tidyMicro: a pipeline for microbiome data analysis and visualization using the tidyverse in R

Impact of land use on antibiotic resistance genes and bacterial communities in rivers

River ecosystems support essential ecosystem functions and services, including supplying water for domestic, agricultural, and industrial activities, provisioning of hydropower and fisheries, supporting navigation and recreational activities, and regulating water quality. In recent decades, the presence and spread of antibiotic resistance genes (ARGs) have emerged as a key threat to ecosystem health and human well-being. Rivers that are surrounded by human-modified landscapes serve as primary repositories and sources of ARGs. However, our understanding of the relationship between the diversity of ARGs and land use remain limited. We collected 30 sediment samples from five rivers in Ningbo, China, and then classified the sampling sites into two groups (i.e., group A with low levels of human impacts and group B with intense human impact) based on land use in their upstream areas. In total, we found 31 types of ARGs and 148 phyla of bacteria in the samples. ARGs abundance had a positive relationship with the levels of anthropogenic activities, and exhibited significant difference between the two groups. Co-occurrence networks showed that the interrelationship between bacteria and ARGs was more complex in group B than in group A. Moreover, Structural Equation Modeling (SEM) revealed that anthropogenic activity not only posed direct effect on ARGs but also indirectly affected ARGs through bacteria. Our results underscore the profound impacts of land-use changes on the diversity of ARGs, bacteria communities, and their relationships, which highlight the need for integrating ARGs in river assessments in regions with human-dominated land use.

Identification and Characterization of WOX Gene Family in Flax (Linum usitatissimum L.) and Its Role Under Abiotic Stress

The WOX (WUSCHEL-related homeobox) gene family plays pivotal roles in plant growth, development, and responses to biotic/abiotic stresses. Flax (Linum usitatissimum L.), a globally important oilseed and fiber crop, lacks a comprehensive characterization of its WOX family. Here, 18 LuWOX genes were systematically identified in the flax genome through bioinformatics analyses. Phylogenetic classification grouped these genes into three clades: Ancient, Intermediate, and WUS Clades, with members within the same clade exhibiting conserved exon-intron structures and motif compositions. Promoter analysis revealed abundant cis-acting elements associated with hormone responses (MeJA, abscisic acid) and abiotic stress adaptation (anaerobic induction, drought, low temperature). Segmental duplication events (nine gene pairs) contributed significantly to LuWOX family expansion. Protein-protein interaction networks implicated several LuWOX proteins in stress-responsive pathways. Expression profiling demonstrated that most LuWOX genes were highly expressed in 5-day-post-anthesis (DPA) flowers and embryonic tissues. qRT-PCR validation further uncovered distinct expression patterns of LuWOX genes under cold, drought, and salt stresses. This study established a foundational framework for leveraging LuWOX genes to enhance stress tolerance in flax breeding and functional genomics.

Coix Seed Oil Alleviates DSS-Induced Ulcerative Colitis via Intestinal Barrier Repair and Ferroptosis Regulation

Background

Ulcerative colitis is a chronic intestinal disease linked to intestinal barrier damage, ferroptosis and dysbiosis. Coix lacryma-jobi is a natural food with food-medicine homology, whose seed-derived oil (Coix seed oil, CSO) has been shown anti-inflammatory activity in vitro. Here, the effects and mechanisms of CSO on ulcerative colitis (UC) in vivo are systematically investigated.

Methods

Firstly, the UC mice was replicated by 3% DSS, and assessed the efficacy of CSO by observing the fecal occult blood, colon length, DAI score and pathological histomorphological changes of colon tissues. The anti-inflammatory and barrier-protective effects of CSO were observed by AB staining and qRT-PCR. Secondly, the biological targets of CSO were obtained from TCMSP database and Swiss Target Prediction database, ferroptosis targets were downloaded from FerrDb platform, and UC-related disease targets were obtained from GEO database, and the intersection of the above three was taken to obtain "CSO-UC-Ferroptosis" intersection targets, which were analysed by GO and KEGG enrichment, GSEA analysis, and immune cell infiltration and validation. Finally, the core genes of "CSO-UC-Ferroptosis" were molecular docking with the potential active components of CSO. In order to further verify the effect of CSO on ferroptosis, the GPX4 agonist RSL-3 was used to stimulate mice in vivo, and the levels of Iron, MDA and SOD were measured, and immunohistochemistry was used to detect the effects of tight junction proteins and the "CSO-UC-Ferroptosis" core protein in mice. Besides, the effect of CSO was further evaluated by observing the intercellular junctions of the colon tissues of each group under electron microscope. In addition, 16sRNA sequencing was performed on the intestinal contents of the mice to observe the effects of CSO on the intestinal flora of UC mice.

Results

CSO improved physiological parameters, reduced inflammation response and intestinal barrier damage, regulated ferroptosis, and restored gut microbiota balance in UC mice. Bioinformatics results showed that G6PD, ABCC1 were core targets at the intersection of CSO, UC and ferroptosis, which also demonstrated the similar expression of the core genes in DSS-induced UC mice models in vivo.

Conclusion

Our findings demonstrate for the first time that CSO ameliorated UC by regulating intestinal barrier damage, ferroptosis and the gut microbiota in DSS-induced mice, suggesting that CSO as a promising candidate for UC treatment and warranting further investigation.

Involvement of Oral Microbiome in the Development of Oral Malignancy

OBJECTIVE

This study aimed to identify periodontal pathogens involved in the onset and progression of OSCC.

METHODS

Saliva samples were collected from 112 patients without oral mucosal diseases (OMDs) as controls; 36 patients with oral potentially malignant disorders (OPMDs); and 104 patients with OSCC. Periodontal examinations were performed on all patients. Endpoint PCR was performed for seven species of oral pathogens. The 16S rRNA analysis was performed using 20 DNA samples from each group.

RESULTS

Periodontitis tended to worsen in the OMDs group compared to the control group. The number of oral bacteria was significantly higher in the OSCC group than in the other groups. The detection rates of P. gingivalis and Aggregatibacter actinomycetemcomitans (A. actinomycetemcomitans) were significantly higher in the OSCC group than those in the control group. From 16S rRNA analysis, the relative abundance of Prevotella buccae and intermedia was significantly higher in OSCC than in the control. Moreover, LPS derived from P. gingivalis contributes to the early development of oral epithelial precancerous lesions and carcinomas in mice.

CONCLUSIONS

Specific periodontal pathogens are present in the oral cavities of patients with OPMDs and OSCC, and changes in the bacterial flora due to their presence may contribute to the onset and progression of OMDs.

Bioinformatics and Machine Learning Methods Identified MGST1 and QPCT as Novel Biomarkers for Severe Acute Pancreatitis

Severe acute pancreatitis (SAP) is a life-threatening gastrointestinal emergency. The study aimed to identify biomarkers and investigate molecular mechanisms of SAP. The GSE194331 dataset from GEO database was analyzed using bioinformatics. Differentially expressed genes (DEGs) associated with SAP were identified, and a protein-protein interaction network (PPI) was constructed. Machine learning algorithms were used to determine potential biomarkers. Gene set enrichment analysis (GSEA) explored molecular mechanisms. Immune cell infiltration were analyzed, and correlation between biomarker expression and immune cell infiltration was calculated. A competing endogenous RNA network (ceRNA) was constructed, and biomarker expression levels were quantified in clinical samples using RT-PCR. 1101 DEGs were found, with two modules most relevant to SAP. Potential biomarkers in peripheral blood samples were identified as glutathione S-transferase 1 (MGST1) and glutamyl peptidyltransferase (QPCT). GSEA revealed their association with immunoglobulin regulation, with QPCT potentially linked to pancreatic cancer development. Correlation between biomarkers and immune cell infiltration was demonstrated. A ceRNA network consisting of 39 nodes and 41 edges was constructed. Elevated expression levels of MGST1 and QPCT were verified in clinical samples. In conclusion, peripheral blood MGST1 and QPCT show promise as SAP biomarkers for diagnosis, providing targets for therapeutic intervention and contributing to SAP understanding.

Construction of a gene model related to the prognosis of patients with gastric cancer receiving immunotherapy and exploration of COX7A1 gene function

BACKGROUND

GC is a highly heterogeneous tumor with different responses to immunotherapy, and the positive response depends on the unique interaction between the tumor and the tumor microenvironment (TME). However, the currently available methods for prognostic prediction are not satisfactory. Therefore, this study aims to construct a novel model that integrates relevant gene sets to predict the clinical efficacy of immunotherapy and the prognosis of GC patients based on machine learning.

METHODS

Seven GC datasets were collected from the Gene Expression Omnibus (GEO) database, The Cancer Genome Atlas (TCGA) database and literature sources. Based on the immunotherapy cohort, we first obtained a list of immunotherapy related genes through differential expression analysis. Then, Cox regression analysis was applied to divide these genes with prognostic significancy into protective and risky types. Then, the Single Sample Gene Set Enrichment Analysis (ssGSEA) algorithm was used to score the two categories of gene sets separately, and the scores differences between the two gene sets were used as the basis for constructing the prognostic model. Subsequently, Weighted Correlation Network Analysis (WGCNA) and Cytoscape were applied to further screen the gene sets of the constructed model, and finally COX7A1 was selected for the exploration and prediction of the relationship between the clinical efficacy of immunotherapy for GC. The correlation between COX7A1 and immune cell infiltration, drug sensitivity scoring, and immunohistochemical staining were performed to initially understand the potential role of COX7A1 in the development and progression of GC. Finally, the differential expression of COX7A1 was verified in those GC patients receiving immunotherapy.

RESULTS

First, 47 protective genes and 408 risky genes were obtained, and the ssGSEA algorithm was applied for model construction, showing good prognostic discrimination ability. In addition, the patients with high model scores showed higher TMB and MSI levels, and lower tumor heterogeneity scores. Then, it is found that the COX7A1 expressions in GC tissues were significantly lower than those in their corresponding paracancerous tissues. Meanwhile, the patients with high COX7A1 expression showed higher probability of cancer invasion, worse clinical efficacy of immunotherapy, worse overall survival (OS) and worse disease-free survival (DFS).

CONCLUSIONS

The ssGSEA score we constructed can serve as a biomarker for GC patients and provide important guidance for individualized treatment. In addition, the COX7A1 gene can accurately distinguish the prognosis of GC patients and predict the clinical efficacy of immunotherapy for GC patients.

Genome-Wide Analysis of Flax (Linum usitatissimum L.) Growth-Regulating Factor (GRF) Transcription Factors

Flax is an important cash crop globally with a variety of commercial uses. It has been widely used for fiber, oil, nutrition, feed and in composite materials. Growth regulatory factor (GRF) is a transcription factor family unique to plants, and is involved in regulating many processes of growth and development. Bioinformatics analysis of the GRF family in flax predicted 17 LuGRF genes, which all contained the characteristic QLQ and WRC domains. Equally, 15 of 17 LuGRFs (88%) are predicted to be regulated by lus-miR396 miRNA. Phylogenetic analysis of GRFs from flax and several other well-characterized species defined five clades; LuGRF genes were found in four clades. Most LuGRF gene promoters contained cis-regulatory elements known to be responsive to hormones and stress. The chromosomal locations and collinearity of LuGRF genes were also analyzed. The three-dimensional structure of LuGRF proteins was predicted using homology modeling. The transcript expression data indicated that most LuGRF family members were highly expressed in flax fruit and embryos, whereas LuGRF3, LuGRF12 and LuGRF16 were enriched in response to salt stress. Real-time quantitative fluorescent PCR (qRT-PCR) showed that both LuGRF1 and LuGRF11 were up-regulated under ABA and MeJA stimuli, indicating that these genes were involved in defense. LuGRF1 was demonstrated to be localized to the nucleus as expected for a transcription factor. These results provide a basis for further exploration of the molecular mechanism of LuGRF gene function and obtaining improved flax breeding lines.

The Regulation of Nucleic Acid Vaccine Responses by the Microbiome

Nucleic acid vaccines, including both RNA and DNA platforms, are key technologies that have considerable promise in combating both infectious disease and cancer. However, little is known about the extrinsic factors that regulate nucleic acid vaccine responses and which may determine their effectiveness. The microbiome is recognized as a significant regulator of immune development and response, whose role in regulating some traditional vaccine platforms has recently been discovered. Using germ-free and specific pathogen-free mouse models in combination with different protein, DNA, and mRNA vaccine regimens, we demonstrate that the microbiome is a significant regulator of nucleic acid vaccine immunogenicity. Although the presence of the microbiome enhances CD8+ T cell responses to mRNA lipid nanoparticle immunization, the microbiome suppresses Ig and CD4+ T cell responses to DNA-prime, DNA-protein-boost immunization, indicating contrasting roles for the microbiome in the regulation of these different nucleic acid vaccine platforms. In the case of mRNA lipid nanoparticle vaccination, germ-free mice display reduced dendritic cell/macrophage activation that may underlie the deficient vaccine response. Our study identifies the microbiome as a relevant determinant of nucleic acid vaccine response with implications for continued therapeutic development and deployment of these vaccines.

Global Transcriptomic Analyses Provide New Insight into the Molecular Mechanisms of Endocarp Formation and Development in Iron Walnut (Juglans sigillata Dode)

Iron walnut (Juglans sigillata Dode) is a native species in southwestern China that exhibits variation in both fruit morphology and shell thickness. However, the underlying molecular processes controlling hardened endocarp development in walnut has not yet been reported. Here, we generated transcriptional profiles of iron walnut endocarp at three developmental stages using “Dapao”, the most common commercial variety. Using pairwise comparisons between these three stages, a total of 8555 non-redundant differentially expressed genes (DEGs) were identified, and more than one-half of the total DEGs exhibited significant differential expression in stage I as compared with stage II or stage III, suggesting that the first stage may ultimately determine the final characteristics of the mature walnut shell. Furthermore, in the clustering analysis of the above DEGs, 3682, 2349, and 2388 genes exhibited the highest expression in stages I, II, and III, respectively. GO enrichment analysis demonstrated that the major transcriptional variation among the three developmental stages was caused by differences in cell growth, plant hormones, metabolic process, and phenylpropanoid metabolism. Namely, using the tissue-specific expression analysis and a gene co-expression network, we identified MADS-box transcription factor JsiFBP2 and bHLH transcription factor JsibHLH94 as candidate regulators of endocarp formation in the early stage, and JsiNAC56 and JsiMYB78 might play key roles in regulating the lignification process of endocarp in the late stage. This study provides useful information for further research to dissect the molecular mechanisms governing the shell formation and development of iron walnut.

The Regulation of Nucleic Acid Vaccine Responses by the Microbiome

Nucleic acid vaccines, including both RNA and DNA platforms, are key technologies that have considerable promise in combating both infectious disease and cancer. However, little is known about the extrinsic factors that regulate nucleic acid vaccine responses and which may determine their effectiveness. The microbiome is recognized as a significant regulator of immune development and response, whose role in regulating some traditional vaccine platforms has recently been discovered. Using germ-free and specific-pathogen-free mouse models in combination with different protein, DNA, and mRNA vaccine regimens, we demonstrate that the microbiome is a significant regulator of nucleic acid vaccine immunogenicity. While the presence of the microbiome enhances CD8+ T cell responses to mRNA lipid nanoparticle (LNP) immunization, the microbiome suppresses immunoglobulin and CD4+ T cell responses to DNA-prime, DNA-protein-boost immunization, indicating contrasting roles for the microbiome in the regulation of these different nucleic acid vaccine platforms. In the case of mRNA-LNP vaccination, germ-free mice display reduced dendritic cell/macrophage activation that may underlie the deficient vaccine response. Our study identifies the microbiome as a relevant determinant of nucleic acid vaccine response with implications for their continued therapeutic development and deployment.

Gut Microbiome Remains Static in Functional Abdominal Pain Disorders Patients Compared to Controls: Potential for Diagnostic Tools

Background: Functional Abdominal Pain disorders (FAPDs) are a group of heterogeneous gastrointestinal disorders with unclear pathophysiology. In children, FAPDs are more common in the winter months than summer months. The possible influence of school stressors has been proposed. Previously, our group showed differences in bacterial relative abundances and alpha diversity in the gut microbiome and its relationship with stressors in a cross-sectional evaluation of children suffering from FAPDs compared to a healthy control group. We present longitudinal data to assess whether the gut microbiome changes over school terms in the control and FAPDs groups. Methods: The longitudinal study included children with FAPDs (n = 28) and healthy controls (n = 54). Gastrointestinal symptoms, as well as stool microbiome, were assessed in both groups. Stool samples were serially collected from all participants during both the school term and summer vacation. The stool samples were subjected to total genomic extraction, 16S rRNA amplicon sequencing, and bioinformatics analysis. The gut microbiome was compared at school and during vacation. Other metrics, alpha diversity, and beta diversity, were also compared between the two school terms in every group. Results: In the healthy group, there were differences in microbiome composition between school terms and summer vacation. Conversely, we found no differences in the FAPDs group between the two terms. The healthy control group revealed differences (p-value < 0.05) in 55 bacterial species between the school term and vacation. Several of the differentially abundant identified bacteria were involved in short-chain fatty acids production (SCFAs), inflammation reduction, and gut homeostasis. Alpha diversity metrics, such as the Shannon index, were different in the control group and remained unchanged in the FAPDs group. Conclusion: Although preliminary, our findings suggest that the gut microbiome is static in FAPDs. This compares with a more dynamic healthy gut microbiome. Further studies are warranted to corroborate this and understand the interplay between stress, symptoms, and a less diverse and static microbiome. Future studies will also account for different variables such as diet and other patient demographic criteria that were missing in the current study.

Construction and validation of a novel prognostic model for thyroid cancer based on N7-methylguanosine modification-related lncRNAs

BACKGROUND

To construct and verify a novel prognostic model for thyroid cancer (THCA) based on N7-methylguanosine modification-related lncRNAs (m7G-lncRNAs) and their association with immune cell infiltration.

METHODS

In this study, we identified m7G-lncRNAs using co-expression analysis and performed differential expression analysis of m7G-lncRNAs between groups. We then constructed a THCA prognostic model, performed survival analysis and risk assessment for the THCA prognostic model, and performed independent prognostic analysis and receiver operating characteristic curve analyses to evaluate and validate the prognostic value of the model. Furthermore, analysis of the regulatory relationship between prognostic differentially expressed m7G-related lncRNAs (PDEm7G-lncRNAs) and mRNAs and correlation analysis of immune cells and risk scores in THCA patients were carried out.

RESULTS

We identified 29 N7-methylguanosine modification-related mRNAs and 116 differentially expressed m7G-related lncRNAs, including 87 downregulated and 29 upregulated lncRNAs. Next, we obtained 8 PDEm7G-lncRNAs. A final optimized model was constructed consisting of 5 PDEm7G-lncRNAs (DOCK9-DT, DPP4-DT, TMEM105, SMG7-AS1 and HMGA2-AS1). Six PDEm7G-lncRNAs (DOCK9-DT, DPP4-DT, HMGA2-AS1, LINC01976, MID1IP1-AS1, and SMG7-AS1) had positive regulatory relationships with 10 PDEm7G-mRNAs, while 2 PDEm7G-lncRNAs (LINC02026 and TMEM105) had negative regulatory relationships with 2 PDEm7G-mRNAs. Survival curves and risk assessment predicted the prognostic risk in both groups of patients with THCA. Forest maps and receiver operating characteristic curves were used to evaluate and validate the prognostic value of the model. Finally, we demonstrated a correlation between different immune cells and risk scores.

CONCLUSION

Our results will help identify high-risk or low-risk patients with THCA and facilitate early prediction and clinical intervention in patients with high risk and poor prognosis.

Exploring the Microbiome Analysis and Visualization Landscape

Research on the microbiome has boomed recently, which resulted in a wide range of tools, packages, and algorithms to analyze microbiome data. Here we investigate and map currently existing tools that can be used to perform visual analysis on the microbiome, and associate the including methods, visual representations and data features to the research objectives currently of interest in microbiome research. The analysis is based on a combination of a literature review and workshops including a group of domain experts. Both the reviewing process and workshops are based on domain characterization methods to facilitate communication and collaboration between researchers from different disciplines. We identify several research questions related to microbiomes, and describe how different analysis methods and visualizations help in tackling them.