Hasil untuk "Microbial ecology"

Gregory J. Cooke, Nikku Madhusudhan, Emily G. Mitchell

New observations are opening the possibility of characterising habitable environments in exoplanetary systems, with the recent example of the candidate hycean world K2-18 b. This motivates an exploration of the possible ecological conditions on such planets to better interpret biosignatures as well as understand the nature of potential life. On Earth, the Lotka-Volterra equations have been used to model numerous coupled populations within ecosystems, from interactions between large vertebrates, to systems with multiple microbial species. In this work, we apply the Lotka-Volterra equations to the ecology of habitable exoplanets for the first time, focusing on hycean worlds. We simulate scenarios in a vertical water column with between 1-5 bacterial species that thrive in anoxic environments on Earth, i.e. similar to predicted hycean conditions. We find that a wide range of ecological diversity is possible for microbial populations under hycean conditions. We demonstrate that dominating phototrophic bacteria at the top of a water column out-compete deeper dwelling phototrophic bacteria, analogous to bacterial blooms on Earth. Incorporating microbial viruses (bacteriophages) within our models can cause ecosystem collapse depending on the time of their introduction, and such phage inclusion can be beneficial to ecological diversity. Finally, our work shows that bacterial populations inhabiting tidally locked exoplanets may be more stable due to constant illumination of the ocean, but can have lower peak population densities in such cases when compared to seasonal scenarios. Our work provides an initial step towards understanding the possible ecological diversity on habitable worlds beyond Earth.

Viktoria Freingruber, Rebeca Gonzalez-Cabaleiro, Havva Yoldaş

We present a mathematical model based on a system of partial differential equations (PDEs) with cross-diffusion and reaction terms to describe ecological interactions between multiple bacterial species and substrates within microaggregates, where bacteria proliferate in response to substrate availability and undergo passive dispersal driven by population pressure gradients. The ecological interactions include interspecific competition for shared substrates, and commensalism, whereby one species benefits from the metabolic by-products of another. The main motivation comes from individual-based models (IBMs) of microbial aggregates, where simulations reveal that substrate-limited conditions can give rise to rich spatial patterns. Our numerical experiments demonstrate that our PDE-based model captures the key qualitative features of three verification scenarios that have previously been investigated with IBMs. Moreover, we formally derive a competition system from an on-lattice biased random walk, and establish local well-posedness for a parameter-symmetric subcase of it. We then formally analyse the travelling wave behaviour of this case in one spatial dimension and compare the minimal travelling wave speed with the wave speed measured in the simulations.

Haoran Shi, Yue Wang, Dan Cheng

The gut microbiome plays a crucial role in human health, yet the mechanisms underlying host-microbiome interactions remain unclear, limiting its translational potential. Recent microbiome multiomics studies, particularly paired microbiome-metabolome studies (PM2S), provide valuable insights into gut metabolism as a key mediator of these interactions. Our preliminary data reveal strong correlations among certain gut metabolites, suggesting shared metabolic pathways and microbial co-metabolism. However, these findings are confounded by various factors, underscoring the need for a more rigorous statistical approach. Thus, we introduce microbial correlation, a novel metric that quantifies how two metabolites are co-regulated by the same gut microbes while accounting for confounders. Statistically, it is based on a partially linear model that isolates microbial-driven associations, and a consistent estimator is established based on semi-parametric theory. To improve efficiency, we develop a calibrated estimator with a parametric rate, maximizing the use of large external metagenomic datasets without paired metabolomic profiles. This calibrated estimator also enables efficient p-value calculation for identifying significant microbial co-metabolism signals. Through extensive numerical analysis, our method identifies important microbial co-metabolism patterns for healthy individuals, serving as a benchmark for future studies in diseased populations.

Elham Gholamzadeh, Kajal Singla, Nico Scherf

Predicting interspecies interactions is a key challenge in microbial ecology, as these interactions are critical to determining the structure and activity of microbial communities. In this work, we used data on monoculture growth capabilities, interactions with other species, and phylogeny to predict a negative or positive effect of interactions. More precisely, we used one of the largest available pairwise interaction datasets to train our models, comprising over 7,500 interactions be- tween 20 species from two taxonomic groups co-cultured under 40 distinct carbon conditions, with a primary focus on the work of Nestor et al.[28 ]. In this work, we propose Graph Neural Networks (GNNs) as a powerful classifier to predict the direction of the effect. We construct edge-graphs of pairwise microbial interactions in order to leverage shared information across individual co-culture experiments, and use GNNs to predict modes of interaction. Our model can not only predict binary interactions (positive/negative) but also classify more complex interaction types such as mutualism, competition, and parasitism. Our initial results were encouraging, achieving an F1-score of 80.44%. This significantly outperforms comparable methods in the literature, including conventional Extreme Gradient Boosting (XGBoost) models, which reported an F1-score of 72.76%.

Hao‐Jun Chen, Yue Liu, Yun‐Shan Zhong et al.

ABSTRACT Surfactin, a lipopeptide antibiotic and quorum‐sensing (QS) mediator from Bacillus subtilis, has dual functions in microbial ecology and plant disease suppression. This study engineered B. subtilis R31 to overproduce comK and phrC, key regulators of surfactin biosynthesis, increasing surfactin yield by 45% compared to the WT strain. While elevated surfactin enhanced antimicrobial potential, comK‐mediated overproduction impaired biofilm formation and swarming motility, but rhizosphere colonisation was mostly unaffected. 16S rRNA sequencing of banana rhizospheres showed that surfactin selectively shaped the microbial community by enriching beneficial Bacillus species. Mechanistic studies confirmed surfactin's dual role as an antimicrobial and an intercellular signalling molecule for coordinated development in Bacillus populations. These results reveal the molecular mechanisms of R31‐mediated suppression of banana Fusarium wilt and offer a strategy for engineering synthetic microbial consortia by manipulating metabolic signalling pathways.

Zhi Ting Yip, Zheng Bin Randolph Quek, Danwei Huang

ABSTRACT Environmental DNA (eDNA) provides a powerful means of monitoring biodiversity, offering high taxonomic resolution and broad spatial coverage beyond traditional methods. To characterize ecological communities, it is critical to understand shifts in species composition through time to potentially differentiate resident from transient species in the studied habitats. This study used eDNA metabarcoding to examine temporal and spatial patterns of α‐ and β‐diversity across three distinct habitat types (sandy, rocky, and mangrove) at four coastal sites in Singapore over 1 year. We targeted invertebrates using the cytochrome oxidase subunit I (COI) gene and vertebrates using the 16S rRNA gene. We recorded lower diversity at nature reserves, which harbor more rare species than unprotected habitats. β‐diversity differed significantly by site and time for both markers, though β‐dispersion generally remained consistent over time within sites for both invertebrate and vertebrate communities. The difference in marine metazoan communities was driven by high spatial and temporal turnover without strong directional trends across Singapore's coastal sites. These patterns reflect distinct, cohesive communities with limited seasonality, characteristic of equatorial climates. However, certain taxa showed monsoon‐associated distributions, except in mangrove habitats. Importantly, we suggest more mid‐ to long‐term surveys to elucidate the community of resident species. Our findings highlight the value of using eDNA methods to identify dynamic biodiversity patterns and support its use in long‐term ecological monitoring and conservation planning.

James C. Kosmopoulos, Karthik Anantharaman

The explosion in known microbial diversity in the last two decades has made it abundantly clear that microbes in the environment do not exist in isolation; they are members of communities. Accordingly, omics approaches such as metagenomics have revealed that interactions between diverse groups of community members such as archaea, bacteria, and viruses (bacteriophage) are common and have significant impacts on entire microbiomes. Thus, to have a well-developed understanding of microbes as they naturally exist in the environment, biological entities of all kinds must be studied together. While numerous protocols for metagenome analysis exist, comprehensive published protocols for the simultaneous analysis of viruses and prokaryotes together are scarce. Further, as bioinformatic methods for microbiology rapidly advance, existing metagenomic tools and pipelines require frequent reevaluation. This ensures the adherence of best practices for microbiome and metagenomic data analysis. Here, we offer an expansive approach for the joint analysis of bulk sequence data from a mixed microbial community (metagenomes) and viral-sized fraction communities (viromes). This chapter serves as a beginner's-level guide for researchers with limited bioinformatics expertise who wish to engage in multi-scale metagenome and virome analyses. We cover steps from initial study design to sequence read processing, metagenome assembly, quality control, virus identification, microbial and viral genome binning, taxonomic characterization, species-level clustering, and host-virus predictions. We also provide the bioinformatic scripts used in our workflow for reuse in one's own computational methods. Lastly, we discuss additional approaches a researcher can take after processing data with this workflow.

Betul Kacar

For more than 3.5 billion years, life experienced dramatic environmental extremes on Earth. These include shifts from oxygen-less to over-oxygenated atmospheres and cycling between hothouse conditions and global glaciations. Meanwhile, an ecological revolution took place. The planet evolved from one dominated by microbial life to one containing the plants and animals that are most familiar today. The activities of many key cellular inventions evolved early in the history of life, collectively defining the nature of our biosphere and underpinning human survival. There is a critical need for a new disciplinary synthesis to reveal how microbes and their molecular systems survived ever changing global conditions over deep time. This review critically examines our current understanding of early microbial life and describes the foundations of an emerging area in microbiology and evolutionary synthetic biology to reconstruct the earliest microbial innovations.

Beatrice Tauer, Elizabeth Trembath-Reichert, L. M. Ward

The rise of complex multicellular ecosystems Neoproterozoic time was preceded by a microbial Proterozoic biosphere, where productivity may have been largely restricted to microbial mats made up of bacteria including oxygenic photosynthetic Cyanobacteria, anoxygenic phototrophs, and heterotrophs. In modern environments, analogous microbial mats can be found in restricted environments such as carbonate tidal flats and terrestrial hot springs. Here, we report metagenomic sequence data from an analog in the hot springs of Waikite Valley, Aotearoa New Zealand, where carbon-rich, slightly-alkaline geothermal waters support diverse phototrophic microbial mats. The Waikite Valley hot spring in the Taupo Volcanic Zone of Aotearoa New Zealand was sampled in duplicate at 8 points along a temperature gradient transect of the outflow, from ~62 C (near the source) to ~37 C (~100 meters downstream). ~686 Gb of shotgun metagenomic sequence data was generated by Illumina Novaseq. Each sample was assembled using SPAdes, followed by binning of metagenome-assembled genomes (MAGs) by MetaBAT. These data are useful for the genomic analysis of novel phototrophic bacteria, as well as for ecological comparisons between thermophilic communities with varying temperatures but otherwise similar conditions.

Orges Koci, Richard K. Russell, M. Guftar Shaikh et al.

Abstract Background Shotgun metagenomics for microbial community survey recovers enormous amount of information for microbial genomes that include their abundances, taxonomic, and phylogenetic information, as well as their genomic makeup, the latter of which then helps retrieve their function based on annotated gene products, mRNA, protein, and metabolites. Within the context of a specific hypothesis, additional modalities are often included, to give host-microbiome interaction. For example, in human-associated microbiome projects, it has become increasingly common to include host immunology through flow cytometry. Whilst there are plenty of software approaches available, some that utilize marker-based and assembly-based approaches, for downstream statistical analyses, there is still a dearth of statistical tools that help consolidate all such information in a single platform. By virtue of stringent computational requirements, the statistical workflow is often passive with limited visual exploration. Results In this study, we have developed a Java-based statistical framework ( https://github.com/KociOrges/cviewer ) to explore shotgun metagenomics data, which integrates seamlessly with conventional pipelines and offers exploratory as well as hypothesis-driven analyses. The end product is a highly interactive toolkit with a multiple document interface, which makes it easier for a person without specialized knowledge to perform analysis of multiomics datasets and unravel biologically relevant patterns. We have designed algorithms based on frequently used numerical ecology and machine learning principles, with value-driven from integrated omics tools which not only find correlations amongst different datasets but also provide discrimination based on case–control relationships. Conclusions CViewer was used to analyse two distinct metagenomic datasets with varying complexities. These include a dietary intervention study to understand Crohn’s disease changes during a dietary treatment to include remission, as well as a gut microbiome profile for an obesity dataset comparing subjects who suffer from obesity of different aetiologies and against controls who were lean. Complete analyses of both studies in CViewer then provide very powerful mechanistic insights that corroborate with the published literature and demonstrate its full potential. Video Abstract

Wendy Lorena Reyes-Ardila, Paula Andrea Rugeles-Silva, Juan Diego Duque-Zapata et al.

<i>Bidens pilosa</i> L., native to South America and commonly used for medicinal purposes, has been understudied at molecular and genomic levels and in its relationship with soil microorganisms. In this study, restriction site-associated DNA markers (RADseq) techniques were implemented to analyze genetic diversity and population structure, and metabarcoding to examine microbial composition in soils from Palmira, Sibundoy, and Bogotá, Colombia. A total of 2,984,123 loci and 3485 single nucleotide polymorphisms (SNPs) were identified, revealing a genetic variation of 12% between populations and 88% within individuals, and distributing the population into three main genetic groups, F_ST = 0.115 (<i>p</i> < 0.001) and F_IT = 0.013 (<i>p</i> > 0.05). In the soil analysis, significant correlations were found between effective cation exchange capacity (ECEC) and apparent density, soil texture, and levels of Mg and Fe, as well as negative correlations between ECEC and Mg, and Mg, Fe, and Ca. Proteobacteria and Ascomycota emerged as the predominant bacterial and fungal phyla, respectively. Analyses of alpha, beta, and multifactorial diversity highlight the influence of ecological and environmental factors on these microbial communities, revealing specific patterns of clustering and association between bacteria and fungi in the studied locations.

Fatai A. Olabemiwo, Claudia Kunney, Rachel Hsu et al.

IntroductionPlastic pollution has surged due to increased human consumption and disposal of plastic products. Microbial communities capable of utilizing plastic as a carbon source may play a crucial role in degrading and consuming environmental plastic. In this study, we investigated the potential of a modified Winogradsky column (WC) to enrich Connecticut landfill soil for plastic-degrading bacteria and genes.MethodsBy filling WCs with landfill soil and inorganic Bushnell Haas medium, and incorporating polyethylene (PE) strips at different soil layers, we aimed to identify bacterial taxa capable of degrading PE. We employed high-throughput 16S rRNA sequencing to identify the microbes cultivated on the plastic strips and the intervening landfill soil. We used PICRUSt2 to estimate the functional attributes of each community from 16S rRNA sequences.Results and discussionAfter 12 months of incubation, distinct colors were observed along the WC layers, indicating successful cultivation. Sequencing revealed significant differences in bacterial communities between the plastic strips and the intervening landfill-soil habitats, including increased abundance of the phyla Verrucomicrobiota and Pseudomonadota (néé Proteobacteria) on the strips. Based on inferred genomic content, the most highly abundant proteins in PE strip communities tended to be associated with plastic degradation pathways. Phylogenetic analysis of 16S rRNA sequences showed novel unclassified phyla and genera enriched on the plastic strips. Our findings suggest PE-supplemented Winogradsky columns can enrich for plastic-degrading microbes, offering insights into bioremediation strategies.

Alann Caderhoussin, David Couvin, Gaëlle Gruel et al.

IntroductionThis study aimed to understand the origin and to explain the maintenance of extended-spectrum β-lactamase (ESBL) Enterobacteriaceae isolated from food-producing animals in a third-generation cephalosporin (3GC)-free farm.MethodsCulture and molecular approaches were used to test molecules other than 3GC such as antibiotics (tetracycline and oxytetracycline), antiparasitics (ivermectin, flumethrin, fenbendazol, and amitraz), heavy metal [arsenic, HNO3, aluminum, HNO3, cadmium (CdSO4), zinc (ZnCl2), copper (CuSO4), iron (FeCl3), and aluminum (Al2SO4)], and antioxidant (butylated hydroxytoluene) as sources of selective pressure. Whole-genome sequencing using short read (Illumina™) and long read (Nanopore™) technologies was performed on 34 genomes. In silico gene screening and comparative analyses were used to characterize the genetic determinants of resistance, their mobility, and the genomic relatedness among isolates.ResultsOur analysis unveiled a low diversity among the animal ESBL-producing strains. Notably, E. coli ST3268 was recurrently isolated from both flies (n = 9) and cattle (n = 5). These E. coli ST3268/blaCTX-M-15/blaTEM-1B have accumulated multiple plasmids and genes, thereby representing a reservoir of resistance and virulence factors. Our findings suggest that flies could act as effective mechanical vectors for antimicrobial gene transfer and are capable of transporting resistant bacteria across different environments and to multiple hosts, facilitating the spread of pathogenic traits. A significantly higher mean minimum inhibitory concentration of oxytetracycline (841.4 ± 323.5 mg/L vs. 36.0 ± 52.6 mg/L, p = 0.0022) in ESBL E. coli than in non-ESBL E. coli and blaCTX-M-15 gene overexpression in oxytetracycline-treated vs. untreated ESBL E. coli (RQOxy = 3.593, p = 0.024) confirmed oxytetracycline as a source of selective pressure in ESBL E. coli.DiscussionThe occurrence of ESBL E. coli in a farm without 3GC use is probably due to an as yet undefined human origin of Enterobacteriaceae blaCTX-M-15 gene transmission to animals in close contact with cattle farm workers and the maintenance of the local ESBL E. coli reservoir by a high fly diversity and oxytetracycline selective pressure. These findings highlight the critical need for stringent vector control to mitigate antimicrobial resistance spread for preserving public health. Addressing this issue necessitates a multifaceted approach combining microbial genetics, vector ecology, and farm management practices.

William Krinsman

An active area of research interest is the inference of ecological models of complex microbial communities. Inferring such ecological models entails understanding the interactions between microbes and how they affect each other's growth. This dissertation employs a statistical perspective to contribute further to the knowledge currently addressing this problem. Part I explains how high-throughput droplet-based microfluidics technology can be used to screen for microbial interactions. An explicit, statistical framework is motivated and developed that can guide the analysis of data from such experiments. Part II explains how it might be possible to predict, based on the experimental setup, how much data will be produced to infer given microbial interactions. Running the experiment once without incubating the droplets turns out to be necessary to make such predictions. Part III demonstrates the feasibility of inferring microbial interactions from the data produced by these experiments. Relevant ideas from the microbiological and ecological literature are recast into an explicit, statistical framework.

Anthony Duncan, Kerrie Barry, Chris Daum et al.

Abstract Background Phytoplankton communities significantly contribute to global biogeochemical cycles of elements and underpin marine food webs. Although their uncultured genomic diversity has been estimated by planetary-scale metagenome sequencing and subsequent reconstruction of metagenome-assembled genomes (MAGs), this approach has yet to be applied for complex phytoplankton microbiomes from polar and non-polar oceans consisting of microbial eukaryotes and their associated prokaryotes. Results Here, we have assembled MAGs from chlorophyll a maximum layers in the surface of the Arctic and Atlantic Oceans enriched for species associations (microbiomes) with a focus on pico- and nanophytoplankton and their associated heterotrophic prokaryotes. From 679 Gbp and estimated 50 million genes in total, we recovered 143 MAGs of medium to high quality. Although there was a strict demarcation between Arctic and Atlantic MAGs, adjacent sampling stations in each ocean had 51–88% MAGs in common with most species associations between Prasinophytes and Proteobacteria. Phylogenetic placement revealed eukaryotic MAGs to be more diverse in the Arctic whereas prokaryotic MAGs were more diverse in the Atlantic Ocean. Approximately 70% of protein families were shared between Arctic and Atlantic MAGs for both prokaryotes and eukaryotes. However, eukaryotic MAGs had more protein families unique to the Arctic whereas prokaryotic MAGs had more families unique to the Atlantic. Conclusion Our study provides a genomic context to complex phytoplankton microbiomes to reveal that their community structure was likely driven by significant differences in environmental conditions between the polar Arctic and warm surface waters of the tropical and subtropical Atlantic Ocean. Video Abstract.

Pankaj Mehta, Robert Marsland

Recent work suggests that cross-feeding -- the secretion and consumption of metabolic biproducts by microbes -- is essential for understanding microbial ecology. Yet how cross-feeding and competition combine to give rise to ecosystem-level properties remains poorly understood. To address this question, we analytically analyze the Microbial Consumer Resource Model (MiCRM), a prominent ecological model commonly used to study microbial communities. Our mean-field solution exploits the fact that unlike replicas, the cavity method does not require the existence of a Lyapunov function. We use our solution to derive new species-packing bounds for diverse ecosystems in the presence of cross-feeding, as well as simple expressions for species richness and the abundance of secreted resources as a function of cross-feeding (metabolic leakage) and competition. Our results show how a complex interplay between competition for resources and cooperation resulting from metabolic exchange combine to shape the properties of microbial ecosystems.

Tancredi Caruso, Giulio Virginio Clemente, Matthias C Rillig et al.

Ecological networks such as plant-pollinator systems and food webs vary in space and time. This variability includes fluctuations in global network properties such as total number and intensity of interactions but also in the local properties of individual nodes such as the number and intensity of species-level interactions. Fluctuations of species properties can significantly affect higher-order network features, e.g. robustness and nestedness. Local fluctuations should therefore be controlled for in applications that rely on null models, especially pattern and perturbation detection. By contrast, most randomization methods for null models used by ecologists treat node-level local properties as hard constraints that cannot fluctuate. Here, we synthesise a set of methods that resolves the limit of hard constraints and is based on statistical mechanics. We illustrate the methods with some practical examples making available open source computer codes. We clarify how this approach can be used by experimental ecologists to detect non-random network patterns with null models that not only rewire but also redistribute interaction strengths by allowing fluctuations in the null model constraints (soft constraints). Null modelling of species heterogeneity through local fluctuations around typical topological and quantitative constraints offers a statistically robust and expanded (e.g. quantitative null models) set of tools to understand the assembly and resilience of ecological networks.

Débora Princepe, Marcus A. M. de Aguiar, Joshua B. Plotkin

Mitochondrial and nuclear genomes must be co-adapted to ensure proper cellular respiration and energy production. Mito-nuclear incompatibility reduces individual fitness and induces hybrid infertility, suggesting a possible role in reproductive barriers and speciation. Here we develop a birth-death model for evolution in spatially extended populations under selection for mito-nuclear co-adaptation. Mating is constrained by physical and genetic proximity, and offspring inherit nuclear genomes from both parents, with recombination. The model predicts macroscopic patterns including a community's long-term species diversity, its species abundance distribution, speciation and extinction rates, as well as intra- and inter-specific genetic variation. We explore how these long-term outcomes depend upon the microscopic parameters of reproduction: individual fitness governed by mito-nuclear compatibility, constraints on mating compatibility, and ecological carrying capacity. We find that strong selection for mito-nuclear compatibility reduces the equilibrium number of species after a radiation, increases the species' abundances, while simultaneously increasing both speciation and extinction rates. The negative correlation between species diversity and diversification rates in our model agrees with the broad empirical pattern of lower species diversity and higher speciation/extinction rates in temperate regions, compared to the tropics. We therefore suggest that these empirical patterns may be caused in part by latitudinal variation in metabolic demands, and corresponding variation in selection on mito-nuclear function.

Anusha Telagathoti, Maraike Probst, Iuliia Khomenko et al.

In ecology, Volatile Organic Compounds (VOCs) have a high bioactive and signaling potential. VOCs are not only metabolic products, but are also relevant in microbial cross talk and plant interaction. Here, we report the first large-scale VOC study of 13 different species of <i>Mortierella sensu lato (s.</i><i>l.</i>) isolated from a range of different alpine environments. Proton Transfer Reaction–Time-of-Flight Mass Spectrometry (PTR–ToF–MS) was applied for a rapid, high-throughput and non-invasive VOC fingerprinting of 72 <i>Mortierella s.</i><i>l.</i> isolates growing under standardized conditions. Overall, we detected 139 mass peaks in the headspaces of all 13 <i>Mortierella s.</i><i>l.</i> species studied here. Thus, <i>Mortierella</i><i>s.</i><i>l.</i> species generally produce a high number of different VOCs. <i>Mortierella</i> species could clearly be discriminated based on their volatilomes, even if only high-concentration mass peaks were considered. The volatilomes were partially phylogenetically conserved. There were no VOCs produced by only one species, but the relative concentrations of VOCs differed between species. From a univariate perspective, we detected mass peaks with distinctively high concentrations in single species. Here, we provide initial evidence that VOCs may provide a competitive advantage and modulate <i>Mortierella s.</i><i>l.</i> species distribution on a global scale.

Anupam Sengupta

Topology transcends boundaries that conventionally delineate physical, biological and engineering sciences. Our ability to mathematically describe topology, combined with our access to precision tracking and manipulation approaches, has triggered a fresh appreciation of topological ramifications, specifically in mediating key functions in biological systems spanning orders of magnitude in length and time scales. Microbial ecosystems, a frequently encountered example of living matter, offer a rich test bed where the role of topological defects and their mechanics can be explored in the context of microbial composition, structure and functions. Emergent processes, triggered by anisotropy and activity characteristic of such structured, out-of-equilibrium systems, underpin fundamental properties in microbial systems. An inevitable consequence of anisotropy is the long-range orientational (or positional) correlations, which give rise to topological defects nucleating due to spontaneous symmetry breaking. The scene stealer of this emerging cross-disciplinary field is the topological defects: singularities embedded within the material field that elicit novel, if not unexpected, dynamics that are at the heart of active processes underpinning soft and living matter systems. In this short review, I have put together a summary of the key recent advances that highlight the interface of liquid crystal physics and the physical ecology of microbes; and combined it with original experimental data on sessile species as a case to demonstrate how this interface offers a biophysical framework that could help to decode and harness active microbial processes in 'true' ecological settings. Topology and its functional manifestations - a crucial and well-timed topic - offer a rich opportunity for both experimentalists and theoreticians willing to take up an exciting journey across scales and disciplines.