A host-microbiota interactome reveals extensive transkingdom connectivity.

The myriad microorganisms that live in close association with humans have diverse effects on physiology, yet the molecular bases for these impacts remain mostly unknown1-3. Classical pathogens often invade host tissues and modulate immune responses through interactions with human extracellular and secreted proteins (the 'exoproteome'). Commensal microorganisms may also facilitate niche colonization and shape host biology by engaging host exoproteins; however, direct exoproteome-microbiota interactions remain largely unexplored. Here we developed and validated a novel technology, BASEHIT, that enables proteome-scale assessment of human exoproteome-microbiome interactions. Using BASEHIT, we interrogated more than 1.7 million potential interactions between 519 human-associated bacterial strains from diverse phylogenies and tissues of origin and 3,324 human exoproteins. The resulting interactome revealed an extensive network of transkingdom connectivity consisting of thousands of previously undescribed host-microorganism interactions involving 383 strains and 651 host proteins. Specific binding patterns within this network implied underlying biological logic; for example, conspecific strains exhibited shared exoprotein-binding patterns, and individual tissue isolates uniquely bound tissue-specific exoproteins. Furthermore, we observed dozens of unique and often strain-specific interactions with potential roles in niche colonization, tissue remodelling and immunomodulation, and found that strains with differing host interaction profiles had divergent interactions with host cells in vitro and effects on the host immune system in vivo. Overall, these studies expose a previously unexplored landscape of molecular-level host-microbiota interactions that may underlie causal effects of indigenous microorganisms on human health and disease.

QNetDiff: a quantitative measurement of network rewiring.

Bacteria in the human body, particularly in the large intestine, are known to be associated with various diseases. To identify disease-associated bacteria (markers), a typical method is to statistically compare the relative abundance of bacteria between healthy subjects and diseased patients. However, since bacteria do not necessarily cause diseases in isolation, it is also important to focus on the interactions and relationships among bacteria when examining their association with diseases. In fact, although there are common approaches to represent and analyze bacterial interaction relationships as networks, there are limited methods to find bacteria associated with diseases through network-driven analysis. In this paper, we focus on rewiring of the bacterial network and propose a new method for quantifying the rewiring. We then apply the proposed method to a group of colorectal cancer patients. We show that it can identify and detect bacteria that cannot be detected by conventional methods such as abundance comparison. Furthermore, the proposed method is implemented as a general-purpose tool and made available to the general public.

Wisconsin bill would restrict pathogen studies.

Efforts to ban "gain-of-function" research on viruses and bacteria worry scientists.

Severe problem of macrolides resistance to common pathogens in China.

With the widespread use of macrolide antibiotics in China, common pathogens causing children's infections, such as Streptococcus pneumoniae, Streptococcus (including Group A streptococcus, Group B streptococcus), Staphylococcus aureus, Bordetella pertussis, and Mycoplasma pneumoniae, have shown varying degrees of drug resistance. In order to provide such problem and related evidence for rational use of antibiotics in clinic, we reviewed the drug resistance of common bacteria to macrolides in children recent 20 years.

Microbial peptides activate tumour-infiltrating lymphocytes in glioblastoma.

Microbial organisms have key roles in numerous physiological processes in the human body and have recently been shown to modify the response to immune checkpoint inhibitors1,2. Here we aim to address the role of microbial organisms and their potential role in immune reactivity against glioblastoma. We demonstrate that HLA molecules of both glioblastoma tissues and tumour cell lines present bacteria-specific peptides. This finding prompted us to examine whether tumour-infiltrating lymphocytes (TILs) recognize tumour-derived bacterial peptides. Bacterial peptides eluted from HLA class II molecules are recognized by TILs, albeit very weakly. Using an unbiased antigen discovery approach to probe the specificity of a TIL CD4+ T cell clone, we show that it recognizes a broad spectrum of peptides from pathogenic bacteria, commensal gut microbiota and also glioblastoma-related tumour antigens. These peptides were also strongly stimulatory for bulk TILs and peripheral blood memory cells, which then respond to tumour-derived target peptides. Our data hint at how bacterial pathogens and bacterial gut microbiota can be involved in specific immune recognition of tumour antigens. The unbiased identification of microbial target antigens for TILs holds promise for future personalized tumour vaccination approaches.

Programming bacteria for multiplexed DNA detection.

DNA is a universal and programmable signal of living organisms. Here we develop cell-based DNA sensors by engineering the naturally competent bacterium Bacillus subtilis (B. subtilis) to detect specific DNA sequences in the environment. The DNA sensor strains can identify diverse bacterial species including major human pathogens with high specificity. Multiplexed detection of genomic DNA from different species in complex samples can be achieved by coupling the sensing mechanism to orthogonal fluorescent reporters. We also demonstrate that the DNA sensors can detect the presence of species in the complex samples without requiring DNA extraction. The modularity of the living cell-based DNA-sensing mechanism and simple detection procedure could enable programmable DNA sensing for a wide range of applications.

U.S. scientists brace for greater scrutiny of risky research.

Expert panel recommends broader reviews of research involving pathogens or toxins that could have "dual use".

MSF-LRR: Multi-Similarity Information Fusion Through Low-Rank Representation to Predict Disease-Associated Microbes.

An Increase in microbial activity is shown to be intimately connected with the pathogenesis of diseases. Considering the expense of traditional verification methods, researchers are working to develop high-efficiency methods for detecting potential disease-related microbes. In this article, a new prediction method, MSF-LRR, is established, which uses Low-Rank Representation (LRR) to perform multi-similarity information fusion to predict disease-related microbes. Considering that most existing methods only use one class of similarity, three classes of microbe and disease similarity are added. Then, LRR is used to obtain low-rank structural similarity information. Additionally, the method adaptively extracts the local low-rank structure of the data from a global perspective, to make the information used for the prediction more effective. Finally, a neighbor-based prediction method that utilizes the concept of collaborative filtering is applied to predict unknown microbe-disease pairs. As a result, the AUC value of MSF-LRR is superior to other existing algorithms under 5-fold cross-validation. Furthermore, in case studies, excluding originally known associations, 16 and 19 of the top 20 microbes associated with Bacterial Vaginosis and Irritable Bowel Syndrome, respectively, have been confirmed by the recent literature. In summary, MSF-LRR is a good predictor of potential microbe-disease associations and can contribute to drug discovery and biological research.

Multiple Receptors Contribute to the Attractive Response of Caenorhabditis elegans to Pathogenic Bacteria.

Nematodes feed mainly on bacteria and sense volatile signals through their chemosensory system to distinguish food from pathogens. Although nematodes recognizing bacteria by volatile metabolites are ubiquitous, little is known of the associated molecular mechanism. Here, we show that the antinematode bacterium Paenibacillus polymyxa KM2501-1 exhibits an attractive effect on Caenorhabditis elegans via volatile metabolites, of which furfural acetone (FAc) acts as a broad-spectrum nematode attractant. We show that the attractive response toward FAc requires both the G-protein-coupled receptors STR-2 in AWC neurons and SRA-13 in AWA and AWC neurons. In the downstream olfactory signaling cascades, both the transient receptor potential vanilloid channel and the cyclic nucleotide-gated channel are necessary for FAc sensation. These results indicate that multiple receptors and subsequent signaling cascades contribute to the attractive response of C. elegans to FAc, and FAc is the first reported ligand of SRA-13. Our current work discovers that P. polymyxa KM2501-1 exhibits an attractive effect on nematodes by secreting volatile metabolites, especially FAc and 2-heptanone, broadening our understanding of the interactions between bacterial pathogens and nematodes. IMPORTANCE Nematodes feed on nontoxic bacteria as a food resource and avoid toxic bacteria; they distinguish them through their volatile metabolites. However, the mechanism of how nematodes recognize bacteria by volatile metabolites is not fully understood. Here, the antinematode bacterium Paenibacillus polymyxa KM2501-1 is found to exhibit an attractive effect on Caenorhabditis elegans via volatile metabolites, including FAc. We further reveal that the attractive response of C. elegans toward FAc requires multiple G-protein-coupled receptors and downstream olfactory signaling cascades in AWA and AWC neurons. This study highlights the important role of volatile metabolites in the interaction between nematodes and bacteria and confirms that multiple G-protein-coupled receptors on different olfactory neurons of C. elegans can jointly sense bacterial volatile signals.

Active antibiotic resistome in soils unraveled by single-cell isotope probing and targeted metagenomics.

Antimicrobial resistance (AMR) in soils represents a serious risk to human health through the food chain and human-nature contact. However, the active antibiotic-resistant bacteria (ARB) residing in soils that primarily drive AMR dissemination are poorly explored. Here, single-cell Raman-D2O coupled with targeted metagenomics is developed as a culture-independent approach to phenotypically and genotypically profiling active ARB against clinical antibiotics in a wide range of soils. This method quantifies the prevalence (contamination degree) and activity (spread potential) of soil ARB and reveals a clear elevation with increasing anthropogenic activities such as farming and the creation of pollution, thereby constituting a factor that is critical for the assessment of AMR risks. Further targeted sorting and metagenomic sequencing of the most active soil ARB uncover several uncultured genera and a pathogenic strain. Furthermore, the underlying resistance genes, virulence factor genes, and associated mobile genetic elements (including plasmids, insertion sequences, and prophages) are fully deciphered at the single-cell level. This study advances our understanding of the soil active AMR repertoire by linking the resistant phenome to the genome. It will aid in the risk assessment of environmental AMR and guide the combat under the One Health framework.

Label-free single-particle imaging approach for ultra-rapid detection of pathogenic bacteria in clinical samples.

Rapid detection of pathogenic bacteria within a few minutes is the key to control infectious disease. However, rapid detection of pathogenic bacteria in clinical samples is quite a challenging task due to the complex matrix, as well as the low abundance of bacteria in real samples. Herein, we employ a label-free single-particle imaging approach to address this challenge. By tracking the scattering intensity variation of single particles in free solution, the morphological heterogeneity can be well identified with particle size smaller than the diffraction limit, facilitating the morphological identification of single bacteria from a complex matrix in a label-free manner. Furthermore, the manipulation of convection in free solution enables the rapid screening of low-abundance bacteria in a small field of view, which significantly improves the sensitivity of single-particle detection. As a proof of concept demonstration, we are able to differentiate the group B streptococci (GBS)-positive samples within 10 min from vaginal swabs without using any biological reagents. This is the most rapid and low-cost method to the best of our knowledge. We believe that such a single-particle imaging approach will find wider applications in clinical diagnosis and disease control due to its high sensitivity, rapidity, simplicity, and low cost.

Within-host evolution of a gut pathobiont facilitates liver translocation.

Gut commensal bacteria with the ability to translocate across the intestinal barrier can drive the development of diverse immune-mediated diseases1-4. However, the key factors that dictate bacterial translocation remain unclear. Recent studies have revealed that gut microbiota strains can adapt and evolve throughout the lifetime of the host5-9, raising the possibility that changes in individual commensal bacteria themselves over time may affect their propensity to elicit inflammatory disease. Here we show that within-host evolution of the model gut pathobiont Enterococcus gallinarum facilitates bacterial translocation and initiation of inflammation. Using a combination of in vivo experimental evolution and comparative genomics, we found that E. gallinarum diverges into independent lineages adapted to colonize either luminal or mucosal niches in the gut. Compared with ancestral and luminal E. gallinarum, mucosally adapted strains evade detection and clearance by the immune system, exhibit increased translocation to and survival within the mesenteric lymph nodes and liver, and induce increased intestinal and hepatic inflammation. Mechanistically, these changes in bacterial behaviour are associated with non-synonymous mutations or insertion-deletions in defined regulatory genes in E. gallinarum, altered microbial gene expression programs and remodelled cell wall structures. Lactobacillus reuteri also exhibited broadly similar patterns of divergent evolution and enhanced immune evasion in a monocolonization-based model of within-host evolution. Overall, these studies define within-host evolution as a critical regulator of commensal pathogenicity that provides a unique source of stochasticity in the development and progression of microbiota-driven disease.

Immunometabolic and potential tumor-promoting changes in 3D cervical cell models infected with bacterial vaginosis-associated bacteria.

Specific bacteria of the human microbiome influence carcinogenesis at diverse anatomical sites. Bacterial vaginosis (BV) is the most common vaginal disorder in premenopausal women that is associated with gynecologic sequelae, including cervical cancer. BV-associated microorganisms, such as Fusobacterium, Lancefieldella, Peptoniphilus, and Porphyromonas have been associated with gynecologic and other cancers, though the pro-oncogenic mechanisms employed by these bacteria are poorly understood. Here, we integrated a multi-omics approach with our three-dimensional (3-D) cervical epithelial cell culture model to investigate how understudied BV-associated bacteria linked to gynecologic neoplasia influence hallmarks of cancer in vitro. Lancefieldella parvulum and Peptoniphilus lacrimalis elicited robust proinflammatory responses in 3-D cervical cells. Fusobacterium nucleatum and Fusobacterium gonidiaformans modulated metabolic hallmarks of cancer corresponding to accumulation of 2-hydroxyglutarate, pro-inflammatory lipids, and signs of oxidative stress and genotoxic hydrogen sulfide. This study provides mechanistic insights into how gynecologic cancer-associated bacteria might facilitate a tumor-promoting microenvironment in the human cervix.

SeqScreen: accurate and sensitive functional screening of pathogenic sequences via ensemble learning.

The COVID-19 pandemic has emphasized the importance of accurate detection of known and emerging pathogens. However, robust characterization of pathogenic sequences remains an open challenge. To address this need we developed SeqScreen, which accurately characterizes short nucleotide sequences using taxonomic and functional labels and a customized set of curated Functions of Sequences of Concern (FunSoCs) specific to microbial pathogenesis. We show our ensemble machine learning model can label protein-coding sequences with FunSoCs with high recall and precision. SeqScreen is a step towards a novel paradigm of functionally informed synthetic DNA screening and pathogen characterization, available for download at www.gitlab.com/treangenlab/seqscreen .

FabT, a Bacterial Transcriptional Repressor That Limits Futile Fatty Acid Biosynthesis.

Phospholipids are vital membrane constituents that determine cell functions and interactions with the environment. For bacterial pathogens, rapid adjustment of phospholipid composition to changing conditions during infection can be crucial for growth and survival. Fatty acid synthesis (FASII) regulators are central to this process. This review puts the spotlight on FabT, a MarR-family regulator of FASII characterized in streptococci, enterococci, and lactococci. Roles of FabT in virulence, as reported in mouse and nonhuman primate infection models, will be discussed. We present FabT structure, the FabT regulon, and changes in FabT regulation according to growth conditions. A unique feature of FabT concerns its modulation by an unconventional corepressor, acyl-acyl-carrier protein (ACP). Some bacteria express two ACP proteins, which are distinguished by their interactions with endogenous or exogenous fatty acid sources, one of which causes strong FabT repression. This system seems to allow preferred use of environmental fatty acids, thereby saving energy by limiting futile FASII activity. Control of fabT expression and FabT activity link various metabolic pathways to FASII. The various physiological consequences of FabT loss summarized here suggest that FabT has potential as a narrow range therapeutic target.

5NosoAE: a web server for nosocomial bacterial antibiogram investigation and epidemiology survey.

5NosoAE is a webserver that can be used for nosocomial bacterial analysis including the identification of similar strains based on antimicrobial resistance profiles (antibiogram) and the spatiotemporal distribution visualization and phylogenetic analysis of identified strains with similar antibiograms. The extensive use of antibiotics has caused many pathogenic bacteria to develop multiple drug resistance, resulting in clinical infection treatment challenges and posing a major threat to global public health. Relevant studies have investigated the key determinants of antimicrobial resistance in the whole-genome sequence of bacteria. However, a web server is currently not available for performing large-scale strain searches according to antimicrobial resistance profiles and visualizing epidemiological information including the spatiotemporal distribution, antibiogram heatmap, and phylogeny of identified strains. Here, we implemented these functions in the new server, referred to as 5NosoAE. This server accepts the genome sequence file in the FASTA format of five nosocomial bacteria, namely Acinetobacter baumannii, Pseudomonas aeruginosa, Klebsiella pneumoniae, Enterococcus faecium and Staphylococcus aureus for query. All visualizations are implemented in JavaScript and PHP. This server will be useful for physicians and epidemiologists involved in research on infectious disease. The 5NosoAE platform is available at https://nosoae.imst.nsysu.edu.tw.

Phenotype-Based Threat Assessment.

Bacterial pathogen identification, which is critical for human health, has historically relied on culturing organisms from clinical specimens. More recently, the application of machine learning (ML) to whole-genome sequences (WGSs) has facilitated pathogen identification. However, relying solely on genetic information to identify emerging or new pathogens is fundamentally constrained, especially if novel virulence factors exist. In addition, even WGSs with ML pipelines are unable to discern phenotypes associated with cryptic genetic loci linked to virulence. Here, we set out to determine if ML using phenotypic hallmarks of pathogenesis could assess potential pathogenic threat without using any sequence-based analysis. This approach successfully classified potential pathogenetic threat associated with previously machine-observed and unobserved bacteria with 99% and 85% accuracy, respectively. This work establishes a phenotype-based pipeline for potential pathogenic threat assessment, which we term PathEngine, and offers strategies for the identification of bacterial pathogens.

Identifying more targeted antimicrobials active against selected bacterial phytopathogens.

AIMS: Phytopathogens are a global threat to the world's food supply. The use of broad-spectrum bactericides and antibiotics to limit or eliminate bacterial infections is becoming less effective as levels of resistance increase, while concurrently becoming less desirable from an ecological perspective due to their collateral damage to beneficial members of plant and soil microbiomes. Bacteria produce numerous antimicrobials in addition to antibiotics, such as bacteriocins with their relatively narrow activity spectra, and inhibitory metabolic by-products, such as organic acids. There is an interest in developing these naturally occurring antimicrobials for use as alternatives or supplements to antibiotics. METHODS AND RESULTS: In this study, we investigate the inhibitory potential of 217 plant-associated bacterial isolates from 44 species including plant pathogens, plant growth promoting rhizobacteria and plant commensals. Over half of the isolates were found to produce antimicrobial substances, of which 68% were active against phytopathogens. Even more intriguing, 98% of phytopathogenic strains were sensitive to the compounds produced specifically by plant growth promoting rhizobacteria. CONCLUSION: These data argue that plant-associated bacteria produce a broad range of antimicrobial substances, and that the substances produced preferentially target phytopathogenic bacteria. SIGNIFICANCE AND IMPACT OF STUDY: There is a need for novel antimicrobials for use in agriculture. The methods presented here reveal the potential for simple phenotypic screening methods to provide a broad range of potential drug candidates.