Heavy metal resistance in the Yanomami and Tunapuco microbiome

Abstract

BACKGROUND

The Amazon Region hosts invaluable and unique biodiversity as well as mineral resources. Consequently, large illegal and artisanal gold mining areas exist in indigenous territories. Mercury has been used in gold mining, and some has been released into the environment and atmosphere, primarily affecting indigenous people such as the Yanomami. In addition, other heavy metals have been associated with gold mining and other metal-dispersing activities in the region.

OBJECTIVE

Investigate the gut microbiome of two semi-isolated groups from the Amazon, focusing on metal resistance.

METHODS

Metagenomic data from the Yanomami and Tunapuco gut microbiome were assembled into contigs, and their putative proteins were searched against a database of metal resistance proteins.

FINDINGS

Proteins associated with mercury resistance were exclusive in the Yanomami, while proteins associated with silver resistance were exclusive in the Tunapuco. Both groups share 77 non-redundant metal resistance (MR) proteins, mostly associated with multi-MR and operons with potential resistance to arsenic, nickel, zinc, copper, copper/silver, and cobalt/nickel. Although both groups harbour operons related to copper resistance, only the Tunapuco group had the pco operon.

CONCLUSION

The Yanomami and Tunapuco gut microbiome shows that these people have been exposed directly or indirectly to distinct scenarios concerning heavy metals.

Key words:
gut microbiome; mercury resistance; silver resistance; hunter-gatherers; shotgun metagenomic sequencing

Heavy metals are naturally occurring elements found in the environment.¹ And in biological systems, they play an essential role in carrying out various physiological and biochemical functions.^2,3 However, the distribution and amount of heavy metals have increased significantly worldwide due to various anthropogenic activities such as mining, industrial practices, and the addition of metals to fertilisers and livestock feed.^4,5 Thus, high concentrations of heavy metals have been detected in soils, sediments, and water bodies. The resulting selective pressure often leads to dramatic changes within their microbial populations and ecological functions.^6-11 This process has exceeded the tolerable baseline level of natural heavy metal cycling in many ecosystems and is currently a problem not only for the environment but also for public health.²

Exposure of humans and animals to heavy metals can occur through water, air, food, or skin, and bioaccumulation of heavy metals can lead to various toxic effects on different tissues and organs.^12,13,14 For example, arsenic, cadmium, and chromium are carcinogenic metals that can impair DNA synthesis and repair.^15,16 To cope with high metal concentrations, microbes have evolved adaptation and resistance mechanisms such as metal resistance (MR) genes.¹⁷ These mechanisms are thought to result from billions of years of selection by geological heavy metals and later by anthropogenic influences.^2,18

The Amazon hosts an invaluable and unique biodiversity of flora and fauna on Earth and is home to 173 ethnic groups, with indigenous territories occupying a significant portion of the region (27% of the forest area).¹⁹ Nevertheless, the Brazilian Amazon is home to the third largest area of artisanal mining in indigenous territories,²⁰ where mercury (Hg) is used to combine with gold in the amalgamation process.²¹ Mercury is considered one of the ten most hazardous substances to health by the World Health Organization (WHO) due to its high toxicity²² and in the amalgamation process, some mercury is dispersed into aquatic environments, soils, and the atmosphere,^23,24 affecting indigenous areas such as the Yanomami.²³

The Yanomami are the largest indigenous, semi-isolated group in the Amazon that still maintains traditional subsistence practices (hunting, fishing, gathering, and swidden horticulture). Their land encompasses the Venezuela-Brazil border and spans two Brazilian states (Roraima and Amazonas).²⁴ Reports indicate that this group has been exposed to mercury from gold mining since 1980,²⁵ and it is a current and growing threat.²⁶ The Yanomami are even more vulnerable because of their dependence on resources in this potentially contaminated environment. Fish in the Amazon, the main source of protein for traditional groups, is also contaminated with mercury,^27,28 and dietary exposure is the major pathway for the accumulation of metals in the body. In addition to mercury, other heavy metals such as copper (Cu), zinc (Zn), arsenic (As), cadmium (Cd), and lead (Pb) have also been associated with gold mining.²⁹ However, other metal-dispersing activities may also be occurring in the region. As our group has previously noted, metal contamination could result from the continuous discharge of batteries by the Yanomami on their land for decades.³⁰

Tunapuco is a rural agriculturalist group situated in the central Andes, at an elevation between 2,500 and 3,100 m above sea level. Their diet is based on local agricultural products (mainly potatoes, oca, and mashua) and home-raised small animals.³¹ The Peruvian Andes hosts a wealth of mineral resources such as copper, silver, zinc, and gold, with silver mining being one of the region’s most important activities.³²

In addition to the toxic effects caused by metal contamination, the gut microbiota of individuals may reflect their long-term metal exposure.³³ However, studies of this nature are extremely limited, and we found none that focused on isolated or semi-isolated groups. Thus, to gain insight into this issue, we compared the gut microbiomes of two different groups, the Brazilian Yanomami and the Tunapuco, focusing on the differences in their heavy metal resistance profiles.

MATERIALS AND METHODS

Data used - For this study, we analysed shotgun metagenomic data from the gut microbiome of the Yanomami (n = 15), a semi-isolated hunter-gatherer community from the Brazilian Amazon,³⁰ and the Tunapuco (n = 12), a traditional agricultural community from the Andean highlands.³¹ The metagenomic data from both groups comprises paired-end sequences generated on Illumina sequencers and can be found in the National Centre for Biotechnology Information Sequence Read Archive (https://www.ncbi.nlm.nih.gov/sra) under the BioProjects PRJNA527208 and PRJNA268964.

Metagenomic assemble and open reading frames (ORFs) prediction - Each metagenome was independently subjected to de novo assembly through metaSPAdes v.3.13³⁴ using default parameters and k-mers of size 21, 33 and 55. This step assembled the short reads of the metagenomes into 2,012,469 contigs (longer sequences). ORFs were predicted in these contigs with FragGeneScan v.1.31³⁵ using the parameters “- w 1 -t complete”.

Sequences classification - The translated ORFs were matched to specific proteins using the Abricate program (https://github.com/tseemann/abricate). This program uses BLAST+³⁶ to compare sequences with reference databases, and in this study, we used the BacMet2 database¹⁷ to analyse metal resistance proteins. The results from each search were filtered, so only proteins with more than 80% coverage and 80% identity were considered for further analysis.

To relate the identified proteins to the bacterial species of the Yanomami/Brazil microbiome, the contigs that showed similarities to MR proteins were taxonomically classified using the Kraken2 program.³⁷

Gene abundance and reads per kilobase per million sequenced reads (RPKM) calculation - Metagenomic reads were aligned to the MR coding genes using BWA mem v0.7.17³⁸ with default parameters. SAMtools v1.13³⁹ was used for file manipulation and assessment of the reads abundance and length of each gene. Abundances were then normalised using a RPKM approach that normalises to the total number of reads sequenced per sample, not the total number of reads mapped.⁴⁰ The following formula was used: RPKM = number of reads mapped to the ORF / (length of the ORF / 1000* total number of reads sequenced per sample / 1,000,000).

Metals determinants operons - To identify the presence of metal resistance operons, we surveyed the presence of coding genes in the metagenomes. Mercury resistance operon was determined by the mer operon, which consists of genes responsible for mercuric reduction (merA), periplasmic binding (merP), transport (merTCEG), and transcriptional regulation (merRD),⁴¹ but also genes associated with organomercury compounds degradation (merB and merG).⁴² Arsenic resistance operon was determined by the ars operon (inorganic arsenic), which has many variants but, at the minimum, it consists of genes responsible for arsenite efflux transporter ATPase subunit (arsB), arsenate reductase (arsC) and a metalloregulatory transcriptional repressor (arsR).⁴³ The copper resistance operon was determined by a system for copper efflux (cue), a system for copper sensing (cus)⁴⁴ and the pco operon.⁴⁵ The cue system consists of the periplasmic multicopper oxidase (cueO), the membrane-embedded ATP-driven copper efflux pumps (copAB), and the transcriptional regulator (cueR).^44,46 The cus system has the potential to also confer silver resistance, and it includes a complex of proton gradient-driven efflux pump (cusABC) and regulators (cusRS).⁴⁴ The pco operon consists of the multicopper oxidase (pcoA), the copper-binding protein (pcoB), metallochaperones (pcoCE), membrane components (pcoD and pcoS), and transcriptional regulator (pcoR).⁴⁵ The zinc resistance operon was determined by the presence of proteins that control zinc homeostasis by importing and exporting Zn ions via Zn uptake systems.⁴⁷ The proteins znuABC are ABC transporters that facilitate zinc ions uptake, which can be regulated by zitB, zur, or soxS.^47,48 Chromate resistance operon was determined by the chromate ion transport (chr) superfamily, which consists of genes responsible for reduction (nfsA and chrR/yieF), transport (chrA), regulation (chrB and chrF) and superoxide dismutase (chrC).⁴⁹ Nickel resistance operon was determined by the nik system of ABC transporters, which is involved in specific nickel uptake and is composed of a periplasmic binding protein (nikA), integral membrane components (nikBC), two ATPases (nikDE), and a repressor that avoids nickel overload.^50,51 Silver resistance was determined by the presence of the sil operon, which consists of efflux pumps (silCBA and silP), binding proteins (silF and silE) and a two-component regulatory system (genes silRS).⁵² Cobalt and nickel resistance operon was determined by the rcn system, which is composed of a regulator (rcnR) that controls the transcription of cobalt and nickel exporters (rcnAB).⁵³

RESULTS

We reanalysed the metagenomic data from the gut microbiome of Yanomami and Tunapuco individuals (Fig. 1). Although both are semi-isolated groups living within the Amazon Region, their lifeways and environments are startlingly different.

Fig. 1:
geographic locations and satellite images of the Yanomami and Tunapuco regions. Map generated in R and satellite images obtained from Google Earth.

Putative proteins from the metagenomic contigs were screened for similarity with MR proteins from the BacMet2 database. Four of the 12 Tunapuco metagenomes and 11 of the 15 Yanomami metagenomes presented MR proteins (Fig. 2).

Fig. 2:
amount of metal resistance (MR) proteins identified per metagenome in the Tunapuco and Yanomami groups.

MR proteins with more copies in the Yanomami are encoded by merT and merP (identified eleven and nine times, respectively), followed by pstB and chrA1 (identified eight times). merT was identified in four metagenomes, having up to five copies each, while merP was identified in five metagenomes with up to three copies each. pstB was identified in six metagenomes, duplicated in two of them. chrA1 was duplicated in the four metagenomes in which it was identified. MR proteins with more copies in the Tunapuco are also the most prevalent, encoded by arsR, cutA, yhcN, and zur, each of which has been identified four times, once in each of the four metagenomes with MR proteins. These four proteins were also identified in the Yanomami.

Out of the 107 and 95 non-redundant MR proteins identified in the Yanomami and Tunapuco, respectively, 77 are shared between the two groups (Fig. 3A), with most being associated with resistance to multi-metals (two or more metals) (Fig. 3B). The majority of unique MR proteins in the Yanomami are associated with mercury resistance, while the majority of unique MR proteins in the Tunapuco are associated with silver resistance. It is noteworthy that the Tunapuco did not present any protein capable of conferring resistance to mercury, and the Yanomami did not present any protein capable of conferring resistance to silver (Fig. 3B).

Fig. 3:
(A) Venn diagram and the (B) amount grouped by metals of the unique and shared metal resistance (MR) proteins between the Tunapuco and Yanomami groups.

Contigs that potentially encode MR proteins in the Yanomami belong to the phylum Proteobacteria (170 contigs) and Firmicutes (four contigs), while in the Tunapuco belong exclusively to Proteobacteria (102 contigs). Taxonomic information regarding the contigs from both groups can be found in Supplementary data (Tables I-II). In the Yanomami, the most abundant genus from the Proteobacteria phylum was Escherichia (114 contigs with 80 MR proteins), followed by Ralstonia (18 contigs with seven MR genes). From the phylum Firmicutes, the genera identified were Enterococcus (three contigs with four MR genes) and Streptococcus (one contig with one MR gene). In the Tunapuco, the contigs were classified as belonging to the genera Escherichia (81 contigs with 91 MR proteins), and the family Enterobacteriaceae (21 contigs with 22 MR proteins).

We further investigated the presence and abundance of heavy metals operons in the metagenomes [Figs 4, 5, Supplementary data (Table III)]. We identified nine operons or systems (mer, ars, chr, nik, znu, cue/cop/pco and sil) that have the potential to confer resistance to mercury, arsenic, chromium, nickel, zinc, copper and silver, respectively. We also identified two operons (cus and rcn) associated with multiple metals (copper/silver and cobalt/nickel).

The mer operon was the most predominant in the Yanomami, being identified in the metagenomes AH08, AL19, TA24, and TA91, which have putative proteins responsible for the main functions necessary for mercury resistance, but the mechanisms for organomercury compound degradation (merB and merG) were not identified (Fig. 4). The metagenome AL19 does not have the merA protein for mercuric reduction and also has the lowest RPKM values for the other proteins from the mer operon. All contigs harbouring putative proteins from the mer operon were classified as Proteobacteria, mainly Aeromonas, Burkholderia, and Paraburkholderia [Supplementary data (Table I)]. These four metagenomes also harbour putative proteins involved in chromium transport (chrA1 and chrC) in contigs classified as Ralstonia. In contrast, the metagenomes CA46 and TA08 harbour genes that encode proteins involved in chromium reduction (nfsA and chrR) in contigs that were classified as Escherichia, Ralstonia, Paraburkholderia, Kinneretia, and Enterobacteriaceae [Fig. 4, Supplementary data (Table I)].

Fig. 4:
dot chart representing the coding genes of the operons identified in each Yanomami metagenome coloured by function and grouped by the metal(s) in which they have the potential to confer resistance. The size of each dot represents the reads per kilobase per million sequenced reads (RPKM) abundance of each gene in each metagenome.

Moreover, the Yanomami metagenomes CA46 and TA08 stand out for having the highest number of operons detected (nickel, cobalt/nickel, arsenic, zinc, copper, and copper/silver). Most contigs (64/70) harbouring these operons were classified as Escherichia coli. The same contig from metagenome CA46 harbours the complete nik and ars operons, while the genes from the rcn and cus operons were identified in different but also single contigs [Supplementary data (Table I)].

The unique proteins associated with silver resistance in the Tunapuco belonged to the sil operon. This operon and all the other operons identified in the Tunapuco group were complete in the metagenome HCO11 (Fig. 5). Compared with the other metagenomes, the putative proteins from these operons in the HCO11 had the highest RPKM values. Yanomami and Tunapuco harbour operons associated with copper resistance, but the pco operon was exclusively identified in the Tunapuco group. In the HCO11 metagenome, the complete pco and nik operons were identified in the same contig, while the complete cus, ars and rcn operons were identified each in a single contig (Fig. 5). These four contigs were classified as E. coli [Supplementary data (Table II)]. The other Tunapuco metagenomes also harbour proteins from these operons; however, they did not present the complete set. The metagenome HCO70 harbours partial operons associated with arsenic, zinc and cobalt/nickel resistance, the metagenome HCO72 harbours a partial operon associated with arsenic, and the metagenome HCO64 harbours a partial operon associated with nickel (Fig. 5).

Fig. 5:
dot chart representing the coding genes of the operons identified in each Tunapuco metagenome coloured by function and grouped by the metal(s) in which they have the potential to confer resistance. The size of each dot represents the reads per kilobase per million sequenced reads (RPKM) abundance of each gene in each metagenome.

DISCUSSION

In general, the structure, composition, and diversity of gut microbiomes reflect the living conditions and environment of the individuals.⁵⁴ Although heavy metals are naturally occurring elements found mainly in soil, heavy metal pollution in mining areas is a serious environmental and human health concern. In our study, we characterised heavy metal resistance genes/proteins associated with the gut microbiome of Yanomami and Tunapuco individuals.

The Yanomami is a semi-nomadic hunter-gatherer group from the Amazon. Although they explore a rich and highly biodiverse territory, gold mining has been practiced in their land for decades.²⁵ This practice usually relies on a process that results in the dispersal of mercury into the atmosphere and environment.^55,56 Remarkably, we identified the mer operon (mercury resistance) and other heavy metal-associated operons in the Yanomami microbiome. The Tunapuco is an agricultural group that inhabits a large area in the Central Andes (Panao, Pachitea, province of Huánuco) where mining activities have not been reported.^31,57 Although most Tunapuco metagenomes did not show any MR protein, we identified complete heavy-metal-associated operons in the microbiome of one individual and partial operons in the microbiomes of three other individuals. These operons include the sil and pco operons (silver and copper resistance), not harboured by the Yanomami. Notably, Peru has been one of the world’s biggest producers of silver, copper, and zinc.³² Identifying MR proteins related to heavy metals associated with mining practices could suggest that the Yanomami and Tunapuco groups have been exposed to diverse heavy metals, which may have cumulative effects and cause higher toxicities.¹³

In the Yanomami, most identified proteins have been associated with resistance to toxic metals such as mercury, copper, and zinc, while the most identified proteins in the Tunapuco have been associated with copper, zinc, and nickel.

The mer operon, only identified in the Yanomami, included the genes (merT and merP) required for the full expression of mercury resistance,⁵⁸ which had the largest number of copies in the Yanomami. The narrow-spectrum mercury resistance merRTPADE operon confers resistance to inorganic mercury, while the broad-spectrum mercury resistance merRTPAGBDE operon confers resistance to inorganic and organic mercury. Both include the central enzyme in the microbial mercury detoxification system: the mercuric reductase (MERA) protein, which catalyses the reduction of Hg(II) to volatile Hg(0). Organic mercury compounds are more toxic than inorganic compounds because they are readily absorbed from the gastrointestinal tract (95%) and distributed throughout the body.¹⁴ Nevertheless, all forms of mercury are toxic and have been associated with neurological damage, renal dysfunction, gastrointestinal ulceration, and hepatotoxicity in humans.^14,59

The sil operon (silver resistance) was only identified in the Tunapuco. The natural sources of silver in the environment include volcanic activity and hydrothermal-sedimentary deposits.⁶⁰ In contrast, anthropogenic sources of environmental Ag pollution include soil pollution, coal and cement plants, metallurgy, waste landfills, sludge fertilisers, and silver nanoparticle combustion.⁶⁰ Silver is generally not considered carcinogenic or toxic to the immune, cardiovascular, nervous, or reproductive systems.⁶¹ However, overexposure can lead to acute symptoms such as low blood pressure, diarrhoea, stomach irritation, and reduced respiration. Prolonged intake can affect the liver and kidneys, alter blood cells, and cause argyria and/or argyrosis. Moreover, small amounts of soluble silver compounds can accumulate in the brain and muscles.⁶¹

Both groups harbour proteins associated with copper resistance; however, the pco system was only identified in the Tunapuco group. This system detoxifies copper in the periplasm and relies on the activity of copA to confer resistance.⁶² The copper systems identified in both groups were cue and cus, both of which catalyse the removal of excess copper from the cells.⁴⁴ The former system contains cueR, which regulates the expression of cueO and copA.⁶² The copA extrudes the excess copper from the cytoplasm, while cueO, as well as the cus system, are involved in periplasmic copper detoxification.⁶² Additionally, the cus system can be activated by silver, but higher concentrations of silver are required compared to those observed for copper.⁴⁴

Other prevalent proteins identified in the Yanomami microbiome are CHRA1 and PSTB. The former reduces chromate accumulation in cells, and the latter is involved in phosphate import and arsenic resistance. Bioaccumulation of both metals in the human body can cause dermal, renal, neurological, and gastrointestinal diseases and several cancers. Arsenic, however, is of particular concern because it can endanger health even at very low concentrations and affect the cardiovascular, hepato-biliary, and respiratory systems, in addition to those already mentioned.^14,63,64,65 In the Amazon Forest, chromium concentrations in a pristine area have been reported to be higher than the background concentration, but its origin was presumed to be geogenic.⁶⁵ A study from 2005 reported that arsenic concentrations in Amazon rivers were higher in those flowing from the Andes, which is not the case for rivers to which the Yanomami have access.⁶⁶ Since then, however, gold mining in the Yanomami region has increased substantially,²⁶ and elevated arsenic concentrations in sediments, soils, and water in other regions of Brazil have been linked to various anthropogenic activities.⁶⁷

For decades, the Yanomami ecological niche has been under the continuous discharge of batteries that also contributes to releasing metals into their environment.³⁰ The most commonly used battery types contain significant amounts of heavy metals whose resistance genes were identified in our study, such as cobalt, copper, and nickel.⁶⁸ Interestingly, cobalt and copper, as well as zinc, are essential for human life. However, they can also be toxic when present in excess.²

The proteins arsR, cutA, yhcN, and zur had the largest number of copies in the Tunapuco. Each protein represents a distinct metal resistance mechanism, with arsR being a transcriptional regulator associated with arsenic resistance,⁴³cutA being potentially involved in copper tolerance,⁶⁹yhcN being a stress-response protein associated with cadmium,⁷⁰ and zur being a regulator of zinc uptake.⁴⁷

We noticed a pattern in the metagenomes, considering different metal operons. Specifically, metagenomes containing genes with higher RPKM abundances usually presented all genes belonging to each operon. In contrast, most genes with lower RPKM abundance were the sole representatives of their respective operons. Therefore, we hypothesise that microbiomes in which operon genes exhibit the highest RPKM abundances are likely expressing the resistance phenotype.

As for the bacteria carrying these MR genes, most contigs were classified as Proteobacteria. This phylum is widespread and ubiquitous, and due to its adaptability and tolerance, it has been reported to harbour a variety of MR determinants in different environments and contexts.² Interestingly, we have previously observed that the gut microbiome of Yanomami individuals differs from that of other semi-isolated human groups in the high abundance of Proteobacteria.³⁰ However, it is not known whether this is the reason why these individuals have more Proteobacteria in their gut microbiome. In fact, we have previously linked this high abundance to the high exposure of the Yanomami to solar UVB light.⁷¹ However, it is also worth considering that most contigs were classified as Proteobacteria, or specifically as E. coli due to the over-representation of E. coli genomes in databases. It is a fact that E. coli strains are the most commonly used prokaryote model in studies related to resistance or virulence genes. Therefore, the classification of contigs harbouring metal resistance genes may be biased, as their genetic content comprises genes typically found in E. coli.

In summary, identifying genes that encode proteins associated with metal resistance in the faeces of Yanomami and Tunapuco individuals suggests a high abundance of heavy metals in the ecological niche they explore. Heavy metals from geological and anthropogenic sources are both widespread in the environment and non-biodegradable, which may act as long-term selection pressures.¹ Therefore, we cannot trace the source or time period of exposure with this study, but the evidence shows that increases in gold mining have direct impacts on the Yanomami group, as do increases in deforestation rates.^20,26 Monitoring and remediation of these activities are urgently important not only for the health and survival of indigenous groups but also for the conservation of the Amazon Region overall, which implies preserving global biodiversity and regulating the climate and hydrological cycle.⁷²

ACKNOWLEDGEMENTS

To the Yanomami people. We also thank the health personnel of the Distrito Sanitário Especial Indígena Yanomami for their overall support during fieldwork.

REFERENCES

• ¹
  Garrett RG. Natural sources of metals to the environment. Hum Ecol Risk Assess Int J. 2000; 6(6): 945-63.
• ²
  Hao X, Zhu J, Rensing C, Liu Y, Gao S, Chen W, et al. Recent advances in exploring the heavy metal(loid) resistant microbiome. Comput Struct Biotechnol J. 2021; 19: 94-109.
• ³
  Mehri A. Trace elements in human nutrition (II) ? An update. Int J Prev Med. 2020; 11: 2.
• ⁴
  Debski B. Supplementation of pigs diet with zinc and copper as alternative to conventional antimicrobials. Pol J Vet Sci. 2016; 19(4): 917-24.
• ⁵
  Tchounwou PB, Yedjou CG, Patlolla AK, Sutton DJ. Heavy metal toxicity and the environment. Exp Suppl. 2012; 101: 133-64.
• ⁶
  Chen J, McIlroy SE, Archana A, Baker DM, Panagiotou G. A pollution gradient contributes to the taxonomic, functional, and resistome diversity of microbial communities in marine sediments. Microbiome. 2019; 7(1): 104.
• ⁷
  Zhao H, Wu Y, Lan X, Yang Y, Wu X, Du L. Comprehensive assessment of harmful heavy metals in contaminated soil in order to score pollution level. Sci Rep. 2022; 12(1): 3552.
• ⁸
  Epelde L, Lanzén A, Blanco F, Urich T, Garbisu C. Adaptation of soil microbial community structure and function to chronic metal contamination at an abandoned Pb-Zn mine. FEMS Microbiol Ecol. 2015; 91(1): 1-11.
• ⁹
  Algül F, Beyhan M. Concentrations and sources of heavy metals in shallow sediments in Lake Bafa, Turkey. Sci Rep. 2020; 10(1): 11782.
• ¹⁰
  Chowdhury S, Mazumder MAJ, Al-Attas O, Husain T. Heavy metals in drinking water: occurrences, implications, and future needs in developing countries. Sci Total Environ. 2016; 569-570: 476-88.
• ¹¹
  Mokarram M, Saber A, Obeidi R. Effects of heavy metal contamination released by petrochemical plants on marine life and water quality of coastal areas. Environ Sci Pollut Res Int. 2021; 28(37): 51369-83.
• ¹²
  Azevedo BF, Furieri LB, Peçanha FM, Wiggers GA, Vassallo PF, Simões MR, et al. Toxic effects of mercury on the cardiovascular and central nervous systems. J Biomed Biotechnol. 2012; 2012: 949048.
• ¹³
  Cobbina SJ, Chen Y, Zhou Z, Wu X, Zhao T, Zhang Z, et al. Toxicity assessment due to sub-chronic exposure to individual and mixtures of four toxic heavy metals. J Hazard Mater. 2015; 294: 109-20.
• ¹⁴
  Balali-Mood M, Naseri K, Tahergorabi Z, Khazdair MR, Sadeghi M. Toxic mechanisms of five heavy metals: mercury, lead, chromium, cadmium, and arsenic. Front Pharmacol. 2021; 12: 643972.
• ¹⁵
  Clancy HA, Sun H, Passantino L, Kluz T, Muñoz A, Zavadil J, et al. Gene expression changes in human lung cells exposed to arsenic, chromium, nickel or vanadium indicate the first steps in cancer. Met Integr Biometal Sci. 2012; 4(8): 784-93.
• ¹⁶
  Koedrith P, Kim H, Weon JI, Seo YR. Toxicogenomic approaches for understanding molecular mechanisms of heavy metal mutagenicity and carcinogenicity. Int J Hyg Environ Health. 2013; 216(5): 587-98.
• ¹⁷
  Pal C, Bengtsson-Palme J, Rensing C, Kristiansson E, Larsson DGJ. BacMet: antibacterial biocide and metal resistance genes database. Nucleic Acids Res. 2014; 42(Database issue): D737-43.
• ¹⁸
  Chen SC, Sun GX, Yan Y, Konstantinidis KT, Zhang SY, Deng Y, et al. The great oxidation event expanded the genetic repertoire of arsenic metabolism and cycling. Proc Natl Acad Sci. 2020; 117(19): 10414-21
• ¹⁹
  Cerejeiras DP. Atlas de pressões e ameaças às terras indígenas na Amazônia brasileira. Confins [Internet]. 2010 [cited 2023 Aug 12]. Available from: https://journals.openedition.org/confins/6539
  » https://journals.openedition.org/confins/6539
• ²⁰
  Castro MC, Peterka C. Malaria is increasing in Indigenous and artisanal mining areas in the Brazilian Amazon. Nat Med. 2023; 29(4): 762-4.
• ²¹
  United States Environmental Protection Agency [homepage on the Internet]. Artisanal and small-scale gold mining without mercury. 2023 [updated 2023 Jan 9; cited 2023 Apr 2]. Available from: https://www.epa.gov/international-cooperation/artisanal-and-small-scale-gold-mining-without-mercury
  » https://www.epa.gov/international-cooperation/artisanal-and-small-scale-gold-mining-without-mercury
• ²²
  WHO - World Health Organization. Mercury and health. 2017 [updated 2017 Mar 31; cited 2023 Apr 2]. Available from: https://www.who.int/news-room/fact-sheets/detail/mercury-and-health
  » https://www.who.int/news-room/fact-sheets/detail/mercury-and-health
• ²³
  Vega CM, Orellana JDY, Oliveira MW, Hacon SS, Basta PC. Human mercury exposure in Yanomami indigenous villages from the Brazilian Amazon. Int J Environ Res Public Health. 2018; 15(6): 1051.
• ²⁴
  Albert B, Tourneau FML. Ethnogeography and resource use among the Yanomami. Curr Anthropol. 2007; 48(4): 584.
• ²⁵
  Sing KA, Hryhorczuk D, Saffirio G, Sinks T, Paschal DC, Sorensen J, et al. Organic mercury levels among the Yanomama of the Brazilian Amazon Basin. Ambio. 2003; 32(7): 434-9.
• ²⁶
  Mataveli G, Chaves M, Guerrero J, Escobar-Silva EV, Conceição K, de Oliveira G. Mining is a growing threat within indigenous lands of the Brazilian Amazon. Remote Sens. 2022; 14(16): 4092.
• ²⁷
  Passos CJS, Da Silva DS, Lemire M, Fillion M, Guimarães JRD, Lucotte M, et al. Daily mercury intake in fish-eating populations in the Brazilian Amazon. J Expo Sci Environ Epidemiol. 2008; 18(1): 76-87.
• ²⁸
  Bastos WR, Dórea JG, Bernardi JVE, Lauthartte LC, Mussy MH, Lacerda LD, et al. Mercury in fish of the Madeira River (temporal and spatial assessment), Brazilian Amazon. Environ Res. 2015; 140: 191-7.
• ²⁹
  Tun AZ, Wongsasuluk P, Siriwong W. Heavy metals in the soils of placer small-scale gold mining sites in Myanmar. J Health Pollut. 2020; 10(27): 200911.
• ³⁰
  Conteville LC, Oliveira-Ferreira J, Vicente ACP. Gut microbiome biomarkers and functional diversity within an Amazonian semi-nomadic hunter-gatherer group. Front Microbiol. 2019; 10: 1743.
• ³¹
  Obregon-Tito AJ, Tito RY, Metcalf J, Sankaranarayanan K, Clemente JC, Ursell LK, et al. Subsistence strategies in traditional societies distinguish gut microbiomes. Nat Commun. 2015; 6: 6505.
• ³²
  The Global Economy [homepage on the Internet]. Peru economic indicators: minerals. 2020 [cited 2023 Jul 10]. Available from: https://www.theglobaleconomy.com/Peru/
  » https://www.theglobaleconomy.com/Peru/
• ³³
  Shao M, Zhu Y. Long-term metal exposure changes gut microbiota of residents surrounding a mining and smelting area. Sci Rep. 2020; 10(1): 4453.
• ³⁴
  Nurk S, Meleshko D, Korobeynikov A, Pevzner PA. metaSPAdes: a new versatile metagenomic assembler. Genome Res. 2017; 27(5): 824-34.
• ³⁵
  Rho M, Tang H, Ye Y. FragGeneScan: predicting genes in short and error-prone reads. Nucleic Acids Res. 2010; 38(20): e191.
• ³⁶
  Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, et al. BLAST+: architecture and applications. BMC Bioinformatics. 2009; 10: 421.
• ³⁷
  Wood DE, Lu J, Langmead B. Improved metagenomic analysis with Kraken 2. Genome Biol. 2019; 20(1): 257.
• ³⁸
  Li H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv preprint. 2013; 1303.3997: 1-3.
• ³⁹
  Danecek P, Bonfield JK, Liddle J, Marshall J, Ohan V, Pollard MO, et al. Twelve years of SAMtools and BCFtools. Gigascience. 2021; 10(2): giab008.
• ⁴⁰
  Zhang X, Xu W, Liu Y, Cai M, Luo Z, Li M. Metagenomics reveals microbial diversity and metabolic potentials of seawater and surface sediment from a hadal biosphere at the Yap trench. Front Microbiol. 2018; 9: 2402.
• ⁴¹
  Nascimento AMA, Chartone-Souza E. Operon mer: bacterial resistance to mercury and potential for bioremediation of contaminated environments. Genet Mol Res. 2003; 2(1): 92-101.
• ⁴²
  Boyd ES, Barkay T. The mercury resistance operon: from an origin in a geothermal environment to an efficient detoxification machine. Front Microbiol. 2012; 3: 349.
• ⁴³
  Rawle R, Saley TC, Kang YS, Wang Q, Walk S, Bothner B, et al. Introducing the ArsR-regulated arsenic stimulon. Front Microbiol. 2021; 12: 630562.
• ⁴⁴
  Outten FW, Huffman DL, Hale JA, O'Halloran TV. The independent cue and cus systems confer copper tolerance during aerobic and anaerobic growth in Escherichia coli. J Biol Chem. 2001; 276(33): 30670-7.
• ⁴⁵
  Brown NL, Barrett SR, Camakaris J, Lee BT, Rouch DA. Molecular genetics and transport analysis of the copper-resistance determinant (pco) from Escherichia coli plasmid pRJ1004. Mol Microbiol. 1995; 17(6): 1153-66.
• ⁴⁶
  Teixeira EC, Franco de Oliveira JC, Novo MTM, Bertolini MC. The copper resistance operon copAB from Xanthomonas axonopodis pathovar citri: gene inactivation results in copper sensitivity. Microbiology. 2008; 154(2): 402-12.
• ⁴⁷
  Hussain S, Khan M, Sheikh TMM, Mumtaz MZ, Chohan TA, Shamim S, et al. Zinc essentiality, toxicity, and its bacterial bioremediation: a comprehensive insight. Front Microbiol. 2022; 13: 900740.
• ⁴⁸
  Warner DM, Levy SB. SoxS increases the expression of the zinc uptake system ZnuACB in an Escherichia coli murine pyelonephritis model. J Bacteriol. 2012; 194(5): 1177-85.
• ⁴⁹
  Viti C, Marchi E, Decorosi F, Giovannetti L. Molecular mechanisms of Cr(VI) resistance in bacteria and fungi. FEMS Microbiol Rev. 2014; 38(4): 633-59.
• ⁵⁰
  Navarro C, Wu LF, Mandrand-Berthelot MA. The nik operon of Escherichia coli encodes a periplasmic binding-protein-dependent transport system for nickel. Mol Microbiol. 1993; 9(6): 1181-91.
• ⁵¹
  De Pina K, Desjardin V, Mandrand-Berthelot MA, Giordano G, Wu LF. Isolation and characterization of the nikR gene encoding a nickel-responsive regulator in Escherichia coli. J Bacteriol. 1999; 181(2): 670-4.
• ⁵²
  Wang H, Li J, Min C, Xia F, Tang M, Li J, et al. Characterization of silver resistance and coexistence of sil operon with antibiotic resistance genes among gram-negative pathogens isolated from wound samples by using whole-genome sequencing. Infect Drug Resist. 2022; 15: 1425-37.
• ⁵³
  Blériot C, Effantin G, Lagarde F, Mandrand-Berthelot MA, Rodrigue A. RcnB is a periplasmic protein essential for maintaining intracellular Ni and Co concentrations in Escherichia coli. J Bacteriol. 2011; 193(15): 3785-93.
• ⁵⁴
  Gupta VK, Paul S, Dutta C. Geography, ethnicity or subsistence-specific variations in human microbiome composition and diversity. Front Microbiol. 2017; 8: 1162.
• ⁵⁵
  Bastos WR, Gomes JPO, Oliveira RC, Almeida R, Nascimento EL, Bernardi JVE, et al. Mercury in the environment and riverside population in the Madeira River Basin, Amazon, Brazil. Sci Total Environ. 2006; 368(1): 344-51.
• ⁵⁶
  Goix S, Maurice L, Laffont L, Rinaldo R, Lagane C, Chmeleff J, et al. Quantifying the impacts of artisanal gold mining on a tropical river system using mercury isotopes. Chemosphere. 2019; 219: 684-94.
• ⁵⁷
  INGEMMET - Instituto Geológico, Minero y Metalúrgico [homepage on the Internet]. Proyectos Mineros [cited 2023 Jul 10]. Available from: https://geocatmin.ingemmet.gob.pe/geocatmin/
  » https://geocatmin.ingemmet.gob.pe/geocatmin/
• ⁵⁸
  Hamlett NV, Landale EC, Davis BH, Summers AO. Roles of the Tn21 merT, merP, and merC gene products in mercury resistance and mercury binding. J Bacteriol. 1992; 174(20): 6377-85.
• ⁵⁹
  Tchounwou PB, Yedjou CG, Patlolla AK, Sutton DJ. Heavy metals toxicity and the environment. Exp Suppl. 2012; 101: 133-64.
• ⁶⁰
  Tsepina N, Kolesnikov S, Minnikova T, Timoshenko A, Kazeev K. Soil contamination by silver and assessment of its ecotoxicity. Rev Agric Sci. 2022; 10: 186-205.
• ⁶¹
  Drake PL, Hazelwood KJ. Exposure-related health effects of silver and silver compounds: a review. Ann Occup Hyg. 2005; 49(7): 575-85.
• ⁶²
  Bondarczuk K, Piotrowska-Seget Z. Molecular basis of active copper resistance mechanisms in Gram-negative bacteria. Cell Biol Toxicol. 2013; 29(6): 397-405.
• ⁶³
  Fang Z, Zhao M, Zhen H, Chen L, Shi P, Huang Z. Genotoxicity of Tri- and hexavalent chromium compounds in vivo and their modes of action on DNA damage in vitro. PLoS One. 2014; 9(8): e103194.
• ⁶⁴
  Tchounwou PB, Patlolla AK, Centeno JA. Carcinogenic and systemic health effects associated with arsenic exposure ? a critical review. Toxicol Pathol. 2003; 31(6): 575-88.
• ⁶⁵
  Moreira LJD, da Silva EB, Fontes MPF, Liu X, Ma LQ. Speciation, bioaccessibility and potential risk of chromium in Amazon forest soils. Environ Pollut. 2018; 239: 384-91.
• ⁶⁶
  Scarpelli W. Arsenic in the rivers of the Amazon. Terrae. 2005; 2(1-2): 20-7.
• ⁶⁷
  Teixeira MC, Santos AC, Fernandes CS, Ng JC. Arsenic contamination assessment in Brazil ? Past, present and future concerns: a historical and critical review. Sci Total Environ. 2020; 730: 138217.
• ⁶⁸
  Melchor-Martínez EM, Macias-Garbett R, Malacara-Becerra A, Iqbal HMN, Sosa-Hernández JE, Parra-Saldívar R. Environmental impact of emerging contaminants from battery waste: a mini review. Case Stud Chem Environ Eng. 2021; 3: 100104.
• ⁶⁹
  Fong ST, Camakaris J, Lee BT. Molecular genetics of a chromosomal locus involved in copper tolerance in Escherichia coli K-12. Mol Microbiol. 1995; 15(6): 1127-37.
• ⁷⁰
  Lee J, Hiibel SR, Reardon KF, Wood TK. Identification of stress-related proteins in Escherichia coli using the pollutant cis-dichloroethylene. J Appl Microbiol. 2010; 108(6): 2088-102.
• ⁷¹
  Conteville LC, Vicente ACP. Skin exposure to sunlight: a factor modulating the human gut microbiome composition. Gut Microbes. 2020; 11(5): 1135-8.
• ⁷²
  Gatti LV, Basso LS, Miller JB, Gloor M, Domingues LG, Cassol HLG, et al. Amazonia as a carbon source linked to deforestation and climate change. Nature. 2021; 595(7867): 388-93.

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  Financial support: FAPERJ, PAEF (IOC-023-FIO-18-2-47).

Publication Dates

• Publication in this collection
  10 Nov 2023
• Date of issue
  2023

History

• Received
  14 May 2023
• Accepted
  05 Oct 2023