Massively parallel screening of synthetic microbial communities

Quantifying microbial interactions: concepts, caveats, and applications

Microbial communities are fundamental to every ecosystem on Earth and hold great potential for biotechnological applications. However, their complex nature hampers our ability to study and understand them. A common strategy to tackle this complexity is to abstract the community into a network of interactions between its members - a phenomenological description that captures the overall effects of various chemical and physical mechanisms that underpin these relationships. This approach has proven useful for numerous applications in microbial ecology, including predicting community dynamics and stability and understanding community assembly and evolution. However, care is required in quantifying and interpreting interactions. Here, we clarify the concept of an interaction and discuss when interaction measurements are useful despite their context-dependent nature. Furthermore, we categorize different approaches for quantifying interactions, highlighting the research objectives each approach is best suited for.

Synthetic microbial ecology: engineering habitats for modular consortia

Microbiomes, the complex networks of micro-organisms and the molecules through which they interact, play a crucial role in health and ecology. Over at least the past two decades, engineering biology has made significant progress, impacting the bio-based industry, health, and environmental sectors; but has only recently begun to explore the engineering of microbial ecosystems. The creation of synthetic microbial communities presents opportunities to help us understand the dynamics of wild ecosystems, learn how to manipulate and interact with existing microbiomes for therapeutic and other purposes, and to create entirely new microbial communities capable of undertaking tasks for industrial biology. Here, we describe how synthetic ecosystems can be constructed and controlled, focusing on how the available methods and interaction mechanisms facilitate the regulation of community composition and output. While experimental decisions are dictated by intended applications, the vast number of tools available suggests great opportunity for researchers to develop a diverse array of novel microbial ecosystems.

Synthetic microbial communities: Novel strategies to enhance the quality of traditional fermented foods

Consumers are attracted to traditional fermented foods due to their unique flavor and nutritional value. However, the traditional fermentation technique can no longer accommodate the requirements of the food industry. Traditional fermented foods produce hazardous compounds, off-odor, and anti-nutritional factors, reducing product stability. The microbial system complexity of traditional fermented foods resulting from the open fermentation process has made it challenging to regulate these problems by modifying microbial behaviors. Synthetic microbial communities (SynComs) have been shown to simplify complex microbial communities and allow for the targeted design of microbial communities, which has been applied in processing traditional fermented foods. Herein, we describe the theoretical information of SynComs, particularly microbial physiological processes and their interactions. This paper discusses current approaches to creating SynComs, including designing, building, testing, and learning, with typical applications and fundamental techniques. Based on various traditional fermented food innovation demands, the potential and application of SynComs in enhancing the quality of traditional fermented foods are highlighted. SynComs showed superior performance in regulating the quality of traditional fermented foods using the interaction of core microorganisms to reduce the hazardous compounds of traditional fermented foods and improve flavor. Additionally, we presented the current status and future perspectives of SynComs for improving the quality of traditional fermented foods.

Automated and miniaturized screening of antibiotic combinations via robotic-printed combinatorial droplet platform

Antimicrobial resistance (AMR) has become a global health crisis in need of novel solutions. To this end, antibiotic combination therapies, which combine multiple antibiotics for treatment, have attracted significant attention as a potential approach for combating AMR. To facilitate advances in antibiotic combination therapies, most notably in investigating antibiotic interactions and identifying synergistic antibiotic combinations however, there remains a need for automated high-throughput platforms that can create and examine antibiotic combinations on-demand, at scale, and with minimal reagent consumption. To address these challenges, we have developed a Robotic-Printed Combinatorial Droplet (RoboDrop) platform by integrating a programmable droplet microfluidic device that generates antibiotic combinations in nanoliter droplets in automation, a robotic arm that arranges the droplets in an array, and a camera that images the array of thousands of droplets in parallel. We further implement a resazurin-based bacterial viability assay to accelerate our antibiotic combination testing. As a demonstration, we use RoboDrop to corroborate two pairs of antibiotics with known interactions and subsequently identify a new synergistic combination of cefsulodin, penicillin, and oxacillin against a model E. coli strain. We therefore envision RoboDrop becoming a useful tool to efficiently identify new synergistic antibiotic combinations toward combating AMR.

Data-driven prediction of colonization outcomes for complex microbial communities

Microbial interactions can lead to different colonization outcomes of exogenous species, be they pathogenic or beneficial in nature. Predicting the colonization of exogenous species in complex communities remains a fundamental challenge in microbial ecology, mainly due to our limited knowledge of the diverse mechanisms governing microbial dynamics. Here, we propose a data-driven approach independent of any dynamics model to predict colonization outcomes of exogenous species from the baseline compositions of microbial communities. We systematically validate this approach using synthetic data, finding that machine learning models can predict not only the binary colonization outcome but also the post-invasion steady-state abundance of the invading species. Then we conduct colonization experiments for commensal gut bacteria species Enterococcus faecium and Akkermansia muciniphila in hundreds of human stool-derived in vitro microbial communities, confirming that the data-driven approaches can predict the colonization outcomes in experiments. Furthermore, we find that while most resident species are predicted to have a weak negative impact on the colonization of exogenous species, strongly interacting species could significantly alter the colonization outcomes, e.g., Enterococcus faecalis inhibits the invasion of E. faecium invasion. The presented results suggest that the data-driven approaches are powerful tools to inform the ecology and management of microbial communities.

Richness and density jointly determine context dependence in bacterial interactions

Pairwise interactions are often used to predict features of complex microbial communities due to the challenge of measuring multi-species interactions in high dimensional contexts. This assumes that interactions are unaffected by community context. Here, we used synthetic bacterial communities to investigate that assumption by observing how interactions varied across contexts. Interactions were most often weakly negative and showed a phylogenetic signal among genera. Community richness and total density emerged as strong predictors of interaction strength and contributed to an attenuation of interactions as richness increased. Population level and per-capita measures of interactions both displayed such attenuation, suggesting factors beyond systematic changes in population size were involved; namely, changes to the interactions themselves. Nevertheless, pairwise interactions retained some explanatory power across contexts, provided those contexts were not substantially divergent in richness. These results suggest that understanding the emergent properties of microbial interactions can improve our ability to predict the features of microbial communities.

Photo-addressable microwell devices for rapid functional screening and isolation of pathogen inhibitors from bacterial strain libraries

Discovery of new strains of bacteria that inhibit pathogen growth can facilitate improvements in biocontrol and probiotic strategies. Traditional, plate-based co-culture approaches that probe microbial interactions can impede this discovery as these methods are inherently low-throughput, labor-intensive, and qualitative. We report a second-generation, photo-addressable microwell device, developed to iteratively screen interactions between candidate biocontrol agents existing in bacterial strain libraries and pathogens under increasing pathogen pressure. Microwells (0.6 pl volume) provide unique co-culture sites between library strains and pathogens at controlled cellular ratios. During sequential screening iterations, library strains are challenged against increasing numbers of pathogens to quantitatively identify microwells containing strains inhibiting the highest numbers of pathogens. Ring-patterned 365 nm light is then used to ablate a photodegradable hydrogel membrane and sequentially release inhibitory strains from the device for recovery. Pathogen inhibition with each recovered strain is validated, followed by whole genome sequencing. To demonstrate the rapid nature of this approach, the device was used to screen a 293-membered biovar 1 agrobacterial strain library for strains inhibitory to the plant pathogen Agrobacterium tumefaciens sp. 15955. One iterative screen revealed nine new inhibitory strains. For comparison, plate-based methods did not uncover any inhibitory strains from the library (n = 30 plates). The novel pathogen-challenge screening mode developed here enables rapid selection and recovery of strains that effectively suppress pathogen growth from bacterial strain libraries, expanding this microwell technology platform toward rapid, cost-effective, and scalable screening for probiotics, biocontrol agents, and inhibitory molecules that can protect against known or emerging pathogens.

Searching for Principles of Microbial Ecology Across Levels of Biological Organization

Microbial communities play pivotal roles in ecosystems across different scales, from global elemental cycles to household food fermentations. These complex assemblies comprise hundreds or thousands of microbial species whose abundances vary over time and space. Unraveling the principles that guide their dynamics at different levels of biological organization, from individual species, their interactions, to complex microbial communities, is a major challenge. To what extent are these different levels of organization governed by separate principles, and how can we connect these levels to develop predictive models for the dynamics and function of microbial communities? Here, we will discuss recent advances that point towards principles of microbial communities, rooted in various disciplines from physics, biochemistry, and dynamical systems. By considering the marine carbon cycle as a concrete example, we demonstrate how the integration of levels of biological organization can offer deeper insights into the impact of increasing temperatures, such as those associated with climate change, on ecosystem-scale processes. We argue that by focusing on principles that transcend specific microbiomes, we can pave the way for a comprehensive understanding of microbial community dynamics and the development of predictive models for diverse ecosystems.

From microbiome composition to functional engineering, one step at a time

SUMMARYCommunities of microorganisms (microbiota) are present in all habitats on Earth and are relevant for agriculture, health, and climate. Deciphering the mechanisms that determine microbiota dynamics and functioning within the context of their respective environments or hosts (the microbiomes) is crucially important. However, the sheer taxonomic, metabolic, functional, and spatial complexity of most microbiomes poses substantial challenges to advancing our knowledge of these mechanisms. While nucleic acid sequencing technologies can chart microbiota composition with high precision, we mostly lack information about the functional roles and interactions of each strain present in a given microbiome. This limits our ability to predict microbiome function in natural habitats and, in the case of dysfunction or dysbiosis, to redirect microbiomes onto stable paths. Here, we will discuss a systematic approach (dubbed the N+1/N-1 concept) to enable step-by-step dissection of microbiome assembly and functioning, as well as intervention procedures to introduce or eliminate one particular microbial strain at a time. The N+1/N-1 concept is informed by natural invasion events and selects culturable, genetically accessible microbes with well-annotated genomes to chart their proliferation or decline within defined synthetic and/or complex natural microbiota. This approach enables harnessing classical microbiological and diversity approaches, as well as omics tools and mathematical modeling to decipher the mechanisms underlying N+1/N-1 microbiota outcomes. Application of this concept further provides stepping stones and benchmarks for microbiome structure and function analyses and more complex microbiome intervention strategies.

Soil microbiome engineering for sustainability in a changing environment

Recent advances in microbial ecology and synthetic biology have the potential to mitigate damage caused by anthropogenic activities that are deleteriously impacting Earth's soil ecosystems. Here, we discuss challenges and opportunities for harnessing natural and synthetic soil microbial communities, focusing on plant growth promotion under different scenarios. We explore current needs for microbial solutions in soil ecosystems, how these solutions are being developed and applied, and the potential for new biotechnology breakthroughs to tailor and target microbial products for specific applications. We highlight several scientific and technological advances in soil microbiome engineering, including characterization of microbes that impact soil ecosystems, directing how microbes assemble to interact in soil environments, and the developing suite of gene-engineering approaches. This Review underscores the need for an interdisciplinary approach to understand the composition, dynamics and deployment of beneficial soil microbiomes to drive efforts to mitigate or reverse environmental damage by restoring and protecting healthy soil ecosystems.

Interactions between plant-beneficial microorganisms in a consortium: Streptomyces microflavus and Trichoderma harzianum

The construction of microbial consortia is challenging due to many variables to be controlled, including the cross-compatibility of the selected strains and their additive or synergistic effects on plants. In this work, we investigated the interactions in vitro, in planta, and at the molecular level of two elite biological control agents (BCAs), that is Streptomyces microflavus strain AtB-42 and Trichoderma harzianum strain M10, to understand their attitude to cooperate in a consortium. In vitro, we observed a strong cross-antagonism between AtB-42 and M10 in agar plates due to diffusible metabolites and volatile organic compounds. In liquid co-cultures, M10 hindered the growth of AtB-42 very likely because of secondary metabolites and strong competition for the nutrients. The interaction in the co-culture induced extensive transcriptional reprogramming in both strains, especially in the pathways related to ribosomes, protein synthesis, and oxidoreductase activity, suggesting that each strain recognized the counterpart and activated its defence responses. The metabolome of both strains was also significantly affected. In contrast, in the soil, M10 growth was partially contrasted by AtB-42. The roots of tomato seedlings inoculated with the consortium appeared smaller than the control and single-strain-inoculated plants, indicating that plants diverted some energy from the development to defence activation, as evidenced by the leaf transcriptome. The consortium induced a stronger transcriptional change compared to the single inoculants, as demonstrated by a higher number of differentially expressed genes. Although the cross-antagonism observed in vitro, the two strains exerted a synergistic effect on tomato seedlings by inducing resistance responses stronger than the single inoculants. Our observations pose a question on the usefulness of the sole in vitro assays for selecting BCAs to construct a consortium. In vivo experiments should be preferred, and transcriptomics may greatly help to elucidate the activity of the BCAs beyond the phenotypic effects on the plant.

Statistical prediction of microbial metabolic traits from genomes

The metabolic activity of microbial communities is central to their role in biogeochemical cycles, human health, and biotechnology. Despite the abundance of sequencing data characterizing these consortia, it remains a serious challenge to predict microbial metabolic traits from sequencing data alone. Here we culture 96 bacterial isolates individually and assay their ability to grow on 10 distinct compounds as a sole carbon source. Using these data as well as two existing datasets, we show that statistical approaches can accurately predict bacterial carbon utilization traits from genomes. First, we show that classifiers trained on gene content can accurately predict bacterial carbon utilization phenotypes by encoding phylogenetic information. These models substantially outperform predictions made by constraint-based metabolic models automatically constructed from genomes. This result solidifies our current knowledge about the strong connection between phylogeny and metabolic traits. However, phylogeny-based predictions fail to predict traits for taxa that are phylogenetically distant from any strains in the training set. To overcome this we train improved models on gene presence/absence to predict carbon utilization traits from gene content. We show that models that predict carbon utilization traits from gene presence/absence can generalize to taxa that are phylogenetically distant from the training set either by exploiting biochemical information for feature selection or by having sufficiently large datasets. In the latter case, we provide evidence that a statistical approach can identify putatively mechanistic genes involved in metabolic traits. Our study demonstrates the potential power for predicting microbial phenotypes from genotypes using statistical approaches.

High-throughput bacterial co-encapsulation in microfluidic gel beads for discovery of antibiotic-producing strains

Bacteria with antagonistic activity inhibit the growth of other bacteria through different mechanisms, including the production of antibiotics. As a result, these microorganisms are a prolific source of such compounds. However, searching for antibiotic-producing strains requires high-throughput techniques due to the vast diversity of microorganisms. Here, we screened and isolated bacteria with antagonistic activity against Escherichia coli expressing the green fluorescent protein (E. coli-GFP). We used microfluidics to co-encapsulate and co-culture single cells from different strains within picoliter gel beads and analyzed them using fluorescence-activated cell sorting (FACS). To test the methodology, we used three bacterial isolates obtained from Mexican maize, which exhibit high, moderate, or no antagonistic activity against E. coli-GFP, as determined previously using agar plate assays. Single cells from each strain were separately co-incubated into gel beads with E. coli-GFP. We monitored the development of the maize bacteria microcolonies and tracked the growth or inhibition of E. coli-GFP using bright-field and fluorescent microscopy. We correlated these images with distinctive light scatter and fluorescence signatures of each incubated bead type using FACS. This analysis enabled us to sort gel beads filled with an antagonistic strain, starting from a mixture of the three different types of maize bacteria and E. coli-GFP. Likewise, culturing the FACS-sorted beads on agar plates confirmed the isolation and recovery of the two antagonistic strains. In addition, enrichment assays demonstrated the methodology's effectiveness in isolating rare antibiotic-producer strains (0.01% abundance) present in a mixture of microorganisms. These results show that associating light side scatter and fluorescent flow cytometry signals with microscopy images provides valuable controls to establish successful high-throughput methods for sorting beads in which microbial interaction assays are performed.

Statistically learning the functional landscape of microbial communities

Microbial consortia exhibit complex functional properties in contexts ranging from soils to bioreactors to human hosts. Understanding how community composition determines function is a major goal of microbial ecology. Here we address this challenge using the concept of community-function landscapes-analogues to fitness landscapes-that capture how changes in community composition alter collective function. Using datasets that represent a broad set of community functions, from production/degradation of specific compounds to biomass generation, we show that statistically inferred landscapes quantitatively predict community functions from knowledge of species presence or absence. Crucially, community-function landscapes allow prediction without explicit knowledge of abundance dynamics or interactions between species and can be accurately trained using measurements from a small subset of all possible community compositions. The success of our approach arises from the fact that empirical community-function landscapes appear to be not rugged, meaning that they largely lack high-order epistatic contributions that would be difficult to fit with limited data. Finally, we show that this observation holds across a wide class of ecological models, suggesting community-function landscapes can be efficiently inferred across a broad range of ecological regimes. Our results open the door to the rational design of consortia without detailed knowledge of abundance dynamics or interactions.

Metaproteomics, metagenomics and 16S rRNA sequencing provide different perspectives on the aerobic granular sludge microbiome

The tremendous progress in sequencing technologies has made DNA sequencing routine for microbiome studies. Additionally, advances in mass spectrometric techniques have extended conventional proteomics into the field of microbial ecology. However, systematic studies that provide a better understanding of the complementary nature of these 'omics' approaches, particularly for complex environments such as wastewater treatment sludge, are urgently needed. Here, we describe a comparative metaomics study on aerobic granular sludge from three different wastewater treatment plants. For this, we employed metaproteomics, whole metagenome, and 16S rRNA amplicon sequencing to study the same granule material with uniform size. We furthermore compare the taxonomic profiles using the Genome Taxonomy Database (GTDB) to enhance the comparability between the different approaches. Though the major taxonomies were consistently identified in the different aerobic granular sludge samples, the taxonomic composition obtained by the different omics techniques varied significantly at the lower taxonomic levels, which impacts the interpretation of the nutrient removal processes. Nevertheless, as demonstrated by metaproteomics, the genera that were consistently identified in all techniques cover the majority of the protein biomass. The established metaomics data and the contig classification pipeline are publicly available, which provides a valuable resource for further studies on metabolic processes in aerobic granular sludge.

Microbial interactions in theory and practice: when are measurements compatible with models?

Most predictive models of ecosystem dynamics are based on interactions between organisms: their influence on each other's growth and death. We review here how theoretical approaches are used to extract interaction measurements from experimental data in microbiology, particularly focusing on the generalised Lotka-Volterra (gLV) framework. Though widely used, we argue that the gLV model should be avoided for estimating interactions in batch culture - the most common, simplest and cheapest in vitro approach to culturing microbes. Fortunately, alternative approaches offer a way out of this conundrum. Firstly, on the experimental side, alternatives such as the serial-transfer and chemostat systems more closely match the theoretical assumptions of the gLV model. Secondly, on the theoretical side, explicit organism-environment interaction models can be used to study the dynamics of batch-culture systems. We hope that our recommendations will increase the tractability of microbial model systems for experimentalists and theoreticians alike.

Artificial intelligence-accelerated high-throughput screening of antibiotic combinations on a microfluidic combinatorial droplet system

Microfluidic platforms have been employed as an effective tool for drug screening and exhibit the advantages of lower reagent consumption, higher throughput and a higher degree of automation. Despite the great advancement, it remains challenging to screen complex antibiotic combinations in a simple, high-throughput and systematic manner. Meanwhile, the large amounts of datasets generated during the screening process generally outpace the abilities of the conventional manual or semi-automatic data analysis. To address these issues, we propose an artificial intelligence-accelerated high-throughput combinatorial drug evaluation system (AI-HTCDES), which not only allows high-throughput production of antibiotic combinations with varying concentrations, but can also automatically analyze the dynamic growth of bacteria under the action of different antibiotic combinations. Based on this system, several antibiotic combinations displaying an additive effect are discovered, and the dosage regimens of each component in the combinations are determined. This strategy not only provides useful guidance in the clinical use of antibiotic combination therapy and personalized medicine, but also offers a promising tool for the combinatorial screenings of other medicines.

User-friendly analysis of droplet array images

Water-in-oil droplets allow performing massive experimental parallelization and high-throughput studies, such as single-cell experiments. However, analyzing such vast arrays of droplets usually requires advanced expertise and sophisticated workflow tools, which limits accessibility for a wider user base in the fields of chemistry and biology. Thus, there is a need for more user-friendly tools for droplet analysis. In this article, we deliver a set of analytical pipelines for user-friendly analysis of typical scenarios in droplet experiments. We built pipelines that combine various open-source image-analysis software with a custom-developed data processing tool called "EasyFlow". Our pipelines are applicable to the typical experimental scenarios that users encounter when working with droplets: i) mono- and polydisperse droplets, ii) brightfield and fluorescent images, iii) droplet and object detection, iv) signal profile of droplets and objects (e.g., fluorescence).

Synthetic phylogenetically diverse communities promote denitrification and stability

Denitrification is an important process of the global nitrogen cycle as some of its intermediates are environmentally important or related to global warming. However, how the phylogenetic diversity of denitrifying communities affects their denitrification rates and temporal stability remains unclear. Here we selected denitrifiers based on their phylogenetic distance to construct two groups of synthetic denitrifying communities: one closely related (CR) group with all strains from the genus Shewanella and the other distantly related (DR) group with all constituents from different genera. All synthetic denitrifying communities (SDCs) were experimentally evolved for 200 generations. The results showed that high phylogenetic diversity followed by experimental evolution promoted the function and stability of synthetic denitrifying communities. Specifically, the productivity and denitrification rates were significantly (P < 0.05) higher with Paracocus denitrificans as the dominant species (since the 50th generation) in the DR community than those in the CR community. The DR community also showed significantly (t = 7.119, df = 10, P < 0.001) higher stability through overyielding and asynchrony of species fluctuations, and showed more complementarity than the CR group during the experimental evolution. This study has important implications for applying synthetic communities to remediate environmental problems and mitigate greenhouse gas emissions.

A combinatorial droplet microfluidic device integrated with mass spectrometry for enzyme screening

Mass spectrometry (MS) enables detection of different chemical species with a very high specificity; however, it can be limited by its throughput. Integrating MS with microfluidics has a tremendous potential to improve throughput and accelerate biochemical research. In this work, we introduce Drop-NIMS, a combination of a passive droplet loading microfluidic device and a matrix-free MS laser desorption ionization technique called nanostructure-initiator mass spectrometry (NIMS). This platform combines different droplets at random to generate a combinatorial library of enzymatic reactions that are deposited directly on the NIMS surface without requiring additional sample handling. The enzyme reaction products are then detected with MS. Drop-NIMS was used to rapidly screen enzymatic reactions containing low (on the order of nL) volumes of glycoside reactants and glycoside hydrolase enzymes per reaction. MS "barcodes" (small compounds with unique masses) were added to the droplets to identify different combinations of substrates and enzymes created by the device. We assigned xylanase activities to several putative glycoside hydrolases, making them relevant to food and biofuel industrial applications. Overall, Drop-NIMS is simple to fabricate, assemble, and operate and it has potential to be used with many other small molecule metabolites.

Accelerated high-throughput imaging and phenotyping system for small organisms

Studying the complex web of interactions in biological communities requires large multifactorial experiments with sufficient statistical power. Automation tools reduce the time and labor associated with setup, data collection, and analysis in experiments that untangle these webs. We developed tools for high-throughput experimentation (HTE) in duckweeds, small aquatic plants that are amenable to autonomous experimental preparation and image-based phenotyping. We showcase the abilities of our HTE system in a study with 6,000 experimental units grown across 2,000 treatments. These automated tools facilitated the collection and analysis of time-resolved growth data, which revealed finer dynamics of plant-microbe interactions across environmental gradients. Altogether, our HTE system can run experiments with up to 11,520 experimental units and can be adapted for other small organisms.

Leveraging interactions in microfluidic droplets for enhanced biotechnology screens

Microfluidic droplet screens serve as an innovative platform for high-throughput biotechnology, enabling significant advancements in discovery, product optimization, and analysis. This review sheds light on the emerging trends of interaction assays in microfluidic droplets, underscoring the unique suitability of droplets for these applications. Encompassing a diverse range of biological entities such as antibodies, enzymes, DNA, RNA, various microbial and mammalian cell types, drugs, and other molecules, these assays demonstrate their versatility and scope. Recent methodological breakthroughs have escalated these screens to novel scales of bioanalysis and biotechnological product design. Moreover, we highlight pioneering advancements that extend droplet-based screens into new domains: cargo delivery within human bodies, application of synthetic gene circuits in natural environments, 3D printing, and the development of droplet structures responsive to environmental signals. The potential of this field is profound and only set to increase.

Bioengineered microbial strains for detoxification of toxic environmental pollutants

Industrialization and other anthropogenic human activities pose significant environmental risks. As a result of the hazardous pollution, numerous living organisms may suffer from undesirable diseases in their separate habitats. Bioremediation, which removes hazardous compounds from the environment using microbes or their biologically active metabolites, is one of the most successful remediation approaches. According to the United Nations Environment Program (UNEP), deteriorating soil health negatively impacts food security and human health over time. Soil health restoration is critical right now. Microbes are widely known for their importance in cleaning up toxins present in the soil, such as heavy metals, pesticides, and hydrocarbons. However, the capacity of local bacteria to digest these pollutants is limited, and the process takes an extended time. Genetically modified organisms (GMOs), whose altered metabolic pathways promote the over-secretion of a variety of proteins favorable to the bioremediation process, can speed up the breakdown process. The need for remediation procedures, degrees of soil contamination, site circumstances, broad adoptions, and numerous possibilities occurring at various cleaning stages are all studied in detail. Massive efforts to restore contaminated soils have also resulted in severe issues. This review focuses on the enzymatic removal of hazardous pollutants from the environment, such as pesticides, heavy metals, dyes, and plastics. There are also in-depth assessments of present discoveries and future plans for efficient enzymatic degradation of hazardous pollutants.

High-throughput microfluidic droplets in biomolecular analytical system: A review

Droplet microfluidic technology has revolutionized biomolecular analytical research, as it has the capability to reserve the genotype-to-phenotype linkage and assist for revealing the heterogeneity. Massive and uniform picolitre droplets feature dividing solution to the level that single cell and single molecule in each droplet can be visualized, barcoded, and analyzed. Then, the droplet assays can unfold intensive genomic data, offer high sensitivity, and screen and sort from a large number of combinations or phenotypes. Based on these unique advantages, this review focuses on up-to-date research concerning diverse screening applications utilizing droplet microfluidic technology. The emerging progress of droplet microfluidic technology is first introduced, including efficient and scaling-up in droplets encapsulation, and prevalent batch operations. Then the new implementations of droplet-based digital detection assays and single-cell muti-omics sequencing are briefly examined, along with related applications such as drug susceptibility testing, multiplexing for cancer subtype identification, interactions of virus-to-host, and multimodal and spatiotemporal analysis. Meanwhile, we specialize in droplet-based large-scale combinational screening regarding desired phenotypes, with an emphasis on sorting for immune cells, antibodies, enzymatic properties, and proteins produced by directed evolution methods. Finally, some challenges, deployment and future perspective of droplet microfluidics technology in practice are also discussed.

Construction and Modeling of a Coculture Microplate for Real-Time Measurement of Microbial Interactions

The dynamic structures of microbial communities emerge from the complex network of interactions between their constituent microorganisms. Quantitative measurements of these interactions are important for understanding and engineering ecosystem structure. Here, we present the development and application of the BioMe plate, a redesigned microplate device in which pairs of wells are separated by porous membranes. BioMe facilitates the measurement of dynamic microbial interactions and integrates easily with standard laboratory equipment. We first applied BioMe to recapitulate recently characterized, natural symbiotic interactions between bacteria isolated from the Drosophila melanogaster gut microbiome. Specifically, the BioMe plate allowed us to observe the benefit provided by two Lactobacillus strains to an Acetobacter strain. We next explored the use of BioMe to gain quantitative insight into the engineered obligate syntrophic interaction between a pair of Escherichia coli amino acid auxotrophs. We integrated experimental observations with a mechanistic computational model to quantify key parameters associated with this syntrophic interaction, including metabolite secretion and diffusion rates. This model also allowed us to explain the slow growth observed for auxotrophs growing in adjacent wells by demonstrating that, under the relevant range of parameters, local exchange between auxotrophs is essential for efficient growth. The BioMe plate provides a scalable and flexible approach for the study of dynamic microbial interactions. IMPORTANCE Microbial communities participate in many essential processes from biogeochemical cycles to the maintenance of human health. The structure and functions of these communities are dynamic properties that depend on poorly understood interactions among different species. Unraveling these interactions is therefore a crucial step toward understanding natural microbiota and engineering artificial ones. Microbial interactions have been difficult to measure directly, largely due to limitations of existing methods to disentangle the contribution of different organisms in mixed cocultures. To overcome these limitations, we developed the BioMe plate, a custom microplate-based device that enables direct measurement of microbial interactions, by detecting the abundance of segregated populations of microbes that can exchange small molecules through a membrane. We demonstrated the possible application of the BioMe plate for studying both natural and artificial consortia. BioMe is a scalable and accessible platform that can be used to broadly characterize microbial interactions mediated by diffusible molecules.

Data-driven prediction of colonization outcomes for complex microbial communities

Complex microbial interactions can lead to different colonization outcomes of exogenous species, be they pathogenic or beneficial in nature. Predicting the colonization of exogenous species in complex communities remains a fundamental challenge in microbial ecology, mainly due to our limited knowledge of the diverse physical, biochemical, and ecological processes governing microbial dynamics. Here, we proposed a data-driven approach independent of any dynamics model to predict colonization outcomes of exogenous species from the baseline compositions of microbial communities. We systematically validated this approach using synthetic data, finding that machine learning models (including Random Forest and neural ODE) can predict not only the binary colonization outcome but also the post-invasion steady-state abundance of the invading species. Then we conducted colonization experiments for two commensal gut bacteria species Enterococcus faecium and Akkermansia muciniphila in hundreds of human stool-derived in vitro microbial communities, confirming that the data-driven approach can successfully predict the colonization outcomes. Furthermore, we found that while most resident species were predicted to have a weak negative impact on the colonization of exogenous species, strongly interacting species could significantly alter the colonization outcomes, e.g., the presence of Enterococcus faecalis inhibits the invasion of E. faecium . The presented results suggest that the data-driven approach is a powerful tool to inform the ecology and management of complex microbial communities.

Resolving metabolic interaction mechanisms in plant microbiomes

Metabolic interactions are fundamental to the assembly and functioning of microbiomes, including those of plants. However, disentangling the molecular basis of these interactions and their specific roles remains a major challenge. Here, we review recent applications of experimental and computational methods toward the elucidation of metabolic interactions in plant-associated microbiomes. We highlight studies that span various scales of taxonomic and environmental complexity, including those that test interaction outcomes in vitro and in planta by deconstructing microbial communities. We also discuss how the continued integration of multiple methods can further reveal the general ecological characteristics of plant microbiomes, as well as provide strategies for applications in areas such as improved plant protection, bioremediation, and sustainable agriculture.

Pulsed, continuous or somewhere in between? Resource dynamics matter in the optimisation of microbial communities

The optimisation of synthetic and natural microbial communities has vast potential for emerging applications in medicine, agriculture and industry. Realising this goal is contingent on a close correlation between theory, experiments, and the real world. Although the temporal pattern of resource supply can play a major role in microbial community assembly, resource dynamics are commonly treated inconsistently in theoretical and experimental research. Here we explore how the composition of communities varies under continuous resource supply, typical of theoretical approaches, versus pulsed resource supply, typical of experiments. Using simulations of classical resource competition models, we show that community composition diverges rapidly between the two regimes, with almost zero overlap in composition once the pulsing interval stretches beyond just four hours. The implication for the rapidly growing field of microbial community optimisation is that the resource supply regime must be tailored to the community being optimised. As such, we argue that resource supply dynamics should be considered both a constraint in the design of novel microbial communities and as a tuning mechanism for the optimisation of pre-existing communities like those found in the human gut.

"What I cannot create, I do not understand": elucidating microbe-microbe interactions to facilitate plant microbiome engineering

Plant-microbe interactions are important for both physiological and pathological processes. Despite the significance of plant-microbe interactions, microbe-microbe interactions themselves represent an important, complex, dynamic network that warrants deeper investigation. To understand how microbe-microbe interactions affect plant microbiomes, one approach is to systematically understand all the factors involved in successful engineering of a microbial community. This follows the physicist Richard Feynman's declaration: "what I cannot create, I do not understand". This review highlights recent studies that focus on aspects that we believe are important for building (ergo understanding) microbe-microbe interactions in the plant environment, including pairwise screening, intelligent application of cross-feeding models, spatial distributions of microbes, and understudied interactions between bacteria and fungi, phages, and protists. We offer a framework for systematic collection and centralized integration of data of plant microbiomes that could organize all the factors that can help ecologists understand microbiomes and help synthetic ecologists engineer beneficial microbiomes.

Core Species Derived from Multispecies Interactions Facilitate the Immobilization of Cadmium

Microbial consortia have opened new avenues for heavy-metal remediation. However, the limited understanding of the overall effect of interspecific interactions on remediation efficacy hinders its application. Here, the effects of multispecies growth and biofilm formation on Cd immobilization were explored from direct and multiple interactions through random combinations of two or three rhizosphere bacteria. In monocultures, Cd stress resulted in an average decrease in planktonic biomass of 26%, but through cooperation, the decrease was attenuated in dual (21%) and triple cultures (13%), possibly involving an increase in surface polysaccharides. More than 65% of the co-cultures exhibited induction of biofilm formation under Cd stress, which further enhanced the role of biofilms in Cd immobilization. Notably, excellent biofilm-forming ability or extensive social induction makes Pseudomonas putida and Brevundimonas diminuta stand out in multispecies biofilm formation and Cd immobilization. These two core species significantly increase the colonization of soil microorganisms on rice roots compared to the control, resulting in a 40% decrease in Cd uptake by rice. Our study enhances the understanding of bacterial interactions under Cd stress and provides a novel strategy for adjusting beneficial soil consortia for heavy-metal remediation.

Microbiome engineering for bioremediation of emerging pollutants

Axenic microbial applications in the open environment are unrealistic and may not be always practically viable. Therefore, it is important to use mixed microbial cultures and their interactions with the microbiome in the targeted ecosystem to perform robust functions towards their sustainability in harsh environmental conditions. Emerging pollutants like phthalates and hydrocarbons that are toxic to several aquatic and terrestrial life forms in the water bodies and lands are an alarming situation. The present review explores the possibility of devising an inclusive eco-friendly strategy like microbiome engineering which proves to be a unique and crucial technology involving the power of microbial communication through quorum sensing. This review discusses the interspecies and intra-species communications between different microbial groups with their respective environments. Moreover, this review also envisages the efforts for designing the next level of microbiome-host engineering concept (MHEC). The focus of the review also extended toward using omics and metabolic network analysis-based tools for effective microbiome engineering. These approaches might be quite helpful in the future to understand such microbial interactions but it will be challenging to implement in the real environment to get the desired functions. Finally, the review also discusses multiple approaches for the bioremediation of toxic chemicals from the soil environment.

Defining the Benefits of Antibiotic Resistance in Commensals and the Scope for Resistance Optimization

Antibiotic resistance is a major medical and public health challenge, characterized by global increases in the prevalence of resistant strains. The conventional view is that all antibiotic resistance is problematic, even when not in pathogens. Resistance in commensal bacteria poses risks, as resistant organisms can provide a reservoir of resistance genes that can be horizontally transferred to pathogens or may themselves cause opportunistic infections in the future. While these risks are real, we propose that commensal resistance can also generate benefits during antibiotic treatment of human infection, by promoting continued ecological suppression of pathogens. To define and illustrate this alternative conceptual perspective, we use a two-species mathematical model to identify the necessary and sufficient ecological conditions for beneficial resistance. We show that the benefits are limited to species (or strain) interactions where commensals suppress pathogen growth and are maximized when commensals compete with, rather than prey on or otherwise exploit pathogens. By identifying benefits of commensal resistance, we propose that rather than strictly minimizing all resistance, resistance management may be better viewed as an optimization problem. We discuss implications in two applied contexts: bystander (nontarget) selection within commensal microbiomes and pathogen treatment given polymicrobial infections. IMPORTANCE Antibiotic resistance is commonly viewed as universally costly, regardless of which bacterial cells express resistance. Here, we derive an opposing logic, where resistance in commensal bacteria can lead to reductions in pathogen density and improved outcomes on both the patient and public health scales. We use a mathematical model of commensal-pathogen interactions to define the necessary and sufficient conditions for beneficial resistance, highlighting the importance of reciprocal ecological inhibition to maximize the benefits of resistance. More broadly, we argue that determining the benefits as well as the costs of resistances in human microbiomes can transform resistance management from a minimization to an optimization problem. We discuss applied contexts and close with a review of key resistance optimization dimensions, including the magnitude, spectrum, and mechanism of resistance.

Microscale combinatorial stimulation of human myeloid cells reveals inflammatory priming by viral ligands

Cells sense a wide variety of signals and respond by adopting complex transcriptional states. Most single-cell profiling is carried out today at cellular baseline, blind to cells' potential spectrum of functional responses. Exploring the space of cellular responses experimentally requires access to a large combinatorial perturbation space. Single-cell genomics coupled with multiplexing techniques provide a useful tool for characterizing cell states across several experimental conditions. However, current multiplexing strategies require programmatic handling of many samples in macroscale arrayed formats, precluding their application in large-scale combinatorial analysis. Here, we introduce StimDrop, a method that combines antibody-based cell barcoding with parallel droplet processing to automatically formulate cell population × stimulus combinations in a microfluidic device. We applied StimDrop to profile the effects of 512 sequential stimulation conditions on human dendritic cells. Our results demonstrate that priming with viral ligands potentiates hyperinflammatory responses to a second stimulus, and show transcriptional signatures consistent with this phenomenon in myeloid cells of patients with severe COVID-19.

Interactions between Culturable Bacteria Are Predicted by Individual Species' Growth

Predicting interspecies interactions is a key challenge in microbial ecology given that interactions shape the composition and functioning of microbial communities. However, predicting microbial interactions is challenging because they can vary considerably depending on species' metabolic capabilities and environmental conditions. Here, we employ machine learning models to predict pairwise interactions between culturable bacteria based on their phylogeny, monoculture growth capabilities, and interactions with other species. We trained our models on one of the largest available pairwise interactions data set containing over 7,500 interactions between 20 species from two taxonomic groups that were cocultured in 40 different carbon environments. Our models accurately predicted both the sign (accuracy of 88%) and the strength of effects (R² of 0.87) species had on each other's growth. Encouragingly, predictions with comparable accuracy could be made even when not relying on information about interactions with other species, which are often hard to measure. However, species' monoculture growth was essential to the model, as predictions based solely on species' phylogeny and inferred metabolic capabilities were significantly less accurate. These results bring us one step closer to a predictive understanding of microbial communities, which is essential for engineering beneficial microbial consortia. IMPORTANCE In order to understand the function and structure of microbial communities, one must know all pairwise interactions that occur between the different species within the community, as these interactions shape the community's structure and functioning. However, measuring all pairwise interactions can be an extremely difficult task especially when dealing with big complex communities. Because of that, predicting interspecies interactions is a key challenge in microbial ecology. Here, we use machine learning models in order to accurately predict the type and strength of interactions. We trained our models on one of the largest available pairwise interactions data set, containing over 7,500 interactions between 20 different species that were cocultured in 40 different environments. Our results show that, in general, accurate predictions can be made, and that the ability of each species to grow on its own in the given environment contributes the most to predictions. Being able to predict microbial interactions would put us one step closer to predicting the functionality of microbial communities and to rationally microbiome engineering.

Prospects for utilizing microbial consortia for lignin conversion

Naturally occurring microbial communities are able to decompose lignocellulosic biomass through the concerted production of a myriad of enzymes that degrade its polymeric components and assimilate the resulting breakdown compounds by members of the community. This process includes the conversion of lignin, the most recalcitrant component of lignocellulosic biomass and historically the most difficult to valorize in the context of a biorefinery. Although several fundamental questions on microbial conversion of lignin remain unanswered, it is known that some fungi and bacteria produce enzymes to break, internalize, and assimilate lignin-derived molecules. The interest in developing efficient biological lignin conversion approaches has led to a better understanding of the types of enzymes and organisms that can act on different types of lignin structures, the depolymerized compounds that can be released, and the products that can be generated through microbial biosynthetic pathways. It has become clear that the discovery and implementation of native or engineered microbial consortia could be a powerful tool to facilitate conversion and valorization of this underutilized polymer. Here we review recent approaches that employ isolated or synthetic microbial communities for lignin conversion to bioproducts, including the development of methods for tracking and predicting the behavior of these consortia, the most significant challenges that have been identified, and the possibilities that remain to be explored in this field.

Assembly of gut-derived bacterial communities follows "early-bird" resource utilization dynamics

Diet can impact host health through changes to the gut microbiota, yet we lack mechanistic understanding linking nutrient availability and microbiota composition. Here, we use thousands of microbial communities cultured in vitro from human feces to uncover simple assembly rules and develop a predictive model of community composition upon addition of single nutrients from central carbon metabolism to a complex medium. Community membership was largely determined by the donor feces, whereas relative abundances were determined by the supplemental carbon source. The absolute abundance of most taxa was independent of the supplementing nutrient, due to the ability of fast-growing organisms to quickly exhaust their niche in the complex medium and then exploit and monopolize the supplemental carbon source. Relative abundances of dominant taxa could be predicted from the nutritional preferences and growth dynamics of species in isolation, and exceptions were consistent with strain-level variation in growth capabilities. Our study reveals that community assembly follows simple rules of nutrient utilization dynamics and provides a predictive framework for manipulating gut commensal communities through nutritional perturbations.

Ecological landscapes guide the assembly of optimal microbial communities

Assembling optimal microbial communities is key for various applications in biofuel production, agriculture, and human health. Finding the optimal community is challenging because the number of possible communities grows exponentially with the number of species, and so an exhaustive search cannot be performed even for a dozen species. A heuristic search that improves community function by adding or removing one species at a time is more practical, but it is unknown whether this strategy can discover an optimal or nearly optimal community. Using consumer-resource models with and without cross-feeding, we investigate how the efficacy of search depends on the distribution of resources, niche overlap, cross-feeding, and other aspects of community ecology. We show that search efficacy is determined by the ruggedness of the appropriately-defined ecological landscape. We identify specific ruggedness measures that are both predictive of search performance and robust to noise and low sampling density. The feasibility of our approach is demonstrated using experimental data from a soil microbial community. Overall, our results establish the conditions necessary for the success of the heuristic search and provide concrete design principles for building high-performing microbial consortia.

Transcriptome, intestinal microbiome and histomorphology profiling of differences in the response of Chinese sea bass (Lateolabrax maculatus) to Aeromonas hydrophila infection

The Chinese sea bass (Lateolabrax maculatus) is an important aquaculture fish, but diseases caused by Aeromonas hydrophila have led to severe economic losses to the aquaculture industry in recent years. To date, only a few studies have focused on the relationship between the intestinal immune response and changes in intestinal microbes by A. hydrophila infection. Here, we report the transcriptome and intestinal changes in infected sea bass. Histopathological results showed that severe steatosis and vacuolation occurred in the liver and that the intestinal villi and mesentery were seriously affected after infection. By extracting total RNA from intestinal tissue and studying the transcriptome profile, 1,678 genes (1,013 upregulated and 665 downregulated) were identified as significantly differentially expressed genes (DEGs). These genes are involved in many immune-related signalling pathways, such as the NOD-like receptor, C-type lectin receptor, and Toll-like receptor signalling pathways. Moreover, the intestinal microbes of sea bass changed significantly after infection. Interestingly, at the genus level, there was an increase in Serratia, Candida arthromitus and Faecalibacterium as well as a decrease in Akkermansia and Parabacteroides after infection. The results also indicated that some of the DEGs involved in the immune response were related to the genus level of intestinal microbiota. Finally, there was a relationship between gene expression patterns and the bacterial structure in the host intestine. Our study provides a reference for the study of the immune response and particular functions of intestinal microbes of sea bass after pathogen infection.

Advances in the design of combination therapies for the treatment of tuberculosis

INTRODUCTION

Tuberculosis requires lengthy multi-drug therapy. Mycobacterium tuberculosis occupies different tissue compartments during infection, making drug access and susceptibility patterns variable. Antibiotic combinations are needed to ensure each compartment of infection is reached with effective drug treatment. Despite drug combinations' role in treating tuberculosis, the design of such combinations has been tackled relatively late in the drug development process, limiting the number of drug combinations tested. In recent years, there has been significant progress using in vitro, in vivo, and computational methodologies to interrogate combination drug effects.

AREAS COVERED

This review discusses the advances in these methodologies and how they may be used in conjunction with new successful clinical trials of novel drug combinations to design optimized combination therapies for tuberculosis. Literature searches for approaches and experimental models used to evaluate drug combination effects were undertaken.

EXPERT OPINION

We are entering an era richer in combination drug effect and pharmacokinetic/pharmacodynamic data, genetic tools, and outcome measurement types. Application of computational modeling approaches that integrate these data and produce predictive models of clinical outcomes may enable the field to generate novel, effective multidrug therapies using existing and new drug combination backbones.

A Transcriptomic Analysis of Higher-Order Ecological Interactions in a Eukaryotic Model Microbial Ecosystem

Nonlinear ecological interactions within microbial ecosystems and their contribution to ecosystem functioning remain largely unexplored. Higher-order interactions, or interactions in systems comprised of more than two members that cannot be explained by cumulative pairwise interactions, are particularly understudied, especially in eukaryotic microorganisms. The wine fermentation ecosystem presents an ideal model to study yeast ecosystem establishment and functioning. Some pairwise ecological interactions between wine yeast species have been characterized, but very little is known about how more complex, multispecies systems function. Here, we evaluated nonlinear ecosystem properties by determining the transcriptomic response of Saccharomyces cerevisiae to pairwise versus tri-species culture. The transcriptome revealed that genes expressed during pairwise coculture were enriched in the tri-species data set but also that just under half of the data set comprised unique genes attributed to a higher-order response. Through interactive protein-association network visualizations, a holistic cell-wide view of the gene expression data was generated, which highlighted known stress response and metabolic adaptation mechanisms which were specifically activated during tri-species growth. Further, extracellular metabolite data corroborated that the observed differences were a result of a biotic stress response. This provides exciting new evidence showing the presence of higher-order interactions within a model microbial ecosystem. IMPORTANCE Higher-order interactions are one of the major blind spots in our understanding of microbial ecosystems. These systems remain largely unpredictable and are characterized by nonlinear dynamics, in particular when the system is comprised of more than two entities. By evaluating the transcriptomic response of S. cerevisiae to an increase in culture complexity from a single species to two- and three-species systems, we were able to confirm the presence of a unique response in the more complex setting that could not be explained by the responses observed at the pairwise level. This is the first data set that provides molecular targets for further analysis to explain unpredictable ecosystem dynamics in yeast.

Low-cost gel-filled microwell array device for screening marine microbial consortium

In order to exploit the microbes present in the environment for their beneficial resources, effective selection and isolation of microbes from environmental samples is essential. In this study, we fabricated a gel-filled microwell array device using resin for microbial culture. The device has an integrated sealing mechanism that enables high-density isolation based on the culture of microorganisms; the device is easily manageable, facilitating observation using bright-field microscopy. This low-cost device made from polymethyl methacrylate (PMMA)/polyethylene terephthalate (PET) has 900 microwells (600 μm × 600 μm × 700 μm) filled with a microbial culture gel medium in glass slide-sized plates. It also has grooves for maintaining the moisture content in the micro-gel. The partition wall between the wells has a highly hydrophobic coating to inhibit microbial migration to neighboring wells and to prevent exchange of liquid substances. After being hermetically sealed, the device can maintain moisture in the agarose gels for 7 days. In the bacterial culture experiment using this device, environmental bacteria were isolated and cultured in individual wells after 3 days. Moreover, the isolated bacteria were then picked up from wells and re-cultured. This device is effective for the first screening of microorganisms from marine environmental samples.

Guided by the principles of microbiome engineering: Accomplishments and perspectives for environmental use

Although the accomplishments of microbiome engineering highlight its significance for the targeted manipulation of microbial communities, knowledge and technical gaps still limit the applications of microbiome engineering in biotechnology, especially for environmental use. Addressing the environmental challenges of refractory pollutants and fluctuating environmental conditions requires an adequate understanding of the theoretical achievements and practical applications of microbiome engineering. Here, we review recent cutting-edge studies on microbiome engineering strategies and their classical applications in bioremediation. Moreover, a framework is summarized for combining both top-down and bottom-up approaches in microbiome engineering toward improved applications. A strategy to engineer microbiomes for environmental use, which avoids the build-up of toxic intermediates that pose a risk to human health, is suggested. We anticipate that the highlighted framework and strategy will be beneficial for engineering microbiomes to address difficult environmental challenges such as degrading multiple refractory pollutants and sustain the performance of engineered microbiomes in situ with indigenous microorganisms under fluctuating conditions.

Pathways to engineering the phyllosphere microbiome for sustainable crop production

Current disease resistance breeding, which is largely dependent on the exploitation of resistance genes in host plants, faces the serious challenges of rapidly evolving phytopathogens. The phyllosphere is the largest biological surface on Earth and an untapped reservoir of functional microbiomes. The phyllosphere microbiome has the potential to defend against plant diseases. However, the mechanisms of how the microbiota assemble and function in the phyllosphere remain largely elusive, and this restricts the exploitation of the targeted beneficial microbes in the field. Here we review the endogenous and exogenous cues impacting microbiota assembly in the phyllosphere and how the phyllosphere microbiota in turn facilitate the disease resistance of host plants. We further construct a holistic framework by integrating of holo-omics, genetic manipulation, culture-dependent characterization and emerging artificial intelligence techniques, such as deep learning, to engineer the phyllosphere microbiome for sustainable crop production.

Nodule-associated diazotrophic community succession is driven by developmental phases combined with microhabitat of Sophora davidii

Nodule-associated nitrogen-fixing microorganisms (diazotrophs) residing in legume root nodules, and they have the potential to enhance legume survival. However, the succession characteristics and mechanisms of leguminous diazotrophic communities remain largely unexplored. We performed a high-throughput nifH amplicon sequencing with samples of root nodules and soil in the three developmental phases (young nodules, active nodules and senescent nodules) of the Sophora davidii (Franch.) Skeels root nodules, aiming to investigate the dynamics of nodule-endophytic diazotrophs during three developmental phases of root nodules. The results demonstrated the presence of diverse diazotrophic bacteria and successional community shifting dominated by Mesorhizobium and Bradyrhizobium inside the nodule according to the nodule development. The relative abundance decreased for Mesorhizobium, while decreased first and then increased for Bradyrhizobium in nodule development from young to active to senescent. Additionally, strains M. amorphae BT-30 and B. diazoefficiens B-26 were isolated and selected to test the interaction between them in co-cultured conditions. Under co-culture conditions: B. diazoefficiens B-26 significantly inhibited the growth of M. amorphae BT-30. Intriguingly, growth of B. diazoefficiens B-26 was significantly promoted by co'culture with M. amorphae BT-30 and could utilize some carbon and nitrogen sources that M. amorphae BT-30 could not. Additionally, the composition of microbial community varied in root nodules, in rhizosphere and in bulk soil. Collectively, our study highlights that developmental phases of nodules and the host microhabitat were the key driving factors for the succession of nodule-associated diazotrophic community.

Positive biofilms to guide surface microbial ecology in livestock buildings

The increase in human consumption of animal proteins implies changes in the management of meat production. This is followed by increasingly restrictive regulations on antimicrobial products such as chemical biocides and antibiotics, used in particular to control pathogens that can spread zoonotic diseases. Aligned with the One Health concept, alternative biological solutions are under development and are starting to be used in animal production. Beneficial bacteria able to form positive biofilms and guide surface microbial ecology to limit microbial pathogen settlement are promising tools that could complement existing biosecurity practices to maintain the hygiene of livestock buildings. Although the benefits of positive biofilms have already been documented, the associated fundamental mechanisms and the rationale of the microbial composition of these new products are still sparce. This review provides an overview of the envisioned modes of action of positive biofilms used on livestock building surfaces and the resulting criteria for the selection of the appropriate microorganisms for this specific application. Limits and advantages of this biosecurity approach are discussed as well as the impact of such practices along the food chain, from farm to fork.

Rapid parallel generation of a fluorescently barcoded drop library from a microtiter plate using the plate-interfacing parallel encapsulation (PIPE) chip

In drop-based microfluidics, an aqueous sample is partitioned into drops using individual pump sources that drive water and oil into a drop-making device. Parallelization of drop-making devices is necessary to achieve high-throughput screening of multiple experimental conditions, especially in time-sensitive studies. Here, we present the plate-interfacing parallel encapsulation (PIPE) chip, a microfluidic chip designed to generate 50 to 90 μm diameter drops of up to 96 different conditions in parallel by interfacing individual drop makers with a standard 384-well microtiter plate. The PIPE chip is used to generate two types of optically barcoded drop libraries consisting of two-color fluorescent particle combinations: a library of 24 microbead barcodes and a library of 192 quantum dot barcodes. Barcoded combinations in the drop libraries are rapidly measured within a microfluidic device using fluorescence detection and distinct barcoded populations in the fluorescence drop data are identified using DBSCAN data clustering. Signal analysis reveals that particle size defines the source of dominant noise present in the fluorescence intensity distributions of the barcoded drop populations, arising from Poisson loading for microbeads and shot noise for quantum dots. A barcoded population from a drop library is isolated using fluorescence-activated drop sorting, enabling downstream analysis of drop contents. The PIPE chip can improve multiplexed high-throughput assays by enabling simultaneous encapsulation of barcoded samples stored in a microtiter plate and reducing sample preparation time.

Maize-soybean relay strip intercropping reshapes the rhizosphere bacterial community and recruits beneficial bacteria to suppress Fusarium root rot of soybean

Rhizosphere microbes play a vital role in plant health and defense against soil-borne diseases. Previous studies showed that maize-soybean relay strip intercropping altered the diversity and composition of pathogenic Fusarium species and biocontrol fungal communities in the soybean rhizosphere, and significantly suppressed soybean root rot. However, whether the rhizosphere bacterial community participates in the regulation of this intercropping on soybean root rot is not clear. In this study, the rhizosphere soil of soybean healthy plants was collected in the continuous cropping of maize-soybean relay strip intercropping and soybean monoculture in the fields, and the integrated methods of microbial profiling, dual culture assays in vitro, and pot experiments were employed to systematically investigate the diversity, composition, and function of rhizosphere bacteria related to soybean root rot in two cropping patterns. We found that intercropping reshaped the rhizosphere bacterial community and increased microbial community diversity, and meanwhile, it also recruited much richer and more diverse species of Pseudomonas sp., Bacillus sp., Streptomyces sp., and Microbacterium sp. in soybean rhizosphere when compared with monoculture. From the intercropping, nine species of rhizosphere bacteria displayed good antagonism against the pathogen Fusarium oxysporum B3S1 of soybean root rot, and among them, IRHB3 (Pseudomonas chlororaphis), IRHB6 (Streptomyces), and IRHB9 (Bacillus) were the dominant bacteria and extraordinarily rich. In contrast, MRHB108 (Streptomyces virginiae) and MRHB205 (Bacillus subtilis) were the only antagonistic bacteria from monoculture, which were relatively poor in abundance. Interestingly, introducing IRHB3 into the cultured substrates not only significantly promoted the growth and development of soybean roots but also improved the survival rate of seedlings that suffered from F. oxysporum infection. Thus, this study proves that maize-soybean relay strip intercropping could help the host resist soil-borne Fusarium root rot by reshaping the rhizosphere bacterial community and driving more beneficial microorganisms to accumulate in the soybean rhizosphere.

Accelerated High-throughput Plant Imaging and Phenotyping System.. [DOI: 10.1101/2022.09.28.509964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

The complex web of interactions in biological communities is an area of study that requires large multifactorial experiments with sufficient statistical power. The use of automated tools can reduce the time and labor associated with experiment setup, data collection, and analysis in experiments aimed at untangling these webs. Here we demonstrate tools for high-throughput experimentation (HTE) in duckweeds, small aquatic plants that are amenable to autonomous experimental preparation and image-based phenotyping. We showcase the abilities of our HTE system in a study with 6,000 experimental units grown across 1,000 different nutrient environments. The use of our automated tools facilitated the collection and analysis of time-resolved growth data, which revealed finer dynamics of plant-microbe interactions across environmental gradients. Altogether, our HTE system can run experiments of up to 11,520 experimental units and can be adapted to studies with other small organisms.

Microfluidics for antibiotic susceptibility testing

The rise of antibiotic resistance is a threat to global health. Rapid and comprehensive analysis of infectious strains is critical to reducing the global use of antibiotics, as informed antibiotic use could slow down the emergence of resistant strains worldwide. Multiple platforms for antibiotic susceptibility testing (AST) have been developed with the use of microfluidic solutions. Here we describe microfluidic systems that have been proposed to aid AST. We identify the key contributions in overcoming outstanding challenges associated with the required degree of multiplexing, reduction of detection time, scalability, ease of use, and capacity for commercialization. We introduce the reader to microfluidics in general, and we analyze the challenges and opportunities related to the field of microfluidic AST.

New opportunities in plant microbiome engineering for increasing agricultural sustainability under stressful conditions

Plant microbiome (or phytomicrobiome) engineering (PME) is an anticipated untapped alternative strategy that could be exploited for plant growth, health and productivity under different environmental conditions. It has been proven that the phytomicrobiome has crucial contributions to plant health, pathogen control and tolerance under drastic environmental (a)biotic constraints. Consistent with plant health and safety, in this article we address the fundamental role of plant microbiome and its insights in plant health and productivity. We also explore the potential of plant microbiome under environmental restrictions and the proposition of improving microbial functions that can be supportive for better plant growth and production. Understanding the crucial role of plant associated microbial communities, we propose how the associated microbial actions could be enhanced to improve plant growth-promoting mechanisms, with a particular emphasis on plant beneficial fungi. Additionally, we suggest the possible plant strategies to adapt to a harsh environment by manipulating plant microbiomes. However, our current understanding of the microbiome is still in its infancy, and the major perturbations, such as anthropocentric actions, are not fully understood. Therefore, this work highlights the importance of manipulating the beneficial plant microbiome to create more sustainable agriculture, particularly under different environmental stressors.