In silico metabolic profiling of an Indian cohort refutes the "One-Diet-Fits-All" paradigm in colorectal cancer

In silico metabolic profiling of an Indian cohort refutes the "One-Diet-Fits-All" paradigm in colorectal cancer | 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 Analysis In silico metabolic profiling of an Indian cohort refutes the "One-Diet-Fits-All" paradigm in colorectal cancer Shivraj nile, Abhilasha Sharma, Aman Sharma, Navdeep Kaur, Amisha Rani, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8201956/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The metabolic impact of diet on colorectal cancer (CRC) is governed by complex, personalized microbial networks that elude static nutritional guidelines. Therefore, we integrated shotgun metagenomics with personalized genome-scale metabolic modeling (GSMM) to simulate metabolic fluxes in an Indian cohort of 30 CRC patients and 110 healthy controls. Under six simulated dietary conditions, the CRC microbiome- characterized by Fusobacterium nucleatum enrichment, exhibited highly variable metabolic responses. While high-fiber interventions restored protective butyrate in 61% of patients, they paradoxically elevated the oncometabolite succinate in 79% of models. Furthermore, high-fat diets resulted in net hydrogen sulfide consumption defects, potentially exacerbating local toxicity. Conversely, Mediterranean and Vegan diets successfully restored microbial diversity and suppressed pathogenic species. These findings indicate that metabolic outcomes are strictly conditional on baseline microbial composition, refuting "one-diet-fits-all" guidelines. Our study highlights the capacity of computational modeling to predict non-intuitive metabolic side effects, providing a framework for precision nutritional oncology. Biological sciences/Computational biology and bioinformatics/Computational models Biological sciences/Cancer/Gastrointestinal cancer/Colorectal cancer/Colon cancer Gut microbiome Colorectal cancer Constraint-based modeling Precision oncology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Diet is a primary modifiable driver of colorectal cancer (CRC), yet the relationship between nutritional intake and oncogenesis is governed by a critical intermediary: the gut microbiome 1 , 2 . Functioning as a complex and highly personalized bioreactor, the intestinal microbial community metabolizes dietary components into a vast suite of bioactive compounds with profoundly divergent effects on host health 3 – 5 . This metabolic activity bifurcates along with two principal axes. On one hand, saccharolytic fermentation of complex carbohydrates, or dietary fiber, by commensal bacteria yields protective short-chain fatty acids (SCFAs) 6 , 7 . Among these, butyrate is paramount; it serves as the preferred energy substrate for healthy colonocytes, reinforces the mucosal barrier, and exhibits potent anti-neoplastic properties by acting as a histone deacetylase (HDAC) inhibitor, thereby promoting cell cycle arrest and apoptosis in transformed cells 8 , 9 . On the other hand, proteolytic fermentation of undigested proteins and microbial transformation of bile acids (a process exacerbated by high-fat diets) can generate a cascade of detrimental oncometabolites 10 , 11 . These include genotoxic compounds such as hydrogen sulfide, which can induce DNA damage at physiological concentrations, and pro-inflammatory secondary bile acids like deoxycholic acid (DCA), which promote tumorigenesis by activating oncogenic signaling pathways and generating reactive oxygen species 11 . This functional duality presents a central paradox in nutritional oncology: the ultimate biological impact of a given diet is not intrinsic to its composition but is conditional upon the metabolic capacity of an individual’s gut microbiome. Consequently, a conventionally "healthy" diet could, in the context of a dysbiotic microbiome, inadvertently fuel oncogenic processes, demanding a shift toward a more personalized understanding of nutrition. This contradiction is not merely a theoretical curiosity but also a fundamental aspect in significant public health challenge. According to the latest GLOBOCAN 2022 estimates, CRC is the third most diagnosed malignancy and the second leading cause of cancer-related mortality worldwide, accounting for 9.6% of new cases and 9.3% of deaths, respectively 12 , 13 . Compounding this burden is an alarming and accelerating increase in the incidence of early-onset CRC (EO-CRC) in individuals under the age of 50, a trend observed globally since the 1990s 14 , 15 . This generational shift is too rapid to be explained by host germline genetics alone and points decisively toward the influence of profound environmental changes, particularly shifts in early-life dietary patterns, lifestyle, and antibiotic exposure 1 . While this crisis is global, its trajectory is manifesting with unique urgency in non-Western nations undergoing rapid economic and nutritional transition. In India, for instance, CRC is emerging as a major oncological challenge, now ranking as the fourth most common cancer with over 64,000 new cases and 38,000 deaths reported in 2022 16 . In stark contrast to the stabilizing or declining rates in many Western countries, India is witnessing a steady rise in CRC incidence, particularly in urban centers where the adoption of Westernized diets and lifestyles is most pronounced 17 . This epidemiological landscape underscores the imperative for population-specific research to understand and mitigate the drivers of CRC in this unique and understudied context. Current mechanistic understanding of CRC-associated dysbiosis is founded almost exclusively on a Western-centric paradigm, derived from studies of North American and European cohorts 18 , 19 . This paradigm is characterized by a consistent and well-defined taxonomic signature in gut micrbiome: a marked depletion of beneficial, butyrate-producing commensals, most notably Faecalibacterium prausnitzii , and a concomitant enrichment of pro-inflammatory pathobionts, including Bacteroides fragilis and Fusobacterium nucleatum 20 , 21 . The oncogenic mechanisms of key drivers like F. nucleatum have been elucidated in remarkable detail. Its unique FadA adhesin directly engages E-cadherin on colon cancer cells, hijacking the Wnt/β-catenin signaling pathway to fuel uncontrolled proliferation 20 . Simultaneously, its Fap2 outer membrane protein mediates both tumor colonization, by binding to Gal-GalNAc moieties overexpressed on cancer cells, and potent immune evasion, by engaging the TIGIT inhibitory receptor on tumor-infiltrating natural killer (NK) cells and T lymphocytes, thereby suppressing anti-tumor immunity 22 . While mechanistically elegant, the global applicability of this model is scientifically tenuous. Extrapolating these taxonomic and functional signatures to non-Western populations—with profoundly different dietary histories, distinct host genetic backgrounds, and unique ancestral microbial landscapes—is a flawed approach that overlooks the fundamental ecological principles governing host-microbiome interactions 23 . To move beyond description and toward prediction, a paradigm shift is needed from static, compositional analysis ("who is there?") to a dynamic, functional understanding ("what will they do with a specific diet?"). This study addresses this need by pioneering a novel, systems-based approach that integrates deep, culture-independent characterization of the microbiome's genetic potential with predictive modeling of its metabolic behavior. We coupled high-resolution shotgun metagenomic sequencing, which provides a comprehensive catalog of all microbial genes and metabolic pathways, with personalized, genome-scale metabolic modeling (GSMM), a computational framework that simulates community-wide metabolic flux under defined nutritional conditions. Our central hypothesis defines the functional output of the CRC-associated microbiome is not a fixed trait but is conditionally expressed, contingent upon the available dietary substrates that serve as metabolic inputs. By applying this integrative framework, we aim to first characterize the unique compositional and functional signatures of CRC-associated dysbiosis in an understudied Indian cohort. Second, we used the data to construct personalized in silico models capable of predicting how different diets trigger paradoxical metabolic outcomes, such as the production of oncometabolites from seemingly benign precursors. The outcome of this work provides a strong, mechanistically grounded framework for dismantling the 'one-diet-fits-all' model of nutrition and to lay the foundation for a new era of predictive, personalized nutritional oncology. Results Profound microbiome dysbiosis with global and population-specific features characterizes the Indian CRC cohort A total of 140 whole-genome sequencing datasets comprising 30 colorectal cancer (CRC) cases and 110 healthy controls from an Indian cohort were retrieved from the NCBI Sequence Read Archive (Fig. 1 , Supplementary Table S1 ) 24 . Following a stringent preprocessing pipeline: removal of adapter sequences, trimming of low-quality bases, and removal of host sequences by aligning reads to the human reference genome (GRCh38), 28 CRC patients and 108 healthy controls were retained for downstream taxonomic analyses ( Supplementary Table S2 ). Taxonomic profiling revealed a pronounced state of dysbiosis in the CRC-associated gut microbiome 25 . At the species level, relative abundance plots showed significant compositional differences between groups (Fig. 2 a, Supplementary Table S3 ). Consistent with global CRC microbiome signatures, the Indian cohort displayed significant enrichment of established oral pathobionts, including Fusobacterium nucleatum, Parvimonas micra, Porphyromonas asaccharolytica , and Peptostreptococcus stomatis —a set of taxa repeatedly implicated in pro-inflammatory tumor microenvironments across cohorts from China, Germany, and France 18 , 19 , 26 . These enrichments coincided with depletion of key butyrate-producing commensals such as Faecalibacterium prausnitzii and Roseburia spp. , both hallmarks of CRC-associated dysbiosis worldwide 27 . Distinct features emerged in this Indian cohort. Lachnospira eligens , a beneficial commensal, was markedly reduced, with the depletion more pronounced than in other populations 28 . Conversely, while taxa such as Bacteroides fragilis and Alistipes finegoldii are often elevated in CRC microbiomes in other population 29 , 30 , they were not prominent drivers of dysbiosis in Indian cohort ( Supplementary Table 3 ). These cohort-specific differences underscore the influence of host genetics, diet, and environmental exposures in shaping the CRC microbiome. Alpha diversity analyses demonstrated a significant reduction in microbial richness and evenness in CRC patients. Shannon diversity indices (mean ± SD, CRC: 2.05 ± 0.283; Healthy: 2.50 ± 0.254) and Simpson indices showed consistently lower values in CRC across genus- and species-level profiles. Violin plots illustrate these reductions (Fig. 2 b). The Wilcoxon rank-sum test confirmed statistical significance (n₁ = 28, n₂ = 108, P = 1.1 × 10⁻⁶), with a large effect size (Cohen’s d = − 1.68). Normality (Shapiro–Wilk, P > 0.4 for CRC; P > 0.49 for Healthy) and variance homogeneity (Levene’s test, P = 0.681) assumptions were met. Beta diversity analyses corroborated the compositional restructuring. Both non-metric multidimensional scaling (NMDS, stress = 0.189, R² = 0.111) and principal coordinate analysis (PCoA) based on Bray–Curtis dissimilarity proved clear separations between CRC and healthy groups, with minimal overlap in their 95% confidence ellipses (Fig. 2 c). A PERMANOVA test (R² = 0.111, F = 16.8, P < 0.001) confirmed that CRC status accounted for 11.1% of the total variation in community structure. Analysis of beta dispersion indicated no significant differences in within-group variability (F = 1.51, P = 0.221). Together, these findings reveal both globally conserved and uniquely Indian signatures of CRC-associated dysbiosis, characterized by loss of beneficial commensals, enrichment of pathobionts, and reduced community diversity—features that may reflect shared disease mechanisms modulated by local environmental, dietary, and genetic contexts. Dietary interventions drive patient-specific and paradoxical metabolic shifts in CRC microbiomes To elucidate the functional consequences of CRC-associated dysbiosis, we further performed flux balance analysis on personalized gut microbiome metabolic models for each of the 28 participants. The metabolic output was simulated under six distinct dietary conditions: Gluten-free, High-fat, High-fiber, High-protein, Mediterranean, and Vegan 31 . The 48 hours simulations revealed pronounced inter-individual variability in the production and consumption of key metabolites across the patient cohort, as visualized in the individual flux profiles (Fig. 3 a, Supplementary Table S4 ). Each patient's microbiome exhibited a unique metabolic response to the dietary shifts, with fluxes for many metabolites, such as the short-chain fatty acid (SCFA) butyrate, deviating substantially from the average flux observed in healthy individuals (Fig. 3 a, red dashed line). This heterogeneity underscores the context-dependent nature of dietary interventions. A collective rescue analysis, which assessed the restoration of metabolite production to a healthy range, further illustrated this variability across patients and diets (Fig. 3 b). Short-Chain Fatty Acids (SCFAs) and Succinate A primary focus was on the production of butyrate, a beneficial SCFA known for its protective role in colon health 8 , 32 . The high-fiber diet, as expected, promoted butyrate production, achieving the highest mean flux across all diets. It successfully rescued butyrate production in 61% of the CRC patient models (17 of 28). However, the Gluten-Free, High-Fat, and High-Protein diets achieved the similar rescue rate, indicating that fiber is not the sole determinant of butyrate rescue in these personalized models. Similarly, the production of acetate, another key SCFA, was most effectively rescued by the High-Fat (rescued in ~ 79% of patients) and High-Fiber (rescued in 75% of patients) diets, with the High-Fiber diet also yielding the highest mean acetate flux. Crucially, this analysis uncovered a paradoxical effect associated with the High-Fiber diet. While increasing beneficial butyrate, this diet also elevated the production of succinate, a known oncometabolite that promotes tumorigenesis 33 . The High-Fiber and High-Fat diets were the most effective at rescuing succinate production, each restoring it in ~ 79% of patients (22 of 28). The High-Fat diet also generated the highest mean succinate flux. This dual effect highlights a potential trade-off where a generally beneficial dietary strategy simultaneously increases a detrimental metabolite. Protein Fermentation and Sulfur Metabolism Metabolites associated with protein fermentation also showed significant diet- and patient-specific responses. The High-Protein diet expectedly influenced these pathways, leading to a high rescue rate for putrescine (~ 93% of patients) but also the most negative mean flux for indole, suggesting high consumption of this tryptophan-derived metabolite 34 . In contrast, the High-Fat diet was markedly ineffective at rescuing putrescine production (rescued in only ~ 11% of patients). Production of hydrogen sulfide (H₂S), a pleiotropic signaling molecule 35 , was also highly variable. The Gluten-Free diet was most effective at rescuing H₂S production (75% of patients), whereas the High-Fat diet was least effective, rescuing production in only ~ 32% of patients and resulting in a negative mean flux. Similarly, the production of trimethylamine (TMA), a precursor to the pro-atherogenic TMAO 36 , was strongly induced by the High-Protein and Gluten-Free diets. Conversely, the High-Fiber diet substantially attenuated TMA production, and the High-Fat diet led to its net consumption. Collectively, these simulation results demonstrate that dietary interventions in the context of CRC produce highly personalized and sometimes contradictory metabolic outputs. Our findings summarize this complex landscape, where the efficacy of a diet in rescuing the production of beneficial metabolites is not uniform, underscoring the critical need for personalized nutritional strategies in managing CRC. Diet acts as an ecological filter, remodeling the structure and diversity of the dysbiotic community Simulated dietary interventions exerted profound effects on the gut microbiome in CRC, acting as a powerful ecological filter that reshaped community composition and diversity. Family-level changes captured in stacked bar plots (Fig. 4 a) revealed pronounced, diet-specific taxonomic shifts that were distinctly different between CRC and healthy groups across all six dietary regimens. For instance, the relative abundance of families such as Odoribacter and Alcaligenes varied markedly, with Odoribacter notably more abundant in CRC under certain diets, indicating disease-specific diet responses. Alpha-diversity, assessed using the Shannon index, demonstrated statistically significant differences (P < 0.01 to P < 0.001) between CRC and healthy cohorts within each diet (Fig. 4 b). Notably, the Mediterranean and vegan diets exhibited substantial improvements in microbial diversity in CRC patients, shifting their profiles closer to those of healthy controls. This partial restoration of diversity aligns with the concept that complex, plant-rich diets foster rich microbial ecosystems and may counteract the diversity loss typically seen in CRC 37 , 38 . Beta-diversity analyses using Principal Coordinate Analysis (PCoA) of Bray-Curtis dissimilarities further underscored the restorative potentials of specific diets. Mediterranean and high-fiber interventions facilitated a convergence of CRC patient communities toward the healthy control cluster (Fig. 4 c), suggesting that these diets can partially reverse community-wide structural dysbiosis associated with CRC. This shift in microbial community architecture implies functional recalibration potentially beneficial to host health. Species-specific growth dynamics illuminated the microbial taxa most responsive to dietary modulation (Fig. 4 d, Supplementary Table S5 ). Across diets, key pathobionts—including certain Prevotella and Succinivibrio species—exhibited significantly slower growth rates in CRC models, indicating their potential suppression by nutritional interventions 2 , 39 . Conversely, beneficial commensals such as Bacteroides faecis , Odoribacter splanchnicus , and Paraprevotella clara displayed enhanced growth under diets like Mediterranean and high-fiber, reflecting targeted promotion of protective taxa through diet 2 , 39 . Together, these findings provide compelling evidence that diet functions as a selective ecological filter in the CRC microbiome, reshaping both taxonomic and functional landscapes 2 , 9 , 25 . The efficacy of Mediterranean and vegan diets in enhancing microbial diversity and shifting community structure reinforces their potential as nutritional strategies to mitigate dysbiosis 40 , 41 . Furthermore, identification of diet-responsive "winners" and "losers" at the species level offers mechanistic insights and targets for precision microbiome modulation in CRC. Species-specific metabolic contributions reveal a disconnect between abundance and function Comprehensive analysis of species-level metabolic contributions across multiple key metabolites revealed a strong disconnect between microbial abundance and functional output (Fig. 5 ). While conventional wisdom often equates high relative abundance with functional dominance, our results challenge this paradigm, demonstrating that rare taxa can exert disproportionately large effects on metabolite pools, while dominant species may contribute minimally. For several metabolites—such as butyrate, acetate, and succinate—the largest contributors within each community were not always the most abundant taxa. For example, within CRC models, Odoribacter splanchnicus and Parabacteroides distasonis emerged as key contributors to formate, acetate, and succinate production, despite not consistently ranking among the most prevalent species. Notably, for butyrate production, the relative contribution of Klebsiella pneumoniae in CRC patients was markedly lower than in healthy controls, underscoring a disease-specific loss of beneficial metabolic function even at similar or elevated abundance levels. By contrast, Bacteroides fragilis , while highly abundant, contributed significantly to hydrogen sulfide production but had a negligible direct impact on beneficial metabolites like butyrate and acetate, supporting the concept that high-abundance taxa can serve as metabolic specialists with deleterious functional roles 42 – 44 . Further, analysis of putrescine and indole production illuminated a similar pattern: in CRC, specialized taxa with moderate to low abundance—such as Paraprevotella clara and Clostridium innocuum —displayed high per-capita contributions, indicating the importance of minority taxa for maintaining metabolic flexibility and resilience within the ecosystem 45 . Disease-specific alterations in these abundance-function relationships were also observed. In CRC, several taxa (e.g., Klebsiella pneumoniae ) exhibited reduced per-cell capacity for beneficial metabolite production, suggesting that CRC is associated not only with compositional shifts but also with functional attenuation within key microbial lineages 43 , 44 . Importantly, dietary interventions were found to modulate these functional hierarchies. In many cases, plant-rich regimens—such as Mediterranean and high-fiber diets—partially restored the contribution of beneficial, low-abundance taxa to key metabolites, suggesting that providing suitable substrates can selectively enhance the metabolic function of these important yet underrepresented microbes. These findings highlight that ecosystem function cannot be inferred solely from taxonomic profiles; instead, an integrated view considering both abundance and per-taxon metabolic output is required 46 . The results also suggest a mechanistic pathway for personalized microbiome restoration: targeting substrate pools to promote functionally critical, but numerically rare, taxa in the CRC gut ecosystem. Temporal dynamics demonstrate rapid, therapeutically relevant ecosystem responses to diet Dietary interventions prompted rapid and extensive shifts in microbial community composition within 48 hours, confirming that the gut microbiome is highly plastic and responsive to nutritional change 47 , 48 . CRC-associated microbiomes were markedly more sensitive to dietary perturbations than those of healthy controls, as indicated by pronounced species turnover and larger log fold changes in abundance for dynamic taxa (Fig. 6 a, Supplementary Table S6 ). Species growth trajectory analysis highlighted that certain taxa, including Elusimicrobium minutum, Streptomyces massiliensis , and Lactobacillus taiwanensis , reproducibly exhibited substantial increases (log fold change exceeding + 7) in CRC subjects across multiple dietary patterns. In contrast, taxa such as Cronobacter sakazakii, Bacteroides eggerthii , and Ruthenibacterium lactatiformans consistently grew more rapidly in healthy controls, illustrating disease-dependent responses and ecosystem instability in CRC. Functional enrichment analysis using Normalized Enrichment Score (NES), captured the collective metabolic consequences of dietary intervention (Fig. 6 b). While trends toward increased short-chain fatty acid (SCFA) synthesis were observed in CRC patients receiving high-fiber and gluten-free interventions, the overall enrichment patterns varied for each diet and disease context. Notably, high-fat diets in CRC tended to favor pathways associated with harmful metabolite production, such as ammonia and succinate, underscoring divergent metabolic outcomes depending on nutritional inputs. These values confirm that dietary composition can selectively influence the metabolic capabilities of the microbiome in a manner that depends on disease status 9 . At a systems level, temporal analysis revealed that major bacterial phyla shifted functional roles and their metabolic outputs in the CRC state versus healthy microbiomes (Fig. 6 c). This reorganization of contributions to key metabolite pools following dietary changes highlights the microbiome’s innate adaptability—and a potential avenue for targeted therapeutic manipulation 9 , 47 , 49 . Collectively, these findings demonstrate that dietary interventions can drive swift, pronounced changes in microbiome composition, growth dynamics, and metabolic function, particularly in the dysbiotic CRC gut. The rapidity and magnitude of these shifts suggest opportunities for using temporal plasticity in the microbiome as a target for therapeutic nutrition. Discussion This study addresses the need to understand diet-microbiome interactions in a non-Western CRC cohort using a functional, systems-level approach. Standard nutritional advice is often unpredictable due to high inter-individual variability 50 , 51 . By employing shotgun metagenomics and personalized GSMMs, we found that the gut microbiome in an Indian CRC cohort has both globally conserved dysbiotic markers and unique population-specific features. Crucially, our functional models predict that dietary interventions, particularly with fiber, yield highly individualized and paradoxical metabolic outputs. A diet designed to produce protective butyrate can simultaneously elevate the oncometabolite succinate, challenging the 'one-diet-fits-all' nutritional model for CRC. Our findings underscore that predicting the functional consequences of diet requires moving beyond taxonomic cataloging to personalized simulations, which reveal how dietary substrates can be diverted toward either protective or pro-tumorigenic pathways depending on the underlying dysbiotic ecosystem 52 . The enrichment of Fusobacterium nucleatum and Parvimonas micra and depletion of Faecalibacterium prausnitzii in the Indian cohort aligns with global studies, confirming a universal CRC microbial signature 18 , 19 . This suggests conserved pathogenic mechanisms, such as inflammation driven by F. nucleatum and the loss of anti-proliferative butyrate from F. prausnitzii . However, the cohort also displayed distinct local features, including the depletion of the anti-inflammatory species Lachnospira eligens and an inconsistent enrichment of Bacteroides fragilis . This divergence from Western cohorts may reflect differences in the baseline Indian microbiome, which is often Prevotella -rich, as well as long-term dietary patterns, host genetics, or other environmental exposures 53 . While the functional outcomes of dysbiosis appear conserved, the specific taxa driving them can be population specific. A key finding is the functional paradox revealed by our models: a simulated high-fiber diet increased beneficial butyrate but also elevated the oncometabolite succinate. Succinate is a metabolic intermediate, and its accumulation suggests a functional bottleneck in the dysbiotic CRC microbiome where succinate-producing pathways are favored over consuming ones. This is clinically significant, as succinate is a known oncometabolite that promotes tumorigenesis. It signals through the SUCNR1 receptor to drive inflammation and angiogenesis and can stabilize Hypoxia-Inducible Factor 1-alpha (HIF-1α) even in normoxic conditions, promoting a pro-tumorigenic transcriptional program 7 . This paradox implies that blanket recommendations to increase fiber may be counterproductive in individuals whose microbiomes are primed to overproduce succinate, strongly supporting the need for personalized nutritional strategies based on the predicted functional output of an individual's gut ecosystem. Our in-silico experiments showed that plant-rich diets, like the Mediterranean and vegan diets, could partially restore microbial diversity in CRC microbiomes, shifting them closer to healthy controls. This supports the concept of diet as an ecological filter 23 . A low-fiber Western diet provides few metabolic niches, favoring a low-diversity community. In contrast, the diverse fibers and polyphenols in plant-rich diets create a multitude of niches, promoting the growth of a wider range of specialist microbes and thus increasing overall diversity. The simulated growth of beneficial commensals like Odoribacter splanchnicus , a known producer of short-chain fatty acids (SCFAs), exemplifies this principle 54 . This frames dietary therapy as a restorative ecological strategy, aiming to create an environment where beneficial bacteria can outcompete pathobionts. This study's functional resolution demonstrates that a microbe's functional contribution is not proportional to its relative abundance, challenging a paradigm common in 16S rRNA-based studies 55 . For example, our models identified the low-abundance Odoribacter splanchnicus as a key producer of formate and acetate, while the more abundant Bacteroides fragilis had a more specialized, and potentially detrimental, role in producing hydrogen sulfide (H 2 S). This aligns with the ecological concept of "keystone species," where low-abundance organisms can have a disproportionate functional impact 56 . This implies that the functional resilience of the gut microbiome may depend on these rare but vital taxa. Consequently, diagnostics must look beyond dominant species, and therapeutics could be developed to boost these functionally important rare microbes. The study's strengths include the use of shotgun metagenomics, which provides species-level and functional resolution, and the novel application of personalized GSMMs to predict dietary responses 57 . The primary limitation is that these predictions are in silico and must be considered hypotheses requiring experimental validation through in vitro , animal, or clinical studies. GSMMs are powerful hypothesis-generation tools, not replacements for experimentation. Additional limitations include the cross-sectional study design, which prevents causal inference, and the cohort size, which requires that findings be validated in larger populations. Based on these findings, future research should prioritize longitudinal studies to track microbiome and metabolome changes in response to dietary interventions in CRC cohorts. Integrating metagenomics with other omics data—such as host transcriptomics and metabolomics—is critical to validate the predicted metabolic fluxes and understand host responses, like confirming pathway upregulation in patients with high succinate. Ultimately, this work should inform pilot clinical trials where patients are stratified by their predicted metabolic response. The long-term goal is to refine these computational tools into "virtual patient" models that can serve as clinical decision-support systems for personalized nutrition. In conclusion, this study provides a functional, systems-level framework demonstrating that the CRC microbiome's response to diet is highly personalized and potentially paradoxical. By moving beyond compositional descriptions to predictive modeling, this work challenges the 'one-diet-fits-all' model of nutritional advice. This predictive approach is a critical step toward developing personalized, mechanistically grounded nutritional strategies to manage colorectal cancer in diverse global populations and advance the field of precision nutritional medicine. Methods Data Acquisition and Cohort Description This study followed an observational case-control design utilizing publicly available whole-genome sequencing (WGS) data. As this was a secondary analysis of existing public datasets, subject randomization and blinding were not applicable to the data collection phase. The cohort consisted of 140 individuals from India, comprising 30 colorectal cancer (CRC) patients (NCBI BioProject, RRID:SCR_004801; PRJNA531273) and 110 healthy, asymptomatic controls (BioProject PRJNA397112), as previously described 24 . All raw metagenomic data, sequenced on the Illumina NextSeq 500 platform, were retrieved from the NCBI Sequence Read Archive ((SRA, RRID:SCR_004891). The SRA Toolkit (v3.1.0) 58 was employed for data download and conversion; specifically, the prefetch command was used to securely download SRR files, and fasterq-dump was used to convert these into paired-end FASTQ format for subsequent analysis. Metagenomic Data Preprocessing and Quality Control A rigorous bioinformatic pipeline was implemented to process the raw sequencing reads. Initial and post-processing quality assessments for all samples were performed using FastQC (v0.12.1, RRID:SCR_014583) 59 , and the results were aggregated into a unified report using MultiQC (v1.27.1, RRID:SCR_014982) 60 . Raw paired-end reads were trimmed for adapter content and low-quality bases using Trimmomatic (v0.39, RRID:SCR_011848) 61 . The trimming process involved removing the first 19 bases (HEADCROP:19), clipping leading and trailing bases with a Phred score below 3, applying a 4-base sliding window to trim regions where the average quality dropped below 20 (SLIDINGWINDOW:4:20), cropping reads to a maximum length of 125 bp (CROP:125), and discarding any reads shorter than 36 bases post-trimming (MINLEN:36). Following quality trimming, host DNA contamination was removed. The processed paired-end reads were aligned against the human reference genome (GRCh38) using Bowtie2 (v2.4.2, RRID:SCR_005476) in its --very-sensitive mode 62 . Only read pairs that failed to align to the human genome were retained (--un-conc-gz ), ensuring a high-purity microbial dataset for downstream taxonomic and functional analysis. Taxonomic Profiling and Abundance Estimation The non-human, quality-filtered reads were subjected to taxonomic classification using Kraken2 (v2.1.3, RRID:SCR_026838), a highly sensitive k-mer-based classifier 63 . Reads were classified against a pre-built Kraken2 database constructed from the standard bacterial reference library. To obtain more accurate species-level abundance estimates, the initial classification reports from Kraken2 were processed with Bracken (Bayesian Reestimation of Abundance with KrakEN, v2.9, RRID:SCR_005484) 64 . Bracken uses the taxonomic assignments from Kraken2 to estimate the true proportion of species in a sample, correcting for biases inherent in k-mer classification. The species-level and genus-level relative abundance tables generated by Bracken formed the foundational data for all subsequent community-level and functional analyses. Microbiome Community Analysis Statistical analyses of the microbiome composition were conducted in R (v4.2.2, RRID:SCR_001905). Alpha-diversity was calculated using the Shannon index on the Bracken-corrected species abundance data, with differences between CRC and healthy groups assessed via the Wilcoxon rank-sum test 65 . Beta-diversity was evaluated using the Bray-Curtis dissimilarity metric, visualized with Principal Coordinate Analysis (PCoA), and statistically tested using a Permutational Multivariate Analysis of Variance (PERMANOVA) with 9,999 permutations ( adonis2 function, vegan package) 66 . Personalized Metabolic Model Construction and Simulation To investigate the functional consequences of the observed dysbiosis, we constructed personalized in silico metabolic models for each microbiome samples. For each sample, a personalized community model was assembled by integrating the Bracken-derived species relative abundances with the AGORA2 database, a resource of 7302 manually curated, genome-scale metabolic models (GEMs) of gut microbes 57 . This was achieved by creating a unified model where the metabolic network of each species present in a sample was included, with the flux constraints of its associated reactions weighted by that species' relative abundance. Each personalized community model was generated by assembling the GEMs corresponding to all microbial species detected in a sample 67 , 68 . The metabolic network of each species was incorporated into a unified community model, with flux bounds on individual reactions scaled according to the relative abundance of that species within the community. This abundance-weighted integration ensured that each model quantitatively reflected the compositional and functional characteristics of the individual’s gut microbiome. For computational implementation, species abundance tables were curated and stored in standardized spreadsheet files (e.g., hi_p.xlsx , di_p.xlsx ), listing AGORA model identifiers alongside their relative abundance values. These datasets were imported and processed using the pandas library in Python, enabling efficient mapping of microbial species to their respective AGORA genome-scale metabolic models. The resulting structured data served as input for the reconstruction and analysis of personalized microbial community models within the COBRA (Constraint-Based Reconstruction and Analysis) framework, implemented through COBRApy (RRID:SCR_012096) 69 , 70 . The core computational approach was Flux Balance Analysis (FBA), a constraint-based method that estimates the steady-state distribution of metabolic fluxes across the network 71 . Mathematically, the metabolic system is represented by the stoichiometric matrix S (of dimension m × n , where m denotes metabolites and n denotes reactions), subject to the mass-balance constraint: \(\:S.v=\) 0 Where S is the stoichiometric matrix ( m x n ), with m metabolites and n reactions. Each entry in the matrix represents the stoichiometric coefficient of a metabolite in each reaction (negative for substrates, positive for products) and v is the vector of all reaction fluxes. where v is the flux vector representing the rate of all reactions in the network. As this system is typically underdetermined, linear programming was employed to optimize a biologically meaningful objective function under flux bounds: \(\:vlb\le\:v\le\:vub\) ​ The objective function, Z, is a linear combination of fluxes, typically formulated to maximize a specific biological goal: $$\:Maximize\:Z=cT\cdot\:v$$ Where c is a weight vector that defines the objective. For these simulations, the objective was to maximize the community biomass production rate, a proxy for overall microbial growth potential. Each personalized community model was simulated under six distinct dietary conditions (Western, High-Fat, High-Protein, High-Fiber, Mediterranean, and Vegan). The model constraints for nutrient uptake and metabolite exchange for each diet were parameterized using empirically derived flux boundaries sourced from the Virtual Metabolic Human (VMH) database ( https://www.vmh.life/ ) 31 . The corresponding nutrient influx tables ( _FLUX.tsv ) were parsed in pandas to adjust the exchange reaction constraints within each model, ensuring that all simulations were physiologically realistic and comparable across dietary conditions. To capture temporal dynamics, dynamic flux balance analysis (dFBA) was performed using COBRApy . Each simulation spanned 480 iterative time steps (0.1-hour intervals, representing 48 hours of gut metabolism). At each iteration, FBA was executed to optimize the community biomass objective, after which individual species’ growth rates were updated based on their flux distributions. Species abundances were subsequently propagated using exponential growth equations and renormalized to maintain total community biomass. At the end of each simulation, predicted secretion fluxes were extracted to quantify key metabolic outputs, including short-chain fatty acids (SCFAs) including acetate, propionate, and butyrate, as well as nitrogen- and sulfur-containing metabolites associated with dysbiosis and gut inflammation. Weighted metabolite fluxes were computed for each model and dietary condition as the sum of each species’ secretion flux multiplied by its final abundance. Statistical Analysis Statistical comparisons were performed using Python and R packages. Differences in Alpha-diversity (Shannon index) between CRC and healthy groups were assessed via the Wilcoxon rank-sum test. Assumptions for the Wilcoxon test were verified, ensuring samples were independent and the data were at least ordinal. Differential abundance of microbial taxa and functional pathways was determined using LEfSe (Linear discriminant analysis Effect Size, RRID:SCR_014609). Predicted metabolic fluxes between groups and conditions were compared using Welch's t-test (two-tailed). This test was selected to account for potential unequal variances between the CRC and healthy groups; normality of the data distribution was assessed prior to testing. For all statistical tests, a p-value of less than 0.05 was considered significant. Results are reported with test statistics, degrees of freedom, and exact p-values where applicable. Corrections for multiple hypothesis testing were applied using the Benjamini-Hochberg procedure where appropriate. Declarations Code availability All code required to reproduce the results is publicly available and documented at https://github.com/SHNLab/CRC_metagenomics_pipeline . Data availability The data sets analyzed during the current study are available in the NCBI BioProject database under project numbers PRJNA531273 and PRJNA397112. References Song, M., Chan, A. T. & Sun, J. Influence of the Gut Microbiome, Diet, and Environment on Risk of Colorectal Cancer. Gastroenterology 158 , 322–340 (2020). Kato, I. & Sun, J. Microbiome and diet in colon cancer development and treatment. Cancer J 29 , 89 (2023). Debnath, N., Kumar, R., Kumar, A., Mehta, P. K. & Yadav, A. K. Gut-microbiota derived bioactive metabolites and their functions in host physiology. Biotechnol Genet Eng Rev 37 , 105–153 (2021). Barabási, A. L., Menichetti, G. & Loscalzo, J. The unmapped chemical complexity of our diet. Nat Food 1 , 33–37 (2020). Gebara, C. H., Berthet, E., Vandenabeele, M. I. D., Jolliet, O. & Laurent, A. 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NPJ Syst Biol Appl 4 , (2018). Scott, W. T. et al. A structured evaluation of genome-scale constraint-based modeling tools for microbial consortia. PLoS Comput Biol 19 , e1011363 (2023). Heirendt, L. et al. Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0. Nat Protoc 14 , 639–702 (2019). Orth, J. D., Thiele, I. & Palsson, B. O. What is flux balance analysis? Nat Biotechnol 28 , 245–248 (2010). Additional Declarations There is NO Competing Interest. Supplementary Files Supplementarydata.xlsx Supplementary Table S1-S6 Cite Share Download PDF Status: Posted Version 1 posted 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. 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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-8201956","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Analysis","associatedPublications":[],"authors":[{"id":551345918,"identity":"eea988b5-c07b-46a8-96e2-296a1d41d879","order_by":0,"name":"Shivraj nile","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYDACCcYGIMnGwMDM3MDwgYEhgQ0qTowWxgbGGcRpgbMYG5h5gFoIukt+dnObdEUNH4M5O2PjY9sddnl8DLwPH/5gsMjDpcXgzsE2yTPH2BgsmxmbjXPPJBezMbAbG/MwSBTj1CKR2GzYwMbGYHCYsU06t405sY2BjU0a6ODEBlwOmwHS8g+spf23ZVs9SAv7zx94tDDcSGx82NgGsYWZse0w2BYGHjxaDMBa+th4gFqaJXvbjhezMbMxS/MY4HNY+oODDd+OyRmcP3zww8+26jz59jbGjz8q6nA7DAKO8SDYzGDb8asHghqCKkbBKBgFo2AEAwDHOExWIsiMqgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-3141-5754","institution":"BRIC-National Agri-Food and Biomanufacturing Institute (NABI)","correspondingAuthor":true,"prefix":"","firstName":"Shivraj","middleName":"","lastName":"nile","suffix":""},{"id":551345919,"identity":"d005f7f6-5ba6-474d-96ab-133ee898c085","order_by":1,"name":"Abhilasha Sharma","email":"","orcid":"","institution":"BRIC-National Agri-Food and Biomanufacturing Institute (NABI)","correspondingAuthor":false,"prefix":"","firstName":"Abhilasha","middleName":"","lastName":"Sharma","suffix":""},{"id":551345920,"identity":"ab88946b-054b-4065-a74b-b56a31d39f4a","order_by":2,"name":"Aman Sharma","email":"","orcid":"","institution":"BRIC-National Agri-Food and Biomanufacturing Institute (NABI)","correspondingAuthor":false,"prefix":"","firstName":"Aman","middleName":"","lastName":"Sharma","suffix":""},{"id":551345921,"identity":"34c7a9a6-ee69-4427-a705-4df4330dfe96","order_by":3,"name":"Navdeep Kaur","email":"","orcid":"","institution":"BRIC-National Agri-Food and Biomanufacturing Institute (NABI)","correspondingAuthor":false,"prefix":"","firstName":"Navdeep","middleName":"","lastName":"Kaur","suffix":""},{"id":551345922,"identity":"dc2a8965-7ea6-41e1-97a4-392e5a9e13a1","order_by":4,"name":"Amisha Rani","email":"","orcid":"","institution":"BRIC-National Agri-Food and Biomanufacturing Institute (NABI)","correspondingAuthor":false,"prefix":"","firstName":"Amisha","middleName":"","lastName":"Rani","suffix":""},{"id":551345923,"identity":"b1af1f06-c75b-47d3-9dc4-3a0587904ad0","order_by":5,"name":"Sumandeep Kaur","email":"","orcid":"","institution":"BRIC-National Agri-Food and Biomanufacturing Institute (NABI)","correspondingAuthor":false,"prefix":"","firstName":"Sumandeep","middleName":"","lastName":"Kaur","suffix":""},{"id":551345924,"identity":"6cc315a8-ba63-491a-b1ab-67594e8cd615","order_by":6,"name":"Kritika Kuksal","email":"","orcid":"","institution":"BRIC-National Agri-Food and Biomanufacturing Institute (NABI)","correspondingAuthor":false,"prefix":"","firstName":"Kritika","middleName":"","lastName":"Kuksal","suffix":""}],"badges":[],"createdAt":"2025-11-25 10:21:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8201956/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8201956/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":97666324,"identity":"00b6bbc0-10ec-4e3a-a5f3-e842529954e9","added_by":"auto","created_at":"2025-12-08 09:21:00","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":541848,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSchematic overview of the personalized metabolic modeling framework.\u003c/strong\u003e The diagram illustrates the experimental workflow and analysis pipeline. \u003cstrong\u003eData acquisition and preprocessing\u003c/strong\u003e began with whole-genome sequencing (WGS) reads from 30 Colorectal Cancer (CRC) patients and 110 healthy controls. The reads were then subjected to quality control, adapter trimming, and host DNA removal, resulting in a final cohort of 28 CRC and 108 healthy individuals. High-quality microbial reads were used for \u003cstrong\u003etaxonomic classification and community analysis\u003c/strong\u003e, which was performed with Kraken2 and Bracken. Subsequently, personalized microbiome models were constructed for each sample using the AGORA2 GEMs. \u003cstrong\u003eFlux balance analysis\u003c/strong\u003e was then performed by simulating various dietary conditions, which determined the diet-dependent metabolic flux. The goal of this analysis was to identify therapeutic biomarkers and develop personalized dietary interventions for CRC patients.\u003c/p\u003e","description":"","filename":"fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8201956/v1/12fc64ea0c78d70e4d9daf85.jpg"},{"id":97422644,"identity":"af5bfa22-72cd-4821-845d-a09ced1b335a","added_by":"auto","created_at":"2025-12-04 08:43:23","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":613876,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComprehensive microbiome community analysis highlights significant dysbiosis in Colorectal Cancer (CRC) patients.\u003c/strong\u003e \u003cstrong\u003ea\u003c/strong\u003e, Stacked bar plots illustrate the species-level relative abundance of the gut microbiome across all individual samples. The left panel shows data from 28 CRC patients, and the right panel displays data from 108 healthy controls. Each unique color in the legend corresponds to a specific bacterial taxon, allowing for visual comparison of community composition between groups. \u003cstrong\u003eb\u003c/strong\u003e, Violin plots compare the within-sample diversity using both Shannon and Simpson indices, calculated at the genus (top row) and species (bottom row) levels. Red plots represent CRC patients, while teal plots represent healthy controls. The central box in each violin indicates the interquartile range (IQR), with the median marked by a horizontal line. \u003cstrong\u003ec\u003c/strong\u003e, Two dimensionality reduction plots showcase the between-sample microbial community dissimilarities. The top panel uses Non-metric Multidimensional Scaling (NMDS) based on Bray-Curtis dissimilarity, and the bottom panel presents Principal Coordinate Analysis (PCoA), also using Bray-Curtis dissimilarity. Each point represents an individual sample, with CRC patients colored red and healthy controls colored teal. Shaded ellipses indicate the 95% confidence regions for each group, demonstrating a clear separation in microbial community structure.\u003c/p\u003e","description":"","filename":"fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8201956/v1/eff58a96c169ad33577e51a0.jpg"},{"id":97422645,"identity":"9c9de190-af2b-488f-bc18-e3d9ef038a9f","added_by":"auto","created_at":"2025-12-04 08:43:23","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":931731,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIndividual and collective metabolite flux across CRC patients by diet.\u003c/strong\u003e This figure presents the simulated metabolic output of the gut microbiome in Colorectal Cancer (CRC) patients under various dietary conditions. \u003cstrong\u003ea\u003c/strong\u003e, The bar plots show the individual metabolite flux for each CRC patient's personalized microbiome model, simulated on six different diets: Gluten-free, High-fat, High-fiber, High-protein, Mediterranean, and Vegan. Each column represents a single patient, and each row corresponds to a specific metabolite. The red dashed line indicates the average flux of the healthy cohort, with bars above the line showing higher-than-average production and bars below the line showing lower-than-average production or consumption. \u003cstrong\u003eb\u003c/strong\u003e, The heat map shows the collective rescue status of key metabolites across all CRC patients, categorized by dietary intervention. Each row represents a metabolite, and each column represents a CRC patient. A blue box indicates that a specific metabolite was produced (positive flux) in that patient's model under the given dietary condition. White boxes signify no production or a negative flux. These results highlight the potential of targeted dietary interventions to modulate the metabolic output of the CRC gut microbiome.\u003c/p\u003e","description":"","filename":"fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8201956/v1/2b258439f4d1ebf2206690db.jpg"},{"id":97422647,"identity":"ed549fde-b7cb-42ce-954d-8b22394d8c61","added_by":"auto","created_at":"2025-12-04 08:43:23","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":801208,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImpact of simulated dietary interventions on microbiome composition and diversity.\u003c/strong\u003e This figure presents the simulated impact of various dietary interventions on the gut microbiome's composition and diversity of Colorectal Cancer (CRC) patients and healthy controls. \u003cstrong\u003ea\u003c/strong\u003e, Stacked bar plots show the relative abundance of bacterial taxa at the family level under six different simulated diets. For each diet, the left bar plots represent CRC patients, while the right plots represent healthy controls. \u003cstrong\u003eb\u003c/strong\u003e, Box plots compare the alpha diversity (Shannon index) between CRC patients (red) and healthy controls (teal) for each of the six diets. Asterisks indicate statistical significance (**P \u0026lt; 0.01; ***P \u0026lt; 0.001). \u003cstrong\u003ec\u003c/strong\u003e, A scatter plot illustrates beta diversity based on Principal Coordinate Analysis (PCoA) of Bray-Curtis dissimilarity, showing the overall clustering of microbiome communities by both diet and group (CRC vs. healthy). Each point represents a sample, with shapes and colors differentiating the groups and diets. \u003cstrong\u003ed\u003c/strong\u003e, Bar plots identify the top 5 species with significantly different growth dynamics between the CRC and healthy groups under each dietary intervention. The left panel shows species that grow slower in CRC patients (teal), while the right panel shows species that grow faster (red). The data highlights how different diets can modulate specific microbial populations, potentially contributing to dysbiosis.\u003c/p\u003e","description":"","filename":"fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8201956/v1/fc714a664b6519977b2761e8.jpg"},{"id":97422650,"identity":"9e94fab6-d97a-48a8-a3b4-874019c783a1","added_by":"auto","created_at":"2025-12-04 08:43:23","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":680730,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional contribution of key species to metabolite production across dietary interventions. \u003c/strong\u003eThis figure presents the simulated contributions of top bacterial species to the production of key metabolites. Each panel displays a heatmap showing the functional contribution of the top 10 species to the production of a specific metabolite, comparing Colorectal Cancer (CRC) patients and healthy controls. The dot size represents the \u003cstrong\u003emean relative abundance\u003c/strong\u003e of each species. The color of the dot indicates the \u003cstrong\u003emean contribution\u003c/strong\u003e of that species to the production of the specified metabolite, with red signifying a high contribution and teal a low contribution. The metabolites examined include butyrate, isobutyrate, succinate, formate, propionate, hydrogen sulfide, and indole, all of which are relevant to gut health. This analysis highlights how the metabolic roles of species differ between healthy and diseased states and across various dietary conditions. The data reveal that while some species may be highly abundant, their contribution to metabolite production can be low, and vice-versa.\u003c/p\u003e","description":"","filename":"fig5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8201956/v1/25ef2b6afb63bf79e4cd9270.jpg"},{"id":97666286,"identity":"85d8e5dc-94fb-472c-8ac3-da8f4a2c94a7","added_by":"auto","created_at":"2025-12-08 09:20:51","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":655639,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMetabolic and taxonomic dynamics of the gut microbiome in response to dietary interventions.\u003c/strong\u003e This figure showcases the dynamic changes in the gut microbiome's composition and function in response to different dietary interventions. \u003cstrong\u003ea\u003c/strong\u003e, A heat map illustrates the \u003cstrong\u003elog fold change\u003c/strong\u003e of the top 50 dynamic bacterial species in Colorectal Cancer (CRC) patients compared to healthy controls, after a 48-hour simulation period. Red indicates a significant increase (positive log fold change), while blue indicates a significant decrease (negative log fold change) in abundance. The dendrograms on the left and top cluster the species and samples based on their dynamic response patterns. \u003cstrong\u003eb\u003c/strong\u003e, A bar chart presents the \u003cstrong\u003eFunctional Enrichment Score (NES)\u003c/strong\u003e of key metabolite sets by diet. Positive NES values indicate a stronger enrichment of that metabolite set in the CRC group, while negative NES values show a stronger enrichment in the healthy group. This analysis highlights how different diets selectively influence the metabolic capabilities of the microbiome in each group. \u003cstrong\u003ec\u003c/strong\u003e, \u003cstrong\u003ePhylum-to-Metabolite contribution plots\u003c/strong\u003e for CRC patients (top) and healthy controls (bottom). These circos plots visually represent the proportional contribution of different bacterial phyla (outer ring) to the production of key metabolites (inner ring, color-coded). The thickness of the bands connecting the phyla to the metabolites indicates the relative magnitude of the contribution. This provides a clear, high-level overview of which major taxonomic groups are driving the production of important metabolites in each host group.\u003c/p\u003e","description":"","filename":"fig6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8201956/v1/abd1c6f5397dcd7559136eec.jpg"},{"id":97677583,"identity":"47622b9d-5c41-43a2-808d-208c0ca20e1e","added_by":"auto","created_at":"2025-12-08 09:53:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5656578,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8201956/v1/c7f8d5ef-55bd-4ced-a7e5-fa8e68750550.pdf"},{"id":97667710,"identity":"4ac5f7bc-88bb-4cc8-bc7a-95b3de3151e3","added_by":"auto","created_at":"2025-12-08 09:24:07","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":4270291,"visible":true,"origin":"","legend":"Supplementary Table S1-S6","description":"","filename":"Supplementarydata.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8201956/v1/9bc5b7b956114bfda65d7cdb.xlsx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"In silico metabolic profiling of an Indian cohort refutes the \"One-Diet-Fits-All\" paradigm in colorectal cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDiet is a primary modifiable driver of colorectal cancer (CRC), yet the relationship between nutritional intake and oncogenesis is governed by a critical intermediary: the gut microbiome \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Functioning as a complex and highly personalized bioreactor, the intestinal microbial community metabolizes dietary components into a vast suite of bioactive compounds with profoundly divergent effects on host health \u003csup\u003e\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. This metabolic activity bifurcates along with two principal axes. On one hand, saccharolytic fermentation of complex carbohydrates, or dietary fiber, by commensal bacteria yields protective short-chain fatty acids (SCFAs) \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Among these, butyrate is paramount; it serves as the preferred energy substrate for healthy colonocytes, reinforces the mucosal barrier, and exhibits potent anti-neoplastic properties by acting as a histone deacetylase (HDAC) inhibitor, thereby promoting cell cycle arrest and apoptosis in transformed cells \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. On the other hand, proteolytic fermentation of undigested proteins and microbial transformation of bile acids (a process exacerbated by high-fat diets) can generate a cascade of detrimental oncometabolites \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. These include genotoxic compounds such as hydrogen sulfide, which can induce DNA damage at physiological concentrations, and pro-inflammatory secondary bile acids like deoxycholic acid (DCA), which promote tumorigenesis by activating oncogenic signaling pathways and generating reactive oxygen species \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. This functional duality presents a central paradox in nutritional oncology: the ultimate biological impact of a given diet is not intrinsic to its composition but is conditional upon the metabolic capacity of an individual\u0026rsquo;s gut microbiome. Consequently, a conventionally \"healthy\" diet could, in the context of a dysbiotic microbiome, inadvertently fuel oncogenic processes, demanding a shift toward a more personalized understanding of nutrition.\u003c/p\u003e\u003cp\u003eThis contradiction is not merely a theoretical curiosity but also a fundamental aspect in significant public health challenge. According to the latest GLOBOCAN 2022 estimates, CRC is the third most diagnosed malignancy and the second leading cause of cancer-related mortality worldwide, accounting for 9.6% of new cases and 9.3% of deaths, respectively \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Compounding this burden is an alarming and accelerating increase in the incidence of early-onset CRC (EO-CRC) in individuals under the age of 50, a trend observed globally since the 1990s \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. This generational shift is too rapid to be explained by host germline genetics alone and points decisively toward the influence of profound environmental changes, particularly shifts in early-life dietary patterns, lifestyle, and antibiotic exposure \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. While this crisis is global, its trajectory is manifesting with unique urgency in non-Western nations undergoing rapid economic and nutritional transition. In India, for instance, CRC is emerging as a major oncological challenge, now ranking as the fourth most common cancer with over 64,000 new cases and 38,000 deaths reported in 2022 \u003csup\u003e16\u003c/sup\u003e. In stark contrast to the stabilizing or declining rates in many Western countries, India is witnessing a steady rise in CRC incidence, particularly in urban centers where the adoption of Westernized diets and lifestyles is most pronounced \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. This epidemiological landscape underscores the imperative for population-specific research to understand and mitigate the drivers of CRC in this unique and understudied context.\u003c/p\u003e\u003cp\u003eCurrent mechanistic understanding of CRC-associated dysbiosis is founded almost exclusively on a Western-centric paradigm, derived from studies of North American and European cohorts \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. This paradigm is characterized by a consistent and well-defined taxonomic signature in gut micrbiome: a marked depletion of beneficial, butyrate-producing commensals, most notably \u003cem\u003eFaecalibacterium prausnitzii\u003c/em\u003e, and a concomitant enrichment of pro-inflammatory pathobionts, including \u003cem\u003eBacteroides fragilis\u003c/em\u003e and \u003cem\u003eFusobacterium nucleatum\u003c/em\u003e \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. The oncogenic mechanisms of key drivers like \u003cem\u003eF. nucleatum\u003c/em\u003e have been elucidated in remarkable detail. Its unique FadA adhesin directly engages E-cadherin on colon cancer cells, hijacking the Wnt/β-catenin signaling pathway to fuel uncontrolled proliferation \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Simultaneously, its Fap2 outer membrane protein mediates both tumor colonization, by binding to Gal-GalNAc moieties overexpressed on cancer cells, and potent immune evasion, by engaging the TIGIT inhibitory receptor on tumor-infiltrating natural killer (NK) cells and T lymphocytes, thereby suppressing anti-tumor immunity\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. While mechanistically elegant, the global applicability of this model is scientifically tenuous. Extrapolating these taxonomic and functional signatures to non-Western populations\u0026mdash;with profoundly different dietary histories, distinct host genetic backgrounds, and unique ancestral microbial landscapes\u0026mdash;is a flawed approach that overlooks the fundamental ecological principles governing host-microbiome interactions \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTo move beyond description and toward prediction, a paradigm shift is needed from static, compositional analysis (\"who is there?\") to a dynamic, functional understanding (\"what will they do with a specific diet?\"). This study addresses this need by pioneering a novel, systems-based approach that integrates deep, culture-independent characterization of the microbiome's genetic potential with predictive modeling of its metabolic behavior. We coupled high-resolution shotgun metagenomic sequencing, which provides a comprehensive catalog of all microbial genes and metabolic pathways, with personalized, genome-scale metabolic modeling (GSMM), a computational framework that simulates community-wide metabolic flux under defined nutritional conditions. Our central hypothesis defines the functional output of the CRC-associated microbiome is not a fixed trait but is conditionally expressed, contingent upon the available dietary substrates that serve as metabolic inputs. By applying this integrative framework, we aim to first characterize the unique compositional and functional signatures of CRC-associated dysbiosis in an understudied Indian cohort. Second, we used the data to construct personalized \u003cem\u003ein silico\u003c/em\u003e models capable of predicting how different diets trigger paradoxical metabolic outcomes, such as the production of oncometabolites from seemingly benign precursors. The outcome of this work provides a strong, mechanistically grounded framework for dismantling the 'one-diet-fits-all' model of nutrition and to lay the foundation for a new era of predictive, personalized nutritional oncology.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eProfound microbiome dysbiosis with global and population-specific features characterizes the Indian CRC cohort\u003c/h2\u003e\u003cp\u003eA total of 140 whole-genome sequencing datasets comprising 30 colorectal cancer (CRC) cases and 110 healthy controls from an Indian cohort were retrieved from the NCBI Sequence Read Archive (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cb\u003eSupplementary Table S1\u003c/b\u003e)\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Following a stringent preprocessing pipeline: removal of adapter sequences, trimming of low-quality bases, and removal of host sequences by aligning reads to the human reference genome (GRCh38), 28 CRC patients and 108 healthy controls were retained for downstream taxonomic analyses (\u003cb\u003eSupplementary Table S2\u003c/b\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTaxonomic profiling revealed a pronounced state of dysbiosis in the CRC-associated gut microbiome \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. At the species level, relative abundance plots showed significant compositional differences between groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, \u003cb\u003eSupplementary Table S3\u003c/b\u003e). Consistent with global CRC microbiome signatures, the Indian cohort displayed significant enrichment of established oral pathobionts, including \u003cem\u003eFusobacterium nucleatum, Parvimonas micra, Porphyromonas asaccharolytica\u003c/em\u003e, and \u003cem\u003ePeptostreptococcus stomatis\u003c/em\u003e\u0026mdash;a set of taxa repeatedly implicated in pro-inflammatory tumor microenvironments across cohorts from China, Germany, and France \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. These enrichments coincided with depletion of key butyrate-producing commensals such as \u003cem\u003eFaecalibacterium prausnitzii\u003c/em\u003e and \u003cem\u003eRoseburia spp.\u003c/em\u003e, both hallmarks of CRC-associated dysbiosis worldwide \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eDistinct features emerged in this Indian cohort. \u003cem\u003eLachnospira eligens\u003c/em\u003e, a beneficial commensal, was markedly reduced, with the depletion more pronounced than in other populations \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Conversely, while taxa such as \u003cem\u003eBacteroides fragilis\u003c/em\u003e and \u003cem\u003eAlistipes finegoldii\u003c/em\u003e are often elevated in CRC microbiomes in other population\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e, they were not prominent drivers of dysbiosis in Indian cohort (\u003cb\u003eSupplementary Table\u0026nbsp;3\u003c/b\u003e). These cohort-specific differences underscore the influence of host genetics, diet, and environmental exposures in shaping the CRC microbiome.\u003c/p\u003e\u003cp\u003eAlpha diversity analyses demonstrated a significant reduction in microbial richness and evenness in CRC patients. Shannon diversity indices (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, CRC: 2.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.283; Healthy: 2.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.254) and Simpson indices showed consistently lower values in CRC across genus- and species-level profiles. Violin plots illustrate these reductions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). The Wilcoxon rank-sum test confirmed statistical significance (n₁ = 28, n₂ = 108, P\u0026thinsp;=\u0026thinsp;1.1 \u0026times; 10⁻⁶), with a large effect size (Cohen\u0026rsquo;s d = \u0026minus;\u0026thinsp;1.68). Normality (Shapiro\u0026ndash;Wilk, P\u0026thinsp;\u0026gt;\u0026thinsp;0.4 for CRC; P\u0026thinsp;\u0026gt;\u0026thinsp;0.49 for Healthy) and variance homogeneity (Levene\u0026rsquo;s test, P\u0026thinsp;=\u0026thinsp;0.681) assumptions were met.\u003c/p\u003e\u003cp\u003eBeta diversity analyses corroborated the compositional restructuring. Both non-metric multidimensional scaling (NMDS, stress\u0026thinsp;=\u0026thinsp;0.189, R\u0026sup2; = 0.111) and principal coordinate analysis (PCoA) based on Bray\u0026ndash;Curtis dissimilarity proved clear separations between CRC and healthy groups, with minimal overlap in their 95% confidence ellipses (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). A PERMANOVA test (R\u0026sup2; = 0.111, F\u0026thinsp;=\u0026thinsp;16.8, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) confirmed that CRC status accounted for 11.1% of the total variation in community structure. Analysis of beta dispersion indicated no significant differences in within-group variability (F\u0026thinsp;=\u0026thinsp;1.51, P\u0026thinsp;=\u0026thinsp;0.221). Together, these findings reveal both globally conserved and uniquely Indian signatures of CRC-associated dysbiosis, characterized by loss of beneficial commensals, enrichment of pathobionts, and reduced community diversity\u0026mdash;features that may reflect shared disease mechanisms modulated by local environmental, dietary, and genetic contexts.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eDietary interventions drive patient-specific and paradoxical metabolic shifts in CRC microbiomes\u003c/h3\u003e\n\u003cp\u003eTo elucidate the functional consequences of CRC-associated dysbiosis, we further performed flux balance analysis on personalized gut microbiome metabolic models for each of the 28 participants. The metabolic output was simulated under six distinct dietary conditions: Gluten-free, High-fat, High-fiber, High-protein, Mediterranean, and Vegan \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. The 48 hours simulations revealed pronounced inter-individual variability in the production and consumption of key metabolites across the patient cohort, as visualized in the individual flux profiles (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, \u003cb\u003eSupplementary Table S4\u003c/b\u003e). Each patient's microbiome exhibited a unique metabolic response to the dietary shifts, with fluxes for many metabolites, such as the short-chain fatty acid (SCFA) butyrate, deviating substantially from the average flux observed in healthy individuals (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, red dashed line). This heterogeneity underscores the context-dependent nature of dietary interventions. A collective rescue analysis, which assessed the restoration of metabolite production to a healthy range, further illustrated this variability across patients and diets (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eShort-Chain Fatty Acids (SCFAs) and Succinate\u003c/h3\u003e\n\u003cp\u003eA primary focus was on the production of butyrate, a beneficial SCFA known for its protective role in colon health \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. The high-fiber diet, as expected, promoted butyrate production, achieving the highest mean flux across all diets. It successfully rescued butyrate production in 61% of the CRC patient models (17 of 28). However, the Gluten-Free, High-Fat, and High-Protein diets achieved the similar rescue rate, indicating that fiber is not the sole determinant of butyrate rescue in these personalized models. Similarly, the production of acetate, another key SCFA, was most effectively rescued by the High-Fat (rescued in ~\u0026thinsp;79% of patients) and High-Fiber (rescued in 75% of patients) diets, with the High-Fiber diet also yielding the highest mean acetate flux.\u003c/p\u003e\u003cp\u003eCrucially, this analysis uncovered a paradoxical effect associated with the High-Fiber diet. While increasing beneficial butyrate, this diet also elevated the production of succinate, a known oncometabolite that promotes tumorigenesis \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. The High-Fiber and High-Fat diets were the most effective at rescuing succinate production, each restoring it in ~\u0026thinsp;79% of patients (22 of 28). The High-Fat diet also generated the highest mean succinate flux. This dual effect highlights a potential trade-off where a generally beneficial dietary strategy simultaneously increases a detrimental metabolite.\u003c/p\u003e\n\u003ch3\u003eProtein Fermentation and Sulfur Metabolism\u003c/h3\u003e\n\u003cp\u003eMetabolites associated with protein fermentation also showed significant diet- and patient-specific responses. The High-Protein diet expectedly influenced these pathways, leading to a high rescue rate for putrescine (~\u0026thinsp;93% of patients) but also the most negative mean flux for indole, suggesting high consumption of this tryptophan-derived metabolite \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. In contrast, the High-Fat diet was markedly ineffective at rescuing putrescine production (rescued in only\u0026thinsp;~\u0026thinsp;11% of patients).\u003c/p\u003e\u003cp\u003eProduction of hydrogen sulfide (H₂S), a pleiotropic signaling molecule \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e, was also highly variable. The Gluten-Free diet was most effective at rescuing H₂S production (75% of patients), whereas the High-Fat diet was least effective, rescuing production in only\u0026thinsp;~\u0026thinsp;32% of patients and resulting in a negative mean flux. Similarly, the production of trimethylamine (TMA), a precursor to the pro-atherogenic TMAO \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, was strongly induced by the High-Protein and Gluten-Free diets. Conversely, the High-Fiber diet substantially attenuated TMA production, and the High-Fat diet led to its net consumption. Collectively, these simulation results demonstrate that dietary interventions in the context of CRC produce highly personalized and sometimes contradictory metabolic outputs. Our findings summarize this complex landscape, where the efficacy of a diet in rescuing the production of beneficial metabolites is not uniform, underscoring the critical need for personalized nutritional strategies in managing CRC.\u003c/p\u003e\n\u003ch3\u003eDiet acts as an ecological filter, remodeling the structure and diversity of the dysbiotic community\u003c/h3\u003e\n\u003cp\u003eSimulated dietary interventions exerted profound effects on the gut microbiome in CRC, acting as a powerful ecological filter that reshaped community composition and diversity. Family-level changes captured in stacked bar plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea) revealed pronounced, diet-specific taxonomic shifts that were distinctly different between CRC and healthy groups across all six dietary regimens. For instance, the relative abundance of families such as \u003cem\u003eOdoribacter\u003c/em\u003e and \u003cem\u003eAlcaligenes\u003c/em\u003e varied markedly, with \u003cem\u003eOdoribacter\u003c/em\u003e notably more abundant in CRC under certain diets, indicating disease-specific diet responses.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAlpha-diversity, assessed using the Shannon index, demonstrated statistically significant differences (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01 to P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) between CRC and healthy cohorts within each diet (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). Notably, the Mediterranean and vegan diets exhibited substantial improvements in microbial diversity in CRC patients, shifting their profiles closer to those of healthy controls. This partial restoration of diversity aligns with the concept that complex, plant-rich diets foster rich microbial ecosystems and may counteract the diversity loss typically seen in CRC \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eBeta-diversity analyses using Principal Coordinate Analysis (PCoA) of Bray-Curtis dissimilarities further underscored the restorative potentials of specific diets. Mediterranean and high-fiber interventions facilitated a convergence of CRC patient communities toward the healthy control cluster (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec), suggesting that these diets can partially reverse community-wide structural dysbiosis associated with CRC. This shift in microbial community architecture implies functional recalibration potentially beneficial to host health.\u003c/p\u003e\u003cp\u003eSpecies-specific growth dynamics illuminated the microbial taxa most responsive to dietary modulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed, \u003cb\u003eSupplementary Table S5\u003c/b\u003e). Across diets, key pathobionts\u0026mdash;including certain \u003cem\u003ePrevotella\u003c/em\u003e and \u003cem\u003eSuccinivibrio\u003c/em\u003e species\u0026mdash;exhibited significantly slower growth rates in CRC models, indicating their potential suppression by nutritional interventions \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Conversely, beneficial commensals such as \u003cem\u003eBacteroides faecis\u003c/em\u003e, \u003cem\u003eOdoribacter splanchnicus\u003c/em\u003e, and \u003cem\u003eParaprevotella clara\u003c/em\u003e displayed enhanced growth under diets like Mediterranean and high-fiber, reflecting targeted promotion of protective taxa through diet \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTogether, these findings provide compelling evidence that diet functions as a selective ecological filter in the CRC microbiome, reshaping both taxonomic and functional landscapes \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. The efficacy of Mediterranean and vegan diets in enhancing microbial diversity and shifting community structure reinforces their potential as nutritional strategies to mitigate dysbiosis \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Furthermore, identification of diet-responsive \"winners\" and \"losers\" at the species level offers mechanistic insights and targets for precision microbiome modulation in CRC.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eSpecies-specific metabolic contributions reveal a disconnect between abundance and function\u003c/h2\u003e\u003cp\u003eComprehensive analysis of species-level metabolic contributions across multiple key metabolites revealed a strong disconnect between microbial abundance and functional output (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). While conventional wisdom often equates high relative abundance with functional dominance, our results challenge this paradigm, demonstrating that rare taxa can exert disproportionately large effects on metabolite pools, while dominant species may contribute minimally.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFor several metabolites\u0026mdash;such as butyrate, acetate, and succinate\u0026mdash;the largest contributors within each community were not always the most abundant taxa. For example, within CRC models, \u003cem\u003eOdoribacter splanchnicus\u003c/em\u003e and \u003cem\u003eParabacteroides distasonis\u003c/em\u003e emerged as key contributors to formate, acetate, and succinate production, despite not consistently ranking among the most prevalent species. Notably, for butyrate production, the relative contribution of \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e in CRC patients was markedly lower than in healthy controls, underscoring a disease-specific loss of beneficial metabolic function even at similar or elevated abundance levels.\u003c/p\u003e\u003cp\u003eBy contrast, \u003cem\u003eBacteroides fragilis\u003c/em\u003e, while highly abundant, contributed significantly to hydrogen sulfide production but had a negligible direct impact on beneficial metabolites like butyrate and acetate, supporting the concept that high-abundance taxa can serve as metabolic specialists with deleterious functional roles \u003csup\u003e\u003cspan additionalcitationids=\"CR43\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Further, analysis of putrescine and indole production illuminated a similar pattern: in CRC, specialized taxa with moderate to low abundance\u0026mdash;such as \u003cem\u003eParaprevotella clara\u003c/em\u003e and \u003cem\u003eClostridium innocuum\u003c/em\u003e\u0026mdash;displayed high per-capita contributions, indicating the importance of minority taxa for maintaining metabolic flexibility and resilience within the ecosystem \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eDisease-specific alterations in these abundance-function relationships were also observed. In CRC, several taxa (e.g., \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e) exhibited reduced per-cell capacity for beneficial metabolite production, suggesting that CRC is associated not only with compositional shifts but also with functional attenuation within key microbial lineages \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eImportantly, dietary interventions were found to modulate these functional hierarchies. In many cases, plant-rich regimens\u0026mdash;such as Mediterranean and high-fiber diets\u0026mdash;partially restored the contribution of beneficial, low-abundance taxa to key metabolites, suggesting that providing suitable substrates can selectively enhance the metabolic function of these important yet underrepresented microbes. These findings highlight that ecosystem function cannot be inferred solely from taxonomic profiles; instead, an integrated view considering both abundance and per-taxon metabolic output is required \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. The results also suggest a mechanistic pathway for personalized microbiome restoration: targeting substrate pools to promote functionally critical, but numerically rare, taxa in the CRC gut ecosystem.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eTemporal dynamics demonstrate rapid, therapeutically relevant ecosystem responses to diet\u003c/h3\u003e\n\u003cp\u003eDietary interventions prompted rapid and extensive shifts in microbial community composition within 48 hours, confirming that the gut microbiome is highly plastic and responsive to nutritional change \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. CRC-associated microbiomes were markedly more sensitive to dietary perturbations than those of healthy controls, as indicated by pronounced species turnover and larger log fold changes in abundance for dynamic taxa (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea, \u003cb\u003eSupplementary Table S6\u003c/b\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSpecies growth trajectory analysis highlighted that certain taxa, including \u003cem\u003eElusimicrobium minutum, Streptomyces massiliensis\u003c/em\u003e, and \u003cem\u003eLactobacillus taiwanensis\u003c/em\u003e, reproducibly exhibited substantial increases (log fold change exceeding\u0026thinsp;+\u0026thinsp;7) in CRC subjects across multiple dietary patterns. In contrast, taxa such as \u003cem\u003eCronobacter sakazakii, Bacteroides eggerthii\u003c/em\u003e, and \u003cem\u003eRuthenibacterium lactatiformans\u003c/em\u003e consistently grew more rapidly in healthy controls, illustrating disease-dependent responses and ecosystem instability in CRC.\u003c/p\u003e\u003cp\u003eFunctional enrichment analysis using Normalized Enrichment Score (NES), captured the collective metabolic consequences of dietary intervention (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb). While trends toward increased short-chain fatty acid (SCFA) synthesis were observed in CRC patients receiving high-fiber and gluten-free interventions, the overall enrichment patterns varied for each diet and disease context. Notably, high-fat diets in CRC tended to favor pathways associated with harmful metabolite production, such as ammonia and succinate, underscoring divergent metabolic outcomes depending on nutritional inputs. These values confirm that dietary composition can selectively influence the metabolic capabilities of the microbiome in a manner that depends on disease status \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAt a systems level, temporal analysis revealed that major bacterial phyla shifted functional roles and their metabolic outputs in the CRC state versus healthy microbiomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec). This reorganization of contributions to key metabolite pools following dietary changes highlights the microbiome\u0026rsquo;s innate adaptability\u0026mdash;and a potential avenue for targeted therapeutic manipulation \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e,\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. Collectively, these findings demonstrate that dietary interventions can drive swift, pronounced changes in microbiome composition, growth dynamics, and metabolic function, particularly in the dysbiotic CRC gut. The rapidity and magnitude of these shifts suggest opportunities for using temporal plasticity in the microbiome as a target for therapeutic nutrition.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study addresses the need to understand diet-microbiome interactions in a non-Western CRC cohort using a functional, systems-level approach. Standard nutritional advice is often unpredictable due to high inter-individual variability \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. By employing shotgun metagenomics and personalized GSMMs, we found that the gut microbiome in an Indian CRC cohort has both globally conserved dysbiotic markers and unique population-specific features. Crucially, our functional models predict that dietary interventions, particularly with fiber, yield highly individualized and paradoxical metabolic outputs. A diet designed to produce protective butyrate can simultaneously elevate the oncometabolite succinate, challenging the 'one-diet-fits-all' nutritional model for CRC. Our findings underscore that predicting the functional consequences of diet requires moving beyond taxonomic cataloging to personalized simulations, which reveal how dietary substrates can be diverted toward either protective or pro-tumorigenic pathways depending on the underlying dysbiotic ecosystem \u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe enrichment of \u003cem\u003eFusobacterium nucleatum\u003c/em\u003e and \u003cem\u003eParvimonas micra\u003c/em\u003e and depletion of \u003cem\u003eFaecalibacterium prausnitzii\u003c/em\u003e in the Indian cohort aligns with global studies, confirming a universal CRC microbial signature \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. This suggests conserved pathogenic mechanisms, such as inflammation driven by \u003cem\u003eF. nucleatum\u003c/em\u003e and the loss of anti-proliferative butyrate from \u003cem\u003eF. prausnitzii\u003c/em\u003e. However, the cohort also displayed distinct local features, including the depletion of the anti-inflammatory species \u003cem\u003eLachnospira eligens\u003c/em\u003e and an inconsistent enrichment of \u003cem\u003eBacteroides fragilis\u003c/em\u003e. This divergence from Western cohorts may reflect differences in the baseline Indian microbiome, which is often \u003cem\u003ePrevotella\u003c/em\u003e-rich, as well as long-term dietary patterns, host genetics, or other environmental exposures \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. While the functional outcomes of dysbiosis appear conserved, the specific taxa driving them can be population specific.\u003c/p\u003e\u003cp\u003eA key finding is the functional paradox revealed by our models: a simulated high-fiber diet increased beneficial butyrate but also elevated the oncometabolite succinate. Succinate is a metabolic intermediate, and its accumulation suggests a functional bottleneck in the dysbiotic CRC microbiome where succinate-producing pathways are favored over consuming ones. This is clinically significant, as succinate is a known oncometabolite that promotes tumorigenesis. It signals through the SUCNR1 receptor to drive inflammation and angiogenesis and can stabilize Hypoxia-Inducible Factor 1-alpha (HIF-1α) even in normoxic conditions, promoting a pro-tumorigenic transcriptional program \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. This paradox implies that blanket recommendations to increase fiber may be counterproductive in individuals whose microbiomes are primed to overproduce succinate, strongly supporting the need for personalized nutritional strategies based on the predicted functional output of an individual's gut ecosystem.\u003c/p\u003e\u003cp\u003eOur \u003cem\u003ein-silico\u003c/em\u003e experiments showed that plant-rich diets, like the Mediterranean and vegan diets, could partially restore microbial diversity in CRC microbiomes, shifting them closer to healthy controls. This supports the concept of diet as an ecological filter \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. A low-fiber Western diet provides few metabolic niches, favoring a low-diversity community. In contrast, the diverse fibers and polyphenols in plant-rich diets create a multitude of niches, promoting the growth of a wider range of specialist microbes and thus increasing overall diversity. The simulated growth of beneficial commensals like \u003cem\u003eOdoribacter splanchnicus\u003c/em\u003e, a known producer of short-chain fatty acids (SCFAs), exemplifies this principle \u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. This frames dietary therapy as a restorative ecological strategy, aiming to create an environment where beneficial bacteria can outcompete pathobionts.\u003c/p\u003e\u003cp\u003eThis study's functional resolution demonstrates that a microbe's functional contribution is not proportional to its relative abundance, challenging a paradigm common in 16S rRNA-based studies\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. For example, our models identified the low-abundance \u003cem\u003eOdoribacter splanchnicus\u003c/em\u003e as a key producer of formate and acetate, while the more abundant \u003cem\u003eBacteroides fragilis\u003c/em\u003e had a more specialized, and potentially detrimental, role in producing hydrogen sulfide (H\u003csub\u003e2\u003c/sub\u003eS). This aligns with the ecological concept of \"keystone species,\" where low-abundance organisms can have a disproportionate functional impact \u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. This implies that the functional resilience of the gut microbiome may depend on these rare but vital taxa. Consequently, diagnostics must look beyond dominant species, and therapeutics could be developed to boost these functionally important rare microbes.\u003c/p\u003e\u003cp\u003eThe study's strengths include the use of shotgun metagenomics, which provides species-level and functional resolution, and the novel application of personalized GSMMs to predict dietary responses\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. The primary limitation is that these predictions are \u003cem\u003ein silico\u003c/em\u003e and must be considered hypotheses requiring experimental validation through \u003cem\u003ein vitro\u003c/em\u003e, animal, or clinical studies. GSMMs are powerful hypothesis-generation tools, not replacements for experimentation. Additional limitations include the cross-sectional study design, which prevents causal inference, and the cohort size, which requires that findings be validated in larger populations. Based on these findings, future research should prioritize longitudinal studies to track microbiome and metabolome changes in response to dietary interventions in CRC cohorts. Integrating metagenomics with other omics data—such as host transcriptomics and metabolomics—is critical to validate the predicted metabolic fluxes and understand host responses, like confirming pathway upregulation in patients with high succinate. Ultimately, this work should inform pilot clinical trials where patients are stratified by their predicted metabolic response. The long-term goal is to refine these computational tools into \"virtual patient\" models that can serve as clinical decision-support systems for personalized nutrition.\u003c/p\u003e\u003cp\u003eIn conclusion, this study provides a functional, systems-level framework demonstrating that the CRC microbiome's response to diet is highly personalized and potentially paradoxical. By moving beyond compositional descriptions to predictive modeling, this work challenges the 'one-diet-fits-all' model of nutritional advice. This predictive approach is a critical step toward developing personalized, mechanistically grounded nutritional strategies to manage colorectal cancer in diverse global populations and advance the field of precision nutritional medicine.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003eData Acquisition and Cohort Description\u003c/h2\u003e\u003cp\u003eThis study followed an observational case-control design utilizing publicly available whole-genome sequencing (WGS) data. As this was a secondary analysis of existing public datasets, subject randomization and blinding were not applicable to the data collection phase. The cohort consisted of 140 individuals from India, comprising 30 colorectal cancer (CRC) patients (NCBI BioProject, RRID:SCR_004801; PRJNA531273) and 110 healthy, asymptomatic controls (BioProject PRJNA397112), as previously described \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. All raw metagenomic data, sequenced on the Illumina NextSeq 500 platform, were retrieved from the NCBI Sequence Read Archive ((SRA, RRID:SCR_004891). The SRA Toolkit (v3.1.0)\u003csup\u003e58\u003c/sup\u003e was employed for data download and conversion; specifically, the \u003cem\u003eprefetch\u003c/em\u003e command was used to securely download SRR files, and \u003cem\u003efasterq-dump\u003c/em\u003e was used to convert these into paired-end FASTQ format for subsequent analysis.\u003c/p\u003e\u003ch2\u003eMetagenomic Data Preprocessing and Quality Control\u003c/h2\u003e\u003cp\u003eA rigorous bioinformatic pipeline was implemented to process the raw sequencing reads. Initial and post-processing quality assessments for all samples were performed using FastQC (v0.12.1, RRID:SCR_014583) \u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e, and the results were aggregated into a unified report using MultiQC (v1.27.1, RRID:SCR_014982) \u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. Raw paired-end reads were trimmed for adapter content and low-quality bases using Trimmomatic (v0.39, RRID:SCR_011848) \u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. The trimming process involved removing the first 19 bases (HEADCROP:19), clipping leading and trailing bases with a Phred score below 3, applying a 4-base sliding window to trim regions where the average quality dropped below 20 (SLIDINGWINDOW:4:20), cropping reads to a maximum length of 125 bp (CROP:125), and discarding any reads shorter than 36 bases post-trimming (MINLEN:36). Following quality trimming, host DNA contamination was removed. The processed paired-end reads were aligned against the human reference genome (GRCh38) using Bowtie2 (v2.4.2, RRID:SCR_005476) in its \u003cem\u003e--very-sensitive mode\u003c/em\u003e \u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. Only read pairs that failed to align to the human genome were retained \u003cem\u003e(--un-conc-gz\u003c/em\u003e), ensuring a high-purity microbial dataset for downstream taxonomic and functional analysis.\u003c/p\u003e\u003ch2\u003eTaxonomic Profiling and Abundance Estimation\u003c/h2\u003e\u003cp\u003eThe non-human, quality-filtered reads were subjected to taxonomic classification using Kraken2 (v2.1.3, RRID:SCR_026838), a highly sensitive k-mer-based classifier \u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. Reads were classified against a pre-built Kraken2 database constructed from the standard bacterial reference library. To obtain more accurate species-level abundance estimates, the initial classification reports from Kraken2 were processed with Bracken (Bayesian Reestimation of Abundance with KrakEN, v2.9, RRID:SCR_005484) \u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e. Bracken uses the taxonomic assignments from Kraken2 to estimate the true proportion of species in a sample, correcting for biases inherent in k-mer classification. The species-level and genus-level relative abundance tables generated by Bracken formed the foundational data for all subsequent community-level and functional analyses.\u003c/p\u003e\u003ch2\u003eMicrobiome Community Analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses of the microbiome composition were conducted in R (v4.2.2, RRID:SCR_001905). Alpha-diversity was calculated using the Shannon index on the Bracken-corrected species abundance data, with differences between CRC and healthy groups assessed via the Wilcoxon rank-sum test \u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. Beta-diversity was evaluated using the Bray-Curtis dissimilarity metric, visualized with Principal Coordinate Analysis (PCoA), and statistically tested using a Permutational Multivariate Analysis of Variance (PERMANOVA) with 9,999 permutations (\u003cem\u003eadonis2\u003c/em\u003e function, \u003cem\u003evegan\u003c/em\u003e package) \u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003ch2\u003ePersonalized Metabolic Model Construction and Simulation\u003c/h2\u003e\u003cp\u003eTo investigate the functional consequences of the observed dysbiosis, we constructed personalized \u003cem\u003ein silico\u003c/em\u003e metabolic models for each microbiome samples. For each sample, a personalized community model was assembled by integrating the Bracken-derived species relative abundances with the AGORA2 database, a resource of 7302 manually curated, genome-scale metabolic models (GEMs) of gut microbes \u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. This was achieved by creating a unified model where the metabolic network of each species present in a sample was included, with the flux constraints of its associated reactions weighted by that species' relative abundance.\u003c/p\u003e\u003cp\u003eEach personalized community model was generated by assembling the GEMs corresponding to all microbial species detected in a sample \u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e,\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e. The metabolic network of each species was incorporated into a unified community model, with flux bounds on individual reactions scaled according to the relative abundance of that species within the community. This abundance-weighted integration ensured that each model quantitatively reflected the compositional and functional characteristics of the individual’s gut microbiome. For computational implementation, species abundance tables were curated and stored in standardized spreadsheet files (e.g., \u003cem\u003ehi_p.xlsx\u003c/em\u003e, \u003cem\u003edi_p.xlsx\u003c/em\u003e), listing AGORA model identifiers alongside their relative abundance values. These datasets were imported and processed using the \u003cem\u003epandas\u003c/em\u003e library in Python, enabling efficient mapping of microbial species to their respective AGORA genome-scale metabolic models. The resulting structured data served as input for the reconstruction and analysis of personalized microbial community models within the COBRA (Constraint-Based Reconstruction and Analysis) framework, implemented through \u003cem\u003eCOBRApy\u003c/em\u003e (RRID:SCR_012096) \u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e,\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe core computational approach was Flux Balance Analysis (FBA), a constraint-based method that estimates the steady-state distribution of metabolic fluxes across the network\u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e. Mathematically, the metabolic system is represented by the stoichiometric matrix S (of dimension \u003cem\u003em × n\u003c/em\u003e, where \u003cem\u003em\u003c/em\u003e denotes metabolites and \u003cem\u003en\u003c/em\u003e denotes reactions), subject to the mass-balance constraint:\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:S.v=\\)\u003c/span\u003e\u003c/span\u003e0\u003c/p\u003e\u003cp\u003eWhere \u003cb\u003eS\u003c/b\u003e is the stoichiometric matrix (\u003cem\u003em\u003c/em\u003e x \u003cem\u003en\u003c/em\u003e), with \u003cem\u003em\u003c/em\u003e metabolites and \u003cem\u003en\u003c/em\u003e reactions. Each entry in the matrix represents the stoichiometric coefficient of a metabolite in each reaction (negative for substrates, positive for products) and \u003cb\u003ev\u003c/b\u003e is the vector of all reaction fluxes.\u003c/p\u003e\u003cp\u003ewhere \u003cb\u003ev\u003c/b\u003e is the flux vector representing the rate of all reactions in the network. As this system is typically underdetermined, linear programming was employed to optimize a biologically meaningful objective function under flux bounds:\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:vlb\\le\\:v\\le\\:vub\\)\u003c/span\u003e\u003c/span\u003e\u003cem\u003e​\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe objective function, Z, is a linear combination of fluxes, typically formulated to maximize a specific biological goal:\u003c/p\u003e\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:Maximize\\:Z=cT\\cdot\\:v$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003eWhere \u003cb\u003ec\u003c/b\u003e is a weight vector that defines the objective. For these simulations, the objective was to maximize the community biomass production rate, a proxy for overall microbial growth potential.\u003c/p\u003e\u003cp\u003eEach personalized community model was simulated under six distinct dietary conditions (Western, High-Fat, High-Protein, High-Fiber, Mediterranean, and Vegan). The model constraints for nutrient uptake and metabolite exchange for each diet were parameterized using empirically derived flux boundaries sourced from the Virtual Metabolic Human (VMH) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.vmh.life/\u003c/span\u003e\u003cspan address=\"https://www.vmh.life/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) \u003csup\u003e31\u003c/sup\u003e. The corresponding nutrient influx tables (\u003cem\u003e_FLUX.tsv\u003c/em\u003e) were parsed in \u003cem\u003epandas\u003c/em\u003e to adjust the exchange reaction constraints within each model, ensuring that all simulations were physiologically realistic and comparable across dietary conditions. To capture temporal dynamics, dynamic flux balance analysis (dFBA) was performed using \u003cem\u003eCOBRApy\u003c/em\u003e. Each simulation spanned 480 iterative time steps (0.1-hour intervals, representing 48 hours of gut metabolism). At each iteration, FBA was executed to optimize the community biomass objective, after which individual species’ growth rates were updated based on their flux distributions. Species abundances were subsequently propagated using exponential growth equations and renormalized to maintain total community biomass.\u003c/p\u003e\u003cp\u003eAt the end of each simulation, predicted secretion fluxes were extracted to quantify key metabolic outputs, including short-chain fatty acids (SCFAs) including acetate, propionate, and butyrate, as well as nitrogen- and sulfur-containing metabolites associated with dysbiosis and gut inflammation. Weighted metabolite fluxes were computed for each model and dietary condition as the sum of each species’ secretion flux multiplied by its final abundance.\u003c/p\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eStatistical comparisons were performed using Python and R packages. Differences in Alpha-diversity (Shannon index) between CRC and healthy groups were assessed via the Wilcoxon rank-sum test. Assumptions for the Wilcoxon test were verified, ensuring samples were independent and the data were at least ordinal. Differential abundance of microbial taxa and functional pathways was determined using LEfSe (Linear discriminant analysis Effect Size, RRID:SCR_014609). Predicted metabolic fluxes between groups and conditions were compared using Welch's t-test (two-tailed). This test was selected to account for potential unequal variances between the CRC and healthy groups; normality of the data distribution was assessed prior to testing. For all statistical tests, a p-value of less than 0.05 was considered significant. Results are reported with test statistics, degrees of freedom, and exact p-values where applicable. Corrections for multiple hypothesis testing were applied using the Benjamini-Hochberg procedure where appropriate.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eCode availability\u003c/h2\u003e\u003cp\u003eAll code required to reproduce the results is publicly available and documented at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/SHNLab/CRC_metagenomics_pipeline\u003c/span\u003e\u003cspan address=\"https://github.com/SHNLab/CRC_metagenomics_pipeline\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eData availability\u003c/h2\u003e\u003cp\u003eThe data sets analyzed during the current study are available in the NCBI BioProject database under project numbers PRJNA531273 and PRJNA397112.\u003c/p\u003e\u003c/div\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSong, M., Chan, A. 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What is flux balance analysis? \u003cem\u003eNat Biotechnol\u003c/em\u003e \u003cstrong\u003e28\u003c/strong\u003e, 245\u0026ndash;248 (2010).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Gut microbiome, Colorectal cancer, Constraint-based modeling, Precision oncology","lastPublishedDoi":"10.21203/rs.3.rs-8201956/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8201956/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe metabolic impact of diet on colorectal cancer (CRC) is governed by complex, personalized microbial networks that elude static nutritional guidelines. Therefore, we integrated shotgun metagenomics with personalized genome-scale metabolic modeling (GSMM) to simulate metabolic fluxes in an Indian cohort of 30 CRC patients and 110 healthy controls. Under six simulated dietary conditions, the CRC microbiome- characterized by \u003cem\u003eFusobacterium nucleatum\u003c/em\u003e enrichment, exhibited highly variable metabolic responses. While high-fiber interventions restored protective butyrate in 61% of patients, they paradoxically elevated the oncometabolite succinate in 79% of models. Furthermore, high-fat diets resulted in net hydrogen sulfide consumption defects, potentially exacerbating local toxicity. Conversely, Mediterranean and Vegan diets successfully restored microbial diversity and suppressed pathogenic species. These findings indicate that metabolic outcomes are strictly conditional on baseline microbial composition, refuting \"one-diet-fits-all\" guidelines. Our study highlights the capacity of computational modeling to predict non-intuitive metabolic side effects, providing a framework for precision nutritional oncology.\u003c/p\u003e","manuscriptTitle":"In silico metabolic profiling of an Indian cohort refutes the \"One-Diet-Fits-All\" paradigm in colorectal cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-04 08:43:18","doi":"10.21203/rs.3.rs-8201956/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6c826c72-60ff-477b-9bb2-cece5d4780f7","owner":[],"postedDate":"December 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":58670382,"name":"Biological sciences/Computational biology and bioinformatics/Computational models"},{"id":58670383,"name":"Biological sciences/Cancer/Gastrointestinal cancer/Colorectal cancer/Colon cancer"}],"tags":[],"updatedAt":"2025-12-04T08:43:18+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-04 08:43:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8201956","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8201956","identity":"rs-8201956","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}