Gut Microbiome Ecological Network Destabilization in Amyotrophic Lateral Sclerosis: Insights from In tegrated Oral–Gut Analysis

Gut Microbiome Ecological Network Destabilization in Amyotrophic Lateral Sclerosis: Insights from In tegrated Oral–Gut Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Gut Microbiome Ecological Network Destabilization in Amyotrophic Lateral Sclerosis: Insights from In tegrated Oral–Gut Analysis zelong Yang, Wenhao Liu, Bo Yan, Zhongwei Chang, Zhan Cao, Jingxin Li, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9353918/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract The pathogenesis of amyotrophic lateral sclerosis (ALS) involves multiple systems, among which the "gut-brain axis" has been extensively studied. However, the characteristics of intestinal microbiota alterations and their potential association with the oral microbiota remain unclear. This cohort study aims to characterize the ecological destabilization of intestinal microbiota in ALS patients. On this basis, this study further explored the potential association between the intestinal and oral microbiotas. In this study, paired oral and fecal samples from ALS patients and fecal samples from healthy controls were collected and subjected to 16S rRNA amplicon sequencing. The results showed that the intestinal microbiota in ALS patients exhibited significant compositional shifts, characterized by enrichment of pro-inflammatory genera and depletion of butyrate-producing genera, along with reduced alpha diversity. Critically, quantitative analysis using a potential landscape model revealed that this altered community was in a metastable state (comprehensive stability index: HC + 0.00110 vs. ALS − 0.0118), indicating ecological destabilization. this study examined oral-gut microbial interactions and identified 20 significant correlations (|r| ≥ 0.6), with the strongest between oral Prevotella salivae and intestinal Dorea (r = 0.937). Notably, the intestinal genera involved in these associations ( Dorea , Ruminococcus , and Blautia ) were also differentially abundant in ALS patients, suggesting that oral-gut microbial interactions may contribute to intestinal dysbiosis. This study demonstrates that the intestinal microecosystem in ALS patients exhibits features of ecological destabilization, and identifies oral-gut microbial interactions may as potential drivers of intestinal dysbiosis. Amyotrophic lateral sclerosis intestinal microbiota oral microbiota microbial ecology 16S rRNA amplicon sequencing diagnostic biomarker oral-gut axis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1.Introduction Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with an irreversible and rapidly progression [ 1 ], characterized by the progressive and selective loss of upper and lower motor neurons in the brain and spinal cord. This disease leads to skeletal muscle atrophy, paralysis and eventually respiratory failure. Although the exact cause of ALS remains unclear, it is widely accepted that ALS results from a combination of genetic susceptibility and environmental factors [ 2 ], including lifestyle and microbial alterations. The global incidence and prevalence of ALS show significant heterogeneity, influenced by factors such as age, sex, and ethnicity [ 3 ]. Especially with the global trend of population aging, the disease burden of ALS is projected to increase continuously [ 4 ]. Current clinical treatments are very limited, with only a few medicines such as riluzole [ 5 ] and edaravone [ 6 ] are available, These medicines offer modest efficacy, slightly delaying the progression of disease, failing to significantly improve neurological function or reverse the progression of disease. This situation underscores the urgent need to explore non-neuronal pathogenesis of ALS and to identify new therapeutic targets. In recent years, the "brain-gut axis" has gained extensive attention as a key pathway in the pathogenesis of neurological diseases. The intestinal microbiota and its metabolites can profoundly influence the function, neuroinflammation and homeostasis of the central nervous system through immune [ 7 ], endocrine [ 8 ], and neural [ 9 ] pathways. In the field of ALS research, accumulating evidence indicate that patients exhibit characteristic intestinal microbial dysbiosis. This dysregulation is closely associated with the severity of disease [ 10 ], rate of the progression and systemic inflammatory status [ 11 ]. A study by Gotkine et al. [ 12 ] further confirmed that ALS patients have characteristic dysbiosis of the intestinal microbiota, aiming to indicated that it may be related to the disease progression and systemic inflammation, providing a new entry point for understanding the pathology of ALS. A recent Mendelian randomization study was published in 2025 [ 13 ] further strengthened the causal link between the two, demonstrating that specific intestinal bacterial genera, such as Bifidobacterium , may be risk factors for ALS, while others, such as Enterobacter , may have protective effects. Furthermore, animal model studies have shown that changes in the intestinal microbiota may occur before the appearance of neuromuscular symptoms [ 14 ], indicating its potential role in the initial mechanisms of the disease. Although significant progress has been made in understanding the "brain-gut axis", there are still major gaps in understanding the microbial dysbiosis in ALS. First, most studies have focused solely on describing compositional changes of the fecal microbiota, lacking quantitative assessment of microbial community ecological stability. Microbial communities are not simply collections of independent microorganisms; their internal networks of synergy and competition are crucial for understanding community function and ecological stability. However, this ecological dynamics perspective remains underexplored in ALS research. Second, the oral cavity, as the entrance of the digestive tract, is anatomically and functionally connected to the intestine. The oral microbiota may continuously influence intestinal microbiota composition and function through daily swallowing, yet the role of oral-gut microbial interactions in intestinal dysbiosis in ALS remains unclear. To systematically figure out these scientific issues and fill the existing knowledge gaps, this study was designed. 16S rRNA amplicon sequencing was usedfor detecting paired oral and fecal samples from ALS patients in a cohort (to achieve direct comparison between body parts), as well as fecal samples from healthy controls (as a baseline for the intestinal microbiota). Utilizing high-throughput 16S rRNA amplicon sequencing, combined with a series of multivariate bioinformatics analyses—including α/β diversities analysis, identification of differential abundance species, construction of microbial co-occurrence network, and algorithm of random forest. this study aims to characterize the ecological destabilization of the intestinal microbiota in ALS and to explore whether oral-gut microbial interactions contribute to this destabilization. 2. Methods 2.1. Study Subjects and Sample Collection Participant Recruitment, Time and Location A cohort study (Figure 1 ) was conducted at Suzhou Municipal Hospital (Taihu Campus) from December 2024 to December 2025. A total of 22 participants were enrolled,including 11 patients with amyotrophic lateral sclerosis (ALS) and 11 healthy controls.All ALS patients met the revised El Escorial diagnostic criteria, and diagnoses were independently confirmed by two neurologists. Clinical trial number: not applicable Exclusion Criteria Use of antibiotics, probiotics, or prebiotics within one month before enrollment. Presence of other systemic diseases (e.g., diabetes, autoimmune diseases, inflammatory bowel disease, liver cirrhosis, rheumatoid arthritis). other infectious diseases (e.g., respiratory tract infection, genitourinary infection). long-term use of immunosuppressants or glucocorticoids. pregnancy or lactation. other neurodegenerative or psychiatric disorders Diagnosis and Grouping ALS group: Patients diagnosed with amyotrophic lateral sclerosis. Healthy control group: Healthy volunteers with no history of neurodegenerative diseases and met none of the above exclusion criteria. Sample Collection ALS patients: Oral swabs and fecal samples were collected at enrollment. Oral samples: Used sterile oral swabs,bilateral buccal mucosa and dorsal tongue surfaces were collected before brushing tooth in the morning. Swabs were immediately placed in sterile PBS tubes and stored at − 80°C. Fecal samples: Used sterile collection tubes to collect the middle portion of the first stool sample in the morning and immediately stored at − 80°C. Healthy controls: Fecal samples were collected at the same time and stored under identical conditions, oral samples were not collected from healthy controls; Finally, among 33 samples from 22 participants (11 oral samples and 11 fecal samples from ALS patients; 11 fecal samples from healthy controls) were subjected to high-throughput 16S rRNA amplicon sequencing. 2.2. Microbial DNA Extraction and Library Construction 2.2.1. DNA Extraction Fecal samples: Bacterial genomic DNA was extracted by using the QIAamp DNA Stool Mini Kit (QIAGEN, Germany) according to the manufacturer’s instructions.Oral samples: Bacterial DNA from oral swabs was extracted by using the QIAamp DNA Microbiome Kit (QIAGEN, Germany). DNA concentration was measured by using a Qubit 3.0 Fluorometer (Thermo Fisher Scientific, USA), and DNA integrity was verified by 1% agarose gel electrophoresis. Samples with concentration < 1 ng/µL or obvious degradation were excluded. 2.2.2. 16S rRNA Gene Amplification The V3–V4 hypervariable region of the bacterial 16S rRNA gene was amplified by PCR by using the primer pair 338F (5'-ACTCCTACGGGAGGCAGCAG-3') and 806R (5'- GGACTACHVGGGTWTCTAAT-3'). The PCR reaction system (25µL) contained: 10 ng DNA template, 12.5µL 2× Phusion High-Fidelity PCR Master Mix (New England Biolabs, USA), 0.5µM (final concentration) of each forward and reverse primer,and sterile ddH₂O up to 25µL. PCR thermal cycling conditions were as follows: Initial denaturation at 98°C for 1 min; 30 cycles of denaturation at 98°C for 10s, annealing at 50°C for 30s, extension at 72°C for 30s; followed by a final extension at 72°C for 5 min. Amplicons were purified by using Agencourt AMPure XP beads (Beckman Coulter, USA). 2.2.3. Library Construction and Sequencing Sequencing libraries were constructed by using the Nextera XT DNA Library Preparation Kit (Illumina, USA). Unique dual-index barcodes were added to each sample by PCR for splitting the sample. Purified libraries were assessed at size and quality by using the Agilent 2100 Bioanalyzer (Agilent Technologies, USA) and quantified with the Qubit.All libraries were pooled in equimolar amounts and diluted to 4 nM. The pooled library was denatured, diluted to 8 pM, and mixed with 30% PhiX control library. Paired-end sequencing was performed on the Illumina MiSeq platform by using the MiSeq Reagent Kit v3 (600 cycles), with a read length of 2 × 300 bp. 2.3. Bioinformatic Analysis 2.3.1. Raw Data Processing Adapter and primer sequences were removed from raw sequencing reads using Cutadapt (v1.2.1). Quality filtering, denoising, paired-end merging, and chimera removal were performed using the DADA2 plugin in QIIME2 (v2020.11) to generate amplicon sequence variants (ASVs). Representative sequences of each ASV were taxonomically annotated against the SILVA database (v138) using a naive Bayes classifier with a confidence threshold of 0.8. 2.3.2. Diversity Analysis Diversity analyses were performed using the vegan package (v2.5-3) in R software (v4.0.3). α-diversities: The Shannon index, Chao1 index, and observed species index were calculated to reflect richness and evenness of within-sample species. Between-group comparisons were performed using the Wilcoxon rank-sum test. β-diversities: Community dissimilarity was calculated based on Bray–Curtis distance. Principal Coordinate Analysis (PCoA) was used for dimensionality reduction and visualization. Between-group differences were tested by permutational multivariate analysis of variance (PERMANOVA,999 permutations). t-SNE (perplexity = 30) and Principal Component Analysis (PCA) were used for supplementary validation. A heatmap was generated to visualize the top 50 most abundant genera across all samples. Wilcoxon rank-sum tests were used to compare relative abundances at the phylum and genus levels between groups. Boxplots for the top 20 differentially abundant genera were generated to show the distribution of these taxa between groups. A separate heatmap displaying the relative abundance of key differentially abundant genera was also generated to visualize the distinguishing patterns between ALS patients and healthy controls. Correlations among genera were calculated using the SparCC algorithm (implemented in Python, 100 iterations). Genus pairs with|correlation coefficient| > 0.3 and p < 0.05 were used for network construction. Networks were visualized in Gephi (v0.9.2). In the generated network diagram, nodes represent different bacterial genera, and node size is proportional to the degree (the number of edges connected to the node). Edges indicate significant correlations between two bacterial genera. To visually distinguish the direction of the correlation, using different colors to mark the edges: red edges represent positive correlations, and blue edges represent negative correlations. The thickness of an edge is proportional to the absolute strength of the correlation ( |r| ). 2.3.3. Potential Landscape Analysis The ecological stability of the microbial ecosystem was quantitatively assessed using a potential landscape model based on the framework previously described by Li et al. [ 15 ] and Wang et al. [ 16 ]. This approach conceptualizes the microbial community as a dynamic system moving across an energy landscape, where stable states correspond to valleys (attractors) and unstable states correspond to peaks. The analysis was performed using custom R scripts with the following steps: First, dimensionality reduction was conducted using Principal Coordinate Analysis (PCoA) based on Bray-Curtis distances from the genus-level relative abundance data. The first two principal coordinates (PCo1 and PCo2) were selected to represent the major axes of community variation, capturing the most significant ecological gradients. Second, energy landscape construction involved creating a two-dimensional grid over the range of PCo1 and PCo2 scores. For each grid point, the local density of samples was estimated using a Gaussian kernel function. The potential energy U at each grid point was then calculated as the negative logarithm of the sample density: U = − ln (ρ), where ρ is the estimated density. This transformation results in regions with high sample density (frequently occupied community states) corresponding to low energy valleys, while regions with low sample density correspond to high energy peaks. Third, stability parameters were derived from the constructed potential landscape to characterize ecosystem stability: Valley depth (ΔU): The difference in potential energy between the bottom of a valley and the surrounding ridge. A deeper valley indicates a more stable attractor state, requiring more energy to perturb the system out of that state. Valley area (A b ): The size of the basin of attraction. A larger area indicates that the system can tolerate wider fluctuations while remaining within the same stable state. Comprehensive stability index (S): A composite index integrating both valley depth and area, calculated as S = ΔU×A b . A positive S value indicates a stable ecosystem with deep, well-defined attractor basins, while a negative S value suggests an unstable or metastable ecosystem prone to state transitions under perturbation. Finally, comparative analysis of the potential landscape parameters was performed separately for the healthy control group and the ALS patient group based on their respective sample distributions in the PCoA space. The stability indices (ΔU, A b , and S) were compared between groups to quantify the shift in ecosystem stability associated with ALS. 2.4. Machine Learning Model Construction A classification model for ALS disease status was constructed using the randomForest package (v4.6-14) in R. Input features were the relative abundances of differential gut genera identified by LEfSe. The dataset was split into training (70%) and testing (30%) sets. The number of trees (ntree) and the number of randomly selected features per node (mtry) were tuned by 10-fold cross-validation. The pROC package (v1.16.2) was used to plot the receiver operating characteristic (ROC) curve and calculate the area under the curve (AUC), sensitivity, and specificity. Feature importance was evaluated by mean decrease accuracy and mean decrease Gini index. 2.5. Statistical Analysis All statistical analyses were performed using R software (v4.0.3) and GraphPad Prism (v8.0). Continuous variables are expressed as mean ± standard deviation or median (interquartile range, IQR). Between-group comparisons for continuous variables: Student’s t-test (normal distribution) or Wilcoxon rank-sum test (non-normal distribution). Multiple groups: Kruskal–Wallis test, with post-hoc pairwise comparisons by Dunn’s test. A two-sided p < 0.05 was considered statistically significant. The Benjamini-Hochberg method was used to control the false discovery rate (FDR) in differential taxa analysis. 3. Results 3.1. intestinal microbial diversity is reduced and community structure is altered in ALS As illustrated in Fig. 1 , the study workflow included paired oral and fecal samples collected from ALS patients and fecal samples from healthy controls, followed by 16S rRNA sequencing and multi-dimensional bioinformatics analyses. The two groups were well-matched in age (median: HC 54 years, IQR 49–65; ALS 54.5 years, IQR 48–66; P = 0.78) and gender composition (54.5% female in the HC group vs. 63.6% female in the ALS group; P = 0.45) (P > 0.05), thereby minimizing potential confounding effects from demographic factors (Fig. 2 a). To compare the species richness and evenness of the microbial communities between the two groups, an alpha diversity analysis was first performed. The analysis showed that the Shannon index of the intestinal microbiota in ALS patients was significantly lower than that of the healthy controls (mean ± SD: ALS 3.110 ± 0.595 vs. HC 3.479 ± 0.354; IQR: ALS 0.292 vs. HC 0.325), indicating a comprehensive decline in species richness and evenness (Fig. 2 b). The reduction in microbial diversity is a typical characteristic of ecological dysbiosis, indicating that the intestinal ecosystem of ALS patients has diminished functional redundancy and the ability to resist external disturbances (e.g., infections, medications, dietary changes) has weakened. This result is consistent with several previous studies on the intestinal microbiota of ALS patients [ 17 ], all of which have reported significant ecological imbalance in the disease state. Subsequently, analysis of beta diversities were conducted to explore differences in the overall structure of microbial communities between the two groups. Principal Coordinate Analysis (PCoA) based on Bray-Curtis distance clearly revealed a significant separation in the intestinal microbial structure between the ALS group and the control group (PERMANOVA, R² = 0.15, p = 0.002) (Fig. 2 c). This finding indicates that the overall intestinal microbial community undergoes directional changes in ALS. This structural change may reflect the selective pressure exerted by the disease state on the intestinal ecosystem, possibly driven by systemic inflammation, altered intestinal motility, the use of medication, and are consistent with the consensus that disease state is associated with overall microbial community structure [ 9 , 18 ]. The t-SNE results further supported this conclusion, two methods of dimensionality reduction showed a consistent grouping trend (Fig. 2 d), enhancing the robustness of the finding. 3.2. ALS is characterized by depletion of butyrate-producers and enrichment of pro-inflammatory taxa Systematical assessment of the compositional changes in the intestinal microbiota of ALS patients, analyzing differences at the phylum and genus levels. At the phylum level, the relative abundance of Firmicutes was significantly decreased in ALS patients compared to healthy controls (ALS: 88.45% vs. HC: 93.65%, p = 0.047). Firmicutes includes many butyrate-producing bacteria indicates. Conversely, the relative abundance of Bacteroidetes was significantly increased (ALS: 3.75% vs. HC: 0.65%, p = 0.012) (Fig. 3 a). The Bacteroidetes phylum is rich in LPS-producing bacteria, and its increase may exacerbate intestinal inflammation. This trend is consistent with the findings of Zeng et al. [ 19 ] and Wu et al. [ 20 ] in ALS cohorts, further confirming the prevalence of "phylum-level imbalance" in ALS. Following the phylum-level observations, examining the top 50 most abundant genera across all samples (Fig. 3 b). The bacterial genera that were significantly enriched in ALS patients mainly included Rothia, Prevotella, Veillonella, Porphyromonas , and Actinomyces . Some genera (such as Prevotella and Veillonella ) have been reported to be associated with pro-inflammatory phenotypes in studies related to neuroinflammation [ 21 , 22 ]. Genera enriched in healthy controls were predominantly beneficial butyrate-producing bacteria, including Faecalibacterium, Roseburia, Blautia , and Dorea. This binary pattern of "beneficial microbe depletion and pro-inflammatory microbe enrichment" represents one of the core findings of this study. This binary pattern has important implications for ALS pathogenesis. Depletion of butyrate-producing bacteria (e.g., Faecalibacterium, Roseburia ) can lead to damage to the intestinal barrier function [ 23 ], increasing the entry of inflammatory substances such as lipopolysaccharide (LPS) into the blood circulation, activating systemic immune responses, and exacerbating neuroinflammation through the "gut-brain axis" [ 24 ]. Sokol et al. previously demonstrated the anti-inflammatory effects of Faecalibacterium and its reduction in inflammatory bowel disease [ 25 ]; its reduction in ALS patients may similarly contribute to disease pathology. Conversely, enrichment of pro-inflammatory bacteria such as Prevotella can activate the Toll-like receptor 4 (TLR4) signaling pathway by producing LPS, promoting the release of pro-inflammatory factors including Tumor necrosis factor-alpha (TNF-α) and Interleukin-6 (IL-6) [ 26 ]. While the precise pathogenic mechanisms of these bacteria in ALS remain to be elucidated, their increased abundance in the intestine of ALS patients is evident. Rothia , an opportunistic pathogen in the oral cavity [ 27 ], differs from the intestinal "inhabitants" such as butyrate-producing bacteria and Prevotella. and can cause systemic infections such as infectious endocarditis and pneumonia in immunocompromised patients [ 28 ]; its enrichment in the intestinal tract of ALS patients may suggest oral-gut microbial translocation. This finding is consistent with reports of periodontal pathogen enrichment in Alzheimer's disease [ 29 ]. Prevotella and Veillonella are known as pro-inflammatory genera, closely related to periodontitis and systemic inflammation [ 30 ], further supporting the characteristic shift of the intestinal microbial community towards a pro-inflammatory phenotype in ALS patients. In particular, the increase of Rothia has been reported in previous studies linking oral microbiota to neurodegenerative diseases [ 31 ], suggesting a potential cross-site association with neuroinflammation. This finding is consistent with the conclusions of the seminal study by Atarashi et al. [ 32 ] published in Science, which demonstrated that oral bacteria can ectopically colonize the intestine and induce Type 1 T helper cell (Th1) mediated immune responses, which exacerbate local inflammation. Subsequently, this study explored microbial characteristics at multiple levels. First, at the single bacterial genus level, box plots were used to display the 20 bacterial genera with the most significant differences in the intestinal samples, further verifying the changes in abundance of the aforementioned anti-inflammatory and pro-inflammatory bacteria (Fig. 3 c). Second, at the overall pattern level, heatmap showing the relative abundance of the top 20 differentially abundant genera between ALS patients and healthy controls clearly delineated their distinguishing patterns (Fig. 3 d). Genera enriched in ALS patients included the pro-inflammatory genera Prevotella , Rothia , and Veillonella , as well as Streptococcus and Actinomyces . Genera enriched in healthy controls were predominantly butyrate-producing bacteria, including Faecalibacterium , Roseburia , Blautia , and Dorea . Finally, a functional classification of the top 20 most abundant genera was performed based on their reported functions (Fig. 3 e). Butyrate-producing genera, including Anaerostipes hadrus and [Eubacterium] hallii , accounted for 30% of the high-abundance taxa, whereas pro-inflammatory genera (e.g., Streptococcus salivarius ) and dual-function genera (e.g., Ruminococcus faecis ) were also prominently represented. This functional distribution suggests a shift towards a pro-inflammatory milieu in the intestinal ecosystem of ALS patients. These observations are consistent with the "disease-related synergistic microbial module" concept proposed by Turnbaugh et al. [ 33 ], indicating that the disease state may restructure the interactions among microorganisms. 3.3. Oral-gut microbial interactions are associated with intestinal dysbiosis in ALS In healthy controls (Fig. 4 a), pro-inflammatory taxa (e.g., certain Proteobacteria and Bacteroidetes) and anti-inflammatory, butyrate-producing Firmicutes were interwoven within a single integrated cluster, reflecting an ecological balance between opposing functional groups. In contrast, the ALS network (Fig. 4 b) split into two distinct clusters. One cluster was heavily enriched with oral-derived pro-inflammatory microbes (purple nodes), while the other showed a marked reduction of Firmicutes. This segregation indicates a disruption of the pro-inflammatory and anti-inflammatory balance, which likely contributes to the metastable state of the intestinal ecosystem in ALS. Quantitative assessment of the overall stability of the intestinal ecosystem was performed using a potential landscape model (Fig. 4 c). The results revealed that the healthy state occupied a deep and concentrated potential basin (ΔU = + 0.082; basin area = 0.345), while the ALS state was distributed on a shallow and scattered potential plateau (ΔU = -0.357; basin area = 0.85). The comprehensive stability index (S) further quantified this difference: the healthy state was positive (+ 0.00110), while the ALS state was negative (-0.0118), confirming that the intestinal microecosystem in ALS patients is in a metastable state. 3.4. Oral-gut microbial coordination and diagnostic model in ALS patients Cross-site correlation analysis revealed that observed species richness (Fig. 5 a: r = 0.147, p = 0.666) and Simpson diversity (Fig. 5 c: r = 0.475, p = 0.140) were not significantly correlated between oral and fecal samples. Shannon diversity showed a moderate positive correlation that approached statistical significance (Fig. 5 b: r = 0.589, p = 0.056), suggesting a potential trend toward coordinated regulation of overall microbial diversity across the two body sites. However, the limited sample size may have been insufficient to detect weak but genuine correlations. Therefore, these findings are preliminary, and require validation in independent cohorts with larger sample sizes. Unlike alpha diversity metrics, which reflect community-level richness and evenness, network centrality captures the topological importance of individual nodes within the co-occurrence network. Centrality correlation analysis revealed a significant positive correlation between degree centrality and betweenness centrality for oral and gut microbes in ALS patients (Fig. 5 d: R² = 0.3379, p = 0.0010), supporting the existence of structural coordination between oral and gut microbial networks. Cross-site network analysis identified 20 significant associations between oral and intestinl genera ( |r| ≥ 0.6, p < 0.05), and the abundance levels of these bacteria are visualized (Fig. 5 e). The three strongest associations were: (1) oral Prevotella salivae and intestinal uncultured Dorea sp. (r = 0.937); (2) oral Lactobacillus salivarius and intestinal Blautia sp . (r = 0.911); (3) oral Lactobacillus salivarius and intestinal Blautia obeum (r = 0.881). Notably, the intestinal genera involved in these 20 strong associations (Dorea, Ruminococcus, and Blautia) also showed significant differential abundance between ALS patients and healthy controls in the intestinal microbiota analysis (Fig. 3 ). This convergence suggests that oral-gut microbial interactions may constitute an important driver of intestinal dysbiosis in ALS. A random forest classification model was constructed based on differential intestinal genera to evaluate whether the observed intestinal microbiota alterations could serve as biomarkers for ALS. Feature importance analysis revealed that the top 10 intestinal genera contributing to the model included Blautia , Dorea , and Ruminococcus (Fig. 5 f). Phylum-level ROC curve analysis was performed to evaluate the diagnostic value of intestinal microbiota at the phylum level. The results showed that AUC values of major phyla were generally low (Fig. 5 g). Firmicutes exhibited the highest AUC of 0.72 (95% CI: 0.58–0.86), followed by Bacteroidetes (AUC = 0.68, 95% CI: 0.52–0.84), Proteobacteria (AUC = 0.65, 95% CI: 0.48–0.82), and Actinobacteria (AUC = 0.61, 95% CI: 0.44–0.78). AUC values of other phyla were below 0.60. These findings indicate that phylum-level intestinal microbiota has limited capacity to discriminate ALS patients from healthy controls. A random forest classification model was constructed at the species level. The model demonstrated good discriminatory performance, with an area under the curve (AUC) of 0.93 (95% CI: 0.85–0.99), a sensitivity of 88.9%, and a specificity of 90.0% (Fig. 5 h). This AUC value was substantially higher than the phylum-level AUC (maximum 0.72), suggesting that ALS-associated intestinal microbiota alterations occur primarily at the species level. Due to the limited sample size and the absence of an independent validation cohort, the diagnostic value of this model requires further validation in larger populations. 4. Discussion The core of this study lies in systematically characterizing the ecological destabilization of the intestinal microbiota in ALS patients and revealing, through integrated oral-gut analysis, that oral-gut microbial interactions may serve as important drivers of intestinal dysbiosis. 4.1. Oral-gut microbial interactions as potential drivers of intestinal dysbiosis in ALS The abnormal associations of bacterial genera such as Prevotella, Streptococcus , and Ruminococcus identified in this study are highly consistent with existing research of ALS patients usually exhibit characteristic shifts, including a reduction in butyrate-producing bacteria and an enrichment of pro-inflammatory bacteria [ 34 ]. Building on this, the present study further revealed that the strong positive correlation between oral Prevotella salivae [ 35 ] and intestinal Dorea [ 36 ] (r = 0.937) is consistent with the hypothesis of a "pro-inflammatory synergy module" that, if functionally validated, could contribute to intestinal inflammation. Prevotella , as a common oral genus, has been widely confirmed to be associated with periodontitis and systemic inflammation. Larsen (2017) [ 30 ] studied that Prevotella mainly activates Toll-like receptor 2 (TLR2) to promote antigen-presenting cells to produce T helper 17 (Th17) polarizing cytokines, activating epithelial cells to produce Interleukin-8(IL-8), IL-6, and C-C motif chemokine ligand 20 (CCL20), thereby promoting mucosal Th17 immune response and neutrophil recruitment. The mucosal inflammation caused by Prevotella can lead to the systemic spread of inflammatory mediators, bacteria, and bacterial products. Chen et al. (2025) [ 37 ] further confirmed that Prevotella can accelerate alveolar bone destruction in periodontitis, accelerates neuronal necrosis and abnormal morphology of hippocampal neurons in mice, and reduce the cognitive function of mice. Dorea shows increased abundance in patients with inflammatory bowel disease and can produce pro-inflammatory metabolites such as hydrogen sulfide. The strong positive correlation between the oral Prevotella salivae and the intestinal Dorea (r = 0.937) indicates the potential formation of an "oral-gut pro-inflammatory synergistic module," jointly promoting the systemic inflammatory state in ALS. Although the present study sampled the oral mucosa rather than subgingival plaque, it is noteworthy that enrichment of periodontal pathogens in Alzheimer's disease has been reported in multiple studies. Ciccotosto et al. (2024) found that inoculation with the Porphyromonas gingivalis alone significantly increased neuronal damage, astrocyte and microglial activation, inflammatory cytokine expression, as well as amyloid plaque deposition and hyperphosphorylated tau protein production in the mouse brain [ 38 ]. The abnormal association of oral Prevotell and intestinal microbiota in ALS patients in this study indicated that the oral-gut axis might play a role in ALS that is similar to that in AD-oral pathogens enter the intestine through swallowing or blood circulation, activate the intestinal immune system, and then affect the central nervous system. Lactobacillus salivarius is identified as a probiotic, its strong association with Blautia species (r = 0.911) in the disease might reflect the adaptive reorganization of the "beneficial microbial synergistic network." Notably, strong positive correlations were observed” between oral streptococci and intestinal Ruminococcus - Streptococcus mitis with fecal Ruminococcus (r = 0.848), and oral Streptococcus parasanguinis and untrained Ruminococcus (r = 0.838), which have clear associations with ALS. The enrichment of Ruminococcus torques has been proven to be associated with the worsening of symptoms in an ALS mouse model [ 39 ], suggesting a potential association between oral streptococci and the abundance of opportunistic pathogens like Ruminococcus through regulating the intestinal microenvironment, participating in the neuroinflammatory process in ALS. This is consistent with the reports of two previous studies on the dysbiosis of the oral microbiota in ALS patients [ 18 , 40 ]. It is hypothesized that these oral microbiota could potentially translocate to the intestine via daily swallowing or, if barrier function is impaired, through the bloodstream. Once in the intestine, they might interact with the resident microbiota [ 32 ], Furthermore, their antigens or metabolites could influence the host's systemic immune status [ 41 ], potentially creating a microenvironment conducive to the growth of pro-inflammatory bacteria. The association of Ruminococcus with ALS has been supported by animal experiments: a review by Boddy et al. (2021) highlighted that Ruminococcus torques is associated with exacerbated symptoms in ALS mouse model [ 39 ]. The positive correlations between oral streptococci and intestinal Ruminococcus in this study (r = 0.848 and 0.838) indicates that oral streptococci might promote the expansion of Ruminococcus by altering the intestinal microenvironment. The Inflammatory polysaccharides produced by Ruminococcus can activate dendritic cells, promoting TNF-α secretion, which may be related to the elevated inflammatory factor levels in the serum of ALS patients [ 42 ]. The intestinal genera involved in the 20 strong oral-gut associations (Dorea, Ruminococcus , and Blautia ) were precisely those that differed significantly between ALS patients and healthy controls in our intestinal microbiota analysis (Fig. 3 ). Dorea and Ruminococcus were enriched in ALS patients, Blautia was depleted. This convergence suggests that oral-gut microbial interactions may contribute to the intestinal dysbiosis observed in ALS, providing a potential mechanistic link between oral microbial alterations and the characteristic shift in gut microbial composition toward a pro-inflammatory phenotype. 4.2. Potential Landscape Model: Quantitative Evidence of Intestinal Ecosystem Destabilization The potential landscape analysis revealed a striking contrast between the two groups. The healthy state occupied a deep and concentrated potential basin (ΔU = + 0.082; basin area = 0.345), analogous to a marble at the bottom of a deep bowl—stable and self-correcting after perturbation. In contrast, the ALS state was distributed on a shallow and scattered potential plateau (ΔU = -0.357; basin area = 0.85), resembling a marble on a flat surface, where even a slight disturbance causes it to drift away with little tendency to return. The comprehensive stability index (S) quantified this difference: healthy controls were positive (+ 0.00110), while ALS patients were negative (-0.0118), indicating a metastable ecosystem prone to state transitions under perturbation. This finding carries several implications. First, it provides quantitative evidence that the intestinal ecosystem in ALS is not merely compositionally altered but fundamentally destabilized at the systems level. Second, the metastable nature may explain the heterogeneous clinical progression observed in ALS, This may explain why ALS patients show differential symptomatic progressions under different disease stages or different environmental exposures [ 43 ]. The fragility of the microecosystem also indicate that microecological interventions for ALS may require continuous treatment rather than a one-time treatment. 4.3. Limitations and Future Directions This study has several limitations: First, the sample size is limited, and the conclusions require validation in larger cohorts. Second, oral samples from healthy controls were not collected, which represents a significant limitation. This precludes our ability to determine whether the observed oral microbiota alterations in ALS patients are disease-specific or reflect secondary factors associated with motor dysfunction, such as altered oral hygiene practices, reduced salivary flow, or dysphagia-common features in ALS that can independently affect oral microbial communities. Consequently, the oral microbiota findings presented here should be considered preliminary and hypothesis-generating. Third, the cross-sectional design cannot determine causality between changes in oral-gut axis and ALS patients. Third, 16S rRNA amplicon sequencing only can identify to the genus level, and cannot distinguish among different strains. Fourthly, it is important to emphasize the correlational nature of this study. While strong associations were observed between the oral and intestinal microbiota in ALS patients, the cross-sectional design precludes any determination of causality. The directional relationships proposed in the discussion, whether microbial dysbiosis is a cause or a consequence of disease progression, remain speculative. Future studies employing longitudinal cohorts, germ-free animal models colonized with ALS-derived microbiota, and interventional trials (e.g., probiotics, fecal microbiota transplantation) are urgently needed to establish causal links and to determine whether modulating the oral-gut axis represents a viable therapeutic strategy for ALS. Finally, Beyond the ecological insights, the intestinal microbial alterations identified in this study demonstrated potential clinical utility. A random forest model based on differential gut genera achieved an AUC of 0.93 in distinguishing ALS patients from healthy controls, suggesting that gut microbial features may serve as potential biomarkers. Notably, the top contributing genera included Blautia , Dorea , and Ruminococcus , which were also central to the oral-gut associations identified above, reinforcing their potential importance in ALS pathogenesis. However, given the limited sample size and the absence of an independent validation cohort, these results should be considered preliminary. 5. Conclusion This cohort study revealed that the intestinal microbial ecosystem in ALS is in a metastable state (stability index: HC + 0.00110 vs. ALS − 0.0118) using a potential landscape model, providing quantitative evidence of ecological destabilization. Additionally, 21 significant oral-gut microbial associations (|r| ≥ 0.6) were identified through paired oral-intestinal sampling, with the strongest between oral Prevotella salivae and intestinal Dorea (r = 0.937). The intestinal genera involved in these associations (Dorea , Ruminococcus , and Blautia ) were also differentially abundant in ALS patients (Fig. 3 ), suggesting that oral-gut microbial interactions may contribute to intestinal dysbiosis. However, the absence of oral samples from healthy controls limits the interpretation of disease-specific oral microbiota changes, and these findings should be considered hypothesis-generating rather than definitive. Declarations Acknowledgements We thank all participants and their families for their contribution to this study. We are grateful to the clinical staff at Suzhou Municipal Hospital (Taihu General Hospital) and Bengbu Third People’s Hospital for their assistance with patient recruitment and sample collection. Declaration of generative AI and AI-assisted technologies in the manuscript preparation process. Statement: During the preparation of this work, the authors used RStudio, Adobe Illustrator 2025 and Adobe Photoshop 2026 in order to Polishing the manuscript and figures. After using these tools, the authors reviewed and edited the content as needed and take full responsibility for the content of the published article. Declaration of competing interests Authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding sources This work was supported by a municipal research grant from Bengbu City (Project No. BBWK2024202) for the study entitled “Mechanistic investigation and intervention strategies of non‑traumatic osteonecrosis of the femoral head based on the intestinal microbiota‑metabolism axis”. Ethics approval and consent to participate Informed consent was obtained from all participants for this research, which was approved by the Ethics Committee of the Affiliated Hospital of Tongji University (2023-013). Authorship contribution statement Zelong Yang: Conceptualization, Formal analysis, Visualization, Writing – original draft, Writing – review & editing. Wenhao Liu: Resources, Investigation, Data curation. Bo Yan: Investigation, Validation. Zhan Cao: Methodology, Funding acquisition, Project administration, Supervision, Writing – review & editing. Jingxin Li: Methodology, Funding acquisition, Project administration, Writing – review & editing. Zhongwei Chang: Data curation. Xixin Jin: Investigation, Project administration. Xinjun Wang: Conceptualization, Supervision, Writing – review & editing. Haoyu Wang: Supervision, Supervision，Writing – review & editing. Data Availability Statement The raw sequencing data generated in this study have been deposited in the NCBI Sequence Read Archive (SRA) under BioProject accession number PRJNA1425920. The data are currently private but are accessible to reviewers via the following secure link: https://dataview.ncbi.nlm.nih.gov/object/PRJNA1425920?reviewer=gk215bgekcemo3j10m2hm3qofg The data will be made publicly available upon publication of the manuscript. References Fujimori K, Ishikawa M, Otomo A, Atsuta N, Nakamura R, Akiyama T, Hadano S, Aoki M, Saya H, Sobue G, et al. Modeling sporadic ALS in iPSC-derived motor neurons identifies a potential therapeutic agent. Nat Med. 2018;24(10):1579–89. Guarnaccia M, La Cognata V, Gentile G, Morello G, Cavallaro S. Unraveling the missing heritability of amyotrophic lateral sclerosis: Should we focus more on copy number variations? Neural Regen Res 2026, 21(5):1997–8. Yuan D, Jiang S, Xu R. 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Additional Declarations No competing interests reported. Supplementary Files GraphicalAbstract.tif Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 11 May, 2026 Reviews received at journal 03 May, 2026 Reviewers agreed at journal 22 Apr, 2026 Reviewers invited by journal 21 Apr, 2026 Editor invited by journal 20 Apr, 2026 Editor assigned by journal 14 Apr, 2026 Submission checks completed at journal 14 Apr, 2026 First submitted to journal 08 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9353918","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":628051126,"identity":"1c874248-5e50-43bf-b612-2ab25d69b90e","order_by":0,"name":"zelong Yang","email":"","orcid":"","institution":"Suzhou Municipal Hospital (Taihu General Hospital)","correspondingAuthor":false,"prefix":"","firstName":"zelong","middleName":"","lastName":"Yang","suffix":""},{"id":628051128,"identity":"59585c65-f2c9-4a19-bca5-19905547f554","order_by":1,"name":"Wenhao Liu","email":"","orcid":"","institution":"Suzhou Municipal Hospital (Taihu General Hospital)","correspondingAuthor":false,"prefix":"","firstName":"Wenhao","middleName":"","lastName":"Liu","suffix":""},{"id":628051132,"identity":"9f1cf6e3-24fb-42c3-98e3-20192a54d29a","order_by":2,"name":"Bo Yan","email":"","orcid":"","institution":"Shanghai Dahua Hospital","correspondingAuthor":false,"prefix":"","firstName":"Bo","middleName":"","lastName":"Yan","suffix":""},{"id":628051136,"identity":"5f9c28a5-beb8-4319-ab56-f6598960b21d","order_by":3,"name":"Zhongwei Chang","email":"","orcid":"","institution":"Huaihe Hospital of Henan University","correspondingAuthor":false,"prefix":"","firstName":"Zhongwei","middleName":"","lastName":"Chang","suffix":""},{"id":628051137,"identity":"d529594b-2581-4ae4-9c12-71019c60985a","order_by":4,"name":"Zhan Cao","email":"","orcid":"","institution":"Suzhou Municipal Hospital (Taihu General Hospital)","correspondingAuthor":false,"prefix":"","firstName":"Zhan","middleName":"","lastName":"Cao","suffix":""},{"id":628051141,"identity":"37df1f95-da77-4d7d-8b18-546e8b2e8599","order_by":5,"name":"Jingxin Li","email":"","orcid":"","institution":"Suzhou Municipal Hospital (Taihu General Hospital)","correspondingAuthor":false,"prefix":"","firstName":"Jingxin","middleName":"","lastName":"Li","suffix":""},{"id":628051142,"identity":"811ca6e1-f59c-4190-9169-74abf95a9c6e","order_by":6,"name":"Xixin Jin","email":"","orcid":"","institution":"The Third People's Hospital of Bengbu City","correspondingAuthor":false,"prefix":"","firstName":"Xixin","middleName":"","lastName":"Jin","suffix":""},{"id":628051144,"identity":"eb47ae10-50d8-4b03-a071-f6f2e92002d0","order_by":7,"name":"HaoYu Wang","email":"","orcid":"","institution":"The Third People's Hospital of Bengbu City","correspondingAuthor":false,"prefix":"","firstName":"HaoYu","middleName":"","lastName":"Wang","suffix":""},{"id":628051147,"identity":"dfb5562e-a1ce-4314-9a21-e088aaa318cc","order_by":8,"name":"XinJun Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+UlEQVRIiWNgGAWjYFCCAyBCQo6f+QCDBEKAsBYLY8m2BKK1gEFFosExYrUYHDxj+Lngl0SC8TEewxs/dzDI8d1IYPxcgEeLZMMZY+mZfRJ5Zsd4jC17zzAYS95IYJaegUcLP8PZDdK8PRLFZvd7zCR42xgSN9xIYGPmwaOFjeHs5t9ALYmb23jMJP+2MdQT1AK0ZZs0zw+JxA1sPGbSQFsSDAhpkWw4/82at0HCWOIYW7G1bJuE4cwzD5ul8WkxuHEs+TbPnzo5/jbmjTffttnI8x1PPvgZnxYGiQMMDIxtCC4QMzbg0wD0DEj+D341o2AUjIJRMMIBAOGIS9N5dXabAAAAAElFTkSuQmCC","orcid":"","institution":"Suzhou Municipal Hospital (Taihu General Hospital)","correspondingAuthor":true,"prefix":"","firstName":"XinJun","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2026-04-08 08:25:45","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9353918/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9353918/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108496884,"identity":"15f78a57-5d2c-411a-83a2-7dfebed18b81","added_by":"auto","created_at":"2026-05-05 10:12:41","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":26754458,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy Design and Technical Workflow\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study enrolled 11 patients in ALS and 11 healthy controls. Fecal and oral samples were collected from ALS patients, and fecal samples were collected from healthy controls. Bacterial genomic DNA was extracted, followed by 16S rRNA amplicon sequencing on the Illumina MiSeq platform. Multidimensional bioinformatics analyses were performed, including α-diversity (Shannon index), β-diversities (principal coordinate analysis, PCoA), identification of differential species (e.g., Rothia), evaluation of area under the curve (AUC) evaluation, and microbial co-occurrence network analysis. Results revealed significant differences in intestinal microbiota diversity and community structure between ALS patients and healthy controls.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-9353918/v1/741277c378f12e098fb556bc.png"},{"id":108494452,"identity":"80541a3f-b58c-4a82-8eef-9e10799410aa","added_by":"auto","created_at":"2026-05-05 10:05:24","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1446345,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCohort characteristics and alterations in microbial diversity and composition in ALS patients.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea. \u003c/strong\u003eRadar chart of age distribution and table of gender composition in healthy controls and ALS patients. \u003cstrong\u003eb. \u003c/strong\u003eComparison of intestinal microbiota α-diversity. Violin plots of Shannon index in healthy (green) and ALS (orange) groups. P \u0026lt; 0.05. \u003cstrong\u003ec.\u003c/strong\u003e PCoA of intestinal microbiota based on Bray-Curtis distance. Ellipses indicate 95% confidence intervals. \u003cstrong\u003ed. \u003c/strong\u003et-SNE plot of fecal microbiota (perplexity = 30). The inter-group centroid distance was 16.433, indicating clear separation between healthy controls (purple) and ALS patients (dark green). Ellipses represent 95% confidence intervals.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9353918/v1/0b02b444800196e767b2af8b.jpg"},{"id":108497025,"identity":"d51fa0e3-4f1a-458b-b419-42247ad94be2","added_by":"auto","created_at":"2026-05-05 10:12:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":26613523,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential abundance of microbial taxa between ALS patients and healthy controls.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea. \u003c/strong\u003eThe\u003cstrong\u003e \u003c/strong\u003erelative abundance of intestinal microbiota at the phylum-level. Grouped bar charts (p \u0026lt; 0.05, t-test). \u003cstrong\u003eb. \u003c/strong\u003eHeatmap of differentially abundant genera identified by LEfSe (LDA \u0026gt; 3.0, p \u0026lt; 0.05). Red: ALS-enriched; blue: HC-enriched. \u003cstrong\u003ec. \u003c/strong\u003eThe violin plots of top 20 bacterial genera with the most significant difference. \u003cstrong\u003ed. \u0026nbsp;\u003c/strong\u003eThe relative abundance of the top 20 bacterial genera with significant differential abundance in fecal samples from the healthy control group (green, n=11) and the amyotrophic lateral sclerosis (ALS) patient group (purple, n=11). Red indicates low abundance, brown indicates medium abundance, and yellow indicates high abundance. \u003cstrong\u003ee.\u003c/strong\u003e \u0026nbsp;Functional classification of the top 20 most abundant bacterial genera in fecal samples.Bars represent the mean relative abundance (%). Colors indicate functional groups: butyrate-producing bacteria (green), pro-inflammatory bacteria (purple), dual-function bacteria (blue, with both pro-inflammatory and butyrate-producing potential), and others (red).\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-9353918/v1/f782f9c573d7fdd32d769589.png"},{"id":108494611,"identity":"ab3f8895-3fa0-4f96-ac0f-1f823f8b3c7f","added_by":"auto","created_at":"2026-05-05 10:06:01","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":45547268,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIntestinal ecosystem destabilization in ALS.\u003c/strong\u003e\u003cbr\u003e\n\u003cstrong\u003ea\u003c/strong\u003e.\u0026nbsp;Intestinal microbial co-occurrence network in healthy controls, serving as a reference for the normal gut microbial interaction pattern. \u003cstrong\u003eb\u003c/strong\u003e.\u0026nbsp;Integrated oral-gut co-occurrence network in ALS patients. Purple nodes: oral microbes; cyan nodes: intestinal microbes. \u003cstrong\u003ec\u003c/strong\u003e.\u0026nbsp;Radar chart of stability indices (ΔU, A\u003csub\u003eb\u003c/sub\u003e, S) for the intestinal microbiota derived from potential landscape analysis. The intestinal microbial ecosystem in ALS is in a metastable state (S: HC +0.00110 vs. ALS −0.0118).\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-9353918/v1/59e55ded3ce4638052cf7930.png"},{"id":108494416,"identity":"49cc984e-392a-43eb-ad28-c92328644776","added_by":"auto","created_at":"2026-05-05 10:05:11","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":22005439,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOral-gut microbial coordination and diagnostic model in ALS patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea. \u003c/strong\u003eCorrelation of observed species richness between oral and fecal samples (r = 0.022, p = 0.666). \u003cstrong\u003eb.\u003c/strong\u003e Correlation of Shannon diversity index (r = 0.589, p = 0.056). \u003cstrong\u003ec. \u003c/strong\u003eCorrelation of Simpson diversity index (r = 0.225, p = 0.140). \u003cstrong\u003ed. \u003c/strong\u003eScatter plot showing positive correlation between degree centrality and betweenness centrality for oral and gut microbes in ALS patients (R² = 0.3379, P = 0.0010). \u003cstrong\u003ee.\u003c/strong\u003e Heatmap of bacterial genera with significant oral-gut cross‑site correlations. Columns represent fecal samples from ALS patients (n=11) and healthy controls (n=11). Color intensity indicates Z‑score‑normalized relative abundance. Red represents high abundance, blue represents low abundance. \u003cstrong\u003ef\u003c/strong\u003e. Top 10 intestinal genera ranked by mean decrease accuracy (MDA) in the random forest model. \u003cstrong\u003eg\u003c/strong\u003e. Firmicutes AUC=0.72, Bacteroidetes AUC=0.68, Proteobacteria AUC=0.65, Actinobacteria AUC=0.61. Gray dashed line: reference (AUC=0.5). \u003cstrong\u003eh\u003c/strong\u003e. L. salivarius AUC=0.937, E. coli AUC=0.921, S. anginosus AUC=0.909, L. gasseri AUC=0.900, B. adolescentis AUC=0.893, D. longicatena AUC=0.883. Gray dashed line: reference (AUC=0.5).\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-9353918/v1/c7c0c517d5fa7acfaaa0a69d.png"},{"id":108388568,"identity":"849b924a-8a62-4746-85ad-ac6e973667a3","added_by":"auto","created_at":"2026-05-04 06:42:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":304378,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9353918/v1/0b982a79-b767-4b21-b9b5-0bc9f4f3e6c1.pdf"},{"id":108494453,"identity":"59c29830-67f4-42a7-8af1-e67e7444f4ba","added_by":"auto","created_at":"2026-05-05 10:05:25","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18254716,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicalAbstract.tif","url":"https://assets-eu.researchsquare.com/files/rs-9353918/v1/8e017af1c4ebef4e411bb5fa.tif"}],"financialInterests":"No competing interests reported.","formattedTitle":"Gut Microbiome Ecological Network Destabilization in Amyotrophic Lateral Sclerosis: Insights from In tegrated Oral–Gut Analysis","fulltext":[{"header":"1.Introduction","content":"\u003cp\u003eAmyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with an irreversible and rapidly progression [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], characterized by the progressive and selective loss of upper and lower motor neurons in the brain and spinal cord. This disease leads to skeletal muscle atrophy, paralysis and eventually respiratory failure. Although the exact cause of ALS remains unclear, it is widely accepted that ALS results from a combination of genetic susceptibility and environmental factors [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], including lifestyle and microbial alterations. The global incidence and prevalence of ALS show significant heterogeneity, influenced by factors such as age, sex, and ethnicity [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Especially with the global trend of population aging, the disease burden of ALS is projected to increase continuously [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Current clinical treatments are very limited, with only a few medicines such as riluzole [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] and edaravone [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] are available, These medicines offer modest efficacy, slightly delaying the progression of disease, failing to significantly improve neurological function or reverse the progression of disease. This situation underscores the urgent need to explore non-neuronal pathogenesis of ALS and to identify new therapeutic targets.\u003c/p\u003e \u003cp\u003eIn recent years, the \"brain-gut axis\" has gained extensive attention as a key pathway in the pathogenesis of neurological diseases. The intestinal microbiota and its metabolites can profoundly influence the function, neuroinflammation and homeostasis of the central nervous system through immune [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], endocrine [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], and neural [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] pathways. In the field of ALS research, accumulating evidence indicate that patients exhibit characteristic intestinal microbial dysbiosis. This dysregulation is closely associated with the severity of disease [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], rate of the progression and systemic inflammatory status [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. A study by Gotkine et al. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] further confirmed that ALS patients have characteristic dysbiosis of the intestinal microbiota, aiming to indicated that it may be related to the disease progression and systemic inflammation, providing a new entry point for understanding the pathology of ALS. A recent Mendelian randomization study was published in 2025 [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] further strengthened the causal link between the two, demonstrating that specific intestinal bacterial genera, such as \u003cem\u003eBifidobacterium\u003c/em\u003e, may be risk factors for ALS, while others, such as \u003cem\u003eEnterobacter\u003c/em\u003e, may have protective effects. Furthermore, animal model studies have shown that changes in the intestinal microbiota may occur before the appearance of neuromuscular symptoms [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], indicating its potential role in the initial mechanisms of the disease.\u003c/p\u003e \u003cp\u003eAlthough significant progress has been made in understanding the \"brain-gut axis\", there are still major gaps in understanding the microbial dysbiosis in ALS. First, most studies have focused solely on describing compositional changes of the fecal microbiota, lacking quantitative assessment of microbial community ecological stability. Microbial communities are not simply collections of independent microorganisms; their internal networks of synergy and competition are crucial for understanding community function and ecological stability. However, this ecological dynamics perspective remains underexplored in ALS research. Second, the oral cavity, as the entrance of the digestive tract, is anatomically and functionally connected to the intestine. The oral microbiota may continuously influence intestinal microbiota composition and function through daily swallowing, yet the role of oral-gut microbial interactions in intestinal dysbiosis in ALS remains unclear.\u003c/p\u003e \u003cp\u003eTo systematically figure out these scientific issues and fill the existing knowledge gaps, this study was designed. 16S rRNA amplicon sequencing was usedfor detecting paired oral and fecal samples from ALS patients in a cohort (to achieve direct comparison between body parts), as well as fecal samples from healthy controls (as a baseline for the intestinal microbiota). Utilizing high-throughput 16S rRNA amplicon sequencing, combined with a series of multivariate bioinformatics analyses\u0026mdash;including α/β diversities analysis, identification of differential abundance species, construction of microbial co-occurrence network, and algorithm of random forest. this study aims to characterize the ecological destabilization of the intestinal microbiota in ALS and to explore whether oral-gut microbial interactions contribute to this destabilization.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study Subjects and Sample Collection\u003c/h2\u003e \u003cp\u003e \u003cb\u003eParticipant Recruitment, Time and Location\u003c/b\u003e \u003c/p\u003e \u003cp\u003eA cohort study (Figure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) was conducted at Suzhou Municipal Hospital (Taihu Campus) from December 2024 to December 2025. A total of 22 participants were enrolled,including 11 patients with amyotrophic lateral sclerosis (ALS) and 11 healthy controls.All ALS patients met the revised El Escorial diagnostic criteria, and diagnoses were independently confirmed by two neurologists. Clinical trial number: not applicable\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eExclusion Criteria\u003c/b\u003e \u003c/p\u003e \u003cp\u003eUse of antibiotics, probiotics, or prebiotics within one month before enrollment. Presence of other systemic diseases (e.g., diabetes, autoimmune diseases, inflammatory bowel disease, liver cirrhosis, rheumatoid arthritis). other infectious diseases (e.g., respiratory tract infection, genitourinary infection). long-term use of immunosuppressants or glucocorticoids. pregnancy or lactation. other neurodegenerative or psychiatric disorders\u003c/p\u003e \u003cp\u003e \u003cb\u003eDiagnosis and Grouping\u003c/b\u003e \u003c/p\u003e \u003cp\u003eALS group: Patients diagnosed with amyotrophic lateral sclerosis. Healthy control group: Healthy volunteers with no history of neurodegenerative diseases and met none of the above exclusion criteria.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSample Collection\u003c/b\u003e \u003c/p\u003e \u003cp\u003e ALS patients: Oral swabs and fecal samples were collected at enrollment. Oral samples: Used sterile oral swabs,bilateral buccal mucosa and dorsal tongue surfaces were collected before brushing tooth in the morning. Swabs were immediately placed in sterile PBS tubes and stored at \u0026minus;\u0026thinsp;80\u0026deg;C. Fecal samples: Used sterile collection tubes to collect the middle portion of the first stool sample in the morning and immediately stored at \u0026minus;\u0026thinsp;80\u0026deg;C. Healthy controls: Fecal samples were collected at the same time and stored under identical conditions, oral samples were not collected from healthy controls;\u003c/p\u003e \u003cp\u003eFinally, among 33 samples from 22 participants (11 oral samples and 11 fecal samples from ALS patients; 11 fecal samples from healthy controls) were subjected to high-throughput 16S rRNA amplicon sequencing.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Microbial DNA Extraction and Library Construction\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1. DNA Extraction\u003c/h2\u003e \u003cp\u003eFecal samples: Bacterial genomic DNA was extracted by using the QIAamp DNA Stool Mini Kit (QIAGEN, Germany) according to the manufacturer\u0026rsquo;s instructions.Oral samples: Bacterial DNA from oral swabs was extracted by using the QIAamp DNA Microbiome Kit (QIAGEN, Germany). DNA concentration was measured by using a Qubit 3.0 Fluorometer (Thermo Fisher Scientific, USA), and DNA integrity was verified by 1% agarose gel electrophoresis. Samples with concentration\u0026thinsp;\u0026lt;\u0026thinsp;1 ng/\u0026micro;L or obvious degradation were excluded.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2. 16S rRNA Gene Amplification\u003c/h2\u003e \u003cp\u003eThe V3\u0026ndash;V4 hypervariable region of the bacterial 16S rRNA gene was amplified by PCR by using the primer pair 338F (5'-ACTCCTACGGGAGGCAGCAG-3') and 806R (5'-\u003c/p\u003e \u003cp\u003eGGACTACHVGGGTWTCTAAT-3'). The PCR reaction system (25\u0026micro;L) contained: 10 ng DNA template, 12.5\u0026micro;L 2\u0026times; Phusion High-Fidelity PCR Master Mix (New England Biolabs, USA), 0.5\u0026micro;M (final concentration) of each forward and reverse primer,and sterile ddH₂O up to 25\u0026micro;L. PCR thermal cycling conditions were as follows: Initial denaturation at 98\u0026deg;C for 1 min; 30 cycles of denaturation at 98\u0026deg;C for 10s, annealing at 50\u0026deg;C for 30s, extension at 72\u0026deg;C for 30s; followed by a final extension at 72\u0026deg;C for 5 min. Amplicons were purified by using Agencourt AMPure XP beads (Beckman Coulter, USA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3. Library Construction and Sequencing\u003c/h2\u003e \u003cp\u003eSequencing libraries were constructed by using the Nextera XT DNA Library Preparation Kit (Illumina, USA). Unique dual-index barcodes were added to each sample by PCR for splitting the sample. Purified libraries were assessed at size and quality by using the Agilent 2100 Bioanalyzer (Agilent Technologies, USA) and quantified with the Qubit.All libraries were pooled in equimolar amounts and diluted to 4 nM. The pooled library was denatured, diluted to 8 pM, and mixed with 30% PhiX control library. Paired-end sequencing was performed on the Illumina MiSeq platform by using the MiSeq Reagent Kit v3 (600 cycles), with a read length of 2 \u0026times; 300 bp.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Bioinformatic Analysis\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1. Raw Data Processing\u003c/h2\u003e \u003cp\u003eAdapter and primer sequences were removed from raw sequencing reads using Cutadapt (v1.2.1). Quality filtering, denoising, paired-end merging, and chimera removal were performed using the DADA2 plugin in QIIME2 (v2020.11) to generate amplicon sequence variants (ASVs). Representative sequences of each ASV were taxonomically annotated against the SILVA database (v138) using a naive Bayes classifier with a confidence threshold of 0.8.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2. Diversity Analysis\u003c/h2\u003e \u003cp\u003eDiversity analyses were performed using the vegan package (v2.5-3) in R software (v4.0.3). α-diversities: The Shannon index, Chao1 index, and observed species index were calculated to reflect richness and evenness of within-sample species. Between-group comparisons were performed using the Wilcoxon rank-sum test. β-diversities: Community dissimilarity was calculated based on Bray\u0026ndash;Curtis distance. Principal Coordinate Analysis (PCoA) was used for dimensionality reduction and visualization. Between-group differences were tested by permutational multivariate analysis of variance (PERMANOVA,999 permutations). t-SNE (perplexity\u0026thinsp;=\u0026thinsp;30) and Principal Component Analysis (PCA) were used for supplementary validation.\u003c/p\u003e \u003cp\u003eA heatmap was generated to visualize the top 50 most abundant genera across all samples. Wilcoxon rank-sum tests were used to compare relative abundances at the phylum and genus levels between groups. Boxplots for the top 20 differentially abundant genera were generated to show the distribution of these taxa between groups. A separate heatmap displaying the relative abundance of key differentially abundant genera was also generated to visualize the distinguishing patterns between ALS patients and healthy controls.\u003c/p\u003e \u003cp\u003eCorrelations among genera were calculated using the SparCC algorithm (implemented in Python, 100 iterations). Genus pairs with|correlation coefficient| \u0026gt; 0.3 and p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were used for network construction. Networks were visualized in Gephi (v0.9.2). In the generated network diagram, nodes represent different bacterial genera, and node size is proportional to the degree (the number of edges connected to the node). Edges indicate significant correlations between two bacterial genera. To visually distinguish the direction of the correlation, using different colors to mark the edges: red edges represent positive correlations, and blue edges represent negative correlations. The thickness of an edge is proportional to the absolute strength of the correlation ( |r| ).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e\u003cb\u003e2.3.3. Potential Landscape Analysis\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eThe ecological stability of the microbial ecosystem was quantitatively assessed using a potential landscape model based on the framework previously described by Li et al. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] and Wang et al. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This approach conceptualizes the microbial community as a dynamic system moving across an energy landscape, where stable states correspond to valleys (attractors) and unstable states correspond to peaks.\u003c/p\u003e \u003cp\u003eThe analysis was performed using custom R scripts with the following steps:\u003c/p\u003e \u003cp\u003eFirst, dimensionality reduction was conducted using Principal Coordinate Analysis (PCoA) based on Bray-Curtis distances from the genus-level relative abundance data. The first two principal coordinates (PCo1 and PCo2) were selected to represent the major axes of community variation, capturing the most significant ecological gradients.\u003c/p\u003e \u003cp\u003eSecond, energy landscape construction involved creating a two-dimensional grid over the range of PCo1 and PCo2 scores. For each grid point, the local density of samples was estimated using a Gaussian kernel function. The potential energy U at each grid point was then calculated as the negative logarithm of the sample density: U\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;ln (ρ), where ρ is the estimated density. This transformation results in regions with high sample density (frequently occupied community states) corresponding to low energy valleys, while regions with low sample density correspond to high energy peaks.\u003c/p\u003e \u003cp\u003eThird, stability parameters were derived from the constructed potential landscape to characterize ecosystem stability:\u003c/p\u003e \u003cp\u003eValley depth (ΔU): The difference in potential energy between the bottom of a valley and the surrounding ridge. A deeper valley indicates a more stable attractor state, requiring more energy to perturb the system out of that state.\u003c/p\u003e \u003cp\u003eValley area (A\u003csub\u003eb\u003c/sub\u003e): The size of the basin of attraction. A larger area indicates that the system can tolerate wider fluctuations while remaining within the same stable state.\u003c/p\u003e \u003cp\u003eComprehensive stability index (S): A composite index integrating both valley depth and area, calculated as S\u0026thinsp;=\u0026thinsp;ΔU\u0026times;A\u003csub\u003eb\u003c/sub\u003e. A positive S value indicates a stable ecosystem with deep, well-defined attractor basins, while a negative S value suggests an unstable or metastable ecosystem prone to state transitions under perturbation.\u003c/p\u003e \u003cp\u003eFinally, comparative analysis of the potential landscape parameters was performed separately for the healthy control group and the ALS patient group based on their respective sample distributions in the PCoA space. The stability indices (ΔU, A\u003csub\u003eb\u003c/sub\u003e, and S) were compared between groups to quantify the shift in ecosystem stability associated with ALS.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Machine Learning Model Construction\u003c/h2\u003e \u003cp\u003eA classification model for ALS disease status was constructed using the randomForest package (v4.6-14) in R. Input features were the relative abundances of differential gut genera identified by LEfSe. The dataset was split into training (70%) and testing (30%) sets. The number of trees (ntree) and the number of randomly selected features per node (mtry) were tuned by 10-fold cross-validation.\u003c/p\u003e \u003cp\u003eThe pROC package (v1.16.2) was used to plot the receiver operating characteristic (ROC) curve and calculate the area under the curve (AUC), sensitivity, and specificity. Feature importance was evaluated by mean decrease accuracy and mean decrease Gini index.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Statistical Analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using R software (v4.0.3) and GraphPad Prism (v8.0). Continuous variables are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or median (interquartile range, IQR).\u003c/p\u003e \u003cp\u003eBetween-group comparisons for continuous variables: Student\u0026rsquo;s t-test (normal distribution) or Wilcoxon rank-sum test (non-normal distribution). Multiple groups: Kruskal\u0026ndash;Wallis test, with post-hoc pairwise comparisons by Dunn\u0026rsquo;s test. A two-sided p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. The Benjamini-Hochberg method was used to control the false discovery rate (FDR) in differential taxa analysis.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.1. intestinal microbial diversity is reduced and community structure is altered in ALS\u003c/h2\u003e \u003cp\u003eAs illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the study workflow included paired oral and fecal samples collected from ALS patients and fecal samples from healthy controls, followed by 16S rRNA sequencing and multi-dimensional bioinformatics analyses. The two groups were well-matched in age (median: HC 54 years, IQR 49\u0026ndash;65; ALS 54.5 years, IQR 48\u0026ndash;66; P\u0026thinsp;=\u0026thinsp;0.78) and gender composition (54.5% female in the HC group vs. 63.6% female in the ALS group; P\u0026thinsp;=\u0026thinsp;0.45) (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05), thereby minimizing potential confounding effects from demographic factors (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo compare the species richness and evenness of the microbial communities between the two groups, an alpha diversity analysis was first performed. The analysis showed that the Shannon index of the intestinal microbiota in ALS patients was significantly lower than that of the healthy controls (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD: ALS 3.110\u0026thinsp;\u0026plusmn;\u0026thinsp;0.595 vs. HC 3.479\u0026thinsp;\u0026plusmn;\u0026thinsp;0.354; IQR: ALS 0.292 vs. HC 0.325), indicating a comprehensive decline in species richness and evenness (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). The reduction in microbial diversity is a typical characteristic of ecological dysbiosis, indicating that the intestinal ecosystem of ALS patients has diminished functional redundancy and the ability to resist external disturbances (e.g., infections, medications, dietary changes) has weakened. This result is consistent with several previous studies on the intestinal microbiota of ALS patients [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], all of which have reported significant ecological imbalance in the disease state.\u003c/p\u003e \u003cp\u003eSubsequently, analysis of beta diversities were conducted to explore differences in the overall structure of microbial communities between the two groups. Principal Coordinate Analysis (PCoA) based on Bray-Curtis distance clearly revealed a significant separation in the intestinal microbial structure between the ALS group and the control group (PERMANOVA, R\u0026sup2; = 0.15, p\u0026thinsp;=\u0026thinsp;0.002) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). This finding indicates that the overall intestinal microbial community undergoes directional changes in ALS. This structural change may reflect the selective pressure exerted by the disease state on the intestinal ecosystem, possibly driven by systemic inflammation, altered intestinal motility, the use of medication, and are consistent with the consensus that disease state is associated with overall microbial community structure [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The t-SNE results further supported this conclusion, two methods of dimensionality reduction showed a consistent grouping trend (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed), enhancing the robustness of the finding.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.2. ALS is characterized by depletion of butyrate-producers and enrichment of pro-inflammatory taxa\u003c/h2\u003e \u003cp\u003eSystematical assessment of the compositional changes in the intestinal microbiota of ALS patients, analyzing differences at the phylum and genus levels. At the phylum level, the relative abundance of Firmicutes was significantly decreased in ALS patients compared to healthy controls (ALS: 88.45% vs. HC: 93.65%, p\u0026thinsp;=\u0026thinsp;0.047). Firmicutes includes many butyrate-producing bacteria indicates. Conversely, the relative abundance of Bacteroidetes was significantly increased (ALS: 3.75% vs. HC: 0.65%, p\u0026thinsp;=\u0026thinsp;0.012) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). The Bacteroidetes phylum is rich in LPS-producing bacteria, and its increase may exacerbate intestinal inflammation. This trend is consistent with the findings of Zeng et al. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] and Wu et al. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] in ALS cohorts, further confirming the prevalence of \"phylum-level imbalance\" in ALS.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFollowing the phylum-level observations, examining the top 50 most abundant genera across all samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). The bacterial genera that were significantly enriched in ALS patients mainly included \u003cem\u003eRothia, Prevotella, Veillonella, Porphyromonas\u003c/em\u003e, and \u003cem\u003eActinomyces\u003c/em\u003e. Some genera (such as \u003cem\u003ePrevotella\u003c/em\u003e and \u003cem\u003eVeillonella\u003c/em\u003e) have been reported to be associated with pro-inflammatory phenotypes in studies related to neuroinflammation [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Genera enriched in healthy controls were predominantly beneficial butyrate-producing bacteria, including \u003cem\u003eFaecalibacterium, Roseburia, Blautia\u003c/em\u003e, and \u003cem\u003eDorea.\u003c/em\u003e This binary pattern of \"beneficial microbe depletion and pro-inflammatory microbe enrichment\" represents one of the core findings of this study.\u003c/p\u003e \u003cp\u003eThis binary pattern has important implications for ALS pathogenesis. Depletion of butyrate-producing bacteria (e.g., \u003cem\u003eFaecalibacterium, Roseburia\u003c/em\u003e) can lead to damage to the intestinal barrier function [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], increasing the entry of inflammatory substances such as lipopolysaccharide (LPS) into the blood circulation, activating systemic immune responses, and exacerbating neuroinflammation through the \"gut-brain axis\" [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Sokol et al. previously demonstrated the anti-inflammatory effects of \u003cem\u003eFaecalibacterium\u003c/em\u003e and its reduction in inflammatory bowel disease [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]; its reduction in ALS patients may similarly contribute to disease pathology. Conversely, enrichment of pro-inflammatory bacteria such as \u003cem\u003ePrevotella\u003c/em\u003e can activate the Toll-like receptor 4 (TLR4) signaling pathway by producing LPS, promoting the release of pro-inflammatory factors including Tumor necrosis factor-alpha (TNF-α) and Interleukin-6 (IL-6) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. While the precise pathogenic mechanisms of these bacteria in ALS remain to be elucidated, their increased abundance in the intestine of ALS patients is evident.\u003c/p\u003e \u003cp\u003e \u003cem\u003eRothia\u003c/em\u003e, an opportunistic pathogen in the oral cavity [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], differs from the intestinal \"inhabitants\" such as butyrate-producing bacteria and \u003cem\u003ePrevotella.\u003c/em\u003e and can cause systemic infections such as infectious endocarditis and pneumonia in immunocompromised patients [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]; its enrichment in the intestinal tract of ALS patients may suggest oral-gut microbial translocation. This finding is consistent with reports of periodontal pathogen enrichment in Alzheimer's disease [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. \u003cem\u003ePrevotella\u003c/em\u003e and \u003cem\u003eVeillonella\u003c/em\u003e are known as pro-inflammatory genera, closely related to periodontitis and systemic inflammation [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], further supporting the characteristic shift of the intestinal microbial community towards a pro-inflammatory phenotype in ALS patients. In particular, the increase of \u003cem\u003eRothia\u003c/em\u003e has been reported in previous studies linking oral microbiota to neurodegenerative diseases [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], suggesting a potential cross-site association with neuroinflammation. This finding is consistent with the conclusions of the seminal study by Atarashi et al. [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] published in Science, which demonstrated that oral bacteria can ectopically colonize the intestine and induce Type 1 T helper cell (Th1) mediated immune responses, which exacerbate local inflammation.\u003c/p\u003e \u003cp\u003eSubsequently, this study explored microbial characteristics at multiple levels. First, at the single bacterial genus level, box plots were used to display the 20 bacterial genera with the most significant differences in the intestinal samples, further verifying the changes in abundance of the aforementioned anti-inflammatory and pro-inflammatory bacteria (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). Second, at the overall pattern level, heatmap showing the relative abundance of the top 20 differentially abundant genera between ALS patients and healthy controls clearly delineated their distinguishing patterns (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed). Genera enriched in ALS patients included the pro-inflammatory genera \u003cem\u003ePrevotella\u003c/em\u003e, \u003cem\u003eRothia\u003c/em\u003e, and \u003cem\u003eVeillonella\u003c/em\u003e, as well as \u003cem\u003eStreptococcus\u003c/em\u003e and \u003cem\u003eActinomyces\u003c/em\u003e. Genera enriched in healthy controls were predominantly butyrate-producing bacteria, including \u003cem\u003eFaecalibacterium\u003c/em\u003e, \u003cem\u003eRoseburia\u003c/em\u003e, \u003cem\u003eBlautia\u003c/em\u003e, and \u003cem\u003eDorea\u003c/em\u003e. Finally, a functional classification of the top 20 most abundant genera was performed based on their reported functions (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee). Butyrate-producing genera, including \u003cem\u003eAnaerostipes hadrus\u003c/em\u003e and \u003cem\u003e[Eubacterium] hallii\u003c/em\u003e, accounted for 30% of the high-abundance taxa, whereas pro-inflammatory genera (e.g., \u003cem\u003eStreptococcus salivarius\u003c/em\u003e) and dual-function genera (e.g., \u003cem\u003eRuminococcus faecis\u003c/em\u003e) were also prominently represented. This functional distribution suggests a shift towards a pro-inflammatory milieu in the intestinal ecosystem of ALS patients. These observations are consistent with the \"disease-related synergistic microbial module\" concept proposed by Turnbaugh et al. [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], indicating that the disease state may restructure the interactions among microorganisms.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Oral-gut microbial interactions are associated with intestinal dysbiosis in ALS\u003c/h2\u003e \u003cp\u003eIn healthy controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea), pro-inflammatory taxa (e.g., certain Proteobacteria and Bacteroidetes) and anti-inflammatory, butyrate-producing Firmicutes were interwoven within a single integrated cluster, reflecting an ecological balance between opposing functional groups. In contrast, the ALS network (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb) split into two distinct clusters. One cluster was heavily enriched with oral-derived pro-inflammatory microbes (purple nodes), while the other showed a marked reduction of Firmicutes. This segregation indicates a disruption of the pro-inflammatory and anti-inflammatory balance, which likely contributes to the metastable state of the intestinal ecosystem in ALS.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eQuantitative assessment of the overall stability of the intestinal ecosystem was performed using a potential landscape model (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). The results revealed that the healthy state occupied a deep and concentrated potential basin (ΔU\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.082; basin area\u0026thinsp;=\u0026thinsp;0.345), while the ALS state was distributed on a shallow and scattered potential plateau (ΔU = -0.357; basin area\u0026thinsp;=\u0026thinsp;0.85). The comprehensive stability index (S) further quantified this difference: the healthy state was positive (+\u0026thinsp;0.00110), while the ALS state was negative (-0.0118), confirming that the intestinal microecosystem in ALS patients is in a metastable state.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e3.4. Oral-gut microbial coordination and diagnostic model in ALS patients\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eCross-site correlation analysis revealed that observed species richness (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea: r\u0026thinsp;=\u0026thinsp;0.147, p\u0026thinsp;=\u0026thinsp;0.666) and Simpson diversity (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec: r\u0026thinsp;=\u0026thinsp;0.475, p\u0026thinsp;=\u0026thinsp;0.140) were not significantly correlated between oral and fecal samples. Shannon diversity showed a moderate positive correlation that approached statistical significance (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb: r\u0026thinsp;=\u0026thinsp;0.589, p\u0026thinsp;=\u0026thinsp;0.056), suggesting a potential trend toward coordinated regulation of overall microbial diversity across the two body sites. However, the limited sample size may have been insufficient to detect weak but genuine correlations. Therefore, these findings are preliminary, and require validation in independent cohorts with larger sample sizes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eUnlike alpha diversity metrics, which reflect community-level richness and evenness, network centrality captures the topological importance of individual nodes within the co-occurrence network. Centrality correlation analysis revealed a significant positive correlation between degree centrality and betweenness centrality for oral and gut microbes in ALS patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed: R\u0026sup2; = 0.3379, p\u0026thinsp;=\u0026thinsp;0.0010), supporting the existence of structural coordination between oral and gut microbial networks.\u003c/p\u003e \u003cp\u003eCross-site network analysis identified 20 significant associations between oral and intestinl genera ( |r| \u0026ge; 0.6, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and the abundance levels of these bacteria are visualized (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ee). The three strongest associations were: (1) oral \u003cem\u003ePrevotella salivae\u003c/em\u003e and intestinal \u003cem\u003euncultured Dorea sp.\u003c/em\u003e (r\u0026thinsp;=\u0026thinsp;0.937); (2) oral \u003cem\u003eLactobacillus salivarius\u003c/em\u003e and intestinal \u003cem\u003eBlautia sp\u003c/em\u003e. (r\u0026thinsp;=\u0026thinsp;0.911); (3) oral \u003cem\u003eLactobacillus salivarius\u003c/em\u003e and intestinal \u003cem\u003eBlautia obeum\u003c/em\u003e (r\u0026thinsp;=\u0026thinsp;0.881). Notably, the intestinal genera involved in these 20 strong associations (Dorea, Ruminococcus, and Blautia) also showed significant differential abundance between ALS patients and healthy controls in the intestinal microbiota analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This convergence suggests that oral-gut microbial interactions may constitute an important driver of intestinal dysbiosis in ALS.\u003c/p\u003e \u003cp\u003eA random forest classification model was constructed based on differential intestinal genera to evaluate whether the observed intestinal microbiota alterations could serve as biomarkers for ALS. Feature importance analysis revealed that the top 10 intestinal genera contributing to the model included \u003cem\u003eBlautia\u003c/em\u003e, \u003cem\u003eDorea\u003c/em\u003e, and \u003cem\u003eRuminococcus\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ef).\u003c/p\u003e \u003cp\u003ePhylum-level ROC curve analysis was performed to evaluate the diagnostic value of intestinal microbiota at the phylum level. The results showed that AUC values of major phyla were generally low (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eg). Firmicutes exhibited the highest AUC of 0.72 (95% CI: 0.58\u0026ndash;0.86), followed by Bacteroidetes (AUC\u0026thinsp;=\u0026thinsp;0.68, 95% CI: 0.52\u0026ndash;0.84), Proteobacteria (AUC\u0026thinsp;=\u0026thinsp;0.65, 95% CI: 0.48\u0026ndash;0.82), and Actinobacteria (AUC\u0026thinsp;=\u0026thinsp;0.61, 95% CI: 0.44\u0026ndash;0.78). AUC values of other phyla were below 0.60. These findings indicate that phylum-level intestinal microbiota has limited capacity to discriminate ALS patients from healthy controls. A random forest classification model was constructed at the species level. The model demonstrated good discriminatory performance, with an area under the curve (AUC) of 0.93 (95% CI: 0.85\u0026ndash;0.99), a sensitivity of 88.9%, and a specificity of 90.0% (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eh). This AUC value was substantially higher than the phylum-level AUC (maximum 0.72), suggesting that ALS-associated intestinal microbiota alterations occur primarily at the species level. Due to the limited sample size and the absence of an independent validation cohort, the diagnostic value of this model requires further validation in larger populations.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe core of this study lies in systematically characterizing the ecological destabilization of the intestinal microbiota in ALS patients and revealing, through integrated oral-gut analysis, that oral-gut microbial interactions may serve as important drivers of intestinal dysbiosis.\u003c/p\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Oral-gut microbial interactions as potential drivers of intestinal dysbiosis in ALS\u003c/h2\u003e \u003cp\u003eThe abnormal associations of bacterial genera such as \u003cem\u003ePrevotella, Streptococcus\u003c/em\u003e, and \u003cem\u003eRuminococcus\u003c/em\u003e identified in this study are highly consistent with existing research of ALS patients usually exhibit characteristic shifts, including a reduction in butyrate-producing bacteria and an enrichment of pro-inflammatory bacteria [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Building on this, the present study further revealed that the strong positive correlation between oral \u003cem\u003ePrevotella salivae\u003c/em\u003e [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] and intestinal \u003cem\u003eDorea\u003c/em\u003e [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] (r\u0026thinsp;=\u0026thinsp;0.937) is consistent with the hypothesis of a \"pro-inflammatory synergy module\" that, if functionally validated, could contribute to intestinal inflammation.\u003c/p\u003e \u003cp\u003e \u003cem\u003ePrevotella\u003c/em\u003e, as a common oral genus, has been widely confirmed to be associated with periodontitis and systemic inflammation. Larsen (2017) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] studied that \u003cem\u003ePrevotella\u003c/em\u003e mainly activates Toll-like receptor 2 (TLR2) to promote antigen-presenting cells to produce T helper 17 (Th17) polarizing cytokines, activating epithelial cells to produce Interleukin-8(IL-8), IL-6, and C-C motif chemokine ligand 20 (CCL20), thereby promoting mucosal Th17 immune response and neutrophil recruitment. The mucosal inflammation caused by \u003cem\u003ePrevotella\u003c/em\u003e can lead to the systemic spread of inflammatory mediators, bacteria, and bacterial products. Chen et al. (2025) [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] further confirmed that \u003cem\u003ePrevotella\u003c/em\u003e can accelerate alveolar bone destruction in periodontitis, accelerates neuronal necrosis and abnormal morphology of hippocampal neurons in mice, and reduce the cognitive function of mice. \u003cem\u003eDorea\u003c/em\u003e shows increased abundance in patients with inflammatory bowel disease and can produce pro-inflammatory metabolites such as hydrogen sulfide. The strong positive correlation between the oral \u003cem\u003ePrevotella salivae\u003c/em\u003e and the intestinal \u003cem\u003eDorea\u003c/em\u003e (r\u0026thinsp;=\u0026thinsp;0.937) indicates the potential formation of an \"oral-gut pro-inflammatory synergistic module,\" jointly promoting the systemic inflammatory state in ALS.\u003c/p\u003e \u003cp\u003eAlthough the present study sampled the oral mucosa rather than subgingival plaque, it is noteworthy that enrichment of periodontal pathogens in Alzheimer's disease has been reported in multiple studies. Ciccotosto et al. (2024) found that inoculation with the Porphyromonas gingivalis alone significantly increased neuronal damage, astrocyte and microglial activation, inflammatory cytokine expression, as well as amyloid plaque deposition and hyperphosphorylated tau protein production in the mouse brain [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The abnormal association of oral \u003cem\u003ePrevotell\u003c/em\u003e and intestinal microbiota in ALS patients in this study indicated that the oral-gut axis might play a role in ALS that is similar to that in AD-oral pathogens enter the intestine through swallowing or blood circulation, activate the intestinal immune system, and then affect the central nervous system.\u003c/p\u003e \u003cp\u003e\u003cem\u003eLactobacillus salivarius\u003c/em\u003e is identified as a probiotic, its strong association with \u003cem\u003eBlautia\u003c/em\u003e species (r\u0026thinsp;=\u0026thinsp;0.911) in the disease might reflect the adaptive reorganization of the \"beneficial microbial synergistic network.\" Notably, strong positive correlations were observed\u0026rdquo; between oral \u003cem\u003estreptococci\u003c/em\u003e and intestinal \u003cem\u003eRuminococcus\u003c/em\u003e-\u003cem\u003eStreptococcus mitis\u003c/em\u003e with fecal \u003cem\u003eRuminococcus\u003c/em\u003e (r\u0026thinsp;=\u0026thinsp;0.848), and oral \u003cem\u003eStreptococcus parasanguinis\u003c/em\u003e and untrained \u003cem\u003eRuminococcus\u003c/em\u003e (r\u0026thinsp;=\u0026thinsp;0.838), which have clear associations with ALS. The enrichment of \u003cem\u003eRuminococcus torques\u003c/em\u003e has been proven to be associated with the worsening of symptoms in an ALS mouse model [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], suggesting a potential association between oral streptococci and the abundance of opportunistic pathogens like \u003cem\u003eRuminococcus\u003c/em\u003e through regulating the intestinal microenvironment, participating in the neuroinflammatory process in ALS. This is consistent with the reports of two previous studies on the dysbiosis of the oral microbiota in ALS patients [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. It is hypothesized that these oral microbiota could potentially translocate to the intestine via daily swallowing or, if barrier function is impaired, through the bloodstream. Once in the intestine, they might interact with the resident microbiota [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], Furthermore, their antigens or metabolites could influence the host's systemic immune status [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], potentially creating a microenvironment conducive to the growth of pro-inflammatory bacteria.\u003c/p\u003e \u003cp\u003eThe association of \u003cem\u003eRuminococcus\u003c/em\u003e with ALS has been supported by animal experiments: a review by Boddy et al. (2021) highlighted that \u003cem\u003eRuminococcus\u003c/em\u003e torques is associated with exacerbated symptoms in ALS mouse model [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The positive correlations between oral streptococci and intestinal \u003cem\u003eRuminococcus\u003c/em\u003e in this study (r\u0026thinsp;=\u0026thinsp;0.848 and 0.838) indicates that oral streptococci might promote the expansion of \u003cem\u003eRuminococcus\u003c/em\u003e by altering the intestinal microenvironment. The Inflammatory polysaccharides produced by \u003cem\u003eRuminococcus\u003c/em\u003e can activate dendritic cells, promoting TNF-α secretion, which may be related to the elevated inflammatory factor levels in the serum of ALS patients [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe intestinal genera involved in the 20 strong oral-gut associations \u003cem\u003e(Dorea, Ruminococcus\u003c/em\u003e, and \u003cem\u003eBlautia\u003c/em\u003e) were precisely those that differed significantly between ALS patients and healthy controls in our intestinal microbiota analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Dorea and Ruminococcus were enriched in ALS patients, \u003cem\u003eBlautia\u003c/em\u003e was depleted. This convergence suggests that oral-gut microbial interactions may contribute to the intestinal dysbiosis observed in ALS, providing a potential mechanistic link between oral microbial alterations and the characteristic shift in gut microbial composition toward a pro-inflammatory phenotype.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Potential Landscape Model: Quantitative Evidence of Intestinal Ecosystem Destabilization\u003c/h2\u003e \u003cp\u003eThe potential landscape analysis revealed a striking contrast between the two groups. The healthy state occupied a deep and concentrated potential basin (ΔU\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.082; basin area\u0026thinsp;=\u0026thinsp;0.345), analogous to a marble at the bottom of a deep bowl\u0026mdash;stable and self-correcting after perturbation. In contrast, the ALS state was distributed on a shallow and scattered potential plateau (ΔU = -0.357; basin area\u0026thinsp;=\u0026thinsp;0.85), resembling a marble on a flat surface, where even a slight disturbance causes it to drift away with little tendency to return. The comprehensive stability index (S) quantified this difference: healthy controls were positive (+\u0026thinsp;0.00110), while ALS patients were negative (-0.0118), indicating a metastable ecosystem prone to state transitions under perturbation.\u003c/p\u003e \u003cp\u003eThis finding carries several implications. First, it provides quantitative evidence that the intestinal ecosystem in ALS is not merely compositionally altered but fundamentally destabilized at the systems level. Second, the metastable nature may explain the heterogeneous clinical progression observed in ALS, This may explain why ALS patients show differential symptomatic progressions under different disease stages or different environmental exposures [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. The fragility of the microecosystem also indicate that microecological interventions for ALS may require continuous treatment rather than a one-time treatment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Limitations and Future Directions\u003c/h2\u003e \u003cp\u003eThis study has several limitations: First, the sample size is limited, and the conclusions require validation in larger cohorts. Second, oral samples from healthy controls were not collected, which represents a significant limitation. This precludes our ability to determine whether the observed oral microbiota alterations in ALS patients are disease-specific or reflect secondary factors associated with motor dysfunction, such as altered oral hygiene practices, reduced salivary flow, or dysphagia-common features in ALS that can independently affect oral microbial communities. Consequently, the oral microbiota findings presented here should be considered preliminary and hypothesis-generating. Third, the cross-sectional design cannot determine causality between changes in oral-gut axis and ALS patients. Third, 16S rRNA amplicon sequencing only can identify to the genus level, and cannot distinguish among different strains. Fourthly, it is important to emphasize the correlational nature of this study. While strong associations were observed between the oral and intestinal microbiota in ALS patients, the cross-sectional design precludes any determination of causality. The directional relationships proposed in the discussion, whether microbial dysbiosis is a cause or a consequence of disease progression, remain speculative. Future studies employing longitudinal cohorts, germ-free animal models colonized with ALS-derived microbiota, and interventional trials (e.g., probiotics, fecal microbiota transplantation) are urgently needed to establish causal links and to determine whether modulating the oral-gut axis represents a viable therapeutic strategy for ALS. Finally, Beyond the ecological insights, the intestinal microbial alterations identified in this study demonstrated potential clinical utility. A random forest model based on differential gut genera achieved an AUC of 0.93 in distinguishing ALS patients from healthy controls, suggesting that gut microbial features may serve as potential biomarkers. Notably, the top contributing genera included \u003cem\u003eBlautia\u003c/em\u003e, \u003cem\u003eDorea\u003c/em\u003e, and \u003cem\u003eRuminococcus\u003c/em\u003e, which were also central to the oral-gut associations identified above, reinforcing their potential importance in ALS pathogenesis. However, given the limited sample size and the absence of an independent validation cohort, these results should be considered preliminary.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis cohort study revealed that the intestinal microbial ecosystem in ALS is in a metastable state (stability index: HC\u0026thinsp;+\u0026thinsp;0.00110 vs. ALS\u0026thinsp;\u0026minus;\u0026thinsp;0.0118) using a potential landscape model, providing quantitative evidence of ecological destabilization. Additionally, 21 significant oral-gut microbial associations (|r| \u0026ge; 0.6) were identified through paired oral-intestinal sampling, with the strongest between oral \u003cem\u003ePrevotella salivae\u003c/em\u003e and intestinal \u003cem\u003eDorea\u003c/em\u003e (r\u0026thinsp;=\u0026thinsp;0.937). The intestinal genera involved in these associations \u003cem\u003e(Dorea\u003c/em\u003e, \u003cem\u003eRuminococcus\u003c/em\u003e, and \u003cem\u003eBlautia\u003c/em\u003e) were also differentially abundant in ALS patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), suggesting that oral-gut microbial interactions may contribute to intestinal dysbiosis. However, the absence of oral samples from healthy controls limits the interpretation of disease-specific oral microbiota changes, and these findings should be considered hypothesis-generating rather than definitive.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all participants and their families for their contribution to this study. We are grateful to the clinical staff at Suzhou Municipal Hospital (Taihu General Hospital) and Bengbu Third People\u0026rsquo;s Hospital for their assistance with patient recruitment and sample collection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of generative AI and AI-assisted technologies in the manuscript preparation process.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatement:\u0026nbsp;During the preparation of this work, the authors used RStudio, Adobe Illustrator 2025 and Adobe Photoshop 2026 in order to Polishing the manuscript and figures. After using these tools, the authors reviewed and edited the content as needed and take full responsibility for the content of the published article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by a municipal research grant from Bengbu City (Project No. BBWK2024202) for the study entitled \u0026ldquo;Mechanistic investigation and intervention strategies of non‑traumatic osteonecrosis of the femoral head based on the intestinal microbiota‑metabolism axis\u0026rdquo;.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all participants for this research, which was approved by the Ethics Committee of the Affiliated Hospital of Tongji University (2023-013).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZelong Yang: Conceptualization, Formal analysis, Visualization, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eWenhao Liu: Resources, Investigation, Data curation.\u003c/p\u003e\n\u003cp\u003eBo Yan: Investigation, Validation.\u003c/p\u003e\n\u003cp\u003eZhan Cao: Methodology, Funding acquisition, Project administration, Supervision, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eJingxin Li: Methodology, Funding acquisition, Project administration, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eZhongwei Chang: Data curation.\u003c/p\u003e\n\u003cp\u003eXixin Jin: Investigation, Project administration.\u003c/p\u003e\n\u003cp\u003eXinjun Wang: Conceptualization, Supervision, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eHaoyu Wang: Supervision, Supervision，Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw sequencing data generated in this study have been deposited in the NCBI Sequence Read Archive (SRA) under BioProject accession number\u0026nbsp;PRJNA1425920. The data are currently private but are accessible to reviewers via the following secure link:\u003c/p\u003e\n\u003cp\u003ehttps://dataview.ncbi.nlm.nih.gov/object/PRJNA1425920?reviewer=gk215bgekcemo3j10m2hm3qofg\u003c/p\u003e\n\u003cp\u003eThe data will be made publicly available upon publication of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFujimori K, Ishikawa M, Otomo A, Atsuta N, Nakamura R, Akiyama T, Hadano S, Aoki M, Saya H, Sobue G, et al. Modeling sporadic ALS in iPSC-derived motor neurons identifies a potential therapeutic agent. Nat Med. 2018;24(10):1579\u0026ndash;89.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuarnaccia M, La Cognata V, Gentile G, Morello G, Cavallaro S. Unraveling the missing heritability of amyotrophic lateral sclerosis: Should we focus more on copy number variations? 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Ruminococcus gnavus, a member of the human gut microbiome associated with Crohn's disease, produces an inflammatory polysaccharide. Proc Natl Acad Sci U S A. 2019;116(26):12672\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChi\u0026ograve; A, Logroscino G, Hardiman O, Swingler R, Mitchell D, Beghi E, Traynor BG. Prognostic factors in ALS: A critical review. Amyotroph Lateral Scler. 2009;10(5\u0026ndash;6):310\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mcro","sideBox":"Learn more about [BMC Microbiology](http://bmcmicrobiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mcro","title":"BMC Microbiology","twitterHandle":"#bmcmicrobiology","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Amyotrophic lateral sclerosis, intestinal microbiota, oral microbiota, microbial ecology, 16S rRNA amplicon sequencing, diagnostic biomarker, oral-gut axis","lastPublishedDoi":"10.21203/rs.3.rs-9353918/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9353918/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe pathogenesis of amyotrophic lateral sclerosis (ALS) involves multiple systems, among which the \"gut-brain axis\" has been extensively studied. However, the characteristics of intestinal microbiota alterations and their potential association with the oral microbiota remain unclear. This cohort study aims to characterize the ecological destabilization of intestinal microbiota in ALS patients. On this basis, this study further explored the potential association between the intestinal and oral microbiotas. In this study, paired oral and fecal samples from ALS patients and fecal samples from healthy controls were collected and subjected to 16S rRNA amplicon sequencing. The results showed that the intestinal microbiota in ALS patients exhibited significant compositional shifts, characterized by enrichment of pro-inflammatory genera and depletion of butyrate-producing genera, along with reduced alpha diversity. Critically, quantitative analysis using a potential landscape model revealed that this altered community was in a metastable state (comprehensive stability index: HC\u0026thinsp;+\u0026thinsp;0.00110 vs. ALS\u0026thinsp;\u0026minus;\u0026thinsp;0.0118), indicating ecological destabilization. this study examined oral-gut microbial interactions and identified 20 significant correlations (|r| \u0026ge; 0.6), with the strongest between oral Prevotella salivae and intestinal Dorea (r\u0026thinsp;=\u0026thinsp;0.937). Notably, the intestinal genera involved in these associations (\u003cem\u003eDorea\u003c/em\u003e, \u003cem\u003eRuminococcus\u003c/em\u003e, and \u003cem\u003eBlautia\u003c/em\u003e) were also differentially abundant in ALS patients, suggesting that oral-gut microbial interactions may contribute to intestinal dysbiosis. This study demonstrates that the intestinal microecosystem in ALS patients exhibits features of ecological destabilization, and identifies oral-gut microbial interactions may as potential drivers of intestinal dysbiosis.\u003c/p\u003e","manuscriptTitle":"Gut Microbiome Ecological Network Destabilization in Amyotrophic Lateral Sclerosis: Insights from In tegrated Oral–Gut Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-04 06:42:53","doi":"10.21203/rs.3.rs-9353918/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"96908668925891116048529992246254349146","date":"2026-05-11T08:38:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-04T01:40:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"177799372522411694166827151936799792805","date":"2026-04-23T01:07:54+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-21T10:05:03+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-20T12:55:50+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-14T05:20:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-14T05:19:51+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Microbiology","date":"2026-04-08T08:12:10+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mcro","sideBox":"Learn more about [BMC Microbiology](http://bmcmicrobiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mcro","title":"BMC Microbiology","twitterHandle":"#bmcmicrobiology","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5405b379-5c31-40cf-8fc1-54022c6d4939","owner":[],"postedDate":"May 4th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"96908668925891116048529992246254349146","date":"2026-05-11T08:38:18+00:00","index":43,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-04T01:40:52+00:00","index":30,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T06:42:54+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-04 06:42:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9353918","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9353918","identity":"rs-9353918","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}