==== Front Cells Cells cells Cells 2073-4409 MDPI 33238430 10.3390/cells9112525 cells-09-02525 Article MicroRNAs and Mammarenaviruses: Modulating Cellular Metabolism https://orcid.org/0000-0002-5039-0604Fernandes Jorlan 1 https://orcid.org/0000-0002-3305-3913Miranda Renan Lyra 2 de Lemos Elba Regina Sampaio 1* https://orcid.org/0000-0001-8323-1477Guterres Alexandro 1* 1 Hantaviruses and Rickettsiosis Laboratory, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro 21040-900, Brazil; jorlan@ioc.fiocruz.br 2 Neurochemistry Interactions Laboratory, Universidade Federal Fluminense, Niterói 24020-150, Brazil; renan_miranda@id.uff.br * Correspondence: elemos@ioc.fiocruz.br (E.R.S.d.L.); guterres@ioc.fiocruz.br (A.G.); Tel.: +55-21-2562-1727 (E.R.S.d.L. & A.G.) 23 11 2020 11 2020 9 11 252513 10 2020 11 11 2020 © 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).Mammarenaviruses are a diverse genus of emerging viruses that include several causative agents of severe viral hemorrhagic fevers with high mortality in humans. Although these viruses share many similarities, important differences with regard to pathogenicity, type of immune response, and molecular mechanisms during virus infection are different between and within New World and Old World viral infections. Viruses rely exclusively on the host cellular machinery to translate their genome, and therefore to replicate and propagate. miRNAs are the crucial factor in diverse biological processes such as antiviral defense, oncogenesis, and cell development. The viral infection can exert a profound impact on the cellular miRNA expression profile, and numerous RNA viruses have been reported to interact directly with cellular miRNAs and/or to use these miRNAs to augment their replication potential. Our present study indicates that mammarenavirus infection induces metabolic reprogramming of host cells, probably manipulating cellular microRNAs. A number of metabolic pathways, including valine, leucine, and isoleucine biosynthesis, d-Glutamine and d-glutamate metabolism, thiamine metabolism, and pools of several amino acids were impacted by the predicted miRNAs that would no longer regulate these pathways. A deeper understanding of mechanisms by which mammarenaviruses handle these signaling pathways is critical for understanding the virus/host interactions and potential diagnostic and therapeutic targets, through the inhibition of specific pathologic metabolic pathways. microRNAsmammarenavirusescellular metabolismamino acid metabolismmetabolism of cofactors and vitamins ==== Body 1. Introduction Mammarenaviruses (Bunyavirales: Arenaviridae: Mammarenavirus) are enveloped bi-segmented ambisense RNA viruses and each segment encodes for two oppositely oriented non-overlapping reading frames separated by an intergenic region, a small (S) segment that encodes the envelope glycoprotein precursor (GPC) and the nucleoprotein (NP) and a large (L) segment coding for the matrix protein (Z) and the RNA-dependent RNA polymerase (RdRp) [1,2]. Based on their genomic features and antigenic properties, mammarenaviruses are historically classified into two large monophyletic clades—the Old World (OW) and New World (NW) groups, both containing important zoonotic pathogens affecting humans. NW mammarenavirus, or Tacaribe serocomplex, includes viruses indigenous to the Americas and can be further divided into Clades A, B, C, and D while the OW or Lassa-lymphocytic choriomeningitis virus (LCMV) serocomplex includes viruses from Africa and, recently, some viruses isolated in Asia [1,3,4]. Some of these emerging, zoonotic viruses are pathogens of major clinical importance to humans, as (i) Lassa virus (OW) is endemic in West Africa and is estimated to cause up to 300,000 infections each year, (ii) Junín (Clade B NW) that periodically cause hemorrhagic fever (HF) outbreaks in Argentina, and (iii) LCMV (OW) which is distributed worldwide and is a neglected human pathogen of clinical significance, to immunocompromised individuals and pregnant women, due to teratogenicity [5,6,7]. Although these two groups share many similarities, important differences with regard to pathogenicity, type of immune response, and molecular mechanisms during virus infection recovery and clearance of the virus is different between and within NW and OW infections. For example, whilst both the OW Lassa virus (LASV) and Junín virus (JUNV) can cause disruption of the vascular endothelium, which is an important pathological feature, the immune responses for LASV infection results in an overall generalized immune suppression, and patients infected with JUNV seem to develop a cytokine storm [8,9]. Understanding the potential differences on a cellular level could aid the development of new vaccines and treatment strategies against these deadly viral infections. MicroRNAs (miRNAs) are the smallest endogenous regulatory non-coding RNAs that play a central role in cell differentiation, proliferation, and survival by binding to complementary target mRNAs leading to translational inhibition or degradation. miRNAs are the crucial factor in diverse biological processes such as antiviral defense, oncogenesis, and cell development [10]. There are complex interaction networks between long non-coding RNAs (lncRNAs), viral genome, and miRNAs. While certain miRNAs can regulated the stability and half-life of lncRNAs and viral genome, lncRNAs and viral genome can also compete for miRNAs acting as miRNA sponges and epigenetic modifications [11,12]. The miRNA-binding sites within viral genomes are mostly located in the 5′ and 3′ non-translated regions (NTRs) but have recently been found in the coding regions of viral proteins [12,13,14]. Viral genomes and gene transcripts affect with host gene expression exploiting passive mechanisms to deregulate host miRNA activity. Various RNA viruses mimic or block the binding between a host miRNA and its target transcript, a phenomenon mediated by the miRNA seed site at the 5′ end of miRNA [12,15]. Based on bioinformatics, we predicted the potential miRNAs, their target genes, and related signaling pathways. To the best of our knowledge, this is the first study to systemically analyze and predict the potential miRNAs and target genes for Mammarenavirus. Our research could help to further assess the roles of miRNAs in virus–host interactions, highlighting potential targets during infection, leading to a better understanding of the metabolic alterations required for the replication of each virus that may lead to novel therapeutic approaches through targeted inhibition of specific cellular metabolic pathways. 2. Materials and Methods The genomic sequences used in the study were all retrieved from the GenBank® database of NCBI (http://www.ncbi.nlm.nih.gov/nuccore/), including all species of the genus Mammarenavirus officially recognized by the International Committee on Taxonomy of Viruses (ICTV) (https://talk.ictvonline.org/taxonomy/). Herein, for a more clear presentation of our results, we considered Clade D New World mammarenaviruses (formally known as Clade A recombinant) as members of Clade A, as well as Xapuri virus that is associated with Clades B, and C was didactically classified here as belonging to Clade B NW mammarenaviruses. Currently, the human genome contains 2654 mature sequences of microRNAs identified in the miRBase database (http://www.mirbase.org). We used BLAST with the Geneious R11.1 software (https://www.geneious.com) to search for miRNAs that interact with the mammarenavirus genome with a perfect alignment of 11 nucleotides encompassing the 8 mer seed region, important specific gene silencing motifs, and regions from the viral RNA [16]. We also sought interactions of miRNAs with transcribed mRNA for the viral proteins and antigenomic viral RNA that is produced during the replication in the Mammarenavirus genus. For microRNA bioinformatic target prediction, we used miRabel, a tool that shows significantly better predictions than other important algorithms such as MBSTAR, miRWalk, ExprTarget, and miRMap [17]. This tool adds all human results of four important prediction algorithms, miRanda [18], PITA [19], SVMicrO [20], and TargetScan [21]. Each of them uses different and complementary features with interactions such as seed match, free energy, site accessibility, and target-site abundance. The miRabel contains data for 2587 human miRNAs which have target mRNAs, 19,799 genes and 275 pathways, representing more than 14.7 million predicted interactions from which 351,298 are experimentally established. These experimentally validated interactions were annotated using miRTarBase [22] and miRecords [23], whereas 5′UTR and CDS predictions were identified with the miRWalk database [24]. miRabel uses genes and pathways information as well as their relationships recovered from the KEGG database [25] while miRNA data were from miRbase (release 22.1) [26] and linked with miRNA target predictions. miRabel produce pathways linked to the resulting interactions that can be retrieved and ranked according to the proportion of its interactions regulated by a given microRNA. Moreover, for each pathway, the number of validated interactions for this miRNA is also indicated [17]. To result in a robust prediction, we consider that the pathways that had 25% of proportion of its interactions are regulated by a given microRNA. The data of mammarenaviruses–miRNA interaction and miRabel results were integrated using the python library pandas [27]. The resulting dataset was used to generate heatmaps to visualize the data with the python library seaborn. We used KEGG pathway classification and its hierarchy relation plotted against all mammarenaviruses. The scale demonstrates how many miRNA–pathway relationships were found for each virus species. If a miRNA targeted multiple pathways that were under the same higher order classification and if multiple miRNA targeted the same pathway, each time would count as a miRNA–pathway relationship in order to represent the possible relevance of miRNAs in that pathway. We started showing higher order pathway classification and subdivided it for better visualization. All procedures were performed using python programming language version 3.6. 3. Results 3.1. MicroRNAs We retrieved a total of 39 genome sequences from GenBank®: 20 NW species, divided into Clade A (8), Clade B (10), and Clade C (2) viruses and 19 OW species. Among the 2654 mature miRNAs identified in the miRBase database, we found that 566 miRNAs can bind to certain regions of the mammarenavirus genome in a total of 755 binding-sites (Supplementary Table S1). We found 316 miRNAs (386 binding-sites) for NW mammarenavirus: 131 of Clade A species (147), 169 Clade B (194), and 45 for Clade C (45). Some of these miRNAs bind to more than one clade, totaling 345. Of these, 231 miRNAs (277) strongly bind in the L segment and 97 to the S segment (109). In total, 313 miRNAs (369) were able to bind in the genome of OW mammarenaviruses: 213 for the L segment (244) and 109 to the S segment (125) (Table 1). The average amount of miRNAs binding-sites by specie was nineteen, with Loie River virus (OW) presenting the lowest amount of predicted miRNAs (8) and Cupixi virus (Clade B NW) the highest (30). Among the 521 binding-sites found within the L segment, 208 were found in the RNA-dependent RNA polymerase (RdRp) region for OW mammarenaviruses and 259 for NW (129 for Clade B, 100 Clade A, and 30 Clade C). We found 27 binding-sites in the Z protein (Z), being 17 for OW and 10 for NW (4 Clade A, 4 Clade B, and 2 Clade C). We identified 19 binding-sites for the noncoding intergenic region of the L segment (L-IGR), with 13 for OW mammarenaviruses, 5 for Clade B, and 1 for Clade A. We have not identified any interaction to L-IGR of the Clade C. Regarding 5′UTR and 3′UTR untranslated region, we found four binding-sites to each region. In total, 233 binding-sites were found within the S segment, being 114 for glycoprotein precursor (GPC), 109 for nucleoprotein (NP), 9 for noncoding intergenic region of the S segment (S-IGR), and 1 bind in the 3′UTR untranslated region. Further information can be found in Supplementary Table S1, where the region with which each miRNA interacts is informed. Fifty-two miRNAs were found binding to more than one virus in the same genomic segment. For example, the hsa-miR-2052 can bind to the S segment of Allpahuayo virus, Bear Canoyn virus, Pichinde virus, and Pirital virus, all belonging to Clade A NW (Table 2). Another interesting example is the hsa-miR-3120-3p binding to L segment of Lujo virus, Lunk virus, Merino Walk virus, and Mopeia virus, all from OW. The complete table with all miRNAs is available in Supplementary Table S2. Thirty-four miRNAs can bind to different regions of L and S segments. For example, hsa-miR-122b-3p can bind to two different positions of Aporé virus L segment, Flexal virus, Machupo virus, Paraná virus, and also to Alxa virus S segment. Another interesting miRNA was miRNA hsa-miR-9-5p that can bind in Pichindé virus L segment, Ryukyu virus, Mobala virus, and to Tacaribe virus S segment (Table 3). The complete table with all miRNAs is available in Supplementary Table S3. Ten miRNAs were predicted to bind different regions of the same virus, especially the miRNA has-miR-8485 binding in three different positions of Ryukyu virus L segment and five positions of Lunk virus L segment, both belonging to OW mammarenaviruses group (Table 4). 3.2. miRNAs and Target Metabolic/Cellular Pathways To further understand the biological implications of miRNAs binding to the mammarenavirus genome, miRabel was used to predict genes targeted by these miRNAs (data not shown). After genes prediction analysis, we performed pathway enrichment analysis of miRNAs target genes based on the KEGG database using miRabel and generated a report of the pathway mapping. Our analysis found a total of 75 pathways (Supplementary Figure S1). Overall, d-Glutamine and d-glutamate metabolism, thiamine metabolism, and valine, leucine, and isoleucine biosynthesis were the main signaling pathways predicted to be regulated by miRNAs (Figure 1). A total of 23 pathways were predicted to Clade A where valine, leucine, and isoleucine biosynthesis, d-Glutamine and d-glutamate metabolism, and biotin metabolism were the main signaling pathways; 55 pathways for Clade B, the top three being thiamine metabolism, biotin metabolism, and valine, leucine, and isoleucine biosynthesis, both in second place and d-Glutamine and d-glutamate metabolism in third place; and 29 for Clade C viruses, where degradation of aromatic compounds and thiamine metabolism were the most common followed by d-Glutamine and d-glutamate metabolism, valine, leucine, and isoleucine biosynthesis, and biotin metabolism, all in second. For miRNAs binding to Old World mammarenavirus, we found a total of 62 predicted pathways, d-Glutamine and d-glutamate metabolism, degradation of aromatic compounds, and thiamine metabolism were main ones (Supplementary Figure S2). The miRabel tool through the KEGG pathway analysis demonstrated that the targets of miRNAs were more associated with amino acid metabolism and metabolism of cofactors and vitamins. d-Glutamine and d-glutamate metabolism pathway was regulated by miRNAs for a total of 49 times (miRNAs encountered in two or more clades were considered multiple times), 8 miRNAs that were found for Clade A, 10 for Clade B, 4 for clade C, and 27 for OW mammarenaviruses, including miRNAs that were found in more than one group of viruses. The thiamine metabolism was targeted 43 times, 3 miRNAs for Clade A, 13 for clade B, 6 for Clade C, and 21 for OW viruses. The results showed a total of 40 miRNAs interactions with the pathway with the valine, leucine, and isoleucine biosynthesis, 12 for Clade A, 12 for Clade B, 4 for Clade C, and 12 for OW viruses. When considering pathways that have at least one experimentally validated interaction for miRNAs, we found a total of 69 pathways out of the 75 initials (Supplementary Figure S3). Dorso-ventral axis formation, degradation of aromatic compounds, and circadian rhythm were the main validated signaling pathways predicted to be regulated by miRNAs (Figure 2). According to each mammarenavirus group, 20 pathways were found in Clade A viruses and valine, leucine, and isoleucine biosynthesis, dorso-ventral axis formation, and d-Glutamine and D-glutamate metabolism were the three main predicted pathways; for Clade B a total of 49 pathways were found with dorso-ventral axis formation first of all, and d-Glutamine and d-glutamate metabolism, Lipoic acid metabolism, valine, leucine, and isoleucine biosynthesis, and Thyroid cancer, equally represented, stand out in proportion. Twenty-two were found for Clade C with degradation of aromatic compounds and thiamine metabolism, biotin metabolism, adherens junction, long-term depression, and thyroid cancer, highlighted in miRNA proportion by pathway. For those miRNAs that were predicted in OW mammarenaviruses, we found a total of 59 pathways, especially dorso-ventral axis formation, degradation of aromatic compounds, and circadian rhythm. Among all predicted pathways, only seven showed no experimentally validated interactions for the set of miRNAs: caffeine metabolism; phenylalanine, tyrosine, and tryptophan biosynthesis; primary bile acid biosynthesis; proximal tubule bicarbonate reclamation; shigellosis; and synthesis and degradation of ketone bodies. 3.3. Mammarenavirus Species, miRNAs, and Target Metabolic/Cellular Pathways We used KEGG pathway classification in order to predict biological relevance of each miRNA found for the different mammarenavirus included in this study. Target genes of miRNAs found were associated with six branches of KEGG pathways: cellular processes, environmental information processing, genetic information processing, human disease, metabolism and, organismal systems. Notably, target genes of miRNAs found are more frequently related to pathways that belong to the metabolism branch, followed by human diseases and organismal systems (Figure 3). Strikingly, the set of miRNAs found for Junín virus, Machupo virus, Lassa virus, and Lymphocytic choriomeningitis virus (LCMV) act strongly in these three branches of KEGG pathways. Within the KEGG metabolism classification, the predicted areas to be more affected by mammarenavirus infection are metabolism of cofactor and vitamins, metabolism of other amino acids, and amino acid metabolism. Pathways related to cancer studies are highlighted within human diseases group. For organismal systems, interactions occurs more frequently in the development and regeneration pathway. We also observed that the miRNAs found for the Junín, Machupo, Oliveros, Lassa, and Lymphocytic choriomeningitis viral genome were the ones with the greatest impact on different pathways (Figure 4). For example, miRNAs that target certain regions of the Lassa genome act in six of the eight metabolism pathways, also acting in four of the five organismal systems pathways, and in three of the five of human diseases pathways. Individually evaluating each virus, we found different sets of mapped pathways. For example, for Pirital virus, miRNAs are mainly associated with the valine, leucine, and isoleucine biosynthesis. Olivero virus degradation of aromatic compounds followed by biotin metabolism were the main predicted pathways. MicroRNAs found for Lujo virus were mainly related to thiamine metabolism (Supplementary Figure S4). 4. Discussion MicroRNAs are noncoding RNAs which downregulate a large number of target mRNAs and modulate cell activity [10]. MicroRNAs functions under normal physiological conditions might be integrated into multilayered control circuits ensuring proper development and cellular homeostasis. However, the dysregulation of miRNA expression or function in response to intrinsic factors (genetic or epigenetic) or extrinsic factors (environmental cues or stress, such as viral infection) may contribute to aberrant gene expression patterns underlying abnormal developmental patterning or metabolic dysfunction [28,29]. Viral genomes could have evolved to directly interact with host miRNAs to facilitate certain steps of their replication and progression. The cellular miRNA composition in infected cells is likely to indirectly affect viruses, because many pathways that promote or limit viral replication or the survival of infected cells are likely to be regulated by cellular miRNAs. In either case, viruses may therefore gain an advantage by reshaping the cellular miRNAs availability [12,15,30,31]. MiRNAs are characterized by variable expression in cells and tissues, which is influenced by the molecular cell environment. Some different miRNAs and miRNA families are predominantly expressed in certain tissues. However, the majority of miRNAs (>80%) is not specific for single tissues [32]. A great example is miR-122 that is highly abundant in liver, with over 60,000 copies in hepatocyte cells [33]. The impact of miR-122 binding on the hepatitis C virus (HCV) genome is critical for viral replication. Upon binding of miR-122, HCV genome translation and initiation of replication are increased by stabilizing and protecting the uncapped HCV RNA genome from degradation [34,35]. MiR-122 in vivo knockout studies have revealed that reduced miR-122 expression in hepatocellular carcinoma correlates with metastasis and poor prognosis [36,37]. These discoveries provide insight into the importance of the roles of miRNAs in maintaining normal cellular function and how disruptions in miRNA expression profiles may heavily impact the development, differentiation, and control of growth leading to diseases. In this article, we predicted several cellular miRNAs potentially regulated by mammarenavirus via direct binding to viral RNAs. Aside from virus specific binding sites, we also predicted cellular pathways that could be dysregulated during viral infection. A large intersection of predicted miRNA sets was found on miRNAs targeting cell metabolism for all mammarenavirus groups, including those considered as human pathogens. As known, viruses are entirely dependent on host metabolism to support its replication, and virally infected cells seem to require complex metabolic alterations in order to deal with the high anabolic demands essential during viral replication [38,39,40]. However, there are highly exclusive patterns of virus-induced remodeling of host cell metabolic machineries, and the mode of cell manipulation appears to be different between RNA and DNA viruses [40,41]. Metabolic studies were successfully applied to a diversity of virus infections in mammals, insects, and plants, besides chronic and acutely infected cell cultures. In all cases, important changes in host cellular metabolism were observed, allowing the identification of specific metabolites and pathways involved in viral infection, and potential cross-talk with the immune system and virus pathogenesis [42,43,44,45,46,47,48]. We identified that d-Glutamine and d-glutamate metabolism, thiamine metabolism, and valine, leucine, and isoleucine biosynthesis seems to be the most affected pathway during mammarenavirus infection. As previous studies have shown for other viral families, mammarenaviruses also could control host-cell metabolism via pos-transcriptional regulations to cope with the pace of the corresponding replication cycles [38,49]. Glutamate metabolism plays a vital role in biosynthesis of nucleic acids and proteins [50]. Glutamine is the most abundant and versatile amino acid in the body and is of fundamental importance to intermediary metabolism, interorgan nitrogen exchange via ammonia (NH3) transport between tissues, and pH homeostasis [51]. It is believed that the viral infection induces glutamine uptake and that glutaminolysis is required to generate cellular energy during infection, allowing the survival of infected cells [52,53]. In fact, LCMV infection of mice was found to change significantly the concentration of α-ketoglutarate, in a concerted fashion, following a response and recovery pattern [54]. The α-ketoglutarate is a product of a set of metabolic reactions that degrade glutamine (glutaminolysis), allowing it to be used anaplerotically to form the intermediates of the tricarboxylic acid cycle (Kreb’s cycle) supporting oxidative phosphorylation [55]. The plasma concentrations followed mice response and recovery dynamics, decreasing at day 3, reaching a minimum concentration at day 7, when viral infection peaks, and returning to the initial concentration at day 14 [54]. These results were also correlated to immune response, including the rise and fall of natural killer cell populations, serum soluble TNF receptor concentration, and viral clearance, providing potential miRNA-mediated pathway targets for molecular diagnostics or therapeutics of mammarenavirus infection and immune response. Similar results have been reposted, where glutamine is important to support HIV-1 replication, when comparing HIV-1 infected with uninfected activated primary human CD4+ T cells. Glutamine concentrations are elevated in HIV-1-infected cells, implying that HIV-1 infection leads to considerable changes in the cellular glutamine metabolism [56]. In another study using metabolomics, researchers showed that the latently infected cells with Kaposi’s Sarcoma-associated herpesvirus (KSHV) have higher levels of glutamine as compared to their mock counterparts [48]. Lysine is another amino acid that has been shown to play an important role on viral infections. It is essential for the replication of viruses and progression of infections [57,58,59]. The extra- and intracellular concentrations of l-lysine amino acid play a limiting role in the synthesis of the virus proteins and in transcription initiation of the retrovirus life cycle, and the deficiency of this essential nutritional element can reduce viral load [60]. Studies have demonstrated serious changes in plasmatic concentrations of l-lysine in HIV-infected patients, where it was found that plasma concentrations of l-lysine were negatively correlated with HIV-1 RNA levels and inversely with CD4 lymphocytes count. Therefore, an excess of l-lysine concentration leads to active HIV replication and reduces the concentration of plasma amino acid [59,61]. In a recent review, Keshavarz and colleagues, based on available metabolomic studies, argued that influenza virus infection can affect with cellular metabolic pathways either directly or indirectly via stimulation of immune system mediators [62]. Curiously, through enhancing the activity of the mTORC1 complex, the influenza virus strengthens numerous metabolic pathways, including glycolysis, glutaminolysis, pentose phosphate, and fatty acid synthesis, to provide more ATP and structural materials for viral replication [62,63,64]. Mammalian target of rapamycin (mTOR) is a conserved serine/threonine kinase that plays a critical role in the control of cellular growth and metabolism [65]. Mapping of the mTOR signaling pathways has revealed that mTOR controls biomass accumulation and metabolism by modulating key cellular processes, including protein synthesis and autophagy [66]. Some important studies have revealed interplays between miRNAs and mTOR pathway during cancer development. These interactions appear to provide a fine-tuning of various cellular functions and contribute qualitatively to the behavior of cancer [67,68]. Furthermore, excessive upregulation of mTOR complex 1 (mTORC1) protein complex pathway could be detrimental because it leads to enhanced production of particular miRNAs [69]. Vital metabolic pathways of host cells are one of the most widely used mechanisms targeted by viruses, resulting in extensive modifications. Some studies have revealed that many different human viruses, such as dengue [70], cytomegalovirus [44,71], and rubella [72], can strongly affect host cell glycolysis, lipid metabolism, and glutaminolysis. Cheng and collaborators demonstrated that enterovirus 71-infected Vero cells had significant changes in glutathione and its related metabolites, and several amino acids, such as glutamate and aspartate. Furthermore, they associated the presence of glutamine in culture medium with an increase in viral replication, and a dimethyl α-ketoglutarate treatment partially mimicked the effect of glutamine supplementation [73]. The small molecule profile of serum from Lassa fever-infected patients indicated a physiological dysregulation affecting pathways mediating blood coagulation, and lipid, amino acid, nucleic acid metabolism changing the levels of numerous metabolites in the circulation [74]. Serum lipids were the most frequently identified molecular class and also the most frequently identified as decreased in fatal Lassa fever and LCMV infected mice as well as proteolytic breakdown products as the dipeptides γ-glutamyl-Valine, γ-glutamyl-Leucine, and prolyl-hydroxyproline, which were present in lower amounts over the course of LCMV infection. Suggesting possible changes in the concentration or activity of enzymes, Gamma glutamyl transferase transfers the gammaglutamyl moiety to various amino acids during viral immune response [54,74]. In this regard, our study predicted several miRNA related to amino acid and fatty acid biosynthesis and metabolic pathways accounting as a potential mammarenavirus strategy to subvert the cell metabolism to its benefits, increasing catabolism of unnecessary compounds and favoring synthesis or suppressing metabolism of those necessary for replication and evasion of immune system. Biotin metabolism was among the most frequent pathways regulated by miRNAs that target mammarenavirus genome. Biotin may affect transcription of genes, biotinylation of proteins in cells, cellular growth, proliferation, and differentiation. The vitamin acts as a co-factor for five carboxylases that are critical for fatty acid, glucose, and amino acid metabolism [75,76]. Biotin deficiency is associated with various diseases, and vitamin deficient mice display enhanced inflammation. Moreover, abnormal cellular growth and differentiation is the underlying cause of fetal malformations [77,78] and impaired immune function observed in biotin-deficient animals [79]. The predicted miRNAs can play an important role in congenital LCMV infection that are marked by permanent, lifelong neurologic deficits, the most common are macrocephaly (secondary to hydrocephalus), and microcephaly [80,81]. Biotin was also shown to be important for the activity of human natural killer lymphocytes, for the generation of cytotoxic T lymphocytes, and for the maturation and responsiveness of immune cells [75,82,83]. One important pathway predicted to be associated with miRNAs targeting mammarenavirus genome was the circadian rhythm which is an internal biological clock that enables to sustain an approximately 24-h rhythm in the absence of environmental cues. The circadian clock system is a main regulatory factor for nearly all physiological activities and its disorder has severe consequences on human health [84,85]. Interestingly, Miller and colleagues performed a study where they infected mice with LCMV and measured plasma corticosterone and cytosolic glucocorticoid receptor (GR) binding at multiple time points throughout the day and throughout infection. Despite a vigorous immune response to LCMV, the infection was associated with minimal and transient increases in corticosterone secretion. However, significant decreases in cytosolic GR were detected in immune tissues. Receptor decreases were characterized by a significant reduction of GR binding during the diurnal rise in corticosterone in the spleen and thymus of infected but not uninfected animals on days 5–10 post infection. In addition, in the morning on these days, GR binding in the spleen of infected mice was decreased compared to uninfected control mice [86]. This indicates effects of LCMV infection in the circadian rhythm with interactions between the neuroendocrine and immune systems modifying at the level of the GR the context of an ongoing immune response during a viral infection. Similar results were found by Edgar and colleagues in infected wild-type mice with a strain of herpes virus and influenza A at different phases of the animals’ circadian clocks. These researchers demonstrated that the time of day of virus infection has an important impact on disease progression, in cellular models as well as in animals. In addition, they observed that clock disruption leads to increased virus replication and dissemination, indicating that severity of acute infections is influenced by circadian timekeeping [87]. Since there is a possibility of circadian rhythms affecting the immune response in the animals, the timing of vaccination should also be important. Indeed, timing of influenza vaccine administration has been shown to be a determinant in the systemic immunization response in people over 65 years of age. In a cluster-randomized trial design, Long and collaborators examined whether manipulating the time of day an older adult received their influenza vaccination would have an effect on the magnitude of the antibody response at one month. Results showed that antibody responses to two of three influenza strains were higher when the vaccination was given in the morning [88]. The identification of the molecular determinants underlying the distinct virulence of pathogenic and nonpathogenic arenaviruses is still a question of great importance for epidemiology and public health [89]. In our study, there was no clear difference in total miRNAs found with the potential to bind to pathogenic and nonpathogenic mammarenavirus genomes, which could be explained by the close genetic relationship of nonpathogenic arenaviruses with their highly pathogenic cousins [1,89,90]. Although the role of miRNA in mammarenavirus pathogenesis is not clear, its role in regulating important molecular pathways during infection cannot be ruled out as an important mechanism to be further investigated. In addition, many studies have suggested that a multifactor pathway could be involved in the different outcomes from arenaviral infection including a relatively low number of adaptive mutations, as well as the incapacity to bind to human receptors and different immune responses elicited by these viruses [91,92,93]. Interaction between host and pathogen has a profound effect on the outcome of an infection. Knowledge about host–pathogen interactions is critical for understanding the pathogenesis of infection. However, it generally overshadows knowledge of metabolic cross-talk between host and pathogen [43,94]. At a cellular level, host and pathogen share similar nutritional substrates generating common metabolic products. The host depends upon nutritional substrates to support its immune responses against the pathogen, while the pathogen is also highly dependent on nutritional substrates being unable to synthesize some or all substrates for its replication [49,94,95]. Post-transcriptional regulation of gene expression plays a pivotal role in various gene regulatory networks including but not limited to metabolism, embryogenesis, and immune responses. Recently, the role of post-transcriptional gene regulation during pathogenic infections and host immune responses has become increasingly important. The role of cellular miRNAs is crucial when they are proviral, or when a longer, persistent infection is established. miRNAs can be used as an entry gate into regulatory networks that could be explored to find new unconventional therapeutic targets. 5. Conclusions Our present study indicates that mammarenavirus infection induces metabolic reprogramming of host cells, probably manipulating cellular microRNAs. A number of metabolic pathways, including valine, leucine, and isoleucine biosynthesis, d-Glutamine and d-glutamate metabolism, thiamine metabolism, and pools of several amino acids were impacted by the predicted miRNAs that would no longer regulate these pathways. The change caused by the effect of the mammarenavirus genome acting as a sponge to miRNAs probably allows activation of anabolic pathways necessary for the production of the viral nucleic acids, capsids, and eventually membrane envelopes. While some studies have demonstrated that viruses reprogram cell metabolism and rely on metabolic changes for optimal virus replication, significant work remains to determine as viral genome interact with host cell machinery to induce such alterations and characterize whether the same metabolic perturbations occur during infection. As well as other viruses, the mammarenavirus has likely evolved to modify the host metabolism for multiple purposes, facilitating viral replication and potentially also reflect antiviral defense mechanisms. The study of metabolism, including metabolomics approach, should not be restricted to energy supply and biosynthetic materials, but should also extend to a better understanding of its role during viral infection and pathogenesis. A deeper understanding of mechanisms by which mammarenaviruses handle these signaling pathways is critical for better understanding the virus/host interactions and potential targets for diagnostic and therapeutic, through inhibition of the specific pathologic metabolic pathways. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Supplementary Materials The following are available online at https://www.mdpi.com/2073-4409/9/11/2525/s1, Figure S1: List of all 75 pathways found, Figure S2: All pathways found for each clade, Figure S3: All 69 pathways with at least one experimentally validated interactions for miRNAs, Figure S4: All 75 pathways distributed according to amount of microRNA interactions for each virus, Table S1: All microRNAs found, their binding sites and the location on the viral genome, Table S2: List of all miRNAs binding to different regions of same the RNA segment of different mammarenavirus species, Table S3: List of all miRNAs binding to S and L segments of different mammarenaviruses. Click here for additional data file. Author Contributions Conceptualization, J.F., R.L.M., and A.G.; methodology, R.L.M. and A.G.; formal analysis, J.F., R.L.M., and A.G.; data curation, A.G.; writing—original draft preparation, J.F., R.L.M., and A.G.; writing—review and editing, E.R.S.d.L. and A.G.; funding acquisition, E.R.S.d.L. All authors have read and agreed to the published version of the manuscript. Funding This research was funded by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brazil (CAPES)—Finance Code 001, and Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)—Brazil. The APC was funded by Instituto Oswaldo Cruz—Fundação Oswaldo Cruz (Fiocruz)—Brazil. Conflicts of Interest The authors declare no conflict of interest. Figure 1 Top 20 main signaling pathways predicted to be regulated by microRNAs targeting mammarenavirus genome. The representative proportion of each pathway individually for the four clades (Clade A, B, C, and Old World) is shown. The graphic scale is represented by the number of microRNAs that were found interacting with each pathway. The larger the scale, the greater the number of microRNAs found that are regulating a specific pathway. Figure 2 Top 20 main signaling pathways predicted to be regulated by microRNAs biding to mammarenavirus genome that had at least one target validated experimentally in proportion for each of the four clades (Clade A, B, C, and Old World viruses). The graphic scale is represented by the number of microRNAs that were found interacting with each pathway. The larger the scale, the greater the number of microRNAs found that are regulating the pathway. Figure 3 Heatmap of predicted miRNA–pathway group interactions for each mammarenavirus. Scale demonstrates how many times a pathway that belonged to a KEGG pathway group was targeted by a miRNA (if a miRNA targeted more than one pathway in the same group it was counted as many times). Figure 4 Heatmap of predicted miRNA–pathway subgroup interactions for each mammarenavirus. Scale demonstrates how many times a pathway that belonged to a KEGG pathway subgroup was targeted by a miRNA (if a miRNA targeted more than one pathway in the same subgroup it was counted as many times). cells-09-02525-t001_Table 1Table 1 Total miRNAs found with the potential to bind to mammarenavirus genome. New World Mammarenaviruses Clade A Viruses Total miRNAs (Binding-Sites) Clade B Viruses Total miRNAs (Binding-Sites) L Segment S Segment L Segment S Segment Allpahuayo virus 12 04 Amapari virus 14 03 Bear Canyon virus 10 05 Aporé virus 15(16) 06 Flexal virus 11 03 Chapare virus 15 04 Paraná virus 09 07 Cupixi virus 23(25) 05 Pichindé virus 14 04 Guanarito virus 12 07 Pirital virus 13 09 Junín virus 13 05 Tamiami virus 12(13) 03(04) Machupo virus 17 08 Whitewater Arroyo virus 22(23) 06 Sabiá virus 09 06(07) Total binding-sites 105 42 Tacaribe virus 14 02 Clade C viruses Total microRNAs Xapuri virus * 07 05 L segment S segment Total binding-sites 140 54 Latino virus 16 03 * Didactically classified here as belonging to Clade B NW mammarenaviruses. Oliveros virus 16 10 Total 32 13 Old World Mammarenaviruses Viruses Total miRNAs (Binding-Sites) Viruses Total miRNAs (Binding-Sites) L Segment S Segment L Segment S Segment Alxa virus 08 12 Mariental virus 12 03 Gairo virus 11 12 Merino Walk virus 14 04 Ippy virus 17 05 Mobala virus 12 08 Lassa virus 16 08 Mopeia virus 14 04 Lijiang virus 12(13) 07 Okahandja virus 09 09(10) Loie River virus 07 01 Ryukyu virus 19(21) 03 Lujo virus 20 06 Solwezi virus 010 04 Luna virus 13 08 Souris virus 05 09 Lunk virus 18(22) 10 Wenzhou virus 09 06 Lymphocytic choriomeningitis virus 11 05 Total binding-sites 244 125 cells-09-02525-t002_Table 2Table 2 List of top 10 miRNAs binding to different regions of same the RNA segment of different mammarenavirus species. miRNAs Mammarenaviruses (Genomic Position) Clade A New World Viruses (L Segment) hsa-miR-122b-3p Flexal (2745–2755: RdRp)/Paraná (2814–2824: RdRp) Clade A New World Viruses (S Segment) hsa-miR-2052 Pirital (954–944: GPC)/Bear Canyon (2422–2412: NP)/Pichindé (948–938: GPC)/Allpahuayo (945–955: GPC) Clade B New World Viruses (L Segment) hsa-miR-122b-3p Aporé (2977–2967: RdRp/7195–7185: 3′UTR)/Machupo (2920–2910: RdRp) hsa-miR-147b-5p Aporé (6826–6816: RdRp)/Machupo (1424–1434: RdRp)/Tacaribe (6771–6761: RdRp) hsa-miR-3149 Machupo (3195–3185: RdRp)/Tacaribe (3173–3163: RdRp)/Xapuri (4629–4639: RdRp) hsa-miR-12122 Amapari (3524–3534: RdRp)/Chapare (3552–3562: RdRp)/Tacaribe (3553–3563: RdRp) Old World Viruses (L Segment) hsa-miR-3120-3p Lujo (3497–3487: RdRp)/Lunk (3599–3589: RdRp)/Merino Walk (5697–5687: RdRp)/Mopeia (3591–3581: RdRp) hsa-miR-3134 Alxa (5200–5190: RdRp)/Wenzhou (2378–2388: RdRp)/Lymphocytic choriomeningitis (1975–1985: RdRp) Old World Viruses (S Segment) hsa-miR-4327 Lunk (2716–2706: NP)/Merino Walk (2721–2711: NP)/Mariental (2721–2711: NP)/Souris (2777–2767: NP) hsa-miR-6516-3p Lujo (1548–1538: NP)/Lijiang (1655–1645: NP)/Mariental (1673–1663: NP) hsa-miR-6740-5p Alxa (2834–2824: NP)/Gairo (2788–2778: NP)/Lassa (2784–2774: NP)/Lymphocytic choriomeningitis (2787–2777: S-IGR) RdRp: RNA-dependent RNA polymerase; GPC: glycoprotein precursor; NP: nucleoprotein; S-IGR: noncoding intergenic region of S segment; 3′UTR: untranslated region. cells-09-02525-t003_Table 3Table 3 List of top 10 miRNAs binding to small (S) and large (L) segments of different mammarenaviruses. miRNAs Species (Genomic Position) L Segment S Segment hsa-miR-122b-3p Aporé (2977–2967: RdRp/7195–7185: 3′UTR) Alxa (1328–1338: GPC) Flexal (2745–2755: RdRp) Machupo (2920–2910: RdRp) Paraná (2814–2824: RdRp) hsa-miR-9-5p Pichindé (4923–4933: RdRp) Tacaribe (2639–2649: NP) Ryukyu (4996–5006: RdRp) Mobala (5050–5060: RdRp) hsa-miR-3611 Aporé (3887–3897: RdRp) Xapuri (1849–1859: NP) Flexal (3774–3784: RdRp) hsa-miR-3617-5p Pichindé (7003–6993: RdRp) Mopeia (2392–2402: NP) Paraná (1198–1208: GPC)) hsa-miR-1229-3p Cupixi (4426–4436: RdRp) Lassa (2783–2793: NP) Oliveros (6625–6635: RdRp) hsa-miR-3085-5p Amapari (3334–3324: RdRp) Lujo (1316–1306: GPC) Lunk (1709–1699: RdRp) hsa-miR-4256 Tamiami (1844–1834: RdRp) Chapare (856–866: GPC) Xapuri (2518–2528: RdRp) hsa-miR-3913-5p Chapare (2959–2969: RdRp) Luna (2453–2463: NP) Lujo (2961–2971: RdRp) hsa-miR-4735-3p Lassa (2553–2563: RdRp) Whitewater Arroyo (1734–1744: NP) Merino Walk (2369–2359: RdRp) hsa-miR-4762-5p Chapare (1305–1315: RdRp) Paraná (2680–2870: NP) Latino (3939–3949: RdRp) RdRp: RNA-dependent RNA polymerase; GPC: glycoprotein precursor; NP: nucleoprotein; 3′UTR: untranslated region. cells-09-02525-t004_Table 4Table 4 MicroRNAs biding to different regions of the same mammarenavirus genomic segment. microRNAs Mammarenaviruses Genomic Position Clade A (L Segment) hsa-miR-6083 Whitewater Arroyo 1597–1607: RdRp/5470–5480: RdRp hsa-miR-7856-5p Tamiami 3946–3956: RdRp/4552–4562: RdRp Clade B (L Segment) hsa-miR-122b-3p Aporé 2977–2967: RdRp/7195–7185: 3′UTR hsa-miR-376a-3p Cupixi 1543–1553: RdRp/3120–3130: RdRp hsa-miR-376b-3p Cupixi 1543–1553: RdRp/3120–3130: RdRp Clade B (S Segment) hsa-miR-5700 Sabiá 1039–1029: GPC/1030–1040: GPC Old World (L Segment) hsa-miR-4460 Lijiang 209–199: Z/4747–4737: RdRp hsa-miR-8485 Ryukyu 354–364/356–366/388–398 (L-IGR) hsa-miR-8485 Lunk 418–428/420–430/422–432/424–434/426–436 (L-IGR) RdRp: RNA-dependent RNA polymerase; GPC: glycoprotein precursor; Z: Z protein; L-IGR: noncoding intergenic region of L segment; 3′UTR: untranslated region. ==== Refs References 1. Radoshitzky S.R. Bào Y. Buchmeier M.J. Charrel R.N. Clawson A.N. Clegg C.S. DeRisi J.L. Emonet S. Gonzalez J.-P. Kuhn J.H. Past, present, and future of arenavirus taxonomy Arch. Virol. 2015 160 1851 1874 10.1007/s00705-015-2418-y 25935216 2. Grande-Pérez A. Martin V. Moreno H. De La Torre J.C. Arenavirus Quasispecies and Their Biological Implications Endogenous ADP-Ribosylation Springer Science and Business Media LLC Berlin, Switzerland 2015 Volume 392 231 275 3. Kuhn J.H. Adkins S. Alioto D. Alkhovsky S.V. Amarasinghe G.K. Anthony S.J. Avšič-Županc T. Ayllón M.A. Bahl J. Balkema-Buschmann A. 2020 taxonomic update for phylum Negarnaviricota (Riboviria: Orthornavirae), including the large orders Bunyavirales and Mononegavirales Arch. Virol. 2020 165 3023 3072 10.1007/s00705-020-04731-2 32888050 4. Maes P. Alkhovsky S.V. Bao Y. Beer M. Birkhead M. Briese T. Buchmeier M.J. Calisher C.H. Charrel R. Choi I.R. Taxonomy of the family Arenaviridae and the order Bunyavirales: Update 2018 Arch. Virol. 2018 163 2295 2310 10.1007/s00705-018-3843-5 29680923 5. Garnett L.E. Strong J.E. Lassa fever: With 50 years of study, hundreds of thousands of patients and an extremely high disease burden, what have we learned? Curr. Opin. Virol. 2019 37 123 131 10.1016/j.coviro.2019.07.009 31479990 6. Ambrosio A. Saavedra M. Mariani M. Gamboa G. Maiza A. Argentine hemorrhagic fever vaccines Hum. Vaccines 2011 7 694 700 10.4161/hv.7.6.15198 7. Delaine M. Weingertner A.-S. Nougairede A. Lepiller Q. Fafi-Kremer S. Favre R. Charrel R. Microcephaly Caused by Lymphocytic Choriomeningitis Virus Emerg. Infect. Dis. 2017 23 1548 1550 10.3201/eid2309.170775 28820372 8. Brisse M. Ly H. Hemorrhagic Fever-Causing Arenaviruses: Lethal Pathogens and Potent Immune Suppressors Front. Immunol. 2019 10 372 10.3389/fimmu.2019.00372 30918506 9. Ly H. Differential Immune Responses to New World and Old World Mammalian Arenaviruses Int. J. Mol. Sci. 2017 18 1040 10.3390/ijms18051040 10. Bartel D.P. MicroRNAs: Genomics, biogenesis, mechanism, and function Cell 2004 116 281 297 10.1016/S0092-8674(04)00045-5 14744438 11. Ang C.E. Trevino A.E. Chang H.Y. Diverse lncRNA mechanisms in brain development and disease Curr. Opin. Genet. Dev. 2020 65 42 46 10.1016/j.gde.2020.05.006 32554106 12. Girardi E. López P. Pfeffer S. On the Importance of Host MicroRNAs During Viral Infection Front. Genet. 2018 9 439 10.3389/fgene.2018.00439 30333857 13. Trobaugh D.W. Gardner C.L. Sun C. Haddow A.D. Wang E. Chapnik E. Mildner A. Weaver S.C. Ryman K.D. Klimstra W.B. RNA viruses can hijack vertebrate microRNAs to suppress innate immunity Nat. Cell Biol. 2014 506 245 248 10.1038/nature12869 24352241 14. Zheng Z. Ke X. Wang M. He S. Li Q. Zheng C. Zhang Z. Liu Y. Wang H. Human MicroRNA hsa-miR-296-5p Suppresses Enterovirus 71 Replication by Targeting the Viral Genome J. Virol. 2013 87 5645 5656 10.1128/JVI.02655-12 23468506 15. Rupaimoole R. Slack F.J. MicroRNA therapeutics: Towards a new era for the management of cancer and other diseases Nat. Rev. Drug Discov. 2017 16 203 222 10.1038/nrd.2016.246 28209991 16. Guterres A. Lima C.H.D.A. Miranda R.L. Gadelha M.R. What is the potential function of microRNAs as biomarkers and therapeutic targets in COVID-19? Infect. Genet. Evol. 2020 85 104417 10.1016/j.meegid.2020.104417 32526370 17. Quillet A. Saad C. Ferry G. Anouar Y. Vergne N. Lecroq T. Dubessy C. Improving Bioinformatics Prediction of microRNA Targets by Ranks Aggregation Front. Genet. 2020 10 1330 10.3389/fgene.2019.01330 32047509 18. Betel D. Koppal A. Agius P. Sander C. Leslie C.S. Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites Genome Biol. 2010 11 R90 10.1186/gb-2010-11-8-r90 20799968 19. Kertesz M. Iovino N. Unnerstall U. Gaul U. Segal E. The role of site accessibility in microRNA target recognition Nat. Genet. 2007 39 1278 1284 10.1038/ng2135 17893677 20. Liu H. Yue D. Chen Y. Gao S.-J. Huang Y. Improving performance of mammalian microRNA target prediction BMC Bioinform. 2010 11 476 10.1186/1471-2105-11-476 21. Agarwal V. Bell G.W. Nam J.-W. Bartel D.P. Predicting effective microRNA target sites in mammalian mRNAs eLife 2015 4 e05005 10.7554/eLife.05005 22. Hsu S.-D. Lin F.-M. Wu W.-Y. Liang C. Huang W.-C. Chan W.-L. Tsai W.-T. Chen G.-Z. Lee C.-J. Chiu C.-M. miRTarBase: A database curates experimentally validated microRNA–target interactions Nucleic Acids Res. 2011 39 D163 D169 10.1093/nar/gkq1107 21071411 23. Xiao F. Zuo Z. Cai G. Kang S. Gao X. Li T. miRecords: An integrated resource for microRNA-target interactions Nucleic Acids Res. 2009 37 D105 D110 10.1093/nar/gkn851 18996891 24. Dweep H. Gretz N. miRWalk2.0: A comprehensive atlas of microRNA-target interactions Nat. Methods 2015 12 697 10.1038/nmeth.3485 26226356 25. Kanehisa M. KEGG: Kyoto Encyclopedia of Genes and Genomes Nucleic Acids Res. 2000 28 27 30 10.1093/nar/28.1.27 10592173 26. Griffiths-Jones S. Grocock R.J. Van Dongen S. Bateman A. Enright A.J. miRBase: microRNA sequences, targets and gene nomenclature Nucleic Acids Res. 2006 34 D140 D144 10.1093/nar/gkj112 16381832 27. McKinney W. Pandas: A Foundational Python Library for Data Analysis and Statistics. Python High Perform Sci. Comput. 2011 14 9 28. Rottiers V. Näär A.M. MicroRNAs in metabolism and metabolic disorders Nat. Rev. Mol. Cell Biol. 2012 13 239 250 10.1038/nrm3313 22436747 29. Vienberg S. Geiger J. Madsen S. Dalgaard L.T. MicroRNAs in metabolism Acta Physiol. 2016 219 346 361 10.1111/apha.12681 30. Bruscella P. Bottini S. Baudesson C. Pawlotsky J.-M. Feray C. Trabucchi M. Viruses and miRNAs: More Friends than Foes Front. Microbiol. 2017 8 824 10.3389/fmicb.2017.00824 28555130 31. Cullen B.R. Viruses and microRNAs Nat. Genet. 2006 38 S25 S30 10.1038/ng1793 16736021 32. Ludwig N. Leidinger P. Becker K. Backes C. Fehlmann T. Pallasch C. Rheinheimer S. Meder B. Stähler C. Meese E. Distribution of miRNA expression across human tissues Nucleic Acids Res. 2016 44 3865 3877 10.1093/nar/gkw116 26921406 33. Jopling C.L. Liver-specific microRNA-122: Biogenesis and function RNA Biol. 2012 9 137 142 10.4161/rna.18827 22258222 34. Masaki T. Arend K.C. Li Y. Yamane D. McGivern D.R. Kato T. Wakita T. Moorman N.J. Lemon S.M. miR-122 Stimulates Hepatitis C Virus RNA Synthesis by Altering the Balance of Viral RNAs Engaged in Replication versus Translation Cell Host Microbe 2015 17 217 228 10.1016/j.chom.2014.12.014 25662750 35. Li Y. Masaki T. Yamane D. McGivern D.R. Lemon S.M. Competing and noncompeting activities of miR-122 and the 5′ exonuclease Xrn1 in regulation of hepatitis C virus replication Proc. Natl. Acad. Sci. USA 2013 110 1881 1886 10.1073/pnas.1213515110 23248316 36. Hsu S.-H. Wang B. Kota J. Yu J. Costinean S. Kutay H. Yu L. Bai S. La Perle K. Chivukula R.R. Essential metabolic, anti-inflammatory, and anti-tumorigenic functions of miR-122 in liver J. Clin. Investig. 2012 122 2871 2883 10.1172/JCI63539 22820288 37. Tsai W.-C. Hsu S.-D. Hsu C.-S. Lai T.-C. Chen S.-J. Shen R. Huang Y. Chen H.-C. Lee C.-H. Tsai T.-F. MicroRNA-122 plays a critical role in liver homeostasis and hepatocarcinogenesis J. Clin. Investig. 2012 122 2884 2897 10.1172/JCI63455 22820290 38. Gualdoni G.A. Mayer K.A. Kapsch A.-M. Kreuzberg K. Puck A. Kienzl P. Oberndorfer F. Frühwirth K. Winkler S. Blaas D. Rhinovirus induces an anabolic reprogramming in host cell metabolism essential for viral replication Proc. Natl. Acad. Sci. USA 2018 115 E7158 E7165 10.1073/pnas.1800525115 29987044 39. Nagy P.D. Strating J.R. Van Kuppeveld F.J. Building Viral Replication Organelles: Close Encounters of the Membrane Types PLoS Pathog. 2016 12 e1005912 10.1371/journal.ppat.1005912 27788266 40. Hsu N.-Y. Ilnytska O. Belov G. Santiana M. Chen Y.-H. Takvorian P.M. Pau C. Van Der Schaar H. Kaushik-Basu N. Balla T. Viral Reorganization of the Secretory Pathway Generates Distinct Organelles for RNA Replication Cell 2010 141 799 811 10.1016/j.cell.2010.03.050 20510927 41. Diamond D.L. Syder A.J. Jacobs J.M. Sorensen C.M. Walters K.-A. Proll S.C. McDermott J.E. Gritsenko M.A. Zhang Q. Zhao R. Temporal Proteome and Lipidome Profiles Reveal Hepatitis C Virus-Associated Reprogramming of Hepatocellular Metabolism and Bioenergetics PLoS Pathog. 2010 6 e1000719 10.1371/journal.ppat.1000719 20062526 42. Manchester M. Anand A. Metabolomics: Strategies to Define the Role of Metabolism in Virus Infection and Pathogenesis Adv. Virus Res. 2017 98 57 81 28433052 43. Noto A. Dessì A. Puddu M. Mussap M. Fanos V. Metabolomics technology and their application to the study of the viral infection J. Matern. Neonatal Med. 2014 27 53 57 10.3109/14767058.2014.955963 25284178 44. Munger J. Bajad S.U. Coller H.A. Shenk T. Rabinowitz J.D. Dynamics of the Cellular Metabolome during Human Cytomegalovirus Infection PLoS Pathog. 2006 2 e132 10.1371/journal.ppat.0020132 17173481 45. Yangfang Y. Ye Y. Chenghua L. Shao Y. Xie X. Zhang W. Liu H.-P. Li C. Metabolic product response profiles of Cherax quadricarinatus towards white spot syndrome virus infection Dev. Comp. Immunol. 2016 61 236 241 10.1016/j.dci.2016.04.006 27068762 46. Suhyuen L. Huang Y.-T. Chen I.-T. Lee D.-Y. Hsieh Y.-C. Li C.-Y. Geendong C. Liang S.-Y. Lin S.-Y. Huang S.-W. An Invertebrate Warburg Effect: A Shrimp Virus Achieves Successful Replication by Altering the Host Metabolome via the PI3K-Akt-mTOR Pathway PLoS Pathog. 2014 10 e1004196 10.1371/journal.ppat.1004196 24945378 47. Munger J. Bennett B.D. Parikh A. Feng X.-J. McArdle J. Rabitz H.A. Shenk T. Rabinowitz J.D. Systems-level metabolic flux profiling identifies fatty acid synthesis as a target for antiviral therapy Nat. Biotechnol. 2008 26 1179 1186 10.1038/nbt.1500 18820684 48. Delgado T. Sanchez E.L. Camarda R. Lagunoff M. Global Metabolic Profiling of Infection by an Oncogenic Virus: KSHV Induces and Requires Lipogenesis for Survival of Latent Infection PLoS Pathog. 2012 8 e1002866 10.1371/journal.ppat.1002866 22916018 49. Wang L. Swevers L. Rombouts C. Meeus I. Van Meulebroek L. Vanhaecke L. Smagghe G. A Metabolomics Approach to Unravel Cricket Paralysis Virus Infection in Silkworm Bm5 Cells Viruses 2019 11 861 10.3390/v11090861 50. Cruzat V.F. Rogero M.M. Keane K.N. Curi R. Newsholme P. Glutamine: Metabolism and Immune Function, Supplementation and Clinical Translation Nutrients 2018 10 1564 10.3390/nu10111564 51. Yelamanchi S.D. Jayaram S. Thomas J.K. Gundimeda S. Khan A.A. Singhal A. Prasad T.S.K. Pandey A. Somani B.L. Gowda H. A pathway map of glutamate metabolism J. Cell Commun. Signal. 2016 10 69 75 10.1007/s12079-015-0315-5 26635200 52. Sanchez E.L. Carroll P.A. Thalhofer A.B. Lagunoff M. Latent KSHV Infected Endothelial Cells Are Glutamine Addicted and Require Glutaminolysis for Survival PLoS Pathog. 2015 11 e1005052 10.1371/journal.ppat.1005052 26197457 53. Sanchez E.L. Lagunoff M. Viral activation of cellular metabolism Virology 2015 609 618 10.1016/j.virol.2015.02.038 25812764 54. Wikoff W.R. Kalisak E. Trauger S.A. Manchester M. Siuzdak G. Response and Recovery in the Plasma Metabolome Tracks the Acute LCMV-Induced Immune Response J. Proteome Res. 2009 8 3578 3587 10.1021/pr900275p 19496611 55. Yu Y. Clippinger A.J. Alwine J.C. Viral effects on metabolism: Changes in glucose and glutamine utilization during human cytomegalovirus infection Trends Microbiol. 2011 19 360 367 10.1016/j.tim.2011.04.002 21570293 56. Hegedus A. Williamson M.K. Khan M.B. Zeidler J.D. Da Poian A.T. El-Bacha T. Struys E.A. Huthoff H. Evidence for Altered Glutamine Metabolism in Human Immunodeficiency Virus Type 1 Infected Primary Human CD4+ T Cells AIDS Res. Hum. Retroviruses 2017 33 1236 1247 10.1089/aid.2017.0165 28844150 57. Sanchez M.D. Ochoa A.C. Foster T.P. Development and evaluation of a host-targeted antiviral that abrogates herpes simplex virus replication through modulation of arginine-associated metabolic pathways Antivir. Res. 2016 132 13 25 10.1016/j.antiviral.2016.05.009 27192555 58. Archard L.C. Williamson J.D. The Effect of Arginine Deprivation on the Replication of Vaccinia Virus J. Gen. Virol. 1971 12 249 258 10.1099/0022-1317-12-3-249 4256170 59. Butorov E.V. Influence of L-lysine amino acid on the HIV-1 RNA replicationin vitro Antivir. Chem. Chemother. 2015 24 39 46 10.1177/2040206614566582 26149265 60. Butorov E.V. Plasma L-Carnitine and L-Lysine Concentrations in HIV-Infected Patients Open Biochem. J. 2017 11 119 131 10.2174/1874091X01711010119 29387270 61. Butorov E.V. Relationship between plasma l -lysine concentrations and levels of HIV-1 RNA Virulence 2013 4 646 653 10.4161/viru.26361 24045422 62. Keshavarz M. Solaymani-Mohammadi F. Namdari H. Arjeini Y. Mousavi M.J. Rezaei F. Metabolic host response and therapeutic approaches to influenza infection Cell. Mol. Biol. Lett. 2020 25 15 19 10.1186/s11658-020-00211-2 32161622 63. Smallwood H.S. Duan S. Morfouace M. Rezinciuc S. Shulkin B.L. Shelat A. Zink E.E. Milasta S. Bajracharya R. Oluwaseum A.J. Targeting Metabolic Reprogramming by Influenza Infection for Therapeutic Intervention Cell Rep. 2017 19 1640 1653 10.1016/j.celrep.2017.04.039 28538182 64. Murray J.L. McDonald N.J. Sheng J. Shaw M.W. Hodge T.W. Rubin D.H. O’Brien W.A. Smee D.F. Inhibition of Influenza a Virus Replication by Antagonism of a PI3K-AKT-mTOR Pathway Member Identified by Gene-Trap Insertional Mutagenesis Antivir. Chem. Chemother. 2012 22 205 215 10.3851/IMP2080 22374988 65. Magaway C. Kim E. Jacinto E. Targeting mTOR and Metabolism in Cancer: Lessons and Innovations Cells 2019 8 1584 10.3390/cells8121584 66. Liu G.Y. Sabatini D.M. mTOR at the nexus of nutrition, growth, ageing and disease Nat. Rev. Mol. Cell Biol. 2020 21 183 203 10.1038/s41580-019-0199-y 31937935 67. Zhang Y. Huang B. Wang H.-Y. Chang A. Zheng X.F.S. Emerging Role of MicroRNAs in mTOR Signaling Cell. Mol. Life Sci. 2017 74 2613 2625 10.1007/s00018-017-2485-1 28238105 68. Wang P. Liu X.-M. Ding L. Zhang X.-J. Ma Z. mTOR signaling-related MicroRNAs and Cancer involvement J. Cancer 2018 9 667 673 10.7150/jca.22119 29556324 69. Ray L.B. miRNAs mediate mTORC1 effects Science 2020 367 753 754 10.1126/science.367.6479.753-d 70. El-Bacha T. Midlej V. Da Silva A.P.P. Da Costa L.S. Benchimol M. Galina A. Da Poian A.T. Mitochondrial and bioenergetic dysfunction in human hepatic cells infected with dengue 2 virus Biochim. Biophys. Acta 2007 1772 1158 1166 10.1016/j.bbadis.2007.08.003 17964123 71. Landini M.P. Early Enhanced Glucose Uptake in Human Cytomegalovirus-infected Cells J. Gen. Virol. 1984 65 1229 1232 10.1099/0022-1317-65-7-1229 6086816 72. Bardeletti G. Respiration and ATP Level in BHK21/13S Cells during the Earliest Stages of Rubella Virus Replication Intervirology 1977 8 100 109 10.1159/000148884 558175 73. Cheng M.-L. Chien K.-Y. Lai C.-H. Li G.-J. Lin J.-F. Ho H.-Y. Metabolic Reprogramming of Host Cells in Response to Enteroviral Infection Cells 2020 9 473 10.3390/cells9020473 32085644 74. Gale T.V. Horton T.M. Grant D.S. Garry R.F. Metabolomics analyses identify platelet activating factors and heme breakdown products as Lassa fever biomarkers PLoS Negl. Trop. Dis. 2017 11 e0005943 10.1371/journal.pntd.0005943 28922385 75. Kung J.T. MacKenzie C.G. Talmage D.W. The requirement for biotin and fatty acids in the cytotoxic T-cell response Cell. Immunol. 1979 48 100 110 10.1016/0008-8749(79)90103-5 315827 76. McMahon R.J. Biotin in metabolism and molecular biology Annu. Rev. Nutr. 2002 22 221 239 10.1146/annurev.nutr.22.121101.112819 12055344 77. Watanabe T. Teratogenic Effects of Biotin Deficiency in Mice J. Nutr. 1983 113 574 581 10.1093/jn/113.3.574 6827377 78. Watanabe T. Dakshinamurti K. Persaud T.V.N. Biotin Influences Palatal Development of Mouse Embryos in Organ Culture J. Nutr. 1995 125 2114 2121 10.1093/jn/125.8.2114 7643245 79. Báez-Saldaña A. Díaz G. Espinoza B. Ortega E. Biotin deficiency induces changes in subpopulations of spleen lymphocytes in mice Am. J. Clin. Nutr. 1998 67 431 437 10.1093/ajcn/67.3.431 9497186 80. Kinori M. Schwartzstein H. Zeid J.L. Kurup S.P. Mets M.B. Congenital lymphocytic choriomeningitis virus—an underdiagnosed fetal teratogen J. Am. Assoc. Pediatr. Ophthalmol. Strabismus 2018 22 79 81 10.1016/j.jaapos.2017.08.011 81. Anderson J.L. Levy P.T. Leonard K.B. Smyser C.D. Tychsen L. Cole F.S. Congenital lymphocytic choriomeningitis virus: When to consider the diagnosis J. Child Neurol. 2014 29 837 842 10.1177/0883073813486295 23666045 82. Okabe N. Urabe K. Fujita K. Yamamoto T. Yao T. Doi S. Biotin effects in Chrohn’s disease Dig. Dis. Sci. 1988 33 1495 1496 10.1007/BF01537009 3180987 83. Agrawal S. Agrawal A. Said H.M. Biotin deficiency enhances the inflammatory response of human dendritic cells Am. J. Physiol. Physiol. 2016 311 C386 C391 10.1152/ajpcell.00141.2016 27413170 84. Mazzoccoli G. Vinciguerra M. Carbone A. Relógio A. The Circadian Clock, the Immune System, and Viral Infections: The Intricate Relationship Between Biological Time and Host-Virus Interaction Pathogens 2020 9 83 10.3390/pathogens9020083 32012758 85. Xie Y. Tang Q. Chen G. Xie M. Yu S. Zhao J. Chen L. New Insights into the Circadian Rhythm and Its Related Diseases Front. Physiol. 2019 10 682 10.3389/fphys.2019.00682 31293431 86. Miller A.H. Spencer R.L. Pearce B.D. Pisell T.L. Tanapat P. Leung J.J. Dhabhar F.S. McEwen B.S. Biron C.A. Effects of viral infection on corticosterone secretion and glucocorticoid receptor binding in immune tissues Psychoneuroendocrinology 1997 22 455 474 10.1016/S0306-4530(97)00028-0 9364623 87. Edgar R.S. Stangherlin A. Nagy A.D. Nicoll M.P. Efstathiou S. O’Neill J.S. Reddy A. Cell autonomous regulation of herpes and influenza virus infection by the circadian clock Proc. Natl. Acad. Sci. USA 2016 113 10085 10090 10.1073/pnas.1601895113 27528682 88. Long J.E. Drayson M.T. Taylor A.E. Toellner K.M. Lord J.M. Phillips A.C. Morning vaccination enhances antibody response over afternoon vaccination: A cluster-randomised trial Vaccine 2016 34 2679 2685 10.1016/j.vaccine.2016.04.032 27129425 89. Moreno H. Möller R. Fedeli C. Gerold G. Kunz S. Comparison of the Innate Immune Responses to Pathogenic and Nonpathogenic Clade B New World Arenaviruses J. Virol. 2019 93 10.1128/JVI.00148-19 90. Fernandes J. Guterres A. De Oliveira R.C. Chamberlain J. Lewandowski K. Teixeira B.R. Coelho T.A. Crisóstomo C.F. Bonvicino C.R. D’Andrea P.S. Xapuri virus, a novel mammarenavirus: Natural reassortment and increased diversity between New World viruses Emerg. Microbes Infect. 2018 7 1 10 10.1038/s41426-018-0119-9 29323102 91. Abraham J. Kwong J.A. Albariño C.G. Lu J.G. Radoshitzky S.R. Salazar-Bravo J. Farzan M. Spiropoulou C.F. Choe H. Host-Species Transferrin Receptor 1 Orthologs Are Cellular Receptors for Nonpathogenic New World Clade B Arenaviruses PLoS Pathog. 2009 5 e1000358 10.1371/journal.ppat.1000358 19343214 92. Ferrer M.F. Thomas P. Ortiz A.O.L. Errasti A.E. Charo N. Romanowski V. Gorgojo J. Rodriguez M.E. Silva E.A.C. Gómez R.M. Junin Virus Triggers Macrophage Activation and Modulates Polarization According to Viral Strain Pathogenicity Front. Immunol. 2019 10 2499 10.3389/fimmu.2019.02499 31695702 93. Schaeffer J. Reynard S. Carnec X. Pietrosemoli N. Dillies M.-A. Baize S. Non-Pathogenic Mopeia Virus Induces More Robust Activation of Plasmacytoid Dendritic Cells than Lassa Virus Viruses 2019 11 287 10.3390/v11030287 30901952 94. Ren W. Rajendran R. Zhao Y. Tan B. Wu G. Bazer F.W. Zhu G. Peng Y. Huang X. Deng J. Amino Acids As Mediators of Metabolic Cross Talk between Host and Pathogen Front. Immunol. 2018 9 319 10.3389/fimmu.2018.00319 29535717 95. Ahmed S. Finkelstein J.L. Stewart A.M. Kenneth J. Polhemus M.E. Endy T.P. Cardenas W. Mehta S. Review article: Micronutrients and dengue Am. J. Trop. Med. Hyg. 2014 91 1049 1056 10.4269/ajtmh.14-0142 25200269