Function Prediction Research Articles

Mutual annotation-based prediction of protein domain functions with Domain2GO.

Identifying unknown functional properties of proteins is essential for understanding their roles in both health and disease states. The domain composition of a protein can reveal critical information in this context, as domains are structural and functional units that dictate how the protein should act at the molecular level. The expensive and time-consuming nature of wet-lab experimental approaches prompted researchers to develop computational strategies for predicting the functions of proteins. In this study, we proposed a new method called Domain2GO that infers associations between protein domains and function-defining gene ontology (GO) terms, thus redefining the problem as domain function prediction. Domain2GO uses documented protein-level GO annotations together with proteins' domain annotations. Co-annotation patterns of domains and GO terms in the same proteins are examined using statistical resampling to obtain reliable associations. As a use-case study, we evaluated the biological relevance of examples selected from the Domain2GO-generated domain-GO term mappings via literature review. Then, we applied Domain2GO to predict unknown protein functions by propagating domain-associated GO terms to proteins annotated with these domains. For function prediction performance evaluation and comparison against other methods, we employed Critical Assessment of Function Annotation 3 (CAFA3) challenge datasets. The results demonstrated the high potential of Domain2GO, particularly for predicting molecular function and biological process terms, along with advantages such as producing interpretable results and having an exceptionally low computational cost. The approach presented here can be extended to other ontologies and biological entities to investigate unknown relationships in complex and large-scale biological data. The source code, datasets, results, and user instructions for Domain2GO are available at https://github.com/HUBioDataLab/Domain2GO. Additionally, we offer a user-friendly online tool at https://huggingface.co/spaces/HUBioDataLab/Domain2GO, which simplifies the prediction of functions of previously unannotated proteins solely using amino acid sequences.

Prediction Method for Dynamic Subsidence Basin in Mining Area Based on SBAS-InSAR and Time Function

Dynamic predictions of surface subsidence are crucial for assessing ground damage and protecting surface buildings. Based on Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technology, a method for making dynamic predictions of large-scale surface subsidence in mining areas can be established; however, the problem of phase coherence loss in InSAR data makes it impossible to predict the complete dynamic subsidence basin. In this study, a method combining the WeiBull time function and the improved probabilistic integral method (IPIM) model was established based on the PIM model, and a method for predicting the dynamic subsidence basin in the mining area was proposed by integrating the IPIM and the combined WeiBull time function. Time-series subsidence data, obtained using SBAS-InSAR, were used as fitting data, and the parameters of the combined WeiBull function were inverted, pixel by pixel, to predict the dynamic subsidence of the working face in the study area. Based on the predicted surface subsidence results of a certain moment in the working face, the parameters of the IPIM model were inverted to predict the subsidence value in the incoherent region. The subsidence predictions of the combined WeiBull time function and the IPIM model were fused using inverse distance weighting (IDW) interpolation to restore the complete subsidence basin in the mining area. This method was tested at the Wannian Mine in Hebei, and the obtained complete subsidence basin was compared with the measured data, with an absolute error range of 0 to 10 mm. The results show that the dynamic subsidence basin prediction method for the SBAS-InSAR mining area, involving the combination of the IPIM model and the combined WeiBull model, can not only accurately fit the time series of surface observation points affected by mining but also accurately restore the subsidence data in the incoherent region to obtain complete subsidence basin information in the mining area.

Ornithine aspartate effects on bacterial composition and metabolic pathways in a rat model of steatotic liver disease

BACKGROUND Metabolic-dysfunction associated steatotic liver disease (MASLD) is a hepatic manifestation of metabolic syndrome. Studies suggest ornithine aspartate (LOLA) as drug therapy. AIM To analyze the influence of LOLA intake on gut microbiota using a nutritional model of MASLD. METHODS Adult male Sprague Dawley rats were randomized into three groups: Control (10 rats fed with a standard diet), MASLD (10 rats fed with a high-fat and choline-deficient diet), and LOLA (10 rats receiving 200 mg/kg/d LOLA, after the 16th week receiving high-fat and choline-deficient diet). After 28 wk of the experiment, animals were euthanized, and feces present in the intestine were collected. Following fecal DNA extraction, the V4 region of the 16S rRNA gene was amplified followed by sequencing in an Ion S5™ system. RESULTS Alpha and beta diversity metrics were comparable between MASLD and LOLA. 3 OTUs were differentially abundant between MASLD and LOLA, which belong to the species Helicobacter rodentium , Parabacteroides goldsteinii , and Parabacteroides distasonis . The functional prediction provided two different metabolic profiles between MASLD and LOLA. The 9 pathways differentially abundant in MASLD are related to a change in energy source, adenosine/purine nucleotides degradation as well as guanosine and adenosine deoxyribonucleotides biosynthesis. The 14 pathways differentially abundant in LOLA are associated with four major metabolic functions primarily influenced by L-aspartate, including tricarboxylic acid cycle pathways, purine/guanosine nucleotides biosynthesis, pyrimidine ribonucleotides biosynthesis and salvage as well as lipid IVA biosynthesis. CONCLUSION Although LOLA had no influence on alpha and beta diversity in this nutritional model of MASLD, it was associated with changes in specific gut microbes and their related metabolic pathways.

Accurate prediction of antibody function and structure using bio-inspired antibody language model.

In recent decades, antibodies have emerged as indispensable therapeutics for combating diseases, particularly viral infections. However, their development has been hindered by limited structural information and labor-intensive engineering processes. Fortunately, significant advancements in deep learning methods have facilitated the precise prediction of protein structure and function by leveraging co-evolution information from homologous proteins. Despite these advances, predicting the conformation of antibodies remains challenging due to their unique evolution and the high flexibility of their antigen-binding regions. Here, to address this challenge, we present the Bio-inspired Antibody Language Model (BALM). This model is trained on a vast dataset comprising 336 million 40% nonredundant unlabeled antibody sequences, capturing both unique and conserved properties specific to antibodies. Notably, BALM showcases exceptional performance across four antigen-binding prediction tasks. Moreover, we introduce BALMFold, an end-to-end method derived from BALM, capable of swiftly predicting full atomic antibody structures from individual sequences. Remarkably, BALMFold outperforms those well-established methods like AlphaFold2, IgFold, ESMFold and OmegaFold in the antibody benchmark, demonstrating significant potential to advance innovative engineering and streamline therapeutic antibody development by reducing the need for unnecessary trials. The BALMFold structure prediction server is freely available at https://beamlab-sh.com/models/BALMFold.

Enhancement of microbial community dynamics and metabolism in compost through ammonifying cultures inoculation

The efficient use of livestock and poultry manure waste has become a global challenge, with microorganisms playing an important role. To investigate the impact of novel ammonifying microorganism cultures (NAMC) on microbial community dynamics and carbon and nitrogen metabolism, five treatments [5% (v/w) sterilized distilled water, Amm-1, Amm-2, Amm-3, and Amm-4] were applied to cow manure compost. Inoculation with NAMC improved the structure of bacterial and fungal communities, enriched the populations of the functional microorganisms, enhanced the role of specific microorganisms, and promoted the formation of tight modularity within the microbial network. Further functional predictions indicated a significant increase in both carbon metabolism (CMB) and nitrogen metabolism (NMB). During the thermophilic phase, inoculated NAMC treatments boosted carbon metabolism annotation by 10.55%–33.87% and nitrogen metabolism annotation by 26.69%–63.11. Structural equation modeling supported the NAMC-mediated enhancement of NMB and CMB. In conclusion, NAMC inoculation, particularly with Amm-4, enhanced the synergistic interaction between bacteria and fungi. This collaboration promoted enzymatic catabolic and synthetic processes, resultng in positive feedback loops with the endogenous microbial community. Understanding these mechanisms not only unravels how ammonifying microorganisms influence microbial communities but also paves the way for the development of the composting industry and global waste management practices.

#909 Role of cystatin C in the development and progression of chronic heart failure in patients with diabetes

Abstract Background and Aims It has been known that cystatin C is a marker for early prediction of renal function. In recent years, studies have shown that cystatin C plays an important role in predicting new or worsening cardiovascular diseases. The aim of our study was to investigate the role of cystatin C in the development and progression of chronic heart failure (CHF) in patients with diabetes. Method A total of 499 patients with DT1 and DT2, aged 34 to 84 years (60 [60; 72]), were examined. All patients underwent standard clinical and laboratory examination, with an assessment of the levels of the natriuretic peptides (BNP and NTproBNP) in baseline plasma samples. Renal function was assessed based on the levels of serum creatinine and cystatin C. Patients were divided into quartiles according to cystatin C levels: Q1 <0.78 mg/l, Q2 0.78-0.92 mg/l, Q3 0.93-1.38 mg/l, Q4 >1.38 mg/l. Results Significant differences in the levels of BNP and NTproBNP were observed in patients with diabetes with cystine C levels ≥0.93 mg/l (32.20 [19.85; 69.35] pg/ml and 104.25 [57.70; 210.08] ng/l, respectively). There were significant correlations between cystatin C and the levels of BNP (r = 0.56, p < 0.05) and NTproBNP (r = 0.58, p < 0.05). Logistic regression analysis showed the prognostic value of cystatin C, and not creatinine, as a predictor of heart failure in patients with diabetes and GFR>60 ml/min/1.73 m2 (1 mg/l increase of cystatin C was associated with 7.085 times increase in CHF odds). ROC analysis revealed that the level of cystatin C ≥0.96 mg/l with a sensitivity of 84% and specificity of 75% predicted increased BNP and NTproBNP in patients with diabetes (ROC AUC = 0.844). Conclusion Cystatin C level could be used as a predictive indicator of CHF in patients with diabetes and GFR>60 ml/min/1.73 m2.

CASTpFold: Computed Atlas of Surface Topography of the universe of protein Folds.

Geometric and topological properties of protein structures, including surface pockets, interior cavitiesand cross channels, are of fundamental importance for proteins to carry out their functions. Computed Atlas of Surface Topography of proteins(CASTp) is a widely used web server for locating, delineating, and measuring these geometric and topological properties of protein structures. Recent developments in AI-based protein structure prediction such as AlphaFold2 (AF2) have significantly expanded our knowledge on protein structures. Here we present CASTpFold, a continuation of CASTp that provides accurate and comprehensive identifications and quantifications of protein topography. It now provides (i) results on an expanded database of proteins, including the Protein Data Bank(PDB) and non-singleton representative structures of AlphaFold2 structures, covering 183 million AF2 structures; (ii) functional pockets prediction with corresponding Gene Ontology (GO) terms or Enzyme Commission (EC) numbers for AF2-predicted structuresand (iii) pocket similarity search function for surface and protein-protein interface pockets. The CASTpFold web server is freely accessible at https://cfold.bme.uic.edu/castpfold/.

Genome-Wide Identification and Characterization of the RWP-RK Proteins in Zanthoxylum armatum

Apomixis is a common reproductive characteristic of Zanthoxylum plants, and RWP-RKs are plant-specific transcription factors known to regulate embryonic development. However, the genome-wide analysis and function prediction of RWP-RK family genes in Z. armatum are unclear. In this study, 36 ZaRWP-RK transcription factors were identified in the genome of Z. armatum, among which 15 genes belonged to the RKD subfamily and 21 belonged to the NLP subfamily. Duplication events of ZaRWP-RK genes were mainly segmental duplication, and synteny analysis revealed a close phylogenetic relationship between Z. armatum and Arabidopsis. The analysis of cis-elements indicated that ZaRWP-RK genes may be involved in the regulation of the embryonic development of Z. armatum by responding to plant hormones such as abscisic acid, auxin, and gibberellin. Results of a real-time PCR showed that the expression levels of most ZaRWP-RK genes were significantly increased from flowers to young fruits. Protein–protein interaction network analysis further revealed the potential roles of the ZaRWP-RK proteins in apomixis. Collectively, this study is expected to improve our understanding of ZaRWP-RK transcription factors and provide a theoretical basis for future investigations into the ZaRWP-RK genes and their regulatory mechanisms in the apomixis process of Z. armatum.

Insights into the Correlation between Microbial Community Succession and Pericarp Degradation during Pepper (Piper nigrum L.) Peeling Process via Retting

White pepper, used both as a seasoning in people’s daily diets and as a medicinal herb, is typically produced by removing the pericarp of green pepper through the retting process. However, the mechanism of the retting process for peeling remains unclear. Therefore, this study aimed to investigate the changes in physicochemical factors, microbial community succession effects, and metabolites of the pepper pericarp during the pepper peeling process. The findings indicated that pre-treatment involving physical friction before the retting process effectively reduced the production time of white pepper. During the retting process, the pectinase activity increased, leading to a decrease in the pectin content in the pepper pericarp. There was a significant correlation observed between the changes in pH, pectin content, and peeling rate and the Shannon diversity index of bacteria and fungi. Prevotella, Lactococcus, and Candida were the dominant microbial genera during the retting. The functional predictions suggested that the monosaccharides degraded from the pepper pericarp could have been utilized by microbes through sugar metabolism pathways. Metabolomic analysis showed that the metabolic pathways of carbohydrates and amino acids were the main pathways altered during the pepper peeling process. The verification experiment demonstrated that the degradation of pectin into galacturonic acid by polygalacturonase was identified as the key enzyme in shortening the pepper peeling time. The structure of the pepper pericarp collapsed after losing the support of pectin, as revealed by scanning electron microscopy. These results suggest that the decomposition of the pepper pericarp was driven by key microbiota. The succession of microbial communities was influenced by the metabolites of the pepper pericarp during retting. These findings provide new insights into the retting process and serve as an important reference for the industrial production of white pepper.

SAFPred: Synteny-aware gene function prediction for bacteria using protein embeddings.

Today, we know the function of only a small fraction of the protein sequences predicted from genomic data. This problem is even more salient for bacteria, which represent some of the most phylogenetically and metabolically diverse taxa on Earth. This low rate of bacterial gene annotation is compounded by the fact that most function prediction algorithms have focused on eukaryotes, and conventional annotation approaches rely on the presence of similar sequences in existing databases. However, often there are no such sequences for novel bacterial proteins. Thus, we need improved gene function prediction methods tailored for bacteria. Recently, transformer-based language models-adopted from the natural language processing field-have been used to obtain new representations of proteins, to replace amino acid sequences. These representations, referred to as protein embeddings, have shown promise for improving annotation of eukaryotes, but there have been only limited applications on bacterial genomes. To predict gene functions in bacteria, we developed SAFPred, a novel synteny-aware gene function prediction tool based on protein embeddings from state-of-the-art protein language models. SAFpred also leverages the unique operon structure of bacteria through conserved synteny. SAFPred outperformed both conventional sequence-based annotation methods and state-of-the-art methods on multiple bacterial species, including for distant homolog detection, where the sequence similarity to the proteins in the training set was as low as 40%. Using SAFPred to identify gene functions across diverse enterococci, of which some species are major clinical threats, we identified 11 previously unrecognized putative novel toxins, with potential significance to human and animal health. https://github.com/AbeelLab/safpred. Supplementary data are available at Bioinformatics online.

Research on the stability prediction for multi-posture robotic side milling based on FRF measurements

In robotic side milling, frequent chatter extremely restricts the acquisition of high surface quality due to weak stiffness, and cutting parameters optimization guided by stability boundary is regarded as an effective solution to solve the chatter problem. In this research, the influence mechanisms of stability were analyzed by evaluating the structural static stiffness and dynamic parameters, and the main factor was characterized as regenerative chatter by means of stability measurements and the theoretical prediction model. The distance-driven multi-posture frequency response function (FRF) prediction model was improved in terms of the dominant modal. Grey correlation analysis was carried out to investigate the influence law of robotic joints to modal parameters, and the difference between far-distance posture and near-distance posture was re-characterized by cross-validation of FRF measurements. Finally, the third-order Hermite–Newton approximation was employed to solve the dynamic model by considering process damping effect, and the results showed the prediction accuracy of the constructed stability boundary was over 85%.

Regulatory effects of microRNAs on monocytic HLA-DR surface expression.

Decreased monocytic HLA-DR expression is the most studied biomarker of immune competency in critically ill and autoimmune disease patients. However, the underlying regulatory mechanisms remain largely unknown. One probable HLA-DR dysregulation is through microRNAs. The aim of this study was to investigate the effects of specific microRNAs on HLA-DR expression in human monocytic cells. Four up- and four down-HLA-DR-regulating microRNAs were identified, with hsa-miR-let-7f-2-3p showing the most significant upregulation and hsa-miR-567 and hsa-miR-3972 downregulation. Anti-inflammatory glucocorticoid medication Dexamethasone-decreased HLA-DR was significantly restored by hsa-miR-let-7f-2-3p and hsa-miR-5693. Contrarily, proinflammatory cytokines IFN-γ and TNF-α-increased HLA-DR were significantly reversed by hsa-miR-567. Clinically, paired plasma samples from patients before and one day after cardiac surgery revealed up-regulated expression of hsa-miR-5693, hsa-miR-567, and hsa-miR-3972, following the major surgical trauma. In silico approaches were applied for functional microRNA-mRNA interaction prediction and candidate target genes were confirmed by qPCR analysis. In conclusion, novel monocytic HLA-DR microRNA modulators were identified and validated in vitro. Moreover, both the interaction between the microRNAs and anti- and proinflammatory molecules and the up-regulated microRNAs identified in cardiac surgery highlight the potential clinical relevance of our findings.

Biochar-bacteria-plant combined potential for remediation of oil-contaminated soil

Oil pollution is a common type of soil organic pollution that is harmful to the ecosystem. Bioremediation, particularly microbe-assisted phytoremediation of oil-contaminated soil, has become a research hotspot in recent years. In order to explore more appropriate bioremediation strategies for soil oil contamination and the mechanism of remediation, we compared the remediation effects of three plants when applied in combination with a microbial agent and biochar. The combined remediation approach of Tagetes erecta, microbial agent, and biochar exhibited the best plant growth and the highest total petroleum hydrocarbons degradation efficiency (76.60%). In addition, all of the remediation methods provided varying degrees of restoration of carbon and nitrogen contents of soils. High-throughput sequencing found that microbial community diversity and richness were enhanced in most restored soils. Some soil microorganisms associated with oil degradation and plant growth promotion such as Cavicella, C1_B045, Sphingomonas, MND1, Bacillus and Ramlibacter were identified in this study, among which Bacillus was the major component in the microbial agent. Bacillus was positively correlated with all soil remediation indicators tested and was substantially enriched in the rhizosphere of T. erecta. Functional gene prediction of the soil bacterial community based on the KEGG database revealed that pathways of carbohydrate metabolism and amino acid metabolism were up-regulated during remediation of oil-contaminated soils. This study provides a potential method for efficient remediation of oil-contaminated soils and thoroughly examines the biochar–bacteria–plant combined remediation mechanisms of oil-contaminated soil, as well as the combined effects from the perspective of soil bacterial communities.

Gut metagenomic analysis of gastric cancer patients reveals Akkermansia, Gammaproteobacteria, and Veillonella microbiota as potential non-invasive biomarkers

The goal of the study was to investigate the changes in the gut microbiota during the advancement of gastric cancer (GC) and identify pertinent taxa associated with the disease. We used a public fecal amplicon gastric cancer dataset from the Sequence Retrieval Archive (SRA), of patients with GC, gastritis, and healthy individuals. We did sequence pre-processing, including quality filtering of the sequences. Then, we performed a diversity analysis, evaluating α- and β-diversity. Next, taxonomic composition analysis was performed and the relative abundances of different taxa at the phylum and genus levels were compared between GC, gastritis, and healthy controls. The obtained results were subsequently subjected to statistical validation. To conclude, metagenomic function prediction was carried out, followed by correlation analysis between the microbiota and KEGG pathways. α analysis revealed a significant difference between male and female categories, while β analysis demonstrated significant distinctions between GC, gastritis, and healthy controls, as well as between sexes within the GC and gastritis groups. The statistically confirmed taxonomic composition analysis highlighted the presence of the microbes Bacteroides and Veillonella. Furthermore, through metagenomic prediction analysis and correlation analysis with pathways, three taxa, namely Akkermansia, Gammaproteobacteria, and Veillonella, were identified as potential biomarkers for GC. Additionally, this study reports, for the first time, the presence of two bacteria, Desulfobacteriota and Synergistota, in GC, necessitating further investigation. Overall, this research sheds light on the potential involvement of gut microbiota in GC pathophysiology; however, additional studies are warranted to explore its functional significance.

Characteristics of Vaginal Microbiota of Women of Reproductive Age with Infections.

The vaginal microbiota can be classified into five major community state types (CSTs) based on the bacterial content. However, the link between different CST subtypes and vaginal infection remains unclear. Here, we analyzed 2017 vaginal microbiota samples from women of a reproductive age with vaginal infections that were published in the last decade. We found that L. iners was the most dominant in 34.8% of the vaginal samples, followed by L. crispatus (21.2%). CST I was common in healthy individuals, whereas CST III and IV were associated with dysbiosis and infection. CST III-B, IV-A, IV-B, and IV-C0 were prevalent in patients with bacterial vaginosis (BV). Based on the relative abundance of bacteria at the (sub)genus level, a random forest classifier was developed to predict vaginal infections with an area under the curve of 0.83. We further identified four modules of co-occurring bacterial taxa: L. crispatus, Gardnerella, Prevotella, and Bacteroides. The functional prediction revealed that nucleotide biosynthesis pathways were upregulated in patients with human papilloma virus, and carbohydrate degradation pathways were downregulated in patients with BV. Overall, our study identified the bacterial signatures of healthy and infected vaginal microbiota, providing unique insights into the clinical diagnosis and health status prediction of women of a reproductive age.

A novel signature incorporating genes related to lipid metabolism and immune for prognostic and functional prediction of breast cancer.

Breast cancer prognosis and functioning have not been thoroughly examined in relation to immunological and lipid metabolism. However, there is a lack of prognostic and functional analyses of the relationship between lipid metabolism and immunity in breast cancer. DEGs in breast cancer were obtained from UCSC database, and lipid metabolism and immune-related genes were obtained from GSEA and Immune databases. A predictive signature was constructed using univariate Cox and LASSO regression on lipid metabolism and immune-related DEGs. The signature's prognostic significance was assessed using Kaplan-Meier, time-dependent ROC, and risk factor survival scores. Survival prognosis, therapeutic relevance, and functional enrichment were used to mine model gene biology. We selected IL18, which has never been reported in breast cancer before, in the signature to learn more about its function, potential to predict outcome, and immune system role. RT-PCR was performed to verify the true expression level of IL18. A total of 136 DEGs associated with breast cancer responses to both immunity and lipid metabolism. Nine key genes (CALR, CCL5, CEPT, FTT3, CXCL13, FLT3, IL12B, IL18, and IL24, p < 1.6e-2) of breast cancer were identified, and a prognostic was successfully constructed with a good predictive ability. IL18 in the model also had good clinical prognostic guidance value and immune regulation and therapeutic potential. Furthermore, the expression of IL18 was higher than that in paracancerous tissue. A unique predictive signature model could effectively predict the prognosis of breast cancer, which can not only achieve survival prediction, but also screen out key genes with important functional mechanisms to guide clinical drug experiments.

Comparative analysis of oropharyngeal microbiota in healthcare workers post-COVID-19

BackgroundTo date, more than 770 million individuals have become coronavirus disease 2019 (COVID-19) convalescents worldwide. Emerging evidence highlights the influence of COVID-19 on the oral microbiome during both acute and convalescent disease phases. Front-line healthcare workers are at an elevated risk of exposure to viral infections, and the effects of COVID-19 on their oral microbiome remain relatively unexplored.MethodsOropharyngeal swab specimens, collected one month after a negative COVID-19 test from a cohort comprising 55 healthcare workers, underwent 16S rRNA sequencing. We conducted a comparative analysis between this post-COVID-19 cohort and the pre-infection dataset from the same participants. Community composition analysis, indicator species analysis, alpha diversity assessment, beta diversity exploration, and functional prediction were evaluated.ResultsThe Shannon and Simpson indexes of the oral microbial community declined significantly in the post-COVID-19 group when compared with the pre-infection cohort. Moreover, there was clear intergroup clustering between the two groups. In the post-COVID-19 group, the phylum Firmicutes showed a significant increase. Further, there were clear differences in relative abundance of several bacterial genera in contrast with the pre-infection group, including Streptococcus, Gemella, Granulicatella, Capnocytophaga, Leptotrichia, Fusobacterium, and Prevotella. We identified Gemella enrichment in the post-COVID-19 group, potentially serving as a recovery period performance indicator. Functional prediction revealed lipopolysaccharide biosynthesis downregulation in the post-COVID-19 group, an outcome with host inflammatory response modulation and innate defence mechanism implications.ConclusionDuring the recovery phase of COVID-19, the oral microbiome diversity of front-line healthcare workers failed to fully return to its pre-infection state. Despite the negative COVID-19 test result one month later, notable disparities persisted in the composition and functional attributes of the oral microbiota.

Bronchopulmonary dysplasia: analysis and validation of ferroptosis-related diagnostic biomarkers and immune cell infiltration features.

Early and precise diagnosis of bronchopulmonary dysplasia (BPD) is essential to improve the prognosis of preterm infants with BPD. Studying ferroptosis-related genes for diagnostic markers of BPD was the objective of this study. Using the GEO database and the FerrDb database, we obtained the GSE32472 dataset and screened the ferroptosis-related differentially expressed mRNAs (FRDE-mRNAs). By using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG), possible biological functions and pathways were identified for FRDE-mRNAs. Three machine learning algorithms (LASSO, SVM-RFE, Random Forest) were used to recognize hub genes, as well as CIBERSORT for exploring the immune landscape of BPD and controls. Functional predictions for hub genes were made using single-gene gene set enrichment analysis (GSEA). Twenty three FRDE-mRNAs were obtained and were mainly involved in autophagy, fatty acid metabolism and ferroptosis. The four hub genes (LPIN1, ACADSB, WIPI1 and SLC7A11) screened were utilized to construct a diagnostic nomogram. The receiver operating characteristic (ROC) curves and calibration curves demonstrateld that the nomogram exhibited good predictive performance. Eight types of immune cell markers differed significantly between BPD and controls. We developed a diagnostic model for BPD, which could facilitate the early diagnosis and timely intervention of BPD. The role of ferroptosis in bronchopulmonary dysplasia is rarely reported. The ferroptosis-related genes (LPIN1, ACADSB, WIPI1 and SLC7A11) we identified could serve as early diagnostic biomarkers for BPD. Immune cell infiltration features in BPD and signaling pathways associated with marker genes give new insight into the disease process and provide a basis for further research.

Effects of Hibernation on Colonic Epithelial Tissue and Gut Microbiota in Wild Chipmunks (Tamias sibiricus)

The gut microbiota plays a crucial role in the host’s metabolic processes. Many studies have shown significant changes in the gut microbiota of mammals during hibernation to adapt to the changes in the external environment, but there is limited research on the colonic epithelial tissue and gut microbiota of the wild chipmunks during hibernation. This study analyzed the diversity, composition, and function of the gut microbiota of the wild chipmunk during hibernation using 16S rRNA gene high-throughput sequencing technology, and further conducted histological analysis of the colon. Histological analysis of the colon showed an increase in goblet cells in the hibernation group, which was an adaptive change to long-term fasting during hibernation. The dominant gut microbial phyla were Bacteroidetes, Firmicutes, and Proteobacteria, and the relative abundance of them changed significantly. The analysis of gut microbiota structural differences indicated that the relative abundance of Helicobacter typhlonius and Mucispirillum schaedleri increased significantly, while unclassified Prevotella-9, unclassified Prevotellaceae-UCG-001, unclassified Prevotellaceae-UCG-003 and other species of Prevotella decreased significantly at the species level. Alpha diversity analysis showed that hibernation increased the diversity and richness of the gut microbiota. Beta diversity analysis revealed significant differences in gut microbiota diversity between the hibernation group and the control group. PICRUSt2 functional prediction analysis of the gut microbiota showed that 15 pathways, such as lipid metabolism, xenobiotics biodegradation and metabolism, amino acid metabolism, environmental adaptation, and neurodegenerative diseases, were significantly enriched in the hibernation group, while 12 pathways, including carbohydrate metabolism, replication and repair, translation, and transcription, were significantly enriched in the control group. It can be seen that during hibernation, the gut microbiota of the wild chipmunk changes towards taxa that are beneficial for reducing carbohydrate consumption, increasing fat consumption, and adapting more strongly to environmental changes in order to better provide energy for the body and ensure normal life activities during hibernation.

Changes in soil organic carbon components and microbial community following spent mushroom substrate application

While spent mushroom substrate (SMS) has shown promise in increasing soil organic carbon (SOC) and improving soil quality, research on the interplay between SOC components and microbial community following the application of diverse SMS types remains scant. A laboratory soil incubation experiment was conducted with application of two types of SMSs from cultivation of Pleurotus eryngii (PE) and Agaricus bisporus (AB), each at three application rates (3, 5.5, and 8%). Advanced techniques, including solid-state 13C nuclear magnetic resonance (NMR) and high-throughput sequencing, were employed to investigate on SOC fractions and chemical structure, microbial community composition and functionality. Compared to SMS-AB, SMS-PE application increased the relative abundances of carbohydrate carbon and O-alkyl C in SOC. In addition, SMS-PE application increased the relative abundance of the bacterial phylum Proteobacteria and those of the fungal phyla Basidiomycota and Ascomycota. The relative abundances of cellulose-degrading bacterial (e.g., Flavisolibacter and Agromyces) and fungal genera (e.g., Myceliophthora, Thermomyces, and Conocybe) were increased as well. The application of SMS-AB increased the aromaticity index of SOC, the relative abundance of aromatic C, and the contents of humic acid and heavy fraction organic carbon. In addition, SMS-AB application significantly increased the relative abundances of the bacterial phyla Firmicutes and Actinobacteria. Notably, the genera Actinomadura, Ilumatobacter, and Bacillus, which were positively correlated with humic acid, experienced an increase in relative abundance. Functional prediction revealed that SMS-PE application elevated carbohydrate metabolism and reduced the prevalence of fungal pathogens, particularly Fusarium. The application of high-rate SMS-AB (8%) enhanced bacterial amino acid metabolism and the relative abundances of plant pathogenic fungi. Our research provides strategies for utilizing SMS to enrich soil organic carbon and fortify soil health, facilitating the achievement of sustainable soil management.