Can Mirna Indicate Risk of Illness After Continuous Exposure to M

International Journal of Molecular Sciences

Article Can miRNA Indicate Risk of Illness after Continuous Exposure to M. tuberculosis?

Cleonardo Augusto Silva 1,2,†, Arthur Ribeiro-dos-Santos 1,†, Wanderson Gonçalves Gonçalves 1,2 , Pablo Pinto 1,2, Rafael Pompeu Pantoja 1, Tatiana Vinasco-Sandoval 3, André Maurício Ribeiro-dos-Santos 1, Mara Helena Hutz 4 , Amanda Ferreira Vidal 1 , Gilderlanio Santana Araújo 1,‡ , Ândrea Ribeiro-dos-Santos 1,2,*,‡ and Sidney Santos 1,2,‡

1 PPG em Genética e Biologia Molecular, Laboratório de Genética Humana e Médica, Universidade Federal do Pará, Belém 66075-110, Brazil; [email protected] (C.A.S.); [email protected] (A.R.-d.-S.); [email protected] (W.G.G.); [email protected] (P.P.); [email protected] (R.P.P.); [email protected] (A.M.R.-d.-S.); [email protected] (A.F.V.); [email protected] (G.S.A.); [email protected] (S.S.) 2 PPG em Oncologia e Ciências Médicas, Núcleo de Pesquisas em Oncologia, Universidade Federal do Pará, Belém 66073-005, Brazil 3 Laboratoire de Génomique et Radiobiologie de la Kératinopoïèse, Institut de Biologie François Jacob, CEA/DRF/IRCM, 91000 Evry, France; [email protected] 4 Instituto de Biociências, Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre 91501-970, Brazil; [email protected] * Correspondence: [email protected] Citation: Silva, C.A.; † These authors contributed equally as First Authors. Ribeiro-dos-Santos, A.; ‡ These authors contributed equally as Senior Authors. Gonçalves, W.G.; Pinto, P.; Pantoja, R.P.; Vinasco-Sandoval, T.; Simple Summary: Tuberculosis is the leading cause of mortality from a single infectious agent and is Ribeiro-dos-Santos, A.M.; Hutz, M.H.; among the top 10 causes of death worldwide. Despite that, few studies focus on regulatory elements Vidal, A.F.; Araújo, G.S.; et al. Can such as small non-coding RNAs in tuberculosis. This pilot work applied Next Generation Sequencing miRNA Indicate Risk of Illness after techniques to evaluate the global miRNA expression profile of patients with active tuberculosis; Continuous Exposure to M. their respective healthy physicians, who are at constant risk of infection; and a second group of tuberculosis?. Int. J. Mol. Sci. 2021, 22, healthy controls. In addition, we observed miRNA–gene interactions affected by exposure to the 3674. https://doi.org/10.3390/ bacteria. Our findings indicate a list of miRNAs that could be used as potential biomarkers to ijms22073674 improve treatment strategies at early stages. We also observed modified pathways related to the immune response due to differential miRNA expression profiles. Finally, we alert and encourage the Academic Editor: Nicoletta Potenza development of new strategies to avoid long-term exposure of healthy physicians, considering how

Received: 9 January 2021 closely related their miRNA profile was to tuberculosis patients using current safety protocols. Accepted: 17 February 2021 Published: 1 April 2021 Abstract: The role of regulatory elements such as small ncRNAs and their mechanisms are poorly understood in infectious diseases. Tuberculosis is one of the oldest infectious diseases of humans and Publisher’s Note: MDPI stays neutral it is still a challenge to prevent and treat. Control of the infection, as well as its diagnosis, are still with regard to jurisdictional claims in complex and current treatments used are linked to several side effects. This study aimed to identify published maps and institutional affil- possible biomarkers for tuberculosis by applying NGS techniques to obtain global miRNA expression iations. profiles from 22 blood samples of infected patients with tuberculosis (n = 9), their respective healthy physicians (n = 6) and external healthy individuals as controls (n = 7). Samples were run through a pipeline consisting of differential expression, target genes, gene set enrichment and miRNA–gene network analyses. We observed 153 altered miRNAs, among which only three DEmiRNAs (hsa- Copyright: c 2021 by the authors. let-7g-5p, hsa-miR-486-3p and hsa-miR-4732-5p) were found between the investigated patients and Licensee MDPI, Basel, Switzerland. their respective physicians. These DEmiRNAs are suggested to play an important role in granuloma This article is an open access article regulation and their immune physiopathology. Our results indicate that miRNAs may be involved distributed under the terms and in immune modulation by regulating gene expression in cells of the immune system. Our findings conditions of the Creative Commons encourage the application of miRNAs as potential biomarkers for tuberculosis. Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Int. J. Mol. Sci. 2021, 22, 3674. https://doi.org/10.3390/ijms22073674 https://www.mdpi.com/journal/ijms Int. J. Mol. Sci. 2021, 22, 3674 2 of 14

Keywords: miRNA; tuberculosis; differential expression analysis

1. Introduction Tuberculosis, a disease caused by Mycobacterium tuberculosis infection, is the leading cause of mortality from a single infectious agent and is among the top 10 causes of death worldwide, accounting for about 1.3 million deaths in 2017 and 10.4 million new cases of active tuberculosis in the world for that same year [1,2]. A third of the world population carries latent tuberculosis infections and, consequently, the potential of developing active tuberculosis [3]. HIV co-infection and age at first exposure are factors that contribute to the progression of the active form of the disease but explain only a fraction of the rate of activation of latent tuberculosis [4]. Disease progression may occur if host cell-mediated immunity fails, as suggested by the fact that an increased risk of latent tuberculosis reactiva- tion is posed by anti-tumor necrosis factor therapy [5], which causes immune suppression, and the considerably high active tuberculosis prevalence among HIV-infected patients with T-cell immunity deficiency [5]. Identifying active tuberculosis high-risk groups would allow strategies for a more effective prophylactic treatment and would prevent the evolution of the disease to highly infectious symptomatic stages, avoiding transmission [6]. Gene regulation is an essential mechanism for both maintenance and normal function- ality of the cell. Changes in gene expression may impact cellular state and even lead to complex and infectious diseases [7]. Non-coding RNAs (ncRNAs) are some of the main molecular elements that act in gene regulation. Among ncRNAs, miRNAs (small fragments of 18–22 nucleotides) are able to regulate the expression of genes at a post-transcriptional level by binding to the 30-UTR of messenger RNAs (mRNAs) and inhibiting their translation or inducing their degradation [7,8]. miRNAs may regulate host response to tuberculosis, being potentially critical for the establishment of the infection. However, the dynamics of the miRNA expression and its implications for immune response in the lungs remains unknown [9]. Thus, considering their impact in cells, miRNAs have the potential to be used as biomarkers for diagnosis, response to treatment and therapeutic interventions [7]. Some studies also provide insight into the role of differentially expressed miRNAs (DEmiRNAs) in tuberculosis, such as discrimination of active tuberculosis and healthy controls [6,10,11] and the regulation of pathogen–host interactions [12–14]. However, there are few studies on the expression profile of miRNAs in tuberculosis using Next Generation Sequencing (NGS) techniques, which offer higher accuracy and sensitivity regarding miRNA sequences, allowing the identification of new and known miRNAs [15]. Risk prediction based on molecular biomarkers of individuals who will develop active tuberculosis within a population exposed to Mycobacterium tuberculosis, is extremely important [13]. Therefore, the aim of the present study was to apply NGS technology to evaluate the global miRNA expression profile (miRnome) of patients with active tuberculosis and to provide a brief overview of altered pathways regulated by these miRNAs using functional enrichment and miRNA–gene network analysis. Such strategies were implemented to identify regulatory elements which may, with further research, be used as biomarkers for early diagnosis of active tuberculosis.

2. Results and Discussion In tuberculosis pathogenesis, host cellular immune response determines whether an infection becomes latent tuberculosis infection or progresses to active infectious or extrapulmonary tuberculosis [16]. It has been established that both adaptive and innate immune response is signiﬁcantly regulated by miRNAs. miRNAs control the differentiation of B cells, antibody generation, T cell development [17] and function and innate immune cell activation [18]. Under mycobacterial infection, components of inﬂammatory and immune pathways are regulated by miRNAs [19,20]. Int. J. Mol. Sci. 2021, 22, 3674 3 of 14

The role of miRNAs in tuberculosis has been reviewed recently [21]. Pathways such as host inﬂammatory response and immune response signaling pathways, as well as a list of regulatory processes such as apoptosis, cytokine production, nitric oxide suppression, T cell proliferation, inhibition of antimicrobial peptides and autophagy, have been associated with miRNA regulation. It has been observed that lipid metabolism, a regulatory function associated with tuberculosis, can also be regulated by miRNAs [22].

2.1. RNA-Seq Data Overview To identify potential biomarkers, miRNA sequence expression of 22 whole blood samples were analyzed. Samples were divided into three groups: External Control (n = 7); Hospital Control (n = 6), which consist of physicians responsible for tuberculosis treatment; and Tuberculosis patients (n = 9). Unfortunately, one sample belonging to the Tuberculosis patients group did not pass the quality control pipeline and was removed from the analysis. RNA-seq data show an average of 3224 mapped reads per sample—ranging from 0 to 925,920 raw read counts. We identified, among 2576 known miRNAs, a total of 210 expressed miRNAs (i.e., miRNAs with total read count ≥ 10 in at least one sample). Of these, hsa-miR-486-5p, hsa-miR-92a-3p and hsa-miR-16-5p were the most abundant in all samples, representing about 82% of the total read count. These miRNAs were later removed from the analysis to avoid statistical bias. Log-normalized mean frequencies for each miRNA and sample were used to generate histograms and boxplots of miRNA expression profiles. We found a clear distinction between the miRNA expression distribution in the External Control group and both Hospital Control and Tuberculosis groups, which showed a higher number of miRNAs with low expression levels (Figure1A,B). Contrary to our initial belief, Hospital Controls had more miRNAs with low expression levels than Tuberculosis patients. The External Control group, as indicated by Figure1C, had a higher expression profile compared to the other groups and presented few miRNAs with low expression values. High peaks of miRNAs expression are represented for each group and sample in Figure1D,E, respectively. We submitted miRNA expression data to statistical analyses to identify differential expression levels between group pairs and further investigate potential biomarkers and genetic mechanisms involved in the process of infection.

2.2. Differential Expression Analysis Differential expression analysis process involves comparing pairs of groups (a case and a control group) to find statistically relevant differences between their miRNA expression profiles. Besides the three possible combinations of our three initial groups, two new comparisons were introduced to the study: (1) a merge of Hospital Controls and External Controls into a new Control group versus Tuberculosis patients (case group); and (2) a merge of Tuberculosis patients and Hospital Controls into a Medical Samples group (case group) versus the External Control group. Among these five comparisons, two presented the most meaningful results and are further discussed: (i) Medical Samples versus External Controls; and (ii) Tuberculosis patients versus Hospital Control. Additionally, these results were used to perform both target genes analyses and gene set enrichment analyses. Int. J. Mol. Sci. 2021, 22, 3674 4 of 14

A Mean of miRNA expression in Tuberculosis B Mean of miRNA expression in Hospital Control C Mean of miRNA expression in External Control 05 070 60 50 40 05 070 60 50 40 05 070 60 50 40 Frequency Frequency Frequency 02 30 20 10 0 02 30 20 10 0 02 30 20 10 0

0 2 4 6 8 10 12 14 0 2 4 6 8 10 12 14 0 2 4 6 8 10 12 14

Mean of Ln (Normalized Expression) Mean of Ln (Normalized Expression) Mean of Ln (Normalized Expression)

D Means of miRNA expression by Group E Means of miRNA expression by Sample 14 14 012 10 8 6 012 10 8 6 Mean of Ln (Normalized Expression) Mean of Ln (Normalized Mean of Ln (Normalized Expression) Mean of Ln (Normalized

Tuberculosis Hospital Control 4 2 0 4 2 0 External Control

Tuberculosis Hospital Control External Control 1 35 7 9 1 3 5 1 3 5 7

Figure 1. Frequency distribution of miRNA reads among sample groups: (A) log-normalized miRNA mean frequency distribution in Tuberculosis samples; (B) log-normalized miRNA mean frequency distribution in Hospital Control samples; (C) log-normalized miRNA mean frequency distribution in External Control samples; (D) log-normalized miRNA mean expression by groups; and (E) log-normalized miRNA mean expression by sample. One sample from the Tuberculosis patients group was ﬁltered and removed due to low sequence quality.

2.2.1. Medical Samples vs. External Control This comparison poses an interesting discussion given it includes all sample groups and showed meaningful DEmiRNA results by providing an overview of similarities and distances between samples and sample groups. DEmiRNAs data obtained from edgeR showed differences in miRNA expression levels between the three initial groups (Figure2A). This analysis is based on Kendall’s hier- archical clustering correlation of miRNA expression profiles in all samples and identified a clear distinction between External Control and Medical Samples groups. This contrast is presented in two clusters of DEmiRNAs. In the first cluster, the External Control group presented higher expression levels, while, in the second, External Controls presented lower expression levels. However, this comparison could not distinguish Tuberculosis patients from Hospital Controls. In total, 130 miRNAs were found to be differentially expressed between Medical Sam- ples and External Controls (Figure2B). This was the second highest number of DEmiRNAs found in all five comparisons. While Tuberculosis versus External Controls yielded a higher number of DEmiRNAs (135), 122 of those were shared between both comparisons. Principal Component Analysis (PCA) was performed to visualize how closely related the three initial groups were (Figure2C) regarding their miRNA expression profile. The results show External Controls as a clearly separated cluster, while Tuberculosis patients largely overlapped with Hospital Controls. This visual representation provides insight into the similarities between the Hospital Control and Tuberculosis patients groups in a miRNA expression level. Int. J. Mol. Sci. 2021, 22, 3674 5 of 14

A miRNA Expression B group hsa−miR−3605−3p hsa−miR−99b−5p hsa−miR−98−5p 80 hsa−miR−126−3p miRNAs hsa−miR−146b−5p hsa−miR−532−5p hsa−miR−4746−5p hsa−miR−5010−3p a hsa−miR−130a−3p non−DE hsa−miR−144−5p hsa−miR−27b−3p a hsa−miR−142−5p DE hsa−miR−30e−5p hsa−miR−144−3p hsa−miR−126−5p hsa−miR−30d−3p hsa−miR−21−5p hsa−miR−15a−5p 60 hsa−miR−451a hsa−let−7f−5p hsa−miR−192−5p hsa−miR−186−5p hsa−miR−424−5p hsa−miR−101−3p hsa−miR−26a−5p hsa−miR−26b−5p hsa−miR−22−3p hsa−miR−425−5p hsa−miR−363−3p hsa−miR−143−3p hsa−miR−4435 hsa−miR−17−3p hsa−miR−19b−3p 40 hsa−miR−20b−5p hsa−miR−1307−5p hsa−miR−148a−3p hsa−miR−20a−5p hsa−miR−17−5p hsa−miR−660−5p

1 hsa−miR−374a−5p hsa−miR−19a−3p hsa−miR−10a−5p − log10 p value hsa−miR−374b−5p hsa−miR−301a−3p hsa−miR−3607−5p hsa−miR−181c−5p hsa−miR−142−3p hsa−miR−32−5p 20 hsa−miR−3688−3p hsa−miR−664a−3p hsa−miR−502−3p hsa−miR−335−5p hsa−miR−29b−3p hsa−miR−4677−3p hsa−miR−101−5p hsa−miR−140−5p hsa−miR−22−5p hsa−miR−3912−3p hsa−miR−10b−5p hsa−miR−148b−5p hsa−miR−362−5p hsa−miR−548k hsa−miR−29c−3p 0 Log(Exp) hsa−miR−3613−5p 5 hsa−miR−190a−5p 0 hsa−miR−7−5p hsa−miR−374a−3p −5 hsa−miR−18a−5p −8−4 0 4 −10 hsa−miR−340−5p hsa−miR−194−5p hsa−miR−421 hsa−miR−29a−3p log2 Fold Change hsa−miR−454−3p hsa−miR−30a−5p hsa−miR−27a−3p hsa−miR−30b−5p hsa−miR−223−3p 1.0 hsa−miR−96−5p C hsa−miR−106b−5p hsa−miR−3158−3p hsa−let−7i−5p hsa−miR−181b−5p hsa−miR−15b−5p hsa−miR−652−3p hsa−miR−584−5p hsa−let−7d−5p hsa−miR−1307−3p hsa−miR−185−3p hsa−miR−532−3p hsa−miR−4732−5p 0.5 hsa−miR−4732−3p hsa−miR−423−3p hsa−miR−3615 hsa−miR−106b−3p hsa−miR−92b−3p hsa−miR−181a−5p hsa−miR−744−5p hsa−miR−423−5p hsa−miR−1294 hsa−miR−320a hsa−miR−320b hsa−miR−1180−3p hsa−let−7b−5p 0.0 hsa−miR−424−3p

2 hsa−let−7d−3p hsa−miR−197−3p hsa−miR−4685−3p hsa−miR−7706 hsa−miR−3940−3p hsa−miR−629−5p hsa−miR−28−3p hsa−miR−16−2−3p hsa−miR−2110 hsa−let−7i−3p hsa−miR−125a−5p PC2 (10.43%) hsa−miR−331−3p −0.5 hsa−miR−6511b−3p hsa−miR−1291 hsa−miR−941 hsa−miR−574−3p hsa−miR−328−3p hsa−miR−324−3p group hsa−miR−3200−3p hsa−miR−486−3p hsa−miR−18a−3p hsa−miR−320c hsa−miR−409−3p External_Control hsa−miR−150−5p hsa−miR−550a−5p −1.0 Hospital_Control hsa−miR−6803−3p Tuberculosis Ext_Control_6 Ext_Control_5 Ext_Control_3 Ext_Control_1 Ext_Control_4 Ext_Control_7 Ext_Control_2 Tub_Sample_1 Tub_Sample_3 Tub_Sample_5 Tub_Sample_6 Tub_Sample_9 Tub_Sample_2 Tub_Sample_7 Tub_Sample_8

Med_Control_3 Med_Control_2 Med_Control_1 Med_Control_6 Med_Control_4 Med_Control_5 −0.4−0.2 0.0 0.2 group External_Control PC1 (47.69%) Hospital_Control Tuberculosis

Figure 2. Differential miRNA expression analysis between Medical Samples and External Controls: (A) differences in miRNA expression levels between the three initial groups (EC, orange; HC, pink; TP, green) represented by a heatmap; (B) volcano plot highlighting 130 DEmiRNAs in Medical Samples versus External Controls; and (C) PCA plot showing distances between the three initial groups based on miRNA expression.

2.2.2. Tuberculosis Patients vs. Hospital Controls This comparison pair provided the most notable expression results, given that Hospital Control group members have had continuous contact with Tuberculosis patients and, thus, have had constant exposure to Mycobacterium tuberculosis. We suggest that this durable exposure promotes changes in the organism, including miRNA expression proﬁle. However, it seems there is a biological mechanism keeping members of the Hospital Control group from acquiring active tuberculosis. Results from differential expression analysis could potentially identify which miRNAs are regulating these mechanisms and use them as biomarkers for the disease. The edgeR statistical analyses found only three DEmiRNAs (hsa-miR-4732-5p, hsa- miR-486-3p and hsa-let-7g-5p) (see Figure3B), the ﬁrst with higher expression in Hospital Controls and the last two with higher expression in Tuberculosis patients. This was the lowest number of miRNAs found in any of the performed comparisons, indicating a lack of DEmiRNAs involved in the process of keeping the organism safe. Both heatmap (Figure3A ) and PCA (Figure3C) analyses did not reveal a clear distinction between the two groups. Results from this comparison are alarming considering both the small number of DEmiRNAs and the distances between the two groups being unexpectedly Int. J. Mol. Sci. 2021, 22, 3674 6 of 14

low, which indicated that Hospital Controls had been exposed to a higher risk level than previously expected.

A miRNA Expression B group 4 hsa−let−7g−5p miRNAs non−DE DE 3

1 hsa−miR−486−3p

2 hsa−miR−486−3p hsa−miR−4732−5p − log10 p value

Log(Exp) 4 0 2 0 hsa−let−7g−5p −2 −4 −6 −1 0 1 log2 Fold Change C 1.0 group

2 Hospital_Control Tuberculosis

0.5

0.0 hsa−miR−4732−5p PC2 (12.16%) −0.5

−1.0

−1.0−0.5 0.0 0.5 1.0 Tub_Sample_7 Tub_Sample_5 Tub_Sample_9 Tub_Sample_3 Tub_Sample_1 Tub_Sample_2 Tub_Sample_8 Tub_Sample_6 Med_Control_2 Med_Control_3 Med_Control_4 Med_Control_1 Med_Control_5 Med_Control_6 group Hospital_Control PC1 (38.34%) Tuberculosis

Figure 3. Differential miRNA expression proﬁles between Tuberculosis patients and Hospital Control samples: (A) difference in expression levels of miRNAs between the two groups (HC, orange; TP, green) indicated by a heatmap; (B) volcano plot highlighting three miRNAs with differential expression; and (C) PCA plot highlighting the homogeneity and separation of the groups based on miRNA expression.

2.3. Gene Set Enrichment Analysis To perform this step, DEmiRNAs found in the previous analyses were submitted to both target gene analysis followed by functional enrichment analysis using Reactome online platform. Reactome requires a list of genes as input and generates a table including data from numerous pathways. Data from Medical Samples versus External Controls were run through a new quality control pipeline to boost target genes analysis. Enrichment results from both comparisons indicated that the DEmiRNAs may interact with cell proliferation inhibition and/or inflammatory response processes, which are some of the affected biological processes expected to have a role in tuberculosis disease. Enriched pathways were selected and classified according to regulatory functions involved in tuberculosis pathogenicity that could potentially be regulated by miRNAs. Inflammatory response and apoptosis inhibition were reported by Chai et al. (2019) through targeting of FOXO1 [23]. Pro-inflammatory properties of Interleukin-6 were observed by Scheller et al. (2011) [24]. Bi et al. (2019) showed the suppression of cell Int. J. Mol. Sci. 2021, 22, 3674 7 of 14

proliferation associated to the inhibition of Egr1/TGF-β/Smad pathway by miRNA-181a- 5p [25]. Spizzo et al. (2010) indicated a TP53 dependent proapoptotic regulatory loop [26]. Finally, Sabir et al. (2018) showed regulation of TLR and TNF by miRNAs [21]. Therefore, in accordance with the aforementioned research, we also investigated and identiﬁed enrichment of pathways related to FOXO, IL-6, TGF-β, TP53, TLR and TNF.

2.3.1. Target Genes Analysis in Medical Samples vs. External Controls Only DEmiRNAs with |log2FoldChange| (|FC|) > 2 were kept for this analysis, resulting in a total of 97 DEmiRNAs. Considering that miRNAs with positive FC values could potentially regulate different mechanisms of the human body compared to the miRNAs with negative FC values, these 97 DEmiRNAs were separated into two new groups, downregulated and upregulated. The downregulated group consisted of 59 DEmiRNAs, while the upregulated group included 38. Target genes investigation revealed that 54 of 59 DEmiRNAs from the downregulated group presented validated experimental interactions with 948 different genes. The upregulated group presented 24 validated interactions with 221 genes, resulting in a total of 1051 genes. To identify which pathways were enriched by each group of DEmiRNAs, we performed a gene set enrichment analysis using Reactome. Gene enrichment analysis for genes associated with upregulated miRNAs resulted in 1161 associated pathways, among which 346 had a significant association (FDR p- value ≤ 0.05). We found 1732 Reactome pathways regarding downregulated miRNAs (465 significant). Pathways related to tuberculosis were selected and their results were presented in Tables1 and2, which contain pathway identification, number of interaction genes and FDR value. Each table was built to include pathways associated to either upregulated or downregulated miRNA group.

Table 1. Gene set enrichment analysis results for genes associated with upregulated miRNAs (Medical Samples vs. External Control analysis).

Pathway Entities Found FDR Value Cytokine signaling in immune system 82 6.2 × 10−14 Apoptosis 23 1.55 × 10−8 FOXO-mediated transcription of cell cycle genes 8 8.98 × 10−6 TP53 regulates transcription of cell death genes 11 9.55 × 10−5 Signaling by TGF-β Receptor complex 9 1.87 × 10−3 Transcriptional regulation of pluripotent stem cells 6 4.48 × 10−3 FceRI signaling 10 1.04 × 10−1 Interleukin-6 signaling 2 1.34 × 10−1 Adaptive immune system 30 1.64 × 10−1 Innate immune system 37 1.64 × 10−1 Regulation of TLR by endogenous ligand 2 1.64 × 10−1 Signaling by B Cell Receptor 7 1.64 × 10−1 Autophagy 6 1.95 × 10−1 Metabolism of nitric oxide: NOS3 activation and regulation 1 6.17 × 10−1 TNF signaling 1 6.97 × 10−1 FcγR dependent phagocytosis 3 8.29 × 10−1 Antimicrobial peptides 1 9.44 × 10−1 ADORA2B mediated anti-inﬂammatory cytokines production 1 9.76 × 10−1 Metabolism of lipids 19 9.99 × 10−1

Intracellular microorganisms such as Mycobacterium tuberculosis, in interaction with the host, establish at least four stages of distinct cellular mechanisms: (i) recognition and engulfment; (ii) inﬂammation; (iii) apoptosis; and (iv) death of the pathogen [21]. These mechanisms involve several genes from various pathways, which act collectively at the time of infection to prevent the onset of the disease and try to defeat the pathogen. These pathways are under epigenetic regulation and the result of these parasite-host Int. J. Mol. Sci. 2021, 22, 3674 8 of 14

interactions can deﬁne the establishment of the disease. Tables1 and2 show the main enriched pathways regulated by upregulated and downregulated miRNAs. Thus, the miRNA expression proﬁle of the host in the face of infection or exposure to Mycobacterium tuberculosis indicates a mechanism capable of combating the bacillus. We can presuppose that these pathways may be relevant in helping to predict the point at which an endangered individual has their natural immune barriers circumvented by the pathogen.

Table 2. Gene set enrichment analysis results for genes associated with downregulated miRNAs (Medical Samples vs. External Controls).

Pathway Entities Found FDR Value Cytokine Signaling in Immune system 278 2.11 × 10−14 FOXO-mediated transcription of cell cycle genes 23 6.02 × 10−13 Signaling by TGF-β Receptor complex 34 3.67 × 10−10 Transcriptional regulation of pluripotent stem cells 21 9.86 × 10−8 Apoptosis 44 1.39 × 10−6 TP53 regulates transcription of cell death genes 23 2.07 × 10−6 Interleukin-6 signaling 10 1.23 × 10−4 FceRI signaling 35 2.17 × 10−2 TNF signaling 11 2.55 × 10−2 Regulation of TLR by endogenous ligand 8 2.85 × 10−2 Autophagy 25 4.44 × 10−2 Signaling by B Cell Receptor 25 1.19 × 10−1 Innate immune system 137 2.33 × 10−1 FcγR dependent phagocytosis 22 2.6 × 10−1 Adaptive immune system 97 3.6 × 10−1 Metabolism of nitric oxide: NOS3 activation and regulation 3 7.34 × 10−1 ADORA2B mediated anti-inﬂammatory cytokines production 9 9.59 × 10−1 Antimicrobial peptides 4 9.97 × 10−1 Metabolism of lipids 69 1

2.3.2. Target Genes Analysis in Tuberculosis Patients vs. Hospital Controls Considering the low number of DEmiRNAs for this comparison (see Figure3), the three DEmiRNAs (hsa-miR-4732-5p,hsa-miR-486-3p and hsa-let-7g-5p) were not divided into upregulated and downregulated groups and target genes analysis was performed. These DEmiRNAs had interactions with 78, 202 and 341 genes, respectively, and interacted with a total of 599 different genes. Interestingly, these DEmiRNAs showed interaction overlaps, as three genes, CDKN1A, ZC3HAV1L and TUBB2A, are common targets to all three DEmiRNAs. A previous study revealed that hsa-let-7g contributed to apoptosis and loss of proliferation in gastric cancer cells under oxidative stress [27]. Additionally, this miRNA is reported to induce the increase of proliferation and reduction of apoptosis in normal cells by targeting antiangiogenic genes or apoptotic genes [28]. It is also suggested that the let-7 miRNA family is involved in the regulation of anti-tuberculosis immune response [5]. Regarding the overlap among all three genes, studies show that CDKN1A may be involved in p53/TP53 mediated inhibition of cellular proliferation (G1 phase) in response to DNA damage [29,30]. CDKN1A encodes p21 protein, a member of Cip/Kip family. High levels of p21 have been previously reported in pulmonary sarcoidosis, and it was hypothesized that in these microenvironments the expression of p21 functions as an inhibitor for apoptosis and as a facilitator for the formation and maturation of granulomas [31–33]. TUBB2A encodes β-tubulin, a protein that plays an important role in cell division, migration and intracellular transport [34]. This gene could act in active macrophage, preventing mature phagolysosome formation and provide comfortable conditions for pathogen survival [35]. Both genes are related to inhibition of cellular proliferation and their regulation may be involved in the formation and maturation of granulomas and inhibition of phagolysosome, important steps for establishment of the disease. Int. J. Mol. Sci. 2021, 22, 3674 9 of 14

2.4. Network Analysis of Target Genes For this analysis, three different complex networks were built, namely two for interactions between miRNA–genes in Medical Samples versus External Controls and one for interactions in Tuberculosis patients versus Hospital Controls. Such networks were built regarding experimentally validated interactions catalogued in miRTArBase. Figure4A is the graphical representation of the miRNA–gene network for genes regulated by downregulated miRNAs in Medical Samples versus External Controls, while Figure4B represents the miRNA–gene network for genes regulated by upregulated miR- NAs. We observed that the network regarding downregulated miRNAs in tuberculosis was much bigger and had a higher number of interactions per miRNA compared to the upregulated network, indicating that there are more genetic interactions being regulated in the External Control group than in both Tuberculosis patients and Hospital Controls. In total, 118 genes were regulated in both networks, as indicated by the Venn diagram in Figure4C.

hsa-miR-423-5p

hsa-miR-1291

hsa-miR-301a-3p A hsa-miR-374a-3p B hsa-miR-18a-3p

hsa-miR-10b-5p hsa-miR-197-3p hsa-miR-532-5p hsa-miR-941 hsa-miR-328-3p

hsa-miR-19a-3p hsa-miR-19b-3p hsa-miR-10a-5p hsa-miR-550a-5p

hsa-miR-1180-3p hsa-miR-148a-3p hsa-miR-181a-5p hsa-miR-192-5p hsa-miR-29b-3p hsa-miR-27b-3p hsa-miR-185-3p hsa-miR-142-5p hsa-miR-744-5p hsa-miR-22-5p hsa-miR-423-3p

hsa-miR-22-3p hsa-miR-320b

hsa-miR-324-3p hsa-miR-27a-3p hsa-miR-142-3p hsa-miR-30e-5p hsa-miR-148b-5p hsa-miR-26b-5p hsa-miR-29a-3p

hsa-let-7b-5p hsa-miR-18a-5p hsa-miR-451a

hsa-miR-190a-5p hsa-miR-106b-3p hsa-miR-320a

hsa-miR-7-5p

hsa-miR-454-3p hsa-let-7i-5p hsa-miR-17-3p hsa-miR-16-2-3p hsa-miR-92b-3p hsa-miR-20a-5p hsa-miR-320c hsa-let-7f-5p hsa-miR-186-5p hsa-miR-574-3p

hsa-miR-29c-3p

hsa-miR-340-5p hsa-miR-106b-5p hsa-miR-424-3p hsa-miR-21-5p hsa-miR-362-5p

hsa-miR-502-3p hsa-miR-98-5p hsa-miR-140-5p hsa-miR-32-5p hsa-miR-374b-5p hsa-miR-181c-5p hsa-miR-424-5p C genes regulated by

hsa-miR-15a-5p upregulated miRNAs

hsa-miR-101-3p

hsa-miR-126-3p hsa-miR-144-5p hsa-miR-101-5p

hsa-miR-660-5p 830 118 hsa-miR-144-3p 103 hsa-miR-374a-5p

hsa-miR-421 hsa-miR-126-5p

hsa-miR-664a-3p hsa-miR-335-5p genes regulated by downregulated miRNAs

Figure 4. miRNA–gene networks for MS vs. EC generated by ncRNA-network tool: (A) graphical representation of the miRNA–gene network of downregulated miRNAs in MS vs. EC; (B) graphical representation of the miRNA–gene network of downregulated miRNAs in MS vs. EC; and (C) Venn diagram of genes regulated by downregulated miRNAs and upregulated miRNAs.

For Tuberculosis patients versus Hospital Controls, we used as input for the ncRNA- network tool a total of 599 different genes. Figure5A represents the miRNA–gene network for this comparison. Among the three DEmiRNAs, an interaction overlap of three genes, CDKN1A, ZC3HAV1L and TUBB2A, was found (Figure5B). Interestingly, hsa-let-7g-5p, which had a higher expression in Tuberculosis patients, also had the highest number of regulated genes in this comparison. Int. J. Mol. Sci. 2021, 22, 3674 10 of 14

hsa-let-7g-5p hsa-miR-4732-5p KIF27 ITGA3 YWHAZ TGFBR1 B A GATM DISC1 MAGEA12 GLO1 ZNF609 MIDN CDKAL1 TIAF1 TMED5 GOLGA4 RBFOX2 ZNF578 BRI3BP RRAD AKAP8 FN1 NAP1L1 TSC22D2 DTX3L PHACTR4 4 TRMO AP1S1 KRAS STAT2 ATG9A GPAT4 PDGFB NCOA3 MARCKSL1 RRM2 69 PM20D2CCND1 324 SLC19A3 RBM12BRAB21 PGM2L1 CEP135 NOA1 C19orf53 AQP6 COL1A2 BRD1 BMI1 TMED4 DCAF8 NAT8L FAM105A ATG12 RHD STX3 ADH5 SYT1 MRPL12 RHBDF2GGA3 SOCS7TOMM40L ZNF584 ECHDC1 RAB40C STRN RDX IGF1R DNAJC28 ZNF644 MSI2 IL6R TRIM71 3 MTUS1 TMTC3 SMAD2 FBXW2 CBX5 SURF4 MTX3 COIL CCNT2ZNF587 CALU CPA4 ANKRD46 SLC11A2 MEF2D FAM83G IFNLR1 FUT10 RNF144B UBXN2B IGF2BP1USP38 CTPS1 NDUFA4P1 SUOX YOD1 FIGN KREMEN1 TNFRSF10B PBX2 OPRL1RNFT1 SMARCAD1 NHLRC2 MLLT10 SLC20A1 CRY2 ZNF556 CELF1 ONECUT2 10 HAND1 RAB19 2 AGO1PPP2R2A NOM1 TGOLN2 SOCS1MXD1 DYRK3 SEMA4C PGRMC1 DNAH9 PARP16 USP47 KIAA0391 ARL8B EFHD2 GABPB1 PDZD8 POLR2D ADIPOR2 HASPIN AKAP11 SOD2 SALL3 ZNF566 TGFBR3 GRPEL2 ERO1A WASL QDPR ABHD17C ARID3B PDP2 ATXN2 NAA20 MAP3K1 ABT1 SLC16A9 C12orf4 ZBTB8OS ZBTB37ATP6V1F ZBTB5C5orf51 AKT2 LIMD2 PRIM2 PLXND1 HERPUD1 DIABLO EMILIN2 KPNA5 POLR3D TBC1D19 RNF44 PEG10 OPA3 PLAGL2COL8A1 FAM43A STK4 PCGF3 MAGEA6 LEFTY1 ZNF200 ZNF8 EDN1 RABL2B 187 UBE3C AHCYL2 MIEF1 INTS7 TXLNG VCL PMAIP1 PMPCA PDE12 BZW1 SLC38A7 PRR5-ARHGAP8 POTEMARIH1 FXN FBXL20 ZNF417 LRIG3 YAE1D1 C19orf47 ZNF738 SNX17 IGDCC4 ACOT9 CASTOR2 C1RL ZFAND4 EPHA4 AREL1 DNA2 SLC10A7 RFC2 IKZF3 MBD2PLCG2 CASP3 SAR1A SYNJ2BP ESPL1 CD59 MEIS3P1 IL13 SLC5A6 RWDD1 ARID3A KIAA0930 RAB11FIP4 POTEG MYC KCTD21RPL12 ZNF774 BACH1 GAB2 HIST1H2BD C1orf21 THYN1 hsa-miR-486-3p TNFSF9 IPO9 KLHDC8B ICOSLG FZD9 GNG5 PLEKHA3 HMGA2 SUMO1TRAPPC10PDLIM5 C11orf57 ZCCHC3 THBS1 FAM222B EIF4G2 AMD1 RANBP2 THEM6 NCKIPSDFMO4 SDR42E1 AK4 DDR1 PLD3 FNDC9 HECTD3 IGF2BP3 DNAL1 DVL3 PRAG1 COX6B1 CXCL8 TXNDC16 HIST1H2BKHMGB1 FPR1 ATXN7L3 GFER RABL2A PRSS22 POLL ATXN7L3B CDV3 IP6K1 ZNF28 MAGEA3 ACER2 MCF2L2EIF4A3 NUCB2 CIAPIN1 FAM167B WDR73 NHLRC3 SART3 LYN TBC1D9 RRM1 C1orf210 TANGO2 REPIN1 KMT2D HMGA1 TXLNA NSD1 C20orf27 KDM6B ZNF460 PLEKHO1 MTMR12 RAD18 ZNF611 CDKN2ALRRC20TUBB4A ACTA1 ZNF799 KRT8 CLDN12 CST9 ECM1 PRKCD FZD7 MAPK6 SFT2D1 EEF1A1 ALPI BAZ2A NAA30 NR6A1 MDM4 NUP155 AHR CRCP TMCC1 CHTOP KIAA1143 ZMAT5 CASKIN1 ATP6AP1 DUSP1 ZNF443 ATP6V1G1 KIAA1328 MKNK2 POU3F1 PAFAH2E2F6 SMCR8 RXRA ENG PAX2 IFITM3 BEND4 SZRD1 SKI PPP1R15B FNDC3ASLC12A7 BCL2L1 LUZP1 COLEC12 UST TAGLN CACNA2D2NID1 ZFHX3 SMC1A CEP120 FAM104APEX11B SLC25A22MBD6 CALR TUBB2A ITGB3 CEBPB CTSA ZNF264 PGAM1 FMNL3 MARS2 SHMT1 MAF STRN4 EDEM3 ZC3HAV1L PLXNA4UBBP4 MACROD2SYTL3 AHDC1 WAC MAP2K7 RNF187 EPHA2 U2AF2 CD276 CDKN1A ADIRF RFWD3 MFSD8 COPZ1 HCN2 CRX GPRC5CTHY1 FASN MRPL34 GPR107 AP1G1 CHD3 RNF41 EMP1 MYH9 CDC42EP4 SH3PXD2APGAM4 ARL4C POFUT2 ERBB2 CAD ATP6V1E1NACC1 NPTXR TUBB8 STIP1 REXO1 CD4 MRNIP RALY CTDNEP1 ZC3H7B RTN4RL1 TMEM245 PPP1R14AAGAP1 SREBF2 NDRG3 KRT80 PTPRF C1orf229 OGT SLC30A5 TOB2 CCDC106 LASP1 NTN1 TMPRSS5 MTA1 LGSN ENTHD1 OSCAR OARD1 POLR2F PKD1 SFN DNAJC6 LY6E HNRNPUL1 NUFIP2 ALG1 NAV1 MFGE8 MAFK HIVEP3 OLFML2A MEN1 ALKBH5 TNNC1 YIPF4 DAZAP2 CRIPT COL18A1 FAM83A PTGES QSER1 CHRNE PRIMA1 ELOVL5 SARS EIF2S3 NFIC CASZ1 GDE1 FEM1A ZNF431 WNT16 KSR2 SPATA2 CALM3 ZNF787 OTUD6A TNC ZNF616 SYK PHF12 HDGF TUBB CYTH2 FXR2 CRABP2 CAMK2A IL20RB ABHD5 NCS1 SIK1 MED28 CC2D1B IER5 ZNF562 ZNF208 MAP2K2 KIAA1549 DDX39B ARHGAP18 PEA15 JMJD4 SLC25A34 BCL7A ZNF138 LAYN VAMP8 UBB EIF5AL1 MBD4 PFN2 TRPV2 SYNDIG1L PTMS SPRED1 APOBEC3C NCBP2 IL31RA TIMM50 ZNF724 ZKSCAN4 KLHL38 MYADM PDHB PTGES2 TNRC6B HSP90AA1 RNF157 ERBB3 UNC119B B4GALNT4 MNT MTHFSD DLGAP4 COX19 ZNF236 ST6GALNAC3 ELFN2 RPL10 SCARF2 CFL1 SRCIN1 FRK RNF20 ZNF383 RPL18A BCL3 ZIK1 UBL5 SEC14L5 CD1D AMH OTUD4 RAF1 PIN1 TGFBR2 EHD3 DOK6 BRSK2 MAP4 TRUB2 SPCS1 C21orf91 HS6ST3 SLC29A1 NACC2 SLC4A2 SYNGR1 MLXIP SNX4 NFATC2IP SBK1 IL36B ENTPD7 BCL11A CAPN15 LRRC58 C8orf33 SC5D EFHC1 SP110 NUBP1 NSD2 ILDR1

Figure 5. miRNA–gene network for TP vs. HC generated by ncRNA-network tool: (A) miRNA–gene network for the three DEmiRNAs (hsa-miR-4732-5p, hsa-miR-486-3p and hsa-let-7g-5p); and (B) Venn diagram indicating intersections between genes for each miRNA.

3. Materials and Methods 3.1. Sample Collection The present study consisted of 22 samples distributed in three groups (Tuberculosis patients, 8 samples; Hospital Control, 6 samples; and External Control, 7 samples). The Tuberculosis patients group was obtained from a set of patients with active pulmonary tuberculosis in regular treatment using standard tuberculosis treatment protocols in Brazil [36]. Eligibility criteria for Tuberculosis patients group include: (i) being over 18 years old; (ii) on sputum smear examination results for acid-fast bacilli and/or the culture of Mycobacterium tuberculosis; (iii) manifesting respiratory symptoms (persistent produc- tive cough, fever and weight loss); (iv) chest radiography/computed tomography images compatible with a speciﬁc pulmonary process; and (v) conﬁrmation by Rapid Molecular Test for tuberculosis (Xpert R MTB/RIF). Tuberculosis patient samples were obtained from patients attending the Pulmonary Tuberculosis Clinic of the Hospital Universitário João Barros Barreto (Universidade Federal do Pará, UFPA). All Tuberculosis patient samples were collected directly by their attending physician. Hospital Control samples were collected from health professionals of the same clinic, annually performing physical examinations and TST tests (advocated by the Ministry of Healthy, Brazil) and had no history of tuberculosis over 15 years of activity. Hospital Control was composed by health professionals without prior history of tuberculosis and that had constant contact (at least 4 h/day workload) with patients with active tuberculosis. The External Control group consisted of volunteers without tuberculosis and no contact with Tuberculosis patients in treatment. External Control samples were acquired from employees of other departments from the same university, who had no contact with the university’s health services and underwent clinical evaluation. Int. J. Mol. Sci. 2021, 22, 3674 11 of 14

Biological material was collected in a 5 mL tube containing RNA later and stored until RNA extraction. Samples from all groups were retrieved during 17–22 May 2017. The data were collected after the research was explained and the patients signed an informed consent form.

3.2. RNA Extraction and Quantification Peripheral blood samples (5 mL) were collected using Tempus Blood RNA Tube (Thermo Fisher Scientific, Waltham, MA, USA) and stored at −20 ◦C until extraction. Total RNA was extracted using MagMAX RNA Isolation Kit (Thermo Fisher Scientific, USA) and quantified with NanoDrop-1000 spectrophotometer (Thermo Fisher Scientific, USA). Agilent RNA ScreenTape assay and 2200 TapeStation Instrument (Agilent Technologies, USA) were used to detect and ensure RNA integrity.

3.3. Library Construction and Sequencing For small RNA-Seq, 1 µg of total RNA per sample was used for library preparation using TruSeq Small RNA Sample Prep Kits (Illumina, San Diego, CA, USA). Size-distribution was measured with the DNA ScreenTape assay on a 2200 TapeStation system (AgilentTech- nologies, US). A total library pool of 4 nM was sequenced using a MiSeq Reagent Kit v3 150 cycle on a MiSeq System (Illumina, San Diego, CA, USA).

3.4. miRNA Quantification and Normalization Biological data obtained from the 22 sequenced samples underwent a quality control pipeline to remove adapters used during sequencing, as well as to trim and filter the obtained sequences. This quality control process was performed using Trimmomatic software version 0.36, “ILLUMINACLIP” (with a custom adapters list), “LEADING:10” and “TRAILING:10” (both cut bases with quality lower than 10 at the beginning or end of the read, respectively), “SLIDINGWINDOW:3:22” (performs a sliding window approach with a three bases window size, cutting bases once the average quality within the window falls below 22) and “MINLEN:16” (remove reads with less than 16 bases) parameters were used. Sequences were then aligned with the human genome (HG19) using STAR software. Result files, generated as “.sam” format by STAR, were manipulated using samtools and converted to “.bam” files. miRNA expression quantification was performed with HTSeq software, using the human genome annotation file (“.gff”) and “type” parameter set to “miRNA”. Data from alignment process were properly classified before being submitted to a new quality control, which kept miRNAs with at least 10 total read count in at least one sample and removed samples with a total read count lower than 1000. This procedure resulted in the removal of one sample from the analysis (sample belonging to TB group); this new data served as the raw data for the differential expression analysis. Two different types of normalization were employed, differential expression analysis was performed using edge package function “calcNormFactors” (which utilizes TMM normalization method as package default). For other analysis such as histograms, boxplots and principal component analysis, raw data was normalised by counts per million (CPM), a measurement of read abundance used to compare the expression of miRNA in different samples or libraries sizes.

3.5. Differential Expression Analysis Exploratory data analysis was performed with R version 3.5.0, RStudio (v1.1) and shell script. Since samples were divided into three different groups, ﬁve differential expression analyses were performed comparing: (i) Tuberculosis patients versus Controls (both Hospital and External Control groups); (ii) Tuberculosis patients versus Hospital Controls; (iii) Tuberculosis patients versus External Controls; (iv) Hospital Controls versus External Controls; and (v) Medical Samples (formed by both Tuberculosis patients and Hospital Control groups) versus External Controls. Differential expression analysis was Int. J. Mol. Sci. 2021, 22, 3674 12 of 14

performed with edgeR package that implements a statistical method for negative binomial distribution analysis. Thus, negative binomial models were used to capture the quadratic mean-variance in RNA-seq data and FDR adjustment method was applied for multiple comparison corrections. miRNAs with adjusted p-value < 0.05 and |FC| > 1 were isolated and considered with differential expression.

3.6. Modeling a miRNA–Gene Networks and Analysis Interactions between miRNAs and their target genes are extremely complex, so it is necessary to implement computational methods to allow their better understanding. Thus, network modeling is a valuable approach to measure and visualize interactions between different components of regulatory networks. We modeled a network of miRNA– gene interaction based on public data of miRTarBase [37]. miRTarBase has catalogued 300,000 interactions between miRNAs and genes, which are validated by different types of experimental studies including microarray data, western blot, report assays and next- generation sequencing. Our miRNA network was modeled as a bipartite graph G = (V, U, E), in which G is a graph comprised by two distinct sets of regulatory elements: U is the set of miRNAs and V is the set of genes. Interactions between miRNA and genes were deﬁned if there are two or more experimentally validation studies based on evidences catalogued in miRTarBase. For the miRNA–gene network, we computed the number of interactions (degree of a node) as a centrality index for both regulatory elements, genes and miRNAs. Af- ter differential expression analysis, we classiﬁed DEmiRNAs on the network as upregulated or downregulated miRNAs to investigate patterns of interactions between both distinct groups. The networks were constructed and graphically represented with NetworkX implemented in Python 3 and an in-house tool available at www.lghm.ufpa.br/ncrnas, ac- cessed on 5 December 2020.

4. Conclusions Our results identify 153 DEmiRNAs among all comparisons. The differential expression analysis between Tuberculosis patients versus Hospital Controls revealed three DEmiRNAs (hsa-miR-4732-5p, hsa-miR-486-3p and hsa-let-7g-5p, which was previously associated with regulation of apoptosis). These three DEmiRNAs are our first suggestion of potential biomarkers for active tuberculosis. However, validation with larger sample numbers are still required. Our analysis indicates that only a small group of miRNAs were potentially associated with absence of active tuberculosis in healthy physicians. The presented results show healthy physicians with highly similar miRNA expression levels to tuberculosis patients. This raises concerns about the efficiency of the current safety measures employed in long- term exposure to tuberculosis environments. Our findings provide a vast number of DEmiRNAs, which can be further studied to provide better insight into the mechanisms involved in host response to tuberculosis as well as for discovering other biomarkers for the disease. Our results can continue to be explored in other molecular research involving genetic interactions in tuberculosis, such as differential co-expression analysis or to re-evaluate safety measures regarding long-term exposure to tuberculosis currently applied in medical centers.

5. Study Limitations There were some limitations to this work: (i) the small sample number could hide important DEmiRNAs; and (ii) hospital and external control groups were not tested for IGRA to denote any infection. However, this is the ﬁrst work to bring data related to health professionals in daily contact with high bacillary loads of tuberculosis, which could indicate possible biomarkers of better diagnostic accuracy, besides proposing a new mechanism of labor monitoring of these professionals regarding the risk of manifesting the disease. Int. J. Mol. Sci. 2021, 22, 3674 13 of 14

Author Contributions: C.A.S., Â.R.-d.-S. and S.S. designed research; C.A.S. enrolled patients and performed and registered clinical diagnosis; P.P., T.V.-S., A.M.R.-d.-S. and A.F.V. performed research; A.R.-d.S., R.P.P., A.M.R.-d.-S. and G.S.A. analyzed the data; and C.A.S., A.R.-d.-S., W.G.G., M.H.H. and G.S.A. wrote the article. All authors have read and agreed to the published version of the manuscript. Funding: We acknowledge the support of CNPq (306815/2018-3 grant for Ândrea Ribeiro-dos-Santos; and 305258/2013-3 grant for Sidney Santos), CAPES PROAMAZONIA (88887.200498/2018-00), CAPES Biocomputacional—Rede PGPH (3381/2013) and PROPESP/UFPA. The funders had no role on the manuscript data or concept. Institutional Review Board Statement: All research procedures were carried out in accordance with the Declaration of Helsinki and the Nuremberg Code, following Research Standards Involving Human Beings (Res. CNS 196/96) of the National Health Council, respecting the ethical standards and the rights of patients. The project was approved by the Human Research Ethics Committee of the Universidade Federal do Pará, under HUJBB’s protocol number 350507. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: The miRNA datasets generated and analysed during the current study are available in the European Nucleotide Repository under accession PRJEB36699. Acknowledgments: The authors are grateful to the entire team of professionals from the Hospital João de Barros Barreto of Universidade Federal do Pará (UFPA) and the tuberculosis patients, without whom it would not be possible to write this paper. Conflicts of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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