Microbial Host Research Articles

Proteomic and single-cell analysis shed new light on the anti-inflammatory role of interferonβ in chronic periodontitis.

Periodontitis, a condition that results in periodontal attachment loss and alveolar bone resorption, contributes to the global burden of oral disease. The underlying mechanism of periodontitis involves the dysbiosis and dyshomeostasis between host and oral microbes, among which the macrophage is one of the major innate immune cell players, producing interferon β (IFNβ) in response to bacterial infection. The objective of this research was to examine the interaction of macrophages with periodontitis and the role and mechanism of IFNβ on macrophages. IFNβ has been shown to have the potential to induce the differentiation of M1 to M2 macrophages, which are stimulated by low levels of lipopolysaccharide (LPS). Additionally, IFNβ has been demonstrated to promote the production of ISG15 by macrophages, which leads to the inhibition of the innate immune response. Moreover, our investigation revealed that IFNβ has the potential to augment the secretion of ISG15 and its downstream cytokine, IL10, in LPS-stimulated macrophages. Single-cell analysis was conducted on the gingival tissues of patients with periodontitis, which revealed a higher proportion of macrophages in the periodontitis-diseased tissue and increased expression of IFNβ, ISG15, and IL10. Gene Set Enrichment Analysis indicated that bacterial infection was associated with upregulation of IFNβ, ISG15, and IL10. Notably, only IL10 has been linked to immunosuppression, indicating that the IFNβ-ISG15-IL10 axis might promote an anti-inflammatory response in periodontitis through IL10 expression. It is also found that macrophage phenotype transitions in periodontitis involve the release of higher levels of IFNβ, ISG15, and IL10 by the anti-inflammatory M2 macrophage phenotype compared to the pro-inflammatory M1 phenotype and myeloid-derived suppressor cells (MDSCs). This implies that the IFNβ-induced production of IL10 might be linked to the M2 macrophage phenotype. Furthermore, cell communication analysis demonstrated that IL10 can promote fibroblast proliferation in periodontal tissues via STAT3 signaling.

CRISPR-dCpf1 mediated whole genome crRNA inhibition library for high-throughput screening of growth characteristic genes in Bacillus amyloliquefaciens LB1ba02

Bacillus amyloliquefaciens LB1ba02 is generally recognized as food safe (GRAS) microbial host and important enzyme-producing strain in the industry. However, autolysis affects the growth of bacteria, further affecting the yield of target products. Besides, the restriction-modification system, existed in B. amyloliquefaciens LB1ba02, results in a low transformation efficiency, which further leads to a lack of high-throughput screening tools. Here, we constructed a genome-wide crRNA inhibition library based on the CRISPR/dCpf1 system and high-throughput screening of related genes affecting the cell growth and autolysis using flow cytometry in B. amyloliquefaciens LB1ba02. The whole genome crRNA library was first validated for resistance to the toxic chemical 5-fluorouracil, and then used for validation of essential genes. In addition, seven gene loci (oppD, flil, tuaA, prmA, sigO, hslU, and GE03231) that affect the growth characteristics of LB1ba02 were screened. Among them, the Opp system had the greatest impact on growth. When the expression of operon oppA-oppB-oppC-oppD-oppF was inhibited, the cell growth difference was most significant. Inhibition of other sites could also promote rapid growth of bacteria to varying degrees; however, inhibition of GE03231 site accelerated cell autolysis. Therefore, the whole genome crRNA inhibition library is well suited for B. amyloliquefaciens LB1ba02 and can be further applied to high-throughput mining of other functional genes.

The microbiome of African penguins (Spheniscus demersus) under managed care resembles that of wild marine mammals and birds

Animals under managed care in zoos and aquariums are ideal surrogate study subjects for endangered species that are difficult to obtain in the wild. We compared the fecal and oral microbiomes of healthy, managed African penguins (Spheniscus demersus) to those of other domestic and wild vertebrate hosts to determine how host identity, diet, and environment shape the penguin microbiome. The African penguin oral microbiome was more similar to that of piscivorous marine mammals, suggesting that diet and a marine environment together play a strong role in shaping the oral microbiome. Conversely, the penguin cloaca/fecal microbiome was more similar to that of other birds, suggesting that host phylogeny plays a significant role in shaping the gut microbiome. Although the penguins were born under managed care, they had a gut microbiome more similar to that of wild bird species compared to domesticated (factory-farmed) birds, suggesting that the managed care environment and diet resemble those experienced by wild birds. Finally, the microbiome composition at external body sites was broadly similar to that of the habitat, suggesting sharing of microbes between animals and their environment. Future studies should link these results to microbial functional capacity and host health, which will help inform conservation efforts.

Microbial Exudates as Biostimulants: Role in Plant Growth Promotion and Stress Mitigation.

Microbes hold immense potential, based on the fact that they are widely acknowledged for their role in mitigating the detrimental impacts of chemical fertilizers and pesticides, which were extensively employed during the Green Revolution era. The consequence of this extensive use has been the degradation of agricultural land, soil health and fertility deterioration, and a decline in crop quality. Despite the existence of environmentally friendly and sustainable alternatives, microbial bioinoculants encounter numerous challenges in real-world agricultural settings. These challenges include harsh environmental conditions like unfavorable soil pH, temperature extremes, and nutrient imbalances, as well as stiff competition with native microbial species and host plant specificity. Moreover, obstacles spanning from large-scale production to commercialization persist. Therefore, substantial efforts are underway to identify superior solutions that can foster a sustainable and eco-conscious agricultural system. In this context, attention has shifted towards the utilization of cell-free microbial exudates as opposed to traditional microbial inoculants. Microbial exudates refer to the diverse array of cellular metabolites secreted by microbial cells. These metabolites enclose a wide range of chemical compounds, including sugars, organic acids, amino acids, peptides, siderophores, volatiles, and more. The composition and function of these compounds in exudates can vary considerably, depending on the specific microbial strains and prevailing environmental conditions. Remarkably, they possess the capability to modulate and influence various plant physiological processes, thereby inducing tolerance to both biotic and abiotic stresses. Furthermore, these exudates facilitate plant growth and aid in the remediation of environmental pollutants such as chemicals and heavy metals in agroecosystems. Much like live microbes, when applied, these exudates actively participate in the phyllosphere and rhizosphere, engaging in continuous interactions with plants and plant-associated microbes. Consequently, they play a pivotal role in reshaping the microbiome. The biostimulant properties exhibited by these exudates position them as promising biological components for fostering cleaner and more sustainable agricultural systems.

Usage of Cultured Human Fecal Microbiota for Colonization of Caenorhabditis elegans to Study Host–Microbe Interaction

The role of the microbiota in health and disease is a research area receiving much attention in academia and industry. A person’s microbiota refers to a community of microorganisms found mainly in the gut. It is estimated that around 39 trillion bacteria can be found on and inside the human body and there is increasing evidence that they influence human health. Advances in sequencing techniques are revolutionizing characterization of the human microbiome. However, causality and underlying molecular mechanisms are still largely unknown due to the complexity of the human microbiome and its interaction with the host. Turning towards simpler host organisms and using well-defined microbiomes are two ways to strengthen studies of causality and mechanism. Here, we show that the nematode Caenorhabditis elegans can be used as host to study sub-microbiomes derived from human feces samples prepared for fecal microbiota transplantation following a simple feeding protocol. Approximately 200 amplicon sequence variants were identified in the worm gut following transplantation with human fecal microbiota samples. We find that the gut microbiome does not simply reflect the bacterial community initially fed to the worms. Hence, our experimental setup can be used to identify and characterize host genetic factors shaping the microbiota and improving our understanding of host–human microbiome interactions.

Identification and functional analysis of novel protein-encoding sequences related to stress-resistance.

Currently, industrial bioproducts are less competitive than chemically produced goods due to the shortcomings of conventional microbial hosts. Thus, is essential developing robust bacteria for improved cell tolerance to process-specific parameters. In this context, metagenomic approaches from extreme environments can provide useful biological parts to improve bacterial robustness. Here, in order to build genetic constructs that increase bacterial resistance to diverse stress conditions, we recovered novel protein-encoding sequences related to stress-resistance from metagenomic databases using an in silico approach based on Hidden-Markov-Model profiles. For this purpose, we used metagenomic shotgun sequencing data from microbial communities of extreme environments to identify genes encoding chaperones and other proteins that confer resistance to stress conditions. We identified and characterized 10 novel protein-encoding sequences related to the DNA-binding protein HU, the ATP-dependent protease ClpP, and the chaperone protein DnaJ. By expressing these genes in Escherichia coli under several stress conditions (including high temperature, acidity, oxidative and osmotic stress, and UV radiation), we identified five genes conferring resistance to at least two stress conditions when expressed in E. coli. Moreover, one of the identified HU coding-genes which was retrieved from an acidic soil metagenome increased E. coli tolerance to four different stress conditions, implying its suitability for the construction of a synthetic circuit directed to expand broad bacterial resistance.

Viral but not bacterial community successional patterns reflect extreme turnover shortly after rewetting dry soils.

As central members of soil trophic networks, viruses have the potential to drive substantial microbial mortality and nutrient turnover. Pinpointing viral contributions to terrestrial ecosystem processes remains a challenge, as temporal dynamics are difficult to unravel in the spatially and physicochemically heterogeneous soil environment. In Mediterranean grasslands, the first rainfall after seasonal drought provides an ecosystem reset, triggering microbial activity during a tractable window for capturing short-term dynamics. Here, we simulated precipitation in microcosms from four distinct dry grassland soils and generated 144 viromes, 84 metagenomes and 84 16S ribosomal RNA gene amplicon datasets to characterize viral, prokaryotic and relic DNA dynamics over 10 days. Vastly different viral communities in each soil followed remarkably similar successional trajectories. Wet-up triggered a significant increase in viral richness, followed by extensive compositional turnover. Temporal succession in prokaryotic communities was much less pronounced, perhaps suggesting differences in the scales of activity captured by viromes (representing recently produced, ephemeral viral particles) and total DNA. Still, differences in the relative abundances of Actinobacteria (enriched in dry soils) and Proteobacteria (enriched in wetted soils) matched those of their predicted phages, indicating viral predation of dominant bacterial taxa. Rewetting also rapidly depleted relic DNA, which subsequently reaccumulated, indicating substantial new microbial mortality in the days after wet-up, particularly of the taxa putatively under phage predation. Production of abundant, diverse viral particles via microbial host cell lysis appears to be a conserved feature of the early response to soil rewetting, and results suggest the potential for 'Cull-the-Winner' dynamics, whereby viruses infect and cull but do not decimate dominant host populations.

A-242 Integrating Respiratory Metagenomics and Metatranscriptomics for Diagnosis of Lung Cancer and Infection in Patients with Pulmonary Diseases

Abstract Background Advances in unbiased metagenomic next generation sequencing (mNGS) technologies have enabled the study of microbial and host genetic material (DNA and RNA) in one test. In this study, we aimed to develop machine learning-based differential diagnostic models (MLBDDMs) using the metagenomic and human transcriptomic data generated by an affordable bronchoalveolar lavage fluid (BLAF) mNGS assay and investigated their clinical utility for early differential diagnosis of lung cancer and pulmonary infection in patients with pulmonary diseases. Methods We recruited 775 patients with respiratory disease, including 160 pathologically diagnosed lung cancer and clinically diagnosed 615 infectious causes (131 tuberculosis, 172 fungal pneumonia and 312 bacterial pneumonia). An affordable mNGS assay on BALF samples collected from these patients on admission were performed. Using the generated mNGS data, we compared the differences in microbial diversity and host gene expression between lung cancer patients and pulmonary infection patients. The BLAF mNGS datasets of lung cancer group and each infection group were then randomly divided into a training dataset and a validation dataset at a ratio of approximately 3:1 for developing optimal MLBDDMs that can be used to distinguish lung cancer from various pulmonary infections. Results By comparing the BALF mNGS data of lung cancer (n = 160) and pulmonary infection (n = 615), we found that the infection group had higher microbial diversity than lung cancer group (P-value < 0.05). Respiratory colonizing microorganisms (e.g., Corynebacterium propinquum and Bacteroides uniformis) and pathogen (Mycobacterium tuberculosis and Cryptococcus neoformans) were found as differential microbes (adjusted p-value < 0.05, LDA score > 2). From BALF gene expression data, we detected 175 genes enriched in NOD-like receptor signaling pathway and chemokine signaling pathway differentially expressed between lung cancer and pulmonary infection groups (False Discovery Rate, FDR < 0.05). Cell composition analysis revealed that macrophage M1 was higher in lung infection group (P-value < 0.001), whereas mast cell activated and DCs activated were higher in lung cancer group (P-value < 0.001, P-value = 0.016). We integrated the metagenomic (microbial composition and human copy number variation) and transcriptomic data (host differentially expressed genes and cell composition) generated by the BALF mNGS assay with eleven machine learning classifiers to establish diagnosis models for distinguishing lung cancer from pulmonary infection (we named LC/PI model). The results showed that a Random Forest diagnostic model (the RF-LC/PI model) had optimal performance, with a sensitivity and specificity of 86.7% and 87.8%, respectively, in distinguishing lung cancer from pulmonary infection (area under the receiver operating characteristic curve [AUC] = 0.838 in the training dataset; AUC = 0.79 in a held-out validation dataset). Similar to the establishment of the LC/PI model, we further developed three diagnostic models for distinguishing lung cancer and tuberculosis (LC/TB model), lung cancer and fungal pneumonia (LC/FP model), and lung cancer and bacterial pneumonia (LC/BP model), respectively. The AUC of these three models were 0.91, 0.88, 0.91, respectively, showing a high differential diagnosis accuracy. Conclusions We have established MLBDDMs using BALF metagenomic and metatranscriptomic data and achieved superior accuracy for differentiating lung cancer and pulmonary infections, which could promote early diagnosis of pulmonary diseases and benefit more patients with one test.

Host‐Variable‐Embedding Augmented Microbiome‐Based Simultaneous Detection of Multiple Diseases by Deep Learning

Microbiome has emerged as a promising indicator or predictor of human diseases. However, previous studies have typically labeled each specimen as either healthy or with a specific disease, ignoring prevalence of complications or comorbidities in actual cohorts, which may confound the microbial–disease associations. For instance, a patient may suffer from multiple diseases, making it challenging to detect their health status accurately. Furthermore, host phenotypes like physiological characteristics and lifestyles can alter microbiome structure, but such information has not yet been fully utilized in data models. To address these issues, a highly explainable deep learning (DL) method called Meta‐Spec is proposed. Using a deep neural network (DNN)‐based approach, it encodes and embeds refined host variables with microbiome features, enabling the detection of multiple diseases simultaneously. Experiments show that Meta‐Spec outperforms regular machine learning (ML) strategies for multilabel disease screening in several cohorts. More importantly, Meta‐Spec successfully detects comorbidities that are often missed by other approaches. In addition, for its high interpretability, Meta‐Spec captures key factors that shape disease patterns from host variables and microbes. Hence, these efforts improve the feasibility and sensitivity of microbiome‐based disease screening in practical scenarios, representing a significant step toward personalized medicine and better health outcomes.

Narrow host range phages infect essential bacteria for water purification reactions in groundwater-fed rapid sand filters

Biofiltration is used worldwide to provide safe potable water due to its low energy demand and excellent treatment performance. For instance, in Denmark, over 95% of drinking water is supplied through groundwater-fed rapid sand filters (RSF). Bacteriophages, viruses that infect bacteria, have been shown to shape the taxonomic and functional composition of microbial communities across a range of natural and engineering systems. However, phages in the biofiltration systems are rarely studied, despite the central role microbes play in water purification. To probe this, metagenomic data from surface water, groundwater and mixed source water biofiltration units (n = 26 from China, Europe and USA) for drinking water production were analysed to characterize prokaryotic viruses and to identify their potential microbial hosts. The source water type and geographical location are found to exert influence on the composition of the phageome in biofilters. Although the viral abundance (71,676 ± 17,841 RPKM) in biofilters is only 14.4% and 17.0% lower than those of the nutrient-rich wastewater treatment plants and fresh surface waters, the richness (1,441 ± 1,046) and diversity (Inverse Simpson: 91 ± 61) in biofiltration units are significantly less by a factor of 2–5 and 3–4, respectively. In depth analysis of data from 24 groundwater-fed RSFs in Denmark revealed a core phageome shared by most RSFs, which was consistently linked to dominant microbial hosts involved in key biological reactions for water purification. Finally, the high number of specific links detected between phages and bacterial species and the large proportion of lytic phages (77%) led to the conjecture that phages regulate bacterial populations through predation, preventing the proliferation of dominant species and contributing to the established functional redundancy among the dominant microbial groups. In conclusion, bacteriophages are likely to play a significant role in water treatment within biofilters, particularly through interactions with key bacterial species.

Recovery of the gut microbiome following enteric infection and persistence of antimicrobial resistance genes in specific microbial hosts

Enteric pathogens cause widespread foodborne illness and are increasingly resistant to important antibiotics yet their ecological impact on the gut microbiome and resistome is not fully understood. Herein, shotgun metagenome sequencing was applied to stool DNA from 60 patients (cases) during an enteric bacterial infection and after recovery (follow-ups). Overall, the case samples harbored more antimicrobial resistance genes (ARGs) with greater resistome diversity than the follow-up samples (p < 0.001), while follow-ups had more diverse gut microbiota (p < 0.001). Although cases were primarily defined by genera Escherichia, Salmonella, and Shigella along with ARGs for multi-compound and multidrug resistance, follow-ups had a greater abundance of Bacteroidetes and Firmicutes phyla and resistance genes for tetracyclines, macrolides, lincosamides, and streptogramins, and aminoglycosides. A host-tracking analysis revealed that Escherichia was the primary bacterial host of ARGs in both cases and follow-ups, with a greater abundance occurring during infection. Eleven distinct extended spectrum beta-lactamase (ESBL) genes were identified during infection, with some detectable upon recovery, highlighting the potential for gene transfer within the community. Because of the increasing incidence of disease caused by foodborne pathogens and their role in harboring and transferring resistance determinants, this study enhances our understanding of how enteric infections impact human gut ecology.

Alternative PCR-Based Approaches for Generation of Komagataella phaffii Strains.

Komagataella phaffii (Pichia pastoris) is a widely known microbial host for recombinant protein production and an emerging model organism in fundamental research. The development of new materials and techniques on this yeast improves heterologous protein synthesis. One of the most prominent ways to enhance protein production efficiency is to select K. phaffii strains with multiple expression cassettes and generate MutS strains using various vectors. In this study, we demonstrate approaches to expand the applications of pPICZ series vectors. Procedures based on PCR amplification and in vivo cloning allow rapid exchange of selectable markers. The combination of PCR amplification with split-marker-mediated transformation allows the development of K. phaffii MutS strains with two expression cassettes using pPICZ vectors. Both PCR-based approaches were applied to efficiently produce interleukin-2 mimetic Neo-2/15 in K. phaffii. The described techniques provide alternative ways to generate and improve K. phaffii strains without the need for obtaining new specific vectors or additional cloning of expression cassettes.

A Review on Soil and Phytomicrobiome for Plant Disease Management

Pests and pathogens significantly reduce crop yields and cost the global economy USD 540 billion annually. The phytomicrobiome is becoming a cornerstone of a comprehensive rethink of agricultural management practices, with microbiome-assisted plant breeding focusing on three principles: minimal soil disturbance, continuous soil cover with crops, cover crops, or a mulch of crop residues, and crop rotation. Plant diseases, phytomicrobiomes, and agricultural practices can all affect plant health. Conservational agricultural practices like minimum tillage and no-tillage have been implemented to reduce anthropogenic activity and preserve microbial diversity. Mulching is a common practice in agriculture to stop moisture loss, maintain soil temperature, control weed growth, and stop soil erosion. Monoculture farming is the practice of cultivating a single crop continuously over several growing seasons on the same field. Intercropping systems encourage the growth of beneficial fungi, such as mycorrhiza, endophytes, saprophytes, decomposers, and bioprotective fungi, and can benefit forest ecosystems by creating disease-suppressive soils. By combining conservation tillage and crop rotation, farmers can reduce disease pressure by disrupting the life cycles of soil-borne pathogens linked to particular crops or genotypes. Composed manure and plant residues can control plant pathogens like Pythium, but the effect is thought to be due to microbial competition or plant host resistance. Green manure can control plant diseases brought on by pathogens in Rhizoctonia, Verticillium, Sclerotinia, Phythophthora, Pythium, Aphanomyces, and Macrophomina, but manure-derived fertilizers may contain antibiotic resistance genes and mobilomes, which could pose risks to both human and animal health. Climate change impacts crop yields by reducing crop physiology and productivity, increasing pathogen diversity, and affecting pathogen genetic traits and speciation. The state of the environment and the availability of suitable hosts significantly impact the ability of these pathogens to survive and spread. However, PGPRs are unpredictable and soil warming can interfere with their effectiveness. Interdisciplinary collaborations between plant biologists, microbiologists, climatologists, and agronomists are required to create effective strategies to reduce the effects of climate change on plant health, crop production, and ecosystem stability.

Deconstructing taxa x taxa x environment interactions in the microbiota: A theoretical examination

A major objective of microbial ecology is to identify how the composition of microbial taxa shapes host phenotypes. However, most studies focus on pairwise interactions and ignore the potentially significant effects of higher-order microbial interactions.Here, we quantify the effects of higher-order interactions among taxa on host infection risk. We apply our approach to an in silico dataset that is built to resemble a population of insect hosts with gut-associated microbial communities at risk of infection from an intestinal parasite across a breadth of nutrient environmental contexts.We find that the effect of higher-order interactions is considerable and can change appreciably across environmental contexts. Furthermore, we show that higher-order interactions can stabilize community structure thereby reducing host susceptibility to parasite invasion.Our approach illustrates how incorporating the effects of higher-order interactions among gut microbiota across environments can be essential for understanding their effects on host phenotypes.

Development of a Novel D-Lactic Acid Production Platform Based on Lactobacillus saerimneri TBRC 5746.

D-Lactic acid is a chiral, three-carbon organic acid, that bolsters the thermostability of polylactic acid. In this study, we developed a microbial production platform for the high-titer production of D-lactic acid. We screened 600 isolates of lactic acid bacteria (LAB) and identified twelve strains that exclusively produced D-lactic acid in high titers. Of these strains, Lactobacillus saerimneri TBRC 5746 was selected for further development because of its homofermentative metabolism. We investigated the effects of high temperature and the use of cheap, renewable carbon sources on lactic acid production and observed a titer of 99.4g/L and a yield of 0.90g/g glucose (90% of the theoretical yield). However, we also observed L-lactic acid production, which reduced the product's optical purity. We then used CRISPR/dCas9-assisted transcriptional repression to repress the two Lldh genes in the genome of L. saerimneri TBRC 5746, resulting in a 38% increase in D-lactic acid production and an improvement in optical purity. This is the first demonstration of CRISPR/dCas9-assisted transcriptional repression in this microbial host and represents progress toward efficient microbial production of D-lactic acid.

Say YES to single-gene lysis!

Viral strategies to lyse host microbes can sometimes involve just a single gene, although the mechanism of action can be hard to discern. In Science, Orta etal. present structures of a protein complex in which protein E of bacteriophage φX174 functions to inhibit host peptidoglycan synthesis, thereby inducing lysis.

Design of an alternate antibody fragment format that can be produced in the cytoplasm of Escherichia coli

With increased accessibility and tissue penetration, smaller antibody formats such as antibody fragments (Fab) and single chain variable fragments (scFv) show potential as effective and low-cost choices to full-length antibodies. These formats derived from the modular architecture of antibodies could prove to be game changers for certain therapeutic and diagnostic applications. Microbial hosts have shown tremendous promise as production hosts for antibody fragment formats. However, low target protein yields coupled with the complexity of protein folding result in production limitations. Here, we report an alternative antibody fragment format ‘FabH3’ designed to overcome some key bottlenecks associated with the folding and production of Fabs. The FabH3 molecule is based on the Fab format with the constant domains replaced by engineered immunoglobulin G1 (IgG1) CH3 domains capable of heterodimerization based on the electrostatic steering approach. We show that this alternative antibody fragment format can be efficiently produced in the cytoplasm of E. coli using the catalyzed disulfide-bond formation system (CyDisCo) in a natively folded state with higher soluble yields than its Fab counterpart and a comparable binding affinity against the target antigen.

Gut microbial carbohydrate metabolism contributes to insulin resistance

Insulin resistance is the primary pathophysiology underlying metabolic syndrome and type 2 diabetes1,2. Previous metagenomic studies have described the characteristics of gut microbiota and their roles in metabolizing major nutrients in insulin resistance3–9. In particular, carbohydrate metabolism of commensals has been proposed to contribute up to 10% of the host’s overall energy extraction10, thereby playing a role in the pathogenesis of obesity and prediabetes3,4,6. Nevertheless, the underlying mechanism remains unclear. Here we investigate this relationship using a comprehensive multi-omics strategy in humans. We combine unbiased faecal metabolomics with metagenomics, host metabolomics and transcriptomics data to profile the involvement of the microbiome in insulin resistance. These data reveal that faecal carbohydrates, particularly host-accessible monosaccharides, are increased in individuals with insulin resistance and are associated with microbial carbohydrate metabolisms and host inflammatory cytokines. We identify gut bacteria associated with insulin resistance and insulin sensitivity that show a distinct pattern of carbohydrate metabolism, and demonstrate that insulin-sensitivity-associated bacteria ameliorate host phenotypes of insulin resistance in a mouse model. Our study, which provides a comprehensive view of the host–microorganism relationships in insulin resistance, reveals the impact of carbohydrate metabolism by microbiota, suggesting a potential therapeutic target for ameliorating insulin resistance.

Identifying and tracking mobile elements in evolving compost communities yields insights into the nanobiome

Microbial evolution is driven by rapid changes in gene content mediated by horizontal gene transfer (HGT). While mobile genetic elements (MGEs) are important drivers of gene flux, the nanobiome—the zoo of Darwinian replicators that depend on microbial hosts—remains poorly characterised. New approaches are necessary to increase our understanding beyond MGEs shaping individual populations, towards their impacts on complex microbial communities. A bioinformatic pipeline (xenoseq) was developed to cross-compare metagenomic samples from microbial consortia evolving in parallel, aimed at identifying MGE dissemination, which was applied to compost communities which underwent periodic mixing of MGEs. We show that xenoseq can distinguish movement of MGEs from demographic changes in community composition that otherwise confounds identification, and furthermore demonstrate the discovery of various unexpected entities. Of particular interest was a nanobacterium of the candidate phylum radiation (CPR) which is closely related to a species identified in groundwater ecosystems (Candidatus Saccharibacterium), and appears to have a parasitic lifestyle. We also highlight another prolific mobile element, a 313 kb plasmid hosted by a Cellvibrio lineage. The host was predicted to be capable of nitrogen fixation, and acquisition of the plasmid coincides with increased ammonia production. Taken together, our data show that new experimental strategies combined with bioinformatic analyses of metagenomic data stand to provide insight into the nanobiome as a driver of microbial community evolution.

Microbiome characteristics description of COVID-19 patients based on bulk RNA-seq and scRNA-Seq data

After infection with SARS-CoV-2, the microbiome inside the human body changes dramatically. By re-annotating microbial sequences in bulk RNA-seq and scRNA-seq data of COVID-19 patients, we described the cellular microbial landscape of COVID-19 patients and identified characteristic microorganisms in various tissues. We found that Acinetobacter lwoffii was highly correlated with COVID-19 symptoms and might disrupt some pathways of patients by interacting with the host and other microbes, such as Klebsiella pneumoniae. We further identified characteristic microorganisms specific to cell type, indicating the enrichment preference of some microbes. We also revealed the co-infection of SARS-CoV-2 with hMPV, which may cause the development of COVID-19. Overall, we demonstrated that the presence of intracellular microorganisms in COVID-19 patients and the synergies between microorganisms were strongly correlated with disease progression, providing a theoretical basis for COVID-19 treatment in a certain extent.