Objective: APBB1IP is a Rap1-binding protein that mainly acts as a regulator of leukocyte recruitment and pathogen clearance through complement-mediated phagocytosis. However, the role of APBB1IP in tumor immunity remains unclear. This study was carried out to evaluate the prognostic landscape of APBB1IP in pan-cancer analysis and investigate the relationship between APBB1IP expression and immune infiltration.Methods: We explored the expression pattern and prognostic value of APBB1IP in pan-cancer analysis through Kaplan-Meier Plotter and multiple databases, including TCGA, Oncomine. We then assessed the correlation between APBB1IP expression and immune cell infiltration using the TIMER database. Furthermore, we identified the proteins that interact with APBB1IP and performed epigenetic and transcriptional analyses. Multivariate Cox regression analyses were applied to construct a prognostic model, which consisted of APBB1IP and its interacting proteins, based on the lung cancer cohorts from the Gene Expression Omnibus (GEO) database.Results: The expression of APBB1IP was correlated with the prognosis of several types of cancer. APBB1IP upregulation was found to be associated with increased immune cell infiltration, especially for CD8+ T cells, natural killer (NK) cells, and immune regulators. A link was found between APBB1IP and immune-related proteins including RAP1A/B, TLN1/2 and VCL in the interaction network.Conclusion: APBB1IP can serve as a prognostic biomarker in pan-cancer analysis. APBB1IP upregulation was correlated with increased immune-cell infiltration, and the expression APBB1IP in different tumors might be related to the tumor immune microenvironment. APBB1IP) was identified as a binding partner of the amyloid β (A4) precursor protein-binding, family B, member 1 (APBB1) APBB1IP was found to interact with the small guanosine triphosphatase (GTPase) Rap1 in a yeast two-hybrid screen APBB1IP belongs to the MRL (Mig-10/RIAM/Lamellipodin) family of adaptor proteins, which have a proline-rich region at the C terminus and a highly conserved pattern of 27 amino acids in a predicted coiled-coil region immediately N-terminal to the RA domain The amyloid β (A4) precursor protein-binding, family B, member 1 interacting protein , programmed death-1 (PD-1), and programmed death ligand-1 (PD-L1) were found to have promising antitumor effects on non-small-cell lung carcinoma and colorectal cancer The tumor microenvironment (TME) contains various cells, with infiltrating immune cells accounting for a large proportion APBB1IP gene in pan-cancer analysis using the TCGA pan-cancer database and Kaplan-Meier Plotter. We then explored the potential relationships between APBB1IP expression and immune infiltration levels. Finally, the potential mechanisms were also explored by bioinformatic analysis.In this study, we evaluated the expression signature and prognostic value of the Mutation data, RNA sequencing data, and clinical data for 33 cancer types were obtained from TCGA using UCSC Xena APBB1IP expression in normal tissue and tumor tissue was compared across cancers using the Wilcoxon test, and the results were visualized using the R packages “ggpubr”.The mRNA expression of APBB1IP in different cancer types was analyzed in the ONCOMINE database. APBB1IP tumors was compared using univariate Cox analysis for each cancer subtype, and genes with a P value < 0.05 were considered as prognostic genes. Overall survival analysis was performed via Kaplan-Meier survival analysis using the “survival” and “survminer” R packages.The prognosis of patients with high- tumors and low-Kaplan-Meier plotter was used to assess the effects of 54,675 genes on survival using 10,461 cancer samples. These samples include 5,143 breast, 1,816 ovarian, 2,437 lung, and 1,065 gastric cancer samples on the HGU133 Plus 2.0 array with a mean follow-up of 69, 40, 49, and 33 months, respectively. The correlation between APBB1IP expression and survival in 21 different cancers was analyzed using Kaplan-Meier plotter APBB1IP expression in several types of cancer and the correlation of APBB1IP expression with the abundance of infiltrating immune cells, after which Spearman's rho value and statistical significance were obtained. The correlation between APBB1IP expression and several immune cell markers was also analyzed by Spearman correlation to identify the potential subtypes of infiltrating immune cells. Immune gene markers were selected from the website of R&D Systems.The TIMER database was used to systematically analyze the tumor-infiltrating immune cells (TIICs) in 32 cancer types in more than 10,000 samples from The Cancer Genome Atlas (TCGA) database Different immune factors lists were obtained from the Tumor Immune System Interactions Database GO functional analysis is a useful method for annotating genes and identifying characteristic biological attributes from high-throughput genome or transcriptome data http://string-db.org) A PPI network was developed using the online database STRING , 746 drug data from Genomics of Drug Sensitivity in Cancer (GDSC) and the Cancer Therapeutics Response Portal (CTRP), as well as normal tissue expression data of 11 688 samples from GTEx . We used GSCALite to analyzed the single nucleotide variation, copy number variation, methylation and pathway activity APBB1IP and associated genes were evaluated by step-wise multivariate Cox regression analysis. Risk scores were calculated based on gene expression multiplied by a linear combination of a regression coefficient obtained from the multivariate Cox analysis. Patients were assigned to high- and low-risk groups according to the median risk score. The survival analysis of patients in the high- and low-risk groups was conducted using the “survival” R package. The receiver operating characteristic (ROC) curve was implemented by the R software package “survival ROC”.APBB1IP mRNA expression levels were analyzed over a cancer-wide range in Oncomine. The results revealed that the expression of APBB1IP was inconsistently up- or down-regulation in different cancer types . These findings demonstrate the intrinsic differences in the expression of APBB1IP between different tumor types, and detailed analyses of APBB1IP expression were considered for further analysis.To explore its role in cancer, the APBB1IP via pan-cancer analysis in different databases. First of all, we used univariate Cox proportional hazard regression models to analyze the association between APBB1IP expression with the overall survival and progression-free survival in various cancers in the TCGA. The criterion for significant association was a P-value of less than 0.05. As shown in the Figure APBB1IP was associated with poor prognosis in LGG  and UVM , while increased expression of APBB1IP was primarily associated with a survival advantage in patients with CESC, HNSC, KIRP, SKCM, THYM, and UCEC. Figure APBB1IP predicted shorter RFS in patients with LGG and PRAD. In patients with ACC, CESC, KIRP and UCEC, high APBB1IP expression predicted better disease‐free survival. Overall- and progression-free survival curves stratified by high- and low-expression of APBB1IP in different types of cancer are shown in the We next investigated the prognostic value of APBB1IP in different cancers, the Kaplan-Meier plotter database was used to evaluate the prognostic value of APBB1IP based on gene chip and RNA-seq data from the GEO and EGA databases. Low APBB1IP expression levels were associated with poorer OS in BRCA, CESC, HNSC, KIRP, READ, SARC, THYM and UCEC. Conversely, low expression of APBB1IP was correlated with better OS in ESCA, LIHC, STAD and TGCT  in SKCM and LGG, in which APBB1IP expression was correlated with a poor prognosis. As shown in Table APBB1IP expression was significantly correlated with most immune cell markers in SKCM, in addition to several markers of M1 macrophages and neutrophils. By contrast, APBB1IP was not significantly correlated with gene markers of CD8+ T cells and NK cells in LGG . Figure Next, 35 genes  that werhttps://string-db.org/) and Cytoscape software , talin 1 (TLN1), talin 2 (TLN2), and vinculin (VCL). These genes were further considered for downstream analysis of the APBB1IP network. Subsequently, a TF regulatory network with 45 nodes was predicted using the iRegulon Cytoscape plugin  as well as in myeloid and B lymphoid differentiation SPI1 might function by regulating the transcription of APBB1IP and other associated proteins. Subsequently, using miRWalk , we predicted 19 miRNAs that could potentially target the 3′UTR of APBB1IP  analysis can reflect the molecular mechanisms of physiological and pathological changes that drive cancer progression. The PPI network of APBB1IP and its protein partners was constructed using the STRING database  profile of APBB1IP and its protein partners using GSCALite APBB1IP, the SNV frequency was 20%, at the cancer level. Additionally APBB1IP-associated SKCM exhibited the highest number of mutations (32), followed by LUSC (22) and UCEC (21). The most frequent DNA alterations of these 6 genes in the pan-cancer analysis were missense mutations. CNVs were reported as frequent pathogenic events in cancers, which could contribute to increased DNA instability and occurrence of genomic imbalance TLN1 and TLN2 was positively associated with CNV in most cancers, except for APBB1IP, RAP1B, and RAP1A.To further understand how APBB1IP. We found that the methylation of APBB1IP was significantly up-regulated in LUSC, BLCA, COAD, HNSC, BRCA, PAAD, and UCEC, while co-methylation patterns of APBB1IP and the protein partners of APBB1IP were not observed +(0.422303 × expression level of RAP1A) +(0.377711 × expression level of RAP1B). All three genes were prognostic for increased risk, including APBB1IP , RAP1A , and RAP1B . Multivariate Cox regression was used to calculate regression coefficients. Risk scores were based on gene expression levels multiplied by the corresponding regression coefficients. Subsequently, 117 lung cancer samples were divided into a high-risk group (n = 59) and a low-risk group (n = 58) based on the median risk score , indicating that this prognostic model exhibited good sensitivity and specificity. To identify the relationship between risk score model and clinicopathological characteristics in lung cancer patients, we further analyzed the risk score level in lung cancer patients at different clinical stages. As revealed in Table P < 0.05). Subsequently, the GSE29016 dataset was used as the testing cohort. The Kaplan-Meier analysis shown in Figure p=3.842e-02). Time-dependent ROC curves showed that the model had good accuracy with a value of 0.710 in 5 years  family of adapter proteins. It was identified as a Rap1-binding protein important for integrin-mediated migration and activation of leukocytes APBB1IP in the immune system, APBB1IP has not been well-studied in immuno-oncology. Here, we conducted a pan-cancer analysis of the expression profile and prognostic significance of APBB1IP and revealed its potential role in tumor immunology via bioinformatics analysis.APBB1IP contains Ras association (RA) and pleckstrin homology (PH) domains and proline-rich regions, which are defining features of the APBB1IP were examined and the prognostic landscape in pan-cancer analysis were visualized using independent datasets in Oncomine and Kaplan-Meier plotter, as well as TCGA data for 33 types of cancer. According to the combined results from the Oncomine and TCGA data, APBB1IP was highly expressed in KIRC, KIRP, STAD, sarcoma, as well as brain and CNS cancers compared to corresponding normal tissues, while it had lower expression levels in BLCA, BRCA, COAD, LUAD, LUSC, PAAD, PCCG, READ, liver cancer and leukemia. From the comprehensive TCGA and Kaplan-Meier plotter data, we found consistent prognostic correlations of APBB1IP. Specifically, decreased APBB1IP expression was correlated with poor prognosis in most tumor types . However, STAD was an exception where high levels of APBB1IP expression indicated a poorer prognosis. The discrepancies in APBB1IP levels and prognosis in different cancer types in different databases might be a reflection of different data collection approaches and underlying mechanisms pertinent to different biological properties. Notably, APBB1IP was up‐regulated in KIRP, but high APBB1IP expression indicated a better prognosis in this cancer. This counterintuitive finding can be explained by the small sample size of the normal group (n = 32), which might have led to unreliable data on the expression of APBB1IP in KIRP. Hence, in future studies, researchers need to collect more samples to verify the expression of APBB1IP in KIRP. In addition, APBB1IP might play distinct roles in the initiation and progression of KIRP. To confirm this, further studies are needed to explore the expression of APBB1IP in tumors with different stages or grades and analyze the relationship between APBB1IP expression and survival of KIRP patients with different tumor stages or grades.In this study, the expression levels of APBB1IP expression is correlated with diverse immune-cell infiltration levels in most cancer types  cytoplasmic adapter proteins  is a key adaptor molecule that links adhesion complexes to actin filaments in integrin-based cell extracellular matrix (ECM) adhesions or cell-cell junctions APBB1IP was reported to promote the migration and invasion of melanoma cells APBB1IP might interact with VCL to achieve this function. According to our results, high APBB1IP expression indicated poor prognosis in SKCM, and we deduced that the promoting effect of APBB1IP on the migration ability of SKCM might not be associated with its prognosis, while the influence on immune infiltration might play a leading role.s Figure A 49, 50.e large 20 kDa cyte large 20 kDa cytRAP1A/B and TLN1 expression is positively associated with APBB1IP (Figure APBB1IP and its interacting partners (Figure APBB1IP could target its interacting partners at the same time (Figure APBB1IP and RAP1A/B were negatively correlated with gene expression (Figure APBB1IP methylation was higher in LUSC, BLCA, LUAD, COAD, BRCA, HNSC, PRAD, and UCEC tumor tissues than in normal control tissues (Figure APBB1IP in most cancers.In this study, we found that P Figure D. Thus, s Figure B. We obse Figure C. In addn Figure E. More ss Figure D. In addAPBB1IP affected patient survival through immune infiltration even though we found that APBB1IP expression was correlated with both immune cell infiltration and patient survival in cancers. Hence, future prospective studies are needed to explore the relationship between APBB1IP expression and immune infiltration in a cancer patient population. Finally, we only conducted a bioinformatic analysis of APBB1IP expression and patient survival across different databases, and further mechanistic studies on APBB1IP at the cellular and molecular levels could help clarify its functions in cancer initiation and progression.Nevertheless, although we integrated information across multiple databases, this study still has some limitations. Since large-scale microarray and sequencing data were initially collected by analyzing tumor tissue information, the cell-level analysis of immune cell markers could have introduced systematic bias. To overcome this problem, future studies relying on methods with a higher resolution, such as single-cell RNA sequencing, should be performed Supplementary figures and tables.Click here for additional data file.


In vitro, a contribution to neuroinflammation processes has been shown for cathepsin X: however, the expression patterns and functional role of cathepsin X in neuroinflammatory brain pathology remain elusive. In this study we analyzed the expression, activity, regional distribution and cellular localization of cathepsin X in the rat brain with neuroinflammation-induced neurodegeneration. The unilateral injection of lipopolysaccharide (LPS) induced a strong upregulation of cathepsin X expression and its activity in the ipsilateral striatum. In addition to the striatum, cathepsin X overexpression was detected in other brain areas such as the cerebral cortex, corpus callosum, subventricular zone and external globus pallidus, whereas the upregulation was mainly restricted to activated microglia and reactive astrocytes. Continuous administration of the cathepsin X inhibitor AMS36 indicated protective effects against LPS-induced striatal degeneration, as seen by the attenuated LPS-mediated dilation of the lateral ventricles and partial decreased extent of striatal lesion. Taken together, our results indicate that cathepsin X plays a role as a pathogenic factor in neuroinflammation-induced neurodegeneration and represents a potential therapeutic target for neurodegenerative diseases associated with neuroinflammation.Neuroinflammation is an important factor in the pathogenesis of neurodegenerative diseases. Microglia-derived lysosomal cathepsins have been increasingly recognized as important inflammatory mediators that trigger signaling pathways that aggravate neuroinflammation.  Accumulating evidence suggests that chronic innate neuroinflammation mediated by microglia and astrocytes is implicated in the progressive nature of these neurodegenerative disorders. Whereas in healthy conditions microglia display key beneficial functions that are crucial for maintaining central nervous system (CNS) homeostasis, their chronic activation in neurodegeneration results in their altered functionality, which is thought to ultimately play an instrumental and detrimental role in disease pathogenesis  and irreversible inhibitor AMS36 treated LPS-hemi-lesioned rats  The animals were kept in IVC , two animals in each cage.Male Wistar rats from the Medical Experimental Center, Medical Faculty, University of Ljubljana, Slovenia, weighing 200–250 g at the beginning of the experiments. The animals were kept under standard laboratory conditions  with free access to food and water. All animal-related procedures were conducted in accordance with the European Communities Council Directive of 2010 (2010/63/UE) and the National Veterinary Institute Guide for the Care and Use of Laboratory Animals. Care was taken to minimize the number of experimental animals and their suffering. The experiments were approved by the Administration of the Republic of Slovenia for Food Safety, Veterinary Sector and Plant Protection (Ref. No. 34401-48/2012/5). The total number of animals used was 11, divided into two groups: DMSO/saline treated LPS-hemi-leisoned rats  and day 2  for protein extraction from cerebellum to determine cathepsin X activity. Based on the obtained cathepsin X activity presented in the In order to investigate the effect of cathepsin X inhibition on the LPS-induced lesion, a specific, irreversible inhibitor of cathepsin X AMS36, synthesized as reported  of animals was treated with DMSO/saline solution ratio 1:10 (vol/vol) (i.p.) 2 days prior to LPS/saline injection  and then every fourth day with DSMO/saline (i.p.) for 4 weeks. After 4 weeks, the animals were sacrificed, and brain tissue from this group of animals was further examined for model evaluation and cathepsin X expression analysis by histological staining, immunohistochemistry and immunofluorescence labeling and protein extraction to determine cathepsin X protein level expression and activity and tyrosine hydroxylase (TH) and inducible nitric oxidase synthase (iNOS) protein level expression analysis. Similarly, the second group of animals  was treated with AMS36 dissolved in DMSO/saline solution ratio 1:10 (vol/vol) at a dose of 50 mg/kg (i.p.) 2 days prior to LPS/saline injection  and then treated every fourth day for 4 weeks. After 4 weeks, the animals were sacrificed and brain tissue was further examined with histological staining and proteins were extracted for TH protein level expression analysis. Animals were killed with CO2. All experiments were performed during day time. A graphical outline of the experiment is presented in For the experiments, the male Wistar rats were randomly allocated in two groups, both receiving unilateral intrastriatal injection of LPS into the right striatum. No exclusion criteria were predetermined and no blinding procedure was performed. The first group of animals was treated with DMSO/saline solution, whereas the second group was treated with cathepsin X inhibitor AMS36. Namely, the first group (n = 5) and the group LPS/AMS36 (n = 6) were sacrificed after 4 weeks of LPS injection and brains were rapidly removed and quickly frozen on dry ice and stored at −80°C until sectioning on a cryostat. Of each subject, coronal brain sections (10–20 μm), identified using a rat brain atlas  or adhered to parafilm. The slides were then vacuum-packed and stored in a freezer at −20°C until being further processed.Subjects of the group LPS (Slices (10 μm) were fixed in 4% formaldehyde in 0.1 M sodium phosphate buffer saline  for 5 min. After washing with potassium phosphate buffer saline , sections were counterstained with 0.2% methylene blue (Sigma-Aldrich) for 1 min, rinsed with distilled water, dehydrated in rising ethanol concentration and coverslipped with the DPX mounting medium .n = 5) or LPS/AMS36 (n = 6) rat brains. The scan area (pixels) of lateral ventricle/striatum was separately measured in the contralateral and ipsilateral side. In the graphs sum of scan area of lateral ventricles/striata in striatal brain slices are shown.Methylene blue-stained brain slices were scanned by MCID, M4 analyzer . Areas of lateral ventricles and striata were manually outlined and measured in striatal brain slices cut at 4 different anterior-posterior levels of LPS (n = 5) or LPS/AMS36 (n = 6) rat brains were fixed in cold 100% methanol for 5 min followed by 15 min in cold methanol with 1% H2O2. Brain slices were incubated in blocking buffer containing 4% normal serum, 1% bovine serum albumin (BSA), and 0.1% Triton X-100 in KPBS for 1 h at room temperature. They were then incubated overnight at 4°C with mouse monoclonal antibody against TH  or goat polyclonal primary antibody against cathepsin X , diluted in blocking solution. Afterwards, the sections were incubated with biotinylated anti-mouse or anti-goat secondary antibodies  diluted in KPBS containing 1% BSA and 0.02% Triton X-100 for 1.5 h at room temperature. Avidin-biotin-peroxidase complex  was added for 30 min. Staining was visualized with 3,3′-diamino-benzidine . All sections were simultaneously immunolabeled to ensure identical DAB staining incubation times. Sections were then dehydrated and coverslipped with DPX mounting medium (BDH Laboratory Supplies). Brain sections were scanned by MCID, M4 analyzer and examined and imaged with an Olympus microscope (Olympus IX81) with an attached digital camera (Olympus DP71) using the same system settings for all samples.Coronal brain sections (10 μm) of LPS  were incubated with 35S-labeled oligodeoxyribonucleotide “antisense” probes (45 bases long) complementary to the rat tyrosine-hydroxylase (TH) mRNA (sequence 5′-AAC CAA ACC AGG GCA CAC AGG GAG AAC CAT GCT CTT AAG-3′) and rat cathepsin X mRNA (sequence 5′-AGG TCT CAT CGG GGA TGC CAT GCT TGT GGG CAT ACT CCC ACA CCG-3′). The GenBank accession numbers used to design the probes were TH M23598 and CTSX NM183330. Hybridized sections were exposed to X-ray film  for 2–3 weeks and developed using standard darkroom techniques.The standard procedure described by Zivin et al. was used for n = 5) or LPS/AMS36 (n = 6) rat brains, separately from the contralateral (Control) and ipsilateral side (Lesion), using a cryostat at −20°C. Tissue was homogenized in ice-cold lysis buffer  supplemented with a cocktail of phosphatase inhibitors , then sonicated and centrifuged at 15.000 g at 4°C for 15 min to collect the supernatant. Total protein concentration was determined by DC™ Protein Assay . All the samples were kept at −70°C until they were used for analysis.For analysis of the protein level of TH or cathepsin X and its activity, the striatum and SNc were dissected out from four 20 μm frozen brain slices (striatum) or eight 20 μm frozen brain slices (SNc) of LPS (n = 5) were determined by ELISA as previously reported , and the concentration of cathepsin X was calculated from the standard calibration curve. Cathepsin X protein level was expressed relative to the contralateral striatum/SNc (Control).The protein levels of cathepsin X in the striatum and SNc of LPS rat brains (n = 5) was measured in tissue lysates with the cathepsin X-specific, intramolecularly quenched fluorogenic substrate Abz-Phe-Glu-Lys(Dnp)-OH synthesized by Jiangsu Vcare Pharmatech Co. (China). An aliquot of 50 μg of the lysate proteins was incubated at 37°C, followed by measurement of cathepsin X activity using 10 μM Abz-Phe-Glu-Lys(Dnp)-OH. The fluorometric reaction was quantified at 37°C at an excitation wavelength of 320 nm and emission wavelength of 420 nm on a microplate reader (Tecan Safire2). Results are presented as change in fluorescence as a function of time (ΔF/Δt) and cathepsin X activity was expressed relative to Control.Cathepsin X activity in the striatum and SNc of LPS rat brains (n = 5) were double-immunostained for the analysis of the cellular localization of cathepsin X using the following primary antibodies: goat polyclonal anti-cathepsin X antibody , mouse monoclonal anti-TH antibody  as a marker for dopaminergic neurons, mouse monoclonal anti-NeuN antibody  as a neuronal marker, mouse monoclonal anti-CD11b antibody  as microglia marker and mouse monoclonal anti-glial fibrillary acidic protein (GFAP) antibody  as astrocyte marker. Brain slices were fixed in cold methanol for 20 min and the immunofluorescence procedure was further performed as reported . Fluorescence microscopy was performed using a Carl Zeiss LSM 710 confocal microscope . Images are presented as single confocal sections and were analyzed using Carl Zeiss ZEN 2011 image software. All images were captured under the same exposure time settings. To improve the signal/noise ratio, 4 frames/image were averaged.The striatal and nigral sections (10 μm) of LPS treated rats (n = 5) and LPS/AMS36 (n = 6) rat brains was determined by western blotting as previously reported , LPS/AMS36 (n = 6)] were analyzed using ANOVA test and post hoc analysis using Student's t-test. When two sets of values were compared; p < 0.05 was considered to be statistically significant. For all the statistical evaluation, GraphPad Prism (version 6) was used.Statistical evaluation was performed with the two-tailed Student's 7A–C). Immunohistochemical analysis of TH expression, a dopaminergic neuronal marker, revealed decreased TH-immunoreactivity in the ipsilateral striatum after LPS injection compared to the contralateral side , corpus callosum (Cc), and external globus pallidus (GPe) . Howeverupper panel). However, LPS-induced cathepsin X expression was not upregulated in NeuN-positive cells in the ipsilateral striatum (lower panel) or in other areas such as the subventricular zone (SVZ), Ctx, Cc and GPe (see lower panel) and throughout different areas including the SVZ (lower panel) and in the SVZ at the ipsilateral side (upper panels). These results further suggest the significant role of cathepsin X in neurodegeneration that is associated with glial activation.In order to further explore which type of cells express cathepsin X following LPS injection, we conducted double immunofluorescent staining to detect the co-localization of cathepsin X with different cell-specific markers, including markers for neurons (NeuN), microglial cells (CD11b) and astrocytes (GFAP). LPS injection induced activation of microglia and astrocytes, as indicated by an increased number of rounded, amoeboid microglia and astrocytes in the ipsilateral striatum . Microgl GPe see . Upregul the SVZ , Cc (Fig the SVZ , Ctx (Fi the SVZ  and GPe  the SVZ  as demonral side . Additioral side , Ctx (Firal side  and GPe ral side  also shoin vivo. After the inhibitor administration, decreased cathepsin X activity was observed in cerebellum extracts; however, a significant decrease in cathepsin X activity was detectable after 2 days of administration (see Intrigued by the results of strongly increased cathepsin X expression and its activity after LPS-induced lesion, we were interested whether the inhibition of cathepsin X enzymatic activity might be useful in preventing the striatal degeneration. We performed a preliminary analysis to determine the potency of cathepsin X inhibition by specific inhibitor AMS36 tion see . Therefotion see . Neverthtion see . There wtion see . Additiotion see . Moreovetion see .Finally, as we observed strong co-localization of cathepsin X with activated microglia marker CD11b in the ipsilateral striatum and throughout other areas, we were prompted to determine the iNOS protein level in lesioned striatum in AMS36-treated rats. iNOS is expressed in microglia during neuroinflammation and produces excessive amount of nitric oxide, which can cause the death of dopaminergic neurons (Bal-Price and Brown, in vivo, i.e., in the rat brain striatum after LPS-induced lesion. Along with striatal upregulation, cathepsin X overexpression was also found in the cerebral cortex, corpus callosum, subventricular zone and external globus pallidus, and in all of these regions, the upregulation was restricted to glial cells. Moreover, we explored the biological role of cathepsin X in neuroinflammation by cathepsin X-specific inhibitor AMS36. The latter indicated moderate protective effect toward the striatal lesion, suggesting that cathepsin X might play a significant role in neuroinflammation related to neurodegeneration.In this study, we utilized a model of neuroinflammation-induced degeneration of the rat brain by intrastriatal injection of LPS, and aimed to evaluate the regional and cellular expression and activity of lysosome cysteine cathepsin X. We described for the first time that cathepsin X expression and its activity are strongly upregulated in vivo (Glanzer et al., Over the past decade, increasing evidence has emerged that implicates cathepsin X in the neurodegeneration. High cathepsin X levels have been observed in degenerating brain regions of amyotrophic lateral sclerosis and AD transgenic mouse models and around senile plaques in brains of AD patients (Wendt et al., In this study, we adopted the LPS induced neuroinflammation rat model that induces neuronal loss thorugh microglial activation (Choi et al., Furthermore, it is important to stress out that the serotype, route of administration, and number of injections of LPS induce varied pathological responses (Batista et al., Whilst the expression and release of cathepsin X has been shown to be associated with neuroinflammation (Glanzer et al., Alterations in the expression patterns and localization of the lysosomal cathepsins in the CNS have been reported in normal and aged brain, and under pathological conditions (reviewed in Pislar and Kos, in vitro studies showing upregulated expression, increased release and activity of microglial cathepsin X following LPS stimulation (Wendt et al., in vivo study using a hemi-parkinsonian 6-OHDA rat model of PD. Four weeks post-lesion, we observed a persistent cathepsin X upregulation restricted to glial cells concentrated in the ipsilateral SNc (Pislar et al., Several lines of evidence support the hypothesis that glial reactive and inflammatory processes contribute to the cascade of events leading to neuronal degeneration (reviewed in More et al., in vivo effects of cysteine protease inhibitors toward neurodegeneration have been demonstrated with cathepsin B inhibition (Hook et al., in vitro (Pislar et al., in vivo study, we first demonstrated lower cathepsin X activity in the rat brain after AMS36 administration as well as selectivity for cathepsin X among other cysteine cathepsins. AMS36 selectivity toward cathepsin X was also reported for tumor tissues, whereas in other tissues such as rat liver and kidney, a significant cross-reactivity of AMS36 with cathepsin B inhibition was observed (Sadaghiani et al., in vivo indicate its protective effect against LPS-induced neurodegeneration. We have showed reduced extent of LPS-induced striatal degeneration in AMS36 injected rats. Namely, TH levels were no longer significantly reduced after LPS lesion when AMS36 was administred. Similarly, lateral ventricles did not show dilation after LPS and AMS36 injection. Furthermore, the increased striatal iNOS protein level in the ipsilateral striatum following LPS injection compared to the contralateral striatum was abolished when AMS36 was continuously administrated together with LPS injection. These results designate glial cathepsin X as a key player in neuroinflammation-induced neurodegeneration. However, further experiments are needed to unequivocally demonstrate cathepsin X inhibitor induced neuroprotection in vivo. Thus, a new generation of selective and reversible cathepsin X inhibitors (Fonovic et al., Due to the harmful action of cysteine cathepsins in pathological processes of neurodegeneration, cathepsin inhibitors constitute a possible tool for therapeutic interventions to inhibit excessive proteolytic activity (reviewed in Pislar and Kos, in vitro evidence showing cathepsin X mediated microglia activation and further support the notion that cathepsin X could participate in the development of neuroinflammation in the CNS.In conclusion, in our current study we used an animal model of neuroinflammation by intrastriatal LPS administration to show the regional distribution and cellular localization of cathepsin X, a lysosomal cysteine protease, in the brain. Unilateral LPS injection into the striatum increased cathepsin X expression and its activity in the striatum and surrounding areas on the ipsilateral side. The prominent cathepsin X upregulation was restricted to glial cells, activated microglia and reactive astrocytes. Moreover, administration of a cathepsin X inhibitor along with LPS injection revealed its potential protective role in neuroinflammation induced striatal lesion. As summarized in The original contributions presented in the study are included in the article/The animal study was reviewed and approved by Administration of the Republic of Slovenia for Food Safety, Veterinary Sector and Plant Protection, Ljubljana, Slovenia.in situ hybridization, generated the data for Figures, participated in carrying out the double immunofluorescence analysis, and reviewed the manuscript. GG reviewed the manuscript. NZ synthesized the cathepsin X inhibitor AMS36. MŽ performed animal surgery. MŽ and JK coordinated the research and reviewed the manuscript. All authors read and approved the final manuscript.AP, MŽ, and JK designed the study. AP prepared coronal sections and protein extractions, performed immunohistochemistry staining, Western blotting, ELISA assay, cathepsin X activity assay and double immunofluorescence labeling, generated the data for Figure's and prepared the draft manuscript. LT performed methylene staining and lateral ventricle and striatum area quantification, immunohistochemistry analysis, The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


For social animals that communicate acoustically, hearing loss and social isolation are factors that independently influence social behavior. In human subjects, hearing loss may also contribute to objective and subjective measures of social isolation. Although the behavioral relationship between hearing loss and social isolation is evident, there is little understanding of their interdependence at the level of neural systems. Separate lines of research have shown that social isolation and hearing loss independently target the serotonergic system in the rodent brain. These two factors affect both presynaptic and postsynaptic measures of serotonergic anatomy and function, highlighting the sensitivity of serotonergic pathways to both types of insult. The effects of deficits in both acoustic and social inputs are seen not only within the auditory system, but also in other brain regions, suggesting relatively extensive effects of these deficits on serotonergic regulatory systems. Serotonin plays a much-studied role in depression and anxiety, and may also influence several aspects of auditory cognition, including auditory attention and understanding speech in challenging listening conditions. These commonalities suggest that serotonergic pathways are worthy of further exploration as potential intervening mechanisms between the related conditions of hearing loss and social isolation, and the affective and cognitive dysfunctions that follow. Social isolation is a major concern among people with hearing loss ,2,3,4. UThe loss of input from the auditory periphery triggers a cascade of plasticity within the central auditory system, multiple aspects of which have been extensively reviewed. Following peripheral loss, the central auditory system undergoes a progressive rebalancing of excitation versus inhibition and synaptic plasticity that encompasses both ascending and descending systems  that show l bodies , or evenl bodies . The denl bodies . Tract tl bodies ,81,82. Ml bodies ,85,86,87Within this anatomical framework, serotonergic function within the auditory system has been assessed by directly measuring serotonin availability during different kinds of events, and by manipulating serotonin or its receptors, and subsequently, monitoring neural or behavioral responses. The direct measurement of serotonin in awake and behaving rodents has shown that serotonin increases in response to both acoustic and non-acoustic stimuli. The presentation of white noise increases serotonergic activity in auditory regions caudal to the thalamus ,90. BothIn the auditory midbrain, serotonergic activity is also elevated during multiple types of social interaction: in males with either male or female social partners, and in females with male social partners ,63,92. IThe responsiveness of auditory neurons to fluctuations in serotonin has been assessed by bypassing endogenous patterns of serotonin release and directly manipulating serotonin or specific types of serotonin receptors within the auditory system. These kinds of manipulations have effects that are observed at the level of individual neurons, specific neural circuits, and neuron populations. The effects of these manipulations depend on the cell type being monitored and the serotonergic receptor type that has been activated or blocked. For example, in the inferior colliculus (IC), 5-HT2A receptors facilitate inhibitory transmission by presynaptically increasing the frequency of spontaneous inhibitory postsynaptic events . SerotonAt the level of the neural population in the IC, manipulating serotonin availability alters the number of neurons expressing the activity-dependent immediate early gene product cFos in female mice . The effGiven these effects of serotonin within many regions of the auditory system, it is hardly surprising that serotonin can influence auditory processing and acoustic perception in human subjects. High levels of serotonin have been associated with a shallower slope of Wave V and the N1/P2 potential of auditory-evoked responses ,97,98, aSummaryPresynaptic and postsynaptic elements of serotonergic pathways are present throughout the auditory system. Serotonin responds to broad classes of events such as stressful events or social interaction. Within the category of social interaction, serotonergic release parallels the salience of specific interactions. Once released, serotonin has cell-specific and receptor-specific effects that functionally reconfigure auditory circuits. Given the scale of serotonergic innervation of the auditory system and the ability of serotonin to induce plasticity in auditory processing, it is striking that the serotonergic system itself exhibits extensive plasticity. Plasticity can result from long-term changes in acoustic input, but also occurs in response to changes in the general social environment. This type of sensitivity to environmental influence can serve an important function in adapting serotonergic regulation to long-term changes in internal and external conditions. Plasticity in serotonin–auditory interactions is expressed at both presynaptic and postsynaptic mechanistic levels. That is, changes occur in both the serotonergic fibers and the dynamics of serotonin release, and in the expression of serotonin receptors by auditory neurons.Sound-induced hearing loss alters the densities of serotonergic fibers and the functional dynamics of serotonin release and reuptake. Following monaural exposure of mice to an 8 kHz tone at 113 dB, axons that are immunopositive for the serotonin transporter (SERT+) in the inferior colliculus (IC) are altered in density . SerotonFollowing bilateral tonal acoustic trauma of rats, densities of SERT+ axons are also lower in multiple auditory regions, including the cochlear nucleus, IC, and auditory cortex relative to a non-trauma group . In paraPostsynaptic changes in serotonergic pathways are also altered by hearing loss. Following hearing loss induced by bilateral exposure to a 10 kHz tone at 116 dB, the expression of multiple types of serotonin receptor are altered in the IC. Receptors in the 5-HT1 family, such as the 5-HT1A and 5-HT1B receptors, are strongly expressed in the IC ,84,85. ASpeculation about the functional consequences of these changes in gene expression can be supported by the distinct roles of these receptor types. The 5-HT1A receptor is often expressed somatodendritically or in the axon initial segment, and often suppresses neural firing ,59,121. Changes in the expression of 5-HT receptors are also observed after hearing loss associated with age or following cochlear ablation ,87. The Some of these effects of exposure to loud sound on the serotonergic system could occur in part through the activation of stress-responsive physiological systems rather than through a primary effect on the auditory system. Noise exposure itself is a stressful event that can increase serum glucocorticoid levels and alter the expression of glucocorticoid receptors, although these effects are dependent on the type of stressor and brain region ,124. ThrSummaryHearing loss is associated with presynaptic and postsynaptic changes in the serotonergic system within auditory regions, but some of these changes also occur in other brain regions. Stressors associated with the induction of hearing loss may independently contribute to the plasticity in local serotonergic pathways.As a factor on its own, social isolation has significant consequences for auditory function. While some work has demonstrated an effect of social interaction on auditory responses in humans , the buAt the level of single cells, the response properties of individual auditory neurons change in ways that suggest that auditory processing is more finely tuned in socially housed birds. Auditory neurons of socially housed birds show less noisy, more precise responses to sound compared to isolated birds ,44. FurtSocial isolation also alters the neural processing of sound on a larger scale. For example, when compared to socially housed birds, birds raised in isolation have more responsive sites across an avian auditory forebrain area, Field L ,44. ThisIn fact, a few studies suggest that social isolation also shapes auditory perception. For example, completely socially isolated birds of several species are unable to discriminate between songs based on frequency; socially deprived birds are also unable to discriminate between different song notes ,49,50. IWhile deprivation of social stimulation alters auditory responses on their own, social deprivation also affects the integration of non-auditory sensory input with auditory processing. In birds raised with adults, images of familiar versus unfamiliar birds differently alter auditory-evoked activity . In contResearch in songbirds suggests that social isolation itself influences auditory processing, but whether this phenomenon extends to other taxa, such as mammals, is relatively underexplored. However, there is a strong body of literature in mammals, particularly in laboratory rodents, demonstrating that social deprivation causes dramatic effects on behavior ,67,135, In addition to the research on the acoustic startle response, one study has explored whether social experience shapes auditory perception in mammals, by investigating the influence of social versus individual housing on auditory discrimination in mice . HousingSummarySimilar to hearing loss, social isolation induces plasticity in auditory processing and may influence the integration of acoustic and non-acoustic information within the auditory system. The majority of this work has been conducted using songbirds as a model system. Future studies should investigate whether social deprivation of mammals causes similar deficits in auditory processing.The serotonergic system provides a source of non-acoustic input to the auditory system and is sensitive to the social environment; it is therefore one potential pathway through which social isolation could influence auditory function. Whether social isolation reconfigures this pathway is just beginning to be explored. Two studies ,147 suggIn the Keesom et al.  study deAlthough there is no effect of social isolation in males on the density of SERT+ projections to the IC, depriving male mice of social contact does alter directly measured serotonergic activity in the IC triggered by the presence of a social partner . In thisSummarySocial isolation influences the interaction between the serotonergic and auditory systems, with effects on serotonergic fiber density and serotonergic activity in the auditory midbrain that match changes in other factors sensitive to isolation, including body weight and behavior.Although this synthesis paper has focused on the auditory system, hearing loss and social isolation both influence the serotonergic system outside of auditory regions. This has been best explored for social isolation. Social isolation influences serotonergic axons, activity, and metabolism throughout the brain, including regions involved in cognition, memory, emotion, and reward. In comparison to the sex-specific decrease in serotonergic fiber density that it causes in the auditory system, social isolation has mixed effects on serotonergic axons across several extra-auditory brain regions. Isolation decreases serotonergic fiber density in the hippocampus  but incrThe extra-auditory effects of both hearing loss and social isolation are particularly interesting in light of the important role of interaction among auditory and non-auditory regions in giving rise to perceptual dysfunction following hearing loss. For example, relative to human subjects with equivalent hearing loss, subjects with tinnitus have anatomical and functional differences in brain regions, including the ventromedial prefrontal cortex and the amygdala ,169,170.The interactive effects of hearing loss and emotional systems on perception are also illustrated by a three-way interaction between hearing loss, the subjective perception of social isolation, and the affective perception of acoustic stimuli. Subjects with hearing loss show dampened emotional ratings of affectively charged sounds, use a smaller range of values to describe such sounds, or take longer to respond to affective sounds but not neutral sounds in comparison to normal hearing listeners ,177,178.SummaryHearing loss and especially social isolation influence presynaptic and postsynaptic elements of the serotonergic system outside of the central auditory system, including in brain areas related to memory, reward, and emotion. This is particularly interesting given that alterations in the functional connectivity between auditory and non-auditory regions are associated with deficits in the perception of acoustic stimuli, and that the perception of social isolation co-occurs with an altered emotional response to sound.Although the current paper presents evidence that hearing loss and social isolation each influence the serotonergic system, whether their effects actually converge on the same targets, and if so, at what levels of organization, have not yet been experimentally explored. For example, multiple subgroups of serotonergic neurons with different responsiveness to external events may innervate the same auditory regions ,82. It iThese uncertainties allow for the possibility of significant nonlinearity in the interactions between hearing loss and social isolation at the level of auditory processing and perception. Possible types of interaction include synergy, in which the effects of hearing loss and social isolation are greater than either alone. In this model, multiple separate ‘hits’ to the serotonergic system caused by hearing loss and social isolation, could create extreme dysregulation of auditory processing. Conversely, maintained social connection could minimize negative effects of hearing loss on serotonin–auditory interaction. This could fall within the ‘social buffering’ hypothesis, in which social contact counteracts some of the negative effects of stressors . In contOn a broader scale, the possibly convergent effects of hearing loss and social isolation on the serotonergic system play out within the context of many interacting sensory and cognitive systems that may themselves be sensitive to these factors. A useful framework for envisioning different types of interactions among auditory and non-auditory systems is the increasingly supported link between hearing loss and cognitive decline in older adults. Difficulty in hearing is predictive of cognitive deficits in a range of tasks assessing memory, executive function, and speed of response ,189,190.The idea that social isolation itself may play an important role in cognitive decline following hearing loss is supported by a recent study modeling the causal interactions of hearing loss, social isolation, and subjective loneliness in cognitive outcomes . The stuModels of the links among hearing loss, isolation, and perceptual or cognitive outcomes that incorporate physiological systems can serve an important role in illustrating the potential for ameliorating some of the consequences of hearing loss and social isolation. For example, some of the hypotheses linking hearing loss with cognitive decline predict that assistive hearing devices should prevent or reduce cognitive deficits. There is increasing evidence that such sensory interventions do decrease cognitive declines, or slow the rate of decline e.g., ,209,210),210209,2Social animals such as rodents, songbirds, and humans may have the opportunity to adapt their behaviors to a wide range of social environments, from enriched to impoverished networks of social contacts. This capability may involve neuromodulatory systems, such as the serotonergic system, given that serotonergic pathways respond to sensory stimuli and external events and are also highly attuned to the social environment. These functions fit the description of a system that could respond to comorbid conditions such as hearing loss and social isolation. Disruptions in neuromodulatory function due to hearing loss and/or social isolation may also have consequences related to the role of these same neurochemical systems in regulating intrinsic neural processing in many brain regions, as well as in regulating connectivity among interacting networks. These dual roles make these systems, including the serotonergic system, a potentially valuable key for understanding the origins of links among social environment, sensory function, communicative dysfunction, and cognition.


Deaf late signers provide a unique perspective on the impact of impoverished early language exposure on the neurobiology of language: insights that cannot be gained from research with hearing people alone. Here we contrast the effect of age of sign language acquisition in hearing and congenitally deaf adults to examine the potential impact of impoverished early language exposure on the neural systems supporting a language learnt later in life. We collected fMRI data from deaf and hearing proficient users (N = 52) of British Sign Language (BSL), who learnt BSL either early (native) or late (after the age of 15 years) whilst they watched BSL sentences or strings of meaningless nonsense signs.STC to show a ‘native-like’ response to a later learnt language.There was a main effect of age of sign language acquisition (late > early) across deaf and hearing signers in the occipital segment of the left intraparietal sulcus. This finding suggests that late learners of sign language may rely on visual processing more than early learners, when processing both linguistic and nonsense sign input – regardless of hearing status. Region-of-interest analyses in the posterior superior temporal cortices (STC) showed an effect of age of sign language acquisition that was specific to deaf signers. In the left posterior STC, activation in response to signed sentences was greater in deaf early signers than deaf late signers. Importantly, responses in the left posterior STC in hearing early and late signers did not differ, and were similar to those observed in deaf early signers. These data lend further support to the argument that robust early language experience, whether signed or spoken, is necessary for left posterior  This situation applies in the vast majority of hearing individuals: only in situations of extreme neglect or severe disability might hearing children not develop a spoken language. In contrast, impoverished early access to language is unfortunately the situation for many children born deaf. Approximately 90–95% of deaf children are born to hearing parents. For these individuals, the first language is typically a spoken language, established on the basis of impoverished auditory input and visual speech (lipreading), which can only afford limited access to the speech signal since many articulators are invisible. The majority of deaf children born to hearing parents are not exposed to sign language in early childhood and often past the point that would normally be considered the sensitive period for language development see . Therefohearing early and late signers. Two previous studies have examined age of sign language acquisition effects on the neural systems supporting sign language processing in deaf signers . Differefirst language (L1), as reported in studies by Mayberry and colleagues  and first language experience (full/impoverished). However, by definition, early signers have BSL as their first language and therefore the early signers/impoverished first language group does not exist. In the current study, we therefore used a full factorial design with hearing status (deaf/hearing) and age of BSL acquisition (early/late) as between subject factors. This design allows us to investigate the effects of late sign language acquisition in groups with very different early language experience. We predicted that any observed age of acquisition effects (early vs. late) would differ between hearing signers and deaf signers, possibly reflecting the impact of impoverished early spoken language exposure. It should be noted that the participants recruited in the current study did not learn BSL in adulthood as a ues e.g. . Rather posterior superior temporal gyrus (STGs) bilaterally (greater activation in earlier learners) in response to ASL sentences relative to a still image of the signer. It could be argued therefore that the earlier use of a signed language by a deaf child may lead to greater recruitment of the posterior STCs for visual processing, than in a deaf child who learns a signed language later in life. Thus, activation in the posterior part of the STC in response to sign stimuli in deaf signers may reflect the interaction between deafness and early sign language exposure. Alternatively, it could be that the region indentified by We were particularly interested in age of sign language acquisition effects in superior temporal cortex (STC). In hearing individuals, middle STC primarily responds to auditory input. In deaf people however, parts of STC have been shown to reliably respond to sign stimuli  as well In the current study we investigated the impact of age of sign language acquisition on STC activation in deaf signers and hearing signers in a whole brain analysis and also using the findings of To address these questions we matched deaf and hearing early signers (age of acquisition = from birth) and late signers (age of acquisition > 15 years) on a BSL grammaticality judgement task  . We the22.1Sixty-two participants were scanned. All participants had learnt BSL, had normal or corrected-to-normal vision and gave informed, written consent to participate in the study, which was approved by the University College London Research Ethics Committee. One participant was excluded due to a data acquisition problem. A further 9 participants were excluded because of poor performance on the experimental task (response sensitivity measured by d’<1.7). See below for which group these participants belonged to. Thus, data from 52 participants were included in the analyses. All participants were right-handed =.922, p = .437, Four participant groups were tested: [1] Deaf native signers who learnt BSL from birth from deaf parents (henceforth DE (deaf early); n = 15 ); [2] deaf non-native signers who began to learn BSL aged 15 or older (henceforth DL (deaf late), 2 were excluded; n = 11 usable datasets); [3] hearing native signers who learnt BSL from birth (henceforth HE (hearing early), 2 were excluded; n = 14  usable datasets); [4] hearing non-native signers who began to learn BSL aged 15 or older (henceforth HL (hearing late), 5 were excluded; n = 12  usable datasets). There were no significant age differences between groups =.895, p = .451, F = 1.400, p = .254,F = 2.865, p = .047, F = 10.38, p < .001, t(21) = 3.316, p = .011, d=.479), DE (t(24) = 5.424, p < .001, d=.783) and DL (t(19) = 3.964, p = .002, d=.572) . There were no significant differences in reading comprehension between the HL, DE and DL groups (p > .05).Participants were tested on a BSL grammaticality judgement task , performAll deaf participants reported being born severely or profoundly deaf. Past audiogram data were available for only half of the participants . The mean hearing loss in the better ear for the DE participants was 95.13 dB; range: 81–108. The mean hearing loss in the DL group was 100.0 dB; range: 91–116. Fourteen out of 26 hearing participants were BSL interpreters (6/14 HE participants and 8/12 HL participants). See Given that deaf and hearing people learn a signed language later in life for very different reasons, it is not surprising that deaf late signers learnt BSL significantly earlier than hearing late signers (t(21) = 2.542, p = .019, d = 1.061). However, deaf and hearing late signers did not differ significantly in their duration of BSL experience (t(21) = 1.010, p = .324, d=.422). Additional background information of the deaf participants is reported in 2.2Two between-subject factors: hearing status (deaf/hearing) and age of BSL acquisition (early/late); and one within-subject factor, stimulus type (BSL/strings of nonsense signs) were included, resulting in a balanced, 2 × 2 × 2 factorial design.2.3One hundred and twenty full colour video clips, 4 s in length, were presented. In each clip, the signer’s hands started from and returned to a rest position in his lap to mark the start and end of each utterance. Sixty of the clips were signed BSL sentences, containing BSL-appropriate mouth and face actions and 60 were nonsense sign sequences. For the signed sentences, participants were required to make a speeded, button response whenever a BSL sentence contained a semantic anomaly. Of the 60 BSL clips, six were ‘yes’ targets containing a semantic anomaly. An example semantic anomaly in English is ‘my favourite colour is a bike’.t(118) = 7.235, p < .001, d = 1.321). To view example videos, click on the image visible below (online version only).During the nonsense sign sequences, participants were required to respond to a target nonsense sign in which the signer placed his left hand in front of his nose with his thumb extended. The 60 nonsense sign sequences consisted of four or five distinct actions, with each action composed of phonotactically legal sub-lexical components  of BSL. Of these 60 clips, six featured a target nonsense sign. During the nonsense sign sequences, the signer displayed meaningless face actions similar to those used during the real sentences, with no mouthing. The anomalous word or target nonsense sign appeared towards the end of the sentence or string of nonsense signs. The mean duration was 2525 ms (SE = 50) for the BSL sentences and 2975 ms (SE = 38) for the nonsense signs. The nonsense sign sequences were longer than the BSL sentences  and nonsense sign sequences (six blocks). One run began with BSL sentences and the other with nonsense sign sequences. The order of the blocks was counterbalanced across participants within group. Video blocks were interspersed with ten 9-sec fixation blocks and two longer 18-sec fixation blocks. Each block began with a 1-sec printed English task prompt: ‘nonsense?’ for the BSL condition or ‘nose?’ for the nonsense sign condition. Five 4-sec video clips followed the task prompt. In each clip, the signer’s hands started from and returned to a rest position in his lap to mark the start and end of each utterance. While each clip  lasted 4 s, the duration of the individual BSL sentences or the nonsense sign strings varied between 1707 ms and 3486 ms, effectively providing jittered inter-stimulus intervals, during which participants saw the signer with his hands on his lap. Stimuli were projected onto a screen positioned at the top of the scanner bore. Participants viewed the stimuli via a mirror placed on the MRI head coil.3.13 were acquired (repetition time (TR): 12.24 msec; echo time (TE): 3.5 msec; inversion time (TI): 530 msec). Functional T2*-weighted echo-planar images with BOLD contrast comprised 38 axial slices of 2 mm thickness (1 mm gap), with 3 × 3 mm in-plane resolution. One hundred and twenty-one volumes were acquired per run (repetition time (TR): 3.42 s; echo time (TE): 50 msec; flip angle = 90°). TR and stimulus onset asynchrony were mismatched, allowing for distributed sampling of slice acquisition across the experiment  sequence. One hundred and seventy-six sagittal partitions with an image matrix of 256 × 224 and a final resolution of one mmperiment , and the3.2The d’ scores and reaction times (RTs) were the dependent measures of behavioral performance. For the calculation of the d’ scores, corrections of ±0.01 were made since some participants had the hit rate of 1 and/or the false alarm rate of zero. RTs were available for ‘yes’ trials only and were recorded from the onset of the stimulus. The RT analysis included correct (target detection) trials only. Behavioral data were analysed in a 2 × 2 ANOVA with hearing status (deaf/hearing) and the age of BSL acquisition (early/late) as between-subject factors; separately for each stimulus type (BSL/nonsense signs).http://www.fil.ion.ucl.ac.uk/spm/). All functional volumes were spatially realigned and unwarped in order to adjust for minor distortions in the B0 field due to head movement . Residual temporal autocorrelations were approximated by an AR(1) model and removed. At the first level, the onsets of trials (4 s video) were modelled as epoch-related responses and convolved with a canonical haemodynamic response function. Correct trials  for each of the two conditions and the errors over two sessions were modelled separately. Button press manual responses and the task prompts were modelled as event-related responses and convolved with a canonical haemodynamic response function. Fixation was not modelled and served as an implicit baseline. The reaction time data were not used in the imaging analyses since they were only from ‘yes’ trials, which made up only 20% of all trials. The contrasts of interest were: 1) BSL, 2) strings of nonsense signs, 3) BSL > strings of nonsense signs, 4) BSL and strings of nonsense signs, averaged over sessions.p < .05, family wise error (FWE) corrected for multiple comparisons at the whole brain level.At the second-level, the contrast images from the first level were used to run random-effects analyses. We investigated main effects of age of BSL acquisition, hearing status and stimulus type; and their interactions using the partitioned error approach. We report activation as significant at voxel-level inference of In order to investigate whether activation is greater in deaf early than deaf late signers in the part of STC where To compare the results with those in 44.1F = 12.685, p < .001, F = 1.363, p = .249, F = 1.758, p = .191, The behavioural data are plotted in F = 4.340, p = .043, F=.654, p = .423, F=.004, p = .950, F=.152, p = .698, F=.071, p = .791, F=.081, p = .777, The reaction time analyses included target detection (correct ‘yes’) trials. For BSL, there was a significant main effect of age of acquisition  but not in reaction times. In contrast, age of acquisition affected reaction times (early learners quicker than late learners) but not response sensitivity.4.24.2.1t(50) = 6.06, Z = 5.19, p = .005 FWE corrected for multiple comparisons] where responses were greater in late (Z = 4.16) than early signers (Z = −2.24), see p < .001 uncorrected & k > 10), this effect extended into both middle and superior occipital gyri (k = 48). There were no significant effects for the opposite contrast of early > late, at the corrected level. Nor were there any significant interactions between age of acquisition and hearing status or stimulus type.There was a significant main effect of age of sign language acquisition at [x = −27, y = −82, z = 29; 4.3p < .05 FWE corrected were found within large areas of the superior temporal cortices (STCs) bilaterally (p < .001 uncorrected (k > 10) in p < .001 uncorrected) to yellow (Z = 5.02). The strongest effects in both height and extent were in the middle part of the STC. The effect in the right STC did not include any voxels within the right STC ROI. The effect in the left STC included just one voxel within the left STC ROI (see Methods). Both STC ROIs were more posterior to the location of the main effect reported here (Significant main effects of hearing status (deaf > hearing) at aterally . The effted here B & C. Th4.4t(49) = 7.12, Z = 5.89, p < .001 FWE corrected, k = 9; see t(25) = 5.27, Z = 4.28, significant at p < .001 uncorrected) and greater for nonsense signs than BSL in deaf signers (t(25) = 4.88, Z = 4.05, significant at p < .001 uncorrected). However, activation did not differ in this region between deaf and hearing signers for either BSL or strings of nonsense signs due to the greater between-subject variance than the within-subject variance.Activation that was greater for BSL than strings of nonsense signs was mostly in bilateral superior temporal cortex, frontal cortex, as well as caudate, thalamus and cerebellum B in red.4.5All statistical details are reported in Left posterior STC ROI: Deaf participants who had learnt BSL early showed greater activation in response to BSL in the left posterior STC ROI compared to deaf participants who learnt BSL late. This effect was not observed for strings of nonsense signs. This difference was reflected in the significant age of BSL acquisition by stimulus type interaction in deaf signers. In hearing signers, there was no effect of age of acquisition for BSL or nonsense signs in this ROI. There was no main effect of hearing status. Critically, the two-way interaction between age of acquisition by hearing status interaction was significant. Activation was greater in deaf early signers than deaf late signers, while there was no difference in activation between hearing early and hearing late signers. Furthermore, responses in early deaf signers did not differ from those of the two hearing groups. The three-way interaction (age of acquisition by hearing status by stimulus type) was not significant. A significant effect of stimulus type indicated that the left posterior STC ROI was significantly more activated by BSL than nonsense signs across both groups, but there was no hearing status by stimulus type interaction.Right posterior STC ROI: There was no significant effect of age of acquisition in the right posterior STC ROI in deaf participants during either condition or across conditions. However, there were main effects of hearing status (greater activation for deaf than hearing signers) and stimulus type (greater activation for strings of nonsense signs than BSL). There was also a significant hearing status by stimulus type interaction such that the effect of hearing status was significant for nonsense signs but not for BSL (with Bonferroni corrections).In summary, we observed two age of BSL acquisition effects. The first was a main effect of age of BSL acquisition across task and hearing status in the occipital segment of the left intraparietal sulcus, where activation was greater in late than early signers. The second was only in deaf signers in the left posterior STC region of interest, where activation was greater in deaf early signers than deaf late signers for BSL but not nonsense signs. There was no effect of age of sign language in hearing signers in this region and no main effect of hearing status. The middle STC, that showed a significant bilateral main effect of hearing status .Deaf and hearing people who learn a sign language after early childhood differ greatly in their early language experience. Hearing late signers learn a sign language upon the basis of a robust first spoken language. In contrast, the majority of deaf people who learn a sign language later in life have, by definition, had impoverished access to an early spoken language. The degree of access to spoken language can depend on many audiological factors. Degree of progress with spoken language acquisition depends on a range of social and cognitive factors, many of which are yet to be clearly determined. Thus, deaf late signers provide a unique perspective on the impact of different degrees of impoverished early language exposure on the neurobiology of language: insights that cannot be gained from research with hearing people alone. The very few previous group studies that have examined the influence of age of sign language acquisition on the neural systems supporting language processing have examined either deaf signers  or heariWe found a main effect of age of sign language acquisition (late > early) in the occipital segment of the left intraparietal sulcus. This effect did not differ between deaf and hearing participants. Surprisingly, despite differences in early language experience, differential effects of age of sign language acquisition between deaf and hearing signers were limited to the left STC ROI, where activation during BSL perception was greater for early than late signers in deaf participants but not in hearing participants. That more extensive group differences were not found may be due to the relatively good English language skills and BSL skills of the deaf late signers who were recruited in the current study in order to minimise differences between deaf early and late signers. Below we consider each of these key findings.5.1Late signers, both deaf and hearing, recruited the occipital segment of the left intraparietal sulcus more than early signers. Similarly, Our study tests this possibility by contrasting deaf and hearing early and late signers. There was no evidence that the effect of sign language acquisition (late > early) we identified, in the occipital segment of the intraparietal sulcus, was influenced by hearing status or stimulus type. Therefore, the effect we observed in occipital regions could not be explained by early language experience because the same effect (late > early) was observed for deaf late signers (with impoverished L1) and hearing late signers (with a robust L1), who are typical L2 learners. Our data therefore suggest that greater activation in late than early signers in the occipital segment of the intraparietal sulcus, should be interpreted as reflecting processes common, in both deaf and hearing late signers, to both BSL sentence and nonsense sign perception.One possible explanation is that the late and early signing groups differed in sign language proficiency. Some support for this explanation comes from the responses to the in-scanner task: early signers were quicker to respond than late signers. However, the data contributing to these analyses were sparse as responses were only required to target trials, which made up only 20% of the total number. Furthermore, the groups did not differ in accuracy on this task. Participants in the current study were also tested on an off-line test of BSL grammaticality judgment  . There In line with the general proposal of Other regions that have previously been shown to be sensitive to effects of age of sign language acquisition by 5.2One aim of the study was to determine whether activation in the STCs in response to sign language input, was influenced by age of sign language acquisition in deaf signers. middle STCs but no influence of age of sign language acquisition  in the uisition . In the In contrast to the left STC ROI, the right posterior STC showed no significant effect of age of sign language acquisition in deaf or hearing signers. There was a significant main effect of hearing status (deaf > hearing) and also a hearing status by stimulus type interaction, indicating that the effect of hearing status was significant for nonsense signs but not for BSL. Given that an effect of age of sign language acquisition was observed in the left posterior STC ROI for BSL stimuli but not nonsense sign sequences and the strong left lateralised processing of signed stimuli e.g. , it is p5.3Replicating numerous previous studies, main effects of hearing status (deaf > hearing) were found in an extensive area of the STCs bilaterally, with the strongest effects in the middle STCs. The location of these effects shared very little overlap with the posterior STC ROIs (one voxel in the left ROI). The responses in deaf signers were of activation, whereas those in hearing signers were mostly of deactivation. There was no evidence that hearing status effects were stronger for BSL than nonsense signs at the whole brain level and no influence of age of sign language acquisition. Therefore, in contrast to the age of acquisition effect in left posterior STC described above, the increased middle STC activation in deaf compared to hearing signers in the current study is likely to be due to processes common to both BSL sentences and nonsense sign sequences.p < .001 uncorrected level. Therefore, the significant difference between deaf signers and hearing signers in the left Heschl’s gyrus was due to hearing signers suppressing task-irrelevant auditory activity, rather than deaf signers recruiting this region during the visual tasks. This interpretation supports that of our previous study (Hearing status effects (deaf > hearing) in the STCs were extensive and included a region of primary auditory cortex in Heschl’s gyrus – here mapped only at the group level, not the individual participant level see . Howeverus study  and thatus study  and agai5.4The finding of an age of sign language acquisition effect (early > late) in left posterior STC in deaf signers has potentially important implications for advice given to parents of deaf children. In some situations hearing parents of deaf children have been advised to not expose their child to sign language for fear of ‘take over’ of parts of auditory cortex by visual input  and hearing signers (BSL > nonsense signs). 6posterior STC: a multisensory region in hearing people, as demonstrated here by the responses to sign stimuli in hearing signers. These data lend further support to the argument that robust early language experience, whether signed or spoken, is necessary for left posterior STC to show a ‘native-like’ response to a later learnt language.We investigated whether the effects of age of sign language acquisition differed between deaf and hearing signers, who differ in early language exposure. There are two main findings. First, we found an effect of age of sign language acquisition that was common to both deaf and hearing signers in the occipital segment of left intraparietal sulcus where activation was greater for late than early signers. This effect may reflect greater reliance on shallower, visual, processing resources when perceiving sign language and nonsense signs in late learners, regardless of hearing status. Second, we found an effect of age of sign language acquisition that was specific to deaf signers in the left posterior STC where activation in response to signed sentences was greater in deaf early signers than deaf late signers. This effect was not observed for nonsense sign sequences and not in hearing signers, whose responses in this region were similar to that of deaf early signers. No effect of age of sign language acquisition was found in the middle STCs. Our findings suggest that any effect of age of acquisition in the left STC is only likely to be observed in the Ref: NIMG 19-1410.Tae Twomey: Data analysis; Manuscript writing.Cathy J. Price: Data analysis supervision; Manuscript writing.Dafydd Waters: Methodology; Data Collection.Mairéad MacSweeney: Conceptualization; Data collection; Supervision; Manuscript writing.None.


Mycoplasma hyopneumoniae infection in pigs, and assess the association between the tracheal microbiome and those from air, feces, and oral fluids. Tracheal fluids (n = 73), feces (n = 71), oropharyngeal fluids (n = 8), and air (n = 12) were collected in seeder pigs (inoculated with M. hyopneumoniae) and contact pigs (113 days post exposure to seeder pigs). After DNA extraction, the V4 region from 16S rRNA gene was sequenced and reads were processed using Divisive Amplicon Denoising Algorithm (DADA2). Clostridium and Streptococcus were among the top five genera identified in all sample types. Mycoplasma hyopneumoniae in tracheal fluids was associated with a reduction of diversity and increment of M. hyorhinis, Glaesserella parasuis, and Pasteurella multocida in tracheal fluids, as well as a reduction of Ruminiclostridium, Barnesiella, and Lactobacillus in feces. Air contributed in a greater proportion to bacteria in the trachea compared with feces and oral fluids. In conclusion, evidence suggests the existence of complex interactions between bacterial communities from distant and distinct niches.The association of the lower respiratory tract microbiome in pigs with that of other tissues and environment is still unclear. This study aimed to describe the microbiome of tracheal and oral fluids, air, and feces in the late stage of  The collection of microorganisms and their genetic elements, persistently or transiently present in a certain ecological niche, is known as its microbiome. The microbiome can be decomposed in different dimensions depending on the type of microorganisms of interest, e.g., the viral microbiome (virome), the fungal microbiome (mycobiome), or the bacterial microbiome. The latter is the most commonly studied and is associated with a plethora of effects in animal hosts, such as breakdown of otherwise indigestible feedstuff (influencing feed efficiency ), competBacteroides, Lactobacillus, Prevotella, Clostridium, Escherichia, and Megasphaera, with evidence of bacterial succession during the pig’s life [Salmonella, Lawsonia, Brachyspira, and porcine epidemic diarrhea virus (PEDV) [The most studied microbiome by far is the gut microbiome. Each segment of the gut has a specific associated microbiome, which can be shaped by diet, host genetics, and environmental factors ,7,8. Zhag’s life ,13,14,15s (PEDV) ,17,18,19Streptococcus, Lactobacillus, and Actinobacillus [The microbiome of the oropharyngeal section of the digestive system in pigs is composed predominantly of bacillus . Shared bacillus  collectebacillus ,23. Althbacillus  describeMycoplasma hyopneumoniae was described to be dominated by bacteria members of the families Mycoplasmataceae, Flavobacteriaceae, and Pasteurellaceae if there are signs of pneumonia, or by members of Mycoplasmataceae, Bradyrhizobiaceae, and Flavobacteriaceae otherwise [Mycoplasma hyopneumoniae is a major respiratory pathogenic bacterium in pigs, causing a chronic disease characterized by transient dry cough and increased susceptibility to other respiratory pathogens [M. hyopneumoniae colonization. This is of interest, since deep tracheal catheter samples are nowadays commonly used for the detection of M. hyopneumoniae in live pigs, as the trachea represents an important site of colonization [The microbiome from certain sections of the respiratory tract in pigs was documented ,25,26. Hathogens ,28,29. Bnization .Recently, researchers strived to characterize the microbiome of the environment of swine facilities ,32,33,34M. hyopneumoniae infection in pigs, to assess their potential association with infection status, and to infer to what extent the tracheal microbiome is related to microbial communities from air, feces, and oral fluids.Currently, there is little evidence of the association of the microbiome residing in the lower respiratory tract in pigs with that of other environments. The lower respiratory tract has long been considered a pristine and privileged section of the host, regarding bacterial colonization. However, recent data uncovered microbial communities that colonize the lower respiratory tract and appear to influence host responses to disease. Therefore, the objectives of this study were to describe the microbiome associated with tracheal and oral fluids, air, and feces during late stages of M. hyopneumoniae infection for the last five years. Pigs were weaned at approximately three weeks of age, then transported and placed in a Midwestern United States commercial wean-to-finish facility designed for research [M. hyopneumoniae and contacts were exposed to seeders following inoculation [M. hyopneumoniae. The majority of contact pigs were confirmed infected at least one month prior to sampling. Therefore, they were considered to be at a late infection stage [Mycoplasma species during the study. Five out of the 73 selected pigs (one seeder and four contacts) were treated for clinical signs suggestive of Streptococcus suis infection using penicillin or ceftiofur, as indicated, at least two months prior to sample collection.All pigs in this study were cared for following the guidelines of the Institutional Animal Care and Use Committee of Pipestone Applied Research (IACUC #3–18). Crossbred pigs were sourced from a commercial sow farm with high health status, known to be positive for influenza A virus, and negative for Porcine Reproductive and Respiratory Syndrome Virus (PRRSV) and research . Pigs weculation . Sampleson stage . Pigs weDeep tracheal catheters were used to collect tracheal fluids in pigs . Fecal sSix cyclonic filter collection devices  distributed throughout the entire air space where pens were located were used to collect air samples (n = 12). Air samples were collected for one hour during each of two samplings. Once the filtration run was complete, the filter was removed and rinsed using the manufacturer provided filter reagent into a sterile cup. All samples were refrigerated immediately and submitted to the Health Management Center  for analysis.TM Pathogen DNA/RNA Kit, Life Technologies, Grand Island, NY, USA) [TM, Life Technologies, Grand Island, NY, USA). A species-specific real-time PCR was run for M. hyopneumoniae detection  using a Roche LightCycler® . Both extraction and PCR methods were performed at the HMC. Real-time PCR samples with a Ct value < 38 were considered positive for M. hyopneumoniae. The M. hyopneumoniae infection status of each pig was based on the real-time PCR result in tracheal fluid samples, as those were individual samples that could be matched to a pig, contrary to environment and pen-based samples, such as air and oral fluids.DNA extraction for oral fluid and air samples  ,36,37 orNY, USA)  was perfFecal samples were submitted to the University of Minnesota Genomic Center (UMGC) for DNA extraction  and sequencing. The V4 region from 16S rRNA gene was amplified for 25 cycles in fecal samples and for 30 cycles in tracheal fluid, air, and oral fluid samples to account for the expected lower biomass in the latter. Samples were also dual indexed during library preparation. Sequencing of the amplicons was performed using MiSeq 2 × 300 bp platform.http://www.bioinformatics.babraham.ac.uk/projects/trim_galore/). The last 15 bases of forward reads and the last 50 bases of reverse reads were also trimmed. Denoising of reads, merging of paired reads, and removal of chimera were performed using the Divisive Amplicon Denoising Algorithm (DADA2) package (version 1.14.0) [All bioinformatics analyses were performed using R version 3.6.2 . The qua 1.14.0) . DADA2-f 1.14.0)  was usedM. hyopneumoniae Ct values, either as a continuous variable or as a binary variable (M. hyopneumoniae status). Detection of M. hyopneumoniae was not evaluated in the fecal samples due to the intrinsic restricted respiratory nature of this bacterium. Thus, M. hyopneumoniae Ct values from the tracheal fluid samples were used for analysis.The inverse Simpson alpha diversity index was computed and used in linear regression models to assess its association with sample type and M. hyopneumoniae status, seeder/contact status, sample type, and the interaction of sample type and M. hyopneumoniae status was evaluated in tracheal fluids and fecal samples, using PERMANOVA stratified by pig [The following beta diversity dissimilarity indices were computed: Bray–Curtis, Jaccard, Unifrac, weighted Unifrac, and Aitchison. The beta diversity dissimilarity indices were used in principal coordinate analysis (PCoA) plots to explore the clustering of samples. The association of beta diversity dissimilarity indices with d by pig  when appM. hyopneumoniae with the rest of the ASVs in tracheal fluids, the counts for all ASVs determined as M. hyopneumoniae through the search of the NCBI 16S ribosomal RNA sequences database (Bacteria and Archaea) using the nucleotide BLAST tool were aggregated. The centered log relative abundance (CLR) for all ASVs was calculated. In order to select the ASVs more strongly associated with M. hyopneumoniae, an elastic net model was built using glmnetUtils and glmnet packages in R [With the aim of assessing the association between the ASVs corresponding to ges in R ,44, seleM. hyopneumoniae in tracheal fluids with the ASVs in paired fecal samples, as well as the association of M. hyopneumoniae Ct values in tracheal fluids with the ASVs in the same samples and with the ASVs in paired fecal samples. Furthermore, ASVs differentially abundant according to M. hyopneumoniae status were detected in tracheal and fecal samples using ANCOM package in R [A similar penalized approach was used to assess the association of the ASVs corresponding to age in R .M. hyopneumoniae status.A Bayesian finite mixture model (SourceTracker package)  was usedM. hyopneumoniae-positive samples categorized by sample type is shown in M. hyopneumoniae was not evaluated in feces due to the respiratory nature of M. hyopneumoniae pathogenesis. The range and median of the sequencing depth (in reads) per sample type were 6079–23,346 and 15,486 for tracheal fluids, 4525–26,202 and 16,323 for air, 10,486–20,337 and 19,188 for oral fluids, and 6925–32,307 and 22,649 for feces, respectively. Rarefaction curves showed that the asymptote of the number of ASVs was reached in all sample types , measured with the inverse Simpson index, when M. hyopneumoniae status was used as a covariate (M. hyopneumoniae status was observed (p = 0.1672). Both overall effects were statistically significant when M. hyopneumoniae Ct values were used as covariate values instead . Since M. hyopneumoniae DNA was detected in one of eight oral fluid samples and undetected in air samples, the linear model assessing the association among alpha diversity, sample types, and M. hyopneumoniae Ct values was reduced to include tracheal fluids and fecal samples. No association between alpha diversity of fecal samples and M. hyopneumoniae Ct values in tracheal fluids was observed (p = 0.6538). However, a quadratic association between alpha diversity of tracheal fluids and M. hyopneumoniae Ct values was detected (p < 0.0001), with a faster reduction in alpha diversity as the Ct values decreased  parasuis is the etiologic agent of Glässer’s disease characterized by polyserositis [P. multocida is associated with atrophic rhinitis and the porcine respiratory disease complex [M. hyopneumoniae as a pathogen capable of inducing dysbiosis in the respiratory tract, potentiating opportunistic pathogens in situ.Beta diversity is intrinsically related to differentially abundant bacteria. Using different approaches to parameterize the presence of  hibisci , Glaesseas rubra , and Paserositis , Glaesseerositis , and P.  complex . HoweverM. hyopneumoniae in tracheal samples and bacteria from the genera Ruminiclostridium, Barnesiella, and Lactobacillus in fecal samples from the same pigs. Ruminiclostridium is member of the family Ruminococcaceae. Interestingly, the relative abundance of Ruminococcus, another genus of the family Ruminococcaceae, in feces was previously documented to be negatively associated with severity of lung lesions caused by M. hyopneumoniae [Barnesiella and Lactobacillus are associated with a healthy gut microbiome [M. hyopneumoniae-induced dysbiosis in the respiratory tract microbiome may be associated through yet unknown mechanisms to changes in gut bacterial colonization patterns.Negative associations were detected between eumoniae . Since Bcrobiome ,59, the The analysis using a Bayesian finite mixture model allowed the estimation of the proportions of the tracheal microbial community originating from fecal, air, or oral fluid sources , providiM. hyopneumoniae status at day 113 post contact was not randomized, but followed experimental infection in seeders and the inherent transmission of the pathogen in contacts, which could potentially affect the associations detected in this study between M. hyopneumoniae and microbiome composition. However, it is important to highlight that common confounders, such as age, breed, biosecurity practices, and diet, were similar for all pigs in the study. Therefore, the residual confounding was considered to be small. It is important to note that the differential number of 16S PCR cycles used to account for low biomass in air, oral, and tracheal fluid samples could have affected the number of ASVs detected in those specimens. This is especially relevant for comparisons between low  and high (feces) biomass specimens. Caution is warranted in the interpretation of the results from the Bayesian finite mixture model approach, since the directionality of the associations, referring to which samples are potential sources and which are the sink, are assumed to be known. In the case of this study, the authors assessed one possible scenario, in which the air, oral fluids, and feces were the potential sources and the tracheal microbiome was the sink. However, the true directionality is unknown. Moreover, the potential contribution of the microbiome from other sites of the respiratory tract, such as nasal microbiome, was not explored in this study. Future research assessing the contribution of nasopharyngeal microbiome to the bacterial communities of the lower respiratory tract is warranted.This was a cross-sectional study, which was not suited to address the temporality of the associations under scrutiny. Since the type of samples collected was part of the design, the authors consider the effect of sample type on the microbiome not to be prone to confounding, i.e., bias due to common causes of the type of samples collected and the microbiome composition. Conversely, M. hyopneumoniae was mainly detected in the tracheal fluid samples, while remaining undetected in the majority of the oral fluid samples. This situation is commonly encountered in infection with this bacterium, even under experimental conditions [M. hyopneumoniae status of pigs was performed based on detection of the bacterium in tracheal fluid samples.An important aspect to mention is the fact that nditions , and mayM. hyopneumoniae [M. hyopneumoniae in this case, and the local microbiome. More importantly, this study provides additional evidence of long-reaching effects of M. hyopneumoniae infection and the fecal microbiome. Future research should dissect these associations to better understand data directionality and temporality. In an era of increasing antibiotic stewardship, the prospect of manipulating the microbiome to prevent and/or mitigate infectious diseases fuels the momentum of the current microbiome research.In recent years, a greater appreciation of the complex pathogen–host-microbiome interactions resulted from studies assessing the effects of the commensal bacterial microbiome not only in situ, but also outside its immediate ecological niche. For instance, protective effects of diverse fecal microbiome were shown against systemic viral diseases in pigs, specifically against PRRSV and PCV2 ,62, and eumoniae . In thisClostridium and Streptococcus were consistently among the top five most abundant bacterial genera across all bacterial niches. Mycoplasma hyopneumoniae infection status was associated with a shift in the tracheal microbiome, reducing its diversity and increasing the relative abundance of the major swine pathogens M. hyorhinis, G. parasuis, and P. multocida. Furthermore, M. hyopneumoniae infection was associated with a decrease in the relative abundance of Ruminiclostridium, Barnesiella, and Lactobacillus in feces. Finally, the air microbial community may have a greater contribution to the tracheal microbiome in comparison with feces and oral fluids.All studied bacterial niches, tracheal and oral fluids, air, and feces, showed intrinsic differences in their bacterial communities. However,