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Biosensors often combine biological recognition elements with nanomaterials of varying compositions and dimensions to facilitate or enhance the operating mechanism of the device. While incorporating nanomaterials is beneficial to developing high-performance biosensors, at the stages of scale-up and disposal, it may lead to the unmanaged release of toxic nanomaterials. Here we attempt to foster connections between the domains of biosensors development and human and environmental toxicology to encourage a holistic approach to the development and scale-up of biosensors. We begin by exploring the toxicity of nanomaterials commonly used in biosensor design. From our analysis, we introduce five factors with a role in nanotoxicity that should be considered at the biosensor development stages to better manage toxicity. Finally, we contextualize the discussion by presenting the relevant stages and routes of exposure in the biosensor life cycle. Our review found little consensus on how the factors presented govern nanomaterial toxicity, especially in composite and alloyed nanomaterials. To bridge the current gap in understanding and mitigate the risks of uncontrolled nanomaterial release, we advocate for greater collaboration through a precautionary One Health approach to future development and a movement towards a circular approach to biosensor use and disposal.
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Modern materials provoke ancient behavior: bacterial resistance to metal nanomaterials
The use of engineered nanomaterials, defined as those smaller than 100 nm, in the health, energy, agricultural, and environmental sectors is expanding rapidly. As such, human and environmental exposure to these materials is increasing every day. For example, metal-based nanomaterials, such as nanosilver, have become ubiquitous in antibacterial applications ranging from socks and baby bottles to healthcare materials, such as oral fillings. Engineered nanomaterials are also used as antibacterial agents and adjuvants to improve antibiotic delivery or efficacy. However, even nanomaterials that were not designed to be antimicrobial can possess potent bactericidal activity. Alarmingly, there are clear connections between nanomaterial exposure, metal resistance, and antibiotic resistance and it is crucial that we dramatically improve our understanding of both the toxicity of these materials and their ability to permanently change the organisms that they encounter. Emerging research indicates that microbes are capable of adapting to nanomaterial toxicity, often with the same generalizable mechanisms used to overcome antibiotic toxicity. In this perspective, we highlight existing knowledge about microbial response to engineered nanomaterials and the key outstanding questions that must be addressed.
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- Award ID(s):
- 2001611
- PAR ID:
- 10464935
- Date Published:
- Journal Name:
- Environmental Science: Nano
- ISSN:
- 2051-8153
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Engineered nanoparticles are incorporated into numerous emerging technologies because of their unique physical and chemical properties. Many of these properties facilitate novel interactions, including both intentional and accidental effects on biological systems. Silver-containing particles are widely used as antimicrobial agents and recent evidence indicates that bacteria rapidly become resistant to these nanoparticles. Much less studied is the chronic exposure of bacteria to particles that were not designed to interact with microorganisms. For example, previous work has demonstrated that the lithium intercalated battery cathode nanosheet, nickel manganese cobalt oxide (NMC), is cytotoxic and causes a significant delay in growth of Shewanella oneidensis MR-1 upon acute exposure. Here, we report that S. oneidensis MR-1 rapidly adapts to chronic NMC exposure and is subsequently able to survive in much higher concentrations of these particles, providing the first evidence of permanent bacterial resistance following exposure to nanoparticles that were not intended as antibacterial agents. We also found that when NMC-adapted bacteria were subjected to only the metal ions released from this material, their specific growth rates were higher than when exposed to the nanoparticle. As such, we provide here the first demonstration of bacterial resistance to complex metal oxide nanoparticles with an adaptation mechanism that cannot be fully explained by multi-metal adaptation. Importantly, this adaptation persists even after the organism has been grown in pristine media for multiple generations, indicating that S. oneidensis MR-1 has developed permanent resistance to NMC.more » « less
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Abstract The widespread misuse of antibiotics has escalated antibiotic resistance into a critical global public health concern. Beyond antibiotics, metals function as antibacterial agents. Metal resistance genes (MRGs) enable bacteria to tolerate metal-based antibacterials and may also foster antibiotic resistance within bacterial communities through co-selection. Thus, predicting bacterial MRGs is vital for elucidating their involvement in antibiotic resistance and metal tolerance mechanisms. The “best hit” approach is mainly utilized to identify and annotate MRGs. This method is sensitive to cutoff values and produces a high false negative rate. Other than the best hit approach, only a few antimicrobial resistance (AMR) detection tools exist for predicting MRGs. However, these tools lack comprehensive annotation for MRGs conferring resistance to multiple metals. To address such limitations, we introduce DeepMRG, a deep learning-based multi-label classifier, to predict bacterial MRGs. Because a bacterial MRG can confer resistance to multiple metals, DeepMRG is designed as a multi-label classifier capable of predicting multiple metal labels associated with an MRG. It leverages bit score-based similarity distribution of sequences with experimentally verified MRGs. To ensure unbiased model evaluation, we employed a clustering method to partition our dataset into six subsets, five for cross-validation and one for testing, with non-homologous sequences, mitigating the impact of sequence homology. DeepMRG consistently achieved high overall F1-scores and significantly reduced false negative rates across a wide range of datasets. It can be used to predict bacterial MRGs in metagenomic or isolate assemblies. The web server of DeepMRG can be accessed athttps://deepmrg.cs.vt.edu/deepmrgand the source code is available athttps://github.com/muhit-emon/DeepMRGunder the MIT license.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1039/D3EN00420A
