Streptococcus and Haemophilus within healthy subjects. The present study was first approach in healthy Koreans to identify the oral microbiome types related to the gut microbiome and investigate their characteristics. Hence, we suggest that our results could be potential healthy control data for identifying differences in microbial composition between healthy people and oral disease patients and studying microbial association with the gut microbial environment .Oral microbial ecosystem could influence intestinal diseases, but there have been insufficient studies demonstrating the association of microbial composition between the oral cavity and the intestinal system. Thus, we aimed to investigate the compositional network within the oral microbiome related to gut enterotype from saliva and stool samples collected from 112 healthy Korean subjects. Here, we performed bacterial 16S amplicon sequencing from clinical samples. Then, we determined oral microbiome type related to individual’s gut enterotype for healthy Korean. The co-occurrence analysis was performed to interactivity prediction of microbiome within saliva samples. As a result, it could be classified into two Korean oral microbiome types (KO) and four oral–gut-associated microbiome types (KOGA) according to distribution and significant differences of oral microflora. The co-occurrence analysis showed various bacterial compositional networks linked around  Next-generation sequencing technology (NGS)-based metagenome profiling classifies the microbial community present in a human living environment, and it is a research method that can predict various biological phenomena generated there in connection with the colonization of different microbial species ,2. It haPorphyromonas gingivalis, one of the ‘red complex’ bacteria associated with periodontal disease, is involved in changes in the intestinal Bacteroidetes/Firmicutes ratio [Bacteroidetes/Firmicutes ratio could lead to obesity and alter the balance of inflammatory responses and insulin resistance [Selenomonas noxia, on the health status of many human body sites, such as the lungs, appendix, and intravenous sites, are ongoing [The oral cavity is the starting point of the digestive and respiratory organs, and saliva especially contains many suspended microbial substances that can reach the intestinal tract and affect microbial diversity . Unlike es ratio ,15. Sevesistance ,17. In a ongoing ,18. As sIn this study, the oral microbiome types of healthy Koreans without oral diseases were classified by their associations with the gut microbiome types of individuals through comprehensive 16S microbiome analysis at the genus level. In addition, we aim to describe the intra-oral clinical environment for each oral microbiome type as its association with changing patterns of relative bacterial frequency by identifying the compositional correlative relationship among bacterial genera within each classified type. Ultimately, through this novel approach, which determines oral microbiome types that reflect individual gut microbiome compositions, we aimed to provide healthy control data for identifying differences in the microbial composition between healthy people and patients with oral diseases, such as periodontitis or dental caries, and studying microbes associated with the gut microbial environment .All clinical experiments  conducted for this study were approved by the Institutional Review Board of Dankook University Hospital (IRB numbers: 2020-10-015). Of those 22 years or older who visited the Department of Periodontics, 112 patients without oral disease participated in this study . Each paThe two types of clinical specimens  were collected in each pair per individual to confirm the oral and gut microbial composition through a non-invasive sampling method. All specimens were collected using the NBgen-GUT NP self-collection tube, which included microbial preservation solution . All participants were requested to avoid eating food and oral hygiene (such as brushing or flossing teeth) at least 2 h before sampling. All collected clinical specimens were used to extract metagenomic DNA (mDNA) for 16S microbiome analysis. The mDNA was extracted using the QIAamp PowerFecal Pro DNA Kit , and all experimental processes were performed according to the formal protocol guide provided in the Kit. The quality and yield of the extracted mDNAs were confirmed using a Bioanalyzer  equipment at the Center for Bio-medical Engineering Core Facility  and stored at 4°C until the following process.TM protocol .A total of 224 16S V3-V4 amplicon libraries (112 samples per each sampling site) were prepared following the Illumina metagenomic sequencing library construction workflow. The metagenome sequencing platform of the Illumina targeted a partial area containing the V3-V4 hypervariable region of the bacterial 16S rRNA gene. PCR for amplifying the target region was performed using the KAPA HiFi Hot Start Ready Mix (2×) , and a pair of 16S V3-V4 target-specific primer recommended by Illumina. The primer sequences were as follows: 16S 341F forward primer is 5’-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG-3“ and 16S 805 R reverse primer is 5”-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC-3’. After the PCR amplification, the purification process of all PCR amplicon products was conducted using the AMPure XP beads . Then, additional PCR amplification was performed using Nextera XT Index Kit , which included Illumina multiplexing dual index barcode and sequencing adapter sequence. The final PCR products were then purified once again using the AMPure XP beads. After the amplicon library construction, the 16S metagenome sequencing was performed using the paired-end 2 × 300 bp Illumina MiSeq16S V3-V4 sequencing raw read data were demultiplexed by using the split_libraries_fastq.py function in the QIIME2 microbiome data analysis pipeline, and quality sequences were trimmed using the Divisive Amplicon Denoising Algorithm 2 (DADA2), which detects and corrects amplicon errors and filters out PhiX and chimeric sequences, in R (version 3.3.2). The R analysis parameters were as follows: EE = 2, TruncL= c , and q = 10. At the same time, the high-quality sequencing data that completed the quality check was used to cluster the Amplicon Sequence Variants (ASVs) using the QIIME2-based DADA2 pipeline. The beta diversity analysis to identify the difference in microbial composition between each comparison group was estimated using Principal Coordinate Analysis (PCoA) based on the unweighted_UniFrac (considering phylogeny), Bray-Curtis , and Jensen–Shannon divergence (in PAM clustering method) distance dissimilarity estimation matrices. Finally, the representative ASV data, generated after denoising, was used to identify bacterial taxonomy using a sklearn-based Naive Bayes classifier trained on the SILVA v138 99% bacterial 16S V3-V4 database. In this process, the feature table assigned to the bacterial taxonomy for each ASV data was formed by setting a confidence threshold of 70% (default) or more value (Supplementary Table S2). Then, the bacterial relative abundance analysis at each biological taxonomic rank was performed to determine the relative frequency of bacterial strains present in each sample to identify differences in the bacterial composition of each group (Supplementary Table S3).To create clusters of the Korean oral microbiome type (KO type) and the Korean oral–gut-associated oral microbiome type (KOGA type), the bacterial genera, which were classified by more than 0.01% relative frequency within healthy subjects (Supplementary Table S3), were preferentially filtered from the relative abundance data classified from each sample. This process is essential to distinguish bacterial genera classified high-relative frequency, which plays a dominant role in discriminating microbial clusters within each sample of healthy subjects group. Based on the relative frequency of the filtered bacterial taxa (at the genus level), the dissimilarity score of bacterial composition for each sample was calculated using the ‘Jensen–Shannon divergence (JSD)’ distance clustering metric. Subsequently, estimating the optimal number of bacterial composition clustering for each sample and evaluating the robustness of the clusters was performed by applying the ‘Calinski–Harabasz (CH)’ index and Silhouette validation method included in the R studio’s ClusterSim library. Finally, the ‘Partitioning Around Medoid (PAM)’ clustering method was applied to cluster each sample according to the optimal number of clusters calculated previously. The KOGA type was designated by applying the same clustering pipeline by correlating the gut microbiome data of samples corresponding to each KO type previously clustered. The Microbiome Multivariable Association with Linear Models (MaAsLin 2) were used to cross-validate about PAM clustering results . The data analysis process using the MaAsLin2 was as follows: (1) The unnamed genera  with ‘;__’ were removed . Then, the bacterial genus abundance profiles were normalized to generate probability distribution. (2) The bacterial genera with very low relative abundance were removed to decrease the noise, if their average abundance across all samples was below 0.01%. (3) Finally, the preprocessed input data was applied to BiocManager’s MaAsLin 2 package in R studio. In the MaAsLin 2 analysis, relative abundance data created by QIIME2 was used as input data. So, normalization and prevalence filtering about input data were set to ‘None’ parameter in MaAsLin 2 package in R studio.The oral microbiome data in other nations  annotated on the NCBI database was used to compare the differences with the oral microbiome type classified in the present study. The sources (Bioproject number on NCBI database) of public data applied in this comparison were as follows: China-Taiwan_PRJNA503603 (76 samples), PRJNA609009 (21 samples), and PRJEB39064 (27 samples); Portugal-Spain_PRJNA774299 (22 samples) and PRJNA427101 (1319 samples); Australia_PRJNA558132 (64 samples).R = SparCC correlation coefficient): |R| > 0.3 and p-value <0.05. The output data generated through the co-occurrence analysis was visualized using the ‘Cytoscape’ bioinformatics tool and the ‘networkx’ tool using the python operating system.Co-occurrence analysis was performed to identify other bacterial genera forming a compositional network with each dominant bacterial genera of KO type. In this process, the ‘Sparse Correlation Network Investigation for Compositional Data (SCNIC)’ analysis-based ‘SparCC’ correlation method  was applThe Linear discriminant analysis Effect Size (LEfSe) analysis was performed to identify distinct oral bacterial genera or species showing a significant difference in relative bacterial frequency within each KOGA type sub-classified from KO type. The threshold on the logarithmic LDA score for discriminative features was set to 2.0 .z-score to confirm its standard normal distribution within each KOGA type. Next, we converted each calculated z-score value into a percentile value (%) and estimated the potential health characteristic of each KOGA type by performing a scoring process based on the quartile formula. In this analysis, since the characteristics of each type based on scoring were standardized to the distribution rate of beneficial bacteria, the scoring process for harmful bacteria was performed by inversely converting the negative and positive signs of each calculated z-score value.First, we converted the relative frequency of each selected bacterial species into a U statistical test was applied to comparative statistical analysis between all comparison groups established for this study. The permutational multivariate analysis of variance (PERMANOVA) non-parametric statistical test was applied to identify the statistical significance of beta-diversity analysis using the Bray–Curtis, unweighted_UniFrac, and Jensen–Shannon divergence (JSD) distance matrices. The Kruskal–Wallis ANOVA test and Mann–Whiteny U test were applied to LEfSe analysis. The Statistical significance evaluated from all comparison set was denoted as asterisk (*) if p-value<0.05, (**) if p-value<0.01, and (***) if p-value<0.001.The Mann–Whitney We comprehensively investigated the bacterial compositional network associations between oral and gut microbiomes by clustering healthy KO types and KOGA types. We checked the integrity of the metagenomic DNA (mDNA) samples extracted from the saliva  and stool (gut) samples of 112 participants in this study, because high-quality mDNA extraction can affect accurate microbial quantification. For the comprehensive 16S microbiome analysis, we performed 16S V3-V4 metagenomic sequencing using constructed sequencing libraries. As a result of the sequencing, the average demultiplexed read counts generated from the oral and gut samples were 47,675 and 59,042, respectively (Suplementary Table S1). Finally, the average high-quality raw read counts (non-chimeric reads) used for the comprehensive 16S microbiome analysis after the filtering process were 32,004 and 35,233, respectively. For comprehensive microbiome analysis, the ASV taxonomy classification numbers of the oral and gut sample types clustered with >70% (default) confidence threshold about the sequence alignment with the SILVA v138 16S rRNA reference database were 4,123 and 4,354, respectively (Supplementary Table S2). Our raw 16S V3-V4 sequencing data processing and ASV taxonomic clustering results allowed for subsequent microbiome analyses based on high-quality sequence data.n = 112; Supplementary Table S3). Prior to this analysis, we filtered 75 bacterial taxa calculated to an average relative frequency of over 0.01% from the 215 bacterial genera classified from the healthy subjects to decrease the analytical noise. Based on the distance dissimilarity score of the bacterial communities between each individual calculated by applying the Jensen–Shannon divergence (JSD) formula, we confirmed that the optimal cluster number was two , we applied relative bacterial abundance data at the genus level for healthy subjects  previously clustered, we applied the relative abundance analysis data for the gut microbiome of individuals corresponding to each KO type (Supplementary Table S4). This approach was performed by applying the same workflow as the clustering method for the KO type. We confirmed that the optimal cluster number between each gut microbial composition within the group corresponding to the S and H types was two, respectively . Then, based on the data in Supplementary Table S4, we found that the major dominant bacterial genera that formed the cluster groups within each KO type group were Bacteroides and Prevotella . Bacteroides and Prevotella as dominant bacterial genera within the group of each KO type. The results of several previous studies that conducted comprehensive 16S microbiome analyses showed that the healthy Korean enterotype was predominantly clustered as Bacteroides and Prevotella types [Streptococcus-gut_Bacteroides dominant microbiome type (n = 43), SP , HB , and HP  types were divided into 38.4, 17.0, 32.1, and 12.5%, respectively  within each KO type (la types . Based oectively . These eStreptococcus (Cluster 1) and Veillonella (Cluster 2); Portugal and Spain (European),_Streptococcus (Cluster 1) and Haemophilus (Cluster 2); and Australia (Oceanian),_Streptococcus (Cluster 1) and Prevotella (Cluster 2). Thus, Streptococcus was a common dominant bacterial genus that formed a microbial cluster within each public dataset. Although both the KO type and European  data formed clusters around the Streptococcus and Haemophilus genera, we confirmed that there were differences in the bacterial composition (relative abundance) within each cluster group.We also compared the clustered KO type to public data in other nations  annotated in the National Center for Biotechnology Information (NCBI) database . The majStreptococcus and Haemophilus genera, major dominant bacterial taxa for determining the characteristics of each KO type, became the criteria for verifying bacterial correlations. In the first network analysis, six (five positive and one negative) and five (three positive and two negative) significant correlations (|R| > 0.3 and p < 0.05) were found within the S and H types, respectively, from which 42 (33 positive and nine negative) and 30 (19 positive and 11 negative) significant (|R| > 0.3 and p < 0.05) correlations were identified through the second network analysis. In the first network of the S type, the Granulicatella, Gemella, and Actinomyces genera were strongly estimated to have a more positive correlation with Streptococcus than the others (R > 0.51). In contrast, the Selenomonas genus was deduced to have a negative correlation with Streptococcus (R = − 0.323). Interestingly, in the second network, the Rothia genus was deduced to have a strong positive correlation with all the genera positively correlated with Streptococcus in the first network except for the Corynebacterium genus. We also found that the Streptococcus, Selenomonas, Gemella, and Granulicatella genera are forming the potential correlation to affect bacterial colonization between each other within the oral cavity. In the H type, the relatively high positive correlations of Veillonella and Megasphaera genera with Haemophilus were found in the first network . In contrast, Treponema and Bacteroides genera were deduced to have a negative correlation with Haemophilus (R < −0.32). In the second network of the H type, we found that many bacterial genera were linked to the second branch with Streptococcus compared to other genera in the first network (seven positive and eight negative correlations). Especially, it was confirmed that the Gemella and Rothia genera had a strong positive correlation with Streptococcus compared to the others . In this network analysis, there were 10 and 9 distinct bacterial genera with statistically significant relative frequency differences between the two KO types in the significant co-occurrence networks identified within the S and H types (Supplementary Table S6). Even though we confirmed the compositional network within each KOGA type by applying the same co-occurrence analysis pipeline, we could not identify a significant correlation distinguished from the KO type. This result was presumed to be because the bacterial composition within each KOGA type was not significantly different from that of the upper-cluster group, the KO type. Therefore, we applied LEfSe analysis to identify distinct bacteria showing significant relative frequency differences between each KOGA type (p < 0.05), we identified the taxonomy by extending the range of the ranks to the species level in the genus. As a results of LEfSe analysis, total of nine distinct bacterial taxa were identified from each KOGA type.We performed co-occurrence analysis to identify the compositional network  between the bacterial taxa classified within each KO type : In the analysis of the oral samples, the calculated potential health score within the SP-type was higher than that of the other types . In the gut samples, a high potential health score was calculated for the two dominant Bacteroides types . Based on the summation value of the scores within each KOGA type , the type with the highest calculated health score value was SB .Finally, we estimated the potential health characteristics that could be described with microorganisms by confirming the distribution of beneficial, normal flora, and harmful bacterial species within the oral and gut samples of each KOGA type ; Table 5Based on large-scale microbiome consortiums, such as HMP and MetaHIT, various human microbiome research has continuously been conducted to promote human health and prevent various systemic diseases ,26. In pStreptococcus (S-type) and Haemophilus (H-type) genera-predominant groups (KO type). Streptococcus and Haemophilus are well known as normal flora (or core microbiome), with high proportions in the human oral cavity [Bacteroides and Prevotella dominant types) corresponding to each KO type. We confirmed the difference between our clustering results and other oral microbiome studies conducted in various locations in different countries. Streptococcus and Haemophilus-dominant. Still, there was a clear difference in the relative bacterial composition within each cluster type classified in the present study. We also suggest that although the major dominant bacteria in each cluster was different, our relative abundance data were somewhat similar to the oral microbiome composition classified from Asian samples. Based on these results, we could predict that our oral microbiome type clustering results showed some Korean specificity compared to other national data.In our study, we identified the oral microbiome types of healthy Korean adults without oral diseases in association with the gut microbiome types and confirmed the potential health characteristics of each type through comprehensive microbiome analysis. Considering the reading of several hypervariable regions (V3-V4) on the 16S rRNA gene in this microbiome study, we minimized the error rate of the bacterial classification by identifying the microbiome type at the genus level. We confirmed that the two bacterial cluster types in the 112 healthy individuals were divided into l cavity . AdditioVeillonella, Megaspaera, Granulicatella, Gemella, Actinomyces, and Neisseria, formed positive correlations in the first network with Streptococcus and Haemophilus within each KO type [Selenomonas and Treponema, had negative correlations [Rothia genus forms second networks with high positive correlation values with many bacterial genera that showed first correlations with the Streptococcus. Although Rothia is normal flora within the human oral cavity, it is opportunistic infectious bacteria with the potential to cause immune diseases in the host by producing enterobactin (a siderophore compound) [In co-occurrence analysis to identify compositional networks between bacterial genera, we found that normal flora in the oral cavity such as  KO type . In contelations . These rompound) ,47. Throz-score-based bacterial distribution of various beneficial and harmful bacterial species within the gut and oral samples. In this analysis, the SB-type was estimated to show the most potentially positive health effects in the oral and visceral samples of each KOGA type. Through this approach, we could simultaneously predict the potential health characteristics of the oral and gut for each KOGA type clustered in this study.Finally, we estimated the potential health characteristics for each KOGA type by confirming the In summary, we clustered the oral microbiome types related to the gut enterotype through comprehensive 16S microbiome analysis of saliva and fecal samples collected from healthy Korean adults and identified the potential health characteristics. The present study was the first approach to identifying the oral microbiome types related to gut enterotypes and investigate their characteristics in healthy Koreans. Hence, we suggest that our results could be potential healthy control data for identifying differences in microbial compositions between healthy people and oral disease patients and studying microbial associations with the gut microbial environment .Click here for additional data file.


Early life vitamin D exposure is linked to later skeletal health with maternal vitamin D status in pregnancy associated with neonatal bone mass. The MAVIDOS study has demonstrated that vitamin D supplementation leads to reduced RXRA DNA methylation. Mice exposed to early life vitamin D deficiency have reduced bone mass and bone accrual in response to mechanical loading. Using the tibiae of these mice, we have examined the effect of diet and mechanical loading on the DNA methylation of promoters of genetic loci important for bone growth and development and their association with bone strength.osterix and Runx2 in these bones. Tibia strength was also demonstrated to be associated with a change in DNA methylation status in CpGs of the vitamin D receptor (VDR), ostrix, and RXRA genes.Mechanical loading of mouse tibiae leads to a reduction of RXRA DNA methylation. Early life vitamin D deficiency is associated with altered methylation of We have shown for the first time that mechanical loading of bone and early life vitamin D deficiency leads to changes in the epigenome of this tissue in key genes in the vitamin D and osteoblast differentiation pathway. Early life exposures have been hypothesised to lead to long-term changes in adult health . SuboptiOne mechanism by which early life environment has been suggested to influence later phenotype is through the altered epigenetic regulation of genes. Epigenetic mechanisms induce heritable changes in gene expression without a change in the gene sequence. Epigenetic mechanisms include DNA methylation which can affect the accessibility of the DNA to the transcriptional machinery . In geneRXRA locus has been observed in umbilical cord tissue stored at birth from the MAVIDOS study  in the maternal supplementation group. This study found a positive effect of maternal vitamin D supplementation during pregnancy on neonatal bone mass for winter births (when background 25(OH)D-vitamin D concentrations are lowest). This suggests vitamin D may affect bone development accrual through the epigenetic of regulation of RXRA and related pathways . Fo. Fo24]. n = 24) . All animals were supplemented with 2 mmol/l Ca2+ in their drinking water.Female mice were fed a vitamin D-supplemented diet (1000 units/kg) or vitamin D-free diet based on AIN93G  diet from 4 weeks of age. Mice were randomly allocated to either vitamin D replete (1000 units/kg) or deplete diet. At 10 weeks of age, female mice were mated with healthy control adult males; dams remained on their respective diets throughout gestation until pup weaning. Female offspring remained with their dams until weaning at day 22 whereupon they were weaned onto the vitamin D-replete diet  Act 1986. The study was approved by the local committee of the University of Sheffield that oversees animal use within the institution. Loading experiments were performed under isoflurane anaesthesia. All efforts were made to minimise animal suffering.A noninvasive method of tibial loading was used at ages 8 and 16 weeks as previously reported . The peaMice were euthanised on day 15  was treated as an internal control for loading  Bones previously stored at −20 °C in PBS-soaked gauze were placed in PBS at 4 °C overnight prior to testing at room temperature. Samples were subjected to three-point bending analysis using a Bose ElectroForce 3200 mechanical testing machine with a 450 N load cell as previously described . BrieflyFollowing biomechanical testing, bones were again stored frozen at −20 °C wrapped in PBS-soaked gauze. DNA was extracted from tibiae using the DNeasy Blood and Tissue Kit as per the manufacturer’s instructions. DNA was stored at 4 °C for short-term use and at −20 °C for longer-term storage.RXRA, VDR, Runx2, and Osterix) .%DNA methylation of specific CpGs upstream of the promotors of the 4 examined genes (ix) Fig.  was measRunx2 promoter, as this was a region previously identified to be differentially methylated [Pyrosequencing primers were designed to measure the DNA methylation status of CpGs sites within the promoter regions of RXRA, VDR, and Osterix, where sequence context permitted the efficient binding of primers. For Runx2, DNA methylation was measured across CpG sites −2484 to −2148 bp upstream of the thylated .t-tests compared %DNA methylation between the non-loaded and loaded limbs of mice exposed to a control or prenatal vitamin D-deficient diet. To determine whether there were any associations between DNA methylation and tibial stiffness or ultimate force, linear regression analyses were adjusted for the treatment group and for mechanical loading.Independent


Oral squamous cell carcinoma (OSCC) in the context of oral submucous fibrosis (OSF) has a high incidence owing to undefined pathogenesis. Identifying key genes and exploring the underlying molecular mechanisms involved in the conversion of OSF into OSCC are in urgent need. Differentially expressed genes (DEGs) between OSCC and OSF were dug from GEO databases and a total of 170 DEGs were acquired. Functional association of DEGs were analyzed by GO and KEGG. Protein–protein interactions (PPIs) analysis was carried out and candidate biomarkers were identified by Gene co-expression analysis and Cox analyses. Hub genes were confirmed by qRT-PCR in tissues and cell lines, of which we found that IRF4 mRNA was successively up-regulated from Normal to OSF and then to OSCC and associated with immune infiltrating levels. In addition, Immunohistochemical (IHC) and Immunofluorescence (IF) assays were conducted to validate the consistent upregulation of IRF4 and the oncogene role of IRF4 in OSF and OSCC at translation level. IRF4 may be indicative biomarker in transformation of OSF into OSCC. High IRF4 expression contribute to increased immune infiltration of OSCC and may provide a novel diagnostic marker for OSCC patients translated from OSF. OSF is a precancerous condition with a propensity for malignant transformation and up to a quarter of cases are present with epithelial dysplasia at biopsy. Malignant transformation rates have been estimated to range from 5.6 to 9.13 percent in recent studies4. OSCC is the most common malignant tumor of the head and neck5, and also the sixth highest incidence of cancer worldwide7. It is further observed that OSCC originating from OSF tends to occur in young adults, commonly in the posterior buccal, gingival and vestibular mucosa, and are more clinically aggressive and metastatic8. The prognosis and clinicopathological features of OSCC patients with OSF are inferior to conventional OSCC patients9, so it is urgent to search for new OSCC diagnostic biomarkers in the context of OSF.It is estimated that 10–20% of the world's population is accustomed to chewing betel nut. Epidemiological study results suggested that betel nut is the main cause of oral submucosal fibrosis (OSF), and consumption of betel nut is also associated with oral squamous cell carcinoma (OSCC)10. The feature of tumor microenvironment (TME) is nutrition competition or coordination between tumor and infiltrating immune cells that influences antitumor immunity. Adaptive immune responses are essential for the clearance of tumors11. In the aspect of OSCC, inflammatory mediators are identified as potential markers for diagnosis and prognosis of OSCC12. The tumor microenvironment impacts evasion of OSCC from immune recognition and destruction13.The role of tumor immunology in tumorigenesis and progression is significant. Numerous types of cancer form ectopic lymphoid aggregates, also called tertiary lymphoid structures (TLSs), which are relevant to superior prognosis and response to immunotherapy14. In various mature lymphoid neoplasms, abnormally expressed IRF4 also acts as an oncogene. IRF4 and its upstream factor NF-κB form a regulatory circuit to promote the oncogenic transcriptional program in malignant lymphoid cells15. However, research on the role of IRF4 in OSCC is still lacking16.As a member of the IRF transcription factor family, interferon regulatory factor 4 (IRF4) is expressed and crucial for the development and function of numerous immunocyte types such as B cell, T cell and dendritic cell. IRF4 plays a significant role in autoimmune diseasesIn this study, we conducted a comprehensive bioinformatics analysis including functional enrichment analysis, CNV and immune infiltration analysis through Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases to identify key genes and explore the underlying molecular mechanisms involved in OSF into OSCC. We finally found the role of IRF4 in predictabilities of the transformation from OSF to the presence of OSCC. Understanding of the potential oncogenic axis may enable the discovery of noninvasive disease-specific diagnostic biomarkers. There is a discovery that IRF4 can be employed as a possible diagnostic and immunological predictor of malignant transformation of OSF into OSCC. This study may broaden the application of IRF4 in immunotherapy.2.Two human cell lines  were obtained from ATCC. The HN4 (OSCC cell line) and human oral keratinocytes  cell line were cultured in Dulbecco’s modified Eagle’s medium containing 10% fetal bovine serum  and 1% penicillin/streptomycin . Cells were incubated at 37 °C with 5% COThis study was approved by the ethical committee department of Affiliated Hospital of Stomatology of Nanjing Medical University (PJ2022-086-001). Research was performed in accordance with relevant guidelines and regulations. Informed consent was obtained from all patients. A total of 8 Normal oral mucosa tissues  and 10 oral squamous cell carcinoma (OSCC) samples were obtained in Stomatological College of Nanjing Medical University. The normal oral mucosa tissues were collected from the wounds of patients from whom impacted third molar were extracted. 10 oral submucosal fibrosis (OSF) samples were collected at Hunan Xiangya Hospital of Central South University (202005151). Written informed consent was acquired from all of the patients.http://www.ncbi.nlm.nih.gov/geo/)19. GSE64216 was downloaded from GPL10558 (Illumina HumanHT-12 V4.0 expression beadchip), which included 4 tissues from patients with oral submucous fibrosis and 2 from healthy controls. GSE23558 was downloaded from GPL6480 (Agilent-014850 Whole Human Genome Microarray 4 × 44 K G4112F), which contained 27 OSCC tissues compared with 4 independent controls and 1 pooled control oral cavity tissue from healthy donors.Two datasets of gene expression profiles  were obtained from the Gene Expression Omnibus (GEO) database . A total of 505 OSCC patients’ tissues and 24 Normal tissues were included from TCGA-OSCC project.Besides, the data of exon RNAseq and the corresponding clinical data of patients with OSCC were publicly available at TCGA (https://www.r-project.org/) Limma package was used to analyze DEGs in the two comparisons 20. The adj. P value was obtained through multiple checking and correcting by the Benjamini and Hochberg method21. DEGs in the two comparisons with the filter criteria of adj. P value < 0.05 and |logFC (fold change)|> 1 may be candidates in the development of OSF and OSCC. Likewise, differentially expressed mRNAs between Normal and tumor in the TCGA database were also obtained.In the GEO database, the GEO2R online analysis tool based on GEO query and R software  pathway25 were used for the database for annotation, visualization and integrated discovery (https://www.bioinformatics.com.cn). For DEGs, P value < 0.05 and enrichment count of at least 5 were set as the cutoff criteria for significance during the development of OSF to OSCC.Gene Ontology (GO) annotation26. A threshold of 0.4 (medium confidence) was used to construct the PPI network with Cytoscape software 27. The degree of connectivity was employed to describe the association between nodes.Protein–protein interaction (PPI) networks were mapped to predict the interactions between gene–encoded proteins using the Search Tool for the Retrieval of Interacting Genes database  database29. For the core candidate genes, we ranked the most overlapped enriched GO and KEGG pathways and visualized them. We used ggplot2 package in R to visualize the top-ranked enriched pathways30.In order to explore the biological signaling pathway, we ranked gene difference and performed gene set enrichment analysis based on their rankings. We used clusterProfiler and GSEABase to analyze the enriched pathwayshttp://www.sangerbox.com/). Copy number variation (CNV) is the most frequent type of genetic variation occurring in cancers and associated with the occurrence and progression of tumors. The frequencies of copy number gains or losses were conducted and waterfall plot of the mutational landscape was generated using the Sangerbox. The plot was conducted with R.Univariate and multivariate Cox analysis and forest plot were conducted with Sangerbox (http://gepia2.cancer-pku.cn/#index)31. The correlation of IRF4 expression with tumor infiltrating cells were conducted with TIMER (https://cistrome.shinyapps.io/timer/) and TISIDB (http://cis.hku.hk/TISIDB/)33.To conduct survival analysis, clinical data and RNA expression data from TCGA dataset were downloaded. Then Kaplan–Meier survival plot and LogRank analysis was done by GEPIA2 database  according to the manufacturer’s protocols. Quantitative real-time PCR primers were designed and synthesized by Generay . The total RNA was converted into cDNA by Hiscript1 III RT SuperMix for qPCR (+gDNA wiper) . The relative mRNA expression level and the internal control GAPDH were quantified on ABI PrismR 7900HT Real-Time PCR System (Applied Biosystems). All experiments were conducted three times independently, and the data were analyzed by the 2IHC was performed on formalin-fixed, paraffin-embedded specimens to quantify the levels of proteins encoded by the signature genes in Normal oral mucosa, OSF and OSCC tissues. The tissue sections were first deparaffinized in xylene and then dehydrated in ethanol. Citrate buffer (pH = 6) was used for antigen extraction, followed by blocking with goat serum for 1 h to prevent nonspecific binding of antibodies. The samples were then incubated in solutions containing specific primary antibodies  followed by incubation in solutions containing secondary antibodies. Finally, the sections were visualized after staining with 3, 3′-diaminobenzidine (DAB).Tissue sections were dewaxed in xylene, and then dehydrated in ethanol. The tissues were then incubated in solutions containing specific primary antibodies  followed by incubation in 488 goat anti-rabbit secondary antibodies .P < 0.05 was determined statistically significant.The experimental results were presented as mean ± standard deviation (SD) and statistically analyzed using GraphPad Prism 9. The samples from the TCGA database were stratified into high- or low- expression groups according to the median of gene expression. Survival curves with log-rank test and Kaplan–Meier method were drawn for mRNAs. A threshold of p value < 0.05)  Fig. B, C. By 05) Fig. D.Figure . . The GO annotations of DEGs consisted of three categories including biological process (BP), cellular component (CC), and molecular function (MF), which were used to analyze the functional enrichment of DEGs. The significant GO terms were conspicuously enriched in tube development, vasculature development, cardiovascular system development, blood vessel development, cell surface, plasma membrane region, stress fiber, growth factor activity, structural constituent of muscle, and heparin binding Fig. A. The KE. Fig. B.Figure .” GO terms  analysis was conducted using GSEA software, which showed that “DEFENSE RESPONSE TO SYMBIONT, DNA DEPENDENT DNA REPLICATION, MITOTIC NUCLEAR DIVISION, etcrms Fig. C and “ARles Fig. D. GSEA aCo-expression analysis of these 170 DEGs were performed via construction PPI network, and the results was visualized through cytoscape. 5 protein coding gene clusters were obtained, of which the cluster centered around CFTR and CXCL12 may play a critical role in transformation of OSF into OSCC .The Cancer Genome Atlas (TCGA) OSCC cohort including 505 OSCC tissues and 24 Normal tissues was used to further screen hub genes Fig. A. GSEA a36. Thus, we performed CNV frequency analysis for those 16 hub genes in TCGA data. Thus, we analyzed the incidence of CNV and somatic mutations of these hub genes in patients with OSCC in TCGA database, which revealed frequent CNV alterations in these genes, with a higher proportion of copy number losses than gains  is one of the main factors affecting gene expression abundance in many cancersP < 0.05)  consisting of CD79A, HLF and IRF4  of OSCC patients (P < 0.01)  in OSCC Fig. F. Tumor ity Fig. F. In parity Fig. A.Figure Besides, the relationship between abundance of tumor-infiltrating lymphocytes (TILs) and expression, copy number and methylation of IRF4 in pan-cancer was analyzed through TISIDB. The expression of IRF4 was positively correlated with majority of TIL levels, and the copy number and methylation of IRF4 was negatively related to immune infiltration in OSCC Fig. B. In add41. There is a need for consensus on sampling, staining and quantification procedures concerning detection of IRF4 protein expression by IF and IHC staining of tissues from Normal, OSF and OSCC patients to improve reproducibility of studies and confirm bioinformatics results. Here, we detected IRF4 protein expression in different tissues from different patients by IF staining  DEGs in total. GO analysis revealed that the DEGs is mainly associated with regulation of tube development, vasculature development, cardiovascular system development, blood vessel development, cell surface, plasma membrane region, stress fiber, growth factor activity, muscle structural constituent, and heparin binding. In addition, KEGG pathways analysis showed that DEGs mainly enriched in adrenergic signaling in cardiomyocytes, drug metabolism, metabolism of xenobiotics by cytochrome P450, chemical carcinogenesis. 170 genes were included in the PPI network and IRF4 was recognized as a potential biomarker by PCR assay and as a hub gene by bioinformatics analysis, which indicated that IRF4 may play a vital role in OSCC carcinogenesis.45. Recent studies have identified the established roles of both the innate and adaptive immune systems in the host defense against highly aggressive cancers, which are driving a remarkable progresses of modern cancer immunotherapies46. The key role of T cells in tumor immunity has been demonstrated by the positive correlation between T-cell infiltration at the tumor bed and prognosis, and the successful clinical application of chimeric antigen receptor (CAR) T-cell infusions in treating some hematologic malignancies47. B cells and Tfh cells can infiltrate into tumors and B cells affect tumor progression through interaction with Tfh cells. B cells can suppress tumor progression through multiple pathways, including promoting T cell response, secreting immunoglobulins, killing cancer cells directly, and increasing tumor activity through immunosuppressive cytokines48. The lymphoid transcription factor, Interferon regulatory factor 4 (IRF4), is also a key regulator in the development of various immune cells, including T and B cells, suggesting its potential significant role in immuno-oncology. It is also well- known that IRF4 controls numerous decision-making processes relating to B cell differentiation, maturation, and signal transduction. Consequently, genetic alterations that affect the functional aspects of IRF4 can result in clonal transformation and have been identified in various lymphoid malignancies50. In the field of OSCC, IRF4 were highly expressed in OSCC tumor samples compared to control samples and involved in tumor immunity-related signaling pathways. IRF4 indicated significant prognostic values and could be potential therapeutic biomarkers in targeting tumor immunity of OSCC51.The contribution of recruited immune cells to solid tumors is now a widely accepted mechanism of cancer pathogenesisWe found IRF4 may play a role in the transformation of fibrosis into cancer. It may provide a new vision on therapeutic targets in OSCC by exploring the underlying mechanisms. Finally, it may be beneficial to realize early-stage diagnosis and provide new therapeutic targets in OSCC.Though the study may have clinical significance, there are still several limitations we should consider. First of all, the sample numbers of both the TCGA database and clinical specimens are far from inadequate. We need to collect more information to continue verifying its accuracy. Second, the function and mechanism of biomarkers in OSF translating into OSCC need to be further studied in vivo and in vitro. In future research, we will try to explore the related molecular mechanism of IRF4 in malignant transformation of OSF into OSCC.In summary, our study identifies that IRF4 might be employed as a diagnostic and immunological predictor of malignant transformation of OSF into OSCC and provides a new vision for OSCC immunotherapy. Meanwhile, we investigated IRF4 from the aspects of methylation level, mutation analysis, qRT-PCR assay, IF and IHC analysis, which may provide ideas for future research on the role of IRF4 in the progression of OSCC.Supplementary Information.


We aimed to evaluate whether depressive symptoms mediated the relationship between frailty phenotype and cognitive function by sex. Frailty phenotype symptoms, cognitive function scores, and depressive symptoms were measured in 3,705 community-dwelling US adults from the Health and Retirement Study (HRS) from 20012-2016. Fried’s frailty phenotype criteria  were used as a continuous score for frailty symptoms (0-5). Global cognition was measured by the Telephone Interview for Cognitive Status (range: 0-35). The Centers for Epidemiologic Studies-Depression Scale (CES-D) was used as a continuous measure to assess depressive symptoms (0-8). We used mediation analysis to estimate the direct and indirect effects of frailty symptoms on cognitive scores to evaluate the role of depressive symptoms as a potential mediator. Males had a larger total effect  for lower cognitive score for each increase in frailty symptom compared to females , suggesting all five frailty symptoms was associated with 2.15 lower cognitive scores for males and 1.50 for females. A significant indirect effect from frailty phenotype to cognition was found through depressive symptoms for females  but not males . These results highlight the importance of identifying individuals with frailty and depressive symptoms to monitor and provide interventions to preserve cognitive function


Mus musculus) are widespread and invasive on many islands where they can have both direct and indirect impacts on native ecological communities. Given their opportunistic, omnivorous nature the consumptive and competitive impacts of house mice on islands have the potential to vary over time in concert with resource availability and mouse population dynamics.House mice (δ13C) and nitrogen (δ15N) stable isotope values of mouse tissue and prey resources in a single year (2013), and isotopic niche and mixing models to quantify seasonal variation in mouse diets and the potential for resource overlap with native species.We examined the ecological niche of invasive house mice on Southeast Farallon Island, California, USA using a combination of mouse trapping, food resource surveys, and stable isotope analysis to better understand their trophic interactions with native flora and fauna. Specifically, we coupled the analysis of seasonal variation in resource availability over a 17-year period (2001–2017), carbon (We found that plants were the most important resource for house mice during the spring months when vegetation is abundant and mouse populations are low following heavy precipitation and declines in mouse abundance during the winter. While still consumed, plants declined in dietary importance throughout the summer and fall as mouse populations increased, and seabird and arthropod resources became relatively more available and consumed by house mice. Mouse abundance peaks and other resource availability are low on the island in the fall months when the isotopic niches of house mice and salamanders overlap significantly indicating the potential for competition, most likely for arthropod prey.Our results indicate how seasonal shifts in both mouse abundance and resource availability are key factors that mediate the consumptive and competitive impacts of introduced house mice on this island ecosystem. As mice consume and/or compete with a wide range of native taxa, eradication has the potential to provide wide-reaching restoration benefits on Southeast Farallon Island. Post-eradication monitoring focused on plant, terrestrial invertebrate, salamander, and seabird populations will be crucial to confirm these predictions. Mus musculus) and other rodents are some of the most widespread invasive mammals on earth; amongst vertebrates, the breadth of their global distribution is second only to that of humans (Rattus spp.) and/or where rat eradication efforts have freed mice from the constraints of competition and predation  is located 48 km west of San Francisco off the coast of central California. This 28 ha island is the largest of the South Farallon Islands, part of the Farallon Islands National Wildlife Refuge, which hosts the largest seabird breeding colony in the contiguous United States . SEFI alAthene cunicularia) to overwinter on SEFI (Hydrobates homochroa) which results in significant predation on this species of conservation concern  constituted 63% of recovered plant material in mouse stomachs  which is endemic to SEFI . As salamanders feed primarily on insects and other small invertebrates  and nitrogen (δ15N) stable isotopes values of consumer tissues reflecting the abundance of these same biomarkers in their food sources  and common murres  inhabiting rocky slopes and cliffs, storm-petrels , auklets , pigeon guillemots (Cepphus columba), and tufted puffins (Fratercula cirrhata) nesting in rock crevices and burrows, black oystercatchers (Haematopus bachmani) nesting along the rocky shoreline, and western gulls  nesting across the island and most common on the flatter or more gently sloped areas (Mirounga angustirostris), harbor seal (Phoca vitulina), Steller sea lion (Eumetopias jubatus), California sea lion , and the northern fur seal  (Aeorestes cinereus), Mexican free-tail bats (Tadarida brasiliensis), and Western red bat (Lasirurs blossevillii), have been recorded visiting the island, house mice are the only breeding terrestrial mammals present on SEFI (Felis catus) and European rabbits (Oryctolagus cuniculus) were intentionally introduced to SEFI in the late 1800s and successfully removed from the island in the early 1970s  . While h on SEFI . Honda e on SEFI  and the  on SEFI . Domestily 1970s .Sampling was approved by the United States Fish and Wildlife Service under a cooperative agreement with Point Blue Conservation Science (no. 81640AJ008) and the California Department of Fish and Game scientific permit (no. SC–008556). All vertebrate sampling protocols were approved by and adhered to statutes of the Institutional Animal Care and Use Committee of the Woods Hole Oceanographic Institution (no. 4855-001).We compiled data over a 17-year period  to quantify seasonal trends in mouse abundance and resource availability on SEFI. This included an index of mouse abundance based on monthly trapping, precipitation data as a proxy for vegetation phenology, and measures of arthropod, seabird, and salamander abundances. For all metrics, monthly values were compiled and averaged to obtain seasonal values for the spring , summer , fall , and winter  for each season and year that data were available .® Ultra Set® covered snap traps baited with peanut butter and oats. Trapping success was determined as the proportion of trap-nights set per monthly session (either 84 (2001–2004) or 99 (2010–2012 and 2016–2018)) in which house mice were captured.Our index of mice abundance was based on monthly trapping success on 4, ∼300 m transect lines  was collected on SEFI using a standard US National Weather Service rain gauge (∼1 m elevation) at the same location at noon Pacific Standard Time every day throughout 2001–2017 . Precipi2) on SEFI was evaluated during four collecting trips during 2014. Each trip lasted roughly 12 continuous days and fell within a season . Arthropod densities were enumerated by systematically surveying standardized (0.3 m2), untreated wooden cover boards placed throughout the island in a previous study to create salamander refuges , as a leading indicator of overall seabird resource abundance , wooden cover boards placed throughout the island between 2008 and 2017  of Farallon arboreal salamanders on SEFI using data from a long-term monitoring program of this species . A proxyand 2017 . One hunand 2017 . CompletPrior to statistical analyses average monthly values were used to calculated seasonal averages for salamander abundance, mouse trapping success, and arthropod density. Total monthly values were compiled to calculate seasonal averages for rainfall and bird carcasses. We then used the Kruskal-Wallis Test with non-parametric post-hoc comparisons to test for differences among seasons in mouse trapping success and the availability of food resources.n = 63 individuals) during three different seasons in the spring (March), summer (August), and fall (October) in 2013 that reflect the natural oscillation of mouse abundance on SEFI (Lasthenia maritima and Spergularia sp) and non-native  plants and four different taxa of whole-bodied terrestrial arthropods  during the spring  and fall  of 2013. We also collected seabird tissues during the summer  once these resources became more readily available on the island (n = 16) and fall  when they are most active on the island. This allowed us to assess the likelihood of house mice consuming arboreal salamanders on SEFI. All samples were stored frozen (−20 °C) prior to processing for stable isotope analysis.To complement our analysis of resource availability between 2001 and 2017, we collected tissue samples form mice and potential prey items in 2013 to quantify seasonal shifts in resource use by house mice and the potential for resource overlap with arboreal salamanders. Specifically, we sampled muscle and liver tissues from house mice  through interfaced PDZ Europa 20-20 continuous-flow stable isotope ratio mass spectrometers (CFIRMS). Raw δ values were normalized using glutamic acid , bovine liver (G-13), peach leaves (G-7) and nylon 5 (G-18) as standard reference materials. Sample precision based on repeated standard reference materials was 0.1‰for both δ13C and δ15N. Stable isotope ratios are expressed in δ notation in per mil units (‰), according to the following equation: 13C or 15N and R is the corresponding ratio 13C /12C or 15N /14N. The Rstandard values are based on Vienna PeeDee Belemnite (VPDB) for δ13C and atmospheric N2 for δ15N.All samples were freeze-dried and then homogenized using a mortar and pestle. Dried homogenized muscle, liver and tail clip samples underwent lipid extraction using 2:1 chloroform:methanol. Samples were placed in a glass vial with a solvent volume 10 times greater than sample volume and sonicated in a water bath for 15 min and then decanted. This procedure was repeated for a total of three cycles and the samples were rinsed in DI water and oven dried at 60 °C for 24 h to remove any remaining solvent. We flash-combusted  approximately 1.0 mg of each animal tissue sample and 3.0 mg of each plant tissue sample loaded into tin cups to analyze for carbon and nitrogen elemental composition (C:N ratio) and carbon and nitrogen isotopes  and tissue (muscle vs. liver) in house mice and between species  within each season using both multivariate and univariate techniques. We compared isotopic niche positions by computing the Euclidean distance (ED) between group centroids (δ13C and δ15N bivariate means) following the methods of δ13C and/or δ15N) contributed to niche differences across seasons or between species  and muscle  stable isotope values using Pearson correlations.We assessed variation in isotopic niche  position species . To examb) for each group to compare two-dimensional niche areas of house mice and arboreal salamanders among species and seasons. We then used the resulting Bayesian posterior probability distributions of SEAb estimates in pairwise-tests to identify significant differences in SEAb at the p < 0.05 level. Specifically, we calculated the probability of whether posterior SEAb values from one group are different than the posterior SEAb value from the comparison group (c overlap). Among other things, this isotopic niche overlap approach provided us with a quantitative estimate of the potential for competitive overlap between mice and salamanders.In addition, we explored variation in niche area and overlaps using standard ellipse areas which can be interpreted as a measure of the core isotopic niche of a population . We calcon group . Lastly,on group  for eachδ13C or δ15N) GLM and Tukey-Kramer multiple comparison tests to examine isotopic differences across major prey resources  collected in each season. As not all prey resources  were collected in every season we grouped prey resources by taxa and season into a single factor when conducting GLM analyses. We then used the SIMMR Bayesian mixing model , we used the same prey resource δ13C and δ15N values averaged across all seasons in each model analyses. As there is no existing evidence to support or refute the possibility that house mice may also consume arboreal salamanders on SEFI we also explored a separate subset of mixing models that include arboreal salamanders as a prey resource for house mice. We used diet to consumer isotopic discrimination factors for liver tissue  derived from captive studies of house mice on a controlled diet of wheat and corn  and by plotting the posterior predictive distributions  with lower trapping success in the spring relative to the late summer and fall  being lowest in the summer, intermediate in the spring and fall, and highest in the winter  with lower values in the summer and higher values in the spring and winter  with higher values in spring and summer relative to fall and winter  with higher average counts in spring and winter relative to summer  and muscle  tissues. House mice stable isotope values differed among seasons  but not by tissue type  or the interactions between these two factors . Seasonal differences in the isotopic niche position of house mice were due to lower tissue δ13C values in the spring relative to the summer and fall as well as lower tissue δ15N values in the summer relative to the spring (liver and muscle) and fall  and δ15N  values.The isotopic niche position of house mice differed significantly in all pairwise comparisons across seasons for both liver  and muscle  tissues. This was due to higher δ13C values in arboreal salamanders’ tail clips relative to mouse liver and muscle tissues in the spring  and muscle tissues only in the fall  or the fall  of house mice estimated using liver tissue (16.3%) or muscle tissues  and δ15N  values. For example, plant and intertidal resource δ13C and δ15N values, and arthropod and arboreal salamander δ15N values did not differ between seasons  relative to the differences observed between major taxonomic groupings  and δ15N  values differed significantly among major taxonomic groups thus providing isotopically unique endmembers for incorporation into the isotopic mixing model  vs. terrestrial  resources by mice across seasons. While terrestrial resources were more important to mice in all three seasons, the relative importance of marine resources were higher in the summer and fall, relative to the spring . Specifically, the use of stable carbon and nitrogen isotope analysis combined with isotopic niche and dietary mixing model approaches allowed us to quantitatively assess the diets and foraging niches of house mice on Southeast Farallon Island to better understand their interactions with native flora and fauna. We found that plants are the most important resource for house mice in the spring when plants are most abundant and house mouse populations are low , Table 3Apodemus sylvaticus hirtensis) on Hirta varied among sub-populations that had differing spatial access to seabird resources, and between the seabird breeding and non-breeding seasons. via shifts in mouse abundance, primary productivity, decomposition, and nutrient cycling , August 13 to August 15 , and October 26 . Given an average half-life of 3.5 to 5.6 days for mouse liver tissue  between these two group’s predicted dietary contributions and broadly overlapping 95% CI  through resource competition for invertebrates. While mice have been shown to have an indirect impact on adult ashy storm-petrel populations via hyper-predation by owls on SEFI  and bionomic  niche axes, respectively  trapping success (%), precipitation (cm/month), arthropod density (indiv./m2), seabird  carcass abundance (indiv./month), arboreal salamanders  abundance (indiv./month) on Southeast Farallon Island, CA during the winter , spring , summer , and fall  seasons using data collected from December of 2000 to January of 2018. SD are not provided when less than three months of data were available in a specific year and season.Click here for additional data file.10.7717/peerj.13904/supp-3Table S2P < 0.05 level.Major taxonomic groups that share a superscript are not significantly different at the Click here for additional data file.10.7717/peerj.13904/supp-4Table S3δ13C) and nitrogen (δ15N) stable isotope values, elemental concentration, and C/N ratio of major prey resource group averaged across all seasons on Southeast Farallon Island, CA in 2013 and used in stable isotope dietary mixing model analyses. Groups that share a superscript are not significantly different at the P < 0.05 level.The carbon (Click here for additional data file.


Lignin is one of the most abundant biopolymers in nature and has great potential to be transformed into high-value chemicals. However, the limited availability of molecular structure data hinders its potential industrial applications. Herein, we present the Lignin Structural (LGS) Dataset that includes the molecular structure of milled wood lignin focusing on two major monomeric units (coniferyl and syringyl), and the six most common interunit linkages . The dataset constitutes a unique resource that covers a part of lignin’s chemical space characterized by polymer chains with lengths in the range of 3 to 25 monomer units. Structural data were generated using a sequence-controlled polymer generation approach that was calibrated to match experimental lignin properties. The LGS dataset includes 60 K newly generated lignin structures that match with high accuracy (~90%) the experimentally determined structural compositions available in the literature. The LGS dataset is a valuable resource to advance lignin chemistry research, including computational simulation approaches and predictive modelling.  Lignin is not used to its fullest potential due to uncertainties in the characterization of its primary molecular structure.Lignin is one of the most abundant biopolymers in nature followed by cellulose. Natural biopolymers such as DNA contain well-defined arrangements of monomers and linkages that can be consistently determined through experimental characterization. In contrast, lignin’s monomer sequence and structure are not well-defined and present extremely complex chemical structures resulting from an irregular biosynthesis process4. The monomers radically couple and cross-link combinatorially5 to form multifaceted lignin structures. During lignin biosynthesis (or lignification), monomers are transported to the cell wall where they are polymerized in a combinatorial fashion by free-radical coupling mechanisms in a reaction mediated by peroxidases, generating a variety of structures within the lignin polymer7. Lignin’s structural composition varies between plant species and changes depending on the tissue, cell location or environmental conditions10. In addition, major structural changes occur during the lignin separation process12. During the last decades, various applications of lignin have been investigated, either as a macromolecule or after depolymerization into lower molecular weight compounds. Although there are numerous studies on lignin polymerization process and depolymerization13, there is still a lack of curated lignin structure datasets that can support lignin chemistry research.Lignin structures are formed primarily by three main repeating units (monolignols), arranged in random sequences. The major contributors to lignin’s primary structure are p-coumaryl (H), coniferyl (G), and sinapyl (S) alcohols14 provide new insight on the structural features of this macromolecule and guide bioengineering strategies for achieving structures with targeted properties. Experimental techniques such as spectroscopy  or wet chemistry  are key to reveal the molecular features of lignin16 and provide statistical descriptions of the polymer structure. Alternatively, computational methods can be used to predict lignin structure and properties. Multiple research efforts have modelled the lignification process using a variety of simulation techniques parameterized by experimental data on monomer and bond distributions21. Simulation studies on lignin structure formation are often carried out by adding monomeric units to reactive sites in the polymer22.Molecular structure and the length of monomer chains determine polymer properties . Since there may be multiple molecular structures consistent with a given average chemical composition, elucidating the correct molecular structure and structural variations responsible of specific property profiles is a key challenge in lignin chemistry. Current advances in polymer science and computational modelling23, can be readily used in studies aimed at understanding lignin’s structural properties using parameterized force fields24 and molecular dynamics approaches25. The structural information contained in the dataset provides a unique resource for simulating the depolymerization of lignin into lower molecular weight compounds. In addition to the structure data, the paper also describes a new computational approach to sequence-controlled polymer generation. Sequence-controlled polymers are macromolecules in which monomer units of different chemical nature are arranged in an orderly fashion26. A polymer formed by radical chain‐growth polymerization is also considered as a sequence‐controlled polymer, although it is also a nonuniform polymer with chains of different lengths and slightly different composition27. The tool can generate chemically correct and legible 2D structures of the MWL for hardwood, softwood and herbaceous wood types. The code used to generate and validate lignin structures is also provided for reproducibility.This paper describes a novel lignin polymer dataset, herein referred as lignin structural dataset (LGS), containing computer-generated molecular structures of milled wood lignin (MWL). The LGS dataset includes structural isomers for experimental data on spruce (softwood) and birch (hardwood) MWL. This resource provides data for 60 K lignin molecules (6.3 K softwood structures and 53.7 K hardwood structures) with varying degree of polymerization (DP) in the range of 3 to 25. The LGS dataset, available on Figshare29. The major functional groups present in lignin structures include hydroxyl, methoxy, carbonyl and carboxylic groups. The proportion of these groups in phenyl propane units depends on the species genetic origin and isolation processes.The computational approach used to generate the LGS dataset is based on sequence-controlled polymer growth parameterized to match experimental lignin structure data. Lignin’s structural heterogeneity can be characterized by the inter-unit linkages and functional groups attached to phenyl propane units. Inter-unit linkages are present in two major categories corresponding to ether bond linkages  and carbon-carbon linkages . The relative abundance of each linkage in native lignin varies from plant to plant with the β-O-4 linkage being the most abundantLignin structure compositions were analyzed from experimental studies on MWL of different wood species. Table 32 distinguishes two main classes of lignin biopolymers, G type lignin which is typical of conifers or softwood, and SG type lignin which is formed in deciduous plants or hardwood. The presence of H monomers is minimal in both of these lignin types33. The main structural variations of lignin include straight chain (linear) structures, branching structures, and cross linking with carbohydrates34. Two forms of coupling reactions could occur during lignin biosynthesis. First, linear reactions which contribute to extending the macromolecule by coupling new monomer units with β-O-4, β–β and β-5 linkages to form monomer-monomer and oligomer-monomer couplings. Second, branching reactions that form oligomer-oligomer couplings with 5-5 and 4-O-5 linkages35. It is important to note that units with 5-5 linkage have an alternate form with an 8-member ring structure named dibenzodioxocin (DBDO)37 which is predominantly present in softwood lignin. Spirodienone (β-1′/α-O-α′) has also been reported as a possible linkage with low prevalence (~3–4 in 100 phenylpropanoid units)38.Experimental characterization30 to generate plausible monomer sequences. Subsequent steps generate all possible molecular arrangements with similar structural features . The validity of the resulting lignin polymers is then corroborated by comparing their features  with experimental data as well as by matching with molecular fingerprints of structures extracted from recent studies21 on lignin structure simulation. Figure The structure generation approach used to create the LGS dataset doesn’t assume that lignin conforms to a specific sequence of monomers and bond types. Instead, our work assumes that lignin structures are created as the result of a random or near-random coupling of primary monomers, where each monomer can couple in any of all possible ways. To capture lignin’s structural variability, we have developed a generative model based on a combinatorial approach that uses a sequence-controlled method to create theoretical lignin structures. The algorithm starts by defining the primary monomer units followed by the application of molecular connectivity rules based on experimental studies40 on softwood and hardwood lignin samples. The degree of polymerization (DP) is computed as the ratio between the weight average molecular weight (Mw) of lignin polymer and the weight average molecular weight of phenylpropane monomer (M0) 41. The degree of polymerization represents the number of monomer units in each lignin structure. The structure generation workflow starts with sequencing lignin monomers and the generation of linkages, followed by the creation of topological matrices and molecular graph representations. In subsequent steps, graphs are converted to canonical molecular structures and the consistency of the generated structures is verified by comparison with experimental observations and simulated data. Finally, all validated structures are assembled to create the LGS dataset. The next subsections provide specific details on each workflow step.The input information needed to execute the workflow includes the S/G ratio, bond ratio, and degree of polymerization. Experimental characterization data used to bootstrap the structure generation were obtained from analytical results reported in previous studies42. The base unit in the backbone contains structural features of p-coumaryl alcohol (H), in which an allylic alcohol moiety is connected to the para position of phenolic ring. The three carbon atoms present in the aliphatic side chain are located at α, β and γ positions respectively. During lignin structure generation, the H monomer is considered as the base unit whereas coniferyl alcohol (G monomer) and syringyl alcohol (S monomer) are modifications of the base unit with additions of one (position 3) and two (position 3 and 5) methoxy groups, respectively.Phenyl propane units Fig.  form theThe lignin structure generator uses a permutation approach to create lignin structures including all possible combinations of H, G, and S monomers. However, since most available experimental data corresponds to G and SG type lignin structures, only S and G monomers were considered for inclusion in the LGS dataset. Table 43 using a recursive function adjusted to generate all possible permutations of n monomer units  and possible linkages between monomers. Table 45 have shown different branching structure patterns associated with 5-5 and 4-O-5 linkages in native/MWL lignin. The different branching patterns considered in this model are phenolic 5-545, phenolic 4-O-545, phenolic dibenzodioxocin (DBDO)45, non-phenolic DBDO45 and etherified 5-545.Experimental analysis data from previous studiesth carbon in the ring structure of the monomer in the second node. Studies on lignin structure report that monomers are bidirectionally linked5. To account for link directionality, labels are stored as tuples of variables    ) that represent parent-child relationship with bonds represented as directed edges denoting the linkage direction. Monomer sequences and bond patterns generated in the previous step are processed to create linear and branched structures. Linear chains are created by adding monomers one-by-one to the polymer . Branching chains are formed by coupling fragmented linear chains  using the coupling patterns described in Table The structure generation mimics the monomer sequencing in chain-growth polymerization by assigning probabilities to the presence of specific monomer pairs. Resulting molecular structures can be represented as graphs where nodes represent monomer units, and the edges correspond to covalent bonds formed during the polymerization process. For example, in case of outgoing edges for linkage type β-O-4, the β position of the monomer represented in the first node is connected to the O-atom attached to the 446 that define the relationship between monomer units. The information in the topological matrices includes linkages  and bond types . These matrices identify the bond direction and occupied bond position for each graph node . Figure Molecular graphs can be efficiently represented in tabular form as a pair of topological matricesSimplified Molecular Input Line Entry System) notation which represents the molecular structure in a string format47. Directed graphs are converted to canonical SMILES by generating the monomer molecular graph and mapping the respective linkages using the Chemistry Development Toolkit (CDK)48, a widely used open-source cheminformatics toolkit. Details on the software architecture used to implement the LGS generation tool are provided in the supporting Information  contains 6.3 K polymers whereas the remaining 53.7 K polymers correspond to birch MWL structures (SG type). Evaluated features of softwood and hardwood MWL structures from the dataset align with structural features reported in experimental studies on lignin structural composition  and data analytics tools:Application specific files: Molecular data files (MOL), which is a widely used chemical structure file format supported by most software packages for molecular dynamics. The data file contains 3D conformation of the generated molecule, which can be used in molecular editors such as Avogadro to visualize and analyze the spatial arrangements.Tabular files: Text files using the Comma Separated Value (CSV) format that represent each molecular graph using connectivity and adjacency matrices.51) format that provide comprehensive information that includes the definition of molecular structures using SMILES together with properties such as molecular weight, functional group counts and bond ratios. This file also serves as catalog of the structural information with respect to specific DP such as S/G ratio, bond frequencies, SMILES string, etc., Lignin id (lg_id) in the JSON object provides a unique identifier to locate the properties of specific structures in *.mol and *.csv files, respectively.Key/value file: Text files using the JavaScript Object Notation . The TMAP projection algorithm is combined with JSmol53 to visualize the lignin molecules in 2D and 3D representations together with their associated property data. A TMAP visualization for SG type structures is provided in the Supporting Information  were compared with properties derived from experimental observations for MWL from Spruce and Birch wood. Results in Table 20. The Lignin-KMC model implements a structure generation approach based on kinetically controlled radical coupling reactions55. Figure Additional validation was performed by comparing the structures in the LGS dataset with structures generated using the Lignin-KMC model56 was used to inspect the differences across lignin structures with DP = 6 generated using each modeling approach. Molecular structures were encoded using binary fingerprints57 and projected onto a 2D space using t-distributed stochastic neighbor embedding (t-SNE)58. The three clusters identified in Fig. The cheminformatics software package DatawarriorAt a higher degree of polymerization , the LGS model generates molecular structures with higher frequency of β-O-4 linkages that result in molecular structures with longer oligomer chains relative to those generated by the lignin-KMC model. Unlike the KMC model, the LGS approach can generate dibenzodioxocin(5-5/β-O-4/α-O-4) linkages resulting in a more diverse set of lignin structures. Figure 59 that can be simulated using the MWL structures included in the LGS dataset.The LGS dataset constitutes a unique resource that provides primary structural data for milled wood lignin. The information included in the dataset can be used for molecular mechanics and predictive modelling studies on lignin. The dataset can also be used as a foundation to simulate lignin depolymerization into low molecular weight compounds. For instance, lignin depolymerization using deep eutectic solvents (DES) produces lower molecular weight compounds through a selective ether bond cleavageAlong with MWL dataset, this study developed a computational framework to derive the structure of the lignin macromolecule by simulating longer polymer structures based on the molecular linkage patterns present on experimental data. Although spatial  arrangements of lignin macromolecules were not explored as part of this study, the current work provides a foundation for future studies and model extensions to incorporate these effects leading to a more comprehensive understanding of the molecular features of lignin.[Supplementary Material] Molecular structural dataset of lignin macromolecule elucidating experimental structural compositions