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ABSTRACT The human skin microbiome is a diverse ecosystem that can help prevent infections by producing biomolecules and peptides that inhibit growth and virulence of bacterial pathogens.Staphylococcus aureusis a major human pathogen responsible for diseases that range from acute skin and soft tissue infections to life-threatening septicemia. Its ability to form biofilms is a key virulence factor contributing to its success as a pathogen as well as to its increased antimicrobial resistance. Here, we investigated the ability of bacterial skin commensals to produce molecules that inhibitS. aureusbiofilm formation. Matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) identified 77 human skin microbiome bacterial isolates fromStaphylococcusandBacillusgenera. Metabolites from cell-free concentrated media (CFCM) from 26 representative isolates were evaluated for their ability to inhibit biofilm formation by both methicillin-resistant (MRSA) and methicillin-sensitive (MSSA)S. aureusstrains. CFCM, derived from most of the isolates, inhibited biofilm formation to varying extents but did not inhibit planktonic growth ofS. aureus. Size fractionation of the CFCM of threeS.epidermidisisolates indicated that they produce different bioactive molecules. Cluster analysis, based on either MALDI-TOF mass spectra or whole-genome sequencing draft genomes, did not show clear clusters associated with levels of biofilm inhibition amongS. epidermidisstrains. Finally, similar biosynthetic gene clusters were detected in allS. epidermidisstrains analyzed. These findings indicate that several bacterial constituents of the human skin microbiome display antibiofilmin vitroactivity, warranting further investigation on their potential as novel therapeutic agents. IMPORTANCEThe skin is constantly exposed to the environment and consequently to numerous pathogens. The bacterial community that colonizes healthy skin is thought to play an important role in protecting us against infections.S. aureusis a leading cause of death worldwide and is frequently involved in several types of infections, including skin and soft tissue infections. Its ability to adhere to surfaces and produce biofilms is considered an important virulence factor. Here, we analyzed the activity of different species of bacteria isolated from healthy skin onS. aureusbiofilm formation. We found that some species ofStaphylococcusandBacilluscan reduceS. aureusbiofilm formation, although a generally lower level of inhibitory activity was observed compared toS. epidermidisisolates. AmongS. epidermidisisolates, strength of activity was dependent on the strain. Our data highlight the importance of mining the skin microbiome for isolates that could help combat skin pathogens. 

The time-series Susceptible-Infectious-Recovered (TSIR) model has been a standard tool for studying the non-linear dynamics of acute, immunizing infectious diseases. The standard assumption of the TSIR model, that vaccination is equivalent to a reduction in the recruitment of susceptible individuals, or the birth rate, can lead to a bias in the estimate of the reporting fraction and of the total incidence. We show that this bias increases with the level of vaccination due to a double counting of individuals who are infected prior to the age of vaccination. We present a simple correction for this bias by discounting the observed number of cases by the product of the number that occur prior to the average age of vaccination and the vaccination coverage during the initial susceptible reconstruction step of the TSIR model fitting. We generate a time series of measles cases using an age-structured SIR transmission model with vaccination after birth (at 9 months of age) and illustrate the bias with the standard TSIR fitting method. We then illustrate that our proposed correction eliminates the bias in the estimated reporting fraction and total incidence. We note further that this bias does not impact the estimates of the seasonality of transmission. 

SUMMARY Engineered microbes are being programmed using synthetic DNA for applications in soil to overcome global challenges related to climate change, energy, food security, and pollution. However, we cannot yet predict gene transfer processes in soil to assess the frequency of unintentional transfer of engineered DNA to environmental microbes when applying synthetic biology technologies at scale. This challenge exists because of the complex and heterogeneous characteristics of soils, which contribute to the fitness and transport of cells and the exchange of genetic material within communities. Here, we describe knowledge gaps about gene transfer across soil microbiomes. We propose strategies to improve our understanding of gene transfer across soil communities, highlight the need to benchmark the performance of biocontainment measuresin situ, and discuss responsibly engaging community stakeholders. We highlight opportunities to address knowledge gaps, such as creating a set of soil standards for studying gene transfer across diverse soil types and measuring gene transfer host range across microbiomes using emerging technologies. By comparing gene transfer rates, host range, and persistence of engineered microbes across different soils, we posit that community-scale, environment-specific models can be built that anticipate biotechnology risks. Such studies will enable the design of safer biotechnologies that allow us to realize the benefits of synthetic biology and mitigate risks associated with the release of such technologies. 

Abstract.We study the third moment for functions on arbitrary compact Lie groups. 

Percolation theory and the associated conductance networks have provided deep insights into the flow and transport properties of a vast number of heterogeneous materials and media. In practically all cases, however, the conductance of the networks’ bonds remains constant throughout the entire process. There are, however, many important problems in which the conductance of the bonds evolves over time and does not remain constant. Examples include clogging, dissolution and precipitation, and catalytic processes in porous materials, as well as the deformation of a porous medium by applying an external pressure or stress to it that reduces the size of its pores. We introduce two percolation models to study the evolution of the conductivity of such networks. The two models are related to natural and industrial processes involving clogging, precipitation, and dissolution processes in porous media and materials. The effective conductivity of the models is shown to follow known power laws near the percolation threshold, despite radically different behavior both away from and even close to the percolation threshold. The behavior of the networks close to the percolation threshold is described by critical exponents, yielding bounds for traditional percolation exponents. We show that one of the two models belongs to the traditional universality class of percolation conductivity, while the second model yields nonuniversal scaling exponents. 

Unfamiliar native and non-native accents can cause word recognition challenges, particularly in noisy environments, but few studies have incorporated quantitative pronunciation distance metrics to explain intelligibility differences across accents. Here, intelligibility was measured for 18 talkers -- two from each of three native, one bilingual, and five non- native accents -- in three listening conditions (quiet and two noise conditions). Two variations of the Levenshtein pronunciation distance metric, which quantifies phonemic differences from a reference accent, were assessed for their ability to predict intelligibility. An unweighted Levenshtein distance metric was the best intelligibility predictor; talker accent further predicted performance. Accuracy did not fall along a native - non-native divide. Thus, phonemic differences from the listener’s home accent primarily determine intelligibility, but other accent- specific pronunciation features, including suprasegmental characteristics, must be quantified to fully explain intelligibility across talkers and listening conditions. These results have implications for pedagogical practices and speech perception theories. 

ABSTRACT Colorectal cancer (CRC) is the second leading cause of cancer mortality worldwide. The dysbiotic gut microbiota and its metabolite secretions play a significant role in CRC development and progression. In this study, we identified microbial and metabolic biomarkers applicable to CRC using a meta-analysis of metagenomic datasets from diverse geographical regions. We used LEfSe, random forest (RF), and co-occurrence network methods to identify microbial biomarkers. Geographic dataset-specific markers were identified and evaluated using area under the ROC curve (AUC) scores and random effect size. Co-occurrence networks analysis showed a reduction in the overall microbial associations and the presence of oral pathogenic microbial clusters in CRC networks. Analysis of predicted metabolites from CRC datasets showed the enrichment of amino acids, cadaverine, and creatine in CRC, which were positively correlated with CRC-associated microbes ( Peptostreptococcus stomatis , Gemella morbillorum , Bacteroides fragilis , Parvimonas spp., Fusobacterium nucleatum , Solobacterium moorei , and Clostridium symbiosum ), and negatively correlated with control-associated microbes. Conversely, butyrate, nicotinamide, choline, tryptophan, and 2-hydroxybutanoic acid showed positive correlations with control-associated microbes ( P < 0.05). Overall, our study identified a set of global CRC biomarkers that are reproducible across geographic regions. We also reported significant differential metabolites and microbe-metabolite interactions associated with CRC. This study provided significant insights for further investigations leading to the development of noninvasive CRC diagnostic tools and therapeutic interventions. IMPORTANCE Several studies showed associations between gut dysbiosis and CRC. Yet, the results are not conclusive due to cohort-specific associations that are influenced by genomic, dietary, and environmental stimuli and associated reproducibility issues with various analysis approaches. Emerging evidence suggests the role of microbial metabolites in modulating host inflammation and DNA damage in CRC. However, the experimental validations have been hindered by cost, resources, and cumbersome technical expertise required for metabolomic investigations. In this study, we performed a meta-analysis of CRC microbiota data from diverse geographical regions using multiple methods to achieve reproducible results. We used a computational approach to predict the metabolomic profiles using existing CRC metagenomic datasets. We identified a reliable set of CRC-specific biomarkers from this analysis, including microbial and metabolite markers. In addition, we revealed significant microbe-metabolite associations through correlation analysis and microbial gene families associated with dysregulated metabolic pathways in CRC, which are essential in understanding the vastly sporadic nature of CRC development and progression. 

ABSTRACT The capacity of the human microbiome to modulate inflammation in the context of cancer is becoming increasingly clear. Myeloproliferative neoplasms (MPNs) are chronic hematologic malignancies in which inflammation plays a key role in disease initiation, progression, and symptomatology. To better understand the composition of the gut microbiome in patients with MPN, triplicate fecal samples were collected from 25 MPN patients and 25 non-MPN controls. Although most of the variance between the microbial community compositions could be attributed to the individual (permutational analysis of variance [PERMANOVA], R 2  = 0.92, P  = 0.001), 1.7% of the variance could be attributed to disease status (MPN versus non-MPN). When a more detailed analysis was performed, significantly fewer reads mapping to a species of Phascolarctobacterium , a microbe previously associated with reduced inflammation, were found in MPNs. Further, our data revealed an association between Parabacteroides and tumor necrosis factor alpha (TNF-α), an inflammatory cytokine elevated in MPNs. Taken together, our results indicate a significant difference in the microbiome of MPN patients compared to non-MPN controls, and we identify specific species which may have a role in the chronic inflammation central to this disease. IMPORTANCE MPNs are chronic blood cancers in which inflammation plays a key role in disease initiation, progression, and symptomatology. The gut microbiome modulates normal blood development and inflammation and may also impact the development and manifestation of blood cancers. Therefore, the microbiome may be an important modulator of inflammation in MPN and could potentially be leveraged therapeutically in this disease. However, the relationship between the gut microbiome and MPNs has not been defined. Therefore, we performed an evaluation of the MPN microbiome, comparing the microbiomes of MPN patients with healthy donors and between MPN patients with various states of disease.