Molecular Mechanisms of Nanomaterial-Bacterial Interactions Revealed by Omics-The Role of Nanomaterial Effect Level.

Monika Mortimer,Ying Wang,Patricia A Holden

doi:10.3389/fbioe.2021.683520

Abstract

Nanotechnology is employed across a wide range of antibacterial applications in clinical settings, food, pharmaceutical and textile industries, water treatment and consumer goods. Depending on type and concentration, engineered nanomaterials (ENMs) can also benefit bacteria in myriad contexts including within the human body, in biotechnology, environmental bioremediation, wastewater treatment, and agriculture. However, to realize the full potential of nanotechnology across broad applications, it is necessary to understand conditions and mechanisms of detrimental or beneficial effects of ENMs to bacteria. To study ENM effects, bacterial population growth or viability are commonly assessed. However, such endpoints alone may be insufficiently sensitive to fully probe ENM effects on bacterial physiology. To reveal more thoroughly how bacteria respond to ENMs, molecular-level omics methods such as transcriptomics, proteomics, and metabolomics are required. Because omics methods are increasingly utilized, a body of literature exists from which to synthesize state-of-the-art knowledge. Here we review relevant literature regarding ENM impacts on bacterial cellular pathways obtained by transcriptomic, proteomic, and metabolomic analyses across three growth and viability effect levels: inhibitory, sub-inhibitory or stimulatory. As indicated by our analysis, a wider range of pathways are affected in bacteria at sub-inhibitory vs. inhibitory ENM effect levels, underscoring the importance of ENM exposure concentration in elucidating ENM mechanisms of action and interpreting omics results. In addition, challenges and future research directions of applying omics approaches in studying bacterial-ENM interactions are discussed.

Highlights

Understanding interactions of engineered nanomaterials [ENMs, materials with at least one dimension ≤100 nm and having unique size-related physico-chemical properties (Hochella et al, 2019)] with bacteria is important for several reasons
In this review, we ask: what are the relationships between ENM effect level and bacterial metabolic pathway effects? Across various ENMs, what metabolic pathway responses are associated with inhibitory, sub-inhibitory and stimulatory effects on bacterial growth or viability? How does choice of ENM concentration causing different effect levels affect understanding bacterial responses to ENMs? To address these questions, we review published literature regarding ENM mechanisms of action in bacteria obtained by transcriptomic, proteomic, and metabolomic analyses
The studies where the ENM concentration selected for the omics assay caused growth inhibition or lethality in bacteria were labeled “inhibitory”; studies where omics assays were conducted using ENM concentrations which did not significantly diminish bacterial growth or viability were labeled as “sub-inhibitory”; and studies where omics assays were performed with bacteria exposed to ENMs at concentrations which stimulated bacterial growth or a beneficial function, were termed “stimulatory.” Among analyzed papers, only three papers focused on other physiological effects than growth or viability: one paper reported chlorophyll a (Chl-a) reduction, another paper decreased nitrate removal efficiency and the third paper increased nitrogen removal rate upon ENM exposures; so they were categorized into “inhibitory,” “inhibitory,” and “stimulatory” effect levels, respectively

Summary

Introduction

Understanding interactions of engineered nanomaterials [ENMs, materials with at least one dimension ≤100 nm and having unique size-related physico-chemical properties (Hochella et al, 2019)] with bacteria is important for several reasons. As an emerging trend, ENMs can stimulate beneficial bacterial functions (Mu et al, 2019; Peng et al, 2019; Ren et al, 2019b; Yan et al, 2019). Successful accomplishment of these aims requires detailed knowledge of the underlying mechanisms of ENMbacterial interactions. Most studies to date have used targeted assays which are limited to assessing just a few endpoints out of hundreds of metabolic pathways in bacterial cells (Le Boulch et al, 2019) To overcome this limitation, omics (e.g., transcriptomics, proteomics, and metabolomics) methods have been increasingly employed for non-targeted analyses of ENM effects (Revel et al, 2017)

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