XV Phospholipase A2 (PLA2G15), are highly expressed in the high-alkalinity treatment group. These results suggest that high-alkalinity stress-induced changes in the skin mucus of grass carp reflect the continuous activation of osmoregulatory mechanisms, and the high expression of PTGS1 and PLA2G15 in the skin mucus may serve as signatures of the survival status of grass carp in alkaline conditions.

Comparative analysis of STP6 and STP10 unravels molecular selectivity in sugar transport proteins

The distribution of sugars is crucial for plant energy, signaling, and defense mechanisms. Sugar Transport Proteins (STPs) are Sugar Porters (SPs) that mediate proton-driven cellular uptake of glucose. Some STPs also transport fructose, while others remain highly selective for only glucose. What determines this selectivity, allowing STPs to distinguish between compounds with highly similar chemical composition, remains unknown. Here, we present the structure of Arabidopsis thaliana STP6 in an inward-occluded conformational state with glucose bound and demonstrate its role as both a glucose and fructose transporter. We perform a comparative analysis of STP6 with the glucose-selective STP10 using in vivo and in vitro systems, demonstrating how different experimental setups strongly influence kinetic transport properties. We analyze the properties of the monosaccharide binding site and show that the position of a single methyl group in the binding site is sufficient to shuffle glucose and fructose specificity, providing detailed insights into the fine-tuned dynamics of affinity-induced specificity for sugar uptake. Altogether, these findings enhance our understanding of sugar selectivity in STPs and more broadly SP proteins.

Viruses in human-impacted estuarine ecotones: Distribution, metabolic potential, and environmental risks

Estuaries, as dynamic ecological interfaces between marine and terrestrial systems, are characterized by high productivity and intricate microbial communities. Viruses exert critical regulatory effects on microbial processes, influencing ecological functions and contributing to environmental dynamics in estuarine ecosystems. Despite their significance, the diversity and ecological roles of estuarine viruses remain insufficiently understood. This study explored the viral biogeographic patterns, metabolic potential, and influencing factors in 30 subtropical estuaries in China. Few estuarine viruses (< 22 %) exhibited homology with known viruses, and the low overlap of virus clusters with other environments highlights their novelty and habitat specificity. Mantel tests and random forest analysis identified salinity, temperature, nutrients, and pollutants as key factors influencing viral composition and functional profiles. In addition, correlation analysis between virus and host confirmed significant virus-host interactions, while functional analyses highlighted the role of environmental conditions and horizontal gene transfer in shaping auxiliary metabolic genes linked to elemental biogeochemical cycles, particularly phosphorus, sulfur, and nitrogen. The detection of antibiotic resistance genes (ARGs) and virulence factors (VFs) within viral genomes underscores the role of viruses as reservoirs of ARGs and VFs in these ecosystems. These results demonstrate the profound influence of abiotic and host factors on viral community structures in subtropical estuarine ecotones and underscore the ecological significance of metabolic genes in biogeochemical cycling. By clarifying these interactions, this study advances the understanding of viral contributions to ecosystem functioning and biogeochemical dynamics in estuarine environments.

Complete genome sequence of Psychrobacter sp. GTC18467 isolated from snake in Japan

The genus Psychrobacter belongs to the family Moraxellaceae within the class Gammaproteobacteria. Here, we report the complete genome sequence of a new Psychrobacter species obtained from snake skin in Japan. The genome comprised a circular chromosome with a length of 2,642,444 bp and three circular plasmids.

Adding Fruit Fermentation Liquid Improves the Efficiency of the Black Soldier Fly in Converting Chicken Manure and Reshapes the Structure of Its Intestinal Microbial Community

This study evaluated how fruit fermentation liquid (FFL) enhances the conversion of chicken manure by black soldier fly larvae (BSFL) and modulates their gut microbiota. Three groups were tested: control (A: 300 g manure + 50 g water), low-dose FFL (B: 300 g manure + 25 g FFL + 25 g water), and high-dose FFL (C: 300 g manure + 50 g FFL). The results show that the dry matter conversion rate significantly increased by 9.5% (p < 0.05), while the feed-to-larvae ratio was reduced by 1.02 (p < 0.01) in group C. NH3 emissions in group C decreased by 24.48 mg·kg-1·DM (dry matter substrate) day-1 (24.48 mg per kilogram of dry matter substrate per day) (p < 0.01), with suppressed H2S release. Gut microbiota analysis revealed that FFL reduced the abundance of Proteobacteria (6.07% decrease in group C) while enriching Actinobacteriota (4.68% increase) and beneficial genera (Corynebacterium, Gallicola). Substrate microbial diversity in group C improved, with Proteobacteria and Firmicutes increasing by 11.07% and 4.83%, respectively, and pathogenic Sphingobacteriaceae declining by 21.16% by day 7. FFL likely introduced organic acids and nutrients, enhancing larval digestion and nutrient absorption while inhibiting the production of harmful gases. These findings demonstrate that FFL optimizes BSFL-driven waste conversion efficiency through modulation of the microbiota, offering a sustainable strategy for organic waste management and contributing to circular agricultural systems.

Chromosome-level genomes of Arctic and Antarctic mosses: Aulacomnium turgidum and Polytrichastrum alpinum

Bryophytes play a crucial role in the ecosystems of polar regions. These simple plants are among the predominant vegetation types in both Arctic and Antarctic landscapes, where they contribute significantly to biodiversity and ecological stability. Here, we report the chromosome-level genomes of two polar moss species, the Arctic Aulacomnium turgidum and Antarctic Polytrichastrum alpinum. Utilizing a combination of Illumina short reads, Nanopore long reads, and Hi-C data, we assembled genomes of 277.84 Mb for A. turgidum and 498.33 Mb for P. alpinum, respectively. These assemblies were anchored to 11 chromosomes for A. turgidum and 8 chromosomes for P. alpinum. Both species exhibited a sex chromosome with distinct genomic characteristics. Gene annotations revealed 25,999 protein-coding genes in A. turgidum and 28,070 in P. alpinum. The high completeness of the gene space was validated via BUSCO, achieving impressive scores of 98.2% and 98.0%. These high-quality genomes provide critical resources for studying the adaptive evolution and stress tolerance mechanisms of mosses in extreme polar environments.

Studying sleep orthologs in Epsilonproteobacteria through an evolutionary lens: investigating sleep mysteries through phylogenomics

The current study employed phylogenomic methods to examine sleep-related genes' evolutionary role and significance in Sulfurimonas paralvinellae of the Epsilonproteobacteria class. This has facilitated the identification of conserved sleep orthologs, including DnaK (Hsp70), serine hydroxymethyltransferase (SHMT), and potassium channel family proteins, exhibiting sequence similarities ranging from 39.13% to 61.45%. These findings align with prior research indicating that chaperones and ion channels are conserved during sleep. This was demonstrated by the observation that proteins with fewer domains exhibited more significant conservation than others, such as adenylate kinase (AK). Distinct adaptations in bifunctional protein-serine/threonine kinases and phosphatases were linked to S. paralvinellae, an extremophilic organism adapted to high-pressure and/or high-temperature conditions, indicating functional divergence influenced by the organism's environment. The Gene Ontology study results indicated catalytic activity, potassium channel function, and cellular processes, underscoring the significance of ion channels in regulating the sleep-wake cycle. Furthermore, the categories not recognized as particularly significant for the over-represented genes encompassed metabolic and signal transduction categories, suggesting enhanced functional flexibility within this protein subfamily. The findings emphasize that orthologous interactions are complex and influenced by subfunctionalization and neofunctionalization of ecology and evolution. These findings enhance the existing understanding of the evolution of sleep-related genes and their association with metabolic and environmental changes, providing a foundation for subsequent experimental investigations and cross-taxonomic comparisons.

Analysis of the Changes in Diversity of Culturable Bacteria in Different Niches of Mulberry Fields and Assessment of Their Plant Growth-Promoting Potential

Microorganisms play a crucial role in agricultural systems. The use of plant growth-promoting bacteria (PGPB) to enhance agricultural production in a sustainable and environmentally friendly manner has been widely recognized as a key technology for the future. In this study, we analyzed the diversity changes of bacteria in different ecological niches of mulberry fields based on culture-dependent methods, and we further evaluated their antibacterial and plant growth-promoting (PGP) activities. A total of 346 cultivable bacteria belonging to 30 genera were isolated from mulberry rhizosphere soil, mulberry plants and silkworm intestines, among which the dominant genera were Bacillus, Pseudomonas, and Enterobacter. The bacterial communities in the mulberry rhizosphere soil were more diverse than those in the mulberry endophytes and in the silkworm intestines. The antibacterial test showed that 30 bacteria exhibited antibacterial activity against the plant pathogen Ralstonia solanacearum. PGP trait assays indicated that 58 bacteria were capable of nitrogen fixation, phosphate solubilization, potassium release and siderophore production simultaneously. The screened functional strains promoted the growth of mulberry saplings. The results of this study highlight new findings on the application of silkworm intestinal bacteria in PGPB.

Robust phylogenetic profile clustering for Saccharomyces cerevisiae proteins

Background

Genes are continually formed and lost as a genome evolves. However, new genes may tend to appear during specific evolutionary epochs rather than others, or disappear together in a more recent organismal clade. Methods to identify gene origination might simply use the last common ancestor to contain an ortholog as the putative gene origination point, or use a heuristic threshold that allows for a certain amount of missing orthologs in the cohort of species examined. Here, to avoid such issues, an alternative approach based on the clustering of phylogenetic profiles is applied, and the results are examined for any evidence of epochal trends in gene origination, and associated trends in specific sequence traits or functional associations.

Methods

A phylogenetic profile is simply an array indicating the presence or absence of a gene in a list of species. These profiles were compared and clustered to discern patterns in gene occurrences across >800 fungal species, centering the analysis on the budding yeast Saccharomyces cerevisiae.

Results

Clear epochs of gene origination were observed linked to the last common ancestors of Saccharomycetaceae and Saccharomycetes, and also to Fungi and earlier ancestors. These trends are independent of the proteome and genome-assembly quality of the underlying data. Clusters of phylogenetic profiles demonstrated some significant functional associations, such as to cellular spore formation and chromosome segregation in genes originating in Saccharomycetaceae. The phylogenetic profile clustering analysis enabled detection of parameter-independent trends in intrinsic disorder, prion-like composition and gene uniqueness as a function of epochal gene age. For example: new proteins with prion-like domains have arisen at a similar rate for most of fungal evolution centred on S. cerevisiae; the most proteins with mild intrinsic disorder have appeared during the early Saccharomycetaceae epoch rather than more recently, and very recently formed genes are the least likely to be single-copy (i.e., 'unique' yeast proteins).

Conclusions

For individual proteins, the profile cluster data generated here are useful for investigating experimental hypotheses, since they provide evidence for functional linkages that have yet to be discerned.

An Amazonian hidden gem: a new metallic-colored species of Ranitomeya (Anura, Dendrobatidae) from Juruá River basin forests, Amazonas state, Brazil

The genus Ranitomeya has 16 known species, and the last of them was described 13 years ago. The forests of the Juruá River basin are known for their enormous vertebrate diversity, despite being one of the least sampled regions in the entire Amazonia. Our recent expeditions to the region resulted in the discovery of a Ranitomeya species with blue-green dorsal stripes and quite peculiar behavior. Here, it is described as a new species using morphological, morphometric, advertisement call, natural history, and genetic data. This new species is strongly nested within the R.vanzolinii clade, with interspecific p-distances ranging from 2.94 to 3.91%, and it was confirmed in all the delimitation methods used. It differs from its closest relatives mainly by (i) its size (male SVL 15.4-17.7 mm, n = 8; female SVL 17.3-18.5 mm, n = 5), (ii) its unique color pattern that is metallic pale yellowish green to metallic pale turquoise-green dorsal stripes pattern, limbs metallic chrome with dark carmine spotting), (iii) presence of a conspicuous sulfur yellow spot on the dorsal surface of the thighs, (iv) tadpoles with posterior tooth rows P1 > P2 > P3 in all stages, head translucent brownish and lack of emarginate lateral papillae, and (v) its advertisement call (composed of 21-45 notes, call duration of 647-1,424 ms, note rate of 28-36 notes/s and dominant frequency of 4,996-6,288 Hz).

GTPLM-GO: Enhancing Protein Function Prediction Through Dual-Branch Graph Transformer and Protein Language Model Fusing Sequence and Local-Global PPI Information

Currently, protein-protein interaction (PPI) networks have become an essential data source for protein function prediction. However, methods utilizing graph neural networks (GNNs) face significant challenges in modeling PPI networks. A primary issue is over-smoothing, which occurs when multiple GNN layers are stacked to capture global information. This architectural limitation inherently impairs the integration of local and global information within PPI networks, thereby limiting the accuracy of protein function prediction. To effectively utilize information within PPI networks, we propose GTPLM-GO, a protein function prediction method based on a dual-branch Graph Transformer and protein language model. The dual-branch Graph Transformer achieves the collaborative modeling of local and global information in PPI networks through two branches: a graph neural network and a linear attention-based Transformer encoder. GTPLM-GO integrates local-global PPI information with the functional semantic encoding constructed by the protein language model, overcoming the issue of inadequate information extraction in existing methods. Experimental results demonstrate that GTPLM-GO outperforms advanced network-based and sequence-based methods on PPI network datasets of varying scales.

Multiscale and global-local U-Net for protein-protein interaction site prediction

Precise prediction of protein-protein interaction sites (PPIS) is fundamental to deciphering cellular mechanisms and accelerating therapeutic discovery. Despite significant advancements in computational approaches, current methods frequently fail to integrate multiscale features that simultaneously capture global context and local interactions. We present Multiscale and Global-Local U-Net for Protein-Protein Interaction Site Prediction (MGU-PPIS), a novel architecture designed to address this critical limitation. Our model leverages a U-Net framework with implemented multi-level pooling to extract comprehensive multiscale features. Within each scale, we synergistically combine Transformer networks, Graph Convolutional Networks (GCNs), and Graph Attention Networks (GATs) to simultaneously capture global patterns and local structural motifs. We implement Laplacian positional encoding to effectively represent global protein structural characteristics. In our framework, proteins are conceptualized as graph structures where individual residues function as nodes and their spatial relationships define edges. The model processes information through an innovative two-stage U-Net architecture, where output features from the initial stage serve as refined inputs for the subsequent stage. This dual-stage design, coupled with our graph-based representation, enables MGU-PPIS to extract a rich spectrum of multiscale features encompassing both global context and local interactions at each scale. Comprehensive experimental validation demonstrates that MGU-PPIS significantly outperforms state-of-the-art methods in predictive accuracy. Beyond introducing a novel computational strategy for PPIS prediction, our work establishes a foundation for advances in protein functional analysis and structure-based drug design.

The First Genome-Wide Survey Analysis of the Tibetan Plateau Tetraploid Schizothorax curvilabiatus Reveals Its Microsatellite Characteristics and Phylogenetic Relationships

Background/Objectives: Schizothorax curvilabiatus, a typical highland polyploid species within the subfamily Schizothoracinae, holds economic value and ecological research significance. Currently, there are no related genomic studies. To obtain its genetic information and lay the foundation for subsequent whole-genome map construction, this study conducted a genome survey analysis, preliminary genome assembly, microsatellite identification, repeat sequence annotation, mitochondrial genome characterization, and phylogenetic relationship research. Methods: DNA was sequenced on a DNBSEQ-T7 platform to obtain paired-end genomic data. The genome was analyzed using GCE, and the draft genome was assembled with SOAPdenovo. Microsatellites were identified using MISA, and the mitochondrial genome was assembled with NOVOPlasty. Genome features were analyzed, and phylogenetic trees were constructed using PhyloSuite and MEGA. Results: The genome size was estimated at 2.53 Gb, with a heterozygosity of 6.55% and 47.66% repeat sequences. A 1.324 Gb preliminary genome draft was obtained, with repeat sequences comprising 47.17%, the majority being DNA transposons (24.64%). Dinucleotide repeats were most abundant (46.91%), followed by mononucleotide repeats (38.31%), with A/T and AC/GT being the most frequent. A complete mitochondrial genome of 16,589 bp was assembled, and a 939 bp D-loop was annotated. Phylogenetic relationships among genera in the Schizothoracinae subfamily were also clarified. Conclusions: This study provides the latest molecular data for analysis of the S. curvilabiatus genome and its related populations, and for the first time offers genomic resources for research on genomic adaptive evolution and polyploidization in high-altitude environments.

The Role of Grass in the Epidemiology of a Phytoplasma Disease Affecting Trees and Other Plants of the Sabana de Bogotá, Colombia

'Candidatus Phytoplasma asteris' and 'Candidatus Phytoplasma fraxini' infect at least nine species of trees, potato, and strawberry crops in the Sabana de Bogotá. We analyzed the epidemiological implications of the presence of these phytoplasma species in trees, grass, and weeds in the Sabana de Bogotá, as well as in Cicadellidae insects. Both phytoplasmas were detected in symptomatic trees of Salix humboldtiana and Sambucus nigra, and in asymptomatic grass Cenchrus clandestinus and 13 weed species. 'Ca. P. asteris' and 'Ca. P. fraxini' sequences of the 16S rRNA gene obtained from positive samples were compared with sequences from plants and insects of the Sabana de Bogotá. In each case, the sequence identity of this gene suggested that closely related strains of each species circulate in the environment, infecting plants of many families and several Cicadellidae species. Ce. clandestinus plays a key role in the epidemiology of the disease since it is a host of both phytoplasmas, of two known insect vectors, and of other Cicadellidae. Ce. clandestinus is extensively distributed in urban and rural areas. Since management efforts are hampered by the practical impossibility to remove Ce. clandestinus from the ecosystem, different strategies are needed to manage this disease.

Machine learning assessment of zoonotic potential in avian influenza viruses using PB2 segment

BACKGROUND

Influenza A virus (IAV) is a major global health threat, causing seasonal epidemics and occasional pandemics. Particularly, Influenza A viruses from avian species pose significant zoonotic threats, with PB2 adaptation serving as a critical first step in cross-species transmission. A comprehensive risk assessment framework based on PB2 sequences is necessary, which should encompass detailed analyses of specific residues and mutations while maintaining sufficient generality for application to non-PB2 segments.

RESULTS

In this study, we developed two complementary approaches: a regression-based model for accurately distinguishing among risk groups, and a SHAP-based risk assessment model for more meaningful risk analyses. For the regression-based risk models, we compared various methodologies, including tree ensemble methods, conventional regression models, and deep learning architectures. The optimized regression model, combined with SHAP value analysis, identified and ranked individual residues contributing to zoonotic potential. The SHAP-based risk model enabled intra-class analyses within the zoonotic risk assessment framework and quantified risk yields from specific mutations.

CONCLUSION

Experimental analyses demonstrated that the Random Forest regression model outperformed other models in most cases, and we validated the target value settings for risk regression through ablation studies. Our SHAP-based analysis identified key residues (271A, 627K, 591R, 588A, 292I, 684S, 684A, 81M, 199S, and 368Q) and mutations (T271A, Q368R/K, E627K, Q591R, A588T/I/V, and I292V/T) critical for zoonotic risk assessment. Using the SHAP-based risk assessment model, we found that influenza A viruses from Phasianidae showed elevated zoonotic risk scores compared to those from other avian species. Additionally, mutations I292V/T, Q368R, A588T/I, V598A/I/T, and E/V627K were identified as significant mutations in the Phasianidae. These PB2-focused quantitative methods provide a robust and generalizable framework for both rapid screening of avians' zoonotic potential and analytical quantification of risks associated with specific residues or mutations.

HPOseq: a deep ensemble model for predicting the protein-phenotype relationships based on protein sequences

BACKGROUND

Understanding the relationships between proteins and specific disease phenotypes contributes to the early detection of diseases and advances the development of personalized medicine. The acquisition of a large amount of proteomics data has facilitated this process. To improve discovery efficiency and reduce the time and financial costs associated with biological experiments, various computational methods have yielded promising results. However, the lack of rich and reliable protein-related information still presents challenges in this process.

RESULTS

In this paper, we propose an ensemble prediction model, named HPOseq, which predicts human protein-phenotype relationships based only on sequence information. HPOseq establishes two base models to achieve objectives. One directly extracts internal information from amino acid sequences as protein features to predict the associated phenotypes. The other builds a protein-protein network based on sequence similarity, extracting information between proteins for phenotype prediction. Ultimately, an ensemble module is employed to integrate the predictions from both base models, resulting in the final prediction.

CONCLUSION

The results of 5-fold cross-validation reveal that HPOseq outperforms seven baseline methods for predicting protein-phenotype relationships. Moreover, we conduct case studies from the points of phenotype annotation and protein analysis to verify the practical significance of HPOseq.

Mesoporous silica and vegetal extracts combined as sustainable stone heritage protection against biodeterioration

Since biodeterioration is considered one of the main issues related to the conservation of cultural heritage stone materials, an investigation was conducted into preventive sustainable antimicrobial alternatives to protect the stone surfaces. The study focuses on using MCM-41 mesoporous silica particles and vegetal extracts: the mesoporous materials act as nanocontainers encapsulating the extracts, which instead serve as green antimicrobic compounds to inhibit microbiological proliferation. In this way, the antimicrobial features of the extracts are sustained for a more extended period, reducing the evaporation rate and diminishing the quantity required; the amount necessary to achieve the minimum inhibitory concentration was reduced due to the decrease in evaporation. Moreover, since the MCM-41 can host a higher quantity of product than is necessary to exert the antimicrobial effect, the duration of activity is further prolonged, releasing the extracts over time. Specifically, the mesoporous particles were impregnated with the vegetal extract of limonene and the essential oils of thyme and oregano. In vitro microbiological tests were conducted on two fungi (i.e., Aspergillus tubingensis and Penicillium chrysogenum), taken as model microorganisms from real-case scenarios. A combination of mesoporous silica and vegetal extracts was employed to develop a protective coating for stone surfaces, and tests were conducted on marble mock-ups. The promising synergic results show that this system could be of interest for preventing microbiological growth over stone surfaces, avoiding a visible aesthetic impact, being non-toxic for the environment or the operator, and preventing the extract from evaporating but holding it for a controlled release. KEY POINTS: • Green antimicrobial system using porous silica as nanocontainer for plant extracts • Encapsulated vegetal extracts to inhibit microbial growth on stone surfaces • Stable and efficient coating against fungal species in vitro and on marble mock-up.

Interspecies relationships of natural amoebae and bacteria with C. elegans create environments propitious for multigenerational diapause

The molecular and physical communication within the microscopic world underpins the entire web of life as we know it. However, how organisms, such as bacteria, amoebae, and nematodes-all ubiquitous-interact to sustain their ecological niches, particularly how their associations generate and influence behavior, remains largely unknown. In this study, we developed a framework to examine long-term interactions between microbes and animals. From soil samples collected in a temperate, semi-arid climate, we isolated culturable bacterial genera, including Comamonas, Stenotrophomonas, Chryseobacterium, and Rhodococcus, as well as the amoeba, Tetramitus. This microbial ensemble was fed to the nematode C. elegans in experiments spanning over 20 nematode generations to assess developmental rate, dauer entry, fertility, and feeding behavior. Our findings reveal that microbes and nematodes create a stable environment where no species are exhausted, and where nematodes enter diapause after several generations. We have termed this phenomenon dauer formation on naturally derived ensembles (DaFNE). DaFNE occurs across a range of optimal temperatures, from 15°C to 25°C, and is dependent on the nematode's pheromone biosynthesis pathway. The phenomenon intensifies with each passing generation, exhibiting both strong intergenerational and transgenerational effects. Moreover, the RNA interference (RNAi) pathway-both systemic and cell-autonomous-is essential for initiating DaFNE, while heritable RNAi effectors are required for its transgenerational effects. These findings indicate that RNA-mediated communication plays a critical role in bacterially induced behaviors in natural environments.IMPORTANCEMicroscopic nematodes are the most abundant multicellular animals on Earth, which implies they have evolved highly successful relationships with their associated microbiota. However, little is known about how nematode behavior is influenced within complex ecosystems where multiple organisms interact. In this study, we used four bacteria and an amoeba from a natural ecosystem to explore behavioral responses in the nematode Caenorhabditis elegans over an 8 week period. The most striking finding was the nematodes' commitment to a form of hibernation known as diapause. We have termed this phenomenon dauer formation on naturally derived ensembles (DaFNE). Our results suggest that nematodes in nature may frequently enter hibernation as a result of communication with their microbial partners. DaFNE requires the production of nematode pheromones, as well as the RNA interference pathway, indicating that the RNA communication between nematodes and their microbiota may play a critical role. Interestingly, at higher temperatures, fewer animals are needed to trigger DaFNE, suggesting that a mild increase in temperature may promote diapause in natural environments without causing stress to the animals.

FastAAI: efficient estimation of genome average amino acid identity and phylum-level relationships using tetramers of universal proteins

Estimation of whole-genome relatedness and taxonomic identification are two important bioinformatics tasks in describing environmental or clinical microbiomes. The genome-aggregate Average Nucleotide Identity is routinely used to derive the relatedness of closely related (species level) microbial and viral genomes, but it is not appropriate for more divergent genomes. Average Amino-acid Identity (AAI) can be used in the latter cases, but no current AAI implementation can efficiently compare thousands of genomes. Here we present FastAAI, a tool that estimates whole-genome pairwise relatedness using shared tetramers of universal proteins in a matter of microseconds, providing a speedup of up to 5 orders of magnitude when compared with current methods for calculating AAI or alternative whole-genome metrics. Further, FastAAI resolves distantly related genomes related at the phylum level with comparable accuracy to the phylogeny of ribosomal RNA genes, substantially improving on a known limitation of current AAI implementations. Our analysis of the resulting AAI matrices also indicated that bacterial lineages predominantly evolve gradually, rather than showing bursts of diversification, and that AAI thresholds to define classes, orders, and families are generally elusive. Therefore, FastAAI uniquely expands the toolbox for microbiome analysis and allows it to scale to millions of genomes.

Novel Lectins from Bauhinia with Differential N-Glycan Binding Profiles

The specific interaction of lectins with carbohydrates and glycoconjugates grants these proteins a distinct ability to decode the glycocode. Essential for various biological processes in all organisms, this carbohydrate-binding activity also establishes lectins as valuable tools in fields such as glycomics, medicine, and biotechnology. Considering that the discovery of novel lectins with unique binding profiles is particularly relevant, this study investigated the binding specificity of two lectins extracted from Bauhinia seeds toward simple sugars, N-glycans and O-glycans. The combination of agglutination-inhibition assays and glycan arrays revealed subtle differences in the binding of the lectins to galactosides and glycans containing specific motifs, such as LewisX, LacdiNAc, and fucosylated LacdiNAc. Despite slight differences in carbohydrate-binding patterns, both lectins showed similar results in toxicity assays using Artemia salina nauplii and cytotoxicity assays on cancer cell lines, with neither lectin exhibiting significant toxicity. Additionally, both lectins demonstrated low cytotoxicity toward HeLa (cervical adenocarcinoma), HT1080 (fibrosarcoma), and NHDF (normal fibroblasts), even at concentrations up to 125 μg/mL. Analysis of the partial amino acid sequences of these lectins revealed conserved residues compared to other lectins of the genus, as well as secondary structure conformations similar to those of other legume lectins. This research represents a significant advancement in the understanding of lectins from the genus Bauhinia, and future structural studies could further elucidate the interactions of these proteins with their ligands, providing fundamental insights into their biological functions and paving the way toward potential applications.

ProAttUnet: Advancing protein secondary structure prediction with deep learning via U-Net dual-pathway feature fusion and ESM2 pretrained protein language model

Protein secondary structure prediction remains a pivotal concern within the domain of bioinformatics. In this innovative research, we introduce a novel methodology to further enhance a protein prediction model grounded in single sequences. Our key contribution lies in integrating the state-of-the-art (SOTA) model ESM2, which hails from the field of universal protein language models. By leveraging ESM2, we are able to acquire residual embeddings and contact maps for the protein sequences under study. Regarding the model architecture, we employ a unique dual-way U-Net framework for effective feature fusion. This framework is complemented by the integration of a cross-attention mechanism, enabling the model to capture more comprehensive context information. Furthermore, In accordance with the distinctive characteristics of protein sequences, we incorporate a so-called GCU_SE module into both the encoder and the decoder components of the model. These innovative enhancements enable the ProAttUnet model to outperform the benchmark model SPOT-1D-Single by 1.6%, 3.5%, 1.0%, 4.6%, and 7.2% for ss3, and by 5.5%, 7.8%, 4.1%, 8.1%, and 10.1% for ss8 across five test sets (SPOT-2016, SPOT-2016-HQ, SPOT-2018, SPOT-2018-HQ and TEST2018, respectively). This significant improvement vividly demonstrates the effectiveness and novelty of our proposed model.

Chromosome-level genome assembly and annotation of Chinese herring (Ilisha elongata)

The Chinese herring (Ilisha elongata) is a commercially and scientifically significant fish species. In this study, we conducted high-precision whole-genome sequencing using two high-throughput platforms: second-generation MGI and third-generation PacBio. Hi-C technology assisted in assembling the contig sequences onto 24 chromosomes, resulting in a high-quality chromosome-level genome map with excellent continuity and coverage. The completed genome size was approximately 815 Mb, with a contig N50 of 4.82 Mb, scaffold N50 of 32.61 Mb, and a chromosome mounting rate of 95.32%. SNP and InDel purity rates were 0.003% and 0.012%, respectively, and the genome assembly completeness was 96.68%, assessed by BUSCO. Repetitive sequences were annotated via ab initio and homology predictions, identifying 295.7 Mb of repetitive sequences, constituting 35.08% of the genome. A total of 26,381 protein-coding genes were predicted, with 24,596 functionally annotated.

Identification, growth characteristics, and genomic potential analysis of indole-3-acetic acid producing strain D1 in the rhizosphere of ancient tea forests

Chryseobacterium is one of the important beneficial microorganisms groups for protecting plant health. It has the functions of promoting host plant growth, stress resistance, inducing systemic resistance, and resisting pathogens, playing an important role in reducing soil biological barriers, and has broad application prospects. Therefore, screening for IAA producing Chryseobacterium and quickly understanding its genomic potential is of great significance in agricultural production. The unique ecological environment of wild ancient tea forests nurtures rhizosphere microbial resources with unique properties. This study identified the high-yielding indole-3-acetic acid (IAA) producing strain D1 from the rhizosphere of ancient tea forests as a new species of the Chryseobacterium genus, which is closely related to Chryseobacterium aureum and is recommended to be named Chryseobacterium tea sp. nov. Strain D1 exhibits excellent fermentation performance in producing IAA, achieving a maximum IAA yield of 149.24 mg/L after 60 h of fermentation in tryptophan medium. The optimal growth temperature for strain D1 is 25 °C, the optimal growth pH is 6, and the tolerance concentration to sodium chloride is 30 g/L. The genome of strain D1 contains abundant genetic resources for carbohydrate active enzymes, heavy metal resistance, secondary metabolite synthesis gene clusters, and plant pathogen resistance. This study enhances our understanding of the cultivation and genomic function of Chryseobacterium tea sp. nov, as well as the understanding of rhizosphere microorganisms in wild ancient tea forests. It also provides a theoretical basis for the development of Chryseobacterium tea sp. nov as functional fertilizers for crops.