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PubMed2026

Single-organ proteomics in Drosophila melanogaster larva.

The combination of genetic accessibility, organ complexity, evolutionary conservation, and cost-efficiency makes Drosophila melanogaster (Dm) a well-known model system for biomedical and fundamental biological research. Proteomic analysis of single organs enables the identification and quantification of proteins expressed in specific organs. This will help to uncover specific biological functions and unique protein profiles that are not detectable in whole-organism analyses. In this study we have isolated single organs form Dm larvae, and we have performed a deep proteomics mapping by following a minimal manipulation preparation procedure. The combined dataset across all organs comprised 9132 identified proteins. As anticipated, principal component analysis (PCA) revealed clear separation between the proteomes of most organs, confirming distinct protein profiles. These findings demonstrate the applicability of the sample preparation strategy for high-resolution proteomic characterization of individual organs in Drosophila. Given the extensive genetic tools available for this model organism, our approach has the potential to open new avenues for proteomic studies in Drosophila melanogaster and any other biological systems where the sample amount is limiting. SIGNIFICANCE STATEMENT: Drosophila melanogaster is a well-known model system for biomedical and fundamental biological research that serves as a valuable in vivo model organism due to its high degree of evolutionary conservation with higher vertebrates, tractable genetics, and logistical efficiency. However, the proteome of Drosophila at single organ level has been elusive to date, due to several factors like low sensitivity of previous generation mass spectrometers and sample preparation procedures, difficult isolation of some organs. In this study we have applied a compilation of advanced methods including minimal sample manipulation together with simple, straightforward and efficient protein extraction and digestion methods. Obtained peptides were minimally handled to be analyzed by applying specific and sensitive nLC methods coupled on-line to state-of-the-art MS/MS system. Altogether, the applied strategy allowed us to get the first single organ study to date for this animal. These datasets represent a significative resource for future genomic, transcriptomic and proteomic studies in Drosophila, as multi-omic integration requires deep proteomics to translate data into functional biochemistry, and serves as a critical bridge and an indispensable standalone resource across the genomic, transcriptomic, and proteomic landscapes.

Animals↗

PubMed2026

Gastrointestinal digestion governs insect protein hydrolysis and predicted bioactive peptide release: Species-dependent implications for functional food applications.

This study investigates the digestion of insect proteins and the release of predicted bioactive peptides during human gastrointestinal digestion. Using the Infogest in vitro model, mealworm, cricket, and black soldier fly larvae (BSFL) proteins were digested and analyzed through discovery proteomics and bioinformatics to identify predicted bioactive peptides. Sequential windowed acquisition of all theoretical fragment ion mass spectra (SWATH-MS) quantified insect proteins including predicted bioactive peptide precursor proteins, the precursors of predicted bioactive peptides. Results indicated that gastrointestinal digestion strongly influences peptide release, with the gastric phase exhibiting a richer predicted bioactive peptide profile than the small intestinal phase. Many predicted bioactive peptides were rapidly hydrolysed under small intestine conditions, which may lead to reduced stability or diminished activity in vivo, potentially explaining why certain peptides show strong bioactivity in vitro but limited effects in vivo. Additionally, predicted bioactive peptide release varied by insect species, influenced by genetic factors and peptide abundance. These findings highlight the importance of species selection and consideration of proteolytic digestion patterns in optimizing insect-derived bioactive peptides for functional foods and nutraceutical applications.

Animals↗

PubMed2026

Artificial intelligence for anticancer drug discovery from natural products of macroalgae and sponges: A systematic review.

Marine natural products (MNPs) from macroalgae and marine sponges have inspired clinically important anticancer agents, including the cytarabine pharmacophore and the eribulin scaffold, while cyanobacterial dolastatin chemistry supplies the auristatin payloads of several marine-inspired antibody-drug conjugates (ADCs) such as brentuximab vedotin. Artificial intelligence (AI) methods, encompassing both classical machine learning (ML) with hand-engineered features and modern deep learning (DL) with many-layered neural networks, are increasingly supporting key decisions in natural-product anticancer drug discovery, including bioactivity prediction, target identification, absorption, distribution, metabolism, excretion and toxicity (ADMET) filtering, generative analogue design, and the selection of preclinical candidates. DL architectures relevant to this field include graph neural networks, transformer-based molecular generators, diffusion models for protein-ligand docking, and convolutional networks for mass spectrometry, while classical ML contributes interpretable fingerprint-based bioactivity models and molecular networking for dereplication. This review follows a systematic literature review methodology to organize the landscape of AI methods now applied to MNP anticancer discovery, distinguishing ML and DL approaches where relevant, situating them within the chemical context of macroalgal and sponge-derived oncology leads, and critically examining published case studies, including validation level (computational, in vitro, in vivo, clinical). The principal bottleneck for medical translation has shifted partly from algorithmic capability toward data infrastructure and experimental validation. Sparse, heterogeneous, and taxonomically biased bioactivity records limit what current models can learn and reduce the reliability of AI-prioritized candidates entering the preclinical pipeline. A roadmap is proposed that prioritizes open MNP-specific benchmarks, symbiont-aware modeling, and active learning loops with synthesizability and ADMET constraints. These AI workflows may accelerate the prioritization of marine-derived anticancer leads and support earlier, more evidence-based translational decisions in oncology drug development.

Biological Products↗

PubMed2026

Towards microplastic bioremediation: Fungal degradation of pristine and pretreated high-density polyethylene and polystyrene.

Microplastic (MP) contamination has become a significant ecological issue because of its enduring existence in the ecosystem and its possible negative impacts. Therefore, using degrading strategies to eliminate these stubborn polymers has been a subject of scientific research. However, the currently used degradation methods are relatively inefficient. Given the pervasiveness of High-Density Polyethylene (HDPE) and Polystyrene (PS) and their resistance to biodegradability, disposal strategies are critical and must be addressed. This manuscript examines the biodegradation of pristine and UV-treated HDPE and PS MPs by Aspergillus flavus species in minimal growth media over 70 days. The maximum weight loss observed at 70 days for pristine HDPE and PS in sole carbon source (SCS) media was (29.33 ± 0.28) % and (17.67 ± 0.35) %, respectively. Whereas, for UV-treated HDPE and PS MPs, the % weight reduction was (33 ± 0.21) % and (25 ± 0.19) %, respectively. UV-treated MPs exhibited greater weight reduction, as UV induced oxygenated functional groups enhance polymer susceptibility to enzymes, thereby promoting biodegradation. HDPE MPs typically show a higher proportion of particles in the lower size range compared to PS MPs. This assertion was based on the weight loss, particle size distribution, and SEM analysis. Furthermore, chemical changes were evaluated using Fourier transform Infrared Spectroscopy (FTIR) analysis, which also displayed chemical oxidation occurring during biodegradation. Liquid Chromatography-Mass Spectrometry (LC-MS) results indicate that UV pretreatment enhances biodegradability by promoting chain scission. These findings further suggest that this fungus's natural and ubiquitous occurrence in terrestrial and marine environments may actively contribute to MP biodegradation while requiring few nutrients.

Microplastics↗

PubMed2026

Herbicolin A, an antifungal lipopeptide produced by Pantoea agglomerans APC 4211 is a promising biocontrol agent against food spoilage fungi.

Fungal contamination of food with yeast and molds is associated with major economic losses due to spoilage and also poses health risks in the form of mycotoxin production. The strain Pantoea agglomerans APC 4211 isolated from leaves of Ilex aquifolium (holly tree) has broad spectrum antifungal activity against a variety of food spoilage fungi. Genomic analysis of the strain confirmed the presence of biosynthetic gene clusters potentially encoding for the enzymatic machinery required for the production of the antifungal lipopeptide herbicolin A. Matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) analysis of the cell-free supernatant (CFS) confirmed the presence of molecular masses corresponding to herbicolin A (1300.8 Da), and herbicolin B (1138 Da). Purified herbicolin A has desirable properties for biotechnological applications, including potent antifungal activity against a range of spoilage fungi, thermal stability and resistance to proteases. The lipopeptide has low cytotoxicity against epithelial cell lines and has minimum inhibitory concentrations (MICs) lower than those of some commercial antifungal drugs (0.2-2.5 mg/L). In a model dairy system (10% skim milk), herbicolin A demonstrated excellent solubility and stability, effectively eliminating Aspergillus niger and Penicillium notatum at a concentration of 5 mg/L. Overall, the study determines herbicolin's A spectrum against food spoilage organisms and examines potential applications in food. In conclusion, herbicolin A is a potent, naturally occurring antifungal agent with the potential to be applied as a biopreservative in food systems, providing a safe, clean-label, and efficient compound for synthetic preservatives replacement.

Pantoea↗

PubMed2026

Effects of apple phenolics on the human metabolome: modulation of key metabolic pathways.

Apples are widely recognized for their potential health benefits, partly attributed to their phenolic compounds. However, their impact on human metabolism remains incompletely understood. This study investigated metabolic effects of apple-derived phenolic compounds using untargeted metabolomics approach across multiple biofluids. In a crossover intervention study, 30 healthy men consumed a phenolic-rich apple juice or a placebo for two weeks. Blood, urine and saliva samples were collected before and after each intervention and analyzed by direct infusion ultra-high resolution mass spectrometry. Consumption of apple phenolic compounds resulted in significant alterations of the human metabolome, including increased levels of phenolic-derived degradation products and microbial-associated metabolites across all biofluids. Pathway enrichment analysis revealed pronounced effects on phenylalanine and tyrosine metabolism, as well as linoleic and arachidonic acid metabolism, Overall, these findings demonstrate that apple phenolic compounds induce measurable, microbiota-associated and systemic metabolic changes, providing new insights into their metabolic fate and biological relevance.

Humans↗

PubMed2026

ScRNA-seq analysis reveals the effects of nitrite stress on the endocrine system of the eyestalk in Litopenaeus vannamei.

Nitrite is a harmful substance generated in Litopenaeus vannamei farming systems, largely originating from the inadequate breakdown of surplus feed and shrimp feces. Its accumulation in the water can affect the growth and physiological functions of shrimp, damage the immune system, and even cause mass mortality, thus becoming a key environmental factor restricting the green development of the industry. Under nitrite stress, the eyestalk, as an important neuroendocrine regulatory center in crustaceans, participates in the stress adaptation of the organism and exerts a protective effect by regulating energy metabolism and immune function. However, the molecular regulatory mechanism of the eyestalk in response to nitrite stress remains unclear. In this study, single-cell RNA sequencing (scRNA-seq) technology was used to analyze the heterogeneity of eyestalk cells in L. vannamei under nitrite stress. A total of 18, 394 high-quality cells were obtained, and six major cell subpopulations, including Neurosecretory cell, Motor neuron, Sensory neuron, Interneuron, Neurogliocyte, and Support cell, were identified. Differential expression analysis identified 839 differentially expressed genes, and different cell types showed distinct specific responses to nitrite stress. Functional enrichment analysis indicated that pathways such as glycolysis, oxidative phosphorylation, ribosome function, and endoplasmic reticulum protein processing were significantly activated, while signal transduction and DNA repair-related pathways were inhibited. Further analysis revealed that nitrite stress could induce mitochondrial function changes and trigger oxidative stress, thereby affecting the neuroendocrine system function of the eyestalk. This study provided insights into transcriptomic responses of the eyestalk to nitrite stress at the single-cell level, laying a theoretical foundation for the management of aquaculture environments.

Animals↗

PubMed2026

Integrative machine learning and transcriptomic analysis reveals molecular mechanisms underlying low survival rate in larval Chinese Bahaba (Bahaba taipingensis).

Chinese Bahaba (Bahaba taipingensis) is a Class I protected marine fish endemic to China. Low larvae survival during artificial breeding severely hinder population recovery. To investigate the molecular mechanism of high mortality in larval fish, this study performed RNA-seq on liver from naturally deceased (ND) and mass-dead (MD) individuals, combined with least absolute shrinkage and selection operator (LASSO) regression and random forest (RF) algorithms to screen for core signature genes. A total of 873 differentially expressed genes (DEGs) were identified, including 112 upregulated and 761 downregulated genes. GO and KEGG enrichment analyses revealed significant enrichment in amino acid metabolism disorders, one‑carbon folate pool impairment, PPAR signaling abnormalities, ECM-receptor interaction, focal adhesion pathway, indicating widespread metabolic suppression accompanied by extracellular matrix remodeling and signaling disturbances in the livers of MD fish. MAD pre-filtering combined with dual machine learning algorithms yielded 18 robust core signature genes, among which SLC38A4, MMP1, FADD, FKBP5, and APOB were consistently identified as high-frequency core genes by both algorithms. SLC38A4 exhibited the highest importance score in the RF model and was significantly downregulated, making it the primary molecule distinguishing ND from MD phenotypes. ROC curve analysis showed that both models achieved an AUC of 1.000 (95% CI lower bound: 0.610), confirming the precise discriminatory ability of the core genes. GSEA further demonstrated significant enrichment of this core gene set in ND samples. This study provides the first systematic elucidation of the molecular mechanisms underlying liver dysfunction in low survival rate B. taipingensis, characterized by amino acid transport impairment, metabolic reprogramming, and structural remodeling, offering theoretical foundations for health assessment, early mortality risk warning, and artificial breeding conservation of this species.

Animals↗

PubMed2026

Proteomics-based analysis of the defense mechanisms of disease-resistant grass carp against Aeromonas veronii.

Sustainable aquaculture of grass carp (Ctenopharyngodon idella, GC) is consistently threatened by bacterial diseases, particularly those caused by Aeromonas veronii. A disease-resistant grass carp (DR-GC) has been developed by backcrossing female gynogenetic GC with normal male GC, exhibiting improved resistance. However, the systemic molecular mechanisms of DR-GC defending against Aeromonas veronii infection remain largely unexplored. Here, a label-free quantitative proteomics approach was employed to systematically compare proteomic profiles across five tissues (intestine, liver, muscle, skin, and kidney) in DR-GC and GC under healthy and infected conditions. The intestine was identified as the central defense tissue, exhibiting the highest number of differentially abundant proteins (DAPs). In DR-GC, A0A3N0YEK7 (small ribosomal subunit protein eS28), A0A3N0YGT8 (ATP synthase-coupling factor 6) and A0A3N0YNS7 (apolipoprotein A-I) were significantly upregulated in intestine, while D5KZW6 (GCHV-induced protein), A0A3N0Z0A1 and Q8JH84 (hemoglobin subunit alpha) were significantly dysregulated across multiple tissues, which playing the critical roles in defense mechanisms at the protein level. Furthermore, cytochrome P450-associated pathways, cytosolic DNA-sensing and RIG-I-like receptor signaling pathways were identified as crucial coordinators mediating immune and metabolic responses. This study provides the first comprehensive proteomic view of multi-tissue defense mechanisms in DR-GC, and identifies key DAPs and pathways for subsequent functional validation.

Animals↗

PubMed2026

Molecular mechanisms underlying umami taste perception: A DIA-based proteomic analysis of Agrocybe aegerita peptides.

The mechanisms underlying the modulation of the salivary perception of umami peptides remain poorly understood. Herein, three umami peptides (DDL, DEL, and ENG) obtained from Agrocybe aegerita were used to investigate the regulatory role of saliva in umami taste perception via a combined approach involving sensory evaluation and proteomics analysis based on 4D-DIA technology. The results revealed that umami intensity peaked at 10 s after ingestion and was accompanied by a significant increase in saliva secretion (p < 0.05). Further proteomics analysis revealed that lactotransferrin and proline-rich proteins are closely associated with the sensory perception of umami peptides. Differentially expressed proteins were mainly enriched in pathways related to saliva secretion and proteasome function. This study provides new insights from the perspectives of salivary proteomics and dynamic salivary secretion, contributing to a deeper understanding of the mechanisms by which saliva regulates umami perception.

Humans↗

PubMed2026

Associations between multiple essential trace metal concentrations and risk of hyperuricemia: insights from a central Chinese population.

Previous studies have indicated that levels of individual essential trace metals are related to hyperuricemia (HUA), but evidence on their combined effects is limited. To address this gap, the associations of individual and joint levels of 12 essential trace metals (manganese, selenium, nickel, chromium, cobalt, tin, iron, molybdenum, zinc, strontium, vanadium, and copper) with the risk of HUA were investigated in 2,021 adults recruited from Hunan Province, China. Inductively coupled plasma mass spectrometry (ICP-MS) was employed to determine urinary metal concentrations. Logistic regression, Bayesian kernel machine regression (BKMR), and quantile g calculation (Qgcomp) were applied to evaluate the associations of single and mixture metal concentrations with HUA. Of the participants, 516 (25.53%) were diagnosed with HUA. Inverse associations were found between vanadium, chromium, manganese, iron, cobalt, selenium, strontium, and molybdenum levels and HUA, with ORs ranging from 0.63 to 0.91. Conversely, a positive association was observed between zinc concentration and HUA [OR (95% CI): 1.17 (1.01, 1.37)]. Both BKMR and Qgcomp models showed a negative overall effect of essential trace metals on HUA risk, with strontium (- 43.6%) and vanadium (- 27.8%) being the main contributors. In addition, formal interaction tests revealed significant effect modification by age for tin and by BMI for zinc. In conclusion, the levels of essential trace metals were linked to a decreased risk of HUA, and these associations were modified by age and BMI only for specific metals.

Humans↗

PubMed2026

Decoding the spatiotemporal patterns of food spoilage microbial communities: Integrating multi-omics and artificial intelligence to enable precision preservation.

In the global food supply chain, food wastage caused by spoilage has resulted in significant economic losses, food shortages, and environmental pressure. This process is fundamentally driven by the spatiotemporal dynamics of microbial communities. However, traditional research methods struggle to elucidate the complex mechanisms of spatial heterogeneity, interspecies interactions, and functional succession. This limits the development of effective preservation strategies. This review systematically reviews the cutting-edge progress of integrating multi-omics technologies and artificial intelligence (AI) to study food spoilage microbial communities, breaking through this bottleneck. We propose an intelligent theoretical framework that could potentially analyze microbial metabolic activities and predict dynamic shelf life if implemented. The conceptual framework integrates multidimensional data, including spatial metabolomics, temporal metatranscriptomics, single-cell transcriptomics, and longitudinal metagenomics. It can also be combined with AI models, such as graph neural networks. The article elaborates on the principles and applications of spatio-temporal monitoring technologies, such as nano secondary ion mass spectrometry, hyperspectral imaging, and the Internet of Things sensing. Through illustrative cases of typical perishable foods, it also explores how such a multi-omics - AI system might be applied to spoilage warning and precise intervention. Additionally, the article addresses the current challenges in data coverage, model generalization, and federated learning implementation. Then the research further explores emerging areas such as engineered probiotics, edge AI, and microfluidic sensing. These areas are targeted at transforming food preservation from an empirical control approach to a data-driven, precise regulatory framework. This transformation provides theoretical support and technical approaches for developing a smart, sustainable food preservation system.

Multiomics↗

PubMed2026

Construction of precision clinical-proteomics risk model based on machine learning for predicting heart failure in type II diabetes mellitus.

BACKGROUND AND AIMS: Heart failure (HF) is a severe complication in type 2 diabetes mellitus (T2DM), but current risk stratification scores have limited predictive accuracy. We aimed to develop novel prediction tools integrating clinical variables with proteomics to improve risk stratification of hospitalization for HF in T2DM. METHODS AND RESULTS: In this study, we included 2111 UK Biobank participants with T2DM but no prior HF, and profiled 2920 proteins to predict 10-year incident HF hospitalization. Participants were randomly divided into training (70%), tuning (10%), and validation (20%) sets.Three prediction models were developed: a Clinical model based on demographic characteristics, comorbidities, medication use, and laboratory indices; a Protein model based on 40 proteins selected by the Light Gradient Boosting Machine (LGBM); and the Clinical OMics and Protein ASSessment for Heart Failure (COMPASS-HF) model, which integrated both clinical variables and the LGBM-selected proteins. Models were evaluated for area under the curve (AUC), sensitivity, and specificity. During follow-up, 168 participants (7.96%) developed incident HF. The COMPASS-HF model showed better discrimination than the Clinical model, with an AUC of 0.897 (95% CI: 0.850-0.945) versus 0.790 (95% CI: 0.723-0.856). It also demonstrated higher sensitivity (0.882; 95% CI: 0.725-0.967) and consistent performance in subgroups. COMPASS-HF effectively stratified risk of hospitalization for HF, with cumulative incidence rates of 31.9% in the high-risk group and 1.2% in the low-risk group. CONCLUSIONS: By combining clinical and proteomic variables, we developed a high-performance HF prediction model for T2DM, enabling precise risk stratification and informing early intervention strategies.

Humans↗

PubMed2026

Clinical and endocrine correlates of genetic etiologies in severe hypospadias: Study from 34 patients.

OBJECTIVE: Hypospadias is a prevalent congenital anomaly (0.3%-1.0%); however, severe hypospadias (defined as proximal cases with the meatus at the penoscrotal junction, scrotum, or perineum) is a rare and clinically challenging entity with a multifactorial etiology. This study aimed to characterize the interrelationships among the clinical, endocrine, and genetic profiles in children with severe hypospadias. MATERIALS AND METHODS: We conducted a comprehensive analysis of 34 male patients with severe hypospadias. Preoperative hormone levels were measured using two methods: chemiluminescent immunoassay for luteinizing hormone and follicle-stimulating hormone, and liquid chromatography-tandem mass spectrometry for testosterone (T), dihydrotestosterone (DHT), dehydroepiandrosterone (DHEA), 17α-hydroxyprogesterone (17α-OHP), and other steroids. Genetic analysis was conducted via whole exome sequencing. RESULTS: The diagnostic yield of clinically relevant genetic variants (including pathogenic and likely pathogenic, and variants of uncertain significance) in our cohort was 41.2% (14/34) of patients. Patients carrying these variants exhibited a more complex phenotypic profile compared to non-carriers, including a significantly higher rate of patients with ≥3 associated malformations and a greater prevalence of cryptorchidism. Furthermore, the group with clinically relevant variants showed selective elevations in adrenal-derived precursors, specifically 17α-OHP and DHEA. Correlation analysis revealed significant positive associations of both 17α-OHP levels and the T/DHT ratio with the number of associated malformations. CONCLUSION: This study reveals significant genetic heterogeneity in patients with severe hypospadias. Those carrying genetic variants was associated with more severe clinical phenotypes, while certain endocrine variations, including the elevation of adrenal-derived hormones, were also observed in this cohort.

Humans↗

PubMed2026

Obesity in obstetric anesthesia: A systematic review.

Maternal obesity presents complex challenges for anesthetic management, with implications spanning neonatal, cardiovascular, airway, neuraxial, and procedural domains. This review synthesizes evidence on how elevated maternal body mass index (BMI) impacts perioperative evaluations, risks, complications, and outcomes, in addition to anesthetic modalities and efficacy in the pregnant population. Given the increasing global prevalence of maternal obesity, anesthesiologists must refine clinical practices, employing tailored, evidence-based strategies to mitigate risks and enhance patient outcomes. This review aims to provide anesthesiologists and obstetricians with key considerations and best practices for managing obstetric anesthesia patients with obesity. Clinical recommendations herein are based on current research and evaluated using Oxford Centre for Evidence-Based Medicine for level of evidence and class of recommendation.

Humans↗

PubMed2026

Ecological Restoration of the Soil-Like Function in the Bauxite Residue: Natural Microbiomes Mediated Molecular Transformation of Dissolved Organic Matter.

Soilization of bauxite residues offers a scalable route for long-term carbon management and ecological restoration. However, the microbial processes that transform exogenous organic inputs into stable soil-like carbon pools remain poorly resolved. Here, we combined cross-ecosystem meta-analysis, machine-learning prediction, native synthetic community (SynCom) construction, 13C-labeled straw microcosms, field validation, Fourier transform ion cyclotron resonance mass spectrometry, and genome-resolved metagenomics to unravel microbiome-mediated carbon transformation at the dissolved organic matter (DOM) molecular scale. Our meta-analysis revealed that alkaline industrial wastes retained soil-like DOM signatures but were enriched in microbial humic- and protein-like components, indicating active yet incomplete carbon processing. Guided by these patterns, native SynCom inoculation increased 13C incorporation into total organic carbon (TOC) and dissolved organic carbon (DOC), enlarged biodegradable and adsorbable DOC fractions, and shifted DOM from recalcitrant aromatic pools toward oxygenated carbohydrate-, tannin-, and phenolic-like molecular classes. Genome-resolved analyses linked this transformation to complementary polymer degradation and nutrient-cycling functions across fungal and bacterial guilds, including enriched carbohydrate-active enzymes in straw-carbon-utilizing metagenome-assembled genomes. Null model and thermodynamic analyses further showed that microbial communities were constrained by homogeneous selection, whereas DOM molecules were diversified through variable selection and redox-dependent transformation. Field-scale validation confirmed that SynCom promoted TOC and DOC accumulation and humic-like, high-density DOM fractions under alkaline conditions. Together, these findings establish a mechanistic framework in which functional microbiomes couple plant carbon depolymerization, DOM molecular diversification, and mineral-interactive carbon stabilization, providing a microbiome-guided strategy for carbon sequestration and soilization in the bauxite residue.

Soil↗

PubMed2026

In situ product monitoring in heterogeneous reaction of gaseous trimethylamine on Fe2O3/Fe(NO3)3: Effect of environmental factor and particle property.

Gas-particle reactions represent an important atmospheric heterogeneous transformation process for organic amines (OAs). Environmental factors and particle properties may impact the gas-particle reaction products. Although the products from gas-particle reactions can be monitored by various in situ techniques, related data remain scarce. Here, the interfacial and gaseous products from the reaction of trimethylamine on Fe2O3/Fe(NO3)3 particles under light irradiation with mixed NO2, O2, SO2 and H2O were monitored using in-situ diffuse reflectance Fourier transform infrared spectroscopy and proton transfer reaction time-of-flight mass spectrometry. Dark reaction of gaseous trimethylamine on Fe2O3/Fe(NO3)3 generated two interfacial products types: N-containing ones (CH3NCH2, CH3NO2, (CH3)2NCHO, and CH3N(OH)CHO) and N-free ones (alcohols, aldehydes and acids), both accumulating with reaction progression. Light irradiation and O2 oxidation enhanced formation of these products, while NO2 promoted the production of CH3NO2 and (CH3)2NCHO. H2O and SO2 occupied the active sites of particles to inhibit the formation of all products. Compared to Fe(NO3)3, Fe2O3 showed absolute dominance in contribution to the formation of products. Considering the smaller particle size of Fe2O3 and excess Fe(NO3)3, the physical mixing of them reduced the generation of interfacial products. Furthermore, gaseous products of CH3OH, HCHO, CH3CHO, HCOOH and CH3COOH detection clarified the N-free interfacial products. The presence of Fe(NO3)3 inhibited the formation of HCOOH and favored the formation of CH3CHO in the gas phase. By combining product information with thermodynamic calculations, the heterogeneous reaction pathways of trimethylamine were tentatively proposed. These findings provide a guiding significance for the migration of OAs in real atmospheric environment.

Methylamines↗

PubMed2026

Comparative analysis of lipopolysaccharide lipid A structure and its biosynthetic genes in the plant-associated bacteria Brucella cytisi and Brucella lupini.

The genus Brucella comprises important human and animal pathogens, as well as numerous environmental and symbiotic species. Lipopolysaccharide (LPS), a major component of the outer membrane of Gram-negative bacteria, plays a crucial role in bacterial physiology and host interactions. In this study, the structures of lipid A, the hydrophobic anchor of lipopolysaccharide, isolated from two plant-associated strains, Brucella cytisi ESC1ᵀ and Brucella lupini LUP21ᵀ, were presented. Lipid A preparations were structurally characterized using chemical methods, MALDI-TOF mass spectrometry, and nuclear magnetic resonance spectroscopy. The obtained results indicated that both lipid A molecules have almost identical structures. Their sugar backbones consist exclusively of 2,3-diamino-2,3-dideoxy-d-glucose (d-GlcpN3N). Phosphate residues were connected to distal and proximal GlcpN3N in approximately half of the lipid A molecules. Fatty acid analysis revealed the presence of C14:0 (3-OH), C16:0 (3-OH), and traces of C18:0 (3-OH). All of these were primary fatty substituents of the sugar backbone and were amide-linked residues. Lactobacillic acid C19:0cyc and 27-hydroxyoctacosanoic acid (C28:0 (27-OH)) were found as ester-linked secondary acyl residues. In turn, C28:0 (27-OH) was partly esterified by a 3-hydroxybutyroyl residue. Two unsubstituted 3-hydroxyfatty acids were linked exclusively to the proximal d-GlcpN3N residue. It was pointed out that sequences of putative genes encoding enzymes required for lipid A biosynthesis and genes encoding specific enzymes involved in structural modifications of lipid A occurring in the genomes of both bacterial species are almost identical. The high sequence similarity of these proteins reflects the observed similarities in the lipid A structures in both investigated Brucella species.

Brucella↗