Cross-species RNA-seq for deciphering host-microbe interactions

Deep-HPI-pred: An R-Shiny applet for network-based classification and prediction of Host-Pathogen protein-protein interactions

Host-pathogen interactions (HPIs) are vital in numerous biological activities and are intrinsically linked to the onset and progression of infectious diseases. HPIs are pivotal in the entire lifecycle of diseases: from the onset of pathogen introduction, navigating through the mechanisms that bypass host cellular defenses, to its subsequent proliferation inside the host. At the heart of these stages lies the synergy of proteins from both the host and the pathogen. By understanding these interlinking protein dynamics, we can gain crucial insights into how diseases progress and pave the way for stronger plant defenses and the swift formulation of countermeasures. In the framework of current study, we developed a web-based R/Shiny app, Deep-HPI-pred, that uses network-driven feature learning method to predict the yet unmapped interactions between pathogen and host proteins. Leveraging citrus and CLas bacteria training datasets as case study, we spotlight the effectiveness of Deep-HPI-pred in discerning Protein-protein interaction (PPIs) between them. Deep-HPI-pred use Multilayer Perceptron (MLP) models for HPI prediction, which is based on a comprehensive evaluation of topological features and neural network architectures. When subjected to independent validation datasets, the predicted models consistently surpassed a Matthews correlation coefficient (MCC) of 0.80 in host-pathogen interactions. Remarkably, the use of Eigenvector Centrality as the leading topological feature further enhanced this performance. Further, Deep-HPI-pred also offers relevant gene ontology (GO) term information for each pathogen and host protein within the system. This protein annotation data contributes an additional layer to our understanding of the intricate dynamics within host-pathogen interactions. In the additional benchmarking studies, the Deep-HPI-pred model has proven its robustness by consistently delivering reliable results across different host-pathogen systems, including plant-pathogens (accuracy of 98.4% and 97.9%), human-virus (accuracy of 94.3%), and animal-bacteria (accuracy of 96.6%) interactomes. These results not only demonstrate the model's versatility but also pave the way for gaining comprehensive insights into the molecular underpinnings of complex host-pathogen interactions. Taken together, the Deep-HPI-pred applet offers a unified web service for both identifying and illustrating interaction networks. Deep-HPI-pred applet is freely accessible at its homepage: https://cbi.gxu.edu.cn/shiny-apps/Deep-HPI-pred/ and at github: https://github.com/tahirulqamar/Deep-HPI-pred.

Artificial intelligence with mass spectrometry-based multimodal molecular profiling methods for advancing therapeutic discovery of infectious diseases

Infectious diseases, driven by a diverse array of pathogens, can swiftly undermine public health systems. Accurate diagnosis and treatment of infectious diseases-centered around the identification of biomarkers and the elucidation of disease mechanisms-are in dire need of more versatile and practical analytical approaches. Mass spectrometry (MS)-based molecular profiling methods can deliver a wealth of information on a range of functional molecules, including nucleic acids, proteins, and metabolites. While MS-driven omics analyses can yield vast datasets, the sheer complexity and multi-dimensionality of MS data can significantly hinder the identification and characterization of functional molecules within specific biological processes and events. Artificial intelligence (AI) emerges as a potent complementary tool that can substantially enhance the processing and interpretation of MS data. AI applications in this context lead to the reduction of spurious signals, the improvement of precision, the creation of standardized analytical frameworks, and the increase of data integration efficiency. This critical review emphasizes the pivotal roles of MS based omics strategies in the discovery of biomarkers and the clarification of infectious diseases. Additionally, the review underscores the transformative ability of AI techniques to enhance the utility of MS-based molecular profiling in the field of infectious diseases by refining the quality and practicality of data produced from omics analyses. In conclusion, we advocate for a forward-looking strategy that integrates AI with MS-based molecular profiling. This integration aims to transform the analytical landscape and the performance of biological molecule characterization, potentially down to the single-cell level. Such advancements are anticipated to propel the development of AI-driven predictive models, thus improving the monitoring of diagnostics and therapeutic discovery for the ongoing challenge related to infectious diseases.

Dual spatial host-bacterial gene expression in Mycobacterium abscessus respiratory infections

Co-localization of spatial transcriptome information of host and pathogen can revolutionize our understanding of microbial pathogenesis. Here, we aimed to demonstrate that customized bacterial probes can be successfully used to identify host-pathogen interactions in formalin-fixed-paraffin-embedded (FFPE) tissues by probe-based spatial transcriptomics technology. We analyzed the spatial gene expression of bacterial transcripts with the host transcriptomic profile in murine lung tissue chronically infected with Mycobacterium abscessus embedded in agar beads. Customized mycobacterial probes were designed for the constitutively expressed rpoB gene (an RNA polymerase β subunit) and the virulence factor precursor lsr2, modulated by oxidative stress. We found a correlation between the rpoB expression, bacterial abundance in the airways, and an increased expression of lsr2 virulence factor in lung tissue with high oxidative stress. Overall, we demonstrate the potential of dual bacterial and host gene expression assay in FFPE tissues, paving the way for the simultaneous detection of host and bacterial transcriptomes in pathological tissues.

Pathogenicity and transcriptomic profiling reveals immunology molecular hallmarks after CA10 virus infection

BACKGROUND

Hand, foot and mouth disease (HFMD) is a common infectious disease caused by viral infection by a variety of enteroviruses, with coxsackievirus A 10 (CA10) having become more prevalent in recent years.

METHODS

In this study, models of CA10 infection were established in 7-day-old Institute of Cancer Research (ICR) mice by intraperitoneal injection to analyze the pathogenicity of the virus. RNA sequencing analysis was used to screen the differentially expressed genes (DEGs) after CA10 infection. Coxsackievirus A 16 (CA16) and enterovirus 71 (EV71) infections were also compared with CA10.

RESULTS

After CA10 virus infection, the mice showed paralysis of the hind limbs at 3 days post infection and weight loss at 5 days post infection. We observed viral replication in various tissues and severe inflammatory cell infiltration in skeletal muscle. The RNA-sequencing analysis showed that the DEGs in blood, muscle, thymus and spleen showed heterogeneity after CA10 infection and the most up-regulated DEGs in muscle were enriched in immune-related pathways. Compared with CA16 and EV71 infection, CA10 may have an inhibitory effect on T helper (Th) cell differentiation and cell growth. Additionally, the common DEGs in the three viruses were most enriched in the immune system response, including the Toll-like receptor pathway and the nucleotide-binding and oligomerization domain (NOD)-like pathway.

CONCLUSIONS

Our findings revealed a group of genes that coordinate in response to CA10 infection, which increases our understanding of the pathological mechanism of HFMD.

Single-cell sequencing to multi-omics: technologies and applications

Cells, as the fundamental units of life, contain multidimensional spatiotemporal information. Single-cell RNA sequencing (scRNA-seq) is revolutionizing biomedical science by analyzing cellular state and intercellular heterogeneity. Undoubtedly, single-cell transcriptomics has emerged as one of the most vibrant research fields today. With the optimization and innovation of single-cell sequencing technologies, the intricate multidimensional details concealed within cells are gradually unveiled. The combination of scRNA-seq and other multi-omics is at the forefront of the single-cell field. This involves simultaneously measuring various omics data within individual cells, expanding our understanding across a broader spectrum of dimensions. Single-cell multi-omics precisely captures the multidimensional aspects of single-cell transcriptomes, immune repertoire, spatial information, temporal information, epitopes, and other omics in diverse spatiotemporal contexts. In addition to depicting the cell atlas of normal or diseased tissues, it also provides a cornerstone for studying cell differentiation and development patterns, disease heterogeneity, drug resistance mechanisms, and treatment strategies. Herein, we review traditional single-cell sequencing technologies and outline the latest advancements in single-cell multi-omics. We summarize the current status and challenges of applying single-cell multi-omics technologies to biological research and clinical applications. Finally, we discuss the limitations and challenges of single-cell multi-omics and potential strategies to address them.

Effect of Different Dietary Iron Contents on Liver Transcriptome Characteristics in Wujin Pigs

Iron is an important trace element that affects the growth and development of animals and regulates oxygen transport, hematopoiesis, and hypoxia adaptations. Wujin pig has unique hypoxic adaptability and iron homeostasis; however, the specific regulatory mechanisms have rarely been reported. This study randomly divided 18 healthy Wujin piglets into three groups: the control group, supplemented with 100 mg/kg iron (as iron glycinate); the low-iron group, no iron supplementation; and the high-iron group, supplemented with 200 mg/kg iron (as iron glycinate). The pre-feeding period was 5 days, and the formal period was 30 days. Serum was collected from empty stomachs before slaughter and at slaughter to detect changes in the serum iron metabolism parameters. Gene expression in the liver was analyzed via transcriptome analysis to determine the effects of low- and high-iron diets on transcriptome levels. Correlation analysis was performed for apparent serum parameters, and transcriptome sequencing was performed using weighted gene co-expression network analysis to reveal the key pathways underlying hypoxia regulation and iron metabolism. The main results are as follows. (1) Except for the hypoxia-inducible factor 1 (HIF-1) content (between the low- and high-iron groups), significant differences were not observed among the serum iron metabolic parameters. The serum HIF-1 content of the low-iron group was significantly higher than that of the high-iron group (p < 0.05). (2) Sequencing analysis of the liver transcriptome revealed 155 differentially expressed genes (DEGs) between the low-iron and control groups, 229 DEGs between the high-iron and control groups, and 279 DEGs between the low- and high-iron groups. Bioinformatics analysis showed that the HIF-1 and transforming growth factor-beta (TGF-β) signaling pathways were the key pathways for hypoxia regulation and iron metabolism. Four genes were selected for qPCR validation, and the results were consistent with the transcriptome sequencing data. In summary, the serum iron metabolism parameter results showed that under the influence of low- and high-iron diets, Wujin piglets maintain a steady state of physiological and biochemical indices via complex metabolic regulation of the body, which reflects their stress resistance and adaptability. The transcriptome results revealed the effects of low-iron and high-iron diets on the gene expression level in the liver and showed that the HIF-1 and TGF-β signaling pathways were key for regulating hypoxia adaptability and iron metabolism homeostasis under low-iron and high-iron diets. Moreover, HIF-1α and HEPC were the key genes. The findings provide a theoretical foundation for exploring the regulatory pathways and characteristics of iron metabolism in Wujin pigs.

Integrative genomic analysis of the lung tissue microenvironment in SARS-CoV-2 and NL63 patients

The coronavirus disease 2019 (COVID-19) pandemic caused by the SARS-CoV-2 virus has affected over 700 million people, and caused over 7 million deaths throughout the world as of April 2024, and continues to affect people through seasonal waves. While over 675 million people have recovered from this disease globally, the lingering effects of the disease are still under study. Long term effects of SARS-CoV-2 infection, known as 'long COVID,' include a wide range of symptoms including fatigue, chest pain, cellular damage, along with a strong innate immune response characterized by inflammatory cytokine production. Three years after the pandemic, data about long covid studies are finally emerging. More clinical studies and clinical trials are needed to understand and determine the factors that predispose individuals to these long-term side effects. In this methodology paper, our goal was to apply data driven approaches in order to explore the multidimensional landscape of infected lung tissue microenvironment to better understand complex interactions between viral infection, immune response and the lung microbiome of patients with (a) SARS-CoV-2 virus and (b) NL63 coronavirus. The samples were analyzed with several machine learning tools allowing simultaneous detection and quantification of viral RNA amount at genome and gene level; human gene expression and fractions of major types of immune cells, as well as metagenomic analysis of bacterial and viral abundance. To contrast and compare specific viral response to SARS-COV-2, we analyzed deep sequencing data from additional cohort of patients infected with NL63 strain of corona virus. Our correlation analysis of three types of RNA-seq based measurements in patients i.e. fraction of viral RNA (at genome and gene level), Human RNA (transcripts and gene level) and bacterial RNA (metagenomic analysis), showed significant correlation between viral load as well as level of specific viral gene expression with the fractions of immune cells present in lung lavage as well as with abundance of major fractions of lung microbiome in COVID-19 patients. Our methodology-based proof-of-concept study has provided novel insights into complex regulatory signaling interactions and correlative patterns between the viral infection, inhibition of innate and adaptive immune response as well as microbiome landscape of the lung tissue. These initial findings could provide better understanding of the diverse dynamics of immune response and the side effects of the SARS-CoV-2 infection and demonstrates the possibilities of the various types of analyses that could be performed from this type of data.

Microbiota and IBD: Current knowledge and future perspectives

Inflammatory Bowel Disease (IBD) is a chronic relapsing-remitting disease with a remarkable increase in incidence worldwide and a substantial disease burden. Although the pathophysiology is not fully elucidated yet an aberrant immune reaction against the intestinal microbiota and the gut microbial dysbiosis have been identified to play a major role. The composition of gut microbiota in IBD patients is distinct from that of healthy individuals, with certain organisms predominating over others. Differences in the microbial dysbiosis have been also observed between Crohn Disease (CD) and Ulcerative Colitis (UC). A disruption of the microbiota's balance can lead to inflammation and intestinal damage. Microbiota composition in IBD can be affected both by endogenous (i.e., interaction with the immune system and intestinal epithelial cells) and exogenous (i.e., medications, surgery, diet) factors. The complex interplay between the gut microbiota and IBD is an area of great interest for understanding disease pathogenesis and developing new treatments. The purpose of this review is to summarize the latest evidence on the role of microbiota in IBD pathogenesis and to explore possible future areas of research.

Folding molecular origami from ribosomal RNA

Approximately 80 percent of the total RNA in cells is ribosomal RNA (rRNA), making it an abundant and inexpensive natural source of long, single-stranded nucleic acid, which could be used as raw material for the fabrication of molecular origami. In this study, we demonstrate efficient and robust construction of 2D and 3D origami nanostructures utilizing cellular rRNA as a scaffold and DNA oligonucleotide staples. We present calibrated protocols for the robust folding of contiguous shapes from one or two rRNA subunits that are efficient to allow folding using crude extracts of total RNA. We also show that RNA maintains stability within the folded structure. Lastly, we present a novel and comprehensive analysis and insights into the stability of RNA:DNA origami nanostructures and demonstrate their enhanced stability when coated with polylysine-polyethylene glycol in different temperatures, low Mg2+ concentrations, human serum, and in the presence of nucleases (DNase I or RNase H). Thus, laying the foundation for their potential implementation in emerging biomedical applications, where folding rRNA into stable structures outside and inside cells would be desired.

High-Throughput Host-Microbe Single-Cell RNA Sequencing Reveals Ferroptosis-Associated Heterogeneity during Acinetobacter baumannii Infection

Interactions between host and bacterial cells are integral to human physiology. The complexity of host-microbe interactions extends to different cell types, spatial aspects, and phenotypic heterogeneity, requiring high-resolution approaches to capture their full complexity. The latest breakthroughs in single-cell RNA sequencing (scRNA-seq) have opened up a new era of studies in host-pathogen interactions. Here, we first report a high-throughput cross-species dual scRNA-seq technology by using random primers to simultaneously capture both eukaryotic and bacterial RNAs (scRandom-seq). Using reference cells, scRandom-seq can detect individual eukaryotic and bacterial cells with high throughput and high specificity. Acinetobacter baumannii (A.b) is a highly opportunistic and nosocomial pathogen that displays resistance to many antibiotics, posing a significant threat to human health, calling for discoveries and treatment. In the A.b infection model, scRandom-seq witnessed polarization of THP-1 derived-macrophages and the intracellular A.b-induced ferroptosis-stress in host cells. The inhibition of ferroptosis by Ferrostatin-1 (Fer-1) resulted in the improvement of cell vitality and resistance to A.b infection, indicating the potential to resist related infections. scRandom-seq provides a high-throughput cross-species dual single-cell RNA profiling tool that will facilitate future discoveries in unraveling the complex interactions of host-microbe interactions in infection systems and tumor micro-environments.

Genome sequencing of drake semen micobiome with correlation with their compositions, sources and potential mechanisms affecting semen quality

Artificial insemination (AI) technology has greatly promoted the development of the chicken industry. Recently, AI technology has also begun to be used in the duck industry, but there are some problems. Numerous researchers have shown that microbes colonizing in semen can degrade semen quality, and AI can increase the harmful microbial load in hen's reproductive tract. Different from the degraded external genitalia of roosters, drakes have well-developed external genitalia, which may cause drake semen to be more susceptible to microbial contamination. However, information on the compositions, sources, and effects of semen microbes on semen quality remains unknown in drakes. In the current study, high-throughput sequencing technology was used to detect microbial communities in drake semen, environmental swabs, cloacal swabs, and the spermaduct after quantifying the semen quality of drakes to investigate the effects of microbes in the environment, cloaca, and spermaduct on semen microbiota and the relationships between semen microbes and semen quality. Taxonomic analysis showed that the microbes in the semen, environment, cloaca, and spermaduct samples were all classified into 4 phyla and 25 genera. Firmicutes and Proteobacteria were the dominant phyla. Phyllobacterium only existed in the environment, while Marinococcus did not exist in the cloaca. Of the 24 genera present in semen: Brachybacterium, Brochothrix, Chryseobacterium, Kocuria, Marinococcus, Micrococcus, Rothia, Salinicoccus, and Staphylococcus originated from the environment; Achromobacter, Aerococcus, Corynebacterium, Desemzia, Enterococcus, Jeotgalicoccus, Pseudomonas, Psychrobacter, and Turicibacter originated from the cloaca; and Agrobacterium, Carnobacterium, Chelativorans, Devosia, Halomonas, and Oceanicaulis originated from the spermaduct. In addition, K-means clustering analysis showed that semen samples could be divided into 2 clusters based on microbial compositions, and compared with cluster 1, the counts of Chelativorans (P < 0.05), Devosia (P < 0.01), Halomonas (P < 0.05), and Oceanicaulis (P < 0.05) were higher in cluster 2, while the sperm viability (P < 0.05), total sperm number (P < 0.01), and semen quality factor (SQF) (P < 0.01) were lower in cluster 2. Furthermore, functional prediction analysis of microbes showed that the activities of starch and sucrose metabolism, phosphotransferase system, ABC transporters, microbial metabolism in diverse environments, and quorum sensing pathways between cluster 1 and cluster 2 were significantly different (P < 0.05). Overall, environmental/cloacal microbes resulted in semen contamination, and microbes from the Chelativorans, Devosia, Halomonas, and Oceanicaulis genera may have negative effects on semen quality in drakes by affecting the activities of starch and sucrose metabolism, phosphotransferase system, ABC transporters, and quorum sensing pathways that are associated with carbohydrate metabolism. These data will provide a basis for developing strategies to prevent microbial contamination of drake semen.

RNA cis-regulators are important for Streptococcus pneumoniae in vivo success

Bacteria have evolved complex transcriptional regulatory networks, as well as many diverse regulatory strategies at the RNA level, to enable more efficient use of metabolic resources and a rapid response to changing conditions. However, most RNA-based regulatory mechanisms are not well conserved across different bacterial species despite controlling genes important for virulence or essential biosynthetic processes. Here, we characterize the activity of, and assess the fitness benefit conferred by, twelve cis-acting regulatory RNAs (including several riboswitches and a T-box), in the opportunistic pathogen Streptococcus pneumoniae TIGR4. By evaluating native locus mutants of each regulator that result in constitutively active or repressed expression, we establish that growth defects in planktonic culture are associated with constitutive repression of gene expression, while constitutive activation of gene expression is rarely deleterious. In contrast, in mouse nasal carriage and pneumonia models, strains with either constitutively active and repressed gene expression are significantly less fit than matched control strains. Furthermore, two RNA-regulated pathways, FMN synthesis/transport and pyrimidine synthesis/transport display exceptional sensitivity to mis-regulation or constitutive gene repression in both planktonic culture and in vivo environments. Thus, despite lack of obvious phenotypes associated with constitutive gene expression in vitro, the fitness benefit conferred on bacteria via fine-tuned metabolic regulation through cis-acting regulatory RNAs is substantial in vivo, and therefore easily sufficient to drive the evolution and maintenance of diverse RNA regulatory mechanisms.

The art of hijacking: how Nsp1 impacts host gene expression during coronaviral infections

Non-structural protein 1 (Nsp1) is one of the first proteins produced during coronaviral infections. It plays a pivotal role in hijacking and rendering the host gene expression under the service of the virus. With a focus on SARS-CoV-2, this review presents how Nsp1 selectively inhibits host protein synthesis and induces mRNA degradation of host but not viral mRNAs and blocks nuclear mRNA export. The clinical implications of this protein are highlighted by showcasing the pathogenic role of Nsp1 through the repression of interferon expression pathways and the features of viral variants with mutations in the Nsp1 coding sequence. The ability of SARS-CoV-2 Nsp1 to hinder host immune responses at an early step, the absence of homology to any human proteins, and the availability of structural information render this viral protein an ideal drug target with therapeutic potential.

Pathogenic Rickettsia spp. as emerging models for bacterial biology

Our understanding of free-living bacterial models like Escherichia coli far outpaces that of obligate intracellular bacteria, which cannot be cultured axenically. All obligate intracellular bacteria are host-associated, and many cause serious human diseases. Their constant exposure to the distinct biochemical niche of the host has driven the evolution of numerous specialized bacteriological and genetic adaptations, as well as innovative molecular mechanisms of infection. Here, we review the history and use of pathogenic Rickettsia species, which cause an array of vector-borne vascular illnesses, as model systems to probe microbial biology. Although many challenges remain in our studies of these organisms, the rich pathogenic and biological diversity of Rickettsia spp. constitutes a unique backdrop to investigate how microbes survive and thrive in host and vector cells. We take a bacterial-focused perspective and highlight emerging insights that relate to new host-pathogen interactions, bacterial physiology, and evolution. The transformation of Rickettsia spp. from pathogens to models demonstrates how recalcitrant microbes may be leveraged in the lab to tap unmined bacterial diversity for new discoveries. Rickettsia spp. hold great promise as model systems not only to understand other obligate intracellular pathogens but also to discover new biology across and beyond bacteria.

Exploring the transcriptional landscape of phage-host interactions using novel high-throughput approaches

In the last decade, powerful high-throughput sequencing approaches have emerged to analyse microbial transcriptomes at a global scale. However, to date, applications of these approaches to microbial viruses such as phages remain scarce. Tailoring these techniques to virus-infected bacteria promises to obtain a detailed picture of the underexplored RNA biology and molecular processes during infection. In addition, transcriptome study of stress and perturbations induced by phages in their infected bacterial hosts is likely to reveal new fundamental mechanisms of bacterial metabolism and gene regulation. Here, we provide references and blueprints to implement emerging transcriptomic approaches towards addressing transcriptome architecture, RNA-RNA and RNA-protein interactions, RNA modifications, structures and heterogeneity of transcription profiles in infected cells that will provide guides for future directions in phage-centric therapeutic applications and microbial synthetic biology.

A metatranscriptomics strategy for efficient characterization of the microbiome in human tissues with low microbial biomass

The high background of host RNA poses a major challenge to metatranscriptome analysis of human samples. Hence, metatranscriptomics has been mainly applied to microbe-rich samples, while its application in human tissues with low ratio of microbial to host cells has yet to be explored. Since there is no computational workflow specifically designed for the taxonomic and functional analysis of this type of samples, we propose an effective metatranscriptomics strategy to accurately characterize the microbiome in human tissues with a low ratio of microbial to host content. We experimentally generated synthetic samples with well-characterized bacterial and host cell compositions, and mimicking human samples with high and low microbial loads. These synthetic samples were used for optimizing and establishing the workflow in a controlled setting. Our results show that the integration of the taxonomic analysis of optimized Kraken 2/Bracken with the functional analysis of HUMAnN 3 in samples with low microbial content, enables the accurate identification of a large number of microbial species with a low false-positive rate, while improving the detection of microbial functions. The effectiveness of our metatranscriptomics workflow was demonstrated in synthetic samples, simulated datasets, and most importantly, human gastric tissue specimens, thus providing a proof of concept for its applicability on mucosal tissues of the gastrointestinal tract. The use of an accurate and reliable metatranscriptomics approach for human tissues with low microbial content will expand our understanding of the functional activity of the mucosal microbiome, uncovering critical interactions between the microbiome and the host in health and disease.

Intracellular bacteria in cancer-prospects and debates

Recent evidence suggests that some human cancers may harbor low-biomass microbial ecosystems, spanning bacteria, viruses, and fungi. Bacteria, the most-studied kingdom in this context, are suggested by these studies to localize within cancer cells, immune cells and other tumor microenvironment cell types, where they are postulated to impact multiple cancer-related functions. Herein, we provide an overview of intratumoral bacteria, while focusing on intracellular bacteria, their suggested molecular activities, communication networks, host invasion and evasion strategies, and long-term colonization capacity. We highlight how the integration of sequencing-based and spatial techniques may enable the recognition of bacterial tumor niches. We discuss pitfalls, debates and challenges in decisively proving the existence and function of intratumoral microbes, while reaching a mechanistic elucidation of their impacts on tumor behavior and treatment responses. Together, a causative understanding of possible roles played by intracellular bacteria in cancer may enable their future utilization in diagnosis, patient stratification, and treatment.

Methodologies for bacterial ribonuclease characterization using RNA-seq

Bacteria adjust gene expression at the post-transcriptional level through an intricate network of small regulatory RNAs and RNA-binding proteins, including ribonucleases (RNases). RNases play an essential role in RNA metabolism, regulating RNA stability, decay, and activation. These enzymes exhibit species-specific effects on gene expression, bacterial physiology, and different strategies of target recognition. Recent advances in high-throughput RNA sequencing (RNA-seq) approaches have provided a better understanding of the roles and modes of action of bacterial RNases. Global studies aiming to identify direct targets of RNases have highlighted the diversity of RNase activity and RNA-based mechanisms of gene expression regulation. Here, we review recent RNA-seq approaches used to study bacterial RNases, with a focus on the methods for identifying direct RNase targets.

Current concepts, advances, and challenges in deciphering the human microbiota with metatranscriptomics

Metatranscriptomics refers to the analysis of the collective microbial transcriptome of a sample. Its increased utilization for the characterization of human-associated microbial communities has enabled the discovery of many disease-state related microbial activities. Here, we review the principles of metatranscriptomics-based analysis of human-associated microbial samples. We describe strengths and weaknesses of popular sample preparation, sequencing, and bioinformatics approaches and summarize strategies for their use. We then discuss how human-associated microbial communities have recently been examined and how their characterization may change. We conclude that metatranscriptomics insights into human microbiotas under health and disease have not only expanded our knowledge on human health, but also opened avenues for rational antimicrobial drug use and disease management.

Droplet-based high-throughput single microbe RNA sequencing by smRandom-seq

Bacteria colonize almost all parts of the human body and can differ significantly. However, the population level transcriptomics measurements can only describe the average bacteria population behaviors, ignoring the heterogeneity among bacteria. Here, we report a droplet-based high-throughput single-microbe RNA-seq assay (smRandom-seq), using random primers for in situ cDNA generation, droplets for single-microbe barcoding, and CRISPR-based rRNA depletion for mRNA enrichment. smRandom-seq showed a high species specificity (99%), a minor doublet rate (1.6%), a reduced rRNA percentage (32%), and a sensitive gene detection (a median of ~1000 genes per single E. coli). Furthermore, smRandom-seq successfully captured transcriptome changes of thousands of individual E. coli and discovered a few antibiotic resistant subpopulations displaying distinct gene expression patterns of SOS response and metabolic pathways in E. coli population upon antibiotic stress. smRandom-seq provides a high-throughput single-microbe transcriptome profiling tool that will facilitate future discoveries in microbial resistance, persistence, microbe-host interaction, and microbiome research.

Transcriptomic Analysis of Mycobacterial Infected Macrophages Reveals a High MOI Specific Type I IFN Signaling

Macrophage (MΦ) infection models are important tools for studying host-mycobacterial interactions. Although the multiplicity of infection (MOI) is an important experimental variable, the selection of MOI in mycobacterial infection experiments is largely empirical, without reference to solid experimental data. To provide relevant data, we used RNA-seq to analyze the gene expression profiles of MΦs 4 or 24 h after infection with Mycobacterium marinum (M. m) at MOIs ranging from 0.1 to 50. Analysis of differentially expressed genes (DEGs) showed that different MOIs are linked to distinct transcriptomic changes and only 10% of DEGs were shared by MΦ infected at all MOIs. KEGG pathway enrichment analysis revealed that type I interferon (IFN)-related pathways were inoculant dose-dependent and enriched only at high MOIs, whereas TNF pathways were inoculant dose-independent and enriched at all MOIs. Protein-protein interaction (PPI) network alignment showed that different MOIs had distinct key node genes. By fluorescence-activated cell sorting and follow-up RT-PCR analysis, we could separate infected MΦs from uninfected MΦs and found phagocytosis of mycobacteria to be the determinant factor for type I IFN production. The distinct transcriptional regulation of RAW264.7 MΦ genes at different MOIs was also seen with Mycobacterium tuberculosis (M.tb) infections and primary MΦ infection models. In summary, transcriptional profiling of mycobacterial infected MΦs revealed that different MOIs activate distinct immune pathways and the type I IFN pathway is activated only at high MOIs. This study should provide guidance for selecting the MOI most appropriate for different research questions.

Transcriptome Revealed the Macrophages Inflammatory Response Mechanism and NOD-like Receptor Characterization in Siberian Sturgeon (Acipenser baerii)

Nucleotide-binding and oligomerization domain-like receptors (NOD-like receptors, NLRs) can regulate the inflammatory response to eliminate pathogens and maintain the host's homeostasis. In this study, the head kidney macrophages of Siberian sturgeon were treated with lipopolysaccharide (LPS) to induce inflammation by evaluating the expression of cytokines. The high-throughput sequencing for macrophages after 12 h treatment showed that 1224 differentially expressed genes (DEGs), including 779 upregulated and 445 downregulated, were identified. DEGs mainly focus on pattern recognition receptors (PRRs) and the adaptor proteins, cytokines, and cell adhesion molecules. In the NOD-like receptor signaling pathway, multiple NOD-like receptor family CARD domains containing 3-like (NLRC3-like) were significantly downregulated, and pro-inflammatory cytokines were upregulated. Based on the transcriptome database, 19 NLRs with NACHT structural domains were mined and named in Siberian sturgeon, including 5 NLR-A, 12 NLR-C, and 2 other NLRs. The NLR-C subfamily had the characteristics of expansion of the teleost NLRC3 family and lacked the B30.2 domain compared with other fish. This study revealed the inflammatory response mechanism and NLRs family characterization in Siberian sturgeon by transcriptome and provided basic data for further research on inflammation in teleost.

Single-cell transcriptomics and data analyses for prokaryotes-Past, present and future concepts

Transcriptomics, or more specifically mRNA sequencing, is a powerful tool to study gene expression at the single-cell level (scRNA-seq) which enables new insights into a plethora of biological processes. While methods for single-cell RNA-seq in eukaryotes are well established, application to prokaryotes is still challenging. Reasons for that are rigid and diverse cell wall structures hampering lysis, the lack of polyadenylated transcripts impeding mRNA enrichment, and minute amounts of RNA requiring amplification steps before sequencing. Despite those obstacles, several promising scRNA-seq approaches for bacteria have been published recently, albeit difficulties in the experimental workflow and data processing and analysis remain. In particular, bias is often introduced by amplification which makes it difficult to distinguish between technical noise and biological variation. Future optimization of experimental procedures and data analysis algorithms are needed for the improvement of scRNA-seq but also to aid in the emergence of prokaryotic single-cell multi-omics. to help address 21st century challenges in the biotechnology and health sector.

High-throughput single nucleus total RNA sequencing of formalin-fixed paraffin-embedded tissues by snRandom-seq

Formalin-fixed paraffin-embedded (FFPE) tissues constitute a vast and valuable patient material bank for clinical history and follow-up data. It is still challenging to achieve single cell/nucleus RNA (sc/snRNA) profile in FFPE tissues. Here, we develop a droplet-based snRNA sequencing technology (snRandom-seq) for FFPE tissues by capturing full-length total RNAs with random primers. snRandom-seq shows a minor doublet rate (0.3%), a much higher RNA coverage, and detects more non-coding RNAs and nascent RNAs, compared with state-of-art high-throughput scRNA-seq technologies. snRandom-seq detects a median of >3000 genes per nucleus and identifies 25 typical cell types. Moreover, we apply snRandom-seq on a clinical FFPE human liver cancer specimen and reveal an interesting subpopulation of nuclei with high proliferative activity. Our method provides a powerful snRNA-seq platform for clinical FFPE specimens and promises enormous applications in biomedical research.

Improved Bacterial Single-Cell RNA-Seq through Automated MATQ-Seq and Cas9-Based Removal of rRNA Reads

Bulk RNA sequencing technologies have provided invaluable insights into host and bacterial gene expression and associated regulatory networks. Nevertheless, the majority of these approaches report average expression across cell populations, hiding the true underlying expression patterns that are often heterogeneous in nature. Due to technical advances, single-cell transcriptomics in bacteria has recently become reality, allowing exploration of these heterogeneous populations, which are often the result of environmental changes and stressors. In this work, we have improved our previously published bacterial single-cell RNA sequencing (scRNA-seq) protocol that is based on multiple annealing and deoxycytidine (dC) tailing-based quantitative scRNA-seq (MATQ-seq), achieving a higher throughput through the integration of automation. We also selected a more efficient reverse transcriptase, which led to reduced cell loss and higher workflow robustness. Moreover, we successfully implemented a Cas9-based rRNA depletion protocol into the MATQ-seq workflow. Applying our improved protocol on a large set of single Salmonella cells sampled over different growth conditions revealed improved gene coverage and a higher gene detection limit compared to our original protocol and allowed us to detect the expression of small regulatory RNAs, such as GcvB or CsrB at a single-cell level. In addition, we confirmed previously described phenotypic heterogeneity in Salmonella in regard to expression of pathogenicity-associated genes. Overall, the low percentage of cell loss and high gene detection limit makes the improved MATQ-seq protocol particularly well suited for studies with limited input material, such as analysis of small bacterial populations in host niches or intracellular bacteria. IMPORTANCE Gene expression heterogeneity among isogenic bacteria is linked to clinically relevant scenarios, like biofilm formation and antibiotic tolerance. The recent development of bacterial single-cell RNA sequencing (scRNA-seq) enables the study of cell-to-cell variability in bacterial populations and the mechanisms underlying these phenomena. Here, we report a scRNA-seq workflow based on MATQ-seq with increased robustness, reduced cell loss, and improved transcript capture rate and gene coverage. Use of a more efficient reverse transcriptase and the integration of an rRNA depletion step, which can be adapted to other bacterial single-cell workflows, was instrumental for these improvements. Applying the protocol to the foodborne pathogen Salmonella, we confirmed transcriptional heterogeneity across and within different growth phases and demonstrated that our workflow captures small regulatory RNAs at a single-cell level. Due to low cell loss and high transcript capture rates, this protocol is uniquely suited for experimental settings in which the starting material is limited, such as infected tissues.

An Overlooked and Underrated Endemic Mycosis-Talaromycosis and the Pathogenic Fungus Talaromyces marneffei

Talaromycosis is an invasive mycosis endemic in tropical and subtropical Asia and is caused by the pathogenic fungus Talaromyces marneffei. Approximately 17,300 cases of T. marneffei infection are diagnosed annually, and the reported mortality rate is extremely high (~1/3). Despite the devastating impact of talaromycosis on immunocompromised individuals, particularly HIV-positive persons, and the increase in reported occurrences in HIV-uninfected persons, diagnostic and therapeutic approaches for talaromycosis have received far too little attention worldwide. In 2021, scientists living in countries where talaromycosis is endemic raised a global demand for it to be recognized as a neglected tropical disease. Therefore, T. marneffei and the infectious disease induced by this fungus must be treated with concern. T. marneffei is a thermally dimorphic saprophytic fungus with a complicated mycological growth process that may produce various cell types in its life cycle, including conidia, hyphae, and yeast, all of which are associated with its pathogenicity. However, understanding of the pathogenic mechanism of T. marneffei has been limited until recently. To achieve a holistic view of T. marneffei and talaromycosis, the current knowledge about talaromycosis and research breakthroughs regarding T. marneffei growth biology are discussed in this review, along with the interaction of the fungus with environmental stimuli and the host immune response to fungal infection. Importantly, the future research directions required for understanding this serious infection and its causative pathogenic fungus are also emphasized to identify solutions that will alleviate the suffering of susceptible individuals worldwide.

Advancing massive transcriptional profiling of single bacteria

Single-bacteria RNA-seq is becoming a transformative technology for elucidating bacterial diversity in ecology, microbial communities, and pathogenicity. In a recent report by Ma et al. published in Cell, the authors present BacDrop, a versatile, high-throughput method for capturing the transcriptomes of millions of bacteria.

Current evidence and clinical relevance of drug-microbiota interactions in inflammatory bowel disease

Background

Inflammatory bowel disease (IBD) is a chronic relapsing-remitting disease. An adverse immune reaction toward the intestinal microbiota is involved in the pathophysiology and microbial perturbations are associated with IBD in general and with flares specifically. Although medical drugs are the cornerstone of current treatment, responses vary widely between patients and drugs. The intestinal microbiota can metabolize medical drugs, which may influence IBD drug (non-)response and side effects. Conversely, several drugs can impact the intestinal microbiota and thereby host effects. This review provides a comprehensive overview of current evidence on bidirectional interactions between the microbiota and relevant IBD drugs (pharmacomicrobiomics).

Methods

Electronic literature searches were conducted in PubMed, Web of Science and Cochrane databases to identify relevant publications. Studies reporting on microbiota composition and/or drug metabolism were included.

Results

The intestinal microbiota can both enzymatically activate IBD pro-drugs (e.g., in case of thiopurines), but also inactivate certain drugs (e.g., mesalazine by acetylation via N-acetyltransferase 1 and infliximab via IgG-degrading enzymes). Aminosalicylates, corticosteroids, thiopurines, calcineurin inhibitors, anti-tumor necrosis factor biologicals and tofacitinib were all reported to alter the intestinal microbiota composition, including changes in microbial diversity and/or relative abundances of various microbial taxa.

Conclusion

Various lines of evidence have shown the ability of the intestinal microbiota to interfere with IBD drugs and vice versa. These interactions can influence treatment response, but well-designed clinical studies and combined in vivo and ex vivo models are needed to achieve consistent findings and evaluate clinical relevance.

RNA-Seq-based transcriptome analysis of methicillin-resistant Staphylococcus aureus growth inhibition by propionate

Staphylococcus aureus is a pathogen that causes a variety of infectious diseases such as pneumonia, endocarditis, and septic shock. Methicillin-resistant S. aureus (MRSA) evades virtually all available treatments, creating the need for an alternative control strategy. Although we previously demonstrated the inhibitory effect of sodium propionate (NaP) on MRSA, the regulatory mechanism of this effect remains unclear. In this study, we investigated the regulatory mechanism responsible for the inhibitory effect of NaP on MRSA using RNA-Seq analysis. Total RNAs were isolated from non-treated and 50 mM NaP-treated S. aureus USA300 for 3 h and transcriptional profiling was conducted by RNA-Seq analysis. A total of 171 differentially expressed genes (DEGs) with log₂ fold change ≥2 and p < 0.05 was identified in the NaP treatment group compared with the control group. Among the 171 genes, 131 were up-regulated and 40 were down-regulated. Upon gene ontology (GO) annotation analysis, total 26 specific GO terms in "Biological process," "Molecular function," and "Cellular component" were identified in MRSA treated with NaP for 3 h. "Purine metabolism"; "riboflavin metabolism"; and "glycine, serine, and threonine metabolism" were identified as major altered metabolic pathways among the eight significantly enriched KEGG pathways in MRSA treated with NaP. Furthermore, the MRSA strains deficient in purF, ilvA, ribE, or ribA, which were the up-regulated DEGs in the metabolic pathways, were more susceptible to NaP than wild-type MRSA. Collectively, these results demonstrate that NaP attenuates MRSA growth by altering its metabolic pathways, suggesting that NaP can be used as a potential bacteriostatic agent for prevention of MRSA infection.

Comparative Genomics of the Genus Pseudomonas Reveals Host- and Environment-Specific Evolution

Each Earth ecosystem has unique microbial communities. Pseudomonas bacteria have evolved to occupy a plethora of different ecological niches, including living hosts, such as animals and plants. Many genes necessary for the Pseudomonas-niche interaction and their encoded functions remain unknown. Here, we describe a comparative genomic study of 3,274 genomes with 19,056,667 protein-coding sequences from Pseudomonas strains isolated from diverse environments. We detected functional divergence of Pseudomonas that depends on the niche. Each group of strains from a certain environment harbored a distinctive set of metabolic pathways or functions. The horizontal transfer of genes, which mainly proceeded between closely related taxa, was dependent on the isolation source. Finally, we detected thousands of undescribed proteins and functions associated with each Pseudomonas lifestyle. This research represents an effort to reveal the mechanisms underlying the ecology, pathogenicity, and evolution of Pseudomonas, and it will enable clinical, ecological, and biotechnological advances. IMPORTANCE Microbes play important roles in the health of living beings and in the environment. The knowledge of these functions may be useful for the development of new clinical and biotechnological applications and the restoration and preservation of natural ecosystems. However, most mechanisms implicated in the interaction of microbes with the environment remain poorly understood; thus, this field of research is very important. Here, we try to understand the mechanisms that facilitate the differential adaptation of Pseudomonas-a large and ubiquitous bacterial genus-to the environment. We analyzed more than 3,000 Pseudomonas genomes and searched for genetic patterns that can be related with their coevolution with different hosts (animals, plants, or fungi) and environments. Our results revealed that thousands of genes and genetic features are associated with each niche. Our data may be useful to develop new technical and theoretical advances in the fields of ecology, health, and industry.

Novel Virus Identification through Metagenomics: A Systematic Review

Metagenomic Next Generation Sequencing (mNGS) allows the evaluation of complex microbial communities, avoiding isolation and cultivation of each microbial species, and does not require prior knowledge of the microbial sequences present in the sample. Applications of mNGS include virome characterization, new virus discovery and full-length viral genome reconstruction, either from virus preparations enriched in culture or directly from clinical and environmental specimens. Here, we systematically reviewed studies that describe novel virus identification through mNGS from samples of different origin (plant, animal and environment). Without imposing time limits to the search, 379 publications were identified that met the search parameters. Sample types, geographical origin, enrichment and nucleic acid extraction methods, sequencing platforms, bioinformatic analytical steps and identified viral families were described. The review highlights mNGS as a feasible method for novel virus discovery from samples of different origins, describes which kind of heterogeneous experimental and analytical protocols are currently used and provides useful information such as the different commercial kits used for the purification of nucleic acids and bioinformatics analytical pipelines.

Tumor microbiome links cellular programs and immunity in pancreatic cancer

Microorganisms are detected in multiple cancer types, including in putatively sterile organs, but the contexts in which they influence oncogenesis or anti-tumor responses in humans remain unclear. We recently developed single-cell analysis of host-microbiome interactions (SAHMI), a computational pipeline to recover and denoise microbial signals from single-cell sequencing of host tissues. Here we use SAHMI to interrogate tumor-microbiome interactions in two human pancreatic cancer cohorts. We identify somatic-cell-associated bacteria in a subset of tumors and their near absence in nonmalignant tissues. These bacteria predominantly pair with tumor cells, and their presence is associated with cell-type-specific gene expression and pathway activities, including cell motility and immune signaling. Modeling results indicate that tumor-infiltrating lymphocytes closely resemble T cells from infected tissue. Finally, using multiple independent datasets, a signature of cell-associated bacteria predicts clinical prognosis. Tumor-microbiome crosstalk may modulate tumorigenesis in pancreatic cancer with implications for clinical management.

Microbiota in Tumors: From Understanding to Application

Microbes with complex functions have been found to be a potential component in tumor microenvironments. Due to their low biomass and other obstacles, intratumor microbiota is poorly understood. Mucosal sites and normal adjacent tissues are important sources of intratumor microbiota, while hematogenous spread also leads to the invasion of microbes. Intratumor microbiota affects the progression of tumors through several mechanisms, such as DNA damage, activation of oncogenic pathways, induction of immunosuppression, and metabolization of drugs. Notably, in different types of tumors, the composition and abundance of intratumor microbiota are highly heterogeneous and may play different roles in the progression of tumors. Because of the concern in this field, several techniques such as omics and immunological methods have been used to study intratumor microbiota. Here, recent progress in this field is reviewed, including the potential sources of intratumor microbiota, their functions and related mechanisms, and their heterogeneity. Techniques that can be used to study intratumor microbiota are also discussed. Moreover, research is summarized into the development of strategies that can be used in antitumor treatment and prospects for possible future research in this field.

Comparison of methods and resources for cell-cell communication inference from single-cell RNA-Seq data

The growing availability of single-cell data, especially transcriptomics, has sparked an increased interest in the inference of cell-cell communication. Many computational tools were developed for this purpose. Each of them consists of a resource of intercellular interactions prior knowledge and a method to predict potential cell-cell communication events. Yet the impact of the choice of resource and method on the resulting predictions is largely unknown. To shed light on this, we systematically compare 16 cell-cell communication inference resources and 7 methods, plus the consensus between the methods' predictions. Among the resources, we find few unique interactions, a varying degree of overlap, and an uneven coverage of specific pathways and tissue-enriched proteins. We then examine all possible combinations of methods and resources and show that both strongly influence the predicted intercellular interactions. Finally, we assess the agreement of cell-cell communication methods with spatial colocalisation, cytokine activities, and receptor protein abundance and find that predictions are generally coherent with those data modalities. To facilitate the use of the methods and resources described in this work, we provide LIANA, a LIgand-receptor ANalysis frAmework as an open-source interface to all the resources and methods.

Transcriptome analysis of infected Crandell Rees Feline Kidney (CRFK) cells by canine parvovirus type 2c Laotian isolates

The advent of RNA sequencing technology provides insight into the dynamic nature of tremendous transcripts within Crandell-Reese feline kidney (CRFK) cells in response to canine parvovirus (CPV-2c) infection. A total of 1,603 genes displayed differentially expressed genes (DEGs), including 789 up-regulated genes and 814 downregulated genes in the infected cells. Gene expression profiles have shown a subtle pattern of defense mechanism and immune response to CPV through significant DEGs when extensively examined via Gene Ontology (GO) and pathway analysis. Prospective GO analysis was performed and identified several enriched GO biological process terms with significant participating roles in the immune system process and defense response to virus pathway. A Gene network was constructed using the 22 most significantly enriched genes of particular interests in defense response to virus pathways to illustrate the key pathways. Eleven genes (C1QBP, CD40, HYAL2, IFNB1, IFNG, IL12B, IL6, IRF3, LSM14A, MAVS, NLRC5) were identified, which are directly related to the defense response to the virus. Results of transcriptome profiling permit us to understand the heterogeneity of DEGs during in vitro experimental study of CPV infection, reflecting a unique transcriptome signature for the CPV virus. Our findings also demonstrate a distinct scenario of enhanced CPV responses in CRFK cells for viral clearance that involved multistep and perplexity of biological processes. Collectively, our data have given a fundamental role in anti-viral immunity as our highlights of this study, thus providing outlooks on future research priorities to be important in studying CPV.

Prime-seq, efficient and powerful bulk RNA sequencing

Cost-efficient library generation by early barcoding has been central in propelling single-cell RNA sequencing. Here, we optimize and validate prime-seq, an early barcoding bulk RNA-seq method. We show that it performs equivalently to TruSeq, a standard bulk RNA-seq method, but is fourfold more cost-efficient due to almost 50-fold cheaper library costs. We also validate a direct RNA isolation step, show that intronic reads are derived from RNA, and compare cost-efficiencies of available protocols. We conclude that prime-seq is currently one of the best options to set up an early barcoding bulk RNA-seq protocol from which many labs would profit.

Human placental biology at single-cell resolution: a contemporaneous review

Single-cell technologies capture cellular heterogeneity to focus on previously poorly described subpopulations of cells. Work by our laboratory and many others has metagenomically characterised a low biomass intrauterine microbial community, alongside microbial transcripts, antigens and metabolites, but the functional importance of low biomass microbial communities in placental immuno-microenvironments is still being elucidated. Given their hypothesised role in modulating inflammation and immune ontogeny to enable tolerance of beneficial microbes while warding off pathogens, there is a need for single-cell resolution. Herein, we summarise the potential for mechanistic understanding of these and other key fundamental early developmental processes by applying single-cell approaches.

The search for Schizosaccharomyces fission yeasts in environmental meta-transcriptomes

Fission yeast is an important model organism in evolutionary genetics and cell biology research. Nevertheless, most research is limited to a single laboratory strain and knowledge of its natural occurrence is limited, which reduces our understanding of its life history and hinders isolation of new strains from nature. Understanding the natural diversity of fission yeast can provide insight into its genetic and phenotypic diversity and the evolutionary processes that shaped these. Here we aimed to identify candidate natural habitats of fission yeasts by searching through a large collection of publicly available environmental metatranscriptomic datasets. Using a custom pipeline, we processed over 13,000 NCBI SRA accessions, from a wide range of 34 different environmental categories. Overall, we found a very low abundance of putative yeast transcripts, with most fission yeast signatures coming from the categories of 'food' and 'terrestrial arthropods'. Additionally, a signal could be found in a variety of marine and fresh aquatic habitats. Our results do not provide a conclusive answer on the natural habitat of fission yeasts, but our analysis further narrows the range of locations where fission yeasts naturally occur.

Future Applications of Metagenomic Next-Generation Sequencing for Infectious Diseases Diagnostics

Metagenomic next-generation sequencing (mNGS) has the theoretical capacity to detect any microbe present in a host. mNGS also has the potential to infer a pathogen's phenotypic characteristics, including the ability to colonize humans, cause disease, and resist treatment. Concurrent host nucleic acid sequencing can assess the infected individual's physiological state, including characterization and appropriateness of the immune response. When the pathogen cannot be identified, host RNA sequencing may help infer the organism's nature. While the full promise of mNGS remains far from realization, the potential ability to identify all microbes in a complex clinical sample, assess each organism's virulence and antibiotic susceptibility traits, and simultaneously characterize the host's response to infection provide opportunities for mNGS to supplant existing technologies and become the primary method of infectious diseases diagnostics.

The Role of Flagellin B in Vibrio anguillarum-Induced Intestinal Immunity and Functional Domain Identification

Vibrio anguillarum, an opportunistic pathogen of aquatic animals, moves using a filament comprised of polymerised flagellin proteins. Flagellins are essential virulence factors for V. anguillarum infection. Herein, we investigated the effects of flagellins (flaA, flaB, flaC, flaD and flaE) on cell apoptosis, TLR5 expression, and production of IL-8 and TNF-α. FlaB exhibited the strongest immunostimulation effects. To explore the functions of flaB in infection, we constructed a flaB deletion mutant using a two-step recombination method, and in vitro experiments showed a significant decrease in the expression of TLR5 and inflammatory cytokines compared with wild-type cells. However in the in vivo study, expression of inflammatory cytokines and intestinal mucosal structure showed no significant differences between groups. Additionally, flaB induced a significant increase in TLR5 expression based on microscopy analysis of fluorescently labelled TLR5, indicating interactions between the two proteins, which was confirmed by native PAGE and yeast two-hybrid assay. Molecular simulation of interactions between flaB and TLR5 was performed to identify the residues involved in binding, revealing two binding sites. Then, based on molecular dynamics simulations, we carried out thirteen site-directed mutations occurring at the amino acid sites of Q57, N83, N87, R91, D94, E122, D152, N312, R313, N320, L97, H316, I324 in binding regions of flaB protein by TLR5, respectively. Surface plasmon resonance (SPR) was employed to compare the affinities of flaB mutants for TLR5, and D152, D94, I324, N87, R313, N320 and H316 were found to mediate interactions between flaB and TLR5. Our comprehensive and systematic analysis of V. anguillarum flagellins establishes the groundwork for future design of flagellin-based vaccines.

The ambivalent role of Bacteroides in enteric infections

Bacteroides spp. are increasingly used as model gut commensals in cocolonization studies with enteropathogens. The collective findings imply common themes of colonization resistance but also pathogen crossfeeding. We discuss how cutting-edge transcriptomics may help to disentangle the molecular basis of the divergent roles of Bacteroides in either protecting against or promoting infection.

Emerging technologies and infection models in cellular microbiology

The field of cellular microbiology, rooted in the co-evolution of microbes and their hosts, studies intracellular pathogens and their manipulation of host cell machinery. In this review, we highlight emerging technologies and infection models that recently promoted opportunities in cellular microbiology. We overview the explosion of microscopy techniques and how they reveal unprecedented detail at the host-pathogen interface. We discuss the incorporation of robotics and artificial intelligence to image-based screening modalities, biochemical mapping approaches, as well as dual RNA-sequencing techniques. Finally, we describe chips, organoids and animal models used to dissect biophysical and in vivo aspects of the infection process. As our knowledge of the infected cell improves, cellular microbiology holds great promise for development of anti-infective strategies with translational applications in human health.

Novel RNA Extraction Method for Dual RNA-seq Analysis of Pathogen and Host in the Early Stages of Yersinia pestis Pulmonary Infection

Pneumonic plague, caused by Yersinia pestis, is a rapidly progressing lethal infection. The various phases of pneumonic plague are yet to be fully understood. A well-established way to address the pathology of infectious diseases in general, and pneumonic plague in particular, is to conduct concomitant transcriptomic analysis of the bacteria and the host. The analysis of dual RNA by RNA sequencing technology is challenging, due the difficulties of extracting bacterial RNA, which is overwhelmingly outnumbered by the host RNA, especially at the critical early time points post-infection (prior to 48 h). Here, we describe a novel technique that employed the infusion of an RNA preserving reagent (RNAlater) into the lungs of the animals, through the trachea, under deep anesthesia. This method enabled the isolation of stable dual mRNA from the lungs of mice infected with Y. pestis, as early as 24 h post-infection. The RNA was used for transcriptomic analysis, which provided a comprehensive gene expression profile of both the host and the pathogen.

Extracellular Vesicles and Host-Pathogen Interactions: A Review of Inter-Kingdom Signaling by Small Noncoding RNA

The focus of this brief review is to describe the role of noncoding regulatory RNAs, including short RNAs (sRNA), transfer RNA (tRNA) fragments and microRNAs (miRNA) secreted in extracellular vesicles (EVs), in inter-kingdom communication between bacteria and mammalian (human) host cells. Bacteria secrete vesicles that contain noncoding regulatory RNAs, and recent studies have shown that the bacterial vesicles fuse with and deliver regulatory RNAs to host cells, and similar to eukaryotic miRNAs, regulatory RNAs modulate the host immune response to infection. Recent studies have also demonstrated that mammalian cells secrete EVs containing miRNAs that regulate the gut microbiome, biofilm formation and the bacterial response to antibiotics. Thus, as evidence accumulates it is becoming clear that the secretion of noncoding regulatory RNAs and miRNAs in extracellular vesicles is an important mechanism of bidirectional communication between bacteria and mammalian (human) host cells. However, additional research is necessary to elucidate how noncoding regulatory RNAs and miRNA secreted in extracellular vesicles mediate inter-kingdom communication.

Global RNA profiles show target selectivity and physiological effects of peptide-delivered antisense antibiotics

Antisense peptide nucleic acids (PNAs) inhibiting mRNAs of essential genes provide a straight-forward way to repurpose our knowledge of bacterial regulatory RNAs for development of programmable species-specific antibiotics. While there is ample proof of PNA efficacy, their target selectivity and impact on bacterial physiology are poorly understood. Moreover, while antibacterial PNAs are typically designed to block mRNA translation, effects on target mRNA levels are not well-investigated. Here, we pioneer the use of global RNA-seq analysis to decipher PNA activity in a transcriptome-wide manner. We find that PNA-based antisense oligomer conjugates robustly decrease mRNA levels of the widely-used target gene, acpP, in Salmonella enterica, with limited off-target effects. Systematic analysis of several different PNA-carrier peptides attached not only shows different bactericidal efficiency, but also activation of stress pathways. In particular, KFF-, RXR- and Tat-PNA conjugates especially induce the PhoP/Q response, whereas the latter two additionally trigger several distinct pathways. We show that constitutive activation of the PhoP/Q response can lead to Tat-PNA resistance, illustrating the utility of RNA-seq for understanding PNA antibacterial activity. In sum, our study establishes an experimental framework for the design and assessment of PNA antimicrobials in the long-term quest to use these for precision editing of microbiota.

An RNA-centric global view of Clostridioides difficile reveals broad activity of Hfq in a clinically important gram-positive bacterium

The gram-positive human pathogen Clostridioides difficile has emerged as the leading cause of antibiotic-associated diarrhea. However, little is known about the bacterium's transcriptome architecture and mechanisms of posttranscriptional control. Here, we have applied transcription start site and termination mapping to generate a single-nucleotide-resolution RNA map of C. difficile 5' and 3' untranslated regions, operon structures, and noncoding regulators, including 42 sRNAs. Our results indicate functionality of many conserved riboswitches and predict cis-regulatory RNA elements upstream of multidrug resistance (MDR)-type ATP-binding cassette (ABC) transporters and transcriptional regulators. Despite growing evidence for a role of Hfq in RNA-based gene regulation in C. difficile, the functions of Hfq-based posttranscriptional regulatory networks in gram-positive pathogens remain controversial. Using Hfq immunoprecipitation followed by sequencing of bound RNA species (RIP-seq), we identify a large cohort of transcripts bound by Hfq and show that absence of Hfq affects transcript stabilities and steady-state levels. We demonstrate sRNA expression during intestinal colonization by C. difficile and identify infection-related signals impacting its expression. As a proof of concept, we show that the utilization of the abundant intestinal metabolite ethanolamine is regulated by the Hfq-dependent sRNA CDIF630nc_085. Overall, our study lays the foundation for understanding clostridial riboregulation with implications for the infection process and provides evidence for a global role of Hfq in posttranscriptional regulation in a gram-positive bacterium.