Hasil untuk "Microbial ecology"

Esteban Pérez, Esther Sanjuán, Miroslav Jůzl et al.

Microbial spoilage and foodborne pathogens remain central challenges in food safety, driven by the metabolic resilience and ecological adaptability of bacteria, yeasts, and molds across diverse food matrices. Active antimicrobial packaging has emerged as a biologically informed strategy that directly targets microbial physiology through controlled release or contact-mediated mechanisms. These systems employ natural antimicrobials, bacteriocins, essential oils, and metal nanoparticles to disrupt cell membranes, inhibit enzymatic pathways, generate reactive oxygen species, or interfere with quorum sensing, resulting in substantial reductions in microorganisms such as <i>Listeria monocytogenes</i>, <i>Salmonella</i> spp., <i>E. coli</i> O157:H7, <i>Pseudomonas</i> spp., <i>Brochothrix thermosphacta</i>, and spoilage fungi. In real food environments, these interventions achieve multi-log reductions and attenuate microbial metabolism, though efficacy varies with pH, water activity, fat content, and storage temperature. Oxygen scavengers further reshape microbial ecology by suppressing aerobic spoilage organisms while inadvertently favoring anaerobic competitors. Despite promising outcomes, concerns regarding nanoparticle migration, microbial resistance potential, and matrix-dependent performance highlight the need for deeper microbiological validation. Future progress will require integrative research linking microbial ecology, packaging material science, and mechanistic toxicology. By aligning with microbial behavior at the cellular and ecosystem levels, active antimicrobial packaging represents a powerful, biologically grounded approach to mitigating foodborne risks.

Mattia Mattei, David Soriano-Paños, Alex Arenas

Bacteria regulate their motility through a variety of mechanisms, including quorum sensing (QS) and other density-dependent responses mediated by diffusible signals. While nonlinear density-dependent motility is well known in active-matter theory to generate nonequilibrium spatial patterns, its consequences for the coexistence of growing, interacting species remain less explored. Here we develop a minimal spatially structured model for two strongly competing species in which local demographic interactions are coupled to an escape response: each species increases its motility nonlinearly (sigmoidal) with the local abundance of its competitor. We show that this sigmoidal motility regulation promotes optimal spatial self-organization and can sustain long term coexistence via segregation, even in parameter regimes that yield competitive exclusion in well-mixed Lotka-Volterra dynamics. On two-dimensional lattices, the interplay between demographic competition and density-dependent motility generates a range of emergent patterns, including regimes in which the weaker competitor counterintuitively has higher total abundance. Overall, our results identify nonlinear, competitor-induced motility as a fundamental mechanism capable of sustaining coexistence in competing microbial populations.

Cristina I. Amador, Sofia Zoe Moscovitz, Lorrie Maccario et al.

Abstract Bacterial fitness and adaptability in microbial communities are influenced by interspecies interactions and spatial organization. This study investigated how these factors shape the evolutionary dynamics of Bacillus thuringiensis. A distinct phenotypic variant of B. thuringiensis emerged consistently under both planktonic and biofilm conditions, as well as in monospecies and mixed-species settings, but was strongly selected in biofilms and during coexistence with Pseudomonas defluvii and/or Pseudomonas brenneri. Compared to its ancestor, the variant exhibited shorter generation times, reduced sporulation, auto-aggregation, and lower biomass in mixed-species biofilms. Mutations in the spo0A regulator, which controls sporulation and biofilm matrix production, were identified in all variants. Proteomics revealed a reduction in TasA, a key matrix protein, in the variant but increased levels in co-culture with P. brenneri. These findings highlight how interspecies interactions drive B. thuringiensis diversification, promoting traits like reduced matrix production and species coexistence, with implications for microbial consortia applications in agriculture and biopesticides.

Sean Robert Schaefer, Fernando Montaño-López, Hannah Holland-Moritz et al.

The rhizosphere contains diverse groups of bacteria and fungi living near plant roots and mycorrhizal hyphae whose composition and function are key drivers of ecosystem and biogeochemical processes. Despite rich literature on rhizosphere communities, no studies have examined the drivers of rhizosphere communities across plants or soil types in the tundra. We collected 513 root samples from 141 individual plants representing six plant species and three mycorrhizal association types across four glacial histories in Northern Alaska. Glacial drifts ranged from 11 000 to 4.5 million years since deglaciation representing a gradient in glacial history and mineralogical weathering. We found that glacial history, a strong proxy for soil mineralogy, explained the most variation in rhizosphere bacterial communities (13.3%) while interactions between glacial history and host plants explained the most variation in fungal rhizosphere communities (11.6%). We found strong correlations between ectomycorrhizal and rhizosphere communities across spatial scales and sites for the shrub Betula nana (30.7%–54.7% correlated), and that ectomycorrhizal composition was most similar among root fragments of the same plant, followed by plants at the same site, and plants at different sites. This work serves to advance the ecological understanding of rhizosphere and ectomycorrhizal communities in response to shrubification.

Yang Zi, Yilin Yang, Mingyue Li et al.

This study investigated the effects of dietary supplementation with an alkaline metal ion complex (AMIC) on growth performance, meat quality, rumen microbiota, and metabolome in Hu lambs. Fifty lambs were randomly assigned to either a control group (basal diet) or an AMIC group (basal diet + 0.15% AMIC) for 60 days. The results showed that AMIC significantly increased carcass weight, <i>Longissimus dorsi</i> area, crude protein, intramuscular fat, ash content, and meat luminosity (L*). Amino acid profiles and key flavor compounds were elevated, while off-flavor hydrocarbons were reduced. 16S rRNA sequencing revealed that AMIC altered rumen microbiota composition, enriching butyrate-producing genera such as Butyrivibrio and Saccharofermentans. Metabolomic analysis identified 398 differentially expressed metabolites, with upregulated pathways including butanoate metabolism and xylene degradation. Correlation analyses indicated strong associations between specific microbial taxa, metabolites, and meat quality traits. These findings suggest that AMIC enhances meat quality by modulating rumen microbial ecology and metabolic pathways, leading to improved nutrient deposition and flavor development. This study provides novel insights into the microbe–metabolite–muscle axis in ruminants and supports the use of AMIC as a dietary strategy for quality lamb production.

Ada Altieri

The study of ecological systems is gaining momentum in modern scientific research, driven by an abundance of empirical data and advancements in bioengineering techniques. However, a full understanding of their dynamical and thermodynamical properties, also in light of the ongoing biodiversity crisis, remains a formidable endeavor. From a theoretical standpoint, modeling the interactions within these complex systems -- such as bacteria in microbial communities, plant-pollinator networks in forests, or starling murmurations -- presents a significant challenge. Given the characteristic high dimensionality of the datasets, alternative elegant approaches employ random matrix formalism and techniques from disordered systems. In these lectures, we will explore two cornerstone models in theoretical ecology: the MacArthur/Resource-Consumer model, and the Generalized Lotka-Volterra model, with a special focus on systems composed of a large number of interacting species. In the second part, we will highlight timely directions, particularly to bridge the gap with empirical observations and detect macroecological patterns.

Marcelo B. Ribeiro

This paper presents an empirical application of the Gause-Witt model of population ecology and ecosystems to the income distribution competitive dynamics of social classes in economic systems. The Gause-Witt mathematical system of coupled nonlinear first-order ordinary differential equations employed to model population of species sharing the same ecological niche and competing for the same resources was applied to the income data of Brazil. Previous studies using Brazilian income data from 1981 to 2009 showed that the complementary cumulative distribution functions built from yearly datasets have two distinct segments: the lower income region comprising of about 99% of the population can be represented by the Gompertz curve, whereas the richest 1% is described by the Pareto power-law. The results of applying the Gause-Witt system to Brazilian income data in order to describe the distributive competition dynamics of these two population shares indicate that the 99% and 1% income classes are mostly in the dynamic state of stable coexistence.

David Peede, Trevor Cousins, Arun Durvasula et al.

Genomes contain the mutational footprint of an organism's evolutionary history, shaped by diverse forces including ecological factors, selective pressures, and life history traits. The sequentially Markovian coalescent (SMC) is a versatile and tractable model for the genetic genealogy of a sample of genomes, which captures this shared history. Methods that utilize the SMC, such as PSMC and MSMC, have been widely used in evolution and ecology to infer demographic histories. However, these methods ignore common biological features, such as gene flow events and structural variation. Recently, there have been several advancements that widen the applicability of SMC-based methods: inclusion of an isolation with migration model, integration with the multi-species coalescent, incorporation of ecological variables (such as selfing and dormancy), inference of dispersal rates, and many computational advances in applying these models to data. We give an overview of the SMC model and its various recent extensions, discuss examples of biological discoveries through SMC-based inference, and comment on the assumptions, benefits and drawbacks of various methods.

David L Miller, Ken Newman, Thomas Cornulier

Generalized additive models (GAMs) connecting a set of scalar covariates that map 1-1 to a response are commonly employed in ecology and beyond. However, covariates are often inherently non-scalar, taking multiple values for each observation of the response. They can sometimes have a temporal structure, e.g., a time series of temperatures, or a spatial structure, e.g., multiple soil pH measurements made at nearby locations. While aggregating or selectively summarizing such covariates to yield a scalar covariate allows the use of standard GAM fitting procedures, exactly how to do so can be problematic and information is necessarily lost. Naively including all $p$ components of a vector-valued covariate as $p$ separate covariates, say, without recognizing the structure, can lead to problems of multicollinearity, data sets that are excessively wide given the sample size, and difficulty extracting the primary signal provided by the covariate. Here we introduce three useful extensions to GAMs that efficiently and effectively handle vector-valued covariates without requiring one to choose aggregations or selective summarizations. These extensions are varying-coefficient, scalar-on-function and distributed lag models. While these models have existed for some time they remain relatively underused in ecology. This article aims to show when these models can be useful and how to fit them with the popular R package \mgcv{}.

Elizabeth T. Miller, R. Svanbäck, B. Bohannan

Interest in host-associated microbiomes has skyrocketed recently, yet our ability to explain microbiome variation has remained stubbornly low. Considering scales of interaction beyond the level of the individual host could lead to new insights. Metacommunity theory has many of the tools necessary for modeling multiscale processes and has been successfully applied to host microbiomes. However, the biotic nature of the host requires an expansion of theory to incorporate feedback between the habitat patch (host) and their local (microbial) community. This feedback can have unexpected effects, is predicted to be common, and can arise through a variety of mechanisms, including developmental, ecological, and evolutionary processes. We propose a new way forward for both metacommunity theory and host microbiome research that incorporates this feedback.

Laura Anthamatten, Philipp Rogalla von Bieberstein, Carmen Menzi et al.

Abstract Background The human gut microbiome produces and consumes a variety of compounds that interact with the host and impact health. Succinate is of particular interest as it intersects with both host and microbiome metabolism. However, which gut bacteria are most responsible for the consumption of intestinal succinate is poorly understood. Results We build upon an enrichment-based whole fecal sample culturing approach and identify two main bacterial taxa that are responsible for succinate consumption in the human intestinal microbiome, Phascolarctobacterium and Dialister. These two taxa have the hallmark of a functional guild and are strongly mutual exclusive across 21,459 fecal samples in 94 cohorts and can thus be used to assign a robust “succinotype” to an individual. We show that they differ with respect to their rate of succinate consumption in vitro and that this is associated with higher concentrations of fecal succinate. Finally, individuals suffering from inflammatory bowel disease (IBD) are more likely to have the Dialister succinotype compared to healthy subjects. Conclusions We identified that only two bacterial genera are the key succinate consumers in human gut microbiome, despite the fact that many more intestinal bacteria encode for the succinate pathway. This highlights the importance of phenotypic assays in functionally profiling intestinal microbiota. A stratification based on “succinotype” is to our knowledge the first function-based classification of human intestinal microbiota. The association of succinotype with IBD thus builds a bridge between microbiome function and IBD pathophysiology related to succinate homeostasis. Video Abstract

Mohammed-Younes Gueddari, Walid Hachem, Jamal Najim

Approximate Message Passing (AMP) algorithmshave recently gathered significant attention across disciplines such as statistical physics, machine learning, and communication systems. This study aims to extend AMP algorithms to non-symmetric (elliptic) matrices, motivated by analyzing equilibrium properties in ecological systems featuring elliptic interaction matrices.In this article, we provide the general form of an AMP algorithm associated to a random elliptic matrix, the main change lying in a modification of the corrective (Onsager) term. In order to establish the statistical properties of this algorithm, we use and prove a generalized form of Bolthausen conditioning argument, pivotal to proceed by a Gaussian-based induction.We finally address the initial motivating question from theoretical ecology. Large foodwebs are often described by Lotka-Volterra systems of coupled differential equations, where the interaction matrix is elliptic random. In this context, we design an AMP algorithm to analyze the statistical properties of the equilibrium point in a high-dimensional regime. We rigorously recover the results established by [Bunin, 2017] and [Galla,2018] who used techniques from theoretical physics, and extend them with the help of propagation of chaos type arguments.

Allison S. Zwarycz, David E. Whitworth

Myxobacteria prey upon a broad range of microorganisms. Lawn assays are commonly used to quantify myxobacterial predation—myxobacterial suspensions are spotted onto prey lawns, and monitored via spot expansion. The diversity in motility behaviours of myxobacterial strains and differing assay protocols in myxobacteriology laboratories led us to develop a highly-specified assay, which was applied to 28 myxobacterial strains preying on seven phytopathogenic prey species. Generally, prey organisms showed no qualitative differences in their susceptibility/resistance to myxobacterial predation. For most myxobacteria, prey did not stimulate, and in ~50% of cases actively hindered colony expansion. Only ~25% of predator/prey strain combinations exhibited greater colony expansion than in the absence of nutrients. The activity of predatory strains against different prey correlated, implying effective predators may have relatively non-specific predation mechanisms (e.g., broad specificity proteases/lipases), but no correlation was observed between predatory activity and phylogeny. Predation on dead (but intact) or lysed prey cells gave greater colony expansion than on live prey. Occasional strains grew substantially faster on dead compared to lysed cells, or vice-versa. Such differences in accessing nutrients from live, dead and lysed cells indicates there are strain-specific differences in the efficiencies/machineries of prey killing and nutrient acquisition, which has important implications for the ecology of myxobacterial predators and their prey.

Kevin S. Myers, Kevin S. Myers, Abel T. Ingle et al.

The liquid residue resulting from various agroindustrial processes is both rich in organic material and an attractive source to produce a variety of chemicals. Using microbial communities to produce chemicals from these liquid residues is an active area of research, but it is unclear how to deploy microbial communities to produce specific products from the different agroindustrial residues. To address this, we fed anaerobic bioreactors one of several agroindustrial residues (carbohydrate-rich lignocellulosic fermentation conversion residue, xylose, dairy manure hydrolysate, ultra-filtered milk permeate, and thin stillage from a starch bioethanol plant) and inoculated them with a microbial community from an acid-phase digester operated at the wastewater treatment plant in Madison, WI, United States. The bioreactors were monitored over a period of months and sampled to assess microbial community composition and extracellular fermentation products. We obtained metagenome assembled genomes (MAGs) from the microbial communities in each bioreactor and performed comparative genomic analyses to identify common microorganisms, as well as any community members that were unique to each reactor. Collectively, we obtained a dataset of 217 non-redundant MAGs from these bioreactors. This metagenome assembled genome dataset was used to evaluate whether a specific microbial ecology model in which medium chain fatty acids (MCFAs) are simultaneously produced from intermediate products (e.g., lactic acid) and carbohydrates could be applicable to all fermentation systems, regardless of the feedstock. MAGs were classified using a multiclass classification machine learning algorithm into three groups, organisms fermenting the carbohydrates to intermediate products, organisms utilizing the intermediate products to produce MCFAs, and organisms producing MCFAs directly from carbohydrates. This analysis revealed common biological functions among the microbial communities in different bioreactors, and although different microorganisms were enriched depending on the agroindustrial residue tested, the results supported the conclusion that the microbial ecology model tested was appropriate to explain the MCFA production potential from all agricultural residues.

Beat Frey, Gilda Varliero, Gilda Varliero et al.

Soil microorganisms such as Bacteria and Archaea play important roles in the biogeochemical cycling of soil nutrients, because they act as decomposers or are mutualistic or antagonistic symbionts, thereby influencing plant growth and health. In the present study, we investigated the vertical distribution of soil metagenomes to a depth of 1.5 m in Swiss forests of European beech and oak species on calcareous bedrock. We explored the functional genetic potential of soil microorganisms with the aim to disentangle the effects of tree genus and soil depth on the genetic repertoire, and to gain insight into the microbial C and N cycling. The relative abundance of reads assigned to taxa at the domain level indicated a 5–10 times greater abundance of Archaea in the deep soil, while Bacteria showed no change with soil depth. In the deep soil there was an overrepresentation of genes for carbohydrate-active enzymes, which are involved in the catalyzation of the transfer of oligosaccharides, as well as in the binding of carbohydrates such as chitin or cellulose. In addition, N-cycling genes (NCyc) involved in the degradation and synthesis of N compounds, in nitrification and denitrification, and in nitrate reduction were overrepresented in the deep soil. Consequently, our results indicate that N-transformation in the deep soil is affected by soil depth and that N is used not only for assimilation but also for energy conservation, thus indicating conditions of low oxygen in the deep soil. Using shotgun metagenomics, our study provides initial findings on soil microorganisms and their functional genetic potential, and how this may change depending on soil properties, which shift with increasing soil depth. Thus, our data provide novel, deeper insight into the “dark matter” of the soil.

Emre Anakok, Pierre Barbillon, Colin Fontaine et al.

The structure of a bipartite interaction network can be described by providing a clustering for each of the two types of nodes. Such clusterings are outputted by fitting a Latent Block Model (LBM) on an observed network that comes from a sampling of species interactions in the field. However, the sampling is limited and possibly uneven. This may jeopardize the fit of the LBM and then the description of the structure of the network by detecting structures which result from the sampling and not from actual underlying ecological phenomena. If the observed interaction network consists of a weighted bipartite network where the number of observed interactions between two species is available, the sampling efforts for all species can be estimated and used to correct the LBM fit. We propose to combine an observation model that accounts for sampling and an LBM for describing the structure of underlying possible ecological interactions. We develop an original inference procedure for this model, the efficiency of which is demonstrated on simulation studies. The pratical interest in ecology of our model is highlighted on a large dataset of plant-pollinator network.

Ruber Rodríguez-Barreras, Eduardo L. Tosado-Rodríguez, Filipa Godoy-Vitorino

Sea urchins play a critical role in marine ecosystems, as they actively participate in maintaining the balance between coral and algae. We performed the first in-depth survey of the microbiota associated with four free-living populations of Caribbean sea urchins: Lytechinus variegatus, Echinometra lucunter, Tripneustes ventricosus, and Diadema antillarum. We compared the influence of the collection site, echinoid species and trophic niche to the composition of the microbiota. This dataset provides a comprehensive overview to date, of the bacterial communities and their ecological relevance associated with sea urchins in their natural environments. A total of sixty-samples, including surrounding reef water and seagrass leaves underwent 16S rRNA gene sequencing (V4 region) and high-quality reads were analyzed with standard bioinformatic approaches. While water and seagrass were dominated by Cyanobacteria such as Prochlorococcus and Rivularia respectively, echinoid gut samples had dominant Bacteroidetes, Proteobacteria and Fusobacteria. Propionigenium was dominant across all species’ guts, revealing a host-associated composition likely responsive to the digestive process of the animals. Beta-diversity analyses showed significant differences in community composition among the three collection sites, animal species, and trophic niches. Alpha diversity was significantly higher among L. variegatus samples compared to the other species. L. variegatus also displayed an increased abundance of Planctomycetes and Cyanobacterial OTUs. The bacterial community of this herbivorous echinoid reflected similarities to the microfilm community found on Thalassia testudinum leaves; a very abundant seagrass and its main food resource. The results of this study elaborate on the microbial ecology of four important Caribbean echinoids, confirming that selection on the microbial community is trophic-niche dependent.

Laura T. Leonard, Eoin L. Brodie, Kenneth H. Williams et al.

Increased drought and temperatures associated with climate change have implications for ecosystem stress with risk for enhanced carbon release in sensitive biomes. Litter decomposition is a key component of biogeochemical cycling in terrestrial ecosystems, but questions remain regarding the local response of decomposition processes to climate change. This is particularly complex in mountain ecosystems where the variable nature of the slope, aspect, soil type, and snowmelt dynamics play a role. Hence, the goal of this study was to determine the role of elevation, soil type, seasonal shifts in soil moisture, and snowmelt timing on litter decomposition processes. Experimental plots containing replicate deployments of harvested lodgepole and spruce needle litter alongside needle-free controls were established in open meadows at three elevations ranging from 2,800–3,500 m in Crested Butte, Colorado. Soil biogeochemistry variables including gas flux, porewater chemistry, and microbial ecology were monitored over three climatically variable years that shifted from high monsoon rains to drought. Results indicated that elevation and soil type influenced baseline soil biogeochemical indicators; however, needle mass loss and chemical composition were consistent across the 700 m elevation gradient. Rates of gas flux were analogously consistent across a 300 m elevation gradient. The additional variable of early snowmelt by 2–3 weeks had little impact on needle chemistry, microbial composition and gas flux; however, it did result in increased dissolved organic carbon in lodgepole porewater collections suggesting a potential for aqueous export. In contrast to elevation, needle presence and seasonal variability of soil moisture and temperature both played significant roles in soil carbon fluxes. During a pronounced period of lower moisture and higher temperatures, bacterial community diversity increased across elevation with new members supplanting more dominant taxa. Microbial ecological resilience was demonstrated with a return to pre-drought structure and abundance after snowmelt rewetting the following year. These results show similar decomposition processes across a 700 m elevation gradient and reveal the sensitivity but resilience of soil microbial ecology to low moisture conditions.

Benjamin Pontiller, Clara Pérez-Martínez, Carina Bunse et al.

ABSTRACT A considerable fraction of organic matter derived from photosynthesis in the euphotic zone settles into the ocean’s interior and, as it progresses, is degraded by diverse microbial consortia that utilize a suite of extracellular enzymes and membrane transporters. Still, the molecular details that regulate carbon cycling across depths remain little explored. As stratification in fjords has made them attractive models to explore patterns in biological oceanography, we here analyzed bacterial and archaeal transcription in samples from five depth layers in the Gullmar Fjord, Sweden. Transcriptional variation over depth correlated with gradients in chlorophyll a and nutrient concentrations. Differences in transcription between sampling dates (summer and early autumn) were strongly correlated with ammonium concentrations, which potentially was linked with a stronger influence of (micro-)zooplankton grazing in summer. Transcriptional investment in carbohydrate-active enzymes (CAZymes) decreased with depth and shifted toward peptidases, partly a result of elevated CAZyme transcription by Flavobacteriales, Cellvibrionales, and Synechococcales at 2 to 25 m and a dominance of peptidase transcription by Alteromonadales and Rhodobacterales from 50 m down. In particular, CAZymes for chitin, laminarin, and glycogen were important. High levels of transcription of ammonium transporter genes by Thaumarchaeota at depth (up to 18% of total transcription), along with the genes for ammonia oxidation and CO2 fixation, indicated that chemolithoautotrophy contributed to the carbon flux in the fjord. The taxon-specific expression of functional genes for processing of the marine pool of dissolved organic matter and inorganic nutrients across depths emphasizes the importance of different microbial foraging mechanisms over spatiotemporal scales for shaping biogeochemical cycles. IMPORTANCE It is generally recognized that stratification in the ocean strongly influences both the community composition and the distribution of ecological functions of microbial communities, which in turn are expected to shape the biogeochemical cycling of essential elements over depth. Here, we used metatranscriptomics analysis to infer molecular detail on the distribution of gene systems central to the utilization of organic matter in a stratified marine system. We thereby uncovered that pronounced shifts in the transcription of genes encoding CAZymes, peptidases, and membrane transporters occurred over depth among key prokaryotic orders. This implies that sequential utilization and transformation of organic matter through the water column is a key feature that ultimately influences the efficiency of the biological carbon pump.

Zhenyu Wang, Yu Bai, Yu Pi et al.

Abstract Background Low dietary fiber intake has been shown to disturb the gut microbiome community, damage the mucus barrier, and promote pathogen susceptibility. However, little is known about the temporal response of the gut microbiome to dietary fiber deprivation and the recovery induced by dietary fiber inclusion in pigs. Objective In the present study, temporal responses of ileal and fecal microbiota to dietary fiber deprivation were profiled using an ileum cannulated growing pig model. In addition, the potential of dietary-resistant starch, β-glucan, and xylan to alleviate gut dysbiosis throughout the gastrointestinal tract, as well as its possible mechanisms were investigated. Methods Six cannulated growing pigs were fed a fiber deprivation diet for 35 days. Ileal digesta and feces were collected at days 0, 7, 21, and 35 for 16S rRNA sequencing and short-chain fatty acid (SCFA) determination. Another twenty-four healthy growing pigs were assigned to one of four dietary treatments including (1) fiber-free diet, (2) resistant starch diet, (3) β-glucan diet, and (4) xylan diet. These twenty-four pigs were fed a corresponding diet for 35 days and slaughtered. Gut microbiome and SCFA concentration were profiled along the gastrointestinal tract. Results Dietary fiber deprivation-induced consistent microbiota extinction, mainly Bifidobacterium and Lactobacillus, and decreased SCFA concentrations in both ileum and feces. The community structure partially recovered at day 35 compared with baseline while SCFA concentrations remained low. Xylan supplementation alleviated gut dysbiosis by selectively promoting Bifidobacterium pseudocatenulatum within the large intestine. SCFA concentration increased significantly after xylan supplementation and exhibited a positive association with B. pseudocatenulatum abundance. An elevated abundance of xylan degradation-related enzyme genes was also observed in the gut microbiome after xylan supplementation. In vitro growth assay further verified the xylan utilization capacity of B. pseudocatenulatum. Conclusions Dietary fiber deprivation could induce probiotic extinction and loss of the SCFA production while potential pathogen was promoted. Xylan intervention could partially restore dietary fiber deprivation-induced gut dysbiosis through selectively promoting B. pseudocatenulatum and therefore normalizing the gut environment. These findings collectively provide evidence that dietary fiber-driven microbiota metabolism bridges the interplay between microbiome and gut health. Video abstract