The most common approach to sampling the bacterial populations within an infected or colonized host is to sequence genomes from a single colony obtained from a culture plate. However, it is recognized that this method does not capture the genetic diversity in the population. Sequencing a mixture of several colonies (pool-seq) is a better approach to detect population heterogeneity, but it is more complex to analyse due to different types of heterogeneity, such as within-clone polymorphisms, multi-strain mixtures, multi-species mixtures and contamination. Here, we compared 8 single-colony isolates (singles) and pool-seq on a set of 2286 Staphylococcus aureus culture samples to identify features that can distinguish pure samples, samples undergoing intraclonal variation and mixed strain samples. The samples were obtained by swabbing 3 body sites on 85 human participants quarterly for a year, who initially presented with a methicillin-resistant S. aureus skin and soft-tissue infection (SSTI). We compared parameters such as sequence quality, contamination, allele frequency, nucleotide diversity and pangenome diversity in each pool to those for the corresponding singles. Comparing singles from the same culture plate, we found that 18% of sample collections contained mixtures of multiple multilocus sequence types (MLSTs or STs). We showed that pool-seq data alone could predict the presence of multi-ST populations with 95% accuracy. We also showed that pool-seq could be used to estimate the number of intra-clonal polymorphic sites in the population. Additionally, we found that the pool may contain clinically relevant genes such as antimicrobial resistance markers that may be missed when only examining singles. These results highlight the potential advantage of analysing genome sequences of total populations obtained from clinical cultures rather than single colonies.

Background: The most widely used technologies for profiling microbial communities are 16S marker-gene sequencing and shotgun metagenomic sequencing. Interestingly, many microbiome studies have performed both sequencing experiments on the same cohort of samples. The two sequencing datasets often reveal consistent patterns of microbial signatures, highlighting the potential for an integrative analysis to improve power of testing these signatures. However, differential experimental biases, partially overlapping samples, and differential library sizes pose tremendous challenges when combining the two datasets. Currently, researchers either discard one dataset entirely or use different datasets for different objectives. Methods: In this article, we introduce the first method of this kind, named Com-2seq, that combines the two sequencing datasets for testing differential abundance at the genus and community levels while overcoming these difficulties. The new method is based on our LOCOM model (Hu et al., 2022), which employs logistic regression for testing taxon differential abundance while remaining robust to experimental bias. To benchmark the performance of Com-2seq, we introduce two ad hoc approaches: applying LOCOM to pooled taxa count data and combining LOCOM p-values from analyzing each dataset separately. Results: Our simulation studies indicate that Com-2seq substantially improves statistical efficiency over analysis of either dataset alone and works better than the two ad hoc approaches. An application of Com-2seq to two real microbiome studies uncovered scientifically plausible findings that would have been missed by analyzing individual datasets. Conclusions: Com-2seq performs integrative analysis of 16S and metagenomic sequencing data, which improves statistical efficiency and has the potential to accelerate the search of microbial communities and taxa that are involved in human health and diseases.

Increasing antibiotic resistance of Neisseria gonorrhoeae, the causative agent of gonorrhea, is a growing global concern that has renewed vaccine development efforts. The gonococcal OmpA protein was previously identified as a vaccine candidate due to its surface exposure, conservation, stable expression, and involvement in host–cell interactions. We previously demonstrated that the transcription of ompA can be activated by the MisR/MisS two-component system. Interestingly, earlier work suggested that the availability of free iron also influences ompA expression, which we confirmed in this study. In the present study, we found that iron regulation of ompA was independent of MisR and searched for additional regulators. A DNA pull-down assay with the ompA promoter from gonococcal lysates obtained from bacteria grown in the presence or absence of iron identified an XRE (Xenobiotic Response Element) family member protein encoded by NGO1982. We found that an NGO1982 null mutant of N. gonorrhoeae strain FA19 displayed a reduced level of ompA expression compared to the wild-type (WT) parent strain. Given this regulation, and the capacity of this XRE-like protein to regulate a gene involved in peptidoglycan biosynthesis (ltgA), along with its presence in other Neisseria sp., we termed the NGO1982-encoded protein as NceR (Neisseria cell envelope regulator). Critically, results from DNA-binding studies indicated that NceR regulates ompA through a direct mechanism. Thus, ompA expression is subject to both iron-dependent (NceR) and -independent (MisR/MisS) pathways. Hence, levels of the vaccine antigen candidate OmpA in circulating gonococcal strains could be influenced by transcriptional regulatory systems and the availability of iron. IMPORTANCE Herein, we report that the gene encoding a conserved gonococcal surface-exposed vaccine candidate (OmpA) is activated by a heretofore undescribed XRE family transcription factor, which we term NceR. We report that NceR regulation of ompA expression in N. gonorrhoeae is mediated by an iron-dependent mechanism, while the previously described MisR regulatory system is iron-independent. Our study highlights the importance of defining the complexity of coordinated genetic and physiologic systems that regulate genes encoding vaccine candidates to better understand their availability during infection.

UNLABELLED: Staphylococcus aureus and Pseudomonas aeruginosa are the most common bacterial pathogens isolated from cystic fibrosis (CF) related lung infections. When both of these opportunistic pathogens are found in a coinfection, CF patients tend to have higher rates of pulmonary exacerbations and experience a more rapid decrease in lung function. When cultured together under standard laboratory conditions, it is often observed that P. aeruginosa effectively inhibits S. aureus growth. Previous work from our group revealed that S. aureus from CF infections have isolate-specific survival capabilities when cocultured with P. aeruginosa . In this study, we designed a serial transfer evolution experiment to identify mutations that allow S. aureus to adapt to the presence of P. aeruginosa . Using S. aureus USA300 JE2 as our ancestral strain, populations of S. aureus were repeatedly cocultured with fresh P. aeruginosa strain, PAO1. After 8 coculture periods, S. aureus populations that survived better in the presence of PAO1 were observed. We found two independent mutations in the highly conserved S. aureus aspartate transporter, gltT , that were unique to evolved P. aeruginosa -tolerant isolates. Subsequent phenotypic testing demonstrated that gltT mutants have reduced uptake of glutamate and outcompete wild-type S. aureus when glutamate is absent from chemically-defined media. These findings together demonstrate that the presence of P. aeruginosa exerts selective pressure on S. aureus to alter its uptake and metabolism of key amino acids when the two bacteria are cultured together. IMPORTANCE: Staphylococcus aureus and Pseudomonas aeruginosa are the two most common bacterial pathogens that infect people with the genetic disease, cystic fibrosis (CF). They are often found together in CF-associated polymicrobial infections that are associated with worse patient prognosis. Understanding how these very different opportunistic pathogens influence each other in a shared environment is pertinent to improving the treatment of polymicrobial infections. While much attention has been brought to the interspecific interactions between S. aureus and P. aeruginosa, few studies have used experimental evolution methods to identify determinants of their competition and coexistence. Here, we use a serial transfer experimental evolution approach and identified a single genetic change associated with improved survival of S. aureus in the presence of P. aeruginosa. Our findings implicate metabolism of shared resources as an important factor in S. aureus's ability to survive in the presence of P. aeruginosa .

Introduction: Chlamydia trachomatis, a gram-negative obligate intracellular bacterium, commonly causes sexually transmitted infections (STIs). Little is known about C. trachomatis transmission within the host, which is important for understanding disease epidemiology and progression. Methods: We used RNA-bait enrichment and whole-genome sequencing to compare rectal, vaginal and endocervical samples collected at the same time from 26 study participants who attended Fijian Ministry of Health and Medical Services clinics and tested positive for C. trachomatis at each anatomic site. Results: The 78 C. trachomatis genomes from participants resolved into two major clades of the C. trachomatis phylogeny (the “prevalent urogenital and anorectal” clade and “non-prevalent urogenital and anorectal” clade). For 21 participants, genome sequences were almost identical in each anatomic site. For the other five participants, two distinct C. trachomatis strains were present in different sites; in two cases, the vaginal sample was a mixture of strains. Discussion: The absence of large numbers of fixed SNPs between C. trachomatis genomes within many of the participants could indicate recent acquisition of infection prior to the clinic visit without sufficient time to accumulate significant genetic variation in different body sites. This model suggests that many C. trachomatis infections may be resolved relatively quickly in the Fijian population, possibly reflecting common prescription or over-the-counter antibiotics usage.

BACKGROUND: Adverse birth and neonatal outcomes disproportionately affect African American women and infants compared to those of other races/ethnicities. While significant research has sought to identify underlying factors contributing to these disparities, current understanding remains limited, constraining prevention, early diagnosis, and treatment. With the development of next generation sequencing techniques, the contribution of the vaginal microbiome to adverse maternal and neonatal outcomes has come under consideration. However, most microbiome in pregnancy studies include few African American women, do not consider the potential contribution of non-vaginal microbiome sites, and do not consider the effects of sociodemographic or behavioral factors on the microbiome. METHODS: We conceived our on-going, 5-year longitudinal study, Biobehavioral Determinants of the Microbiome and Preterm Birth in Black Women, as an intra-race study to enable the investigation of risk and protective factors within the disparate group. We aim to recruit over 500 pregnant African American women, enrolling them into the study at 8-14 weeks of pregnancy. Participants will be asked to complete questionnaires and provide oral, vaginal, and gut microbiome samples at enrollment and again at 24-30 weeks. Chart review will be used to identify pregnancy outcomes, infections, treatments, and complications. DNA will be extracted from the microbiome samples and sequencing of the V3 and V4 regions of the 16S rRNA gene will be conducted. Processing and mapping will be completed with QIIME and operational taxonomic units (OTUs) will be mapped to Greengenes version 13_8. Community state types (CSTs) and diversity measures at each site and time will be identified and considered in light of demographic, psychosocial, clinical, and biobehavioral variables. DISCUSSION: This rich data set will allow future consideration of risk and protective factors, between and within groups of women, providing the opportunity to uncover the roots of the persistent health disparity experienced by African American families.

Clinical microbiology has long relied on growing bacteria in culture to determine antimicrobial susceptibility profiles, but the use of whole-genome sequencing for antibiotic susceptibility testing (WGS-AST) is now a powerful alternative. This review discusses the technologies that made this possible and presents results from recent studies to predict resistance based on genome sequences. We examine differences between calling antibiotic resistance profiles by the simple presence or absence of previously known genes and single-nucleotide polymorphisms (SNPs) against approaches that deploy machine learning and statistical models. Often, the limitations to genome-based prediction arise from limitations of accuracy of culture-based AST in addition to an incomplete knowledge of the genetic basis of resistance. However, we need to maintain phenotypic testing even as genome-based prediction becomes more widespread to ensure that the results do not diverge over time. We argue that standardization of WGS-AST by challenge with consistently phenotyped strain sets of defined genetic diversity is necessary to compare the efficacy of methods of prediction of antibiotic resistance based on genome sequences.

The genomes of 10 Aeromonas isolates identified and designated Aeromonas hydrophila WI, Riv3, and NF1 to NF4; A. dhakensis SSU; A. jandaei Riv2; and A. caviae NM22 and NM33 were sequenced and annotated. Isolates NF1 to NF4 were from a patient with necrotizing fasciitis (NF). Two environmental isolates (Riv2 and -3) were from the river water from which the NF patient acquired the infection. While isolates NF2 to NF4 were clonal, NF1 was genetically distinct. Outside the conserved core genomes of these 10 isolates, several unique genomic features were identified. The most virulent strains possessed one of the following four virulence factors or a combination of them: cytotoxic enterotoxin, exotoxin A, and type 3 and 6 secretion system effectors AexU and Hcp. In a septicemic-mouse model, SSU, NF1, and Riv2 were the most virulent, while NF2 was moderately virulent. These data correlated with high motility and biofilm formation by the former three isolates. Conversely, in a mouse model of intramuscular infection, NF2 was much more virulent than NF1. Isolates NF2, SSU, and Riv2 disseminated in high numbers from the muscular tissue to the visceral organs of mice, while NF1 reached the liver and spleen in relatively lower numbers on the basis of colony counting and tracking of bioluminescent strains in real time by in vivo imaging. Histopathologically, degeneration of myofibers with significant infiltration of polymorphonuclear cells due to the highly virulent strains was noted. Functional genomic analysis provided data that allowed us to correlate the highly infectious nature of Aeromonas pathotypes belonging to several different species with virulence signatures and their potential ability to cause NF.

Background. It is possible to detect bacterial species in shotgun metagenome datasets through the presence of only a few sequence reads. However, false positive results can arise, as was the case in the initial findings of a recent New York City subway metagenome project. False positives are especially likely when two closely related are present in the same sample. Bacillus anthracis, the etiologic agent of anthrax, is a high-consequence pathogen that shares > 99% average nucleotide identity with Bacillus cereus group (BCerG) genomes. Our goal was to create an analysis tool that used k-mers to detect B. anthracis, incorporating information about the coverage of BCerG in the metagenome sample. Methods. Using public complete genome sequence datasets, we identified a set of 31-mer signatures that differentiated B. anthracis from other members of the B. cereus group (BCerG), and another set which differentiated BCerG genomes (including B. anthracis) from other Bacillus strains. We also created a set of 31-mers for detecting the lethal factor gene, the key genetic diagnostic of the presence of anthrax-causing bacteria. We created synthetic sequence datasets based on existing genomes to test the accuracy of a k-mer based detection model. Results. We found 239,503 B. anthracis-specific 31-mers (the Ba31 set ), 10,183 BCerG 31-mers (the BCerG31 set ), and 2,617 lethal factor k-mers (the lef31 set). We showed that false positive B. anthracis k-mers-which arise from random sequencing errors- are observable at high genome coverages of B. cereus. We also showed that there is a "gray zone" below 0.184× coverage of the B. anthracis genome sequence, in which we cannot expect with high probability to identify lethal factor k-mers. We created a linear regression model to differentiate the presence of B. anthracis-like chromosomes from sequencing errors given the BCerG background coverage. We showed that while shotgun datasets from the New York City subway metagenome project had no matches to lef31 k-mers and hence were negative for B. anthracis, some samples showed evidence of strains very closely related to the pathogen. Discussion. This work shows how extensive libraries of complete genomes can be used to create organism-specific signatures to help interpret metagenomes. We contrast "specialist" approaches to metagenome analysis such as this work to "generalist" software that seeks to classify all organisms present in the sample and note the more general utility of a k-mer filter approach when taxonomic boundaries lack clarity or high levels of precision are required.

Improved knowledge regarding the tissue penetration of antituberculosis drugs may help optimize drug management. Patients with drug-resistant pulmonary tuberculosis undergoing adjunctive surgery were enrolled. Serial serum samples were collected, and microdialysis was performed using ex vivo lung tissue to measure pyrazinamide concentrations. Among 10 patients, the median pyrazinamide dose was 24.7 mg/kg of body weight. Imaging revealed predominant lung lesions as cavitary (n 6 patients), mass-like (n 3 patients), or consolidative (n 1 patient). On histopathology examination, all tissue samples had necrosis; eight had a pH of ≤5.5. Tissue samples from two patients were positive for Mycobacterium tuberculosis by culture (pH 5.5 and 7.2). All 10 patients had maximal serum pyrazinamide concentrations within the recommended range of 20 to 60μg/ml. The median lung tissue free pyrazinamide concentration was 20.96 μg/ml. The median tissue-To-serum pyrazinamide concentration ratio was 0.77 (range, 0.54 to 0.93). There was a significant inverse correlation between tissue pyrazinamide concentrations and the amounts of necrosis (R -0.66, P 0.04) and acid-fast bacilli (R -0.75, P 0.01) identified by histopathology. We found good penetration of pyrazinamide into lung tissue among patients with pulmonary tuberculosis with a variety of radiological lesion types. Our tissue pH results revealed that most lesions had a pH conducive to pyrazinamide activity. The tissue penetration of pyrazinamide highlights its importance in both drug-susceptible and drug-resistant antituberculosis treatment regimens.