Metal-Resistance in Bacteria: Why Care?

Turner, Raymond J.; Huang, Li-Nan; Viti, Carlo; Mengoni, Alessio

doi:10.3390/genes11121470

Cited by 11 publications

(6 citation statements)

References 38 publications

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“…What are considered VME indicators for chemosynthetic ecosystems varies regionally, some include endemic taxa, some specifically list bivalves, others decapods, and still others specifically list polychaetes ( Table 2 ); however, any taxa that create structure would count. Like reefs, chemosynthetic ecosystems meet many of the FAO criteria including Structural Complexity—tubeworms and molluscs often harbor significant epifauna ( Van Dover & Trask, 2000 ; Van Dover, 2002 ; Van Dover et al, 2003 ; Guillon et al, 2017 ; Gollner et al, 2021 ); Functional Significance—both vents and seeps have been observed to act as nurseries for chemosynthetic and non-chemosynthetic taxa ( Gollner et al, 2021 ; Salinas-de-León et al, 2018 ; Sen et al, 2019 ; Turner et al, 2020 ; Fig. 7B ); Uniqueness or Rarity, as they generally occur in discrete areas and exhibit regional endemism of fauna ( Gollner et al, 2021 ); Fragility and Life History characters–at seeps, tubeworms have been documented to live for 200–800 years, fitting with the characteristics of being slow-growing and long-lived ( Durkin, Fisher & Cordes, 2017 ).…”

Section: Resultsmentioning

confidence: 99%

Towards a scientific community consensus on designating Vulnerable Marine Ecosystems from imagery

Baco,

Ross,

Althaus

et al. 2023

PeerJ

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Management of deep-sea fisheries in areas beyond national jurisdiction by Regional Fisheries Management Organizations/Arrangements (RFMO/As) requires identification of areas with Vulnerable Marine Ecosystems (VMEs). Currently, fisheries data, including trawl and longline bycatch data, are used by many RFMO/As to inform the identification of VMEs. However, the collection of such data creates impacts and there is a need to collect non-invasive data for VME identification and monitoring purposes. Imagery data from scientific surveys satisfies this requirement, but there currently is no established framework for identifying VMEs from images. Thus, the goal of this study was to bring together a large international team to determine current VME assessment protocols and establish preliminary global consensus guidelines for identifying VMEs from images. An initial assessment showed a lack of consistency among RFMO/A regions regarding what is considered a VME indicator taxon, and hence variability in how VMEs might be defined. In certain cases, experts agreed that a VME could be identified from a single image, most often in areas of scleractinian reefs, dense octocoral gardens, multiple VME species’ co-occurrence, and chemosynthetic ecosystems. A decision flow chart is presented that gives practical interpretation of the FAO criteria for single images. To further evaluate steps of the flow chart related to density, data were compiled to assess whether scientists perceived similar density thresholds across regions. The range of observed densities and the density values considered to be VMEs varied considerably by taxon, but in many cases, there was a statistical difference in what experts considered to be a VME compared to images not considered a VME. Further work is required to develop an areal extent index, to include a measure of confidence, and to increase our understanding of what levels of density and diversity correspond to key ecosystem functions for VME indicator taxa. Based on our results, the following recommendations are made: 1. There is a need to establish a global consensus on which taxa are VME indicators. 2. RFMO/As should consider adopting guidelines that use imagery surveys as an alternative (or complement) to using bycatch and trawl surveys for designating VMEs. 3. Imagery surveys should also be included in Impact Assessments. And 4. All industries that impact the seafloor, not just fisheries, should use imagery surveys to detect and identify VMEs.

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Section: Resultsmentioning

confidence: 99%

Towards a scientific community consensus on designating Vulnerable Marine Ecosystems from imagery

Baco,

Ross,

Althaus

et al. 2023

PeerJ

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“…As a consequence, the antioxidant system within the microbial cells, including enzymes like superoxide dismutase (SOD), glutathione-Stransferase (GST), and catalase (CAT), plays an active role in combating the stress induced by ROS generation in bacterial cells [108]. Researchers have made a noteworthy discovery that several bacterial species possess the capability to exhibit resistance towards heavy metals [109]. In Table 2, the bioremediation efficacy of heavy-metal-tolerant bacterial strains has been meticulously outlined.…”

Section: Bioaccumulationmentioning

confidence: 99%

Microbial-Based Heavy Metal Bioremediation: Toxicity and Eco-Friendly Approaches to Heavy Metal Decontamination

2023

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There are several industrial processes in which heavy metals are used, including but not limited to chrome plating and tanning. Amongst the most toxic heavy metals to human health are arsenic, cadmium, chromium, lead, copper, nickel, and mercury. The aforementioned toxic metals possess the ability to cause contamination upon their release into the environment. Humans and aquatic and terrestrial animals are at risk from heavy metals in water and soil. Heavy metal toxicity has the potential to result in several health complications, such as renal and hepatic impairment, dermatological afflictions, cognitive lethargy, and potentially oncogenic manifestations. The removal of heavy metals from wastewater and soil can be accomplished using a variety of conventional methods, such as membrane filtration, reverse osmosis, chemical reduction, and adsorption. These methods have several disadvantages, such as generating an abundance of secondary pollutants, and entail significantly higher costs in comparison to biological methods. Conversely, eco-friendly techniques based on microbes have numerous advantages. This review provides a comprehensive overview of biological processes that remove heavy metal ions, both metabolically dependent and metabolically independent. Additionally, we also focused on the source and toxicity of these heavy metals. This study is expected to be particularly beneficial for the development of biological heavy metal treatment systems for soil and water.

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“…In response, there has been an increased reliance on metal-based antibacterial agents. However, it is important to note that bacteria can develop resistance to metals through exposure [3]. Moreover, the use of these antibacterial metals can 1/20 contribute to the emergence and persistence of antibiotic resistance in bacterial populations through co-selection [4,5].…”

Section: Introductionmentioning

confidence: 99%

DeepMRG: a multi-label deep learning classifier for predicting bacterial metal resistance genes

Emon,

Zhang

2023

Preprint

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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. Currently, the “best hit” approaches are utilized to identify and annotate MRGs. This method is sensitive to cutoff values and produces a high false negative rate. 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.

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Metal-Resistance in Bacteria: Why Care?

Abstract: Heavy metal resistance is more than the tolerance one has towards a particular music genera [...]

Cited by 11 publications

References 38 publications

Towards a scientific community consensus on designating Vulnerable Marine Ecosystems from imagery

Towards a scientific community consensus on designating Vulnerable Marine Ecosystems from imagery

Microbial-Based Heavy Metal Bioremediation: Toxicity and Eco-Friendly Approaches to Heavy Metal Decontamination

DeepMRG: a multi-label deep learning classifier for predicting bacterial metal resistance genes

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