remote populations: Increased S100B mRNA in blood in exposed riverine inhabitants of the Amazon

Accepted Manuscript Title: Assessing mercury intoxication in isolated/remote populations: increased S100B mRNA in blood in exposed riverine inhabitants of the Amazon Authors: Gabriela de Paula Fonseca Arrifano, Rosa Del Carmen Rodriguez Martin-Doimeadios, Mar´ıa Jim´enez-Moreno, Marcus Augusto-Oliveira, Jos´e Rog´erio Souza-Monteiro, Ricardo Paraense, Camila Rodrigues Machado, Marcelo Farina, Barbarella Macchi, Jos´e Luiz Martins do Nascimento, Maria Elena Crespo-Lopez PII: DOI: Reference:

S0161-813X(18)30308-5 https://doi.org/10.1016/j.neuro.2018.07.018 NEUTOX 2375

To appear in:

NEUTOX

Received date: Revised date: Accepted date:

31-10-2017 3-7-2018 30-7-2018

Please cite this article as: de Paula Fonseca Arrifano G, Del Carmen Rodriguez MartinDoimeadios R, Jim´enez-Moreno M, Augusto-Oliveira M, Rog´erio Souza-Monteiro J, Paraense R, Rodrigues Machado C, Farina M, Macchi B, do Nascimento JLM, CrespoLopez ME, Assessing mercury intoxication in isolated/remote populations: increased S100B mRNA in blood in exposed riverine inhabitants of the Amazon, Neurotoxicology (2018), https://doi.org/10.1016/j.neuro.2018.07.018 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

NEUROTOX_INA 2017 Special Issue

Assessing mercury intoxication in isolated/remote populations: increased S100B mRNA in blood in exposed riverine inhabitants of the Amazon Gabriela de Paula Fonseca Arrifano 1, Rosa Del Carmen Rodriguez Martin-Doimeadios2, María Jiménez-Moreno2, Marcus Augusto-Oliveira3, José Rogério Souza-Monteiro 1, Ricardo Martins do Nascimento5,6 and Maria Elena Crespo-Lopez1,* 1

Laboratório

de

Farmacologia

Molecular,

Neurodegeneração e Infecção and

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Laboratório

Investigações

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Facultad de Ciencias Ambientales y Bioquímica, Universidad de Castilla-La Mancha, Toledo,

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Spain.

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Departamento de Bioquímica, Centro de Ciências Biológicas, Universidade Federal de Santa

Catarina, Florianópolis-SC, Brazil.

*Corresponding

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Universidade CEUMA, Pesquisa em Neurociências, São Luís-MA, Brazil.

author:

Maria Elena Crespo López

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Laboratório de Neuroquímica e Biologia Celular;

Universidade Federal do Pará, Belém-PA, Brazil. 2

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Paraense1, Camila Rodrigues Machado1, Marcelo Farina4, Barbarella Macchi5, José Luiz

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Laboratório de Farmacologia Molecular , Instituto de Ciências Biológicas , Universidade Federal do Pará (UFPA) , Rua Augusto Corrêa 01, Campus do Guamá. 66075-110 Belém-PA,

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Brazil.

Phone: +559132018212; Fax: +559132017930.

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E-mail: [email protected]

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Graphical Abstract

HIGHLIGHTS  Circulating S100B protein was proposed as a biomarker of MeHg-related neurotoxicity  We quantified blood mRNA as alternative to protein in isolated populations  Approximately 20% of participants showed mercury levels above the recommended limit  Rigorous exclusion criteria were aplied a posteriori to avoid confounding factors  S100B mRNA in blood of exposed participants was over two times higher 2

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ABSTRACT Mercury is a heavy metal responsible for human intoxication worldwide and especially in the Amazon, where both natural and anthropogenic sources are responsible for exposure in riverine populations. Methylmercury is the most toxic specie with recognized neurotoxicity due to its

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affinity for the central nervous system. S100B protein is a well-established biomarker of brain damage and it was recently associated with mercury-related neurotoxicity. Accurate

measurement is especially challenging in isolated/remote populations due to the difficulty of adequate sample conservation, therefore here we use S100B mRNA levels in blood as a way to

assay mercury neurotoxicity. We hypothesized that individuals from chronically exposed populations showing mercury levels above the limit of 10 µg/g in hair would present increased

levels of S100B mRNA, likely due to early brain damage. A total of 224 riverine individuals

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were evaluated for anthropometric data (age, body mass index), self-reported symptoms of

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mercury intoxication, c-reactive protein in blood, and mercury speciation in hair. Approximately 20% of participants showed mercury levels above the limit, and prevalence for

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most symptoms was not different between individuals exposed to high or low mercury levels.

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Rigorous exclusion criteria were applied to avoid confounding factors and S100B mRNA in blood was tested by RT-qPCR. Participants with ≥10 µg/g of mercury had S100B mRNA levels

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over two times higher than that of individuals with lower exposure. A significant correlation was also detected between mercury content in hair and S100B mRNA levels in blood,

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supporting the use of the latter as a possible candidate to predict mercury-induced neurotoxicity. This is the first report of an association between S100B mRNA and mercury exposure in

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humans. The combination of both exposure and intoxication biomarkers could provide additional support for the screening and early identification of high-risk individuals in isolated

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populations and subsequent referral to specialized centers.

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Keywords: methylmercury, Tapajos, Tucurui, biomarker, real time, riverine.

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1. Introduction Mercury is a heavy metal responsible for human intoxication worldwide. Mercury levels usually found in hair varies between 0-2 µg/g in non-exposed populations (WHO, 2008), being, the limit for human exposure previously recommended of 10 µg/g of total mercury content in hair (Grandjean et al., 1997; Harada et al., 1999; NRC, 2000), based primarily on acute

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outbreaks. A variety of chemical forms of mercury can be found in nature, and methylmercury (MeHg) is especially neurotoxic due to its ability to accumulate to high levels in the central nervous system (CNS) (Crespo-Lopez et al., 2009). Mercury exposure has the potential to affect different biological systems and the brain is the main target organ for this toxicant, leading to

psychomotor alterations, visual damage, tremors and genotoxicity among other consequences (Ekino et al., 2007; Crespo-Lopez et al., 2007, 2011 and 2016). The severity of the damage

following mercury exposure seems to be influenced by individual genetic susceptibility

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(Arrifano et al., 2018a,b; Bjorklund et al., 2017), and an accurate diagnosis of mercury

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intoxication can be complicated since it has been associated with hundreds of symptoms (Rice et al., 2014). It is also difficult to find a direct correlation between mercury levels (i.e.,

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exposure) and symptoms (Rice et al., 2014; Yilmaz et al., 2014b), therefore the development

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of peripheral biomarkers to support the diagnosis of neurotoxicity is essential (Branco et al., 2017).

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S100B protein is a well-stablished biomarker used to detect brain damage (Goncalves et al., 2008; Yardan et al., 2011). S100B is a calcium-binding protein synthesized by astrocytes

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and other cells that acts as a neurotrophic factor for both neurons and glia at physiological (nanomolar) concentrations (Streitburger et al., 2012). S100B protein levels in the cerebrospinal

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fluid (CSF) and serum are increased after brain damage (Goncalves et al., 2008; Streitburger et al., 2012; Yardan et al., 2011), and this protein can show deleterious effects by increasing the

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expression of proinflammatory cytokines and inducing apoptosis at micromolar concentrations (Yardan et al., 2011). S100B protein was already associated with mercury neurotoxicity in humans (Yilmaz et al., 2014b) and animal models (Farina et al., 2005), because astrocytes are

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known to play an important role in mercury-induced neurotoxicity (Aschner, 1996). This protein was recently proposed as a strong candidate biomarker for mercury-related neurotoxicity in humans (Yilmaz et al., 2014a). Accurate measurement of this protein is especially challenging in isolated/remote populations due to the difficulty of adequate sample conservation and storage of blood and serum. Immediate freezing and low temperature storage is desirable, if not essential, for a reliable protein analysis. 4

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Isolated/remote populations in developing countries such as the riverine populations of the Amazon usually have no electrical power and are distant from main centers (Arrifano et al., 2018b,c; Crespo-Lopez et al., 2011). Mercury is present at significant levels in the Amazonian environment due to both natural (i.e., soil) and anthropogenic sources (i.e. gold mining) (Berzas Nevado et al., 2010). Riverine populations (including those from the Amazon) are chronically

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exposed to MeHg through the consumption of contaminated fish because environmental mercury is bioaccumulated and biomagnified through the food chain (Berzas Nevado et al.,

2010; Rodriguez Martin-Doimeadios et al., 2014). Unfortunately, studies addressing mercury intoxication in addition to exposure in these Amazonian populations are scarce (Berzas Nevado

et al., 2010) and there are no reports testing a peripheral biomarker for the assessment of the neurotoxicity.

Here we quantify circulating mRNA as alternative to protein, as has been done before

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for other diseases (Li et al., 2006). Samples can be stored at room temperature with a RNA

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stabilizing reagent for 24h, which allows enough time to transport the samples to an area with electricity for better conservation. Given that circulating S100B protein was previously

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proposed as a biomarker of mercury-related neurotoxicity, we hypothesized that individuals

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from chronically exposed populations showing mercury levels above the recommended limit would present increased levels of S100B mRNA, likely due to early brain damage. Rigorous

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exclusion criteria were applied to reduce the possibility of possible confounding factors after

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mercury analysis.

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2. Material and Methods

2.1. Participants

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Participants (n=224) were from riverine populations within the State of Pará, Brazilian Amazon (Figure 1) with confirmed exposure to mercury by consumption of contaminated fish, the main dietary protein (Arrifano et al., 2018b,c; Berzas Nevado et al., 2010; Khoury et al.,

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2015; Rodriguez Martin-Doimeadios et al., 2014). All participants understood the purpose of this work and agreed to participate in the

project. This study followed the guidelines established by both the Declaration of Helsinki and the Conselho Nacional de Ética em Pesquisa com Seres Humanos (CONEP, Brazil; CAAE nº 43927115.4.0000.0018) and involved adult (≥18 years) consumers of large quantities of fish (7 or more meals per week). Individuals living in the area for less than two years were excluded.

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Figure 1. Map of Brazil (obtained from the Instituto Brasileiro de Geografia e Estatística, Brazil) with the locations (-4.287121, -55.984106 and -3.800897, -49.811848) of riverine populations of the State of Pará participating of this study. Photographs illustrates some conditions of the riverine population and the sample collection (white arrow shows Styrofoam box for samples storing).

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Additional exclusion criteria were applied for the analysis of S100B mRNA, including the presence of pre-existent chronic diseases, recent head/body traumas, drug dependency (including tobacco and alcohol), chronic pharmaceutical treatments, and positive results for creactive protein in blood. The 24 included individuals were then classified into groups defined

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as high or low levels of mercury exposure (see Results).

2.2. Anthropometric data and self-reported symptoms

The weight and height of each participant was measured and body mass index (BMI)

was calculated. Age and self-reported symptoms were registered using a simplified

questionnaire based on Wojcik et al. (2006) and the International Academy of Oral Medicine

and Toxicology (IAOMT) test for symptoms and signs of mercury intoxication (http://old.iaomt.org/wp-content/uploads/IAOMT-symptom-survey-1-column.pdf) (Wojcik et

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exclusion criteria was registered for each participant.

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al., 2006). A total of 217 participants agreed complete the questionnaire and the presence of

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2.3. Sample collection

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Approximately 0.1 g of hair from the occipital region and 2 ml of venous blood were collected. Blood samples were divided equally between an EDTA vacutainer tube with

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RNAlater® and a BD Vacutainer SST II Advance® tube. Serum was separated by

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centrifugation in the latter sample.

2.4. Analysis of mercury species

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Methylmercury (MeHg) and inorganic mercury (IHg) in hair samples were analyzed as previously described (Arrifano et al., 2018b,c), and the results were expressed as

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methylmercury (µg/g) and total mercury (MeHg+IHg, µg/g). Human hair ERM-DB001 (Sigma-Aldrich, Brazil) was used as a certified reference material and no statistically significant differences were found between the certified and the measured values (95% confidence level)

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(Arrifano et al., 2018b,c).

2.5. Blood analysis Serum was used to detect c-reactive protein using commercial kits (Labtest, Brazil). Samples of blood with RNAlater were used to analyze S100B protein expression by reversetranscriptase real-time PCR. Total RNA was extracted in these samples using a RiboPure™ – Blood Kit (Ambion, Brazil) according to manufacturer instructions, and treated with RNase7

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free DNase I. Total RNA concentration was determined by fluorimetry using the Qubit™ RNA BR Assay kit (Invitrogen, Brazil) and the cDNA was immediately synthesized using a High Capacity cDNA Reverse Transcription kit (Applied Biosystems, Brazil). RT-qPCR reactions were performed using a StepOne Plus equipment (Applied Biosystems, Brazil), and the TaqMan® Gene Expression Assay (TaqMan® MGB probes,

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FAM™ dye-labeled) (Applied Biosystems, Brazil). The probes were G3PDH (Hs 99999905_m1, endogenous control) and S100B (Hs 00389217_m1) and all reactions were

performed in triplicate with a final volume of 10 µL, including 5 µL of TaqMan® Universal PCR Master Mix, 0.5 µL of TaqMan® Gene Expression Assay, 3.5 µL of sterile Milli-Q water, and 1 µL of cDNA. Relative quantification was calculated by the ∆∆Cq method and expressed

as fold-change (Livak and Schmittgen, 2001). G3PDH expression levels and the low mercury exposure groups were used for normalization as the endogenous control and the calibrator,

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respectively.

2.6. Statistical analysis

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The Gaussian distribution of the data was tested by the D'Agostino-Pearson normality

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test. Data were then analyzed with Student or Mann–Whitney tests to compare groups, as appropriate. A cumulative odds ordinal logistic regression with proportional odds was run to

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determine the effect of total mercury levels, age, sex and BMI, on the presence of self-reported symptoms (divided into three categories: up to 3 symptoms, 4-6 symptoms and more than 6

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symptoms). Considering that many of the symptoms may be relatively frequent (for example, insomnia), we assumed that the presence of more than 3 symptoms would be more characteristic

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of the mercury intoxication and the presence of more than 6 would indicate a serious clinical picture, compatible with mercury neurotoxicity. The Spearman test was used to study possible

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correlations between S100B mRNA and mercury levels. Also, Pearson's partial correlation was run to assess the relationship between transformed data of mercury and S100B mRNA levels,

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after controlled for age and BMI. The p-value was set at ≤ 0.05 for all analyses.

3. Results A total of 224 individuals were included in this study (Table 1). The median level of total mercury in hair was 4.19 µg/g (ranging 0.18 to 75.8) and the proportion of MeHg ranged

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from 71-97%, with a mean value of approximately 90%, which may point to exposure via the food chain. Although the median level of total mercury in hair was below the limit of 10 µg/g (Grandjean et al., 1997; Harada et al., 1999; NRC, 2000), we detected a high prevalence of

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participants showing mercury levels above this value (19.6%, 95% CI: 14.7 - 25.5; Figure 2). Table 1. Anthropometric characteristics and mercury levels of the participants. Non-parametric data are presented as median and interquartile range (IQR).

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224

Min – Max Median (IQR)

63.0 (55.5 - 74.3)

Min – Max

39.8 - 106.0

Median (IQR) 156.0 (151.0 - 163.0) Min – Max Median (IQR)

BMI

18-74

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Height (cm)

42 (30 - 52)

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Weight (Kg)

Median (IQR)

136.0 - 185.0

25.2 (23.3 - 29.2)

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Age (years)

MeHg (%)

Min – Max

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MeHg (µg/g)

Median (IQR)

Median (IQR)

4.2 (1.7 - 9.1) 0.2 - 75.8

3.7 (1.4 - 7.9)

Min – Max

0.1 - 69.2

Min – Max

71-97

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Total Hg (µg/g)

17.0 - 43.7

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Min – Max

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Note: BMI, body mass index; MeHg, methylmercury

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Figure 2. Prevalence of participants with levels of total mercury (THg) content in hair above and below the recommended limit of 10 µg/g (Grandjean et al., 1997; Harada et al., 1999; NRC, 2000). n=224.

Participants were distributed in two groups with high (≥10µg/g) or low (<10µg/g) levels

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of mercury (Table 2) according to the total mercury content in hair, and the prevalence of selfreported symptoms of mercury intoxication was evaluated (Table 3). There were no significant

differences in age, weight, height, or BMI between participants with high and low mercury levels (Table 2), and no differences were also detected between the two groups for most symptoms. Notably, symptoms defined as lack of attention, fine tremors, and fatigue/tiredness were more prevalent in low mercury individuals (Table 3).

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Table 2. Anthropometric characteristics and exposure levels in participants with total mercury content in hair ≥10

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µg/g (High Hg) and <10 µg/g (Low Hg). Non-parametric data are presented as median and interquartile range

Low Hg

43

174

45 (34 - 51)

41 (30 - 52)

19-70

18-74

61.5 (51.0 – 82.9)

63.0 (56.1 - 73.5)

30.2 - 103.0

39.8 - 106.0

156.0 (148.0 -

156.0 (151.0 -

171.0)

162.0)

141.0 - 181.0

136.0 - 185.0

24.7 (23.5 - 29.6)

25.7 (23.0 – 28.9)

17.9 – 36.6

17.0 - 43.7

ns

15.9 (12.1 – 22.7)

2.9 (1.5 – 5.8)

p < 0.0001

10.2 - 75.8

0.2 – 9.9

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(IQR)

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Median Age (years)

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Min – Max Median (IQR)

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Weight (Kg)

Min – Max

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Median

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Height (cm)

BMI

(IQR) Min – Max Median (IQR) Min – Max

Total Hg (µg/g)

Median (IQR) Min – Max

Mann Whitney

High Hg

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(IQR).

test

ns

ns

ns

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Note: ns, non-significant.

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Table 3. Prevalence of self-reported symptoms of mercury intoxication in participants with total mercury content

High Hg

Low Hg

Fisher's

% (n)

% (n)

exact test

Total

100 (43)

100 (174)

Irritability

86.0 (37)

89.7 (156)

Insomnia

41.9 (18)

47.7 (83)

ns

Lack of attention

9.3 (4)

29.3 (51)

p <0.01

Depression

14.0 (6)

17.2 (30)

ns

Loss of memory

37.2 (16)

47.1 (82)

ns

Dizziness

48.8 (21)

63.8 (111)

ns

Fine tremors (hands/feet)

27.9 (12)

34.5 (60)

ns

Fine tremors (lips/eyelids/tongue)

23.2 (10)

44.9 (78)

p <0.01

Paresthesia

39.5 (17)

45.4 (79)

Paralysis

0 (0)

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ns

7.5 (13)

ns

Ringing or noises in ears

27.9 (12)

34.5 (60)

ns

Blurred vision

44.2 (19)

41.4 (72)

ns

Reduced peripheral vision

9.3 (4)

16.7 (29)

ns

Fatigue/tiredness

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in hair ≥10 µg/g (High Hg) and <10 µg/g (Low Hg), n=217.

34.9 (15)

55.2 (96)

p <0.05

Muscle weakness

34.9 (15)

44.8 (78)

ns

41.9 (18)

54.6 (95)

ns

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ns

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Hair loss

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Symptoms

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Note: ns, non-significant.

The effects of total mercury levels, age, sex and BMI, on the presence of self-reported symptoms were assessed by a cumulative odds ordinal logistic regression with proportional

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odds. The assumption of proportional odds was met, as observed by a full likelihood ratio test comparing the fit of the proportional odds model to a model with varying location parameters,

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χ2(4) = 5.487, p = 0.241. The deviance goodness-of-fit test indicated that the model was a good fit to the observed data, χ2(415) = 414.936, p = 0.478, but most cells were sparse with zero frequencies in 66.7% of cells. However, the final model statistically significantly predicted the dependent variable (self-reported symptoms) over and above the intercept-only model, χ2(4) = 24.325, p < 0.001. The advanced age had a statistically significant effect on the prediction of whether participant reported more symptoms, with an odds ratio of 1.032 (95% CI, 1.012 to 1.052), Wald χ2(1) = 9.677, p < 0.002. The odds ratio of reporting more symptoms for women 12

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versus men was 2.364 (95% CI, 1.325 to 4.217), a statistically significant effect, χ2(1) = 8.481, p = 0.004. Neither the mercury levels nor the BMI presented significant associations with symptoms. Rigorous exclusion criteria were applied to analyze S100B mRNA levels (which are not always present in studies of human exposure), in order to reduce the possibility of most

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confounding factors and the potential for mercury levels to be significantly influenced by altered hepatic and/or renal function. The exclusion criteria included older age (>60 years), smoking, alcohol drinking (more than 200 mL/day), chronic diseases (such as cancer,

cardiovascular disease, hypertension, diabetes or inflammatory diseases, among others), drug dependency, recent pharmaceutical treatment, and head/body trauma. The inflammation status

of all participants (224) was verified by assaying c-reactive protein and a positive result for this

test was used as an additional exclusion criterion as an additional precaution. Twenty-four

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individuals were then classified into high and low mercury content groups (Table 4 and Figure

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3). Because age or body mass index (BMI) can interfere on S100B levels, we analyzed those factors in the two groups and found no statistically significant difference (Table 4). No self-

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reported symptom showed higher prevalence in the high mercury content group, and in fact,

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association with mercury levels.

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some symptoms were more prevalent in low mercury content group, pointing to the absence of

Table 4. Characteristics of the selected participants for S100B analysis (anthropometric data, mercury content in

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hair and inflammation status). After applying rigorous inclusion/exclusion criteria, selected participants were grouped into ≥10 µg/g of total mercury content (High Hg) and <10 µg/g (Low Hg). Parametric data are presented

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as mean ± SD and non-parametric data are presented as median and interquartile range (IQR). Low Hg

High Hg

Difference between groups

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Student t test

Mann-Whitney test

Mean ± SD

47 ± 10

44 ± 11

ns

-

BMI

Mean ± SD

25.5 ± 3.5

26.6 ± 3.1

ns

-

Median (IQR)

0.9 (0.6 – 2.4)

12.0 (10.3 -15.0)

Min - Max

0.3 – 6.8

10.1 – 24.6

-

p < 0.0001

Negative

Negative

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Age (years)

Total Hg (µg/g) in hair

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CRP

Note: BMI, body mass index; MeHg, methylmercury; CRP, c-reactive protein; ns, non-significant.

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Figure 3. Mercury content in hair of participants with ≥10 µg/g (High Hg), before (a) and after (b) applying

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exclusion criteria (n=43 and 10, respectively), and <10 µg/g (Low Hg, n=14). Inset includes exclusion criteria

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detected in High Hga group (pre-existent diseases were cancer, diabetes, cardiovascular diseases (mainly hypertension), alcoholism and arthritis). Data are presented as median, interquartile ranges and minimum and

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maximum values. Kruskal-Wallis test; *p < 0.05 and ***p < 0.0001 vs Low Hg group.

The mRNA levels of S100B protein in individuals with ≥10 µg/g of mercury were more

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than twice the levels of participants with <10 µg/g of mercury (Figure 4).

Figure 4. Fold-change difference of S100B mRNA levels in blood of participants with high (≥10 µg/g) or low (<10 µg/g) mercury content in hair. Data are presented as geometric mean and SD (n=24). ****P<0.0001

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Interestingly, the total mercury content in hair of all selected participants was significantly correlated with S100B mRNA levels in blood according to the Spearman test (Table 5). Additionally, multiple variables analysis (Pearson´s partial correlation) was assessed to analyze the association between the transformed data of mercury and S100B mRNA, after

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controlled for age and BMI. Linear relationships between parameters were observed in the scatterplots and partial regression plots. There was univariate normality, as demonstrated by Shapiro-Wilk's test (p >0.05), and no univariate or multivariate outliers, as assessed by boxplots

and Mahalanobis Distance, respectively. A bivariate Pearson's correlation established that there

was a moderate, positive linear relationship between transformed data of mercury and S100B mRNA (r = 0.425, p = 0.038). No significant relationship was detected between any of the latter variables and age or BMI (Table 5). Moreover, Pearson's partial correlation showed that the

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strength of the linear relationship between transformed data of mercury and S100B mRNA was

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practically the same when age and BMI were controlled for, (rpartial = 0.416), and it was still

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statistically significant (p = 0.05).

Variables

r

p

THg vs S100B mRNA

0.431

<0.05

log THg vs log S100B mRNA age vs log THg BMI vs log THg age vs log S100B mRNA BMI vs log S100B mRNA log THg vs log S100B mRNA (after controlled for both age and BMI)

0.425 - 0.131 0.066 - 0.085 0.279

0.038 0.543 0.759 0.695 0.186

0.416

0.050

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Test Spearman correlation

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Bivariate Pearson´s correlations

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Pearson´s partial correlation

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Table 5. Statistical results of correlations between variables (n=24).

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4. Discussion This work demonstrated for the first time that the mRNA level of S100B protein in blood is associated with total mercury levels in Amazonian riverine populations. These populations are chronically exposed to a contaminated environment (mainly due to anthropogenic activities, such as artisanal small-scale gold mining or large-scale projects

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such as dams) (Arrifano et al., 2018b; Khoury et al., 2015). These sources of mercury are not likely disappear from the Amazon, as is the case for other regions chronically exposed to this

hazardous metal around the world (Debes et al., 2016; Gibb and O'Leary, 2014; Oulhote et al., 2017).

Although we found that the median level of total mercury in hair was below the

recommended limit of 10 µg/g previously established (Grandjean et al., 1997; Harada et al., 1999; NRC, 2000) (Table 1), approximately 20% of participants presented levels above this

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limit (Figure 2). There has been growing controversy about whether these limits should be

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considered truly safe over the last decade, given that chronic exposure to low doses of this metal is enough to cause alterations including changes in psychomotor development, cardiovascular

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effects, or genotoxicity (Crespo-Lopez et al., 2016; Crespo-Lopez et al., 2007; Crespo-Lopez

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et al., 2011; Crespo-Lopez et al., 2009; Debes et al., 2016; Oulhote et al., 2017). Interestingly, a limit of 2 µg/g has been proposed as the typical level for total mercury content in human

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populations (WHO, 2008). The prevalence of mercury levels over 2 µg/g in this study was 70%

the studied area.

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(95% CI; 64.1; 76.4), highlighting the environmental concerns regarding mercury exposure in

These data contribute to the literature demonstrating the continuing exposure in

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Amazonian populations (Arrifano et al., 2018b,c; Berzas Nevado et al., 2010; Costa et al., 2017; Crespo-Lopez et al., 2011; Khoury et al., 2015) and indicate that the biomonitoring of human

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populations is essential for the development of prevention strategies and adequate management of these populations. This was recognized by the Minamata Convention on Mercury (www.mercuryconvention.org), an initiative to reduce and combat human and environmental

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mercury exposure supported by 129 countries throughout the world. Unfortunately, few studies simultaneously analyze human intoxication and exposure parameters in Amazonian populations and the majority of those studies only record exposure (i.e. they only quantify mercury levels in tissues and not the deleterious consequences of prolonged exposure) (reviewed by Berzas Nevado et al., 2010).

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The quantification of mercury refers to a relatively recent exposure (mercury has an approximate half-life of 70 days in the human body), without providing information about past exposures or their deleterious consequences. Accurate diagnosis of mercury intoxication is not always possible because chronic exposure to mercury has been associated with more than 250 symptoms (Rice et al., 2014).

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Here we evaluated the main effects associated with mercury intoxication (paresthesia, tremors, visual problems, loss of memory, etc.) using a simplified questionnaire already tested in

exposed populations (Wojcik et al., 2006) and based on information verified by the IAOMT.

The amount of mercury is known to be poorly correlated with the neurological symptoms of mercury intoxication in humans (Yilmaz et al., 2014b), and conflicting results have been found

in Amazonian populations (Costa et al., 2017; Dolbec et al., 2000; Fillion et al., 2011; Khoury et al., 2015; Rodrigues et al., 2007). Therefore, it is not surprising that we found no difference

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between prevalence in individuals with low and high levels of mercury (Table 3). Present

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exposure is not necessarily associated with the past exposure (the latter likely being responsible for the symptoms in cases of chronic exposure), and symptoms can develop over a long time

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period (as long as that occurring after prenatal exposure, for example) (Debes et al., 2016;

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Oulhote et al., 2017).

Neurofunctional tests have been used to detect MeHg-neurotoxicity in the Amazon

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region (Dolbec et al., 2000; Fillion et al., 2011; Grandjean et al., 1999; Khoury et al., 2015; Rodrigues et al., 2007), but they can be influenced by a high number of confounding factors

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(such education level, precarious health care, or endemic diseases, among others) and the high variability in the responses inherent to these tests. These confounding factors, frequently

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present in isolated/remote populations, could also influence the prevalence of clinical symptoms. For example, previous reports in Amazonian populations revealed that individuals

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with low levels of mercury in hair (0.66 ± 0.54 µg/g) showed a higher prevalence of motor and emotional symptoms than those with high levels (9.15 ± 8.17 µg/g), even when the diagnosis was carried out by a neurologist (Costa et al., 2017). We also find a higher prevalence of lack

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of attention, fine tremors, and fatigue in participants with low mercury levels (Table 2). Moreover, when multi-variables analysis such as ordinal logistic regression was assessed with our data to detect the influence of total mercury levels, age, sex and BMI on the self-reported symptoms, only age and sex had a significant effect, with older participants and women presenting odds ratios of 1.032 and 2.364, respectively, to report a higher number of symptoms. These results contribute to the idea that such clinical symptoms may not necessarily be correlated with exposure level from a quantitative point of view. Neurological symptoms 17

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(including self-reported symptoms) may be influenced by subjective factors that are not included in the quantification of biochemical markers of brain damage. So, the use of a combination of exposure markers (mercury content) and biochemical markers of brain damage (intoxication) may provide a more reliable diagnosis. Considering that the CNS is the main target organ for MeHg, the use of reliable

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biochemical indicators of CNS damage such as S100B protein has been proposed (Farina et al., 2005; Vicente et al., 2004; Yilmaz et al., 2014b), which may be more sensitive than behavioral testing.

S100B protein is primarily produced by astrocytes. Although there are other

extracerebral sources of S100B such as adipocytes and lymphocytes, they do not significantly alter serum S100B levels (Pham et al., 2010). Astrocytes protect neurons by accumulating

hazardous metals including lead and mercury (Berzas Nevado et al., 2009; Tiffany-Castiglion

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and Qian, 2001), but this intracellular accumulation eventually leads to astrogliosis and cellular

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death releasing high quantities of S100B protein to the cerebrospinal fluid (CSF) and blood (Farina et al., 2005; Yardan et al., 2011). Therefore, the increased levels of S100B protein in

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CSF or blood may be associated with the loss of neuronal protection by astrocytes and thus

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represent an early biomarker of brain damage. S100B protein levels in both CSF and blood correlate with the brain damage severity and outcome in neurological disorders such as trauma,

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stroke, or neurodegenerative diseases (Rezaei et al., 2017; Rodriguez-Rodriguez et al., 2016; Yilmaz et al., 2017), and this biomarker may be a better predictor than other blood biomarkers

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of brain damage such as GFAP (Bohmer et al., 2011; Mondello et al., 2017; RodriguezRodriguez et al., 2016).

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Mercury exposure increases S100B protein in the CSF (Farina et al., 2005; Vicente et al., 2004). Increased levels of S100B protein can be also detected in the serum of individuals

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intoxicated with mercury, revealing deleterious neurodegenerative effects, even before the onset of clinical symptoms (Yilmaz et al., 2014b). However, the detection of blood proteins is very limited in a fieldwork context, particularly in isolated and/or remote populations, due to

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practical difficulties such as sample storage (the lack of electricity prevents the adequate conservation of proteins) (Figure 1). A possible alternative is to use the mRNA along with a RNA stabilizing reagent (eliminating the need for immediate freezing of the sample for preservation of the mRNA content and making it simple and easy to detect the desired target. This is the first time that the S100B mRNA levels were detected in blood of humans exposed to mercury. Blood mRNA has been shown to be a novel resource for disease identification (Li et al., 2006). We applied rigorous exclusion criteria for the detection of S100B 18

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mRNA to avoid any interference with other sources of S100B (Figure 3). No difference was detected in age or BMI between the ≥10 µg/g of mercury group and the <10 µg/g group (Table 4), allowing us to eliminate age or body fat as confounding factors influencing S100B mRNA levels. Additionally, the negative results for c-reactive protein in the selected participants confirm the absence of a possible bias related to general inflammatory status. The use of these

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rigorous exclusion criteria indicates that the difference between the two groups shown in Figure 4 is likely due to the different levels of mercury.

A significant increase (more than 2-fold change) in S100B expression in blood was found for individuals with total mercury content above the recommended limit (Figure 4), and

the intragroup dispersions were extremely low in both the high and low exposure groups (Figure 4), highlighting the usefulness of this approach to detect the effects of neurotoxicants.

Interestingly, a significant correlation was also detected between S100B mRNA in blood and

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mercury levels in hair (Table 5). This moderate, positive relationship was still significant after

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controlled for confounding factors such as age and BMI (Table 5). These data suggest that the increase of S100B mRNA in blood may be associated with mercury exposure in chronically

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exposed populations, as previously demonstrated for S100B protein. However, finding an

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epidemiological association is only the first step in demonstrating a cause-effect relationship and future studies with in vivo animal models in a controlled environment are needed to

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definitively establish the direct correlation between mercury-induced neurotoxicity and S100B mRNA in blood.

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The possibility of using blood S100B mRNA as a biomarker is an important step for the advancement of the detection of mercury neurotoxicology in isolated/remote populations.

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Recent works with human subjects and quantitative real time PCR used sample sizes similar to those used here (Zhang et al., 2018; Stefanutti et al., 2017; Zhang et al., 2017; Bondar et al.,

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2017; Hidayat et al., 2017; Ding et al., 2016), supporting the validity of this approach. Future large epidemiological studies will analyze the possible influence of confounding factors and the reference values for this biomarker more thoroughly.

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In addition to its importance as a potential biomarker, the increased expression of

S100B protein is highly relevant information towards understanding the molecular and cellular pathways involved in methylmercury-induced neurotoxicity. One of these pathways is neuroinflammation, where S100 protein is known to play a major role as a signaling molecule of damage-associated molecular patterns that interacts with specific receptors and triggers events such as microglial activation and neuronal apoptosis (Braun et al., 2017).

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The differential diagnosis of mercury intoxication is currently based on patient history, physical examination for the evaluation of classical signs, and symptoms of poisoning and the assessment of mercury body burden (by blood, hair or urine analysis) (Berzas Nevado et al., 2010; Rice et al., 2014). The identification and adequate management of intoxicated patients in remote locations such as the Amazon is sometimes very difficult due to the limited access to

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specialized neurologists. Therefore, the combination of exposure and intoxication biomarkers could provide additional support for the screening and early identification of high-risk individuals and subsequent referral to specialized centers.

This work demonstrates that blood S100B mRNA levels are associated with mercury exposure in humans. These findings could assist in the development of prevention programs

and adequate health care strategies to address human mercury intoxication in isolated and/or

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remote populations worldwide.

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Funding

This work was supported by Conselho Nacional de Ciência e Tecnologia em Pesquisa (CNPq,

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Brazil; grant numbers 467143/2014-5 and 447568/2014-0), Pró-Reitoria de Pesquisa da

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Universidade Federal do Pará (PROPESP-UFPA, Brazil), Ministerio de Economía y Competitividad (MINECO, Spain; grant numbers CTQ-2013-48411-P and CTQ2016-78793-

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P). J.L.M. do Nascimento, M.E. Crespo-López and R. Paraense thank CNPq for their research fellowships. Also, M.A. Oliveira and G.P.F. Arrifano thank Coordenação de Aperfeiçoamento

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de Pessoal de Nivel Superior (CAPES, Brazil), for their fellowships.

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Conflict of interests

Authors declare that no conflict of interests exists. The funders had no role in study design, data

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collection and analysis, decision to publish, or preparation of the manuscript.

Acknowledgments

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We are truly grateful to the health staff, community leaders and all participants of the communities for their warm, welcome, and essential support of this study. Contribution of the reviewers is also acknowledged for helping us to improve our work.

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