Next Article in Journal
Investigation of Yellow Fever Virus at the Human–Animal Interface after a Zika Virus Outbreak in Midwest Brazil
Previous Article in Journal
Enhancing Benzo[a]pyrene Degradation by Pantoea dispersa MSC14 through Biostimulation with Sodium Gluconate: Insights into Mechanisms and Molecular Regulation
Previous Article in Special Issue
Metagenomic of Liver Tissue Identified at Least Two Genera of Totivirus-like Viruses in Molossus molossus Bats
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Microorganisms
Volume 12
Issue 3
10.3390/microorganisms12030593
microorganisms-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Identification of Viruses in Molossus Bats from the Brazilian Amazon: A Descriptive Metagenomic Analysis

by
Lucas Rafael Santana Pinheiro
1,
Érika Dayane Leal Rodrigues
2,
Francisco Amilton dos Santos Paiva
2,
Ana Cecília Ribeiro Cruz
2,
Daniele Barbosa de Almeida Medeiros
2,
Alexandre do Rosário Casseb
3,
Sandro Patroca da Silva
2 and
Livia Medeiros Neves Casseb
2,*
1
Graduate Program in Virology, Hemorrhagic Fevers and Arbovirology Section (SAARB), Evandro Chagas Institute (IEC), BR-316 Highway, km 7, Levilândia, Ananindeua 67030-000, PA, Brazil
2
Hemorrhagic Fevers and Arbovirology Section (SAARB), Evandro Chagas Institute (IEC), BR-316 Highway, km 7, Levilândia, Ananindeua 67030-000, PA, Brazil
3
Health and Animal Production Institute, Federal and Rural University of Amazon (UFRA), Belém 66077-830, PA, Brazil
*
Author to whom correspondence should be addressed.
Microorganisms 2024, 12(3), 593; https://doi.org/10.3390/microorganisms12030593
Submission received: 31 October 2023 / Revised: 17 November 2023 / Accepted: 22 November 2023 / Published: 16 March 2024
(This article belongs to the Special Issue Advances in Viral Metagenomics)
Browse Figures
Review Reports Versions Notes

Abstract

:
Bats are widely distributed in Brazil, including the Amazon region, and their association with viral pathogens is well-known. This work aimed to evaluate the metavirome in samples of Molossus sp. bats captured in the Brazilian Amazon from 2019 to 2021. Lung samples from 58 bats were divided into 13 pools for RNA isolation and sequencing followed by bioinformatic analysis. The Retroviridae family showed the highest abundance of viral reads. Although no complete genome could be recovered, the Paramyxoviridae and Dicistroviridae families showed the formation of contigs with satisfactory identity and size characteristics for further analysis. One contig of the Paramyxoviridae family was characterized as belonging to the genus Morbillivirus, being grouped most closely phylogenetically to Porcine morbillivirus. The contig related to the Dicistroviridae family was identified within the Cripavirus genus, with 94%, 91%, and 42% amino acid identity with Culex dicistrovirus 2, Rhopalosiphum padi, and Aphid lethal paralysis, respectively. The presence of viruses in bats needs constant updating since the study was able to identify viral sequences related to families or genera still poorly described in the literature in association with bats.

1. Introduction

Bats of the Molossus genus (Molossidae family) are insectivorous animals found all over the American continent. In Brazil, records of their occurrence have been described in all regions, including the Amazon region. Specimens of the genus are found in both urban and rural areas [1,2,3]. Viral investigations in Molossus spp. carried out in Brazil and other South American countries provide evidence of infection by viruses classified into several families, such as Nairoviridae, Picornaviridae, Astroviridae, Retroviridae, Coronaviridae, Rhabdoviridae, and Paramyxoviridae [4,5,6].
Paramyxoviridae is a family of enveloped, negative-sense, and single-stranded RNA viruses. The representatives of this family have genomes ranging from 14,296 to 20,148 nucleotide (nt). The subfamily Orthoparamyxovirinae is where this genus is included, along with the genera Aquaparamyxovirus, Ferlavirus, Henipavirus, Jeilongvirus, Morbillivirus, Narmovirus, Respirovirus, and Salemvirus [7], which encompass some species known to be pathogenic, either in humans or in animals, such as Hendra henipavirus, Nipah henipavirus, Human respirovirus 1, Human respirovirus 3, Murine respirovirus, Bovine respirovirus 3, and others [8].
Morbillivirus representatives encode eight proteins: phosphoprotein (P), large polymerase protein (L), RNA-binding protein, matrix protein (M), fusion protein (F), and an attachment protein (H) as structural proteins, and C and V as non-structural proteins [9]. They are also well-known agents responsible for causing diseases in their natural hosts. Among them are Measles morbillivirus (the causative agent of measles) and Canine morbillivirus (the causative agent of canine distemper) [10,11].
A morbillivirus infection can occur through the respiratory tract when the host inhales suspended viral particles. These particles can be present in the air because of the aerosols generated by other contaminated hosts, for example, through urine [12]. The first association between bats and morbilliviruses occurred in research by Drexler et al. [6].
Members of the Dicistroviridae family have a positive-sense RNA genome, ranging from 8500 to 10,000 nt in length, comprising two ORFs that decode the non-structural (ORF1) and structural (ORF2) proteins of the viruses [13]. Representatives of this family infect primarily arthropods, such as bees and flies, but they are also present, for example, in shrimp [14]. However, viruses of this family have already been detected in fecal samples from vertebrate animals, such as geese [15]. In bats, these viruses have also been detected, either in feces [16,17], tissue samples [13,18], or even in blood [19].
The purpose of this study was to evaluate the viral diversity among bats of the Molossus sp. genus living in the Amazon region, aiming at the identification of viral pathogen presence or new virus species, especially regarding human or veterinary potential health importance.

2. Materials and Methods

2.1. Samples

Lung samples were obtained from 58 bats of the Molossus molossus and Molossus rufus species that were captured, from 2019 to 2021, in five Brazilian states: Pará, Rondônia, Amazonas, Roraima, and Tocantins. All bat samples were sent to the Department of Arbovirology and Hemorrhagic Fevers (SAARB) in Evandro Chagas Institute (IEC) by units of the health departments of states and municipalities in accordance with the rabies surveillance program and were submitted to direct immunofluorescence and mouse inoculation tests for the Rabies virus with negative results.
This study consisted of a descriptive and univariate metagenomic investigation with non-probabilistic samples selected by the following factors: genus of bat (Molossus sp.), states of the Brazilian Amazon region, and years of collection. To visualize the collection points and sample distribution, a map was constructed using the R program v4.3.0 with the help of the ggplot2 and geobr libraries [20], and using the Brazilian Institute of Geography and Statistics (IBGE) 2020 database (Figure 1).

2.2. Sample Extraction, Synthesis of cDNA and Genomic Library Preparation

To perform the extraction of RNA, lung samples were taken from the quiropterygians. 13 pools were formed, containing tissues from up to 5 bats per pool. The pools were formed based on the year and state of bats collection.
For the extraction of the viscera, approximately 10 mg of tissue was placed in a 2 mL microtube with 1 mL of TRIzol Plus RNA Purification Kit (Thermo Fischer Scientific Inc., Waltham, MA, USA) and a 5-mm diameter steel ball and then agitated in the TissueLyser II equipment for 2 min at a frequency of 25 Hz. At the end of this process, the samples were taken to a biological safety cabinet to proceed with the extraction of the genetic material.
In the same tube, 200 µL of chloroform were added, and then all this material was transferred to a tube containing a phase separator polymer (Phasemaker™ Tubes-Thermo Fischer Scientific Inc., Waltham, MA, USA) and then the separation procedure were carried out according to manufacturer’s recommendations. At the end, approximately 560 µL of the phase above the polymer were transferred to a new 1.5 mL microtube with the subsequent addition of 530 µL of 70% ethanol. The contents of this new tube were purified using the PureLink RNA Mini Kit (Thermo Fischer Scientific Inc., Waltham, MA, USA), following the manufacturer’s recommendations.
After purification, the RNA was quantified using the Qubit RNA HS Assay Kit (Thermo Fischer Scientific Inc., Waltham, MA, USA) along with the Qubit 4.0 equipment, following the manufacturer’s recommendations.
The preparation of cDNA from RNA started with the first and second strand cDNA synthesis, using the SuperScriptTM VILOTM MasterMix kit (Thermo Fischer Scientific Inc., Waltham, MA, USA) and NEBNext® Second Strand Synthesis Module (New England Biolabs Inc., Ipswich, MA, USA) respectively. The cDNA purification was performed by the PureLink® PCR Purification Kit (Thermo Fischer Scientific Inc., Waltham, MA, USA). All steps followed the recommendations of the manufacturer of the respective kits. The synthesized cDNA was quantified using the Assay DNA HS Kit (Thermo Fischer Scientific Inc., Waltham, MA, USA) in the Qubit 4.0 equipment.
The genomic library was prepared according to the guidelines of the Nextera XT DNA kit (Illumina, Inc., San Diego, CA, USA) and sequenced using the NextSeq 500 platform (Illumina, Inc., San Diego, CA, USA) using the NextSeq 500/550 High Output Kit v2.5 (300 cycles) (Illumina, Inc., San Diego, CA, USA), using the paired-end methodology, according to the manufacturer’s recommendations.

2.3. Bioinformatics

2.3.1. Filtering, Evaluation, and Processing of Readings

The fastq files were obtained after demultiplexing the sequencing output files (BCL-Basecall file) using the bcl2fastq software v1.8.4. As a result of this process, two fastq files were generated, R1 and R2, which correspond to the raw reads for each sample.
Initially, the quality of the reads originated from the sequencing step was evaluated using the Fastp program v0.23.2 [21], with the removal of short reads (less than 75 nt), adaptor fragments, and reads with indeterminate bases (reads with more than 10% N), Next, the Bowtie2 program v2.4.5 [22] was used to remove host reads using the Molossus molossus [23] reference genome mMolMol1.p (Aug. 2020 Genome Browser—GCF_014108415.1 assembly) available in NCBI and access by UCSC Genome Browser Gateway (https://genome.ucsc.edu/cgi-bin/hgGateway, accessed on 15 August 2022). Finally, SortMeRNA v.2.1 [24] was used for ribosomal RNA (rRNA) removal. At the end of the treatment, analysis with the Fastp program was applied comparatively to evaluate the efficiency of the data treatment.
Subsequently, the DIAMOND software v2.0.15 [25] was applied to annotate the reads for each file, using the viruses’ database of non-redundant (nr) proteins, considering an e-value of 1 × 10−4 and amino acid identity. The output files generated by DIAMOND were visualized in Krona v2.8 [26]. The results obtained were tabulated and saved in a file with the extension csv, and these tables were loaded and evaluated in the R program v4.3.0 through the heatmaply library in order to observe patterns of abundance of the different viruses found for each of the sequenced samples. The Kraken program v2.1.2 was also employed, using the nr database and displaying results in the Pavian program v1.0.

2.3.2. De Novo Assembly, Prediction and Gene Annotation

The files generated in the treatment step were used for assembly through De Novo methodology using SPAdes v3.13.1 [27] and MEGAHIT v1.2.9 [28] programs, with k-mer values of 21, 33, 55, and 77; and of 21, 31, 41, 51, 61, 71, 81, 91, and 99, respectively. The clustered contigs were analyzed using DIAMOND v2.0.15 [25] for BlastX analysis utilizing the viruses nr protein database, considering an e-value of 1 × 10−4 and amino acid identity values.
After a more general analysis in Krona v2.8.1 to filter possible viruses of interest, the files relative to the contigs generated for each pool were visualized in the Megan program v6.24.11, using an e-value filter of 1 × 10−10.
The contigs that presented similarity with viral sequences underwent gene prediction analysis using the GeneMarkS tool [29]. The amino acid sequences of the probable genes were compared to different protein databases available in the InterProScan5 program [30], which performs a search for protein domains. The tabulated data were inspected for viral domains of interest and curated by the program Geneious, version 9.1.8 (https://www.geneious.com/, accessed on 29 August 2022).

2.4. Phylogenetic Analysis

Phylogenetic inference was performed according to the analysis of the amino acid sequences obtained from the samples and the sequences available in the international database National Center for Biotechnology Information (http://www.ncbi.nlm.nih.gov, accessed on 5 September 2022), using the coding regions of non-structural proteins.
The dataset generated alongside the samples from the study was submitted to Multiple Sequence Alignment (MSA) using Mafft v.7 [31]. The result of the alignment was inspected manually to perform corrections when necessary, using the program Geneious v.9.1.8 (https://www.geneious.com/, accessed on 6 September 2022).
Initially, the aligned dataset was submitted to analysis to identify the best nucleotide substitution model and verify the phylogenetic signal. Then, the phylogenetic trees were constructed using Maximum Likelihood (ML) methodology [32]. These methodologies were employed using the IQ-TREE v.1.6.12 program [33]. In conjunction with these analyses, the bootstrap test was applied, setting 1000 replicates in order to provide greater reliability to the grouping values [34].
The phylogeny visualization was performed by the FigTree v.1.4.4 software. The data set used did not have a root sequence, so the methodology of midpoint rooting was used, a tool available in the phylogeny visualization program.
After assessment and editing of the phylogeny, a file with the extension “sgv” (Scalable Vector Graphics) was generated for editing and manipulation of the image using the Inkscape v.1.1 program (https://inkscape.org/, accessed on 26 September 2022).

3. Results

3.1. Overall Results

The sequencing of the 13 sample pools resulted in a total of 830,981,690 reads. Of these, 74,805 corresponded to RNA viral sequences. Table 1 shows the quantity of reads generated for each pool and how many of them remained after the data processing steps.
The treated reads have their identity distribution displayed in the Supplementary Materials (Table S1). Most reads were identified as string sequences (12.15%), followed by bacteria (7.91%), fungi (1.72%), protozoa (0.38%), and, to a lesser extent, viruses, which accounted for only 0.072% of the reads treated. More than 70% of the reads could not be identified.
Among the viral reads, 32 families were identified, distributed among the 13 pools. The families Poxviridae, Mimiviridae, Retroviridae, Siphoviridae, and Myoviridae were the only ones to have compatible reads in all pools. Among them, Retroviridae was the most represented, followed by Siphoviridae and Myoviridae, in that order. The Herpesviridae, Picornaviridae, Phycodnaviridae, Inoviridae, and Podoviridae families were also widely detected.
Pools 7 (5RO20) and 12 (4TO19) showed the highest diversity in viral families (18 in total) and were also the only pools with a positive detection of reads for the Dicistroviridae family. Pool 4 (5PA21) had the lowest viral diversity, with only eight families identified by reads.
The families Nimaviridae, Rhabdoviridae, Drexlerviridae, Permutotetraviridae, Solemoviridae, Microviridae, Genomoviridae, Tymoviridae, Totiviridae, Iflaviridae, and Paramyxoviridae all had positivity in only one pool. This information is displayed in Figure 2.
Regarding viral diversity per state, Rondônia was the most diverse with twenty-one viral families total, while Pará had twenty, Tocantins eighteen, Roraima fourteen, and Amazonas nine.
A total of 5,605,280 contigs were generated, of which slightly more than 13% were from MEGAHIT and slightly less than 87% from SPADES (Table 2). Of this total, only 0.8% had a positive correlation for viruses, with pool 5 (4PA20/21) presenting the most virus-positive contigs and pool 6 (5RO19) presenting the fewest.

3.2. Morbillivirus Identification

The identity analysis implemented via BlastX of a contig measuring 1142 bp relative to pool 3 (5PA20) showed amino acid identity of 85.77%, e-value = 0.0, and query cover of 99% with L protein from Porcine morbillivirus (GenBank QWQ56143). After refinement of the analysis by reference map, it was possible to extend the sequence to 1498 bp, equivalent to 499 amino acids. The nucleotide sequence comprised 9.56% of the Porcine morbillivirus genome (GenBank MT511667), with 6× coverage, ranging from position 11,810 to 13,380, referring to a portion of the L gene.
In addition to the identity with Porcine morbillivirus, it was possible to observe the proximity with other viruses from the identity matrix in the Supplementary Materials (Table S2), both amino acid and nucleotide, which revealed identity over 70% (nucleotide) and 80% (amino acid) between the sequence obtained in this study with three other viral genomes belonging to the Morbillivirus genus, namely Canine morbillivirus, Phyllostomus bat morbillivirus, and Phocine morbillivirus. Allied to this data, the analysis of protein motifs displayed in the Supplementary Materials (Figure S1) showed very similar and conserved regions between the studied sequence and the sequences of ten other morbilliviruses. The studied sequence was then named Molossus bat morbillivirus (MBMV).
The analysis of the protein domains revealed a strong similarity between the MBMV domain positions and domains of other morbillivirus sequences, indicating that the portion recovered in the present study was a part of the RNA-dependent RNA polymerase (RdRp) domain (Figure 3).
The phylogenetic tree (Figure 4) was generated using reference sequences (refseq) for viruses of all Paramyxoviridae family genera, plus two reference sequences of bat morbilliviruses and one sequence of Porcine morbillivirus, which was identified as the most similar to the sequence of interest in a curation step using the BlastX tool of NCBI. The dataset assembled to generate the phylogenetic tree presented a phylogenetic signal above 60% in the summation of the vertices and less than 30% in the central triangle.
It is possible to identify that the MBMV sequence forms a monophyletic clade with other viral species belonging to the Morbillivirus genus, being more closely related to Phyllostomus bat morbillivirus and Porcine morbillivirus sequences, although on a more external branch in comparison to these two viruses.

3.3. Dicistroviridae

The Dicistroviridae family showed identity, confirmed by BlastX, to a contig from pool 7 (5RO20) containing 1426 nt with 95% amino acid identity to the non-structural polyprotein (GenBank AXQ04777) of Culex dicistrovirus 2 (GenBank MH188005; e-value = 0; Query cover = 97%). Attempts to extend the sequence using a reference map were unsuccessful.
An identity matrix was obtained and is presented in the Supplementary Materials (Table S3), where 91% and 42% amino acid similarity were obtained with Rhopalosiphum padi and Aphid lethal paralysis viruses, respectively, and 94% identity to Culex dicistrovirus 2, which is not a reference sequence for this family. Then, it was possible to assemble a phylogenetic tree using ORF1 related to the non-structural polyprotein of Dicistroviridae reference sequences. The sequence obtained presented a helicase protein domain from InterProScan analysis (Figure 5) and formed a monophyletic clade within the Cripavirus genus (Figure 6), being more closely related to Culex dicistrovirus 2 and Rhopalosiphum padi virus. Given these results, the genomic fragment in question was identified as a Culex dicistrovirus 2 strain.
The database used to assemble the phylogeny presented a value above 60% in the summation of the vertices and less than 30% in the central triangle from the phylogenetic signal test, and the alignment of the sequences is presented in the Supplementary Materials (Figure S2).

4. Discussion

4.1. Overall Discussion

Microviridae, Siphoviridae, Myoviridae, Podoviridae, Herelleviridae, Drexlerviridae, Inoviridae, Ackermannviridae, and Salasmaviridae are viral families infecting bacteria. In accordance with the study objectives, bacteriophage sequences were not investigated further. Encountering bacterial virus sequences is common in studies of chiropteran viromes [18,35,36].
Solemoviridae, Tymoviridae, Genomoviridae, Totiviridae, Marseilleviridae, Mitoviridae, Mimiviridae, Phycodnaviridae, and Partitiviridae are viral families related to infections in fungi, protozoa, amoebas, algae, and plants. The presence of these viruses here is unlikely to come from the occurrence of viremia. Another issue reinforcing this idea is that the number of readings generated for these families was low, like family Solemoviridae represented by four reads only. A higher abundance of viral genome would be expected in the event of viremia. Furthermore, the assembly of these reads generated only a 349 nt contig and 55% identity. The same was observed for the other families, which either had low coverage of reads, did not form contigs, or formed short sequences with low identity.
The Iridoviridae family has insects, fish, and amphibians as known hosts. The Poxviridae family infects birds, reptiles, mammals, and even humans [37]. For the Iridoviridae family, the contigs formed presented identities lower than 40%, and further analysis indicated their association with bat proteins through BlastX at NCBI. For the Poxviridae family, further analysis also showed identity with bat sequences. In addition, the standard number of reads for this family was very low, being less than 50 reads per pool.
Phycodnaviridae, Iridoviridae, Mimiviridae, and Porxviridae are viral families that comprise the phylum Nucleocytoviricota, known as large nucleocytoplasmic DNA viruses (NCLDV) [38,39]. A possibility for the identification of those families might be the fact that NCDLV can integrate eukaryotic genomes, and there are theories that propose the assimilation of parts of their genomes by protoeukaryotes [40]. Thus, the positivity found for these viruses may be related to parts of their genomes that also compose the genome of bats.
The families Nairoviridae, Nimaviridae, Rhabdoviridae, Iflaviridae, and Permutotetraviridae, although present in reads of bank matches, did not form contigs, while the families Polycipiviridae, Polydnaviridae, Picornaviridae, and Herpesviridae formed small contigs (>500 nt) that, in further analysis, did not confirm virus positivity.
The Retroviridae family was the most abundant in reads. The understanding of this viral family has grown a great deal due to the study of sequences that are integrated into the genome of chiropteran hosts due to the viral mechanism of insertion of its cDNA into the genetic material of the host, which occasionally ends up attaching itself there, giving rise to the so-called “endogenous retroviruses” (ERVs). The first associations between retroviruses and bats occurred precisely in cell lines of molossid bat lung (Tadarida brasiliensis) [41,42]. Most ERVs are “genetic fossils”, meaning they are inactive. However, some may still possess the ability to transcribe active elements or may reactivate in a way that is harmful to the host’s health [43]. ERVs might even possess genes commonly identified in exogenous retroviruses, such as gag, pol, pro, and env [44].
There were many contigs formed for the Retroviridae family, ranging from 190 to over 600 among the pools. Contigs that had a size above 900 bp and a 65% identity were evaluated using BlastX. Most of the sequences in this second analysis had a higher identity than endogenous retroviruses or sequences from other mammalian organisms.

4.2. Morbillivirus

The first appearance of the association of Morbillivirus with bats in the literature was a study published in 2012 [6]. Several genomic sequences were classified as “morbilli-related”, coming from several bat species, such as Desmodus rotundus, Myotis myotis, and Coleura afra, and several countries, such as Germany, Ghana, and Brazil. Two of the sequences found in the mentioned study came from spleen samples of bats of the species Desmodus rotundus that were collected in Brazil in 2008 and 2009. According to the phylogenetic tree assembled, these two sequences would be phylogenetically closer to Phocine morbillivirus.
In another work published in 2022 [45], the genomes of two Morbilliviruses were found and named Myotis bat morbillivirus and Phyllostomus bat morbillivirus. The samples used came from bats of the Myotis riparius and Phyllostomus hastatus species. The Myotis samples were collected in the Brazilian Amazon region, in the state of Amazonas in December 2013. Those of Phyllostomus were collected in the Atlantic Forest region in June 2013.
These findings indicate that the circulation of Morbillivirus species in bats from Brazilian forest regions, both Amazonian and Atlantic, has been occurring for almost a decade, so we can infer that viruses of this genus have found a favorable environment and opportunity to spread and maintain a cycle of infection, at least among bats. However, the way this circulation occurs and whether there is any correlation regarding the origin of circulating viruses remain to be determined.
Another fact to be noted is that, in these studies, the samples where the viral sequences were found were from rectal swabs, indicating that Morbillivirus in bats may be infecting both the respiratory and intestinal tracts. It is important to note that these systems may also serve as excretory pathways for the virus, so its transmission becomes more likely. These findings are consistent with those reported by Black (1991) [12], who points out that morbilliviruses cause respiratory and gastrointestinal infections. In addition, given the continuous contact of humans with a variety of bat species both in urban and rural areas, there is the possibility of transmission to humans in a possible spillover event.
A Morbillivirus infection can occur through the respiratory tract when the host inhales viral particles in suspension [12]. Given the habitat of bats, with the formation of colonies and constant exposure to an environment where feces and urine are present, infection through aerosols with viral particles and contaminated feces may be important routes of transmission for Morbillivirus amongst these animals. There are also precedents for vertical transmission of Morbillivirus species [46].
To better assess the possibility of human infection by bat morbillivirus, it is worth looking at the results presented by Lee and colleagues (2021) [11]. In the study, the ability of Myotis bat morbillivirus (MBM) to use cells expressing the CD150/SLAMF1 and NECTIN-4 receptors to replicate is evaluated. The former is expressed by immune cells, and the latter by epithelial cells. The results show that the virus has limited ability to use CD150 to infect human and dog cells, presenting a higher affinity for CD150 expressed by bats. As for NECTIN-4, the MBM was able to replicate in human lung cells that have a higher expression of this receptor.
Considering that the CD150 receptor shows greater variability between species and that NECTIN-4 is more conserved [47], it makes sense that Morbilliviruses coming from bats would be able to infect human epithelial cells that express NECTIN-4 but would not have the same success with cells expressing CD150 as this receptor would be the major one responsible for the specificity of Morbillivirus species for their hosts. It is fair to say that the occurrence of human Morbillivirus infections from bats seems to have little potential to cause great harm to those infected given the difficulty that the virus would have in replicating and spreading throughout the human body, especially if we look at the fact that, in Measles morbillivirus infection, cells expressing CD150 play a vital role in the systemic spread of infection. In addition, it is worth stating that the human-interferon-related defense process seems to be quite efficient in fighting a possible chiropters Morbillivirus infection, and that the eventual success of these viruses in human hosts would very much depend on improving their ability to resist or evade human host defenses [11].
The infection cycle of a Morbillivirus usually begins by entering cells that display CD150 (or SLAM-F1), such as activated lymphocytes, macrophages, and some types of dendritic cells. The virus then reaches secondary lymphoid organs, immune tissues, and then invades epithelial tissues via NECTIN-4 [48]. Taking this infection cycle into consideration, the finding of a Morbillivirus genomic sequence in bat lung may indicate that the virus managed to affect the entire infection pathway in the animal, even though it is not possible to determine if it can cause pathology, particularly due to the failure to obtain viral particles from the tissue used, and further studies are needed to elucidate this issue.

4.3. Dicistroviridae

Currently, the Dicistroviridae family is composed of the Aparavirus, Cripavirus, and Triatovirus genera [39]. This family presents a low degree of similarity among its species. Regarding its structural polyproteins, the aminoacidic similarity varies from 37% to 80% [49]. Moreover, some studies have already performed allocations of new viruses within this family with amino acid identity percentages lower than 40% [13,15]. Based on the nucleotide (89%, 83%, and 51%) and amino acid (94, 91%, and 42%) identity values of Culex dicistrovirus 2, Rhopalosiphum padi, and Aphid lethal paralysis viruses, the presence of common protein domains, and phylogenetic inference, it was possible to classify a contig within the genus Cripavirus, as well as other reference sequences classified in ICTV.
The detection of viruses known to infect insects and other arthropods in the feces of vertebrate animals can occur due to the process of insect oral entry into the studied animal, either by chance or on purpose due to food characteristics, which ends up reaching the intestine, and then, if the insect is carrying a dicistrovirus, it is detected in stool samples. When it comes to detection in tissues and blood, this justification seems more implausible. In those cases, we can discuss an interspecies barrier crossing occurrence, where the contact of these viruses with other hosts may have eventually led to an adaptation of them in these animals. However, in order to verify this possibility, studies involving viral isolation from vertebrate animal samples would be necessary. Even more complicated is to conclude whether the detection of these viruses is related to the presence of a disease process in the vertebrate host, especially when it comes to bats, animals that are known to carry viruses without having an associated clear pathogenic process. This would also require studies to prove the occurrence of pathogenicity in bat cells caused by dicistroviruses in cell culture. There has already been an attempt to culture a dicistrovirus from contaminated liver samples using vertebrate (Vero E6) and invertebrate (C6/36) cells, but there was no success [13].
The papers reviewed for the present work that detected dicistroviruses in chiropterans encompassed especially insectivorous bat species [13,16,17,18], but also fruit bats [19]. Frugivorous bats, although not as a primary goal, also end up ingesting insects through fruits due to the close relationship of these arthropods with this type of food [19]. Thus, it is valid to say that these groups of chiropterans have greater possibilities for a virus originated from insects to end up adapting to their organisms due to the constant interaction between them, which enhances the chances of a spillover event.
Another worthwhile point to mention is that bat species where dicistroviruses have been shown to be present involve the genera Molossus sp. and Pipistrellus pipistrellus [16,18]. These chiropterans are commonly cataloged near human habitations [50,51]. Thus, it is possible to imagine a scenario where the human population may end up coming into contact with viruses of the Dicistroviridae family, not only through insects but also through the feces of bats, for example.
Some studies have already reported the presence of this viral family in human serum samples. In these studies, the patients tested had febrile states. None of the research has ever determined whether the presence of the Dicistroviridae family viruses in these patients was involved in any way with their febrile condition or their ability to replicate in human cells [52,53].

5. Conclusions

The metagenomic analysis of lung samples from bats (Malossus sp.) captured in different states of the Brazilian Amazon region revealed the presence of several viral families. However, none of these families showed the presence of a virus with known significant medical importance in humans or animals, and there was no discernible correlation between the viruses of different samples, their location, or year of collection.
Nevertheless, this research showed a partial genome of a Morbillivirus that may represent a new species, identified by its similarity to the Porcine morbillivirus and Phyllostomus bat morbillivirus genomic sequences. Additionally, the presence of a genomic sequence of a possible new strain of Culex dicistrovirus 2 suggests a possible association with the feeding habits of these bats.
Overall, metagenomic surveillance studies are important tools for identifying and monitoring viruses in bat populations, with the aim of predicting outbreaks of viruses of medical importance.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/microorganisms12030593/s1, Figure S1: Eleven morbilliviruses’ amino acid sequences alignment, including the Molossus bat morbillivirus sequence; Figure S2: Alignment of the amino acid sequences of six Dicistroviruses, including the Culex dicistrovirus 2 5RO20/CDV sequence; Table S1: Classification of the remaining readings—post treatment step—through the Kraken program; Table S2: Nucleotide and amino acid identity matrix of the MBMV sequence with sequences from other Morbilliviruses; Table S3: Nucleotide and amino acid identity matrix of Culex dicistrovirus 2 5RO20/CDV with sequences of other Cripaviruses.

Author Contributions

Conceptualization and Methodology: L.R.S.P., L.M.N.C. and S.P.d.S.; investigation: L.R.S.P., S.P.d.S., É.D.L.R. and F.A.d.S.P.; data curation: S.P.d.S.; resources: A.C.R.C. and L.M.N.C.; writing—original draft preparation: L.R.S.P.; visualization: L.R.S.P. and S.P.d.S.; writing—review and editing: S.P.d.S., L.M.N.C., D.B.d.A.M. and A.d.R.C.; project administration: L.M.N.C. and S.P.d.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Coordination of Superior Level Staff Improvement (CAPES—process number: 88887.636483/2021-00) for financial support.

Institutional Review Board Statement

Ethical review and approval were waived for this study because the bats used in this research were sent dead to Evandro Chagas Institute as part of the rabies control program carried out by Brazilian health agencies. Therefore, there was no need to request authorization from the institution’s ethics committee as no animal was killed in order to collect the lung samples.

Data Availability Statement

The data generated in this work are available at NCBI under the BioProject identification PRJNA956836. The SRA archives are identified by the accession identifications SAMN34231019 to SAMN34231031. Molossus bat morbillivirus nucleotide sequence is available at NCBI under the accession number OQ891082, and Culex dicistrovirus 2 strain 5RO20/CDV is available under the accession number OQ915114.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Silva, L.H.Q.D.; Cunha, E.M.S.; Pedro, W.A.; Cardoso, T.C.; Souza, M.D.C.C.D.; Ferrari, C.I.L. Isolamento Do Vírus Rábico Em Molossus Ater (Chiroptera: Molossidae) No Estado de São Paulo. Rev. Saúde Pública 1999, 33, 626–628. [Google Scholar] [CrossRef] [PubMed]
  2. Silva, L.A.M.; Santos, E.M.; Amorim, F.O. Predação oportunística de Molossus molossus (Pallas, 1766) (Chiroptera: Molossidae) por Rhinella jimi (Stevaux, 2002) (Anura: Bufonidae) na Caatinga, Pernambuco, Brasil. Biotemas 2010, 23, 215–218. [Google Scholar]
  3. Costa, L.J.C.; Andrade, F.A.A.; Uieda, W.; Gregorin, R.; Barroncas, M.E.F. First record of Molossus coibensis (Chiroptera: Molossidae) in the brazilian amazon. Mastozoología Neotrop. 2013, 20, 143–147. [Google Scholar]
  4. Corrêa, M.M.O.; Lazar, A.; Dias, D.; Bonvicino, C.R. Quirópteros hospedeiros de zoonoses no Brasil. Bol. Soc. Bras. Mastozool 2013, 67, 23–38. [Google Scholar]
  5. Salmier, A.; Tirera, S.; Thoisy, B.; Franc, A.; Darcissac, E.; Donato, D.; Bouchier, C.; Lacoste, V.; Lavergne, A. Virome analysis of two sympatric bat species (Desmodus rotundus and Molossus molossus) in French Guiana. PLoS ONE 2017, 12, e0186943. [Google Scholar] [CrossRef] [PubMed]
  6. Drexler, J.F.; Corman, V.M.; Müller, M.A.; Maganga, G.D.; Vallo, P.; Binger, T.; Gloza-Rausch, F.; Cottontail, V.M.; Rasche, A.; Yordanov, S.; et al. Bats Host Major Mammalian Paramyxoviruses. Nat. Commun. 2012, 3, 796. [Google Scholar] [CrossRef]
  7. Rima, B.; Balkema-Buschmann, A.; Dundon, W.G.; Duprex, P.; Easton, A.; Fouchier, R.; Kurath, G.; Lamb, R.; Lee, B.; Rota, P.; et al. ICTV virus taxonomy profile: Paramyxoviridae. J. Gen. Virol. 2019, 100, 1593–1594. [Google Scholar] [CrossRef] [PubMed]
  8. Strauss, J.H.; Strauss, E.G. Minus-Strand RNA Virus. In Viruses and Human Disease, 2nd ed.; Strauss, J.H., Strauss, E.G., Eds.; EUA; Elsevier: Berkeley, CA, USA, 2008; pp. 137–191. [Google Scholar] [CrossRef]
  9. King, A.M.; Lefkowitz, E.; Adams, M.J.; Carstens, E.B. (Eds.) Virus Taxonomy: Ninth Report of the International Committee on Taxonomy of Viruses; EUA; Elsevier Science: Amsterdam, The Netherlands, 2012. [Google Scholar]
  10. Portela, V.A.D.B.; Lima, T.M.; Maia, R.D.C.C. Cinomose Canina: Revisão de Literatura. Med. Vet. (UFRPE) 2017, 11, 162. [Google Scholar] [CrossRef]
  11. Lee, B.; Ikegame, S.; Carmichael, J.; Wells, H.; Furler, R.; Acklin, J.; Chiu, H.-P.; Oguntuyo, K.; Cox, R.; Patel, A.; et al. Zoonotic Potential of a Novel Bat Morbillivirus. Res. Sq. 2021. preprint. [Google Scholar] [CrossRef]
  12. Black, F.L. Epidemiology of Paramyxoviridae. In The Paramyxoviruses, 1st ed.; Kingsbury, D.W., Ed.; The Viruses; EUA; Springer: New York, NY, USA, 1991; pp. 509–536. [Google Scholar]
  13. Fumagalli, M.J.; De Souza, W.M.; De Araujo, J.; Modha, S.; Queiroz, L.H.; Durigon, E.L.; Murcia, P.R.; Figueiredo, L.T.M. Krykféie Dicistrovirus: A Novel Dicistrovirus in Velvety Free-Tailed Bats from Brazil. Infect. Genet. Evol. 2019, 75, 104036. [Google Scholar] [CrossRef]
  14. Bonning, B.C.; Miller, W.A. Dicistroviruses. Annu. Rev. Entomol. 2010, 55, 129–150. [Google Scholar] [CrossRef]
  15. Greninger, A.L.; Jerome, K.R. Draft Genome Sequence of Goose Dicistrovirus. Genome Announc. 2016, 4, e00068-16. [Google Scholar] [CrossRef]
  16. Reuter, G.; Pankovics, P.; Gyöngyi, Z.; Delwart, E.; Boros, Á. Novel Dicistrovirus from Bat Guano. Arch. Virol. 2014, 159, 3453–3456. [Google Scholar] [CrossRef]
  17. Bourgarel, M.; Noël, V.; Pfukenyi, D.; Michaux, J.; André, A.; Becquart, P.; Cerqueira, F.; Barrachina, C.; Boué, V.; Talignani, L.; et al. Next-Generation Sequencing on Insectivorous Bat Guano: An Accurate Tool to Identify Arthropod Viruses of Potential Agricultural Concern. Viruses 2019, 11, 1102. [Google Scholar] [CrossRef]
  18. Dacheux, L.; Cervantes-Gonzalez, M.; Guigon, G.; Thiberge, J.-M.; Vandenbogaert, M.; Maufrais, C.; Caro, V.; Bourhy, H. A Preliminary Study of Viral Metagenomics of French Bat Species in Contact with Humans: Identification of New Mammalian Viruses. PLoS ONE 2014, 9, e87194. [Google Scholar] [CrossRef]
  19. Bennett, A.J.; Bushmaker, T.; Cameron, K.; Ondzie, A.; Niama, F.R.; Parra, H.-J.; Mombouli, J.-V.; Olson, S.H.; Munster, V.J.; Goldberg, T.L. Diverse RNA Viruses of Arthropod Origin in the Blood of Fruit Bats Suggest a Link between Bat and Arthropod Viromes. Virology 2019, 528, 64–72. [Google Scholar] [CrossRef] [PubMed]
  20. Pereira, R.H.M.; Gonçalves, C.N. Geobr: Loads Shapefiles of Official Spatial Data Sets of Brazil. GitHub Repository. 2019. Available online: https://github.com/ipeaGIT/geobr (accessed on 21 November 2023).
  21. Chen, S. Ultrafast One-pass FASTQ Data Preprocessing, Quality Control, and Deduplication Using Fastp. iMeta 2023, 2, e107. [Google Scholar] [CrossRef]
  22. Langmead, B.; Salzberg, S.L. Fast Gapped-Read Alignment with Bowtie 2. Nat. Methods 2012, 9, 357–359. [Google Scholar] [CrossRef] [PubMed]
  23. Jebb, D.; Huang, Z.; Pippel, M.; Hughes, G.M.; Lavrichenko, K.; Devanna, P.; Winkler, S.; Jermiin, L.S.; Skirmuntt, E.C.; Katzourakis, A.; et al. Six Reference-Quality Genomes Reveal Evolution of Bat Adaptations. Nature 2020, 583, 578–584. [Google Scholar] [CrossRef]
  24. Kopylova, E.; Noé, L.; Touzet, H. SortMeRNA: Fast and Accurate Filtering of Ribosomal RNAs in Metatranscriptomic Data. Bioinformatics 2012, 28, 3211–3217. [Google Scholar] [CrossRef]
  25. Buchfink, B.; Xie, C.; Huson, D.H. Fast and Sensitive Protein Alignment Using DIAMOND. Nat. Methods 2015, 12, 59–60. [Google Scholar] [CrossRef] [PubMed]
  26. Ondov, B.D.; Bergman, N.H.; Phillippy, A.M. Interactive Metagenomic Visualization in a Web Browser. BMC Bioinform. 2011, 12, 385. [Google Scholar] [CrossRef] [PubMed]
  27. Bankevich, A.; Nurk, S.; Antipov, D.; Gurevich, A.A.; Dvorkin, M.; Kulikov, A.S.; Lesin, V.M.; Nikolenko, S.I.; Pham, S.; Prjibelski, A.D.; et al. SPAdes: A New Genome Assembly Algorithm and Its Applications to Single-Cell Sequencing. J. Comput. Biol. 2012, 19, 455–477. [Google Scholar] [CrossRef] [PubMed]
  28. Li, D.; Liu, C.-M.; Luo, R.; Sadakane, K.; Lam, T.-W. MEGAHIT: An Ultra-Fast Single-Node Solution for Large and Complex Metagenomics Assembly via Succinct de Bruijn Graph. Bioinformatics 2015, 31, 1674–1676. [Google Scholar] [CrossRef]
  29. Besemer, J. GeneMarkS: A Self-Training Method for Prediction of Gene Starts in Microbial Genomes. Implications for Finding Sequence Motifs in Regulatory Regions. Nucleic Acids Res. 2001, 29, 2607–2618. [Google Scholar] [CrossRef]
  30. Jones, P.; Binns, D.; Chang, H.-Y.; Fraser, M.; Li, W.; McAnulla, C.; McWilliam, H.; Maslen, J.; Mitchell, A.; Nuka, G.; et al. InterProScan 5: Genome-Scale Protein Function Classification. Bioinformatics 2014, 30, 1236–1240. [Google Scholar] [CrossRef]
  31. Katoh, K.; Standley, D.M. MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability. Mol. Biol. Evol. 2013, 30, 772–780. [Google Scholar] [CrossRef]
  32. Myung, I.J. Tutorial on Maximum Likelihood Estimation. J. Math. Psychol. 2003, 47, 90–100. [Google Scholar] [CrossRef]
  33. Nguyen, L.-T.; Schmidt, H.A.; Von Haeseler, A.; Minh, B.Q. IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies. Mol. Biol. Evol. 2015, 32, 268–274. [Google Scholar] [CrossRef]
  34. Felsenstein, J. Confidence Limits on Phylogenies: An Approach Using the Bootstrap. Evolution 1985, 39, 783. [Google Scholar] [CrossRef]
  35. Cibulski, S.P.; De Sales Lima, F.E.; Teixeira, T.F.; Varela, A.P.M.; Scheffer, C.M.; Mayer, F.Q.; Witt, A.A.; Roehe, P.M. Detection of Multiple Viruses in Oropharyngeal Samples from Brazilian Free-Tailed Bats (Tadarida brasiliensis) Using Viral Metagenomics. Arch. Virol. 2021, 166, 207–212. [Google Scholar] [CrossRef]
  36. Wiederkehr, M.A.; Qi, W.; Schoenbaechler, K.; Fraefel, C.; Kubacki, J. Virus Diversity, Abundance, and Evolution in Three Different Bat Colonies in Switzerland. Viruses 2022, 14, 1911. [Google Scholar] [CrossRef]
  37. Kutikhin, A.G.; Yuzhalin, A.E.; Brusina, E.B. Mimiviridae, Marseilleviridae, and Virophages as Emerging Human Pathogens Causing Healthcare-Associated Infections. GMS Hyg. Infect. Control 2014, 9, Doc16. [Google Scholar] [CrossRef]
  38. Schulz, F.; Abergel, C.; Woyke, T. Giant Virus Biology and Diversity in the Era of Genome-Resolved Metagenomics. Nat. Rev. Microbiol. 2022, 20, 721–736. [Google Scholar] [CrossRef]
  39. International Committee on Taxonomy of Viruses (ICTV). Current ICTV Taxonomy Release. 2021. Available online: https://ictv.global/taxonomy/ (accessed on 17 January 2023).
  40. Livingstone Bell, P.J. Viral Eukaryogenesis: Was the Ancestor of the Nucleus a Complex DNA Virus? J. Mol. Evol. 2001, 53, 251–256. [Google Scholar] [CrossRef]
  41. Hayward, J.A.; Tachedjian, G. Retroviruses of Bats: A Threat Waiting in the Wings? mBio 2021, 12, e01941-21. [Google Scholar] [CrossRef]
  42. Graves, D.C.; Ferrer, J.F. In vitro transmission and propagation of the bovine leukemia virus in monolayer cell cultures. Cancer Res. 1976, 36, 4152–4159. [Google Scholar]
  43. Escalera-Zamudio, M.; Mendoza, M.L.Z.; Heeger, F.; Loza-Rubio, E.; Rojas-Anaya, E.; Méndez-Ojeda, M.L.; Taboada, B.; Mazzoni, C.J.; Arias, C.F.; Greenwood, A.D. A Novel Endogenous Betaretrovirus in the Common Vampire Bat (Desmodus rotundus) Suggests Multiple Independent Infection and Cross-Species Transmission Events. J. Virol. 2015, 89, 5180–5184. [Google Scholar] [CrossRef]
  44. Sinha, A.; Johnson, W.E. Retroviruses of the RDR Superinfection Interference Group: Ancient Origins and Broad Host Distribution of a Promiscuous Env Gene. Curr. Opin. Virol. 2017, 25, 105–112. [Google Scholar] [CrossRef]
  45. Wells, H.L.; Loh, E.; Nava, A.; Solorio, M.R.; Lee, M.H.; Lee, J.; Sukor, J.R.A.; Navarrete-Macias, I.; Liang, E.; Firth, C.; et al. Classification of New Morbillivirus and Jeilongvirus Sequences from Bats Sampled in Brazil and Malaysia. Arch. Virol. 2022, 167, 1977–1987. [Google Scholar] [CrossRef]
  46. Chiba, M.E.; Saito, M.; Suzuki, N.; Honda, Y.; Yaegashi, N. Measles Infection in Pregnancy. J. Infect. 2003, 47, 40–44. [Google Scholar] [CrossRef]
  47. Ohishi, K.; Maruyama, T.; Seki, F.; Takeda, M. Marine Morbilliviruses: Diversity and Interaction with Signaling Lymphocyte Activation Molecules. Viruses 2019, 11, 606. [Google Scholar] [CrossRef]
  48. Pfeffermann, K.; Dörr, M.; Zirkel, F.; Von Messling, V. Morbillivirus Pathogenesis and Virus–Host Interactions. In Advances in Virus Research; Elsevier: Amsterdam, The Netherlands, 2018; Volume 100, pp. 75–98. ISBN 9780128152010. [Google Scholar]
  49. Christian, P.D.; Scotti, P.D. Dicistroviruses. In Encyclopedia of Virology; Elsevier: Amsterdam, The Netherlands, 2008; pp. 37–44. ISBN 9780123744104. [Google Scholar]
  50. Santos, J.L. Manual de Manejo e Monitoramento de Morcegos em Áreas Urbanas. Final Paper, Biologist. Ph.D. Thesis, Federal University of Pernambuco, Pernambuco, Brazil, 10 December 2019. [Google Scholar]
  51. FCTViva. Available online: https://www.viva.fct.unl.pt/mamiferos/pipistrellus-pipistrellus (accessed on 5 February 2023).
  52. Phan, T.G.; Del Valle Mendoza, J.; Sadeghi, M.; Altan, E.; Deng, X.; Delwart, E. Sera of Peruvians with Fever of Unknown Origins Include Viral Nucleic Acids from Non-Vertebrate Hosts. Virus Genes 2018, 54, 33–40. [Google Scholar] [CrossRef]
  53. Cordey, S.; Laubscher, F.; Hartley, M.-A.; Junier, T.; Pérez-Rodriguez, F.J.; Keitel, K.; Vieille, G.; Samaka, J.; Mlaganile, T.; Kagoro, F.; et al. Detection of Dicistroviruses RNA in Blood of Febrile Tanzanian Children. Emerg. Microbes Infect. 2019, 8, 613–623. [Google Scholar] [CrossRef]
Microorganisms 12 00593 g001
Figure 1. Identification map of the states and marking the collection sites of the bats used in the analyses. Legend: (A) in red is the north Brazilian region, which includes the states where the bats were collected; (B) states that had bat samples included in this work and the marking in black dots of the places in which the samples were collected. The maps were assembled using data from IBGE (2020) and with the programs ggplot2 and geobr.
Figure 1. Identification map of the states and marking the collection sites of the bats used in the analyses. Legend: (A) in red is the north Brazilian region, which includes the states where the bats were collected; (B) states that had bat samples included in this work and the marking in black dots of the places in which the samples were collected. The maps were assembled using data from IBGE (2020) and with the programs ggplot2 and geobr.
Microorganisms 12 00593 g001
Microorganisms 12 00593 g002
Figure 2. Heatmap for the reads of each pool related to their classification by viral family.
Figure 2. Heatmap for the reads of each pool related to their classification by viral family.
Microorganisms 12 00593 g002
Microorganisms 12 00593 g003
Figure 3. Identification of the MBMV protein domains, their comparison with domains in the same region of the Porcine morbillivirus genome, and positioning in the L protein ORF. Legend: PM = Porcine morbillivirus; MBMV = Molossus bat morbillivirus; PBM = Phyllostomus bat morbillivirus.
Figure 3. Identification of the MBMV protein domains, their comparison with domains in the same region of the Porcine morbillivirus genome, and positioning in the L protein ORF. Legend: PM = Porcine morbillivirus; MBMV = Molossus bat morbillivirus; PBM = Phyllostomus bat morbillivirus.
Microorganisms 12 00593 g003
Microorganisms 12 00593 g004
Figure 4. Phylogenetic tree of the Paramyxoviridae family. Legend: the Maximum Likelihood (ML) method was applied based on the amino acid sequences of a partial L protein obtained from metagenomic analysis. The LG+F+I+G4 model was used as the best amino acid substitution model. The sample identified in this study is highlighted in red. The different genera are highlighted by colors. The numbers at each main node in the tree correspond to bootstrap values in percentage (1000 replicates). The scale bar corresponds to the amino acid divergence per site between sequences.
Figure 4. Phylogenetic tree of the Paramyxoviridae family. Legend: the Maximum Likelihood (ML) method was applied based on the amino acid sequences of a partial L protein obtained from metagenomic analysis. The LG+F+I+G4 model was used as the best amino acid substitution model. The sample identified in this study is highlighted in red. The different genera are highlighted by colors. The numbers at each main node in the tree correspond to bootstrap values in percentage (1000 replicates). The scale bar corresponds to the amino acid divergence per site between sequences.
Microorganisms 12 00593 g004
Microorganisms 12 00593 g005
Figure 5. Culex dicistrovirus 2 5RO20/CDV protein domains, their comparison with domains in the same region of the Culex dicistrovirus 2, and Rhopalosiphum padi virus genomes and their positioning in the non-structural polyprotein ORF. Legend: CD = Culex dicistrovirus 2; CDV = Culex dicistrovirus 2 strain 5RO20; RhPV = Rhopalosiphum padi virus.
Figure 5. Culex dicistrovirus 2 5RO20/CDV protein domains, their comparison with domains in the same region of the Culex dicistrovirus 2, and Rhopalosiphum padi virus genomes and their positioning in the non-structural polyprotein ORF. Legend: CD = Culex dicistrovirus 2; CDV = Culex dicistrovirus 2 strain 5RO20; RhPV = Rhopalosiphum padi virus.
Microorganisms 12 00593 g005
Microorganisms 12 00593 g006
Figure 6. Phylogenetic tree of the Dicistroviridae family. Legend: the Maximum Likelihood (ML) method was applied based on the amino acid sequences of a partial non-structural polyprotein region containing 1454 nucleotides obtained from metagenomic analysis. The VT+F+R4 model was used as the best amino acid substitution model. The sample identified in this study is highlighted in red. The different genera are highlighted by colors. The numbers at each main node in the tree correspond to bootstrap values in percentage (1000 replicates). The scale bar corresponds to the amino acid divergence per site between sequences.
Figure 6. Phylogenetic tree of the Dicistroviridae family. Legend: the Maximum Likelihood (ML) method was applied based on the amino acid sequences of a partial non-structural polyprotein region containing 1454 nucleotides obtained from metagenomic analysis. The VT+F+R4 model was used as the best amino acid substitution model. The sample identified in this study is highlighted in red. The different genera are highlighted by colors. The numbers at each main node in the tree correspond to bootstrap values in percentage (1000 replicates). The scale bar corresponds to the amino acid divergence per site between sequences.
Microorganisms 12 00593 g006
Table 1. Total reads generated by sequencing and remaining after each step in the data cleaning process.
Table 1. Total reads generated by sequencing and remaining after each step in the data cleaning process.
Pool1 Pool IdentificationRaw ReadsAfter FastpAfter Host Genome Sequences RemovalAfter SortMeRNA
Pool 15AM2029,907,99627,369,0148,950,9028,788,973
Pool 25PA1976,284,19471,366,1807,303,2745,792,842
Pool 35PA2080,318,83270,481,54411,806,56610,503,106
Pool 45PA2178,606,73668,321,9689,240,9387,950,931
Pool 54PA20/2177,126,06067,638,4548,610,3167,034,313
Pool 65RO1936,750,81632,750,3203,913,7903,665,172
Pool 75RO2036,711,94833,125,9728,523,7127,023,625
Pool 85RO21108,272,43496,549,53014,148,22211,945,547
Pool 95RO20/2122,753,75220,550,0605,807,6985,697,788
Pool 104RR1959,314,23052,754,68010,355,1189,924,522
Pool 114RR20/2185,614,88479,378,24011,628,98410,669,511
Pool 124TO1936,099,76033,144,5407,288,2825,801,459
Pool 135TO20/21103,219,94891,761,25411,082,91010,401,188
1 Pool identification: the initial number consists of the number of bats, the letters correspond to the state sample origin, and the final numbers correspond to the year of collecting (e.g., 4PA20/21 to the pool of four chiropterans from Pará State, collected in the years 2020 and 2021).
Table 2. Total contigs generated and positive for viruses using the treated reads and the MEGAHIT and SPADES assemblers.
Table 2. Total contigs generated and positive for viruses using the treated reads and the MEGAHIT and SPADES assemblers.
PoolsContigs MegahitContigs SpadesContigs Identified for Viruses
5AM2058,144465,1652885
5PA1945,984294,2802561
5PA2059,724354,5122911
5PA2138,614221,1681633
4PA20/2132,277190,4894579
5RO1920,548152,3331208
5RO2063,143443,2494857
5RO2166,122341,6958452
5RO20/2157,360437,2102207
4RR1972,500461,1122647
4RR20/21111,111663,0044182
4TO1944,285381,6863521
5TO20/2179,031450,5343169
Total748,8434,856,43744.812
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Pinheiro, L.R.S.; Rodrigues, É.D.L.; Paiva, F.A.d.S.; Cruz, A.C.R.; Medeiros, D.B.d.A.; Casseb, A.d.R.; Silva, S.P.d.; Casseb, L.M.N. Identification of Viruses in Molossus Bats from the Brazilian Amazon: A Descriptive Metagenomic Analysis. Microorganisms 2024, 12, 593. https://doi.org/10.3390/microorganisms12030593

AMA Style

Pinheiro LRS, Rodrigues ÉDL, Paiva FAdS, Cruz ACR, Medeiros DBdA, Casseb AdR, Silva SPd, Casseb LMN. Identification of Viruses in Molossus Bats from the Brazilian Amazon: A Descriptive Metagenomic Analysis. Microorganisms. 2024; 12(3):593. https://doi.org/10.3390/microorganisms12030593

Chicago/Turabian Style

Pinheiro, Lucas Rafael Santana, Érika Dayane Leal Rodrigues, Francisco Amilton dos Santos Paiva, Ana Cecília Ribeiro Cruz, Daniele Barbosa de Almeida Medeiros, Alexandre do Rosário Casseb, Sandro Patroca da Silva, and Livia Medeiros Neves Casseb. 2024. "Identification of Viruses in Molossus Bats from the Brazilian Amazon: A Descriptive Metagenomic Analysis" Microorganisms 12, no. 3: 593. https://doi.org/10.3390/microorganisms12030593

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop