Recent advances in genomic DNA sequencing of microbial species from single cells

Metabolic Labeling and Digital Microfluidic Single-Cell Sequencing for Single Bacterial Genotypic-Phenotypic Analysis

Accurate assessment of phenotypic and genotypic characteristics of bacteria can facilitate comprehensive cataloguing of all the resistance factors for better understanding of antibiotic resistance. However, current methods primarily focus on individual phenotypic or genotypic profiles across different colonies. Here, a Digital microfluidic-based automated assay for whole-genome sequencing of single-antibiotic-resistant bacteria is reported, enabling Genotypic and Phenotypic Analysis of antibiotic-resistant strains (Digital-GPA). Digital-GPA can efficiently isolate and sequence antibiotic-resistant bacteria illuminated by fluorescent D-amino acid (FDAA)-labeling, producing high-quality single-cell amplified genomes (SAGs). This enables identifications of both minor and major mutations, pinpointing substrains with distinctive resistance mechanisms. Digital-GPA can directly process clinical samples to detect and sequence resistant pathogens without bacterial culture, subsequently provide genetic profiles of antibiotic susceptibility, promising to expedite the analysis of hard-to-culture or slow-growing bacteria. Overall, Digital-GPA opens a new avenue for antibiotic resistance analysis by providing accurate and comprehensive molecular profiles of antibiotic resistance at single-cell resolution.

Closing the genome of unculturable cable bacteria using a combined metagenomic assembly of long and short sequencing reads

Many environmentally relevant micro-organisms cannot be cultured, and even with the latest metagenomic approaches, achieving complete genomes for specific target organisms of interest remains a challenge. Cable bacteria provide a prominent example of a microbial ecosystem engineer that is currently unculturable. They occur in low abundance in natural sediments, but due to their capability for long-distance electron transport, they exert a disproportionately large impact on the biogeochemistry of their environment. Current available genomes of marine cable bacteria are highly fragmented and incomplete, hampering the elucidation of their unique electrogenic physiology. Here, we present a metagenomic pipeline that combines Nanopore long-read and Illumina short-read shotgun sequencing. Starting from a clonal enrichment of a cable bacterium, we recovered a circular metagenome-assembled genome (5.09 Mbp in size), which represents a novel cable bacterium species with the proposed name Candidatus Electrothrix scaldis. The closed genome contains 1109 novel identified genes, including key metabolic enzymes not previously described in incomplete genomes of cable bacteria. We examined in detail the factors leading to genome closure. Foremost, native, non-amplified long reads are crucial to resolve the many repetitive regions within the genome of cable bacteria, and by analysing the whole metagenomic assembly, we found that low strain diversity is key for achieving genome closure. The insights and approaches presented here could help achieve genome closure for other keystone micro-organisms present in complex environmental samples at low abundance.

PB-LKS: a python package for predicting phage-bacteria interaction through local K-mer strategy

Bacteriophages can help the treatment of bacterial infections yet require in-silico models to deal with the great genetic diversity between phages and bacteria. Despite the tolerable prediction performance, the application scope of current approaches is limited to the prediction at the species level, which cannot accurately predict the relationship of phages across strain mutants. This has hindered the development of phage therapeutics based on the prediction of phage-bacteria relationships. In this paper, we present, PB-LKS, to predict the phage-bacteria interaction based on local K-mer strategy with higher performance and wider applicability. The utility of PB-LKS is rigorously validated through (i) large-scale historical screening, (ii) case study at the class level and (iii) in vitro simulation of bacterial antiphage resistance at the strain mutant level. The PB-LKS approach could outperform the current state-of-the-art methods and illustrate potential clinical utility in pre-optimized phage therapy design.

Alterations in microbiome of COVID-19 patients and its impact on forensic investigations

The human microbiome is vital for maintaining human health and has garnered substantial attention in recent years, particularly in the context of the coronavirus disease 2019 (COVID-19) outbreak. Studies have underscored significant alterations in the microbiome of COVID-19 patients across various body niches, including the gut, respiratory tract, oral cavity, skin, and vagina. These changes manifest as shifts in microbiota composition, characterized by an increase in opportunistic pathogens and a decrease in beneficial commensal bacteria. Such microbiome transformations may play a pivotal role in influencing the course and severity of COVID-19, potentially contributing to the inflammatory response. This ongoing relationship between COVID-19 and the human microbiome serves as a compelling subject of research, underscoring the necessity for further investigations into the underlying mechanisms and their implications for patient health. Additionally, these alterations in the microbiome may have significant ramifications for forensic investigations, given the microbiome's potential in establishing individual characteristics. Consequently, changes in the microbiome could introduce a level of complexity into forensic determinations. As research progresses, a more profound understanding of the human microbiome within the context of COVID-19 may offer valuable insights into disease prevention, treatment strategies, and its potential applications in forensic science. Consequently, this paper aims to provide an overarching review of microbiome alterations due to COVID-19 and the associated impact on forensic applications, bridging the gap between the altered microbiome of COVID-19 patients and the challenges forensic investigations may encounter when analyzing this microbiome as a forensic biomarker.

Dissecting the human gut microbiome to better decipher drug liability: A once-forgotten organ takes center stage

BACKGROUND

The gut microbiome is a diverse system within the gastrointestinal tract composed of trillions of microorganisms (gut microbiota), along with their genomes. Accumulated evidence has revealed the significance of the gut microbiome in human health and disease. Due to its ability to alter drug/xenobiotic pharmacokinetics and therapeutic outcomes, this once-forgotten "metabolic organ" is receiving increasing attention. In parallel with the growing microbiome-driven studies, traditional analytical techniques and technologies have also evolved, allowing researchers to gain a deeper understanding of the functional and mechanistic effects of gut microbiome.

AIM OF REVIEW

From a drug development perspective, microbial drug metabolism is becoming increasingly critical as new modalities (e.g., degradation peptides) with potential microbial metabolism implications emerge. The pharmaceutical industry thus has a pressing need to stay up-to-date with, and continue pursuing, research efforts investigating clinical impact of the gut microbiome on drug actions whilst integrating advances in analytical technology and gut microbiome models. Our review aims to practically address this need by comprehensively introducing the latest innovations in microbial drug metabolism research- including strengths and limitations, to aid in mechanistically dissecting the impact of the gut microbiome on drug metabolism and therapeutic impact, and to develop informed strategies to address microbiome-related drug liability and minimize clinical risk.

KEY SCIENTIFIC CONCEPTS OF REVIEW

We present comprehensive mechanisms and co-contributing factors by which the gut microbiome influences drug therapeutic outcomes. We highlight in vitro, in vivo, and in silico models for elucidating the mechanistic role and clinical impact of the gut microbiome on drugs in combination with high-throughput, functionally oriented, and physiologically relevant techniques. Integrating pharmaceutical knowledge and insight, we provide practical suggestions to pharmaceutical scientists for when, why, how, and what is next in microbial studies for improved drug efficacy and safety, and ultimately, support precision medicine formulation for personalized and efficacious therapies.

Aggregation-induced emission biomaterials for anti-pathogen medical applications: detecting, imaging and killing

Microbial pathogens, including bacteria, fungi and viruses, greatly threaten the global public health. For pathogen infections, early diagnosis and precise treatment are essential to cut the mortality rate. The emergence of aggregation-induced emission (AIE) biomaterials provides an effective and promising tool for the theranostics of pathogen infections. In this review, the recent advances about AIE biomaterials for anti-pathogen theranostics are summarized. With the excellent sensitivity and photostability, AIE biomaterials have been widely applied for precise diagnosis of pathogens. Besides, different types of anti-pathogen methods based on AIE biomaterials will be presented in detail, including chemotherapy and phototherapy. Finally, the existing deficiencies and future development of AIE biomaterials for anti-pathogen applications will be discussed.

A fast machine-learning-guided primer design pipeline for selective whole genome amplification

Addressing many of the major outstanding questions in the fields of microbial evolution and pathogenesis will require analyses of populations of microbial genomes. Although population genomic studies provide the analytical resolution to investigate evolutionary and mechanistic processes at fine spatial and temporal scales-precisely the scales at which these processes occur-microbial population genomic research is currently hindered by the practicalities of obtaining sufficient quantities of the relatively pure microbial genomic DNA necessary for next-generation sequencing. Here we present swga2.0, an optimized and parallelized pipeline to design selective whole genome amplification (SWGA) primer sets. Unlike previous methods, swga2.0 incorporates active and machine learning methods to evaluate the amplification efficacy of individual primers and primer sets. Additionally, swga2.0 optimizes primer set search and evaluation strategies, including parallelization at each stage of the pipeline, to dramatically decrease program runtime. Here we describe the swga2.0 pipeline, including the empirical data used to identify primer and primer set characteristics, that improve amplification performance. Additionally, we evaluate the novel swga2.0 pipeline by designing primer sets that successfully amplify Prevotella melaninogenica, an important component of the lung microbiome in cystic fibrosis patients, from samples dominated by human DNA.

Advances in mining and expressing microbial biosynthetic gene clusters

Natural products (NPs) especially the secondary metabolites originated from microbes exhibit great importance in biomedical, industrial and agricultural applications. However, mining biosynthetic gene clusters (BGCs) to produce novel NPs has been hindered owing that a large population of environmental microbes are unculturable. In the past decade, strategies to explore BGCs directly from (meta)genomes have been established along with the fast development of high-throughput sequencing technologies and the powerful bioinformatics data-processing tools, which greatly expedited the exploitations of novel BGCs from unculturable microbes including the extremophilic microbes. In this review, we firstly summarized the popular bioinformatics tools and databases available to mine novel BGCs from (meta)genomes based on either pure cultures or pristine environmental samples. Noticeably, approaches rooted from machine learning and deep learning with focuses on the prediction of ribosomally synthesized and post-translationally modified peptides (RiPPs) were dramatically increased in recent years. Moreover, synthetic biology techniques to express the novel BGCs in culturable native microbes or heterologous hosts were introduced. This working pipeline including the discovery and biosynthesis of novel NPs will greatly advance the exploitations of the abundant but unexplored microbial BGCs.

Advances in Single-Cell Toxicogenomics in Environmental Toxicology

The toxicity evaluation system of environmental pollutants has undergone numerous changes due to the application of new technologies. Single-cell toxicogenomics is rapidly changing our view on environmental toxicology by increasing the resolution of our analysis to the level of a single cell. Applications of this technology in environmental toxicology have begun to emerge and are rapidly expanding the portfolio of existing technologies and applications. Here, we first summarized different methods involved in single-cell isolation and amplification in single-cell sequencing process, compared the advantages and disadvantages of different methods, and analyzed their development trends. Then, we reviewed the main advances of single-cell toxicogenomics in environmental toxicology, emphatically analyzed the application prospects of this technology in identifying the target cells of pollutants in early embryos, clarifying the heterogeneous response of cell subtypes to pollutants, and finding pathogenic bacteria in unknown microbes, and highlighted the unique characteristics of this approach with high resolution, high throughput, and high specificity by examples. We also offered a prediction of the further application of this technology and the revolution it brings in environmental toxicology. Overall, these advances will provide practical solutions for controlling or mitigating exogenous toxicological effects that threaten human and ecosystem health, contribute to improving our understanding of the physiological processes affected by pollutants, and lead to the emergence of new methods of pollution control.

Identification of housekeeping genes of Candidatus Branchiomonas cysticola associated with epitheliocystis in Atlantic salmon (Salmo salar L.)

Candidatus Branchiomonas cysticola is an intracellular, gram-negative Betaproteobacteria causing epitheliocystis in Atlantic Salmon (Salmo salar L.). The bacterium has not been genetically characterized at the intraspecific level despite its high prevalence among salmon suffering from gill disease in Norwegian aquaculture. DNA from gill samples of Atlantic salmon PCR positive for Cand. B. cysticola and displaying pathological signs of gill disease, was, therefore, extracted and subject to next-generation sequencing (mNGS). Partial sequences of four housekeeping (HK) genes (aceE, lepA, rplB, rpoC) were ultimately identified from the sequenced material. Assays for real-time RT-PCR and fluorescence in-situ hybridization, targeting the newly acquired genes, were simultaneously applied with existing assays targeting the previously characterized 16S rRNA gene. Agreement in both expression and specificity between these putative HK genes and the 16S gene was observed in all instances, indicating that the partial sequences of these HK genes originate from Cand. B. cysticola. The knowledge generated from the present study constitutes a major prerequisite for the future design of novel genotyping schemes for this bacterium.

Microbial diversity and community structure in deep-sea sediments of South Indian Ocean

Microbial communities composed of bacteria, archaea and fungi play a pivotal role in driving the biogeochemical cycles in the marine ecosystem. Despite the vastness of the South Indian Ocean, only a few studies reported the simultaneous analysis of bacterial, archaeal and fungal diversity therein, particularly archaeal and fungal communities in deep-sea environments received less attention previously. In this study, microbial diversity, community composition and dynamics in microbial community structure in eight deep-sea sediment samples collected from different sites at varying depths of the South Indian Ocean were explored using Next-Generation Sequencing. In total, 21 bacterial phyla representing 541 OTUs were identified from the eight samples, where phylum Proteobacteria was found as the most abundant bacterial phylum in five out of eight samples. Firmicutes and Chloroflexi were the dominant phyla in the rest of the three samples. In the case of archaea, uncultured species belonging to the phyla Thaumarchaeota and Euryarchaeota were the abundant taxa in all the samples. Similarly, Ascomycota and Basidiomycota were the most abundant fungal phyla present therein. In all the eight samples studied here, about 10-58% and 19-26% OTUs in archaeal and fungal communities were mapped to unclassified taxa respectively, suggesting the lack of representation in databases. Co-occurrence network analysis further revealed that bacterial communities tend to be more dynamic than archaeal and fungal communities. This study provides interesting insights into the microbial diversity, community composition and dynamics in microbial community structure in the deep-sea sediments of the South Indian Ocean.

Prochlorococcus have low global mutation rate and small effective population size

Prochlorococcus are the most abundant free-living photosynthetic carbon-fixing organisms in the ocean. Prochlorococcus show small genome sizes, low genomic G+C content, reduced DNA repair gene pool and fast evolutionary rates, which are typical features of endosymbiotic bacteria. Nevertheless, their evolutionary mechanisms are believed to be different. Evolution of endosymbiotic bacteria is dominated by genetic drift owing to repeated population bottlenecks, whereas Prochlorococcus are postulated to have extremely large effective population sizes (N_e) and thus drift has rarely been considered. However, accurately extrapolating N_e requires measuring an unbiased global mutation rate through mutation accumulation, which is challenging for Prochlorococcus. Here, we managed this experiment over 1,065 days using Prochlorococcus marinus AS9601, sequenced genomes of 141 mutant lines and determined its mutation rate to be 3.50 × 10^-10 per site per generation. Extrapolating N_e additionally requires identifying population boundaries, which we defined using PopCOGenT and over 400 genomes related to AS9601. Accordingly, we calculated its N_e to be 1.68 × 10⁷, which is only reasonably greater than that of endosymbiotic bacteria but surprisingly smaller than that of many free-living bacteria extrapolated using the same approach. Our results therefore suggest that genetic drift is a key driver of Prochlorococcus evolution.

Species-resolved sequencing of low-biomass or degraded microbiomes using 2bRAD-M

Microbiome samples with low microbial biomass or severe DNA degradation remain challenging for amplicon-based or whole-metagenome sequencing approaches. Here, we introduce 2bRAD-M, a highly reduced and cost-effective strategy which only sequences ~ 1% of metagenome and can simultaneously produce species-level bacterial, archaeal, and fungal profiles. 2bRAD-M can accurately generate species-level taxonomic profiles for otherwise hard-to-sequence samples with merely 1 pg of total DNA, high host DNA contamination, or severely fragmented DNA from degraded samples. Tests of 2bRAD-M on various stool, skin, environmental, and clinical FFPE samples suggest a successful reconstruction of comprehensive, high-resolution microbial profiles.

Metabolomics-Microbiome Crosstalk in the Breast Cancer Microenvironment

Breast cancer, the most frequent cancer diagnosed among females, is associated with a high mortality rate worldwide. Alterations in the microbiota have been linked with breast cancer development, suggesting the possibility of discovering disease biomarkers. Metabolomics has emerged as an advanced promising analytical approach for profiling metabolic features associated with breast cancer subtypes, disease progression, and response to treatment. The microenvironment compromises non-cancerous cells such as fibroblasts and influences cancer progression with apparent phenotypes. This review discusses the role of metabolomics in studying metabolic dysregulation in breast cancer caused by the effect of the tumor microenvironment on multiple cells such as immune cells, fibroblasts, adipocytes, etc. Breast tumor cells have a unique metabolic profile through the elevation of glycolysis and the tricarboxylic acid cycle metabolism. This metabolic profile is highly sensitive to microbiota activity in the breast tissue microenvironment. Metabolomics shows great potential as a tool for monitoring metabolic dysregulation in tissue and associating the findings with microbiome expression.

Synergies of Systems Biology and Synthetic Biology in Human Microbiome Studies

A number of studies have shown that the microbial communities of the human body are integral for the maintenance of human health. Advances in next-generation sequencing have enabled rapid and large-scale quantification of the composition of microbial communities in health and disease. Microorganisms mediate diverse host responses including metabolic pathways and immune responses. Using a system biology approach to further understand the underlying alterations of the microbiota in physiological and pathological states can help reveal potential novel therapeutic and diagnostic interventions within the field of synthetic biology. Tools such as biosensors, memory arrays, and engineered bacteria can rewire the microbiome environment. In this article, we review the computational tools used to study microbiome communities and the current limitations of these methods. We evaluate how genome-scale metabolic models (GEMs) can advance our understanding of the microbe-microbe and microbe-host interactions. Moreover, we present how synergies between these system biology approaches and synthetic biology can be harnessed in human microbiome studies to improve future therapeutics and diagnostics and highlight important knowledge gaps for future research in these rapidly evolving fields.

Identification of Microbiome Etiology Associated With Drug Resistance in Pleural Empyema

Identification of the offending organism and appropriate antimicrobial therapy are crucial for treating empyema. Diagnosis of empyema is largely obscured by the conventional bacterial cultivation and PCR process that has relatively low sensitivity, leading to limited understanding of the etiopathogenesis, microbiology, and role of antibiotics in the pleural cavity. To expand our understanding of its pathophysiology, we have carried out a metagenomic snapshot of the pleural effusion from 45 empyema patients by Illumina sequencing platform to assess its taxonomic, and antibiotic resistome structure. Our results showed that the variation of microbiota in the pleural effusion is generally stratified, not continuous. There are two distinct microbiome clusters observed in the forty-five samples: HA-SA type and LA-SA type. The categorization is mostly driven by species composition: HA-SA type is marked by Staphylococcus aureus as the core species, with other enriched 6 bacteria and 3 fungi, forming a low diversity and highly stable microbial community; whereas the LA-SA type has a more diverse microbial community with a distinct set of bacterial species that are assumed to be the oral origin. The microbial community does not shape the dominant antibiotic resistance classes which were common in the two types, while the increase of microbial diversity was correlated with the increase in antibiotic resistance genes. The existence of well-balanced microbial symbiotic states might respond differently to pathogen colonization and drug intake. This study provides a deeper understanding of the pathobiology of pleural empyema and suggests that potential resistance genes may hinder the antimicrobial therapy of empyema.

Sediment-associated bacterial community and predictive functionalities are influenced by choice of 16S ribosomal RNA hypervariable region(s): An amplicon-based diversity study

Meta-omics approaches such as high-throughput sequencing of 16S hypervariable region(s) [HVR(s)] is extensively applied for profiling microbial community. Several studies have deciphered the influence of HVR(s) on bacterial diversity; most of these were devoted to human body habitats. Extent to which targeted HVR(s) influences the diversity estimates of environmental samples is rather unclear. Here, we evaluated the performance of five widely used universal primer pairs spanning V1-V3, V3-V4, V4, V5-V6 and V7-V9 HVRs to characterize bacterial diversity and predictive functionality of complex marine sediments. Obtained results revealed that the HVR(s) V4 and V5-V6 represented the higher species richness than others while, V1-V3 and V7-V9 were unsuccessful to detect Bacteroidetes and Planctomycetes. Further, PICRUSt analysis showed that the selected HVR(s) also had significant impact on the predictive functional profile. Conclusively, this study proved that HVR selection has a profound effect on overall results and thus should be selected with utmost caution.

In vitro evolution of phi29 DNA polymerases through compartmentalized gene expression and rolling-circle replication

Phi29 DNA polymerase is widely used for DNA amplification through rolling-circle replication or multiple displacement amplification. Here, we performed completely in vitro artificial evolution of phi29 DNA polymerase by combining the in vitro compartmentalization and the gene expression-coupled rolling-circle replication of a circular DNA encoding the polymerase. We conducted the experiments in six different conditions composed of three different levels of inhibitor concentrations with two different DNA labeling methods. One of the experiments was performed in our previous study and the other five experiments were newly conducted in this study. Under all conditions, we found several mutations that enhance the rolling-circle amplification by the polymerase when it was expressed in the reconstituted gene expression system. Especially, a combinatorial mutant polymerase (K555T/D570N) exhibits significantly higher rolling-circle activity than the wild type. These highly active mutant polymerases would be useful for various applications.

Phenome-Genome Profiling of Single Bacterial Cell by Raman-Activated Gravity-Driven Encapsulation and Sequencing

The small size and low DNA amount of bacterial cells have hindered establishing phenome-genome links in a precisely indexed, one-cell-per-reaction manner. Here, Raman-Activated Gravity-driven single-cell Encapsulation and Sequencing (RAGE-Seq) is presented, where individual cells are phenotypically screened via single-cell Raman spectra (SCRS) in an aquatic, vitality-preserving environment, then the cell with targeted SCRS is precisely packaged in a picoliter microdroplet and readily exported in a precisely indexed, "one-cell-one-tube" manner. Such integration of microdroplet encapsulation to Raman-activated sorting ensures high-coverage one-cell genome sequencing or cultivation that is directly linked to metabolic phenotype. For clinical Escherichia coli isolates, genome assemblies derived from precisely one cell via RAGE-Seq consistently reach >95% coverage. Moreover, directly from a urine sample of urogenital tract infection, metabolic-activity-based antimicrobial susceptibility phenotypes and genome sequence of 99.5% coverage are obtained simultaneously from precisely one cell. This single-cell global mutation map corroborates resistance phenotype and genotype, and unveils epidemiological features with high specificity and sensitivity. The ability to profile and correlate bacterial metabolic phenome and high-quality genome sequences at one-cell resolution suggests broad application of RAGE-Seq.

Draft Genome Sequence Resource for Phyllachora maydis-An Obligate Pathogen That Causes Tar Spot of Corn with Recent Economic Impacts in the United States

Phyllachora maydis is an important fungal pathogen that causes tar spot of corn and has led to significant yield loss in the United States and other countries. P. maydis is an obligate biotroph belonging to the Sordariomycetes class of Ascomycota. Due to the challenges posed by their obligate nature, there is no genome sequence available in the Phyllachora genus. P. maydis isolate PM01 was collected from a corn field in Indiana and the genome was determined by next-generation sequencing. The assembly size is 45.7 Mb, with 56.46% repetitive sequences. There are 5,992 protein-coding genes and 59 are predicted as effector proteins. This genome resource will increase our understanding of genomic features of P. maydis and will assist in studying the corn-P. maydis interaction and identifying potential resistant candidates for corn breeding programs.

Single-Cell Genomics and the Oral Microbiome

The human oral cavity is one of the first environments where microbes have been discovered and studied since the dawn of microbiology. Nevertheless, approximately 200 types of bacteria from the oral microbiota have remained uncultured in the laboratory. Some are associated with a healthy oral microbial community, while others are linked to oral diseases, from dental caries to gum disease. Single-cell genomics has enabled inferences on the physiology, virulence, and evolution of such uncultured microorganisms and has further enabled isolation and cultivation of several novel oral bacteria, including the discovery of novel interspecies interactions. This review summarizes some of the more recent advances in this field, which is rapidly moving toward physiologic characterization of single cells and ultimately cultivation of the yet uncultured. A combination of traditional microbiological approaches with genomic-based physiologic predictions and isolation strategies may lead to the oral microbiome being the first complex microbial community to have all its members cultivable in the laboratory. Studying the biology of the individual microbes when in association with other members of the community, in controlled laboratory conditions and in vivo, should lead to a better understanding of oral dysbiosis and its prevention and reversion.

Multiplex bacterial polymerase chain reaction in a cohort of patients with pleural effusion

Background

The identification of the pathogens in pleural effusion has mainly relied on conventional bacterial culture or single species polymerase chain reaction (PCR), both with relatively low sensitivity. We investigated the efficacy of a commercially available multiplex bacterial PCR assay developed for pneumonia to identify the pathogens involved in pleural infection, particularly empyema.

Methods

A prospective, monocentric, observational study including 194 patients with pleural effusion. Patients were evaluated based on imaging, laboratory values, pleura ultrasound and results of thoracentesis including conventional microbiology studies during hospitalisation. Multiplex bacterial PCR (Curetis Unyvero p55) was performed in batch and had no influence on therapeutic decisions.

Results

Overall, there were 51/197 cases with transudate and 146/197 with exudate. In 42% (n = 90/214) there was a clinical suspicion of parapneumonic effusion and the final clinical diagnosis of empyema was made in 29% (n = 61/214) of all cases. The most common microorganisms identified in the cases diagnosed with empyema were anaerobes [31] followed by gram-positive cocci [10] and gram-negative rods [4]. The multiplex PCR assay identified more of the pathogens on the panel than the conventional methods (23.3% (7/30) vs. 6.7% (2/30), p = 0.008).

Conclusion

The multiplex PCR-based assay had a higher sensitivity and specificity than conventional microbiology when only the pathogens on the pneumonia panel were taken into account. A dedicated pleural empyema multiplex PCR panel including anaerobes would be needed to cover most common pathogens involved in pleural infection.

Single cell ecology

Cells are the building blocks of life, from single-celled microbes through to multi-cellular organisms. To understand a multitude of biological processes we need to understand how cells behave, how they interact with each other and how they respond to their environment. The use of new methodologies is changing the way we study cells allowing us to study them on minute scales and in unprecedented detail. These same methods are allowing researchers to begin to sample the vast diversity of microbes that dominate natural environments. The aim of this special issue is to bring together research and perspectives on the application of new approaches to understand the biological properties of cells, including how they interact with other biological entities. This article is part of a discussion meeting issue 'Single cell ecology'.

Identification of Microbial Profiles in Heavy-Metal-Contaminated Soil from Full-Length 16S rRNA Reads Sequenced by a PacBio System

Heavy metal pollution is a serious environmental problem as it adversely affects crop production and human activity. In addition, the microbial community structure and composition are altered in heavy-metal-contaminated soils. In this study, using full-length 16S rRNA gene sequences obtained by a PacBio RS II system, we determined the microbial diversity and community structure in heavy-metal-contaminated soil. Furthermore, we investigated the microbial distribution, inferred their putative functional traits, and analyzed the environmental effects on the microbial compositions. The soil samples selected in this study were heavily and continuously contaminated with various heavy metals due to closed mines. We found that certain microorganisms (e.g., sulfur or iron oxidizers) play an important role in the biogeochemical cycle. Using phylogenetic investigation of communities by reconstruction of unobserved states (PICRUSt) analysis, we predicted Kyoto Encyclopedia of Genes and Genomes (KEGG) functional categories from abundances of microbial communities and revealed a high proportion belonging to transport, energy metabolism, and xenobiotic degradation in the studied sites. In addition, through full-length analysis, Conexibacter-like sequences, commonly identified by environmental metagenomics among the rare biosphere, were detected. In addition to microbial composition, we confirmed that environmental factors, including heavy metals, affect the microbial communities. Unexpectedly, among these environmental parameters, electrical conductivity (EC) might have more importance than other factors in a community description analysis.

Systematic prediction of functionally linked genes in bacterial and archaeal genomes

Functionally linked genes in bacterial and archaeal genomes are often organized into operons. However, the composition and architecture of operons are highly variable and frequently differ even among closely related genomes. Therefore, to efficiently extract reliable functional predictions for uncharacterized genes from comparative analyses of the rapidly growing genomic databases, dedicated computational approaches are required. We developed a protocol to systematically and automatically identify genes that are likely to be functionally associated with a 'bait' gene or locus by using relevance metrics. Given a set of bait loci and a genomic database defined by the user, this protocol compares the genomic neighborhoods of the baits to identify genes that are likely to be functionally linked to the baits by calculating the abundance of a given gene within and outside the bait neighborhoods and the distance to the bait. We exemplify the performance of the protocol with three test cases, namely, genes linked to CRISPR-Cas systems using the 'CRISPRicity' metric, genes associated with archaeal proviruses and genes linked to Argonaute genes in halobacteria. The protocol can be run by users with basic computational skills. The computational cost depends on the sizes of the genomic dataset and the list of reference loci and can vary from one CPU-hour to hundreds of hours on a supercomputer.

Antibiotic-Producing Beneficial Bacteria in the Gut of the Burying Beetle Nicrophorus vespilloides

The increasing prevalence of antibiotic-resistant human pathogens is a growing public concern and there is intense pressure to identify new antibacterial compounds that can be developed into antibiotics with novel mode of action. Evolutionary theory predicts that insects that have evolved to occupy sophisticated ecological niches by feeding and reproducing on carcasses will depend on their gut microbiome to prevent colonization by invading pathogens taken up with the diet. This inspired our hypothesis that the complex interactions between the core microbiome and the more flexible microbial communities dependent on the environment may promote the outsourcing of antibiotic synthesis to beneficial microbes. We tested this hypothesis by cultivating and characterizing bacteria isolated from the gut of the burying beetle Nicrophorus vespilloides, which feeds and reproduces on small vertebrate carcasses buried in the soil to avoid competitors such as fly maggots. The extracts of isolated bacteria were screened for activity against human pathogens such as Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus, and Candida albicans. More than 400 strains were isolated, among which the crude extract of Serratia marcescens 2MH3-2 displayed promising activity against Staphylococcus aureus. Bioactivity-guided fractionation enabled purification of the primary antimicrobial compound of the extract. By LC-MS and NMR experiments, it was identified as serrawettin W2 (C₃₈H₆₁N₅O₉), the antibacterial and nematostatic activity of which was corroborated in our study. We postulate that this antibiotic could contribute to the control of both bacteria and phoretic nematodes in the gut, which compete for food when transferred to the carcass. Our study shows that the gut microbiome of N. vespilloides is a promising resource for the screening of antibiotic-producing bacteria.

Metagenomics Approaches in Discovery and Development of New Bioactive Compounds from Marine Actinomycetes

Marine actinomycetes are prolific sources of marine drug discovery system contributing for several bioactive compounds of biomedical prominence. Metagenomics, a culture-independent technique through its sequence- and function-based screening has led to the discovery and synthesis of numerous biologically significant compounds like polyketide synthase, Non-ribosomal peptide synthetase, antibiotics, and biocatalyst. While metagenomics offers different advantages over conventional sequencing techniques, they also have certain limitations including bias classification, non-availability of quality DNA samples, heterologous expression, and host selection. The assimilation of advanced amplification and screening methods such as φ29 DNA polymerase, Next-Generation Sequencing, Cosmids, and recent bioinformatics tools like automated genome mining, anti-SMASH have shown promising results to overcome these constrains. Consequently, functional genomics and bioinformatics along with synthetic biology will be crucial for the success of the metagenomic approach and indeed for exploring new possibilities among the microbial consortia for the future drug discovery process.

The profiles of dysbiotic microbial communities

Alterations in the human gut microbiota play an important role in disease pathogenesis. Although next-generation sequencing has provided observational evidence linking shifts in gut microbiota composition to alterations in the human host, underlying mechanisms remain elusive. Metabolites generated within complex microbial communities and at the crossroads with host cells may be able to explain the impact of the gut microbiome on human homeostasis. Emerging technologies including novel culturing protocols, microfluidic systems, engineered organoids, and single-cell imaging approaches are providing new perspectives from which the gut microbiome can be studied paving the way to new diagnostic markers and personalized therapeutic interventions.

Genetics of Alzheimer's Disease

Alzheimer's disease (AD) related genes have been elucidated by advanced genetic techniques. Familial autosomal dominant AD genes founded by linkage analyses are APP, PSEN1, PSEN2, ABCA7, and SORL1. Genome-wide association studies have found risk genes such as ABCA7, BIN1, CASS4, CD33, CD2AP, CELF1, CLU, CR1, DSG2, EPHA1, FERMT2, HLA-DRB5-HLA-DRB1, INPP5D, MEF2C, MS4A6A/MS4A4E, NME8, PICALM, PTK2B, SLC24A4, SORL1, and ZCWPW1. ABCA7, SORL1, TREM2, and APOE are proved to have high odds ratio (>2) in risk of AD using next generation sequencing studies. Thanks to the promising genetic techniques such as CRISPR-CAS9 and single-cell RNA sequencing opened a new era in genetics. CRISPR-CAS9 can directly link genetic knowledge to future treatment. Single-cell RNA sequencing are providing useful information on cell biology and pathogenesis of diverse diseases.

Introduction to Genome Biology and Diversity

Organisms display astonishing levels of cell and molecular diversity, including genome size, shape, and architecture. In this chapter, we review how the genome can be viewed as both a structural and an informational unit of biological diversity and explicitly define our intended meaning of genetic information. A brief overview of the characteristic features of bacterial, archaeal, and eukaryotic cell types and viruses sets the stage for a review of the differences in organization, size, and packaging strategies of their genomes. We include a detailed review of genetic elements found outside the primary chromosomal structures, as these provide insights into how genomes are sometimes viewed as incomplete informational entities. Lastly, we reassess the definition of the genome in light of recent advancements in our understanding of the diversity of genomic structures and the mechanisms by which genetic information is expressed within the cell. Collectively, these topics comprise a good introduction to genome biology for the newcomer to the field and provide a valuable reference for those developing new statistical or computation methods in genomics. This review also prepares the reader for anticipated transformations in thinking as the field of genome biology progresses.

High-throughput identification and diagnostics of pathogens and pests: Overview and practical recommendations

High-throughput identification technologies provide efficient tools for understanding the ecology and functioning of microorganisms. Yet, these methods have been only rarely used for monitoring and testing ecological hypotheses in plant pathogens and pests in spite of their immense importance in agriculture, forestry and plant community dynamics. The main objectives of this manuscript are the following: (a) to provide a comprehensive overview about the state-of-the-art high-throughput quantification and molecular identification methods used to address population dynamics, community ecology and host associations of microorganisms, with a specific focus on antagonists such as pathogens, viruses and pests; (b) to compile available information and provide recommendations about specific protocols and workable primers for bacteria, fungi, oomycetes and insect pests; and (c) to provide examples of novel methods used in other microbiological disciplines that are of great potential use for testing specific biological hypotheses related to pathology. Finally, we evaluate the overall perspectives of the state-of-the-art and still evolving methods for diagnostics and population- and community-level ecological research of pathogens and pests.

Viral Diversity and Its Relationship With Environmental Factors at the Surface and Deep Sea of Prydz Bay, Antarctica

A viral metagenomic analysis of five surface and two bottom water (878 meters below surface, mbs, and 3,357 mbs) samples from Prydz Bay, was conducted during February-March 2015. The results demonstrated that most of the DNA viruses were dsDNA viruses (79.73-94.06%, except at PBI1, 37.51%). Of these, Caudovirales (Siphoviridae, Myoviridae, and Podoviridae) phages were most abundant in surface seawater (67.67-71.99%), while nucleocytoplasmic large DNA viruses (NCLDVs) (Phycodnaviridae, Mimiviridae, and Pandoraviridae accounted for >30% of dsDNA viruses) were most abundant in the bottom water (3,357 mbs). Of the ssDNA viruses, Microviridae was the dominant family in PBI2, PBI3, PBOs, and PBI4b (57.09-87.55%), while Inoviridae (58.16%) was the dominant family in PBI1. Cellulophaga phages (phi38:1 and phi10:1) and Flavobacterium phage 11b, infecting the possible host strains affiliated with the family Flavobacteriaceae of the phylum Bacteroidetes, were abundant in surface water dsDNA viromes. The long contig (PBI2_1_C) from the viral metagenomes were most similar to the genome architectures of Cellulophaga phage phi10:1 and Flavobacterium phage 11b from the Arctic Ocean. Comparative analysis showed that the surface viral community of Prydz Bay could be clearly separated from other marine and freshwater environments. The deep sea viral community was similar to the deep sea viral metagenome at A Long-term Oligotrophic Habitat Assessment Station (ALOHA, at 22°45'N, 158°00'W). The multivariable analysis indicated that nutrients probably played an important role in shaping the local viral community structure. This study revealed the preliminary characteristics of the viral community in Prydz Bay, from both the surface and the deep sea. It provided evidence of the relationships between the virome and the environment in Prydz Bay and provided the first data from the deep sea viral community of the Southern Ocean.

Impact of hydrologic boundaries on microbial planktonic and biofilm communities in shallow terrestrial subsurface environments

Subsurface environments contain a large proportion of planetary microbial biomass and harbor diverse communities responsible for mediating biogeochemical cycles important to groundwater used by human society for consumption, irrigation, agriculture and industry. Within the saturated zone, capillary fringe and vadose zones, microorganisms can reside in two distinct phases (planktonic or biofilm), and significant differences in community composition, structure and activity between free-living and attached communities are commonly accepted. However, largely due to sampling constraints and the challenges of working with solid substrata, the contribution of each phase to subsurface processes is largely unresolved. Here, we synthesize current information on the diversity and activity of shallow freshwater subsurface habitats, discuss the challenges associated with sampling planktonic and biofilm communities across spatial, temporal and geological gradients, and discuss how biofilms may be constrained within shallow terrestrial subsurface aquifers. We suggest that merging traditional activity measurements and sequencing/-omics technologies with hydrological parameters important to sediment biofilm assembly and stability will help delineate key system parameters. Ultimately, integration will enhance our understanding of shallow subsurface ecophysiology in terms of bulk-flow through porous media and distinguish the respective activities of sessile microbial communities from more transient planktonic communities to ecosystem service and maintenance.

Machine Learning Reveals Missing Edges and Putative Interaction Mechanisms in Microbial Ecosystem Networks

Microbes affect each other's growth in multiple, often elusive, ways. The ensuing interdependencies form complex networks, believed to reflect taxonomic composition as well as community-level functional properties and dynamics. The elucidation of these networks is often pursued by measuring pairwise interactions in coculture experiments. However, the combinatorial complexity precludes an exhaustive experimental analysis of pairwise interactions, even for moderately sized microbial communities. Here, we used a machine learning random forest approach to address this challenge. In particular, we show how partial knowledge of a microbial interaction network, combined with trait-level representations of individual microbial species, can provide accurate inference of missing edges in the network and putative mechanisms underlying the interactions. We applied our algorithm to three case studies: an experimentally mapped network of interactions between auxotrophic Escherichia coli strains, a community of soil microbes, and a large in silico network of metabolic interdependencies between 100 human gut-associated bacteria. For this last case, 5% of the network was sufficient to predict the remaining 95% with 80% accuracy, and the mechanistic hypotheses produced by the algorithm accurately reflected known metabolic exchanges. Our approach, broadly applicable to any microbial or other ecological network, may drive the discovery of new interactions and new molecular mechanisms, both for therapeutic interventions involving natural communities and for the rational design of synthetic consortia. IMPORTANCE Different organisms in a microbial community may drastically affect each other's growth phenotypes, significantly affecting the community dynamics, with important implications for human and environmental health. Novel culturing methods and the decreasing costs of sequencing will gradually enable high-throughput measurements of pairwise interactions in systematic coculturing studies. However, a thorough characterization of all interactions that occur within a microbial community is greatly limited both by the combinatorial complexity of possible assortments and by the limited biological insight that interaction measurements typically provide without laborious specific follow-ups. Here, we show how a simple and flexible formal representation of microbial pairs can be used for the classification of interactions via machine learning. The approach we propose predicts with high accuracy the outcome of yet-to-be performed experiments and generates testable hypotheses about the mechanisms of specific interactions.

Multi-omics at single-cell resolution: comparison of experimental and data fusion approaches

Biological samples are inherently heterogeneous and complex. Tackling this complexity requires innovative technological and analytical solutions. Recent advances in high-throughput single-cell isolation and nucleic acid barcoding methods are rapidly changing the technological landscape of biological sciences and now make it possible to measure the (epi)genomic, transcriptomic, or proteomic state of individual cells. In addition, few experimental approaches enable multi-omics measurements of the same cell. However, merging-omics data collected from different experiments remains a considerable challenge. Although several strategies for merging transcriptomics datasets have recently been introduced, cell-to-cell variability and heterogeneity remains one of the confounding factors limiting data fusion and integration. Here, we focus our discussion on the latest single-cell technological and analytical solutions to achieve high data dimensionality and resolution. Obtaining datasets with a wealth of multi-omics information will undoubtedly provide new avenues for researchers to unravel the complexity of biological samples encountered in modern biological research and molecular diagnostics.

Global Ramifications of Dust and Sandstorm Microbiota

Dust and sandstorm events inject substantial quantities of foreign microorganisms into global ecosystems, with the ability to impact distant environments. The majority of these microorganisms originate from deserts and drylands where the soil is laden with highly stress-resistant microbes capable of thriving under extreme environmental conditions, and a substantial portion of them survive long journeys through the atmosphere. This large-scale transmission of highly resilient alien microbial contaminants raises concerns with regards to the invasion of sensitive and/or pristine sink environments, and to human health-concerns exacerbated by increases in the rate of desertification. Further increases in the transport of dust-associated microbiota could extend the spread of foreign microbes to new ecosystems, increase their load in present sink environments, disrupt ecosystem balance, and potentially introduce new pathogens. Our present understanding of these microorganisms, their phylogenic affiliations and functional significance, is insufficient to determine their impact. The purpose of this review is to provide an overview of available data regarding dust and sandstorm microbiota and their potential ramifications on human and ecosystem health. We conclude by discussing current gaps in dust and sandstorm microbiota research, and the need for collaborative studies involving high-resolution meta-omic approaches in conjunction with extensive ecological time-series studies to advance the field towards an improved and sufficient understanding of these invisible atmospheric travelers and their global ramifications.

Where are we going with genomics in plant pathogenic bacteria?

Genome sequencing is commonly used in research laboratories right now thanks to the rise of high-throughput sequencing with higher speed and output-to-cost ratios. Here, we summarized the application of genomics in different aspects of plant bacterial pathosystems. Genomics has been used in studying the mechanisms of plant-bacteria interactions, and host specificity. It also helps with taxonomy, study of non-cultured bacteria, identification of causal agent, single cell sequencing, population genetics, and meta-transcriptomic. Overall, genomics has significantly improved our understanding of plant-microbe interaction.

Deciphering the Human Virome with Single-Virus Genomics and Metagenomics

Single-cell genomics has unveiled the metabolic potential of dominant microbes inhabiting different environments, including the human body. The lack of genomic information for predominant microbes of the human body, such as bacteriophages, hinders our ability to answer fundamental questions about our viral communities. Here, we applied single-virus genomics (SVGs) to natural human salivary samples in combination with viral metagenomics to gain some insights into the viral community structure of the oral cavity. Saliva samples were processed for viral metagenomics (n = 15) and SVGs (n = 3). A total of 1328 uncultured single viruses were sorted by fluorescence-activated virus sorting followed by whole genome amplification. Sequencing of 24 viral single amplified genomes (vSAGs) showed that half of the vSAGs contained viral hallmark genes. Among those bona fide viruses, the uncultured single virus 92-C13 putatively infecting oral Streptococcus-like species was within the top ≈10 most abundant viruses in the oral virome. Viral gene network and viral metagenomics analyses of 439 oral viruses from cultures, metagenomics, and SVGs revealed that salivary viruses were tentatively structured into ≈200 major viral clusters, corresponding to approximately genus-level groupings. Data showed that none of the publicly available viral isolates, excepting an Actinomyces phage, were significantly abundant in the oral viromes. In addition, none of the obtained viral contigs and vSAGs from this study were present in all viromes. Overall, the data demonstrates that most viral isolates are not naturally abundant in saliva, and furthermore, the predominant viruses in the oral cavity are yet uncharacterized. Results suggest a variable, complex, and interpersonal viral profile. Finally, we demonstrated the power of SVGs in combination with viral metagenomics to unveil the genetic information of the uncultured viruses of the human virome.

Unrealistic phylogenetic trees may improve phylogenetic footprinting

Motivation

The computational investigation of DNA binding motifs from binding sites is one of the classic tasks in bioinformatics and a prerequisite for understanding gene regulation as a whole. Due to the development of sequencing technologies and the increasing number of available genomes, approaches based on phylogenetic footprinting become increasingly attractive. Phylogenetic footprinting requires phylogenetic trees with attached substitution probabilities for quantifying the evolution of binding sites, but these trees and substitution probabilities are typically not known and cannot be estimated easily.

Results

Here, we investigate the influence of phylogenetic trees with different substitution probabilities on the classification performance of phylogenetic footprinting using synthetic and real data. For synthetic data we find that the classification performance is highest when the substitution probability used for phylogenetic footprinting is similar to that used for data generation. For real data, however, we typically find that the classification performance of phylogenetic footprinting surprisingly increases with increasing substitution probabilities and is often highest for unrealistically high substitution probabilities close to one. This finding suggests that choosing realistic model assumptions might not always yield optimal predictions in general and that choosing unrealistically high substitution probabilities close to one might actually improve the classification performance of phylogenetic footprinting.

Availability and Implementation

The proposed PF is implemented in JAVA and can be downloaded from https://github.com/mgledi/PhyFoo

Supplementary information

Supplementary data are available at Bioinformatics online.

The Emergency Medical Service Microbiome

Emergency medical services (EMS) personnel are an integral component of the health care framework and function to transport patients from various locations to and between care facilities. In addition to physical injury, EMS personnel are expected to be at high risk to acquire and transmit health care-associated infections (HAIs) in the workplace. However, currently, little is known about EMS biosafety risk factors and the epidemiological contribution of EMS to pathogen transmission within and outside the health care sector. Health care facility microbiomes contain diverse bacterial, fungal, and viral pathogens that cause over 1.7 million HAIs each year in the United States alone. While hospital microbiomes have been relatively well studied, there is scant information about EMS infrastructure and equipment microbiomes or the role(s) they play in HAI transmission between health care facilities. We review recent literature investigating the microbiome of ambulances and other EMS service facilities which consistently identify antibiotic-resistant pathogens causing HAIs, including methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant Enterococcus, and Klebsiella pneumoniae Our review provides evidence that EMS microbiomes are dynamic and important pathogen reservoirs, and it underscores the need for more widespread and in-depth microbiome studies to elucidate patterns of pathogen transmission. We discuss emerging DNA sequencing technologies and other methods that can be applied to characterize and mitigate EMS biosafety risks in the future. Understanding the complex interplay between EMS and hospital microbiomes will provide key insights into pathogen transmission mechanisms and identify strategies to minimize HAIs and community infection.

Microbiota changes impact on sexually transmitted infections and the development of pelvic inflammatory disease

The integrity of the human urogenital microbiome is crucial for women's health and well-being. An imbalance of the urogenital microbiota increases the risk for sexually transmitted infections. In this review, we discuss the microbiota composition of the female urogenital tract and its role in protecting from sexually transmitted infections and the emergence of pelvic inflammatory disease.

Obtaining high-quality draft genomes from uncultured microbes by cleaning and co-assembly of single-cell amplified genomes

Single-cell genomics is a straightforward approach to obtain genomes from uncultured microbes. However, sequence reads from a single-cell amplified genome (SAG) contain significant bias and chimeric sequences. Here, we describe Cleaning and Co-assembly of a Single-Cell Amplified Genome (ccSAG), a novel analytical workflow to obtain composite single-cell genomes with elimination of sequence errors. By the integration of ccSAG with a massively parallel single-cell genome amplification platform based on droplet microfluidics, we can generate multiple SAGs and effectively integrate them into the composite genomes quality equivalent to the data obtained from bulk DNA. We obtained two novel draft genomes from single gut microbial cells with high completeness (>96.6%) and extremely low contamination (<1.25%). Moreover, we revealed the presence of single nucleotide polymorphisms in the specific gene by sequence comparison at the single-cell level. Thus, the workflow yields near-complete genomes from uncultured microbes, and enables analyses of genetic heterogeneity within identical strains.

Clinical Relevance of Gastrointestinal Microbiota During Pregnancy: A Primer for Nurses

Emerging evidence about the human microbiome, a collective term for all the microorganisms living in and on the human body, consistently demonstrates the critical influence it has on host physiology and disease risk. The microbiota in the gastrointestinal (GI) tract has the most significant and far-reaching effect on human physiology. The maternal GI microbiota can decrease the risk of adverse pregnancy outcomes by modulating energy extraction, glucose metabolism, vitamin production, and host immunity essential for optimal maternal and neonatal health. Moreover, the maternal GI microbiota is thought to influence colonization of the fetus and neonate that may predispose them to different health trajectories. This article provides a basic understanding about the influence of the structure of the maternal GI microbiota, the fundamental role it plays during pregnancy, and the factors that influence the structure, and subsequently function, of the GI microbiota in the general and pregnant population. While only a small number of studies have examined this topic during pregnancy, the preponderance of the evidence supports the need to clarify baseline structure and function of GI microbiota and its associations with pregnancy outcomes. In addition, the results from the studies conducted in the general population can be extrapolated to pregnancy in many cases. This knowledge is essential for clinicians who need to understand the implications of the microbiota for disease and wellness in order to address the care factors that may adversely influence the GI microbiota during pregnancy.

Sequencing of Genomes from Environmental Single Cells

Sequencing of single bacterial and archaeal cells is an important methodology that provides access to the genetic makeup of uncultivated microorganisms. We here describe the high-throughput fluorescence-activated cell sorting-based isolation of single cells from the environment, their lysis and strand displacement-mediated whole genome amplification. We further outline 16S rRNA gene sequence-based screening of single-cell amplification products, their preparation for Illumina sequencing libraries, and finally propose computational methods for read and contig level quality control of the resulting sequence data.

Genetic circuit characterization and debugging using RNA-seq

Genetic circuits implement computational operations within a cell. Debugging them is difficult because their function is defined by multiple states (e.g., combinations of inputs) that vary in time. Here, we develop RNA‐seq methods that enable the simultaneous measurement of: (i) the states of internal gates, (ii) part performance (promoters, insulators, terminators), and (iii) impact on host gene expression. This is applied to a three‐input one‐output circuit consisting of three sensors, five NOR/NOT gates, and 46 genetic parts. Transcription profiles are obtained for all eight combinations of inputs, from which biophysical models can extract part activities and the response functions of sensors and gates. Various unexpected failure modes are identified, including cryptic antisense promoters, terminator failure, and a sensor malfunction due to media‐induced changes in host gene expression. This can guide the selection of new parts to fix these problems, which we demonstrate by using a bidirectional terminator to disrupt observed antisense transcription. This work introduces RNA‐seq as a powerful method for circuit characterization and debugging that overcomes the limitations of fluorescent reporters and scales to large systems composed of many parts.

Beyond the bulk: disclosing the life of single microbial cells

Microbial single cell analysis has led to discoveries that are beyond what can be resolved with population-based studies. It provides a pristine view of the mechanisms that organize cellular physiology, unbiased by population heterogeneity or uncontrollable environmental impacts. A holistic description of cellular functions at the single cell level requires analytical concepts beyond the miniaturization of existing technologies, defined but uncontrolled by the biological system itself. This review provides an overview of the latest advances in single cell technologies and demonstrates their potential. Opportunities and limitations of single cell microbiology are discussed using selected application-related examples.