SMN2 copy number. Despite limited proteomic overlap, enriched canonical pathways common to two of three SMA severities with at least one differentially expressed protein from the third included mTOR signalling, regulation of eIF2 and eIF4 signalling, and protein ubiquitination. Network expression clustering analysis identified protein profiles that may discriminate or correlate with SMA severity. From these clusters, the differential expression of PYGB (SMA I), RAB3B (SMA II), and IMP1 and STAT1 (SMA III) was verified by Western blot. All SMA fibroblasts were transfected with an SMN-enhanced construct, but only RAB3B expression in SMA II fibroblasts demonstrated an SMN-dependent response. The diverse proteomic profiles and pathways identified here pave the way for studies to determine their utility as biomarkers for patient stratification or monitoring treatment efficacy and for the identification of severity-specific treatments.Most research to characterise the molecular consequences of spinal muscular atrophy (SMA) has focused on SMA I. Here, proteomic profiling of skin fibroblasts from severe (SMA I), intermediate (SMA II), and mild (SMA III) patients, alongside age-matched controls, was conducted using SWATH mass spectrometry analysis. Differentially expressed proteomic profiles showed limited overlap across each SMA type, and variability was greatest within SMA II fibroblasts, which was not explained by  Precl protein . Most hu of SMN1 ,13. Althseverity ,15.SMN1 using a viral vector (AAV9) to the cells (Onasemnogene abeparvovec) or by manipulating SMN2 mRNA splicing so that exon 7 is included (Nusinersen and Risdiplam), thus upregulating functional protein production [Traditionally, SMA is classified into five clinical sub-types depending on the developmental milestones reached: Type 0 , Type I (severe), Type II (intermediate), Type III (mild) and Type IV (adult-onset) ,2. Curreoduction . These toduction .Preclinical and clinical studies have shown that SMN has an important role during development, suggesting that increasing SMN levels after birth may not be sufficient to successfully treat every aspect of the disease. It is known, for example, that reduced SMN levels in neonatal mice result in an SMA-like phenotype, but depletion of SMN in adult mice has relatively little effect ,19. The A key issue in our understanding of SMA at present, however, is that almost all preclinical work has focused on identifying and characterising molecular pathways in cells from severe SMA patients  and relatively severe mouse models of SMA. It is unclear, for example, whether molecular pathways implicated in SMA Type I are also relevant to SMA Types II and III and, consequently, whether therapeutic strategies and molecular biomarkers for patient stratification and/or therapeutic monitoring would translate effectively across the different types and severities of SMA. The aim of this work, therefore, was to quantitatively compare the proteomic profiles of skin fibroblasts from patients with Type I, Type II and Type III SMA to identify whether a core molecular response to reduced SMN is conserved across the different types of SMA, and then to highlight detectable molecular differences that exist between SMA type and severity.p < 0.0001), which could not be explained by SMN2 copy number, as all SMA II patients carried three SMN2 copies (SMN2 copy number (ranging from two–four copies) and the widest range of ages (17 years–66 years), SMA III fibroblasts showed a statistically significantly lower level of variation in the proteomic profiles between patients compared to SMA I and II fibroblasts (p < 0.0001) , but these levels did not, however, differentiate between SMA types  A. The exMA types B. This ip-value of >0.05 assigned to their fold changes  and, finally, the exclusion of proteins with a  changes . When co changes , differe changes . None of changes A).®) highlighted an enrichment of molecular and cellular functions relating to the differentially expressed proteins in each SMA type compared to their respective age-matched controls (p = 2.44 × 10−5), RNA damage and repair (p = 2.00 × 10−3–1.06 × 10−5), gene expression (p = 4.96 × 10−3–1.06 × 10−5), protein synthesis (p = 8.33 × 10−4–2.57 × 10−8) and RNA post-transcriptional modification (p = 9.89 × 10−3–1.13 × 10−8). For SMA II, the top enriched annotations were carbohydrate metabolism (p = 4.47 × 10−2–2.71 × 10−4), cellular assembly and organisation (p = 4.54 × 10−2–1.63 × 10−4), cell morphology (p = 4.36 × 10−2–1.63 × 10−4), small molecular biochemistry (p = 4.91 × 10−2–5.46 × 10−5) and nucleic acid metabolism (p = 1.92 × 10−2–5.46 × 10−5), but fewer differentially expressed proteins were associated with the enriched terms compared to SMA I and III, possibly reflecting the lower number of differentially expressed proteins consistently identified across this group. The top enriched terms in the SMA III dataset included cellular function and maintenance (p = 9.43 × 10−3–2.94 × 10−5), cellular development (p = 1.01 × 10−2–2.94 × 10−5), cellular movement (p = 1.08 × 10−2–1.60 × 10−5), post-translational modification (p = 6.29 × 10−3–9.80 × 10−6) and cell death and survival (p = 9.69 × 10−3–6.95 × 10−6). A three-way comparison in IPA® in which the enriched “cellular and molecular functions” annotations across each SMA type were ranked by absolute p-value indicated that there is very little overlap of enrichment per comparison (SMA type vs. control) , SMA II (p = 9.93 × 10−3) and SMA III (p = 2.8 × 10−5)), autophagy (SMA I (p = 1.75 × 10−4), SMA II (p = 3.08 × 10−3) and SMA III (p = 7.96 × 10−3)), and metabolism of protein (SMA 1 (p = 3.72 × 10−7) and SMA III (p = 2.05 × 10−4) only)  C. While solation B. After 4) only) C.® found far fewer enriched terms associated with the SMA II dataset compared to SMA I and III  and SMA III (p = 4.55 × 10−3)), regulation of eIF4 and p70S6K signalling (SMA I (p = 3.17 × 10−6) and SMA III (p = 1.9 × 10−2)) and eIF2 signalling SMA I (p = 1.64 × 10−5) and SMA II (p = 9.28 × 10−3) (p = 4.52 × 10−4) and SMA II (p = 1.95 × 10−3) but just missed the statistical cut-off for significance in SMA III (p = 5.62 × 10−2) . Similar to the cellular and molecular function analysis, canonical pathway analysis using IPA and III D, and a mparison E. Of themparison D, but se × 10−3) E. Protei × 10−2) E.® identified several networks with which the differentially expressed proteins from each SMA comparison were associated  for Canonical Pathways with the current SMA I dataset (p = 4.86 × 10−8) when Canonical Pathways were considered (p = 6.52 × 10−12–8.25 × 10−27) with seven datasets utilizing peripheral blood relating to individuals with multiple sclerosis (MS) and four datasets using skin from individuals with amyotrophic lateral sclerosis , SMA I (n = 1) and neurologic manifestations (n = 2) (p = 5.42 × 10−7–1.87 × 10−15) for the Diseases and Functions outputs, with 20 involving peripheral blood and six using skin (n = 1), neurologic manifestations (n = 2), spinocerebellar ataxia type 6 (n = 1) and SMA I (n = 2), whilst datasets using peripheral blood studied MS (n = 17), Phelan-McDermid syndrome (n = 2) and spinocerebellar ataxia type 6 (n = 1). No matches with a z-score of 50% or more were made between the curated analyses within IPA and the SMA II fibroblast data for Canonical Pathways, Causal Networks or Diseases and Functions. The only positive overlap was found between four studies in Causal Networks , with two studies involving skin  and two studies involving peripheral blood (one each of MS and Friedreich ataxia).Analysis Match (IPA) applied to the current fibroblast datasets identified a study using SMA Type I fibroblasts  that ove dataset , whilst nsidered  with a s (n = 2) . For thep < 0.0001) and statistically significant differences between each SMA type corrected for control levels, i.e., SMA I vs. SMA II and SMA I vs. SMA III (both p < 0.0001) and SMA II vs. SMA III (p < 0.001) (p = 0.023), SMA I vs. SMA III (p = 0.03) and SMA II vs. SMA III (p < 0.0001). Three separate profiles were identified from the remaining clusters based on statistically significant differences between comparisons, allowing for the identification of protein candidates that discriminate one SMA severity type from the other two types (p < 0.0001); cluster 5: SMA I vs. SMA III (p = 0.0098) and SMA II vs. SMA III (p < 0.001). Cluster 4 grouped 18 proteins with expression changes that were significantly different when adjusted for controls in SMA I compared to SMA II and III, i.e., SMA I vs. SMA II and SMA I vs. SMA III (both p < 0.0001), and cluster 8 grouped 11 proteins with expression changes compared to controls that were significantly different in SMA II compared to SMA I and III, i.e., SMA I vs. SMA II (p < 0.0001) and SMA II vs. SMA III (p < 0.0042). Four of the remaining five clusters grouped proteins that showed significantly different expression changes in one SMA type compared to just one other  . In clus< 0.001) , i.e., Swo types . Clusterne other .p = 0.031) whilst significantly increased in SMA I fibroblasts  compared to their respective controls (p = 0.221). Subsequent quantitative Western blot analysis of replicate fibroblast extracts confirmed the significant decrease in IMP1 levels in SMA III compared to age-matched controls .Having identified fibroblast molecular profiles that correlate with SMA severity and those which appear to discriminate SMA I, SMA II and III from each other, four proteins were selected from clusters as potential biomarker candidates for further study. From the SWATH-MS analysis, insulin-like growth factor 2 mRNA-binding protein 1 (IMP1)  but not significantly changed in either SMA II or III when compared to controls  (p = 0.012)  but were comparable to those found in the SWATH-MS analyses    . This fi= 0.012) B. PYGB lctively) B.p = 0.032), with no significant changes being detected in SMA I or III  (p = 0.022) , with the corresponding ratios being comparable to those observed in the SWATH-MS analysis   B was idectively) . Quantit= 0.022) C. Expresctively) C.p = 0.030) whilst levels in SMA I fibroblasts did not quite meet the significance criteria , and no changes in STAT1 levels were observed in SMA II  (p = 0.0008) and found expression levels in SMA I and II fibroblasts to mirror the trends observed by SWATH-MS  , a component of cluster 6 B, was id= 0.816) . Quantitctively) D.p = 0.007) and SMA I vs. SMA I +SMN1 , with no significant difference between SMA I +EGFP vs. SMA I +SMN1 (p = 0.470). Transient transfection with the plasmid containing SMN1 significantly decreased the levels of RAB3B in the SMA II fibroblasts, whereas transfection with the empty plasmid did not significantly alter RAB3B levels (p = 0.471) and SMA II vs. SMA II +SMN1 , with significant differences also observed between SMA II +EGFP vs. SMA II +SMN1 (p = 0.027). Transfection of SMA III fibroblasts with the empty plasmid elicited an increase in STAT1 levels, whilst transfection with the SMN1-containing plasmid reduced STAT1 levels to those found in non-electroporated SMA III fibroblasts (p = 0.044) and SMA III vs. SMA III +SMN1 , with no significant difference between SMA III +EGFP vs. SMA III +SMN1 (p = 0.057). In the transiently transfected Type III fibroblasts, no change in IMP1 levels was observed irrespective of whether the cells were electroporated with the empty plasmid or the plasmid containing SMN1 (p = 0.273), SMA III vs. SMA III +SMN1  and no significant difference between SMA III +EGFP vs. SMA III +SMN1 (p = 0.431).To establish whether the expression levels of the potential biomarker candidates identified and verified in B levels B, i.e., roblasts B, i.e., ing SMN1 B, i.e., SMN2 copy number. Previously, differences in the age of fibroblast donors have been shown to impact protein expressed in vitro [SMN1 and carrying only 3–4 SMN2 copies [The focus of this study was to determine the molecular consequences of reduced SMN levels in fibroblasts from patients with different severities of SMA. Using a quantitative proteomic approach, dysregulated proteins were identified, and their likely impacts on molecular pathways were determined. Interestingly, there was very little overlap between the proteomic profiles of the different SMA types, with not one single protein being significantly altered across all types, resulting in a lack of conserved molecular responses to reduced SMN levels with SMA severity. Of note was the significantly greater variability in the proteomic profile of the SMA II dataset compared to the SMA I and III datasets, even though these fibroblasts were from patients with the same in vitro , and othin vitro ,32. Whilin vitro  and modi2 copies ,34,35. A2 copies . Indeed,n = 1 and 4, respectively), but our findings concur with a larger study of SMN protein expression in patient fibroblasts  and blood  [n = 17; SMA II, n = 49; and SMA III, n = 42) were determined [SMN levels, although reduced in each SMA type compared with age-matched controls, did not correlate with SMA severity. A small, earlier study  showed l n = 52) , plus a termined . Both stn = 4;) and 10 genes in muscles from patients with SMA III (n = 5) to be significantly altered when compared to control samples [p-value of 0.077. Other differentially expressed genes  in the transcriptomic study were also detected in the fibroblast dataset, but none of the changes were statistically significant.Surprisingly, not a single protein was found to be significantly altered across all SMA types, and only a small proportion (14 out of 232) were significantly changed in two SMA types. Few studies have investigated the proteomic profiles of cells from patients with differing SMA severity, but a previous transcriptional study did find 33 genes in muscles from SMA I patients  and fibroblast cell lines derived from SMA I patients also provides evidence for impaired de novo protein synthesis efficiency related to reduced mTOR activity in SMA [Enriched molecular and cellular processes with which the dysregulated proteins from each SMA type were associated also showed very little overlap, except for necrosis and autophagy for all three types and the metabolism of protein for SMA I and III. Necrosis has been noted as a process that occurs in displaced motor neurons in SMA I and II patients in the later disease stages , whilst l of SMA . SMN is l of SMA ), but thl of SMA , in priml of SMA , in SMA l of SMA  and in ml of SMA , with iml of SMA , and altl of SMA ). Evidenl of SMA ). A study in SMA . Evidency in SMA , and as Biological processes impacted in the SMA II fibroblast dataset included carbohydrate metabolism, cellular assembly and organisation, cell morphology, small molecule biochemistry and nucleic acid metabolism. Carbohydrate metabolism has been highlighted in two recent reviews ,53, withFrom the SMA III dataset, cellular movement, development and function, and maintenance included some functions that were relatable to SMA, such as depolarisation of mitochondria, cellular homeostasis and autophagy (reviewed in ), but thThe set of networks identified for each SMA type are reflective of the functions and pathways already highlighted above for the different SMA severities. Networks associated with the SMA I fibroblasts show the greatest similarity to findings from other SMA studies and include the network with the terms hereditary disorder and neurological disease. Networks associated with SMA II fibroblasts have a greater emphasis on connective tissue disorders and include nephrosis, which has previously been described in SMA I patients . The netNone of the enriched canonical pathways identified were statistically significant across all of the three SMA datasets, but several were significantly enriched across two of three datasets, with at least one differentially expressed protein from the third having been associated, including mTOR signalling, regulation of eIF4 and p70S6K signalling, eIF2 signalling and protein ubiquitination. The association of these pathways with SMA is supported by findings in previous studies, where low levels of SMN have been implicated in mTOR signalling ,55,56, rTo further investigate the relevance of the current datasets and to determine the possible utility of the proteomic changes as prognostic or treatment efficacy biomarkers, the datasets were compared to other published studies of SMA or similar neurological conditions using Analysis Match within IPA. Only studies of skin (fibroblasts) or peripheral blood were included, as obtaining these tissues is less invasive than taking CSF samples or biopsies of internal organs for patients. The finding of a substantial overlap in canonical pathways between the current SMA I fibroblast dataset and that of a previous study of SMA Type I fibroblasts  and a fuSMN2 copy number sometimes correlates with the SMA phenotype, this is not the case for all SMA patients [From the BioLayout Express3D analysis, IMP1 was chosen as a candidate for discriminating both Types I and III SMA fibroblasts, as the SWATH-MS data showed an increase in Type I fibroblasts and a decrease in Type III, with Western blots confirming the latter. IMP1 is involved in various biological processes, including nervous system development, neuronal stem cell maintenance and mRNA stabilisation and transport. It is actively transported in motor neuron axons, where it associates with SMN in individual granules . When SMpatients ,15, as sOverall, this study has highlighted the utility of using fibroblasts to identify the molecular profiles and pathways associated with different SMA severities. With further work to verify findings, some of these proteins and pathways may form the foundation to elicit biomarkers for SMA research and treatment monitoring or alternative pathways to target for severity-specific treatments. Although the main degradative process in SMA is loss of motor neurons and muscle wasting, SMN is ubiquitously expressed in all cells, and in this and other studies, there is a clear decrease in SMN levels in fibroblasts. This study has highlighted proteomic and, consequently, altered biological processes that can be attributed to a specific SMA severity. In addition, using IPA, the findings of the current study were matched to other research data, demonstrating robust findings that were matched not only to studies carried out on other fibroblasts but also to peripheral blood analyses. A study in which SMA fibroblasts were used to generate induced pluripotent stem (iPS) cells that were subsequently used to generate motor neurons enabled specific analysis of motor neurons affected by SMA . The aimIn conclusion, this work demonstrates that there is a limited core molecular response to reduced SMN levels across the different severities of SMA and opens the “field” up for a more targeted approach to SMA treatment with respect to SMA type. While four proteins from the datasets were verified, and the impact of increasing SMN levels on these proteins was determined, there remains considerable potential for the remaining candidates found within this study to be explored as specific SMA type biosignatures and/or as biomarkers of both SMN-dependent and SMN-independent SMA treatment efficacy.Fibroblast cell lines with GM prefixes were obtained from the Coriell Cell Repository , whilst those with the prefix F were acquired from the Newcastle MRC Centre Biobank for Rare & Neuromuscular Diseases . Availab3 per cm2 and grown to 70–80% confluency in standard fibroblast medium  containing 10% foetal bovine serum , 1% minimum essential medium non-essential amino acids  and 1% Penicillin–Streptomycin ) in a humified incubator at 37 °C and 5% CO2. Cells were passaged using trypsin/EDTA solution  for 5 min at 37 °C. Cell pellets for mass spectrometry, Western blotting or SMN2 copy number analysis were stored at −80 °C until required.Fibroblast cell lines  were expanded in cell culture . Cells were seeded at 5 × 10E. coli NovaBlue cells, and following purification using an EndoFree Plasmid Maxi Kit (QIAGEN), the resulting plasmid preparations were analysed by agarose gel electrophoresis and quantified via UV spectrophotometry [® Ezporator® Electroporation System  and adhering to the manufacturer’s instructions. In brief, the fibroblasts were harvested (approximately 1 × 106 cells mL−1) and mixed with 250 µL Ingenio® Electroporation solution containing 5 µg of plasmid DNA and then placed into a 4mm electroporation cuvette. The fibroblasts and plasmids were electroporated at 220 V LV (capacitance = 1050 µF), with the actual peak voltage ranging from 200–222 V and the time constant for the delivered pulse varying from 28–38 ms. Following transfection, the fibroblasts were returned and maintained in standard fibroblast medium for 24 h prior to protein extraction.Plasmids containing full-length human SMN  fused tootometry . Transfe® Tissue Kit (QIAGEN) protocol for cultured animal cells. In brief, cells were lysed with proteinase K  for 10 min at 70 °C. Ethanol (96–100%) was added to each sample, and the mixture was then applied to a DNeasy spin column. Following centrifugation steps of ≥6000× g and subsequent buffer washes to remove contaminants from the bound DNA, DNA was eluted in the buffer provided. dsDNA concentration and purity were determined using the LVis Plate on a FLUOstar Omega microplate reader .Genomic DNA was extracted from thawed fibroblast cell pellets resuspended in phosphate-buffered saline (PBS) using the DNeasySMN2 copy number was determined from gDNA extracted from the SMA fibroblast cells using ddPCR™ technology and the ddPCR SMN2 Copy Number Kit . Briefly, a master ddPCR reaction mix was prepared as per kit instructions with HaeIII restriction enzyme , Ipswich, MA, USA) and nuclease-free water (NEB). Each gDNA sample was diluted to a concentration ranging between 6 and 35 ng/µL, and 4 µL was added to the ddPCR reaction mix , as were the positive  and no-template controls (NTC). Reaction mixtures were then loaded into an Automated Droplet Generator (#10043138) to produce up to 20,000 droplets per well, which were dispensed in a fresh 96-well plate and subsequently sealed using foil seals and a PX1 plate sealer (#1814000). PCR was carried out using the following steps: 10 min at 95 °C; 40 cycles of 30 s at 94 °C, then 1 min at 55 °C; 10 min at 98 °C, then a hold at 12 °C. The sealed ddPCR plate was then placed into a QX100 Droplet Reader (Bio-Rad), and using the QuantaSoft™ Software CNV application, SMN2 copy number was determined using RPP30 as the reference gene. Thresholds were set manually between positive and negative droplets for each well.Following passage, cell pellets were washed with PBS. Cell pellets for mass spectrometry (MS) analysis were lysed in 8 M urea dissolved in 100 mM ammonium bicarbonate in sterile distilled water plus 2% sodium deoxycholate. Cell pellets for Western blot (WB) analyses were lysed on ice for 15–20 min with an equivalent volume of RIPA buffer , 0.25% deoxycholic acid , 1mM ethylenediaminetetraacetic acid , 150 mM sodium chloride  and 50 mM Tris-HCl b, pH 7.4 ). All samples were sonicated for 10 s and then centrifugated at 13,000 RPM  for 5 min at 4 °C to pellet any insoluble material, and their protein concentrations were determined via Pierce™ BCA Protein Assay Kit . Absorbance was measured at 562 nm with a FLUOstar Omega microplate reader (BMG LABTECH), and a standard curve was generated from a plot of the average blank-corrected absorbance for each standard versus its concentration.A 30 μg aliquot of each sample in 8 M urea/100 mM Ammonium Bicarbonate/2% sodium deoxycholate was reduced with Tris (2-carboxyethyl) phosphine (5 mM) at 30 °C for 1 h, followed by alkylation with iodoacetamide (10 mM) at room temperature (RT) for 30 min in the dark. The reaction was quenched with 20 mM DTT. Prior to trypsin digestion, each sample was diluted to 1.5 M urea and then digested with trypsin  overnight. Samples were acidified to 0.05% trifluoroacetic acid (TFA).Peptides were desalted by reversed-phase C-18 and then dried and resuspended to a concentration of 1 µg/µL in loading buffer (2% acetonitrile and 0.05% TFA). SWATH-MS was performed on individual samples. In addition, a pool of all samples was prepared and subjected to nanoLC MS/MS analysis (information-dependent acquisition (IDA)-LC-MS/MS). The remaining peptides were pooled and fractionated by high-pH reverse-phase fractionation  into 12 fractions, which were analysed individually in IDA mode.m/z 400–1250) and MS2 (m/z 230–1800) scans, respectively, and 15 s dynamic exclusion. Rolling collision energy with a collision energy spread of 5 eV was used for fragmentation. The data files were searched using Mascot against the Swissprot database (January 2019), restricted to only proteins from humans, with trypsin as the cleavage enzyme and carbamidomethylation as a fixed modification of cysteines. Note that the iRT peptides were added to this database.For IDA, a combined sample (1 μg) or a high-pH RP fraction was injected onto a reverse-phase trap  for pre-concentration and desalted with loading buffer at 5 μL/min for 10 min on a nanoLC-MS system . The peptide trap was then switched into line with the analytical column . Peptides were eluted from the column at a flow rate of 300 nL/min using a linear solvent gradient: linear 2–20% of buffer B  over 90 min, linear 20–40% of buffer B for 30 min, linear 40–98% of buffer B for 10 min, isocratic 98% of buffer B for 5 min, linear 98–2% of buffer B for 2.5 min and isocratic 2% solvent buffer B for 12.5 min. The mass spectrometer was operated in IDA top 20 positive ion mode, with 250 and 150 ms acquisition time for the MS1  was injected onto the same LCMS set up as above with the same gradient, except that data acquisition was performed in SWATH mode. The method uses 100 variable window widths with a 1 Da overlap, as developed and optimised by Sciex on plasma. Each window has a dwell time of 150 ms to cover the mass range of 400–1250 ng model . Sum totng model  was thenp-value ≤ 0.05 were included in further analyses. Venn diagrams were generated with InteractiVenn [p-value determining the probability that each cellular and molecular function or canonical pathway assigned to that dataset is due to chance alone, and the final lists of functions and pathways were ranked accordingly to the resulting p-value.Only proteins with a fold change ≥1.25 or ≤0.80 and a actiVenn , and heaactiVenn ) to explp-value representing the probability that the overlaps reported between the input dataset and the base pathway/network  are due to chance alone. The resulting networks are a graphical representation of the molecular relationships between proteins, where proteins are represented as nodes, and the biological relationship between two nodes is represented as an edge (line). All edges are supported by at least one reference from the literature, from a textbook or from canonical information stored in the Ingenuity Knowledge Base, all supporting an in vivo or in vitro observation of a protein–protein or protein–DNA interaction (as opposed to a merely predicted interaction from in silico experimentation).The same datasets of proteins were used for network generation in IPA. Each identifier was mapped to its corresponding entry in Ingenuity’s Knowledge Base, and these proteins were overlaid onto a global molecular network developed from information contained in the Ingenuity Knowledge Base. Networks were then algorithmically generated based on their connectivity. The Functional Analysis of each network identified the biological functions and/or diseases that were most significant to the proteins in the network. A right-tailed Fisher’s Exact Test was used to calculate the p-values ≤ 0.05 and absolute z-scores ≥ 2 are included [p-value with Fisher’s Exact Test, which takes into account the number of overlapping entities, the number of non-overlapping entities in both sets of datasets, and the total number of entities in all signatures across all analyses that are not in the two signatures being assessed.Analysis Match builds a signature from the highest-confidence predictions for a set of data and compares it with those of other analyses generated from public gene expression datasets within IPA’s compendium . A signaincluded , with enp-value of <0.05) fold change of ≥1.25 or ≤0.80) in one or more of the SMA types compared to respective age-matched control fibroblasts were imported separately into BioLayout Express3D [Protein levels across all three SMA types in which proteins met the criteria for differential expression . The coefficient of variance (CV) was calculated for each protein identified by 2 peptides or more for each SMA type and the associated control group. The CV data of each group were checked for normality using the Anderson–Darling, D’Agostino–Pearson, Shapiro–Wilk and Kolmogorov–Smirnov tests, and Kruskal-Wallis with Dunn’s multiple comparisons test was used to establish any significant differences between the CVs of the groups. To determine whether changes in protein expression of SMA types were significantly different to age-matched controls following SWATH-MS analysis, multiple unpaired t-tests with Welch correction were carried out assuming individual variance for each group and applying no correction for multiple comparisons. Western blot densitometry measurements were assessed using unpaired, two-tailed or one-tailed t-tests (as appropriate) with Welch’s correction. Equal standard deviations were not assumed, and significant differences were determined between age-matched controls and their corresponding SMA type. For comparison of the Western blots of non-transfected and transiently transfected cells, paired t-tests were used. For clusters generated from the BioLayout analysis, Kruskal–Wallis incorporating multiple comparisons and corrected with Dunn’s was used to determine significant differences between groups, whilst Spearman Rank was used to identify any significant correlations.All statistical analyses were carried out using GraphPad Prism version 9.0.0 (121) for Windows, GraphPad Software, San Diego, CA, USA,


With regards to protective efficiency, an extended dosing interval (14 days) induced greater antibody production than 3- or 7-day intervals in mice. Finally, post-exposure immunization against RABV was performed to evaluate the survival rates of dogs receiving two 25 μg doses of LVRNA001 vs. five doses of inactivated vaccine over the course of three months. Survival rate in the LVRNA001 group was 100%, whereas survival rate in the inactivated vaccine control group was only 33.33%. In conclusion, these results demonstrated that LVRNA001 induced strong protective immune responses in mice and dogs, which provides a new and promising prophylactic strategy for rabies.Rabies is a lethal zoonotic disease that is mainly caused by the rabies virus (RABV). Although effective vaccines have long existed, current vaccines take both time and cost to produce. Messenger RNA (mRNA) technology is an emergent vaccine platform that supports rapid vaccine development on a large scale. Here, an optimized mRNA vaccine construct (LVRNA001) expressing rabies virus glycoprotein (RABV-G) was developed in vitro and then evaluated in vivo for its immunogenicity and protective capacity in mice and dogs. LVRNA001 induced neutralizing antibody production and a strong Th1 cellular immune response in mice. In both mice and dogs, LVRNA001 provided protection against challenge with 50-fold lethal dose 50 (LD Lyssavirus, family Rhabdoviridae [Rabies is an ancient zoonosis of the central nervous system caused by the rabies virus (RABV) that affects numerous species of warm-blooded animals , 2. Clinoviridae , 6. The oviridae . Among toviridae . RABV-G oviridae  and low-oviridae , and facoviridae , 12. Theoviridae , 14. In oviridae , 14. RABoviridae –17 and vThere are currently no effective treatments for rabies. However, as RABV can stay at the entry site of infection for days or weeks before arriving at the central nervous system and causing symptoms, immunizations either prior to or soon after exposure can be an effective strategy against the disease. Ideal rabies vaccines that provide successful postexposure prophylaxis should rapidly stimulate potent protective immune responses . ClassicmRNA, or messenger RNA, technology is a recent advent in the treatment of infectious diseases and cancer , 24. TheIn the past few years, non-replicating mRNA-based rabies vaccines with exclusively unmodified nucleosides have been attempted, phase I clinical studies demonstrated that the vaccine candidates induced boostable functional antibodies against RABV-G, and were generally safe with a reasonable tolerability profile , 34. In 2. RABV strain CVS-24 (GenBank: ADR03123.1) was provided by the Institute of Animal Health, Guangdong Academy of Agricultural Sciences . RABV strain BD06 (GenBank: ACB38373.1) was provided by the Institute of Military Veterinary Medicine, Academy of Military Medical Sciences . BD06 strain could cause 80% mortality in unvaccinated dogs after challenge [BHK-21 cells were cultured with Dulbecco’s modified Eagle’s medium (DMEM) containing 10% FBS at 37 °C with 5% COhallenge  and is rhallenge .mRNA vaccines were produced based on the Liverna Therapeutics platform (China patent ZL201911042634.2). The mRNA molecules included a 5’ cap structure, a 5’ UTR, an ORF, a 3’ UTR and a poly(A) tail. The ORF in this study encodes the glycoprotein (RABV-G) of the CTN-1 strain (GenBank: ACR39382.1), which has been used for production of human rabies vaccine in China and is Chinese domestic isolates , 38. ThrmRNAs were produced by in vitro transcription (IVT). DNA templates were linearized from plasmids containing the open reading frames flanked by 5’/ 3’UTR and Poly-A sequences. IVT reactions were performed using DNA templates, an optimized T7 RNA polymerase , NTP and Cap GAG m7G(5′)ppp(5′ (2′-OMeA)pG . The reaction was terminated by addition of DNase I . mRNAs were purified using Oligo-dT affinity column  and Tangential Flow Filtration . Microfluidic capillary electrophoresis  was used to assess RNA integrity, and the characterization including concentration, pH, residual DNA, proteins, and dsRNA impurities of the solution were also performed. To prepare the mRNA vaccines, purified mRNAs were encapsulated in LNPs according to a modified procedure wherein cholesterol, 1,2-distearoyl-sn-glycero-3-phosphocholine (DSPC), a polyethylene glycol-lipid and a cationic lipid was rapidly mixed with an aqueous solution containing mRNA. Then, the analytical characterization of product was carried out, including the determination of particle size and polydispersity, encapsulation, pH, endotoxin, and bioburden. The final products were lyophilized into powder in 2 ml glass vials. Sterile water (1 ml) was added to each vial to produce a 25 μg/ml (mRNA) solution before use. Appropriate volumes were taken from the vial for animal injection according to the experimental plan.Inactivated vaccines were purchased from an animal hospital  or a domestic vaccine manufacturer .Protein expression was measured using flow cytometry analysis as previously described . BrieflyThe morphology of the nanoparticles was analyzed using TEM. Briefly, a drop of aqueous nanoparticle sample was dropped onto a carbon-coated copper grid. Subsequently, the grid was air-dried completely at room temperature. TEM micrograph images were captured at an accelerating voltage of 80 kV.50 pre-determined by the Institute of Animal Health, Guangdong Academy of Agricultural Sciences for mouse in this experiment) intracerebrally, followed by observation of animal survival and last round of blood sample collection on day 21 post challenge for neutralization antibody test.BALB/c mice  were purchased from the Institute of Animal Health, Guangdong Academy of Agricultural Sciences . Dogs  were obtained from the Institute of Military Veterinary Medicine, Academy of Military Medical Sciences . All experiments were approved by the Research Ethics Committee of the College of Animal Health, Guangdong Academy of Agricultural Sciences, and the Research Ethics Committee of the College of the Institute of Military Veterinary Medicine, Academy of Military Medical Sciences (IACUC of AMMS-11–2021-19). Mouse immunizations were carried out according to the methods described previously , LVRNA0050 . Observation of animal survival continued through 3 months after challenge, when blood samples were collected from survived dogs for neutralization antibody test.For dog challenge studies, LVRNA001  or an inactivated vaccine made for human use  were i.m. injected into the lateral thigh of hind limb. Blood samples were collected on days 0, 7, 9, 11, 13, 35 post first injection for antibody tests. Viruses (strain BD06) were given to the test dogs on day 35 by i.m. injection to the biceps femoris of the hind limb at 50-fold LD50 of virulent RABV-BD06 strain in the biceps femoris of the hind limb. Six hours after challenge infection, dogs were i.m. injected with LVRNA001 or inactivated vaccine. Immunization procedures and experimental steps were the same as pre-exposure protocols shown above.Dogs were intramuscularly injected with 50-fold LD6 cells were seeded into a 24-well flat-bottom tissue culture plate and incubated with 5 μg of a synthesized peptide library of RABV-G for 72 h at 37°C. The cell supernatants were collected, the interferon (IFN)-γ and interleukin (IL)-4 levels were measured using ELISA kits  according to the manufacturer’s instructions.Spleen lymphocytes from mice in each immunization group were collected and resuspended in RPMI 1640 medium containing 10% fetal bovine serum (FBS). Then, 4 × 10+/CD4+ or CD3+/CD8+ T cells from mouse spleen lymphocytes (collected 7 days after booster immunization) were analyzed using flow cytometry. Spleen lymphocytes (4 × 106 cells/ml) were seeded into a 24-well flat-bottom tissue culture plate and incubated with 5 μg of a synthesized peptide library of RABV-G for 72 h at 37°C. Cells were then stained with the following antibodies at 4°C for 20 min: CD8-FITC (1:200), CD3-PerCP-Cy5.5 (1:200), CD4-BV605 (1:200) , and TNF-α-PE (1:100) . The fluorescence signals from staining were measured by flow cytometry, and data analysis was conducted using FlowJo software .TNF-α-producing CD3450 > 0.2.Mouse serum samples were collected and scanned for RABV-G-specific immunoglobulin using a commercially available RABV antibody detection kit  following the manufacturer’s instructions. A positive antibody titer was recognized as OD50) of challenge virus standard strain 11  was added to each well, and the plates were incubated at 37°C for 1 h. BHK-21 cells were cultured with DMEM containing 10% FBS and then added to the wells, and the plates were incubated at 37°C in a humidified incubator with 5% CO2 for 48 h. Afterward, the cells were washed and fixed in 80% cold acetone for 30 min. Last, the cells were covered with a RABV-specific monoclonal antibody  followed by FITC-conjugated goat anti-mouse IgG . Fluorescent signals in each well were detected with a fluorescence microscope.Viral neutralizing antibody (VNA) titers against RABV were determined by fluorescent antibody virus neutralization (FAVN) tests according to standards set forth by the World Organization for Animal Health. Serum samples were serially diluted into a 96-well plate along with WHO reference serum diluted to 0.5 IU/ml. A 50 μL suspension containing the 50% tissue culture infectious dose  and incubated with a RABV-specific primary antibody for 1 h. Samples were then washed with an FITC-conjugated secondary antibody and imaged with a fluorescence microscope.Data are presented as the mean ± standard deviation (SD). Statistical analyses were conducted using GraphPad Prism 6.0 software. Comparisons between groups were performed with one-way ANOVA followed by Tukey’s test. A p value less than 0.05 was considered statistically significant.RABV-G is the major viral antigen that induces neutralizing antibody production in infected species , 42. To To evaluate the immunogenicity of LVRNA001, BALB/c mice were vaccinated via intramuscular (i.m.) injection with LVRNA001 at different dosages: 5 μg, 1 μg, and 0.2 μg. ELISA detected anti-RABV-G IgG levels and demonstrated that the IgG levels induced by LVRNA001 were dose-dependent , the IgG levels in the mice that received LVRNA001 were above the cutoff line and slightly higher than those in mice immunized with inactivated vaccine  were compared. Dogs that received 2 doses or 3 doses of LVRNA001  demonstrated 100% survival  for either two doses (0d/7d) or three doses (0d/7d/21d). Antibody response following pre-exposure immunization against RABV was measured. For all dosage and interval combinations, the induced neutralizing antibody levels exceeded 0.5 IU/ml at 9, 11, 13, and 35 dpi Fig. A. Dogs vval Fig. B, as didval Fig. B. Neutraval Fig. C.Fig. 6P50 of the virus, six hours later, inoculated twice (0d/7d) or three times (0d/7d/21d) with LVRNA001 (25 μg or 5 μg dosages). Three months post-infection, the survival rates of the dogs immunized with 25 µg of LVRNA001 (0d/7d or 0d/7d/21d) were 100%, and the survival rates of the 5 μg dosage groups were 83.33%. In contrast, only 33.33% of dogs vaccinated with inactivated vaccine (0d/3d/7d/14d/28d) survived, and all dogs given PBS died  with 50-fold LDtwice 0d/d or thretwice 0d/d or threIn the current study, an optimized mRNA construct (LVRNA001) expressing the RABV-G protein was designed and developed in vitro. In vivo, LVRNA001 effectively induced antibody production and stimulated cellular immune responses. Inoculation with LVRNA001 provided mice with protective immunity against RABV infection, the highest protection observed when mice received two doses of LVRNA001 over an extended dosing interval of 14 days. Protection against infectious challenge was also stringently verified in dogs, as dogs administered with LVRNA001 produced protective antibody levels and demonstrated 100% survival at 3 months post-infection.RABV-G is an important viral component that induces the host antibody response, allowing it to serve as a main immunogen in rabies vaccines , 46. In In the immune response, macrophages and dendritic cells activate a Th1 response by secreting cytokines such as IFN-γ and TNF-α, and B cells favor a Th2 response by releasing other cytokines, including IL-4 –54. Th1-With dogs, pre-exposure immunization of LVRNA001 induced neutralizing antibody levels as high as or even more than did by classic inactivated vaccine Fig. . In the Most rabies vaccines in current medical usage are inactivated viruses derived from cell culture with complicated manufacturing processes and quality control procedures, leading to an unstable market supply and product safety concerns. Moreover, individuals need 4–5 doses of inactivated vaccine to achieve efficient protection. In this report, we demonstrated that immunization with two doses of an mRNA vaccine can achieve equal or better efficacy in mice and dogs without observing any adverse clinical effects. From a manufacturing perspective, large-scale production of mRNA vaccines is less time-consuming and more effective than inactivated vaccines. Taking together, we believe that LVRNA001 could be a good vaccine candidate for future rabies control. However, as our current study focused on only mice and dogs, clinical studies are needed to further evaluate the safety and efficacy of this mRNA vaccine in human.


Pseudomonas aeruginosa infection using single‐cell transcriptomics, multiplex immunohistochemistry, and flow cytometry. Our single‐cell transcriptomic analysis revealed large‐scale comprehensive changes in immune cell composition and high variation in cell–cell interactions after acute and chronic P. aeruginosa infection. Bacterial infection reprograms the genetic architecture of immune cell populations. We identified specific immune cell types, including Cxcl2+ B cells and interstitial macrophages, in response to acute and chronic infection, such as their proportions, distribution, and functional status. Importantly, the patterns of immune cell response are drastically different between acute and chronic infection models. The distinct molecular signatures highlight the importance of the highly dynamic cell–cell interaction process in different pathological conditions, which has not been completely revealed previously. These findings provide a comprehensive and unbiased immune cellular landscape for respiratory pathogenesis in mice, enabling further understanding of immunologic mechanisms in infection and inflammatory diseases.Knowledge of the changes in the immune microenvironment during pulmonary bacterial acute and chronic infections is limited. The dissection of immune system may provide a basis for effective therapeutic strategies against bacterial infection. Here, we describe a single immune cell atlas of mouse lungs after acute and chronic  + B cells, neutrophils, and inflammatory monocytes are indicated by the Kyoto Encyclopedia of Genes and Genomes (KEGG) and CellChat analysis. In contrast, signaling pathways in the Stmn1+ alveolar macrophages (AMs) and Stmn1– AMs are illustrated by the gene set enrichment analysis. Besides, in the chronic infection condition, expansion of interstitial macrophages and related signaling pathways are shown by the KEGG analysis.Mouse lung cell subtypes with the most significant gene expression changes in acute and chronic pulmonary infection. In the acute infection condition, activated receptors and their involved biological pathways in Cxcl2 Previous studies have shown that lung tissue is composed of more than 40 distinct cell types.Pseudomonas aeruginosa is a versatile gram‐negative pathogen with a large genome size that causes acute and chronic infections clinically in patients with compromised immune systems, especially those with bronchiectasis, chronic obstructive pulmonary disease, and cystic fibrosis (CF).P. aeruginosa is difficult to treat because of its innate and acquired resistance to antibiotics and extraordinarily intricate intracellular regulatory networks. Despite recent research insights, many aspects of the interaction between hosts and P. aeruginosa that can either facilitate or impair immune responses remain unknown. To understand the underlying mechanisms of the host–pathogen interaction, it is crucial to illuminate the molecular characteristics of immune cell intrinsic transcriptional changes and cell–cell interactions in acute and chronic pulmonary infections of P. aeruginosa.The latest progress of single‐cell RNA sequencing (scRNA‐seq) and the concomitant calculation methods in recent years provide an extraordinary opportunity to solve the above outstanding problem.Here, we performed an in‐depth characterization of immune landscapes across the lungs of healthy, acute, and chronically infected mice using single‐cell transcriptomics combined with bulk RNA‐seq, flow cytometry, and multiplex tissue imaging. This yielded extensive insights into the immune cell complexities in lung tissues and revealed the specific signatures of the molecular components in various immune cells. Specifically, excess accumulation of interstitial macrophages (IMs) is associated with the progression of chronic infection, indicating a previously unrevealed role of IMs in shaping the lung immune contexture. These data may offer opportunities for translating defined subpopulations of immune cells as diagnostic and therapeutic targets to control bacterial infectious diseases.22.1P. aeruginosa reference strain PAO1 for 12 h and a well‐established and widely recognized chronic infection model by transtracheal injection of agar bead‐embedded PAO1 into C57BL/6 female mice for 10 days, similar to our previous report.P. aeruginosa acute (Acute), and chronic (Chronic) infected mice with two biological replicates for each condition and dissociated these samples into a single‐cell suspension to carry out scRNA‐seq and bulk RNA‐seq  cells, natural killer T (NKT) cells, neutrophils, monocytes, and basophils using well‐characterized cell marker genes  during acute infection but relatively few alterations in multiple facets between healthy samples and chronically infected lungs Figures  and S1C.2.2+ naïve T cells, T helper type 1 cells (Th1), regulatory T cells (Treg), CD8+ naïve T cells, effector T cells (Teff), tissue resident memory T cells (Trm), type 1, 2, 3 innate lymphoid cells , NK cells, and NKT cells. The definitions of these cell subsets referred to the databases  and previous reports3,+ and CD8+ naïve T cells increased considerably after acute infection, indicating activation of the adaptive immune response in the early stage at 12 h. The majority of subpopulations showed no significant changes in chronic infection samples, with only Trm cells increasing to a certain extent, indicating that the process of chronic infection exhibits a constant state  were consistent with the state changes  related to Janus kinase/signal transducer and activator of transcription (JAK/STAT), the RIG‐I receptor signaling pathway, and the MAPK signaling pathway were enriched in the acute infection group, indicating that CD4+ and CD8+ naïve T cells respond intensively and upregulate genes to activate the immune response to cope with invasion challenges in acute infection , regulator of lymphocyte differentiation,P. aeruginosa infection.Next, we profiled transcription factors (TFs) of T‐ and NK‐cell subtypes, showing that several TFs were highly expressed in each cluster Figure . We foun2.3+ B cells, follicular B cells, Gzmk+ B cells, Il7r+ B cells, memory B cells, marginal zone B cells, and plasma B cells, according to their distinct markers  and its multimeric Cd74 and Cxcr4 receptors increased when Cxcl2+ B cells interacted with memory B cells , Stmn1− alveolar macrophages (Stmn1− AMs), type 1 interstitial macrophages (IM1), type 2 interstitial macrophages (IM2), conventional dendritic cells, inflammatory monocytes, Ly6d+ monocytes, and Nkg7+ monocytes, according to their typical marker genes  and Ccl2 (C‐C Motif Chemokine Ligand 2), while Stmn1− AMs expressed higher levels of Mrc1  and Slc7a2   signaling pathways were enriched in Stmn1− AMs  Figure . We focus Figure , suggestP. aeruginosa and Escherichia coli.P. aeruginosa infection , apolipoprotein E (ApoE), and lysozyme 2 (Lyz2), and these genes were enriched in the lysosome, peroxisome, and Toll‐like receptor signaling pathways, implying that IM1 expanded after chronic infection to activate and mediate the elimination of pathogens Figure . Notably Figures  and S7C.e Figure . Thus, I+ AMs, and Stmn1− AMs interacted with each other in the Acute group, while all four cell clusters interacted with each other in the other two groups. Notably, an increase in the interaction between IM1 and other subsets occurred in the Chronic group  interacted with each other more strongly in the Chronic group than in the Ctrl group , and tumor necrosis factor were activated after acute infection , was routinely cultured in Luria–Bertani broth with shaking (220 r.p.m.) at 37°C.The 4.2Eight‐week‐old female C57BL/6 mice were purchased from Beijing Huafukang Bioscience Co. Ltd.  and housed in a specific‐pathogen‐free facility at the State Key Laboratory of Biotherapy, Sichuan University.4.3Acute and chronic infection model in mice were established according to previous study.4.4+ and CD8+ T cells were conducted after a week.IMs were depleted by the CSF1r inhibitor PLX5622 . C57BL/6 female mice were chowed by PLX5622 for 3 days. IMs were or not reinfused into the mice. Then, all mice were chronically infected for a week. Detection of survival curve, colony‐forming unit (CFU), H&E, and CD44.5Freshly harvested control, acutely infected (12 h after infection) and chronically infected (10 days after infection) mouse lungs were digested with a Lung Dissociation Kit , and single‐cell suspensions were generated according to the manufacturer's protocol.4.6The single‐cell suspension was loaded into Chromium microfluidic chips. Sequencing libraries were constructed with reagents from a Chromium Single Cell 3’ reagent kit (10X Genomics) according to the manufacturer's protocol. The count matrices were generated by Cell Ranger Single Cell Software (v3.1.0). Further analyses were performed in R using Seurat (v3.1), Monocle2, GSEA, CellChat, etc.4.7The six samples include Ctrl1, Ctrl2, Acute1, Acute2, Chronic1, and Chronic2. The sequencing saturation of them are 71.5%, 76.6%, 80.9%, 75.1%, 69%, and 71.6%. Their number of reads are 456,488,848; 521,856,623; 488,500,735; 418,427,751; 472,993,752; and 473,312,146. The total genes detected of the samples are 17,620; 18,049; 17,423; 17,172; 18,016; and 17,894. And their number of cells are 10,818; 10,276; 10,172; 9,750; 13,352; and 13,243. The chemistry used in this study was Single Cell 3’ (v3). Based on the detailed information, we merged and integrated the metrics of six samples together after removing genes expressed in fewer than 150 UMIs and cells that had more than 10% mitochondrial genes. We filtered the ribosomal and red cell genes with Rp‐ and Hbb‐ initially expressed. Then, principal component analysis and uniform manifold approximation and projection dimension reduction (resolution = 0.5) with 30 principal components were performed. The RNA slot of the merged object was used as an input for Harmony.4.8+ and CD8+ T‐cell subpopulations, trajectory analysis was performed as previously described.Filtered and merged datasets were imported into Monocle2 and generated a Seurat object. For unbiased trajectory and pseudotime analysis of the CD44.9p‐value < 0.05.We applied the clusterProfiler R package to analyze the statistical enrichment of marker genes in KEGG pathways. We also used GSEA to analyze differentially expressed genes following obtaining enriched KEGG pathways. In addition, specific exploration of the gene lists for each comparison was also performed to identify relevant themes for genes whose function is described in the text . For this purpose, we only considered genes whose fold change was absolute >1.0 and whose 4.10p‐value threshold included three sections: p < 0.01, 0.01 < p < 0.05, and p > 0.05.Cell–cell interactions based on the expression of known ligand–receptor pairs in different cell types were calculated using the CellChat R package as previously described.4.11Total RNA was extracted from cell suspension from the six samples. The samples were constructed into libraries through NEBNext Ultra RNA Library Prep Kit for Illumina  following manufacturer's manuals. Sequencing data were generated on Illumina HiSeq 2000 platform by Novogene. The further analysis like differential expressed genes, GO, and KEGG was conducted by clusterProfiler R package.4.12After the single‐cell suspensions were generated and red blood cells were lysed using red blood cell lysis buffer , the cells were filtered through a 40 μm cell strainer . Cells were counted and stained with the following fluorochrome‐conjugated antibodies from Biolegend: CD45 , CD3 , CD4 , CD8a , CD19 , CCR7 , CD62L , CD11b , Ly‐6G , CD11c , Mertk , and Siglec‐F . Multicolor analysis was performed on a BD FACSymphony analyzer, and data were analyzed with FlowJo (V4) software.4.13+ B cells, antigen retrieval was performed in AR6 solution using the MWT. A primary rat antibody recognizing CD19  was incubated for 1 h at 37°C followed by detection using an anti‐rat antibody labeled by HRP. Visualization of CD19 was accomplished using Opal 690 TSA Plus (1:100), after which the slides were placed in AR6 solution and heated using MWT. The slides were then incubated with a primary rat antibody recognizing Cxcl2  for 1 h at 37°C, followed by detection using an anti‐rat antibody labeled by HRP. Cxcl2 was visualized using Opal 520 TSA Plus (1:100). Nuclei were subsequently stained with 4′,6‐diamidino‐2‐phenylindole (DAPI) , and sections were cover slipped using anti‐fluorescence quenching agent. The slides were scanned using a Mantra System (PerkinElmer).Samples of lung tissues were extracted and fixed in 4% paraformaldehyde overnight, followed by paraffin embedding, sectioning (4 μm), deparaffinization, and rehydration, according to standard protocols. For AMs, antigen retrieval was performed in AR6 solution  using the means microwave treatment (MWT). Primary rat antibody against Siglec‐F  was incubated for 1 h at 37°C followed by detection using an anti‐rat antibody labeled with horseradish peroxidase (HRP). Visualization of Siglec‐F was accomplished using Opal 690 TSA Plus , after which the slides were placed in AR6 solution and heated using MWT. The slides were then incubated with primary rat antibody for CD11c  for 1 h at 37°C, followed by detection using the anti‐rat antibody labeled by HRP . CD11c was visualized using Opal 520 TSA Plus . For IMs, antigen retrieval was performed in AR6 solution using MWT. A primary rat antibody recognizing Mertk  was incubated for 1 h at 37°C followed by detection using the anti‐rat antibody labeled with HRP. Visualization of Mertk was accomplished using Opal 690 TSA Plus (1:100), after which the slides were placed in AR6 solution and heated using MWT. The slides were then incubated a primary rat antibody recognizing CD11b  for 1 h at 37°C, followed by detection using an anti‐rat antibody labeled by HRP. CD11b was visualized using Opal 520 TSA Plus (1:100). For Cxcl24.14Lung tissues were fixed in 4% paraformaldehyde overnight at room temperature, followed by paraffin embedding, sectioning (4 μm), deparaffinization, and rehydration. Immunodetection of neutrophils were performed on sections with the primary antibody goat anti‐human/mouse myeloperoxidase antigen affinity‐purified polyclonal antibody  at 15 μg/ml for 3 h at room temperature. Then, cells were stained using NorthernLights 557‐conjugated anti‐goat immunoglobulin G secondary antibody  and counterstained with DAPI .4.15t‐test; comparisons between more than two groups were performed by one‐way analysis of variance (with Dunnett's multiple comparisons test). All p‐values less than 0.05 were considered significant. p‐Values are indicated on plots and in figure legends . Statistical analysis was performed with GraphPad Prism 8.Comparisons between two different groups were performed by unpaired two‐tailed Student's T.L. and X.Z. initiated and conceived the project. X.H. and M.W. designed and performed the experiments. X.H. performed computational analyses. T.M., Y.G.Z., Y.X.Z., R.W., and C.Z. assisted with mouse experiments and prepared mouse samples. Y.R., H.L., Q.L., H.L., and X.S. assisted with the flow cytometry experiments. Y.Y., T.W., and M.T. assisted with mIHC experiments. X.H., J.L., T.L., X.G., X.L., and X.Z. wrote the manuscript with extensive input from all authors. All authors have read and approved the final manuscript.The authors declare that they have no conflicts of interest.All animal experiments were reviewed and approved by the Ethics Committee of the State Key Laboratory of Biotherapy, West China Hospital, Sichuan University (No. 20140104) and carried out in compliance with institutional guidelines concerning animal use and care of Sichuan University.Supporting InformationClick here for additional data file.


PFUSION = 5.12 × 10−6, PeMAGMA = 1.94 × 10−7 for whole blood), IL-6R , and Fas . Some newly plausible JIA-associated genes are also reported, including IL-27 , LAT , and MAGI3 . Enrichment analysis further highlighted 4 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and 10 Gene Ontology (GO) terms. Our findings can benefit the understanding of genetic determinants and potential therapeutic targets for JIA.Genome-wide association study (GWAS) of Juvenile idiopathic arthritis (JIA) suffers from low power due to limited sample size and the interpretation challenge due to most signals located in non-coding regions. Gene-level analysis could alleviate these issues. Using GWAS summary statistics, we performed two typical gene-level analysis of JIA, transcriptome-wide association studies (TWAS) using FUnctional Summary-based ImputatiON (FUSION) and gene-based analysis using eQTL Multi-marker Analysis of GenoMic Annotation (eMAGMA), followed by comprehensive enrichment analysis. Among 33 overlapped significant genes from these two methods, 11 were previously reported, including TYK2 ( Juvenile idiopathic arthritis (JIA) is one of the most common rheumatic diseases characterized by arthritis in childhood . It can Genome-wide association studies (GWAS) have successfully identified 22 risk loci associated with JIA ,8,9, howp values. We would like to emphasize that using different gene-level methods with different model statistical principles to obtain the common genes can avoid the risk of false discoveries from using single method. Actually, the results from different analysis could complement to each other [With the increase of publicly available GWAS summary data and the well-developed efficient tools, it is feasible to conduct the gene-level analysis for JIA . There ach other ,17.In the present study, using the GWAS summary data of JIA with relatively larger sample size and case proportions (3305 cases and 9196 controls), we performed TWAS analysis and the gene-based association analysis to identify the tissue-specific JIA-associated genes. We used the false discovery rate (FDR) correction on each tissue to declare the significant genes. Finally, we overlapped the genes significantly detected from these two gene-level methods, and performed the enrichment analysis for these overlapped genes on the Metascape website to identify the significant Gene Ontology (GO) terms as well as the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways.The analysis flowchart of this study is shown in http://gusevlab.org/projects/fusion/, accessed on 8 April 2022) to conducted TWAS analysis of JIA. FUSION is the most commonly used TWAS method and has shown great promise in large-scale integrative omics data analysis [p value for each gene across different tissues. We finally performed FDR correction on each tissue, and genes with FDR less than 0.05 are declared to be significant.TWAS aim to integrate GWAS and expression quantitative trait loci (eQTL) studies to identify tissue-specific gene-trait associations , which hanalysis . Once inanalysis . Here, wanalysis , but alshttps://github.com/eskederks/eMAGMA-tutorial, accessed on 28 March 2022) to conduct gene-based association analysis for JIA. eMAGMA follows the same statistical framework as MAGMA, which is based on a multiple linear principal component regression model and can provide better statistical performance in gene-based association analysis [We used eQTL Multi-marker Analysis of GenoMic Annotation (eMAGMA) method  to better understand the biological mechanisms. Metascape essentially utilizes the hypergeometric test and Benjamini-Hochberg p value correction algorithm to identify all ontology terms. A large number of terms would make the results redundant and complicate the interpretation, Kappa consistency test was thus performed and terms with Kappa > 0.3 were grouped into a cluster, and the most statistically significant term in the cluster was selected to represent the cluster [p Value Cutoff, and Min Enrichment are set to be the default values, respectively. In addition, we also made a protein–protein interaction (PPI) network for the overlapped significant genes on the STRING website .We conducted gene set enrichment analysis for the overlapped genes that were significantly identified by both TWAS and eMAGMA analysis. Specifically, these overlapped significant genes were subjected to GO term and KEGG pathway enrichment analysis on the Metascape website (p = 1.53 × 10−4 and FDR = 5.40 × 10−2 for Artery Aorta). These TWAS significant genes included some established JIA-associated genes that have been reported previously, such as CCDC101 , CLN3 (p = 5.82 × 10−8 and FDR= 2.53 × 10−4 for Whole Blood), ERAP2 (p = 5.49 × 10−6 and FDR = 2.16 × 10−3 for Cells EBV-transformed lymphocytes), LNPEP . The consistent results with previous GWAS partly indicates the correctness of the FUSION analysis. All significant genes in FUSION analysis were displayed in We analyzed all genes involved in the 18 tissues, among which 275 tissue-specific genes were significantly detected by FUSION with FDR less than 0.05. Note that LAT is significantly detected at the border line (p = 2.22 × 10−8 and FDR = 3.38 × 10−5 for Whole Blood), ANKRD55 (p = 3.09 × 10−8 and FDR = 1.08 × 10−4 for Spleen), ATP8B2 (p = 7.53 × 10−7 and FDR = 6.59 × 10−4 for Stomach), PTPN2 (p = 3.96 × 10−6 and FDR = 2.75 × 10−3 for Spleen). Again, all these genes are included in the 22 risk loci identified by previous GWAS illustrate the eMAGMA results more reliable. All significant genes from eMAGMA analysis were summarized in Similarly, we analyzed all genes involved in the 18 tissues, among which 380 tissue-specific genes were significantly detected by eMAGMA with FDR less than 0.05. These included some well-known JIA-associated genes, such as SGF29 , IL(Interleukin)-6R , and Fas . The remaining newly discovered 22 genes are APOBR, ATXN2L, GSDMB, IKZF3, IL27, KIAA1109, LAT, LMAN2L, MAGI3, NFATC2IP, NUPR1, ORMDL3, PHTF1, PSMB7, RGS14, SBK1, SH2B1, STEAP1B, TNFSF15, TUFM, UXS1, ZNF197. Among them, IL-27 , LAT  and MAGI3  are the novel genes that are more likely to be associated with JIA. Detailed information for 33 genes were summarized in We intersected the significant genes from both TWAS analysis and eMAGMA gene-based association analysis according to different tissues, and found a total of 132 tissue-specific genes. A total of 33 unique genes were further identified after removing the duplicated ones, among which 11 genes have been reported in previous studies ,8,9, sucp = 5.83 × 10−6), cytokine–cytokine receptor interaction (p = 2.92 × 10−4), spinocerebellar ataxia (p = 5.11 × 10−4), and Rap1 signaling pathway (p = 1.55 × 10−3). For GO enrichment analysis, we totally found ten significant GO terms (p = 2.25 × 10−5), apoptotic signaling pathway (p = 2.09 × 10−4), cytokine receptor binding (p = 2.14 × 10−4), regulation of blood pressure (p = 1.16 × 10−3), positive regulation of protein phosphorylation (p = 1.18 × 10−3), inflammatory response (p = 2.44 × 10−3), steroid metabolic process (p = 2.66 × 10−3), regulation of autophagy (p = 5.96 × 10−3), protein homodimerization activity (p = 6.40 × 10−3), and regulation of MAPK cascade (p = 6.47 × 10−3). In addition, chord graphs  sites in the non-coding regions and gain insights through RE digestion patterns ,52. ThirIn summary, we performed gene-level analysis as well as enrichment analysis on the largest GWAS summary data of JIA. We identified novel JIA-associated genes including IL-27, LAT, and MAGI3, and highlight the important role of Th17 cell differentiation, Rap1 signaling pathway in the development of JIA. Our results can provide new insights into the pathogenic mechanisms as well as potential therapeutic targets of JIA; however, further studies are still required to validate these findings.


The as-prepared ZFAC nanocomposite was characterized using Fourier transform infrared (FTIR), X-ray diffraction (XRD), nitrogen adsorption, scanning electron microscope (SEM), thermogravimetric analysis (TGA), and ultraviolet–visible (UV–Vis). Batch adsorption operating conditions such as the pH (3–11), CV concentration (25–200 ppm), ZFAC dose (10–50 mg), temperature (23–45 °C), and contact time were evaluated. The results indicate pH-dependent uptake (optimum at pH 7.2) increased with temperature and CV concentration increase and decreased as adsorbent dose increased. Modeling of experimental data revealed better fit to the Langmuir than Freundlich and Temkin isotherms, with maximum monolayer capacities (mQ) of 208.29, 234.03, and 246.19 mg/g at 23, 35, and 45 °C, respectively. Kinetic studies suggest pseudo-second order; however, the intra-particle diffusion model indicates a rate-limiting step controlled by film diffusion mechanism. Based on the thermodynamic parameters, the sorption is spontaneous (−ΔG°), endothermic (+ΔH°), and random process (+ΔS°), and their values support the physical adsorption mechanism. In addition to the ease of preparation, the results confirm the potential of ZFAC as a purifier for dye removal from polluted water.The aim of this study was to investigate the potential advantage of ZnFe Over the last few years, water dirtiness has become a global concern, being a major threat to human health and the environment. Water pollutants can be chemicals, trash, and microbes. Among the many chemicals present in polluted water are organic substances, of which dyes are considered the worst and certainly necessitate urgent solutions . Water cCrystal violet (CV) or gentian violet  is a basSeveral techniques have been utilized for remediation of dyes from polluted water such as adsorption-based strategies, membrane filtration, photodegradation, coagulation, chemical oxidation, and biological processes ,13,14,152O4 by simple impregnation method. The obtained AC/ZnFe2O4 (termed ZFAC) nanocomposite was fully characterized and explored as a potential adsorbent for the removal of cationic CV dye from aqueous solutions. The operating variables such as the pH of polluted solution, pollutant concentration, system temperature, adsorbent dosage, and contact time were investigated in batch method. Based on the kinetic, isothermal, and thermodynamic parameters, ZFAC’s adsorbent performance and adsorption mechanism were discussed.In this present study, activated carbon was modified with ZnFe3COO)2·2H2O) (98.9%), and crystal violet , which was dried at 80 °C for about 2 h before use, were purchased from BDH Chemicals Ltd. . Ferric nitrate nonahydrate (Fe(NO3)3·9H2O) (99.99%) and polyvinylpyrrolidone (PVP. (C6H9NO)x) powder were obtained from Sigma-Aldrich . Ethanol , hydrochloric acid , and sodium hydroxide  were bought from Fisher Chemical . Unless otherwise stated, materials were used as received, and deionized water was used wherever necessary.Activated carbon , zinc acetate dihydrate (Zn(CH2O4 nanoparticles were synthesized in the presence of polyvinylpyrrolidone (PVP) as an organic spacer. Typically, 2 g of PVP was dissolved in 50 mL deionized water at 50 °C. After complete dissolution, the solution was brought to room temperature and a solution of Fe(NO3)3·9H2O  and Zn(CH3COO)2·2H2O  in 10 mL deionized water was slowly added with stirring, then left to stir for an additional 60 min at room temperature. Subsequently, the mixture was poured into a Teflon plate and dried at 60 °C for 24 h. Next, the dried metal oxide–polymer composite was hand ground to fine powder using mortar and pestle tools followed by calcination at 500 °C for 5 h under air atmosphere.ZnFe2O4 (15 wt% of the composite) was mixed with activated carbon (AC) in deionized water  with vigorous stirring for 15 min, followed by sonication for 3 h. The obtained composite (termed as ZFAC) was filtered, washed three times with water, and dried at 80 °C for 24 h. It is important to emphasize that various ratios of ZnFe2O4-to-AC, ranging between 5 and 30 wt%, were prepared, and their efficiency in the removal of cationic (methylene blue (MB) and crystal violet (CV)) and anionic (methyl orange (MO)) dyes was examined in the preliminary experiments; however, the ratio 15:85 was satisfactory, being more effective against cationic substances than anionic ones. Accordingly, following experiments were designed to investigate the efficiency of ZFAC (15:85 wt%) in the removal of CV dye. For comparison, the adsorption performance of individual metal oxide and AC in the removal of CV was also tested in the pilot test.The metal oxide-activated carbon adsorbent, ZFAC nanocomposite, was prepared using a wet homogenization method in deionized water. Thus, ZnFe−1 with 4 cm−1 wavenumber resolution and 32 runs per spectrum. X-ray diffraction (XRD) was measured in a Rigaku XtaLAB mini II benchtop X-ray crystallography system  with copper Kα radiation (λ = 1.5418 Å) and a scan speed of 3°/min over two-theta (2θ) range of 10–90. Field emission scanning electron microscope (FESEM) micrographs were captured on a JSM-7610F LV SEM  at an accelerating voltage of 15 kV. Thermogravimetric (TGA) analysis was performed using a Mettler Toledo TGA/DSC 1 Star system . Samples (5–10 mg) were heated from 25–800 °C at a heating rate of 10 °C/min under nitrogen flow of 20 mL/min. Curves of thermal decomposition were depicted for TGA weight loss percentage and derivative-TGA (DTG). Surface area measurements were obtained from nitrogen adsorption isotherms at 77 K using NOVA 2200e surface area analyzer . Prior to measurement, samples were vacuum degassed at 180 °C for at least 2 h. The BET (Brunauer–Emmett–Teller)-specific surface area was calculated at P/P0 = 0.05–0.25, and the pore size distribution and volume were derived from the adsorption branch by using the Barrett–Joyner–Halenda (BJH) model. The electronic spectra were acquired using a double-beam UV–Vis spectrophotometer  on the range of 200–600 nm, while the dye concentration was measured at λmax of 590 nm at room temperature (23 ± 1 °C). The calibration curve was established using standard concentrations of CV on the range of 1–10 mg/L (R2 = 0.9914).The FTIR spectra were recorded on a Nicolet iS10 spectrophotometer  using the KBr–disc technique. Spectra were taken on the wavenumber range of 400–4000 cmPZC) was studied using the pH drift method [3 solution of various initial pHs (pHi) ranging from 3 to 11. The pHs of the solutions were adjusted using 0.1 M of either HCl or NaOH, which were monitored using a bench-top pH-meter  previously calibrated for the test range) before addition of the adsorbents. The mixtures were shacked at 100 rpm at room temperature (23 ± 1 °C) overnight, then centrifuged, and the final pHs (pHf) of the solutions were measured. The pHPZC was assessed by plotting the change in the pH values before and after addition of the adsorbent (ΔpH = pHf−pHi) versus the pHi, and the point crossing the zero ΔpH was considered the pHPZC. The reported values are the average of two independent experiments.The pH at which the surface net charge of the prepared ZFAC adsorbent is zero  using 30 mg adsorbent dose, 100 ppm adsorbate concentration, 150 rpm agitation speed, unadjusted pH of the dye solution in distilled water, room temperature, and contact time of 24 h. Then, the best performing adsorbent  was further tested for its performance on the removal of cationic (CV and MB) and anionic (MO) dyes and compared with the unloaded AC under the screening conditions. Accordingly, the experiments herein were decided to be accomplished further.The target adsorbent was affirmed after pre-experiment adsorption efficacy screening performed on various ZnFeThe adsorption experiments were carried out, unless otherwise stated, under the following conditions: adsorbate (CV) initial concentration 100 ppm, adsorbate volume 50 mL, adsorbent (ZFAC) dose 30 mg, agitation speed 150 rpm, room temperature 23 ± 1 °C, solution pH 7.2, and adsorption duration of 24 h. Parameters affecting the adsorption efficiency were optimized in the following order. First, the pH effect was investigated on the range from 3 to 11, adjusted to the target values through dropwise addition of 0.1 M HCl or NaOH, and monitored with the help of pH-meter. Second, the optimal adsorption dosage was obtained by screening the adsorption efficiency on the range 10–50 mg, keeping other parameters mentioned above fixed. Third, the influence of adsorbate initial concentration was studied over a range of 25–200 mg/L. Finally, the effect of temperature and adsorption thermodynamic parameters was determined by applying adsorption at room temperature (23 ± 1 °C), 35, and 45 °C. Focusing on the kinetic behavior, the adsorption kinetics were investigated at selected expressive initial concentrations of 25, 50, and 100 ppm using the optimized condition.The reusability of ZFAC adsorbent was also tested. The adsorption was performed for a 100 ppm CV concentration at the optimal condition. The adsorbent was collected by filtration, fairly washed with distilled water, dried at about 40 °C overnight, weighed, and used for desorption process. Regeneration experiment was carried out in batches of 5 mL × 5 time, using ethanol–water solution (25%) . After dmax) was found to be 0.1436 ppm−1 cm−1  at room temperature.A stock solution of CV at a concentration of 500 ppm was prepared in deionized water from which the working solutions were obtained by dilution. The dye concentration to be analyzed using the UV–Vis method was determined in reference to an established standard curve. According to the Beer–Lambert law, the extension coefficient at 590 nm  and adsorption capacity (mg/g) at time t (tq) and at equilibrium (eq) were calculated according to Equations (1)–(3), respectively.C0, tC, and eC (mg/L) are the adsorbate initial concentrations in the liquid phase and the ones at time t (min) and at equilibrium. V (L) is the solution volume, and m (g) is the dry mass of adsorbent used.The removal efficiency  is the maximum monolayer capacity, and LK (L/mg), FK (mg/g)(L/mg)1/n, and TK (J/mol) are, respectively, the model isotherm constants; T (K) is the absolute temperature, R (8.314 J/mol·K) is the universal gas constant, and A (L/mg) is the Temkin isotherm equilibrium binding constant.The adsorption isotherm was computed using the Langmuir ,24, FreuG°), enthalpy change (∆H°), and entropy change (∆S°), were analyzed following expressions given in Equations (7) and (8) [dK (L/g) is the apparent equilibrium constant, while other notations have the same meaning as above.The thermodynamic parameters, including Gibbs free energy change (∆ and (8) ,27.(7)lk1 (min−1), k2 (g/(mg·min)), and idk (mg/(g·min0.5)) are the rate constants of the three models, respectively, and C (mg/g) is the y-intercept of the IPD model. The reciprocal value of the PSO plot intercept describes the initial rate (h) (mg/(g·min)) of the adsorption process, as in Equation (12).The time-dependent adsorption study was performed at different initial dye concentrations . The obtained data were fitted to the linear pseudo-first-order (PFO), pseudo-second-order (PSO), and intra-particle diffusion (IPD) models, described by Lagergren–Svenska , Ho–McKa2O4, ZFAC, and ZFAC–CV. As can be seen, the spectra share common bands for OH stretching and bending vibrations at about 3435 and 1605 cm−1, respectively. The latter, however, is broad due to overlapping, with major contribution from C=C stretching [−1 corresponding to νC=O stretching, νC=C stretching, νC–OH, asymmetric and symmetric νC–O–C, and structure deformation modes, respectively. The ZnFe2O4 spectrum revealed a dominant band at 543 cm−1, characteristic for Fe–O. The M-O bands can be traced in ZFAC and ZFAC–CV as well, thus indicating composite formation with a slight shift in the peak positions due to interactions. Although the Zn-O band is typically bellow 400 cm−1, which is, unfortunately, out of the recorded range, the traced peaks around 418 cm−1 in the spectra of ZFAC and ZFAC–CV could be assigned to Zn–O [−1, attributed to the asymmetric and symmetric stretching of methyl group, N–H bending, and C–N stretching vibrations in the dye, respectively [−1 was observed. This suggests that C=O and OH are involved in the adsorption process, providing active sites for CV settlement.retching ,32. The  to Zn–O ,34. Afte2O4 for comparison. The diffractogram of the obtained spinel metal oxide NPs typically agrees with the literature [2O4 NPs. The resulting cubic ZnFe2O4 has a unit cell parameter of 8.423 Å, which is close to the reported values (8.438 and 8.389 Å) in the literature [2O4 were still identifiable; however, peak intensities were slightly diminished due to AC. All diffraction peaks were indexed to the cubic structure of spinel-doped metal oxide.terature ,37 and Jterature ,40. The 2 adsorption–desorption isotherms of AC, ZnFe2O4, and ZFAC, while the corresponding surface area, pore size, and pore volume are summarized in 2·g−1) was close to that of AC (955 m2·g−1), while the one of ZnFe2O4 was as low as 34 m2·g−1, assuming a negligible effect of ZnFe2O4 on the overall surface area of the adsorbent. On the other hand, ZnFe2O4 has the highest BJH pore size and the lowest pore volume compared with both AC and ZFAC  is more condensed, with an average particle size of 23.59 nm, and arranged in more randomly adhered segments. After composition, the SEM image of the obtained ZFAC revealed a slight alteration of the AC surface structure, with less aggregations and shortened bead-like based chains. The SEM graph of CV-loaded adsorbent (ZFAC–CV), showed more adhesives particles, due to CV filling ZFAC cavities.The SEM micrographs of AC, ZnFe2O4, and ZFAC were also recorded  of 14.30 ± 4.34 nm, which is less than that suggested by SEM. Such structural morphology of ZnFe2O4 was also seen by Abbasian et al. [2O4 nanoparticles, providing comparative surface area with higher active sites, supporting the observed sorption enhancement of ZFAC nanocomposite compared with AC.The TEM micrographs of AC, ZnFerecorded . The AC n et al. , and then et al. . Figure 2O4 (34 m2·g−1) compared with AC (955 m2·g−1) is a result of its particles’ high agglomeration, which led to less porosity per g of ZnFe2O4, as can be seen in the SEM and TEM micrographs as well.The low BET surface area of ZnFe2O4 alone, the adsorbent ZFAC nanocomposite, and the dye-loaded ZFAC were further examined. As shown in 2O4 or organic-based backbone in ZFAC and ZFAC–CV. The last region  is due to the decomposition of functional groups and partial gasification of the least thermally stable fragments of the carbon structure [2O4 shows a relatively high thermal stability; however, the observed total mass loss of about 8.5 wt% at 800 °C could be ascribed to organic matrix (PVP) residues, a spacer used to facilitate production of ZnFe2O4 NPs [The thermal property of AC and ZnFetructure . An AC te2O4 NPs . The cure2O4 NPs , in whic2O4 were prepared and applied for removal of CV dye from water solutions. As shown in 2O4, reflecting the rule, as well as the benefit, of ZnFe2O4 in decorating the AC surface for CV settling [2O4 wt% and up to 15 wt%, beyond which the adsorptive properties of ZnFe2O4 dominate as a result of quantity increase. It is worth mentioning that ZnFe2O4 alone has low activity as an adsorbent for CV dye removal as examined during the pilot studies. Moreover, it is reported that ZnFe2O4 with certain structural morphologies and particle sizes may have photocatalytic properties [2O4 occurs, batch experiments of pure AC, pure ZnFe2O4, and ZFAC nanocomposite were separately performed under the adsorption operating condition . As stated above, there was no change in the CV concentration for the system containing ZnFe2O4 alone, indicating negligible adsorption and further confirming no catalytic effect at the adsorption condition. The time-dependent adsorption of CV onto AC and ZFAC is illustrated in The adsorption performance of various composites was also analyzed. ZFAC composites containing 0, 10, 15, 20, 25, and 30 wt% of ZnFesettling . This imoperties ,48. ThusPZC value was found at 4.3 . With pH increase from 4.3 to 7.2, the available negatively charged sites for CV residency become plentiful, reaching the maximum at pH of 7.2. Next, the adsorption was slightly decreased with a pH increase from 7.2 to 8, above which  the pH has no visible effect on the Re%. However, the removal was sufficient all over the investigated pHs, with Re% ranging between 99.0 and 96.5%.The adsorbent surface charge was assessed using the pH-drift method. The pHd at 4.3 . pHPZC tve sites , leadingRe, %), are illustrated in Re% increased as adsorbent doses increased from 0.01 to 0.03 g, then, no notable difference in removal efficiency can be seen up to 0.05 g. The general enhancement could be attributed to the increase in the number of available active sites reaching maxima at 0.03 g, beyond which the available sites for adsorption decrease due to overlapping or aggregation [The performance of various ZFAC doses on the removal of CV dye from its aqueous solution was examined for a series of 0.2–1.0 g/L (10–50 mg adsorbent per 50 mL dye solution) keeping other parameters constant . The resR2, , the goodness of fit for the experimental data was found to be in the order of Langmuir (R2 = 0.864), Temkin (R2 = 0.724), and Freundlich (R2 = 0.663); the same trend was kept at 308 and 318 K. The maximum theoretical adsorption capacities  were greater than the experimental ones, increased from 208.29 to 246.19 mg/g as temperature increased from 296 to 318 K, thus demonstrating favored adsorption at higher temperature. The Langmuir dimensionless constant RL was also calculated (Equation (13)), drawn in LR = 0 (irreversible), 0 < LR < 1 (favorable), LR = 1 (linear), and LR > 1 (unfavorable). As seen in LR values support favorable adsorption all over the investigated ranges of concentrations and temperatures. Moreover, the tendency of adsorption equilibrium can also be assessed using 1/n; the Freundlich heterogeneity factor with 0.1 < 1/n < 1.0 values support a favored sorption process.The adsorption equilibrium was evaluated at temperatures of 23, 35, and 45 °C and modeled using the Langmuir, Freundlich, and Temkin isotherms (Equations (4)–(6)). As shown in C0 = 50, 100, 150, and 200 mg/L). The thermodynamic behavior was evaluated using Equation (7), and the results are depicted in G° was negative, directing the feasibility and spontaneity of the sorption process. As temperature increased from 23 to 45 °C, the extent of ΔG° negativity was slightly increased, whereas it decreased as CV concentration increased from 50 to 200 mg/L. As seen in H° and ΔS° endorse the endothermic nature of the adsorption process, and randomness increases at the solid–liquid interface. Although ΔS° and ΔH° were virtually temperature-independent [The effect of temperature on the adsorption process of CV onto ZFAC was studied in batch at three temperatures  and for four initial CV concentrations , the mechanism for adsorption of CV onto ZFAC is predominantly physical in nature [The adsorption mechanism can be assessed through the magnitudes of the thermodynamic parameters. According to eq, mg/g) was found to be increased as initial dye concentration increased from 25 to 100 ppm ). As seen in h were C0-dependent, being lower at higher C0. However, the increase in C0 led to an increase in the loading capacity of the adsorbent, possibly due to the higher driving force for mass at higher C0 [ 100 ppm A. Accord ppm  the uptake is mainly film-diffusion-controlled.Since neither PFO nor PSO models could assess the diffusion mechanism and rate-limiting step, the IPD model described by the Weber–Morris equation (Equation (11)) was applied as well. This model assumes that the adsorption can occur through three sequential steps: (1) film diffusion, in which the external adsorbate molecules transfer across the liquid film to the adsorbent exterior surface; (2) intra-particle diffusion, the transport of adsorbent particles from its surface to its internal pores; and (3) sorption, the interaction step. Step (3) is known to be fast and cannot be treated as a rate-limiting step, thus the rate-controlling step can be (1) and/or (2) ,56. AccoC  were relThe reuse study tried to prove the recycling ability of the prepared ZFAC adsorbent. It was found that the dye might be desorbed from ZFAC nanoparticles with diluted ethanol up to 86.3% after three cycles of the adsorption–desorption process . HowevermQ, mg/g) as the base for comparison. The compared adsorbents represent a variety of materials close to the investigated ZFAC and selected as metal oxides/activated carbon composites [mQ value of ZFAC at the operating condition is 208.3 mg/g, which is relatively higher than the alike composites shown in mQ of nanomagnetic iron oxide is low (12.7 mg/g), which could support the observed negligible results in this work. On the other hand, synthetic activated carbons may be more efficient than commercial and bio-based ones, while ZFAC nanocomposite performance is still comparatively high. However, the differences in adsorption properties and adsorbent performance are a result of various factors, including adsorbent nature and properties such as morphology and particle size, adsorbate structure and ionization state, and adsorption operating conditions. The suitability of a potential adsorbent is generally assessed in terms of efficiency, reusability, and availability. Thus, both naturally and industrially based adsorbents may be inexpensive but commonly less efficient. By some engineered modification, adsorbent performance commonly improved, validating the advantage of metal oxides’  addition to activated carbon, as discussed for ZFAC.The performance of ZFAC in CV dye removal compared with some other adsorbents taken from the literature is given in mposites ,59,60,61mposites , commercmposites , bio-basmposites ,63, and mposites . For eas2O4 (ZFAC) nanocomposite was successfully prepared by simple wet homogenization method. A composition containing 15 wt% ZnFe2O4 exhibited adsorption properties superior to its pure precursors, ZnFe2O4 and AC, in terms of remediation of cationic-dye-polluted water, CV dye in particular. The as-combined adsorbent nanoparticles were characterized by FTIR, XRD, SEM, N2 adsorption-based surface area, and TGA. Results of adsorption indicate a pH-dependent process with efficiency increased with temperature and dye concentration increase, while adsorbent dosage increase promotes efficiency reduction. The adsorption process followed Langmuir and pseudo-second-order equations with a maximum capacity of, e.g., 208.29 mg/g at 23 °C and a rate-limiting step controlled by a film-diffusion mechanism. The overall adsorption mechanism, assessed by the values of thermodynamic parameters including ΔG°, ΔH°, and ΔS°, was physical and endothermic, operating with randomness increase at the adsorption interface. This result supports the advantage of such a system in the enhancement of activated carbon adsorptive properties towards application for the removal of selected organic pollutants such as cationic CV dye.In this work, an activated carbon/ZnFe