Metatranscriptomic Profiling of Host-Microbiome Interactions in the Telencephalon and Liver of Carollia perspicillata.

RNA-seq data provide valuable insights into both host transcriptomes and microbial transcripts from active microbiota within host tissues. The presence of microbial transcripts within specific tissues indicates the replication and transcriptional activity of microorganisms. Integrative analyses of the host transcriptome and the meta-transcriptome allow for the characterization of microbial gene expression and the interactive response of the host to the microbiome in each sample. In this study, we employed metatranscriptomics to explore microbial communities in the telencephalon and liver of Carollia perspicillata. By combining host and microbial RNA-seq data, we identified 287 microbial species in the liver and 283 in the telencephalon, revealing tissue-specific microbial diversity. Bacteria were the most abundant taxa in both tissues, followed by notable eukaryotic, archaeal, and viral populations. Using the Metatranscriptome Detector pipeline and NCBI databases, we identified species of potential epidemiological relevance and characterized the host's transcriptional response to the microbiome. Functional analyses indicated differential expression of microbial and host genes across tissues, with enriched metabolic pathways and Gene Ontology terms aligning with hepatic and neural functions. This research underscores the tissue-specific adaptation of the microbiome to host physiology, offering new insights into host-microbiome dynamics in the telencephalon and liver of this frugivorous bat. TITLE: Metatranscriptomic Profiling of Host-Microbiome Interactions in the Telencephalon and Liver of Carollia perspicillata . AUTHORS: Emanuel Ramos da Costa (1,8,†), Patrick Douglas Corrêa Pereira (5,†), Daniel Guerreiro Diniz (8,9,10,†), Nara Gyzely Morais Magalhães (1), Anderson de Jesus Falcão da Silva (1), Jéssica Gizele Sousa Leite (1), Natan Ibraim Pires Almeida (1), Kelle de Nazaré Cunha (1), Mauro André Damasceno de Melo (1), Pedro Fernando da Costa Vasconcelos (2,7), José Antonio Picanço Diniz (3), Dora Brites (3,6), Daniel Clive Anthony (4), (*) Cristovam Wanderley Picanço Diniz (8), Cristovam Guerreiro-Diniz (1) AFFILIATIONS: (1) Instituto Federal de Educação, Ciência e Tecnologia do Pará, IFPA, Campus de Bragança, Laboratório de Biologia Molecular e Neuroecologia, Bragança, Pará, Brazil. (2) Universidade do Estado do Pará, Centro de Ciências Biológicas e da Saúde, Belém, Pará, Brazil. (3) Universidade de Lisboa, Research Institute for Medicines (iMed.ULisboa), Faculty of Pharmacy, Lisbon, Portugal, (4) University of Oxford, Department of Pharmacology, Laboratory of Experimental Neuropathology, Oxford, England, UK. (5) McGill University, Department of Biology, Montreal, Canada. (6) Universidade de Lisboa, Department of Biochemistry and Human Biology, Faculty of Pharmacy, Lisbon, Portugal. (7) Instituto Evandro Chagas, Seção de Arbovirologia e Febres Hemorrágicas, Ananindeua, Pará, Brazil. (8) Universidade Federal do Pará, Instituto de Ciências Biológicas, Hospital Universitário João de Barros Barreto, Laboratório de Investigações em Neurodegeneração e Infecção, Belém, Pará, Brazil. (9) Instituto Evandro Chagas, Seção de Hepatologia, Laboratório de Microscopia Eletrônica, Belém, Pará, Brazil. (10) Universidade Federal do Pará, Núcleo de Pesquisas em Oncologia, Hospital Universitário João de Barros Barreto, Belém, Pará, Brazil. (†) These authors contributed equally to this work. (*) Correspondent author: PhD Cristovam Wanderley Picanço Diniz, address: Laboratório de Investigações em Neurodegeneração e Infecção, Rua dos Mundurucus, 4487, Hospital Universitário João de Barros Barreto, Instituto de Ciências Biológicas, Universidade Federal do Pará, Belém, Brasil. E-mail: [email protected]. ABREVIATIONS COI: Cytochrome Oxidase subunit I GO: Gene Ontology MTD: Meta-Transcriptome Detector DEGs: Differentially expressed genes GSEA: Gene Set Enrichment Analysis KEGG: Kyoto Encyclopedia of Genes and Genomes FDR: False Discovery Rate PCK2: Phosphoenolpyruvate Carboxykinase 2 ALDOB: Aldolase B CPS1: Carbamoyl-Phosphate Synthase 1 MAP1A: Microtubule-Associated Protein 1A NCDN: Neurochondrin SERPINA4: Serpin Family A Member 4 SERPING1: Serpin Family G Member 1 HRG: Histidine-Rich Glycoprotein GABRD: Gamma-Aminobutyric Acid Type A Receptor Subunit Delta SYP: Synaptophysin GRM1: Glutamate Metabotropic Receptor 1 PLG: Plasminogen LOC102426649: (Gene not characterized) HGF: Hepatocyte Growth Factor LOC102422972: (Gene not characterized) VTN: Vitronectin GDP2: GTPase Domain Containing 2 SERPING1: Serpin Family G Member 1 LOC102433381: (Gene not characterized) HRG: Histidine Rich Glycoprotein HSPA1L: Heat Shock Protein Family A (Hsp70) Member 1 Like SERPINF2: Serpin Family F Member 2 SERPINA4: Serpin Family A Member 4 APOB: Apolipoprotein B CTSL: Cathepsin L LOC102422871: (Gene not characterized) MASP1: MBL Associated Serine Protease 1 SERPINA7: Serpin Family A Member 7 PLBD1: Phospholipase B Domain Containing 1 THH: Indian Hedgehog Signaling Molecule SERPINA6: Serpin Family A Member 6 LOC102430228: (Gene not characterized) LOC102442843: (Gene not characterized) ITIH2: Inter-Alpha-Trypsin Inhibitor Heavy Chain 2 HPX: Hemopexin LOC102422416: (Gene not characterized) HABP2: Hyaluronan Binding Protein 2 AFM: Afamin PLA2G12B: Phospholipase A2 Group XIIB TANC2: Tetratricopeptide Repeat, Ankyrin Repeat and Coiled-Coil Containing 2 GABRB3: Gamma-Aminobutyric Acid Type A Receptor Subunit Beta3 TMEM108: Transmembrane Protein 108 GRIK3: Glutamate Ionotropic Receptor Kainate Type Subunit 3 LOC102418872: (Gene not characterized) LOC102432091: (Gene not characterized) PPFIA2: PTPRF Interacting Protein Alpha 2 GABRD: Gamma-Aminobutyric Acid Type A Receptor Subunit Delta CDH9: Cadherin 9 CDH18: Cadherin 18 LOC102429736: (Gene not characterized) SYP: Synaptophysin KCNA1: Potassium Voltage-Gated Channel Subfamily A Member 1 CDH7: Cadherin 7 UNC13A: Unc-13 Homolog A CACNG2e: Calcium Voltage-Gated Channel Auxiliary Subunit Gamma 2 MAP1B: Microtubule Associated Protein 1B PTPRO: Protein Tyrosine Phosphatase Receptor Type O CBLN1: Cerebellin 1 Precursor GLRA2: Glycine Receptor Alpha 2 ADORA1: Adenosine A1 Receptor GABRQ: Gamma-Aminobutyric Acid Type A Receptor Subunit Theta GABRA4: Gamma-Aminobutyric Acid Type A Receptor Subunit Alpha4 KCNA2: Potassium Voltage-Gated Channel Subfamily A Member 2 GRM1: Glutamate Metabotropic Receptor 1 LRFN2: Leucine Rich Repeat and Fibronectin Type III Domain Containing 2 ATP1A3: ATPase Na+/K+ Transporting Subunit Alpha 3 ALDOC: Aldolase, Fructose-Bisphosphate C HPCA: Hippocalcin NCDN: Neurochondrin PHYHIP: Phytanoyl-CoA 2-Hydroxylase Interacting Protein APLP1: Amyloid Beta Precursor Like Protein 1 EEF1A2: Eukaryotic Translation Elongation Factor 1 Alpha 2 MAP1A: Microtubule Associated Protein 1A C1R: Complement C1r ASS1: Argininosuccinate Synthase 1 KHK: Ketohexokinase VTN: Vitronectin PCK2: Phosphoenolpyruvate Carboxykinase 2, Mitochondrial TRFC: Triokinase and FMN Cyclase FBP1: Fructose-Bisphosphatase 1 CPS1: Carbamoyl Phosphate Synthase 1 MIOX: Myo-Inositol Oxygenase GPX3: Glutathione Peroxidase 3 HPN: Hepsin CFB: Complement Factor B BHMT: Betaine–Homocysteine S-Methyltransferase FGB: Fibrinogen Beta Chain COL18A1: Collagen Type XVIII Alpha 1 Chain SERPINF2: Serpin Family F Member 2 FN1: Fibronectin 1 PCK1: Phosphoenolpyruvate Carboxykinase 1 HPX: Hemopexin GRHPR: Glyoxylate and Hydroxypyruvate Reductase KCNAB: Potassium Voltage-Gated Channel Subfamily a Regulatory Beta Subunit 2 CYP27A1: Cytochrome P450 Family 27 Subfamily A Member 1 GNMT: Glycine N-Methyltransferase F2: Coagulation Factor II, Thrombin FGG: Fibrinogen Gamma Chain GJB1: Gap Junction Protein Beta 1 OTC: Ornithine Transcarbamylase SERPINC1: Serpin Family C Member 1 OSGIN1: Oxidative Stress Induced Growth Inhibitor 1

Carollia perspicillata, liver, metatranscriptomics, microbiome-host interaction, telencephalon Total number of words: 8985

In recent years, metatranscriptomics has emerged as a powerful tool for studying host-microbiome interactions by capturing host and microbial gene expression within specific tissues. This approach allows researchers to investigate the functional activity of microbial communities and their influence on host physiology across diverse environments. Moving beyond simply identifying microbial presence, it enables the exploration of the dynamic roles of the microbiota in immune modulation, metabolic processes, and disease resistance (Franzosa et al., 2014; Knight et al., 2018; Van Rossum, Ferretti, Maistrenko, & Bork, 2020). Studies have demonstrated that the ability of the host to tolerate or resist infections is deeply influenced by the transcriptional modulation of immune responses and other protective mechanisms (Bäumler & Sperandio, 2016; Pickard, Zeng, Caruso, & Núñez, 2017). This balance between tolerance (the capacity of the host to limit damage) and resistance (the ability of the host to control microbial load) is pivotal in determining the outcome of host-microbe interactions, particularly in tissues exposed to diverse and dynamic microbial populations (Medzhitov, Schneider, & Soares, 2012; Schneider & Ayres, 2008). The importance of understanding these interactions has intensified due to the rising incidence of zoonotic spillover events, where pathogens from animal reservoirs infect humans, leading to epidemics and pandemics. The potential for spillover is especially pronounced in bat hosts, which are natural reservoirs for many viruses, including coronaviruses, filoviruses, and paramyxoviruses (Calisher, Childs, Field, Holmes, & Schountz, 2006; Olival et al., 2017). Bats, unlike other mammals, possess a distinctive immune tolerance and disease resistance, which is governed by a diverse array of immune gene adaptations (Morales et al., 2025). These adaptations enable bats to serve as reservoirs for potentially pathogenic microbes without experiencing disease, highlighting their unique immunological mechanisms. This phenomenon appears to be underpinned by a finely tuned-gene expression that minimizes damaging inflammation while maintaining control over microbial populations (Brook & Dobson, 2015; O’Shea et al., 2014; Wang & Anderson, 2019). Such modulation of gene expression, particularly within immune and metabolic pathways, appears to play a central role in reducing the likelihood of microbial spillover under natural conditions, suggesting that host-microbiome interactions may be critical to zoonotic risk (Letko, Seifert, Olival, Plowright, & Munster, 2020; Zhou et al., 2020). In this study, we investigated the microbiome of two distinct tissues, the liver and telencephalon in the frugivorous bat C. perspicillata, utilizing metatranscriptomic analysis to provide a comprehensive overview of tissue-specific host-microbe interactions. The liver is a metabolic hub involved in detoxification and immune regulation and has been shown to host microbial communities that interact with these functions in mammals (Schroeder & Bäckhed, 2016; Turnbaugh et al., 2009). In contrast, the telencephalon, a large brain area responsible for multiple parallel and hierarchical neural processes, presents a unique environment where microbial interactions with host neural pathways are less understood but may play a role in neural health and disease (Dinan & Cryan, 2017; Kuijer & Steenbergen, 2023). By profiling the gene expression of both host and microbiome in these tissues, we sought to elucidate how microbial communities adapt to different tissue environments and how host regulatory mechanisms foster microbial tolerance or resistance. The present work provides insights into the tissue-specific dynamics of host-microbiome interactions and offers a framework for understanding the role of such interactions in zoonotic spillover, with a particular focus on the disease tolerance mechanisms of the bat (Demian, Cormier, & Mossman, 2024; Morales et al., 2025; Zhang, Shi, & Holmes, 2018).

Sampling and RNA-later intracardiac perfusion Four individuals of C . perspicillata were randomly captured using mist nets on the campus of IFPA University in Bragança, Pará, Brazil. (Coordinates: -1.054406, -46.785087). The activities were conducted under authorization for the use of wild animals granted by the Chico Mendes Institute for Biodiversity Conservation-ICMBio-Brazil, (SISBIO No.: 78638-1). The bats were anesthetized via Isoflurane inhalation for 90 seconds before transcardiac perfusion. The perfusion process began with a saline solution administered for 10 minutes, followed by the RNA later® RNA Stabilization Solution (Ambion, Life Technologies, USA) for an additional 10 minutes to preserve tissue integrity. Brain and liver tissue samples were collected from C. perspicillata and preserved in RNA later® for subsequent RNA sequencing, resulting in a total of eight samples, (4 brain and 4 liver samples). The time from bat capture to brain extraction ranged from 15 to 20 minutes. All samples were stored at -80°C for 12 hours before proceeding to RNA extraction, library preparation, and sequencing. 2.2 RNA Extraction, Library Preparation, and Sequencing RNA was extracted from collected tissues using a standard TRIzol™ protocol adapted from Simms, Cizdziel, and Chomczynski (1993). Messenger RNA (mRNA) was isolated from total RNA using the Dynabeads mRNA DIRECT Micro Kit (Thermo Fisher Scientific, PN: 61012). cDNA synthesis was conducted using the Ion Total RNA-Seq Kit (Thermo Fisher Scientific, PN: 4479789). Template preparation was automated using the Ion Chef System (Thermo Fisher Scientific, PN: 4484177) per the manufacturer’s protocol. Sequencing was performed on the Ion 540™ Chip (Thermo Fisher Scientific, PN: A27765) using the Ion S5 GeneStudio System (Thermo Fisher Scientific, PN: A37904). The procedure yielded FASTQ files containing single end reads corresponding to biological replicates. 2.3 Data Trimming and Quality Control FASTQ files were evaluated for quality using FastQC software (v0.11.0) (Bioinformatics, 2011). Reads were filtered with Trimmomatic (v0.36) (Bolger, Lohse, & Usadel, 2014) using a PHRED-05 quality score threshold. This approach was chosen based on recommendations for non-model organisms (Macmanes, 2014; Williams, Baccarella, Parrish, & Kim, 2016), as overly aggressive filtering could negatively impact transcriptome analyses by altering detectable expression levels (Williams et al., 2016). Species confirmation combined morphological analysis with De novo reconstruction of the Cytochrome Oxidase subunit I (COI) gene. Transcriptomes were assembled De novo using Trinity software (v2.15) (Grabherr et al., 2011; Haas et al., 2013), and COI sequences were compared against the Barcode of Life Data System (BOLD) (Ratnasingham & Hebert, 2007) for species identification. 2.4 RNA-seq Data Processing with Meta-transcriptome Detector (MTD) RNA-seq data were processed using the Meta-Transcriptome Detector (MTD) pipeline (Wu, Liu, & Ling, 2022), adapted for host and microbiome analysis in C. perspicillata . Reads shorter than 40 bp were removed using Fastp (v0.20.1) (Chen, Zhou, Chen, & Gu, 2018) and the remaining reads were classified into ”host” and ”non-host” categories using Kraken2 (v2.1.1) (Wood, Lu, & Langmead, 2019), generating separate BAM files for downstream analyses. 2.5 Host Transcriptome Analysis The host reads were aligned to the genome of Myotis lucifugus (Ensembl release 112) (Martin et al., 2023), the closest available reference genome for C. perspicillata in the MTD pipeline. Gene expression was quantified using FeatureCounts (v2.0.1) (Liao, Smyth, & Shi, 2014) and differential expression analysis was conducted with DESeq2 (v1.34.0) (Love, Huber, & Anders, 2014). Gene annotations were retrieved using the biomaRt R package (v2.50.2) (Durinck, Spellman, Birney, & Huber, 2009). Enrichment analyses of differentially expressed genes (DEGs) employed ClusterProfiler (v4.2.2) (Yu, Wang, Han, & He, 2012), for Gene Ontology (GO) (Ashburner et al., 2000), KEGG pathways, and Gene Set Enrichment Analysis (GSEA) (Subramanian et al., 2005). 2.6 Microbiome Analysis Non-host reads were processed using microbiome modules within the MTD pipeline. Kraken2 performed taxonomic classification, and Bracken (v2.6.0) estimated species-level abundances (Lu, Breitwieser, Thielen, & Salzberg, 2017). DESeq2 (Love et al., 2014) was used to identify microbial species and genes showing significant abundance changes, providing insights into the functional and ecological roles of the microbiome. Diversity analyses included alpha diversity metrics (Shannon and Simpson indices) and beta diversity (Bray-Curtis distance) using the Phyloseq (v1.38.0) (McMurdie & Holmes, 2013) and Vegan (v2.5-7) (Oksanen et al., 2007) R packages. Microbial community structures were visualized using heatmaps made with DESeq2 normalized data. Phylogenetic relationships among microbial species were analyzed using Graphlan (Asnicar, Weingart, Tickle, Huttenhower, & Segata, 2015), providing insights into taxonomic and evolutionary patterns within the microbiome.

Using RNA-Seq and metagenomics, the microbiome of the liver and telencephalon of the frugivorous bat Carollia perspicillata was analyzed (Figure 1) . Panel A presents an overview of the richness and taxonomic composition of the microbiome in each tissue, illustrating the total species diversity detected across the liver and telencephalon.A high level of species richness is evident, with 287 species identified in the liver and 283 in the telencephalon. This distribution highlights the complex and varied microbial communities associated with these tissues, suggesting distinct microbial niches dependent on the environment. Panel B presents the phylum-level distribution of detected taxa across both tissue types. Among the phyla identified, Bacteria dominate, comprising 63.1% of the total microbiome composition with 550 species. Eukaryota also represents a significant portion of the microbiome, accounting for 31.1% and including 271 species. Archaea, although less abundant, make up 3% of the detected taxa with 26 species, while viruses comprised 2.9% with a total of 25 species. This information reveals the diversity of microbial communities in the analyzed tissues, a necessary step in further exploring microorganism-host interactions. Figure 1. Microbial Diversity and Taxonomic Composition in Liver and Telencephalon. Microbiome diversity and taxonomic composition in liver and telencephalon tissues of C. perspicillata . (A) Number of species detected in each tissue, with 287 species identified in the liver and 283 in the telencephalon. (B) Distribution of microbial phyla across tissues, highlighting the dominance of bacteria (63.1%), followed by eukaryotes (31.1%), archaea (3%), and viruses (2.9%). This figure provides an overview of microbial diversity in the two tissue types. A comprehensive microbial taxa phylogenetic analysis captures bacterial and fungal communities in a circular cladogram format (Figure 2) . The central node represents the root of the phylogenetic tree, with branches extending outward to various taxonomic groups, organized hierarchically from the phylum to genus levels. Distinct color-coded segments differentiate major microbial clades, facilitating a clear visualization of microbial diversity within the sample. Key bacterial groups such as Bacteroidota, Proteobacteria, and Actinobacteria are prominent, as indicated by the clustering of branches and their corresponding color zones. Similarly, fungal groups, notably Ascomycota and Basidiomycota, are shown in unique color regions, capturing the diversity of fungal taxa present. The node sizes represent the relative abundance of each taxon, with larger nodes indicating higher abundances within the sample. The colored bars around the outer perimeter provide additional context on differential abundance, potentially across different environmental conditions, treatment groups, or temporal variations, depending on the study’s context. Notably, specific taxa within Bacteroidota and Ascomycota appear more abundant, suggesting their ecological significance or adaptation to specific conditions within the sampled environment. This visualization underscores the taxonomic complexity of the microbial community. It highlights specific clades that may contribute to functional processes within the sampled habitat, such as nutrient cycling, symbiosis, or pathogenicity. Figure 2. Phylogenetic Distribution of Microbial Taxa in Liver and Telencephalon. Phylogenetic analysis of microbial taxa in liver and telencephalon tissues of C. perspicillata . The circular cladogram illustrates taxonomic diversity, displaying bacterial and fungal communities organized hierarchically from phylum to genus levels. Node sizes indicate relative abundances, while color-coded branches represent distinct taxonomic groups. A comparative analysis of metabolic pathways and biological processes in the telencephalon and liver highlights both shared and tissue-specific functional aspects (Figure 3). Panel A shows a Venn diagram of KEGG metabolic pathways, revealing that 1,159 pathways are shared between the telencephalon and liver, indicating a substantial overlap in core metabolic functions as expected. However, 357 pathways are exclusive to the telencephalon, while 182 are unique to the liver, suggesting specialized metabolic roles in each tissue. The telencephalon-specific pathways are likely involved in neural functions such as neurotransmitter synthesis and signaling pathways, while liver-specific pathways may support detoxification, lipid metabolism, and glucose regulation. Panel B illustrates the overlap and tissue-specific Gene Ontology (GO) terms. Of the total GO terms identified, 7,511 are shared, representing fundamental biological processes necessary for cellular maintenance and function in both tissues. In contrast, 1,090 GO terms are unique to the telencephalon, and 627 are exclusive to the liver, emphasizing distinct biological requirements. The telencephalon-specific GO terms may be related to neural development, synaptic transmission, and cognition. In contrast, the liver-specific terms likely correspond to metabolic processes critical for homeostasis, such as amino acid metabolism and xenobiotic processing. These findings underscore the balance between shared core functions and specialized roles in different tissues, reflecting the diverse functional demands of the telencephalon and liver in the organism. Figure 3. Comparative Analysis of KEGG Pathways and GO Terms. Comparison of KEGG metabolic pathways and Gene Ontology (GO) terms between the telencephalon and liver tissues of C. perspicillata . (A) Venn diagram showing shared and unique KEGG pathways across tissues. (B) Venn diagram depicting common and tissue-specific GO terms, indicating distinct functional roles in each tissue. To further characterize tissue-specific metabolic activity, Figure 4 presents a heatmap analysis comparing the differential expression of KEGG metabolic pathways and GO terms in the liver and telencephalon. In Panel A, the heatmap showcases distinct clustering patterns for KEGG metabolic pathways between the liver and the telencephalon, as evident from the separation along the top dendrogram. Pathways with higher expressions in the liver are displayed in red, indicating increased metabolic activity, while lower expression levels appear in blue. The liver exhibits heightened expression in metabolism, detoxification, and energy production pathways, reflecting its critical role in metabolic homeostasis. Conversely, pathways with higher expression in the telencephalon are likely involved in neurotransmitter synthesis, synaptic signaling, and other neural-specific functions, emphasizing its specialized role in neural processing. Panel B presents the distribution of enriched GO terms, revealing a clear distinction between liver and telencephalon gene expression profiles. GO terms enriched in the telencephalon, shown in red, are associated with neural development, cell signaling, and cognitive processes, underscoring the telencephalon’s role in sensory processing and cognition. On the other hand, liver-enriched GO terms highlight processes related to metabolic regulation, lipid metabolism, detoxification, and other essential hepatic functions. These findings emphasize the liver’s and telencephalon’s functional specialization, with each tissue displaying unique metabolic and biological pathways that cater to their distinct physiological roles in C. perspicillata . Figure 4. Heatmap of KEGG Pathways and GO Terms Across Tissues. Heatmap analysis of KEGG metabolic pathways and GO terms in liver and telencephalon tissues of C. perspicillata . (A) Clustering of KEGG pathways with color-coded expression levels, highlighting differences between tissues. (B) Distribution of enriched GO terms, emphasizing functional specialization in each tissue. To explore how microbial and host gene activity varies between the liver and telencephalon, Figure 5 presents differential expression analyses of microbial taxa and host genes. The figure highlights the molecular differentiation between these tissues, suggesting specific microbial and genetic adaptations aligned with their functional requirements. Panel A displays a volcano plot illustrating differential gene expression between the liver and telencephalon. Genes with significant differential expression (FDR ±2) are highlighted in red, with key genes labeled for clarity. The liver exhibits significant upregulation of genes such as PCK2, ALDOB, and CPS1, which are associated with critical hepatic activities as previously described. In contrast, genes such as MAP1A and NCDN show higher expression in the telencephalon, aligning with their roles in neural function, synaptic plasticity, and cognitive processes. Panel B presents a similar volcano plot for microbial taxa, showing distinct microbial compositions between the liver and telencephalon. Microbial taxa with significant differential abundance (FDR ±2) are highlighted in red, with selected taxa labeled. Notably, several Streptomyces species, such as Streptomyces sp. SJL17-4, Streptomyces sp. S1A1-7 and Streptomyces xiamenensis are more abundant in the telencephalon, suggesting a unique microbial niche in the neural environment. In contrast, microbial species such as Rothia kristinae, Streptomyces cinereoruber, and Diaphorobacter sp. HDW4A are more prevalent in the liver, potentially contributing to metabolic processing and immune modulation specific to hepatic function. These findings highlight the distinct molecular landscapes of the liver and telencephalon, emphasizing how tissue-specific gene expression and microbial compositions contribute to their specialized functions in C. perspicillata . Figure 5. Differential Expression of Microbial Taxa and Host Genes . Differential expression analysis of microbial taxa and host genes in liver and telencephalon tissues of C. perspicillata . (A) Volcano plot depicting differentially expressed genes, with significantly up-and down-regulated genes highlighted. (B) Volcano plot showing differentially abundant microbial taxa, highlighting key differences between tissues. These findings highlight the distinct molecular and microbial landscapes of the liver and telencephalon, underscoring their specialized functional and ecological adaptations in C. perspicillata . The distribution of microbial taxa between the liver and telencephalon of C. perspicillata reveals distinct tissue-specific microbial compositions (Figure 6). Each bar represents the log₂ fold change in abundance for specific microbial taxa, with positive values (red) indicating enrichment in the telencephalon and negative values (green) signifying higher abundance in the liver. Notably, numerous Streptomyces species, including Streptomyces xiamenensis, Streptomyces SJ1-7, and Streptomyces S1A1-7, are highly enriched in the telencephalon. This suggests that Streptomyces may play specific roles or thrive in the biochemical environment of neural tissue. In contrast, taxa such as Rothia kristinae, Streptomyces cinereoruber, and Diaphorobacter sp . HDW4A are more abundant in the liver, indicating a microbial community adapted to hepatic metabolic and detoxification processes. The unique metabolic functions of the liver may select for taxa capable of processing metabolic byproducts or contributing to immune regulation. These findings underscore the specialization of microbial communities in adapting to the distinct functional environments of the liver and telencephalon. Figure 6. Differential Abundance of Microbial Taxa Across Tissues. Differential abundance of microbial taxa between liver and telencephalon tissues of C. perspicillata . The bar plot presents log ₂ fold changes in microbial abundance, with positive values indicating enrichment in the telencephalon and negative values indicating enrichment in the liver. Gene Set Enrichment Analysis (GSEA) was performed to identify key functional gene sets enriched in C. perspicillata tissue samples (Figure 7). The x-axis shows the gene ratio, which represents the proportion of genes that are significantly differentially expressed within each functional category. Dot size corresponds to the count of genes within each category, indicating the relative prevalence of each function. At the same time, color intensity reflects the adjusted p-value (p.adjust), with a blue-to-red gradient indicating increasing statistical significance. Among the enriched gene sets, molecular functions such as ”enzyme inhibitor activity,” ”end peptidase inhibitor activity,” and ”peptidase regulator activity” are prominent, suggesting a solid presence of regulatory and inhibitory processes in the tissue. These functions are likely relevant for modulating enzymatic reactions, which could be essential in maintaining cellular homeostasis and protecting against excessive proteolytic activity. Additionally, the ”synapse” and ”cell junction” are significantly enriched, pointing to the involvement of genes linked to neural connectivity and intercellular communication, particularly in tissues associated with neural function, like the telencephalon. Enriched terms ”extracellular space” and ”extracellular region” indicate a high degree of extracellular interaction, potentially supporting cell signaling, immune response, or structural maintenance in both liver and neural tissues. These findings suggest that C. perspicillata tissues exhibit specialized gene expression patterns that support diverse functional roles, including regulatory control of enzymatic activity, neural connectivity, and extracellular interactions. Such specialization reflects the unique physiological demands of the analyzed tissues, with inhibitory activities and extracellular components playing crucial roles in maintaining tissue-specific functions and integrity. Figure 7. Gene Set Enrichment Analysis (GSEA) of Functional Categories. Dot plot for Gene Set Enrichment Analysis (GSEA) in liver and telencephalon tissues of C. perspicillata . The x-axis represents the gene ratio, while the dot size indicates the count of genes in each functional category. Color intensity reflects the adjusted p-value, with enriched functional categories related to enzymatic activity, cellular communication, and extracellular processes. A network visualization of enriched gene sets associated with extracellular and synaptic functions further illustrates the functional organization of C. perspicillata tissues (Figure 8). The top network focuses on genes related to ”extracellular space” and ”enzyme inhibitor activity,” illustrating a dense cluster of genes involved in extracellular interactions and regulatory functions. The genes serpina4, serping1, and hrg are central to this network and strongly associated with extracellular space and enzyme inhibition. These genes are likely to contribute to protease inhibition and extracellular matrix stability, essential for maintaining structural integrity and regulating extracellular enzymatic activity, particularly in tissue environments where controlled degradation and interaction with external molecules are crucial. The bottom network highlights genes associated with ”cell junction” and ”synapse” categories, capturing elements critical for neural connectivity and intercellular communication. Genes like gabrd, syp, and grm1 are highly connected within this network, playing roles in synaptic transmission and cell junction formation. The presence of these genes underscores the specialization of telencephalon tissue for neural communication and synaptic regulation. Additionally, the differential fold changes, represented by the red and blue color gradient, reveal variations in gene expression, suggesting tissue-specific modulation of these genes. The visualization emphasizes the distinct gene networks underlying key functional processes in maintaining extracellular structure and synaptic connectivity. It reflects how each tissue in C. perspicillata is equipped with specialized molecular machinery to meet its unique physiological demands. Figure 8. Network Visualization of Enriched Functional Gene Sets. Network visualization of enriched gene sets related to extracellular and synaptic functions. The top network represents interactions in the extracellular space, presenting genes related to liver functions, while the bottom network illustrates genes related to the telencephalon with synaptic functions. Node size corresponds to gene count, and color indicates fold change, highlighting functional specialization between tissues Enrichment analysis of biological functions was conducted to determine the localization and intensity of functional activities (Figure 9). The running enrichment score on the y-axis represents cumulative values across the dataset’s rank, with peaks indicating significant enrichment of specific biological functions. The running enrichment score on the y-axis shows cumulative values across the dataset’s rank, highlighting peaks where certain functions are significantly enriched. Each line represents a distinct biological function, with colors corresponding to specific activities like ”cell junction,” ”endopeptidase inhibitor activity,” and ”synapse.” Peaks in these curves suggest areas within the dataset where genes or proteins linked to these functions are concentrated, indicating their potential importance in the underlying biological context. The highest peaks in the enrichment scores, such as those for ”cell junction” and ”synapse,” suggest strong associations within certain dataset regions, implying these functions may be pivotal in cellular connectivity and signaling processes. The presence of ”endopeptidase inhibitor activity” and ”enzyme inhibitor activity” at other notable peaks may indicate the involvement of regulatory mechanisms, potentially controlling proteolytic processes or modulating extracellular interactions. The lower panel further emphasizes the distribution of ranked list metrics, showing where these functional categories appear most intensely within the dataset. This figure underscores the significance of cellular communication, inhibition mechanisms, and extracellular regulatory functions, suggesting their integral roles as players in the studied biological processes. Figure 9. Functional Enrichment Analysis of Biological Processes (Liver and Telencephalon) . Enrichment analysis of biological functions across a ranked dataset. The running enrichment score (y-axis) shows cumulative values across the ranked dataset, with peaks indicating significant enrichment of specific biological functions. The lower panel displays ranked list metrics, illustrating the data distribution. A network analysis of differentially regulated gene clusters reveals the presence of distinct molecular functional shifts across tissues (Figure 10). The network is organized into two main clusters based on regulation status, with red nodes representing up-regulated functions and blue nodes indicating down-regulated functions. The size of each node corresponds to the number of genes linked to that specific function, while edges between nodes indicate shared genes, reflecting functional interconnections. The up-regulated cluster highlights critical activities such as ”ATP hydrolysis activity,” ”endopeptidase regulator activity,” and ”peptidase inhibitor activity,” suggesting an increase in proteolytic regulation and energy-related processes. This up-regulation may imply an enhanced requirement for protein processing, degradation, and molecular chaperoning in the studied biological context, potentially in response to cellular stress or increased metabolic demand. Notably, endopeptidase and peptidase inhibition genes are upregulated, which could modulate protease activity and prevent excessive proteolysis, ensuring controlled protein turnover and cellular stability. Conversely, the down-regulated cluster includes functions associated with ion transport, such as ”calcium ion transmembrane transporter activity” and ”metal ion transmembrane transporter activity.” This decrease suggests a reduction in ion exchange and homeostasis mechanisms, which may reflect altered cellular signaling or reduced ionic demand in the given biological state. Several interconnected nodes related to the ion channel and transporter activity underline the coordinated down-regulation of ionic fluxes across cellular membranes, potentially impacting processes such as neurotransmission or cellular excitability. Overall, Figure 10 highlights a distinct regulatory pattern, where up-regulated functions are primarily associated with energy metabolism and protease regulation, while down-regulated functions predominantly involve ion transport. This functional shift may play a critical role in the physiological adaptation or potential pathological processes observed in the studied tissues. Figure 10. Network of Differentially Regulated Gene Clusters. Functional network of differentially regulated gene clusters. Red nodes indicate up-regulated functions, while blue nodes represent down-regulated functions. Node size corresponds to the number of associated genes, and edges indicate functional interconnections, visualizing distinct regulatory patterns across tissues. A dot plot analysis of enriched molecular functions for up- and down-regulated genes provides further insights into functional shifts within the dataset (Figure 11). The y-axis lists molecular functions, separated into up-regulated functions on the left and down-regulated on the right. The dot sizes represent the Gene Ratio or the proportion of genes associated with each function. The colors of the dots indicate the false discovery rate (FDR), with warmer tones (red) signifying higher significance levels. In the up-regulated category, prominent functions include ”endopeptidase regulator activity,” ”ATP hydrolysis activity,” and ”protein folding chaperone,” suggesting an increased need for protein regulation and stress response mechanisms. The presence of ”ATP hydrolysis activity” and ”unfolded protein binding” highlights an elevated energy demand and protein folding activity, potentially in response to cellular stress or an active metabolic state. Conversely, the down-regulated functions focus on ion transport and neurotransmission, with critical functions such as ”calcium ion transmembrane transporter activity,” ”GABA receptor activity,” and ”glutamate receptor activity.” This suggests a reduction in cellular activities related to ion exchange, signaling, and neurotransmitter function, which could indicate shifts in cellular excitability or signaling demands in this biological context. Overall, Figure 11 reveals a contrasting regulatory landscape, where up-regulation emphasizes protein management and energy-related processes. At the same time, down-regulation reduces ion transport and receptor-mediated signaling, potentially reflecting a reallocation of cellular resources or a shift in physiological priorities. Figure 11. Enriched Molecular Functions of Differentially Expressed Genes. Dot plot of enriched molecular functions for up- and down-regulated genes. Functional categories are shown on the y-axis, with dot sizes representing gene ratios and colors indicating significance levels (FDR-adjusted p-values). The molecular functions with vertically upper-left dots correspond to liver-related functions, while the functions with lower-right dots correspond to the telencephalon.

Our metatranscriptomic analysis of C. perspicillata reveals significant insights into how bats achieve unique microbiome-host interactions that likely underpin their remarkable immune tolerance and resistance mechanisms. Focusing on two functionally distinct tissues, the liver, and the telencephalon, we observed contrasting microbial diversity and host gene expression patterns that highlight how the physiological environment of each tissue influences its microbial ecosystem. The high microbial diversity of the liver, consistent with its metabolic and detoxification functions, suggests that bats rely on specialized microbial communities to support hepatic tolerance to pathogens and potentially toxic metabolic byproducts (Schroeder & Bäckhed, 2016; Turnbaugh et al., 2009). This diversity may contribute to an immune environment that limits inflammation, aligning with the principle of tolerance as defined in host-pathogen interactions, where the host minimizes damage rather than solely focusing on pathogen elimination (Medzhitov et al., 2012; Schneider & Ayres, 2008). The telencephalon, with a lower microbial load but enriched in specific taxa such as Streptomyces spp., reflects a potential neuromodulatory role, as certain microbes are implicated in neurotransmitter synthesis and cognitive processes (Dinan & Cryan, 2017; Kuijer & Steenbergen, 2023). The immune-privileged of telencephalon status and reduced inflammation likely create a unique niche for microbes adapted to neural environments, and this low-inflammatory state is key to the ability of the bat to harbor microbes without adverse effects, a phenomenon well-documented in the capacity of the bat to act as reservoirs without succumbing to infection (Brook & Dobson, 2015; Demian et al., 2024; O’Shea et al., 2014; Pereira et al., 2023). This immune tolerance is hypothesized to be driven by a combination of gene expression regulation and selective immune pathways that allow bats to avoid harmful inflammatory responses while maintaining pathogen control, which may be critical for zoonotic pathogens to remain in a latent or low-virulence state within the host (Pereira et al., 2023). Our functional enrichment analysis supports these observations, showing that liver tissue was enriched for pathways associated with metabolism, detoxification, and immune regulation, essential functions that help manage the high diversity of microbial taxa without inciting detrimental immune responses. Previous studies have highlighted that the bat liver, due to its role in metabolic homeostasis, may support immune tolerance through transcriptional pathways that modulate inflammation, a strategy possibly employed to prevent excessive immune activation while maintaining pathogen resistance (Franzosa et al., 2014; Schroeder & Bäckhed, 2016). Pereira et al. (2023) reviewed these adaptations and highlighted that bats achieve immune tolerance primarily by downregulating inflammatory cytokines while simultaneously enhancing cellular mechanisms that preserve tissue integrity and restrict pathogen proliferation. These processes contribute to tolerance as a defense strategy, allowing bats to control microbial communities without the collateral damage seen in other mammals (David, Schountz, Schwemmle, & Ciminski, 2022; Guito et al., 2021; Medzhitov et al., 2012; Sia et al., 2022). In contrast, the telencephalon is enriched for cellular signaling, synaptic function, and neuromodulation pathways. These findings support the idea that bats might have evolved to support microbial communities that participate in or support neural processes with adaptations that enhance the immune-privileged state of the brain. The microbial composition of the telencephalon aligns with emerging studies that suggest certain microbes may play beneficial roles in neural health, potentially influencing cognitive processes or contributing to neuroprotection. This neural tolerance, coupled with resistance mechanisms, such as selective immune activation, could explain how bats support diverse microbial populations in neural tissues without compromising brain function, thus maintaining resilience against neuropathogenic microbes (Brook & Dobson, 2015; Zhang et al., 2018). The enrichment of the telencephalon in cellular signaling, synaptic function, and neuromodulation pathways suggests that bats may have evolved to support microbial communities that participate in or support neural processes, thereby enhancing the brain’s immune-privileged state. While direct studies on bat brain microbiomes are limited, research on the microbiota-gut-brain axis in mammals provides relevant insights. For instance, Nakhal et al. (2024) discuss how gut microbiota communicate with the brain through neural, endocrine, immune, and humoral pathways, influencing neurological functions and potentially contributing to the immune-privileged environment of the brain (Nakhal et al., 2024). This review supports the concept that microbial communities can modulate neural processes and immune responses within the central nervous system. Indeed, the gut microbiome is known to influence the behavior of microglia, and astrocytes, by the release of microbiome-derived molecules with neuro- and immune-active potential in humans (Loh et al., 2024; Ratsika, Cruz Pereira, Lynch, Clarke, & Cryan, 2023). Interestingly, mental states, including stress, have also been shown to influence the composition of gut bacteria (Chang et al., 2024). Recent metatranscriptomic studies have demonstrated that the human brain also harbours a diverse spectrum of microbial communities, with variations in microbial composition influenced by both brain region and disease state ((Kriesel et al., 2019). These studies, utilizing RNA sequencing and metatranscriptomic analysis, identified bacteria, fungi, viruses, and even chloroplastida within human brain tissue. Notably, microbial diversity was found to be higher in individuals with Alzheimer’s disease (AD) and multiple sclerosis (MS) compared to non-diseased controls. AD brains exhibited an overrepresentation of specific bacterial ( Streptococcus, Staphylococcus, Sphingomonas ) and fungal ( Acrocalymma, Altenaria, Tausonia ) taxa, while MS samples showed an enrichment of anaerobic bacteria such as Nitrosospira and Fusobacterium . Regional differences were also observed, with the cingulate cortex showing the highest microbial burden. Furthermore, MS brains displayed increased expression of immune-related pathways, suggesting a potential link between microbial activity and neuroinflammation. The findings of our study on differential gene expression in the liver and telencephalon of C. perspicillata may have broader implications for understanding zoonotic spillover. The ability of the bat to tolerate and resist a range of microbes, including viruses with zoonotic potential, hinges on their capacity to manage microbial load without escalating inflammatory responses that could lead to pathogen shedding (Calisher et al., 2006; Letko et al., 2020; Zhou et al., 2020). The differential expression profiles observed, particularly in pathways associated with immune regulation, underscore a finely tuned host-microbe balance that may prevent these pathogens from reaching spillover conditions. Such modulation of host immune responses, by reducing inflammation while maintaining control over microbial populations, lowers the likelihood of high-virulence pathogen replication, as suggested by Letko et al. (2020) and Pereira et al. (2023). The ability of C. perspicillata to maintain distinct microbial populations in different tissues may be a key factor in its role as a reservoir for pathogens. Previous research has shown that bats harbor many viruses without apparent disease symptoms, likely due to finely regulated immune responses that avoid the damaging effects of inflammation while keeping microbial populations in check (Wang & Anderson, 2019; Zhou et al., 2020). This modulation of immune responses, facilitated by tissue-specific microbial communities, might reduce the likelihood of pathogen spillover under natural conditions, as the host environment does not foster the high pathogen replication rates that typically precede spillover events (Brook & Dobson, 2015; Letko et al., 2020). It is important to highlight, however, that human-driven expansions and the resulting alterations to natural habitats are increasingly forcing bats to seek alternative environments. These changes can create novel interactions with other species, including humans, thereby facilitating the potential dissemination of viruses and bacteria of epidemiological significance into new ecological niches. Such interactions may elevate the risk of zoonotic spillover, increasing the likelihood of transmission to humans, domestic animals, and wildlife that coexist within these modified environments. Numerous studies have demonstrated that habitat destruction and fragmentation contribute to altered host-pathogen dynamics, leading to increased contact between wildlife and human populations (Allen et al., 2017; Plowright, Becker, McCallum, & Manlove, 2019). The ecological and evolutionary adaptations of bats, combined with their unique immune tolerance, make them efficient reservoirs for various zoonotic pathogens, including coronaviruses, filoviruses, and paramyxoviruses (Olival et al., 2017; Wang & Anderson, 2019). Therefore, understanding the consequences of habitat encroachment and developing strategies to mitigate human-wildlife interactions are critical to reducing future spillover events. DATA ACCESSIBILITY All raw sequencing data generated in this study have been deposited in the NCBI Sequence Read Archive (SRA) under the BioProject accession PRJNA1233355. The individual SRA accession numbers for each sample are provided in Supplementary File 1. These datasets include transcriptomic and metatranscriptomic reads from Carollia perspicillata liver and telencephalon, enabling further exploration of host-microbiome interactions. Additionally, all supplementary files, including processed gene expression data, microbiome analyses, and other relevant resources supporting this study, are available in the following GitHub repository: https://github.com/patrick-douglas/da_Costa_et_al_2025/tree/main. Additionally, all supplementary files, including processed gene expression data, microbiome analyses, and other relevant resources supporting this study, are available in the following GitHub repository: https://github.com/patrick-douglas/da_Costa_et_al_2025/tree/main. . ACKNOWLEDGMENTS We would like to thank all participants for their collaboration and the continued support of the University Hospital João de Barros Barreto/EBSERH administration office, Universidade Federal do Pará, Instituto Federal de Educação, Ciência e Tecnologia do Pará, Laboratório de Investigações em Neurodegeneração e Infecção e Laboratório de Biologia Molecular e Neuroecologia to carry out this work. We thank Fundação para a Ciência e a Tecnologia through iMed.ULisboa (UID 04138). COMPETING INTERESTS All the authors declare that there were no conflicts of interest. AUTHOR CONTRIBUTIONS ERC, PDCP, AJFS, JGSL, NIPA, KNC: Field sampling. ERC, PDCP, CWPD, CGD: investigation, statistical analysis, and interpretation of results. PDCP and DGD: conceptualization, data analysis, and illustration editions. CWPD, DB, NGMM, MADM, JAPD, PFCV, CGD, PDCP, and DA: critical review, interpretation of results, writing, and final edition of the manuscript. All authors have read and approved the manuscript’s final version. FUNDING DGD was supported by Brazilian National Research Council - CNPq (101716/2024-9), CWPD was supported by Brazilian National Research Council - CNPq (407075/2021-6 & 301268/2019-3), Instituto Nacional de Ciência e Tecnologia em Víroses Emergentes e Reemergentes INCT-VER (406360/2022-7), CAPES, UFPA/EBSERH and UFPA/FINEP/FADESP-SOS (Equipment 2021). ERC received support from CAPES during his Doctoral’s degree. DB was supported by grants from Fundação para a Ciência e Tecnologia FCT and PTDC/MED-NEU (2382/2021 & LISBOA-01-0145-FEDER-031395), La Caixa Foundation and Fundación Luzón (HR21-00931) and Instituto de Investigação do Medicamento (UIDB/04138). SUPPORTING INFORMATION Graphical Abstract Text and Figure: The workflow began with the collection of tissue samples from the telencephalon and liver of Carollia perspicillata . Total RNA was extracted and prepared for next-generation sequencing through library preparation. Following sequencing, raw reads were processed to identify host transcripts and unaligned reads. The analysis of the host transcriptome included differential gene expression profiling. Meanwhile, unaligned reads were used to facilitate microbiome discovery. To distinguish host-derived sequences from microbial components, the Myotis lucifugus reference genome was used for alignment. This approach enabled the imultaneous exploration of host gene expression and microbiome composition within each tissue.

1. http://www Allen, T., Murray, K. A., Zambrana-Torrelio, C., Morse, S. S., Rondinini, C., Di Marco, M., . . . Daszak, P. (2017). Global hotspots and correlates of emerging zoonotic diseases. Nat Commun, 8 (1), 1124. M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., . . . Sherlock, G. (2000). Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet, 25 (1), 25-29. F., Weingart, G., Tickle, T. L., Huttenhower, C., & Segata, N. (2015). Compact graphical representation of phylogenetic data and metadata with GraPhlAn. PeerJ, 3, e1029. A. J., & Sperandio, V. (2016). Interactions between the microbiota and pathogenic bacteria in the gut. Nature, 535 (7610), 85-93. B. (2011). FastQC A quality control tool for high throughput sequence data. Cambridge, UK: Babraham Institute . Bolger, A. M., Lohse, M., & Usadel, B. (2014). Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics, 30 (15), 2114-2120. C. E., & Dobson, A. P. (2015). Bats as ’special’ reservoirs for emerging zoonotic pathogens. Trends Microbiol, 23 (3), 172-180. C. H., Childs, J. E., Field, H. E., Holmes, K. V., & Schountz, T. (2006). Bats: important reservoir hosts of emerging viruses. Clin Microbiol Rev, 19 (3), 531-545. H., Perkins, M. H., Novaes, L. S., Qian, F., Zhang, T., Neckel, P. H., . . . de Araujo, I. E. (2024). Stress-sensitive neural circuits change the gut microbiome via duodenal glands. Cell, 187 (19), 5393-5412 e5330. S., Zhou, Y., Chen, Y., & Gu, J. (2018). fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics, 34 (17), i884-i890. Q., Schountz, T., Schwemmle, M., & Ciminski, K. (2022). Different but Not Unique: Deciphering the Immunity of the Jamaican Fruit Bat by Studying Its Viriome. Viruses, 14 (2). W. L., Cormier, O., & Mossman, K. (2024). Immunological features of bats: resistance and tolerance to emerging viruses. Trends Immunol, 45 (3), 198-210. T. G., & Cryan, J. F. (2017). Gut-brain axis in 2016: Brain-gut-microbiota axis - mood, metabolism and behaviour. Nat Rev Gastroenterol Hepatol, 14 (2), 69-70. S., Spellman, P. T., Birney, E., & Huber, W. (2009). Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt. Nature protocols, 4 (8), 1184-1191. Franzosa, E. A., Morgan, X. C., Segata, N., Waldron, L., Reyes, J., Earl, A. M., . . . Huttenhower, C. (2014). Relating the metatranscriptome and metagenome of the human gut. Proc Natl Acad Sci U S A, 111 (22), E2329-2338. M. G., Haas, B. J., Yassour, M., Levin, J. Z., Thompson, D. A., Amit, I., . . . Zeng, Q. (2011). Trinity: reconstructing a full-length transcriptome without a genome from RNA-Seq data. Nature biotechnology, 29 (7), 644. Guito, J. C., Prescott, J. B., Arnold, C. E., Amman, B. R., Schuh, A. J., Spengler, J. R., . . . Towner, J. S. (2021). Asymptomatic Infection of Marburg Virus Reservoir Bats Is Explained by a Strategy of Immunoprotective Disease Tolerance. Curr Biol, 31 (2), 257-270.e255. B. J., Papanicolaou, A., Yassour, M., Grabherr, M., Blood, P. D., Bowden, J., . . . Lieber, M. (2013). De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis. Nature protocols, 8 (8), 1494-1512. Knight, R., Vrbanac, A., Taylor, B. C., Aksenov, A., Callewaert, C., Debelius, J., . . . Dorrestein, P. C. (2018). Best practices for analysing microbiomes. Nat Rev Microbiol, 16 (7), 410-422. J. D., Bhetariya, P., Wang, Z. M., Renner, D., Palmer, C., & Fischer, K. F. (2019). Spectrum of Microbial Sequences and a Bacterial Cell Wall Antigen in Primary Demyelination Brain Specimens Obtained from Living Patients. Sci Rep, 9 (1), 1387. E. J., & Steenbergen, L. (2023). The microbiota-gut-brain axis in hippocampus-dependent learning and memory: current state and future challenges. Neurosci Biobehav Rev, 152, 105296. M., Seifert, S. N., Olival, K. J., Plowright, R. K., & Munster, V. J. (2020). Bat-borne virus diversity, spillover and emergence. Nat Rev Microbiol, 18 (8), 461-471. Y., Smyth, G. K., & Shi, W. (2014). featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics, 30 (7), 923-930. J. S., Mak, W. Q., Tan, L. K. S., Ng, C. X., Chan, H. H., Yeow, S. H., . . . Khaw, K. Y. (2024). Microbiota-gut-brain axis and its therapeutic applications in neurodegenerative diseases. Signal Transduct Target Ther, 9 (1), 37. M. I., Huber, W., & Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol, 15 (12), 550. J., Breitwieser, F., Thielen, P., & Salzberg, S. (2017). Bracken: estimating species abundance in metagenomics data. Peer J Computer Science, 3, e104. M. D. (2014). On the optimal trimming of high-throughput mRNA sequence data. Front Genet, 5, 13. F. J., Amode, M. R., Aneja, A., Austine-Orimoloye, O., Azov, A. G., Barnes, I., . . . Bhai, J. (2023). Ensembl 2023. Nucleic acids research, 51 (D1), D933-D941. McMurdie, P. J., & Holmes, S. (2013). phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One, 8 (4), e61217. R., Schneider, D. S., & Soares, M. P. (2012). Disease tolerance as a defense strategy. Science, 335 (6071), 936-941. A. E., Dong, Y., Brown, T., Baid, K., Kontopoulos, D., Gonzalez, V., . . . Hiller, M. (2025). Bat genomes illuminate adaptations to viral tolerance and disease resistance. Nature, 638 (8050), 449-458. M. M., Yassin, L. K., Alyaqoubi, R., Saeed, S., Alderei, A., Alhammadi, A., . . . Hamad, M. I. K. (2024). The Microbiota-Gut-Brain Axis and Neurological Disorders: A Comprehensive Review. Life (Basel), 14 (10). T. J., Cryan, P. M., Cunningham, A. A., Fooks, A. R., Hayman, D. T., Luis, A. D., . . . Wood, J. L. (2014). Bat flight and zoonotic viruses. Emerg Infect Dis, 20 (5), 741-745. J., Kindt, R., Legendre, P., O’Hara, B., Stevens, M. H. H., Oksanen, M. J., & Suggests, M. (2007). The vegan package. Community ecology package, 10 (631-637), 719. Olival, K. J., Hosseini, P. R., Zambrana-Torrelio, C., Ross, N., Bogich, T. L., & Daszak, P. (2017). Host and viral traits predict zoonotic spillover from mammals. Nature, 546 (7660), 646-650. P. D. C., Diniz, D. G., da Costa, E. R., Magalhães, N. G. M., da Silva, A. J. F., Leite, J. G. S., . . . Guerreiro-Diniz, C. (2023). Genes, inflammatory response, tolerance, and resistance to virus infections in migratory birds, bats, and rodents. Front Immunol, 14, 1239572. J. M., Zeng, M. Y., Caruso, R., & Núñez, G. (2017). Gut microbiota: Role in pathogen colonization, immune responses, and inflammatory disease. Immunol Rev, 279 (1), 70-89. R. K., Becker, D. J., McCallum, H., & Manlove, K. R. (2019). Sampling to elucidate the dynamics of infections in reservoir hosts. Philos Trans R Soc Lond B Biol Sci, 374 (1782), 20180336. S., & Hebert, P. D. (2007). BOLD: The Barcode of Life Data System (. barcodinglife. org). Molecular ecology notes, 7 (3), 355-364. Ratsika, A., Cruz Pereira, J. S., Lynch, C. M. K., Clarke, G., & Cryan, J. F. (2023). Microbiota-immune-brain interactions: A lifespan perspective. Curr Opin Neurobiol, 78, 102652. D. S., & Ayres, J. S. (2008). Two ways to survive infection: what resistance and tolerance can teach us about treating infectious diseases. Nat Rev Immunol, 8 (11), 889-895. B. O., & Bäckhed, F. (2016). Signals from the gut microbiota to distant organs in physiology and disease. Nat Med, 22 (10), 1079-1089. W. R., Zheng, Y., Han, F., Chen, S., Ma, S., Wang, L. F., & Leeansyah, E. (2022). Exploring the Role of Innate Lymphocytes in the Immune System of Bats and Virus-Host Interactions. Viruses, 14 (1). D., Cizdziel, P. E., & Chomczynski, P. (1993). TRIzol: A new reagent for optimal single-step isolation of RNA. Focus, 15 (4), 532-535. Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., . . . Mesirov, J. P. (2005). Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A, 102 (43), 15545-15550. P. J., Ridaura, V. K., Faith, J. J., Rey, F. E., Knight, R., & Gordon, J. I. (2009). The effect of diet on the human gut microbiome: a metagenomic analysis in humanized gnotobiotic mice. Sci Transl Med, 1 (6), 6ra14. Rossum, T., Ferretti, P., Maistrenko, O. M., & Bork, P. (2020). Diversity within species: interpreting strains in microbiomes. Nat Rev Microbiol, 18 (9), 491-506. L. F., & Anderson, D. E. (2019). Viruses in bats and potential spillover to animals and humans. Curr Opin Virol, 34, 79-89. C. R., Baccarella, A., Parrish, J. Z., & Kim, C. C. (2016). Trimming of sequence reads alters RNA-Seq gene expression estimates. BMC Bioinformatics, 17 (1), 103. D. E., Lu, J., & Langmead, B. (2019). Improved metagenomic analysis with Kraken 2. Genome Biol, 20 (1), 257. F., Liu, Y.-Z., & Ling, B. (2022). MTD: a unique pipeline for host and meta-transcriptome joint and integrative analyses of RNA-seq data. Briefings in Bioinformatics, 23 (3), bbac111. Yu, G., Wang, L.-G., Han, Y., & He, Q.-Y. (2012). clusterProfiler: an R package for comparing biological themes among gene clusters. Omics: a journal of integrative biology, 16 (5), 284-287. Zhang, Y. Z., Shi, M., & Holmes, E. C. (2018). Using Metagenomics to Characterize an Expanding Virosphere. Cell, 172 (6), 1168-1172. P., Yang, X. L., Wang, X. G., Hu, B., Zhang, L., Zhang, W., . . . Shi, Z. L. (2020). A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature, 579 (7798), 270-273. Information & Authors Information Version history Copyright This work is licensed under a Non Exclusive No Reuse License.

Authors Metrics & Citations Metrics Article Usage 329views 258downloads Citations Download citation Emanuel Ramos da Costa, Patrick Douglas Correa Pereira, Daniel Diniz, et al. Metatranscriptomic Profiling of Host-Microbiome Interactions in the Telencephalon and Liver of Carollia perspicillata.. Authorea. 20 March 2025. DOI: https://doi.org/10.22541/au.174248319.93763000/v1 DOI: https://doi.org/10.22541/au.174248319.93763000/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu.