Flow Cytometric and 16S Sequencing Methodologies for Monitoring the Physiological Status of the Microbiome in Powdered Infant Formula Production

Tracking defined microbial communities by multicolor flow cytometry reveals tradeoffs between productivity and diversity

Cross feeding between microbes is ubiquitous, but its impact on the diversity and productivity of microbial communities is incompletely understood. A reductionist approach using simple microbial communities has the potential to detect cross feeding interactions and their impact on ecosystem properties. However, quantifying abundance of more than two microbes in a community in a high throughput fashion requires rapid, inexpensive assays. Here, we show that multicolor flow cytometry combined with a machine learning-based classifier can rapidly quantify species abundances in simple, synthetic microbial communities. Our approach measures community structure over time and detects the exchange of metabolites in a four-member community of fluorescent Bacteroides species. Notably, we quantified species abundances in co-cultures and detected evidence of cooperation in polysaccharide processing and competition for monosaccharide utilization. We also observed that co-culturing on simple sugars, but not complex sugars, reduced microbial productivity, although less productive communities maintained higher community diversity. In summary, our multicolor flow cytometric approach presents an economical, tractable model system for microbial ecology using well-studied human bacteria. It can be extended to include additional species, evaluate more complex environments, and assay response of communities to a variety of disturbances.

'To be, or not to be' - the dilemma of 'silent' antimicrobial resistance genes in bacteria

Antimicrobial resistance is a serious threat to public health that dramatically undermines our ability to treat bacterial infections. Microorganisms exhibit resistance to different drug classes by acquiring resistance determinants through multiple mechanisms including horizontal gene transfer. The presence of drug resistance genotypes is mostly associated with corresponding phenotypic resistance against the particular antibiotic. However, bacterial communities harboring silent antimicrobial resistance genes - genes whose presence is not associated with a corresponding resistant phenotype, do exist. Under suitable conditions, the expression pattern of such genes often revert and regain resistance, and could potentially lead to therapeutic failure. We often miss the presence of silent genes, since the current experimental paradigms are focused on resistant strains. Therefore, the knowledge on the prevalence, importance, and mechanism of silent antibiotic resistance genes in bacterial pathogens is very limited. Silent genes, therefore, provide an additional level of complexity in the war against drug-resistant bacteria, reminding us that not only phenotypically resistant strains but also susceptible strains should be carefully investigated. In this review, we discuss the presence of silent antimicrobial resistance genes in bacteria, their relevance, and their importance in public health.

A 16S rRNA Sequencing Study Describing the Environmental Microbiota of Two Powdered Infant Formula Built Facilities

Microbial safety is critically important for powdered infant formula (PIF) fed to neonates, with under-developed immune systems. The quality and safety of food products are dictated by those microorganisms found in both raw materials and the built production environment. In this study, a 2-year monitoring program of a production environment was carried out in two PIF factories located in the Republic of Ireland, and the environmental microbiome in different care areas of these sites was studied by using a 16S ribosomal RNA (rRNA)-based sequencing technique. Results highlighted a core microbiome associated with the PIF factory environment containing 24 bacterial genera representing five phyla, with Acinetobacter and Pseudomonas as the predominant genera. In different care areas of the PIF factory, as hygiene standards increased, deciphered changes in microbial community compositions became smaller over time and approached stability, and bacteria dominating the care area became less influenced by the external environment and more by human interactions and raw materials. These observations indicated that the microbial composition can be altered in response to environmental interventions. Genera Cronobacter and Salmonella were observed in trace amounts in the PIF factory environment, and bacterial genera known to be persistent in a stressed environment, such as Acinetobacter, Bacillus, Streptococcus, and Clostridium, were likely to have higher abundances in dry environment-based care areas. To our knowledge, this is the first study to characterize the PIF production environment microbiome using 16S rRNA-based sequencing. This study described the composition and changing trends of the environmental microbial communities in different care areas of the PIF manufacturing facility, and it provided valuable information to support the safer production of PIF in the future.

Next-Generation Food Research: Use of Meta-Omic Approaches for Characterizing Microbial Communities Along the Food Chain

Microorganisms exist along the food chain and impact the quality and safety of foods in both positive and negative ways. Identifying and understanding the behavior of these microbial communities enable the implementation of preventative or corrective measures in public health and food industry settings. Current culture-dependent microbial analyses are time-consuming and target only specific subsets of microbes. However, the greater use of culture-independent meta-omic approaches has the potential to facilitate a thorough characterization of the microbial communities along the food chain. Indeed, these methods have shown potential in contributing to outbreak investigation, ensuring food authenticity, assessing the spread of antimicrobial resistance, tracking microbial dynamics during fermentation and processing, and uncovering the factors along the food chain that impact food quality and safety. This review examines the community-based approaches, and particularly the application of sequencing-based meta-omics strategies, for characterizing microbial communities along the food chain. Expected final online publication date for the Annual Review of Food Science and Technology, Volume 13 is March 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Processing environment monitoring in low moisture food production facilities: Are we looking for the right microorganisms?

Processing environment monitoring is gaining increasing importance in the context of food safety management plans/HACCP programs, since past outbreaks have shown the relevance of the environment as contamination pathway, therefore requiring to ensure the safety of products. However, there are still many open questions and a lack of clarity on how to set up a meaningful program, which would provide early warnings of potential product contamination. Therefore, the current paper aims to summarize and evaluate existing scientific information on outbreaks, relevant pathogens in low moisture foods, and knowledge on indicators, including their contribution to a "clean" environment capable of limiting the spread of pathogens in dry production environments. This paper also outlines the essential elements of a processing environment monitoring program thereby supporting the design and implementation of better programs focusing on the relevant microorganisms. This guidance document is intended to help industry and regulators focus and set up targeted processing environment monitoring programs depending on their purpose, and therefore provide the essential elements needed to improve food safety.

Computational Analysis of Microbial Flow Cytometry Data

Flow cytometry is an important technology for the study of microbial communities. It grants the ability to rapidly generate phenotypic single-cell data that are both quantitative, multivariate and of high temporal resolution. The complexity and amount of data necessitate an objective and streamlined data processing workflow that extends beyond commercial instrument software. No full overview of the necessary steps regarding the computational analysis of microbial flow cytometry data currently exists. In this review, we provide an overview of the full data analysis pipeline, ranging from measurement to data interpretation, tailored toward studies in microbial ecology. At every step, we highlight computational methods that are potentially useful, for which we provide a short nontechnical description. We place this overview in the context of a number of open challenges to the field and offer further motivation for the use of standardized flow cytometry in microbial ecology research.

Mobile Technologies for the Discovery, Analysis, and Engineering of the Global Microbiome

The microbiome has been heralded as a gauge of and contributor to both human health and environmental conditions. Current challenges in probing, engineering, and harnessing the microbiome stem from its microscopic and nanoscopic nature, diversity and complexity of interactions among its members and hosts, as well as the spatiotemporal sampling and in situ measurement limitations induced by the restricted capabilities and norm of existing technologies, leaving some of the constituents of the microbiome unknown. To facilitate significant progress in the microbiome field, deeper understanding of the constituents' individual behavior, interactions with others, and biodiversity are needed. Also crucial is the generation of multimodal data from a variety of subjects and environments over time. Mobile imaging and sensing technologies, particularly through smartphone-based platforms, can potentially meet some of these needs in field-portable, cost-effective, and massively scalable manners by circumventing the need for bulky, expensive instrumentation. In this Perspective, we outline how mobile sensing and imaging technologies could lead the way to unprecedented insight into the microbiome, potentially shedding light on various microbiome-related mysteries of today, including the composition and function of human, animal, plant, and environmental microbiomes. Finally, we conclude with a look at the future, propose a computational microbiome engineering and optimization framework, and discuss its potential impact and applications.

Analysis of Staphylococcus aureus cell viability, sublethal injury and death induced by synergistic combination of ultrasound and mild heat

This study was designed to investigate the combined effects of ultrasound and mild heat on the viability of S. aureus in association with the cell membrane integrity and intracellular enzyme activity. Cells were treated by ultrasound under 55°C for 3, 5, 7, 10, and 15min. The dynamic changes of S. aureus cell viability, sublethal injury and death were evaluated using flow cytometric assay. Microscopies were applied to identify the morphological appearance, ultrastructure and topography of S. aureus. The results showed the membrane damage was synchronous with esterase inhibition during the exposure to sonication, leading to the immediate lethal effect. On the other hand, bacteria under the mild heat at 55°C were inactivated via a sublethal injury process. The different lethal modes were observed between sonication and mild heat treatments, which could synergistically inactivate S. aureus. The antibacterial value of thermo-sonication was greater than the sum of the individual treatments. The thermo-sonication combination synergistically reduced the number of sublethal cells and also resulted in severe cell damage.

A Review on the Applications of Next Generation Sequencing Technologies as Applied to Food-Related Microbiome Studies

The development of next generation sequencing (NGS) techniques has enabled researchers to study and understand the world of microorganisms from broader and deeper perspectives. The contemporary advances in DNA sequencing technologies have not only enabled finer characterization of bacterial genomes but also provided deeper taxonomic identification of complex microbiomes which in its genomic essence is the combined genetic material of the microorganisms inhabiting an environment, whether the environment be a particular body econiche (e.g., human intestinal contents) or a food manufacturing facility econiche (e.g., floor drain). To date, 16S rDNA sequencing, metagenomics and metatranscriptomics are the three basic sequencing strategies used in the taxonomic identification and characterization of food-related microbiomes. These sequencing strategies have used different NGS platforms for DNA and RNA sequence identification. Traditionally, 16S rDNA sequencing has played a key role in understanding the taxonomic composition of a food-related microbiome. Recently, metagenomic approaches have resulted in improved understanding of a microbiome by providing a species-level/strain-level characterization. Further, metatranscriptomic approaches have contributed to the functional characterization of the complex interactions between different microbial communities within a single microbiome. Many studies have highlighted the use of NGS techniques in investigating the microbiome of fermented foods. However, the utilization of NGS techniques in studying the microbiome of non-fermented foods are limited. This review provides a brief overview of the advances in DNA sequencing chemistries as the technology progressed from first, next and third generations and highlights how NGS provided a deeper understanding of food-related microbiomes with special focus on non-fermented foods.