While often obvious for macroscopic organisms, determining whether a microbe is dead or alive is fraught with complications. Fields such as microbial ecology, environmental health, and medical microbiology each determine how best to assess which members of the microbial community are alive, according to their respective scientific and/or regulatory needs. Many of these fields have gone from studying communities on a bulk level to the fine-scale resolution of microbial populations within consortia. For example, advances in nucleic acid sequencing technologies and downstream bioinformatic analyses have allowed for high-resolution insight into microbial community composition and metabolic potential, yet we know very little about whether such community DNA sequences represent viable microorganisms. In this review, we describe a number of techniques, from microscopy- to molecular-based, that have been used to test for viability (live/dead determination) and/or activity in various contexts, including newer techniques that are compatible with or complementary to downstream nucleic acid sequencing. We describe the compatibility of these viability assessments with high-throughput quantification techniques, including flow cytometry and quantitative PCR (qPCR). Although bacterial viability-linked community characterizations are now feasible in many environments and thus are the focus of this critical review, further methods development is needed for complex environmental samples and to more fully capture the diversity of microbes (e.g., eukaryotic microbes and viruses) and metabolic states (e.g., spores) of microbes in natural environments.

Mass transit environments, specifically, urban subways, are distinct microbial environments with high occupant densities, diversities, and turnovers, and they are thus especially relevant to public health. Despite this, only three culture-independent subway studies have been performed, all since 2013 and all with widely differing designs and conclusions. In this study, we profiled the Boston subway system, which provides 238 million trips per year overseen by the Massachusetts Bay Transportation Authority (MBTA). This yielded the first high-precision microbial survey of a variety of surfaces, ridership environments, and microbiological functions (including tests for potential pathogenicity) in a mass transit environment. Characterizing microbial profiles for multiple transit systems will become increasingly important for biosurveillance of antibiotic resistance genes or pathogens, which can be early indicators for outbreak or sanitation events. Understanding how human contact, materials, and the environment affect microbial profiles may eventually allow us to rationally design public spaces to sustain our health in the presence of microbial reservoirs.

Oral squamous cell carcinomas are a major cause of morbidity and mortality, and tobacco usage, alcohol consumption, and poor oral hygiene are established risk factors. To date, no large-scale case-control studies have considered the effects of these risk factors on the composition of the oral microbiome, nor microbial community associations with oral cancer. We compared the composition, diversity, and function of the oral microbiomes of 121 oral cancer patients to 242 age- and gender-matched controls using a metagenomic multivariate analysis pipeline. Significant shifts in composition and function of the oral microbiome were observed with poor oral hygiene, tobacco smoking, and oral cancer. Specifically, we observed dramatically altered community composition and function after tooth loss, with smaller alterations in current tobacco smokers, increased production of antioxidants in individuals with periodontitis, and significantly decreased glutamate metabolism metal transport in oral cancer patients. Although the alterations in the oral microbiome of oral cancer patients were significant, they were of substantially lower effect size relative to microbiome shifts after tooth loss. Alterations following tooth loss, itself a major risk factor for oral cancer, are likely a result of severe ecological disruption due to habitat loss but may also contribute to the development of the disease.

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Antibiotic resistance is increasingly widespread, largely due to human influence. Here, we explore the relationship between antibiotic resistance genes and the antimicrobial chemicals triclosan, triclocarban, and methyl-, ethyl-, propyl-, and butylparaben in the dust microbiome. Dust samples from a mixed-use athletic and educational facility were subjected to microbial and chemical analyses using a combination of 16S rRNA amplicon sequencing, shotgun metagenome sequencing, and liquid chromatography tandem mass spectrometry. The dust resistome was characterized by identifying antibiotic resistance genes annotated in the Comprehensive Antibiotic Resistance Database (CARD) from the metagenomes of each sample using the Short, Better Representative Extract Data set (ShortBRED). The three most highly abundant antibiotic resistance genes were tet(W), blaSRT-1, and erm(B). The complete dust resistome was then compared against the measured concentrations of antimicrobial chemicals, which for triclosan ranged from 0.5 to 1970 ng/g dust. We observed six significant positive associations between the concentration of an antimicrobial chemical and the relative abundance of an antibiotic resistance gene, including one between the ubiquitous antimicrobial triclosan and erm(X), a 23S rRNA methyltransferase implicated in resistance to several antibiotics. This study is the first to look for an association between antibiotic resistance genes and antimicrobial chemicals in dust.

Currently, proteomic tools are able to establish a complete list of the most abundant proteins present in a sample, providing the opportunity to study at high resolution the physiology of any bacteria for which the genome sequence is available. For a comprehensive list, proteins should be first resolved into fractions that are then proteolyzed by trypsin. The resulting peptide mixtures are analyzed by a high-throughput tandem mass spectrometer that records thousands of MS/MS spectra for each fraction. These spectra are then assigned to peptides, which are used as evidence of the existence of proteins. In addition to generating a list of protein identifications, this shortcut to proteomics uses the number of spectra recorded for each protein to quantify the observations. Here, we describe one of the most simple sample preparation methods for high-throughput proteomics of bacteria, as well as the subsequent data processing to extract quantitative information based on the spectral count approach.

BackgroundMicrobial communities associated with indoor dust abound in the built environment. The transmission of sunlight through windows is a key building design consideration, but the effects of light exposure on dust communities remain unclear. We report results of an experiment and computational models designed to assess the effects of light exposure and wavelengths on the structure of the dust microbiome. Specifically, we placed household dust in replicate model “rooms” with windows that transmitted visible, ultraviolet, or no light and measured taxonomic compositions, absolute abundances, and viabilities of the resulting bacterial communities.ResultsLight exposure per se led to lower abundances of viable bacteria and communities that were compositionally distinct from dark rooms, suggesting preferential inactivation of some microbes over others under daylighting conditions. Differences between communities experiencing visible and ultraviolet light wavelengths were relatively minor, manifesting primarily in abundances of dead human-derived taxa. Daylighting was associated with the loss of a few numerically dominant groups of related microorganisms and apparent increases in the abundances of some rare groups, suggesting that a small number of microorganisms may have exhibited modest population growth under lighting conditions. Although biological processes like population growth on dust could have generated these patterns, we also present an alternate statistical explanation using sampling models from ecology; simulations indicate that artefactual, apparent increases in the abundances of very rare taxa may be a null expectation following the selective inactivation of dominant microorganisms in a community.ConclusionsOur experimental and simulation-based results indicate that dust contains living bacterial taxa that can be inactivated following changes in local abiotic conditions and suggest that the bactericidal potential of ordinary window-filtered sunlight may be similar to ultraviolet wavelengths across dosages that are relevant to real buildings.Electronic supplementary materialThe online version of this article (10.1186/s40168-018-0559-4) contains supplementary material, which is available to authorized users.

Proteogenomics consists of the annotation or reannotation of protein-coding nucleic acid sequences based on the empirical observation of their gene products. While functional annotation of predicted genes is increasingly feasible given the multiplicity of genomes available for many branches of the tree of life, the accurate annotation of the translational start sites is still a point of contention. Extensive coverage of the proteome, including specifically the N-termini, is now possible, thanks to next-generation mass spectrometers able to record data from thousands of proteins at once. Efforts to increase the peptide coverage of protein sequences and to detect low abundance proteins are important to make proteomic and proteogenomic studies more comprehensive. In this review, we present the panoply of N-terminus-oriented strategies that have been developed over the last decade.