Microbial Phenotypes Research Articles

Ecophysiological Study of Paraburkholderia sp. Strain 1N under Soil Solution Conditions: Dynamic Substrate Preferences and Characterization of Carbon Use Efficiency.

We used time-resolved metabolic footprinting, an important technical approach used to monitor changes in extracellular compound concentrations during microbial growth, to study the order of substrate utilization (i.e., substrate preferences) and kinetics of a fast-growing soil isolate, Paraburkholderia sp. strain 1N. The growth of Paraburkholderia sp. 1N was monitored under aerobic conditions in a soil-extracted solubilized organic matter medium, representing a realistic diversity of available substrates and gradient of initial concentrations. We combined multiple analytical approaches to track over 150 compounds in the medium and complemented this with bulk carbon and nitrogen measurements, allowing estimates of carbon use efficiency throughout the growth curve. Targeted methods allowed the quantification of common low-molecular-weight substrates: glucose, 20 amino acids, and 9 organic acids. All targeted compounds were depleted from the medium, and depletion followed a sigmoidal curve where sufficient data were available. Substrates were utilized in at least three distinct temporal clusters as Paraburkholderia sp. 1N produced biomass at a cumulative carbon use efficiency of 0.43. The two substrates with highest initial concentrations, glucose and valine, exhibited longer usage windows, at higher biomass-normalized rates, and later in the growth curve. Contrary to hypotheses based on previous studies, we found no clear relationship between substrate nominal oxidation state of carbon (NOSC) or maximal growth rate and the order of substrate depletion. Under soil solution conditions, the growth of Paraburkholderia sp. 1N induced multiauxic substrate depletion patterns that could not be explained by the traditional paradigm of catabolite repression.IMPORTANCE Exometabolomic footprinting methods have the capability to provide time-resolved observations of the uptake and release of hundreds of compounds during microbial growth. Of particular interest is microbial phenotyping under environmentally relevant soil conditions, consisting of relatively low concentrations and modeling pulse input events. Here, we show that growth of a bacterial soil isolate, Paraburkholderia sp. 1N, on a dilute soil extract resulted in a multiauxic metabolic response, characterized by discrete temporal clusters of substrate depletion and metabolite production. Our data did not support the hypothesis that compounds with lower energy content are used preferentially, as each cluster contained compounds with a range of nominal oxidation states of carbon. These new findings with Paraburkholderia sp. 1N, which belongs to a metabolically diverse genus, provide insights on ecological strategies employed by aerobic heterotrophs competing for low-molecular-weight substrates in soil solution.

Quantitative Microbial Risk Assessment Based on Whole Genome Sequencing Data: Case of Listeria monocytogenes.

The application of high-throughput DNA sequencing technologies (WGS) data remain an increasingly discussed but vastly unexplored resource in the public health domain of quantitative microbial risk assessment (QMRA). This is due to challenges including high dimensionality of WGS data and heterogeneity of microbial growth phenotype data. This study provides an innovative approach for modeling the impact of population heterogeneity in microbial phenotypic stress response and integrates this into predictive models inputting a high-dimensional WGS data for increased precision exposure assessment using an example of Listeria monocytogenes. Finite mixture models were used to distinguish the number of sub-populations for each of the stress phenotypes, acid, cold, salt and desiccation. Machine learning predictive models were selected from six algorithms by inputting WGS data to predict the sub-population membership of new strains with unknown stress response data. An example QMRA was conducted for cultured milk products using the strains of unknown stress phenotype to illustrate the significance of the findings of this study. Increased resistance to stress conditions leads to increased growth, the likelihood of higher exposure and probability of illness. Neglecting within-species genetic and phenotypic heterogeneity in microbial stress response may over or underestimate microbial exposure and eventual risk during QMRA.

Growth hormone deficiency and excess alter the gut microbiome in adult male mice

The gut microbiome has been implicated in host metabolism, endocrinology, and pathophysiology. Furthermore, several studies have shown that gut bacteria impact host growth, partially mediated through the growth hormone (GH)/insulin-like growth factor 1 (IGF-1) axis. Yet, no study to date has examined the specific role of GH on the gut microbiome. Our study thus characterized the adult gut microbial profile and intestinal phenotype in GH gene-disrupted (GH-/-) mice (a model of GH deficiency) and bovine GH transgenic (bGH) mice (a model of chronic, excess GH action) at 6 months of age. Both the GH-/- and bGH mice had altered microbial signatures, in opposing directions at the phylum and genus levels. For example, GH-/- mice had significantly reduced abundance in the Proteobacteria, Campylobacterota, and Actinobacteria phyla, whereas bGH mice exhibited a trending increase in those phyla compared with respective controls. Analysis of maturity of the microbial community demonstrated that lack of GH results in a significantly more immature microbiome while excess GH increases microbial maturity. Several common bacterial genera were shared, although in opposing directions, between the 2 mouse lines (e.g., decreased in GH-/- mice and increased in bGH mice), suggesting an association with GH. Similarly, metabolic pathways like acetate, butyrate, heme B, and folate biosynthesis were predicted to be impacted by GH. This study is the first to characterize the gut microbiome in mouse lines with altered GH action and indicates that GH may play a role in the growth of certain microbiota thus impacting microbial maturation and metabolic function.

Elucidating host-microbe interactions in vivo by studying population dynamics using neutral genetic tags.

Host-microbe interactions are highly dynamic in space and time, in particular in the case of infections. Pathogen population sizes, microbial phenotypes and the nature of the host responses often change dramatically over time. These features pose particular challenges when deciphering the underlying mechanisms of these interactions experimentally, as traditional microbiological and immunological methods mostly provide snapshots of population sizes or sparse time series. Recent approaches - combining experiments using neutral genetic tags with stochastic population dynamic models - allow more precise quantification of biologically relevant parameters that govern the interaction between microbe and host cell populations. This is accomplished by exploiting the patterns of change of tag composition in the microbe or host cell population under study. These models can be used to predict the effects of immunodeficiencies or therapies (e.g. antibiotic treatment) on populations and thereby generate hypotheses and refine experimental designs. In this review, we present tools to study population dynamics in vivo using genetic tags, explain examples for their implementation and briefly discuss future applications.

An inexpensive, high-throughput μPAD assay of microbial growth rate and motility on solid surfaces using Saccharomyces cerevisiae and Escherichia coli as model organisms.

Many microbial phenotypes are differentially or exclusively expressed on agar surfaces, including biofilms, motility, and sociality. However, agar-based assays are limited by their low throughput, which increases costs, lab waste, space requirements, and the time required to conduct experiments. Here, we demonstrate the use of wax-printed microfluidic paper-based analytical devices (μPADs) to measure linear growth rate of microbes on an agar growth media as a means of circumventing the aforementioned limitations. The main production materials of the proposed μPAD design are a wax printer, filter paper, and empty pipet boxes. A single wax-printed μPAD allowing 8 independent, agar-grown colonies costs $0.07, compared to $0.20 and $9.37 for the same number of replicates on traditional microtiter/spectrophotometry and Petri dish assays, respectively. We optimized the μPAD design for channel width (3 mm), agar volume (780 μL/channel), and microbe inoculation method (razor-blade). Comparative analyses of the traditional and proposed μPAD methods for measuring growth rate of nonmotile (Saccharomyces cerevisiae) and motile (flagellated Escherichia coli) microorganisms suggested the μPAD assays conferred a comparable degree of accuracy and reliability to growth rate measurements as their traditional counterparts. We substantiated this claim with strong, positive correlations between the traditional and μPAD assay, a significant nonzero slope in the model relating the two assays, a nonsignificant difference between the relative standard errors of the two techniques, and an analysis of inter-device reliability. Therefore, μPAD designs merit consideration for the development of enhanced-throughput, low-cost microbial growth and motility assays.

Reconstructing organisms in silico: genome-scale models and their emerging applications.

Escherichia coli is considered to be the best-known microorganism given the large number of published studies detailing its genes, its genome and the biochemical functions of its molecular components. This vast literature has been systematically assembled into a reconstruction of the biochemical reaction networks that underlie E. coli's functions, a process which is now being applied to an increasing number of microorganisms. Genome-scale reconstructed networks are organized and systematized knowledge bases that have multiple uses, including conversion into computational models that interpret and predict phenotypic states and the consequences of environmental and genetic perturbations. These genome-scale models (GEMs) now enable us to develop pan-genome analyses that provide mechanistic insights, detail the selection pressures on proteome allocation and address stress phenotypes. In this Review, we first discuss the overall development of GEMs and their applications. Next, we review the evolution of the most complete GEM that has been developed to date: the E. coli GEM. Finally, we explore three emerging areas in genome-scale modelling of microbial phenotypes: collections of strain-specific models, metabolic and macromolecular expression models, and simulation of stress responses.

Differential Gut Microbiota and Fecal Metabolites Related With the Clinical Subtypes of Myasthenia Gravis.

Myasthenia gravis (MG) is a devastating acquired autoimmune disease. Previous studies have observed that disturbances of gut microbiome may attribute to the development of MG through fecal metabolomic signatures in humans. However, whether there were differential gut microbial and fecal metabolomic phenotypes in different subtypes of MG remains unclear. Here, our objective was to explore whether the microbial and metabolic signatures of ocular (OMG) and generalized myasthenia gravis (GMG) were different, and further identify the shared and distinct markers for patients with OMG and GMG. In this study, 16S ribosomal RNA (rRNA) gene sequencing and gas chromatography-mass spectrometry (GC/MS) were performed to capture the microbial and metabolic signatures of OMG and GMG, respectively. Random forest (RF) classifiers was used to identify the discriminative markers for OMG and GMG. Compared with healthy control (HC) group, GMG group, but not OMG group, showed a significant decrease in α-phylogenetic diversity. Both OMG and GMG groups, however, displayed significant gut microbial and metabolic disorders. Totally, we identified 20 OTUs and 9 metabolites specific to OMG group, and 23 OTUs and 7 metabolites specific to GMG group. Moreover, combinatorial biomarkers containing 15 discriminative OTUs and 2 differential metabolites were capable of discriminating OMG and GMG from each other, as well as from HCs, with AUC values ranging from 0.934 to 0.990. In conclusion, different subtypes of MG harbored differential gut microbiota, which generated discriminative fecal metabolism.

Topological and kernel-based microbial phenotype prediction from MALDI-TOF mass spectra.

MotivationMicrobial species identification based on matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) has become a standard tool in clinical microbiology. The resulting MALDI-TOF mass spectra also harbour the potential to deliver prediction results for other phenotypes, such as antibiotic resistance. However, the development of machine learning algorithms specifically tailored to MALDI-TOF MS-based phenotype prediction is still in its infancy. Moreover, current spectral pre-processing typically involves a parameter-heavy chain of operations without analyzing their influence on the prediction results. In addition, classification algorithms lack quantification of uncertainty, which is indispensable for predictions potentially influencing patient treatment.ResultsWe present a novel prediction method for antimicrobial resistance based on MALDI-TOF mass spectra. First, we compare the complex conventional pre-processing to a new approach that exploits topological information and requires only a single parameter, namely the number of peaks of a spectrum to keep. Second, we introduce PIKE, the peak information kernel, a similarity measure specifically tailored to MALDI-TOF mass spectra which, combined with a Gaussian process classifier, provides well-calibrated uncertainty estimates about predictions. We demonstrate the utility of our approach by predicting antibiotic resistance of three clinically highly relevant bacterial species. Our method consistently outperforms competitor approaches, while demonstrating improved performance and security by rejecting out-of-distribution samples, such as bacterial species that are not represented in the training data. Ultimately, our method could contribute to an earlier and precise antimicrobial treatment in clinical patient care.Availability and implementationWe make our code publicly available as an easy-to-use Python package under https://github.com/BorgwardtLab/maldi_PIKE.

Influence of polymer type and carbon nanotube properties on carbon nanotube/polymer nanocomposite biodegradation

The interaction of anaerobic microorganisms with carbon nanotube/polymer nanocomposites (CNT/PNC) will play a major role in determining their persistence and environmental fate at the end of consumer use when these nano-enabled materials enter landfills and encounter wastewater. Motivated by the need to understand how different parameters (i.e., polymer type, microbial phenotype, CNT characteristics) influence CNT/PNC biodegradation rates, we have used volumetric biogas measurements and kinetic modeling to study biodegradation as a function of polymer type and CNT properties. In one set of experiments, oxidized multiwall carbon nanotubes (O-MWCNTs) with a range of CNT loadings 0–5% w/w were incorporated into poly-ε-caprolactone (PCL) and polyhydroxyalkanoates (PHA) matrices and subjected to biodegradation by an anaerobic microbial community. For each CNT/PNC, complete polymer biodegradation was ultimately observed, although the rate of biodegradation was inhibited above certain critical CNT loadings dependent upon the polymer type. Higher loadings of pristine MWCNTs were needed to decrease the rate of polymer biodegradation compared to O-MWCNTs, an effect ascribed principally to differences in CNT dispersion within the polymer matrices. Above certain CNT loadings, a CNT mat of similar shape to the initial PNC was formed after polymer biodegradation, while below this threshold, CNT aggregates fragmented in the media. In situations where biodegradation was rapid, methanogen growth was disproportionately inhibited compared to the overall microbial community. Analysis of the results obtained from this study indicates that the inhibitory effect of CNTs on polymer biodegradation rate is greatest under conditions (i.e., polymer type, microbial phenotype, CNT dispersion) where biodegradation of the neat polymer is slowest. This new insight provides a means to predict the environmental fate, persistence, and transformations of CNT-enabled polymer materials.

Rhizosphere microbiome functional diversity and pathogen invasion resistance build up during plant development.

The rhizosphere microbiome is essential for plant growth and health, and numerous studies have attempted to link microbiome functionality to species and trait composition. However, to date little is known about the actual ecological processes shaping community composition, complicating attempts to steer microbiome functionality. Here, we assess the development of microbial life history and community-level species interaction patterns that emerge during plant development. We use microbial phenotyping to experimentally test the development of niche complementarity and life history traits linked to microbiome performance. We show that the rhizosphere microbiome assembles from pioneer assemblages of species with random resource overlap into high-density, functionally complementary climax communities at later stages. During plant growth, fast-growing species were further replaced by antagonistic and stress-tolerant ones. Using synthetic consortia isolated from different plant growth stages, we demonstrate that the high functional diversity of 'climax' microbiomes leads to a better resistance to bacterial pathogen invasion. By demonstrating that different life-history strategies prevail at different plant growth stages and that community-level processes may supersede the importance of single species, we provide a new toolbox to understand microbiome assembly and steer its functionality at a community level.

Moving toward precision: Understanding the heterogeneity of obesity.

12054 Background: Obesity is a global health epidemic and has been linked to detrimental impact on cancer incidence, recurrence, and mortality. Growing evidence have recognized the complex biopsychosocial relationship including microbial phenotypes that undermines the carcinogenic potential and heterogeneity of obesity. A precision understanding on obesity while at its infancy is necessary to accelerate reduction of its impact on cancer outcomes. Methods: With our aim to better understand the biopsychosocial relationship on obesity, we conducted a cross sectional study in healthy and obese individuals. Univariate and multivariate logistic regression models were used to examine obesity and its association with sociodemographic (age, gender, ethnicity, education, income, and marital status), clinical (waist to hip ratio), dietary-behavioral (daily calorie, fat, carbohydrate, protein consumption, and preference on cultural diet), and biological factors (gut microbiome). Parameters were controlled and corrected for multiple hypothesis testing. Gut microbial data using 16S rRNA sequencing were analyzed for alpha diversity, beta diversity, and association of taxa abundance. Results: Among 171 participants between July 2013 and August 2018, individuals were found to have a higher BMI if they were Hispanic [Adjusted Odds Ratio (AOR) 3.36, 95% CI 1.27-8.90], had an obese waist to hip ratio (AOR 8.51, 95% CI 3.45-21.02), and consumed an American diet (AOR 4.82, 95% CI 1.74-13.34). Multivariate permutation analysis controlling for BMI, sociodemographic, clinical, and dietary parameters found that Hispanic have a significantly different microbiome profile than non-Hispanic (p = 0.042). While microbial species richness (Chao1) were similar (p = 0.22), Hispanic had a lower microbial species evenness (Shannon) compared to non-Hispanic (p = 0.029). Differential expression of microbial species revealed a positive correlation of Firmicutes:Bacteroidetes ratio in individuals with higher BMI and consumed an American diet whereas a negative correlation to Hispanic ethnicity. Conclusions: Obesity association to Hispanic ethnicity uniquely expressed through microbial signature despite sociodemographic, clinical, and dietary differences. Microbial characterization as an emerging predictive marker for oncology therapeutics may also serve as selection biomarker in onco-obesity practices and clinical trials. Addressing ethnic disparities guided by microbial phenotypes may unlock novel understanding of obesity heterogeneity and transform its impact on cancer care.

Predicting community dynamics of antibiotic-sensitive and -resistant species in fluctuating environments.

Microbes occupy almost every niche within and on their human hosts. Whether colonizing the gut, mouth or bloodstream, microorganisms face temporal fluctuations in resources and stressors within their niche but we still know little of how environmental fluctuations mediate certain microbial phenotypes, notably antimicrobial-resistant ones. For instance, do rapid or slow fluctuations in nutrient and antimicrobial concentrations select for, or against, resistance? We tackle this question using an ecological approach by studying the dynamics of a synthetic and pathogenic microbial community containing two species, one sensitive and the other resistant to an antibiotic drug where the community is exposed to different rates of environmental fluctuation. We provide mathematical models, supported by experimental data, to demonstrate that simple community outcomes, such as competitive exclusion, can shift to coexistence and ecosystem bistability as fluctuation rates vary. Theory gives mechanistic insight into how these dynamical regimes are related. Importantly, our approach highlights a fundamental difference between resistance in single-species populations, the context in which it is usually assayed, and that in communities. While fast environmental changes are known to select against resistance in single-species populations, here we show that they can promote the resistant species in mixed-species communities. Our theoretical observations are verified empirically using a two-species Candida community.

Prevalence of Antimicrobial Resistance and Hemolytic Phenotypes in Culturable Arctic Bacteria.

Many Arctic biomes, which are populated with abundant and diverse microbial life, are under threat: climate change and warming temperatures have raised concerns about diversity loss and possible emergence of pathogenic microorganisms. At present, there is little information on the occurrence of Arctic virulence-associated phenotypes. In this study we worked with 118 strains of bacteria (from 10 sampling sites in the Arctic region, located in Greenland and the Svalbard Archipelago) isolated using R2A medium. These strains belong to 4 phyla and represent 36 different bacterial genera. Phenotypic resistance to 8 clinically important antimicrobials (ampicillin, chloramphenicol, ciprofloxacin, cefotaxime, erythromycin, imipenem, kanamycin, and tetracycline) and thermotolerance range were determined. In addition, a screening of all isolates on blood agar media and erythrocytes suspension of bovine and sheep erythrocytes for virulence-linked hemolytic activity was performed. Although antimicrobial resistance profiles varied among the isolates, they were consistent within bacterial families and genera. Interestingly, a high number of isolates (83/104) were resistant to the tested concentration of imipenem (4 mg/L). In addition, one third of the isolates showed hemolytic activity on blood agar, however, in only 5% of the isolates hemolytic activity was also observed in the cell extracts when added to erythrocyte suspensions for 60 min. The observed microbial phenotypes contribute to our understanding of the presence of virulence-associated factors in the Arctic environments, while highlighting the potential risks associated with changes in the polar areas in the light of climate change.

Raman-activated sorting of antibiotic-resistant bacteria in human gut microbiota.

SummaryThe antibiotic‐resistant bacteria (ARB) and antibiotic‐resistant genes (ARGs) in human gut microbiota have significant impact on human health. While high throughput metagenomic sequencing reveals genotypes of microbial communities, the functionality, phenotype and heterogeneity of human gut microbiota are still elusive. In this study, we applied Raman microscopy and deuterium isotope probing (Raman–DIP) to detect metabolic active ARB (MA‐ARB) in situ at the single‐cell level in human gut microbiota from two healthy adults. We analysed the relative abundances of MA‐ARB under different concentrations of amoxicillin, cephalexin, tetracycline, florfenicol and vancomycin. To establish the link between phenotypes and genotypes of the MA‐ARB, Raman‐activated cell sorting (RACS) was used to sort MA‐ARB from human gut microbiota, and mini‐metagenomic DNA of the sorted bacteria was amplified, sequenced and analysed. The sorted MA‐ARB and their associated ARGs were identified. Our results suggest a strong relation between ARB in human gut microbiota and personal medical history. This study demonstrates that the toolkit of Raman–DIP, RACS and DNA sequencing can be useful to unravel both phenotypes and genotypes of ARB in human gut microbiota at the single‐cell level.

Quick microbial molecular phenotyping by differential shotgun proteomics.

SummaryDifferential shotgun proteomics identifies proteins that discriminate between sets of samples based on differences in abundance. This methodology can be easily applied to study (i) specific microorganisms subjected to a variety of growth or stress conditions or (ii) different microorganisms sampled in the same condition. In microbiology, this comparison is particularly successful because differing microorganism phenotypes are explained by clearly altered abundances of key protein players. The extensive description and quantification of proteins from any given microorganism can be routinely obtained for several conditions within a few days by tandem mass spectrometry. Such protein‐centred microbial molecular phenotyping is rich in information. However, well‐designed experimental strategies, carefully parameterized analytical pipelines, and sound statistical approaches must be applied if the shotgun proteomic data are to be correctly interpreted. This minireview describes these key items for a quick molecular phenotyping based on label‐free quantification shotgun proteomics.

Current metabolic perspective on malnutrition in obesity: towards more subgroup-based nutritional approaches?

Lifestyle intervention may be effective in reducing type 2 diabetes mellitus incidence and cardiometabolic risk. A more personalised nutritional approach based on an individual or subgroup-based metabolic profile may optimise intervention outcome. Whole body insulin resistance (IR) reflects defective insulin action in tissues such as muscle, liver, adipose tissue, gut and brain, which may precede the development of cardiometabolic diseases. IR may develop in different organs but the severity may vary between organs. Individuals with more pronounced hepatic IR have a distinct plasma metabolome and lipidome profile as compared with individuals with more pronounced muscle IR. Additionally, genes related to extracellular modelling were upregulated in abdominal subcutaneous adipose tissue in individuals with more pronounced hepatic IR, whilst genes related to inflammation as well as systemic low-grade inflammation were upregulated in individuals with primarily muscle IR. There are indications that these distinct IR phenotypes may also respond differentially to dietary macronutrient composition. Besides metabolic phenotype, microbial phenotype may be of importance in personalising the response to diet. In particular fibres or fibre mixtures, leading to a high distal acetate and SCFA production may have more pronounced effects on metabolic health. Notably, individuals with prediabetes may have a reduced response to diet-induced microbiota modulation with respect to host insulin sensitivity and metabolic health outcomes. Overall, we need more research to relate metabolic subphenotypes to intervention outcomes to define more optimal diets for individuals with or predisposed to chronic metabolic diseases.

Responses of soil microbes and their interactions with plant community after nitrogen and phosphorus addition in a Tibetan alpine steppe

Nitrogen (N) and phosphorus (P) additions are the widely used restoration management for degraded grasslands. However, soil microbial community responses to N and P additions are still not well understood, especially multiple levels or/and combined N and P additions in high-altitude grasslands. In this study, the single and interactive effects of N and P additions on the soil microbes were explored. A field experiment with two factors (N 0, 7.5, and 15 g N m−2 year−1; P 0, 3.27, and 6.55 g P m−2 year−1) was conducted by a completely randomized block design in a Tibetan plateau steppe. Total and specific microbial biomass (bacteria, fungi, G+/G− bacteria, saprotrophs, actinomycetes, and arbuscular mycorrhizal fungi) were analyzed by using phospholipid fatty acids (PLFAs). Additionally, based on their fixed weight composites, we used a composite variable (phenotype) to represent the microbial community. The results showed that N addition decreased the microbe phenotype, total microbial biomass, and specific microbial biomass (total bacteria, G+, and G− bacteria). P application and its interaction with N application all increased the microbial phenotype, total microbial biomass, and specific microbial biomass (total bacteria, G+, G−, saprotrophs, and actinomycetes). Besides, the microbial phenotype was positively correlated with P application rates and not correlated with N application rates. In conclusion, P was the key limiting factor for soil microbes, and its interaction with N is also important for some specific microbes. In addition, long-term responses of soil microbes after N and P amendments need further investigation in the Tibetan alpine steppe.

Benchmarking bacterial genome-wide association study methods using simulated genomes and phenotypes.

Genome-wide association studies (GWASs) have the potential to reveal the genetics of microbial phenotypes such as antibiotic resistance and virulence. Capitalizing on the growing wealth of bacterial sequence data, microbial GWAS methods aim to identify causal genetic variants while ignoring spurious associations. Bacteria reproduce clonally, leading to strong population structure and genome-wide linkage, making it challenging to separate true ‘hits’ (i.e. mutations that cause a phenotype) from non-causal linked mutations. GWAS methods attempt to correct for population structure in different ways, but their performance has not yet been systematically and comprehensively evaluated under a range of evolutionary scenarios. Here, we developed a bacterial GWAS simulator (BacGWASim) to generate bacterial genomes with varying rates of mutation, recombination and other evolutionary parameters, along with a subset of causal mutations underlying a phenotype of interest. We assessed the performance (recall and precision) of three widely used single-locus GWAS approaches (cluster-based, dimensionality-reduction and linear mixed models, implemented in plink, pyseer and gemma) and one relatively new multi-locus model implemented in pyseer, across a range of simulated sample sizes, recombination rates and causal mutation effect sizes. As expected, all methods performed better with larger sample sizes and effect sizes. The performance of clustering and dimensionality reduction approaches to correct for population structure were considerably variable according to the choice of parameters. Notably, the multi-locus elastic net (lasso) approach was consistently amongst the highest-performing methods, and had the highest power in detecting causal variants with both low and high effect sizes. Most methods reached the level of good performance (recall >0.75) for identifying causal mutations of strong effect size [log odds ratio (OR) ≥2] with a sample size of 2000 genomes. However, only elastic nets reached the level of reasonable performance (recall=0.35) for detecting markers with weaker effects (log OR ~1) in smaller samples. Elastic nets also showed superior precision and recall in controlling for genome-wide linkage, relative to single-locus models. However, all methods performed relatively poorly on highly clonal (low-recombining) genomes, suggesting room for improvement in method development. These findings show the potential for multi-locus models to improve bacterial GWAS performance. BacGWASim code and simulated data are publicly available to enable further comparisons and benchmarking of new methods.

Differences in Gut Microbial and Serum Biochemical Indices Between Sows With Different Productive Capacities During Perinatal Period.

Maternal gut microflora changes dramatically during perinatal period and plays a vital role in animal health and reproductive performance. However, little is known about the microbial differences between sows with different productive capacities during perinatal period. Hence, this study explored fecal microbial diversity, composition, metabolic functions, and phenotypes differences between high productive capacity (HPC, litter size ≥ 15) and low productive capacity (LPC, litter size ≤ 7) sows during late pregnancy (LP, the third day before due date) and early stage after parturition (EAP, the third day after parturition) as well as serum biochemical indices differences after parturition. Results showed that HPC sows had lower microbial richness at LP stage and higher microbial diversity at EAP stage than LPC sows. Several genera belonging to the Prevotellaceae family exhibited higher abundance, while some genera belonging to the Ruminococcaceae family exhibited lower abundance in HPC sows compared to LPC sows at LP stage. Moreover, the relative abundance of Eubacterium_coprostanoligenes_group and Ruminococcaceae_UCG-014 in HPC sows was significantly higher than that in LPC sows at EAP stage. The predicted metabolic functions related to Lipopolysaccharide biosynthesis were significantly higher in HPC sows at LP stage. Further, HPC sows had significantly higher blood urea nitrogen (BUN) and high-density lipoprotein cholesterol (HDL-C) levels after parturition, and there were strong correlations between BUN level and the relative abundance of genera belonging to the Ruminococcaceae families. These results indicated that the HPC sows may experience greater inflammation than LPC sows at LP stage. Inflammation environment might impact health but promote parturition. The microbial differences at EAP stage might be beneficial to hemostasis and anti-inflammation, which might contribute to postpartum recovery in HPC sow.

Navigating the Third Frontier of Antimicrobial Therapy to Support Women’s Health

This paper explores one of the underappreciated reasons for lack of efficacy in certain cases of antimicrobial therapy, namely the occurrence of a non-genetic resistance to antimicrobial drugs due to a metabolic quiescence of microorganisms. This review has centered on those microorganisms of particular importance in obstetrics and gynecology and accordingly has reviewed the nature and extent of the persister phenotype in relation to infectious agents affecting women’s health. We show how the quiescent persister microbial phenotype represents the next significant issue that could compromise successful antibiotic therapy. A brief history of antimicrobial therapy is provided as context for the problem posed by the persister phenotype. This review has been focused on the current literature having relevance for physicians concerned with women’s health. The study of this phenotype has led to increasing understanding of the molecular mechanisms for this state which also provides ideas for rational development of drug candidates to interdict these organisms in human disease and explores the possibility of developing specifically targeted molecules to address persisters, research on screening botanicals, existing drugs and chemicals to discover novel approaches to the clinical consequence of microbial persisters. Of interest in this review, is the return to naturally occurring botanical substances, first to be used as anti-infectives, now being considered as possible agents to address persister microorganisms. Overall this paper aims to provide information tailored especially to the obstetrics and gynecology specialists.