Loss of Fmr1 reorganizes the multi-elemental composition of neural and somatic tissues in Fragile X mice

3 Fragile X Syndrome (FXS) is a leading genetic cause of autism spectrum disorder (ASD) and 4

from a genetic mutation which silences the expression of Fragile X Messenger 5 Ribonucleoprotein (FMRP). FMRP serves various roles regulating cellular protein synthesis and 6 ion flux . However, a comprehensive comparison of multidimensional elemental balance (i.e., 7 ionome) between FXS genotypes and tissues remains absent from the literature. Here, w e 8 measured the multivariate balance of 10 elements (i.e., ionome) in tissues of wild-type and Fmr1-9 knockout mice to compare ionomic composition of brain and somatic tissues within and across 10 genotypes. We found that homogenized brain tissue including several regions (brain PMHTH ; 11 define at first use) differed in elemental balance between genotypes, according to MANOV A. We 12 failed to observe differences between genotypes in the mean ratio of any individual element in 13 PMHTH, but s odium displayed lower variance in knockout than wild-type PMHTH. Knockout 14 striatum displayed lower variance in potassium than wild -type. Knockout olfactory bulbs 15 contained higher mean iron and displayed higher variance in sodium and copper than wild-type. 16 Wild-type feces contained higher mean magnesium and zinc than knockout. These results align 17 with previous work showing FXS pathologies alter electrolytic and metal ion regulation, neuronal 18 excitability, and gastrointestinal function. Further work is needed to identify the source of overall 19 ionomic differences in heterogeneous brain tissue (PMHTH), which could be due to differences 20 among regions. Future work should additionally test how elemental differences relate to function 21 at the cellular level , as well as patterns of individual intake, digestion, assimilation, and /or 22 excretion. 23 .CC-BY-NC 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted January 30, 2026. ; https://doi.org/10.64898/2026.01.27.702117doi: bioRxiv preprint

24 Fragile X Syndrome (FXS) is a neurodevelopmental disorder which results from a mutation 25 in a single gene, Fmr1 (Fragile X Messenger Ribonucleoprotein 1), on the X chromosome 26 (Hagerman et al., 2017; Salcedo -Arellano et al., 2023) . Mutation of this gene reduces the 27 expression of its protein product, Fragile X Messenger Ribonucleoprotein (FMRP) (Bagni et al., 28 2012; Mila et al., 2018) . Because FMRP is an RNA -binding protein, it affects a wide range of 29 biological processes, including gastrointestinal function, synaptic activity, and neural 30 development, among others. The diversity of these effects makes it difficult to catalog all 31 downstream consequences of Fmr1 disruption at the molecular level . Because all bio logical 32 systems (e.g., cell, tissue) are made up of approximately 20 chemical elements (depending on 33 structure, and process; Williams and Fraústo da Silva, 2003), the relative abundance of all elements 34 encompassing a system (i.e., the ionome) are appropriately conceptualized as a working unit 35 (Baxter, 2010; Salt et al., 2008). Thus, rather than tracing single molecular pathways or patterns in 36 molecular systems, like transcriptomic ( Ding et al., 2020; Donnard et al., 2022; Ebrahimiazar et 37 al., 2025) and proteomic analyses (Abbasi et al., 2024; Gao et al., 2023), responses of the ionome 38 to genetic perturbations are quantized and interpreted to advance understanding of various 39 phenotypes (Huang and Salt, 2016) including disease (Cabral et al., 2021; Dubey et al., 2020; 40 Sarafanov et al., 2011; Zhang et al., 2020; Zhang et al., 2023). 41 Fmr1 encodes an RNA -binding protein (FMRP), and its loss affects diverse aspects of 42 cellular physiology by disrupting post -transcriptional regulation of many target mRNAs. FMRP 43 associates with ribosomes and polyribosomes to control the translation of transcripts involved in 44 synaptic signaling, ion channel regulation, and metabolic processes (Darnell and Klann, 2013; 45 Ferron, 2016; Stefani et al., 2004; Zhou et al., 2025) . Consequently, Fmr1 disruption alters both 46 the synthesis and localization of proteins responsible for ion transport and storage (e.g., voltage -47 .CC-BY-NC 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted January 30, 2026. ; https://doi.org/10.64898/2026.01.27.702117doi: bioRxiv preprint gated channels, metalloregulatory enzymes; (Cheng et al., 2021; Deng and Klyachko, 2021; Napoli 48 et al., 2011) . Thus, FXS may secondarily influence elemental balance across tissues. These 49 alterations in translational efficiency and post translational modifications can cascade into 50 systemic changes in ionomic composition, potentially influencing neuronal function. 51 Individuals with FXS are often diagnosed with autism spectrum disorder (ASD), and 52 exhibit overlapping clinical features such as antisocial behavior, communication deficits, cognitive 53 impairments, and language delays (Kaufmann et al., 2017) . Several studies have identified 54 associations between ASD and specific elements: supplementation with zinc promotes restoration 55 of synaptic proteins such as Shank3 and Shank2 and helps restore excitatory –inhibitory balance 56 (Hagmeyer et al., 2018) . M agnesium, particularly in combination with vitamin B6, further 57 mitigates neurobehavioral disorders in ASD (Khan et al., 2021; M Mousain -Bosc et al., 2006) . 58 Moreover, recent research demonstrates that ASD -related symptoms extend beyond neural 59 dysfunction, including disruptions in balance across gut and peripheral tissues (iron - Lin et al., 60 2024, Talvio et al., 2021; zinc - Vela et al., 2015 ; various trace metals - Grabrucker, 2020) . 61 However, focusing on single elements or tissues, while informative, risks missing the broader 62 systemic interactions that emerge from the coordination of multiple elements. Without a 63 comprehensive ionomic perspective, the integrative patterns linking neural, gastrointestinal, and 64 metabolic functions may remain unresolved. 65 In this context, we conducted an experiment to examine how the multielemental 66 composition, the ionome, of neural and somatic tisssues differs in the context of FXS between 67 wild-type and knockout strains of mice. Because elemental concentrations are interdependent and 68 constrained to a constant sum, these data were analyzed within a compositional framework that 69 captures relative, rather than absolute, changes among elements. Using this approach, we explored 70 .CC-BY-NC 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted January 30, 2026. ; https://doi.org/10.64898/2026.01.27.702117doi: bioRxiv preprint the potential influence of Fmr1 loss on overall elemental balance among brain regions and somatic 71 tissues. We hypothesized that male mice lacking Fmr1 would exhibit distinct multielement 72 compositions relative to wild-type males. To test this hypothesis, we quantified and compared the 73 ionomic composition of gut, brain, and noninvasive tissues such as fur and feces between 74 genotypes. This compositional data analysis provides insight into systemic elemental 75 reorganization in FXS (Greenacre, 2021) and identifies tissues that may serve as noninvasive 76 proxies for elemental diagnostics in clinical contexts (Austin et al., 2022). 77 .CC-BY-NC 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted January 30, 2026. ; https://doi.org/10.64898/2026.01.27.702117doi: bioRxiv preprint Methodology 78 Experimental Animals and Design 79 In our study, we used an Fmr1 K O (stock # 003025, Fmr1 KO) mouse model which 80 recapitulates some core symptoms of FXS patients. We performed our experiments on C57BL/6J 81 (stock #000664, B6) wild -type background (control animals) , which were obtained from the 82 Jackson Laboratory and bred at Oklahoma State University (The Dutch -Belgian Fragile X 83 Consorthium et al., 1994). All animals we used in this study were male WT (n = 8) and KO (n = 84 6) mice between 99-156 days old. All mice were on a 12-hour light cycle (6 AM- 6 PM. Since FXS 85 is an X-linked trait and therefore more common in males, we limited the study to male animals. 86 All experimental procedures were conducted under appropriate laws and NIH guidelines and our 87 study principles received approval from the Oklahoma State University Institutional Animal Care 88 and Use Committee. 89 Tissue Collection and Preparation 90 Firstly, we euthanized mice by exposing them to isoflurane overdose. After confirming 91 death of the mice by lack of respiration and toe pinch reflex , we decapitated them and harvested 92 their brains. Then we placed brains in a Petri dish and dissected the whole brains into specific brain 93 regions including cerebellum, cortex, midbrain, olfactory bulb, brainstem, and striatum. After 94 removing the above-mentioned dissected brain regions, we labeled all leftover brain regions (pons, 95 medulla, hippocampus, thalamus, hypothalamus) as brain PMHTH (Figure S1). We then 96 transferred all dissected brain regions into a 2 mL Eppendorf tube for subsequent processing. 97 Additionally, we collected samples from the gut tissues like cecum, feces and also included fur for 98 each individual mouse. To collect the cecum, we opened the abdominal cavity with a midline 99 incision from the lower abdomen to the sternum , which exteriorized the intestine to expose the 100 cecum—a large, pouch-like structure located at the junction between the ileum and the colon. We 101 .CC-BY-NC 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted January 30, 2026. ; https://doi.org/10.64898/2026.01.27.702117doi: bioRxiv preprint immediately transferred the cecum to a sterile, 2 mL Eppendorf tube. Later, to separate the cecal 102 contents from the cecum tissue, we opened the cecum longitudinally on a sterile Pedri dish, then 103 collected cecal contents and discarded the cecal tissue. To collect feces, we gently h eld the mice 104 by the base of their tails and waited around 5 minutes until they defecate d. After defecation, we 105 used forceps to collect fresh fecal pellets, and immediately placed those pellets into pre -labeled 106 sterile tubes. For fur samples, we used clean, sterile scissors to trim the fur from the abdomen. 107 Then, we transferred the clipped fur into a pre -weighed and labeled Eppendorf tube using sterile 108 tweezers. 109 Ionomics and compositional data analysis: 110 Once tissues were collected and stored , we dried them at room temperature in an oven at 111 55 °C for 72 hours. Then, we homogenized dried tissues using a mortar and pestle and weighed 4 112 – 12 mg for each sample. We digested homogenized samples using 100% trace metal grade HNO3 113 and 100% trace metal grade H2O2 in a 2:1 ratio; allow ing them at least 24 hours for complete 114 digestion before performing elemental analysis using inductively coupled plasma optical emission 115 spectroscopy ( ICP-OES, iCAP7400; ThermoScientific, Waltham, MA). We measured the 116 concentrations of 10 biologically relevant elements in brain tissue using ICP-OES: Calcium (Ca), 117 Copper (Cu), Iron (Fe), Potassium (K), Magnesium (Mg), Manganese (Mn), sodium (Na), 118 Phosphorus (P), Sulfur (S), and Zinc (Zn). We retained wavelengths with 90% of measured 119 samples within the limits of detection established by standard curves and replaced values over and 120 under limits of detection with upper and lower limits. Then, we averaged emittance of multiple 121 wavelengths when more than one wavelength quantified an individual element. 122 Of the 84 total samples analyzed for 10 elements (840 readings), 4 readings of sulfur, and 123 7 readings of zinc were missing values. To account for this, we imputed missing values "impCoda" 124 .CC-BY-NC 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted January 30, 2026. ; https://doi.org/10.64898/2026.01.27.702117doi: bioRxiv preprint function from the "robCompositions" R package, which is specifically designed for compositional 125 data (Templ et al., 2011). A few missing values are common in ICP-based elemental analyses due 126 to matrix effects and instrument detection limits (Reimann et al., 2008) .Tissue elemental 127 concentrations represent compositional data and were transformed to account for their inherent 128 constraints. Concentrations of each element (µg/ mg) were converted into percentages. We 129 calculated the fill value (Fv) as the remaining unmeasured percentage of samples (i.e., 100 – sum 130 of all measured elements ). For each element, we then calculated its proportion of the total 131 remaining unmeasured mass (Element/Fv) to standardize comparisons across samples in log space 132 (i.e., additive log ratios ; ALRs). These proportional values (Element/Fv) represent the relative 133 abundances of each element within the measured elemental pools that are suitable for robust 134 statistical analysis and inference (Greenacre, 2021). 135 Additive log ratios (ALRs) were visualized in multi -dimensional space using a principle 136 components analysis (PCA) to test ionomic differences due to Fmr1 knockout (WT vs KO) and 137 tissue (i.e., feces, striatum, cecum, olfactory bulb, fur, and PMHTH). The contribution of each 138 element in explaining variance due to these factors was evaluated using a multivariate analysis of 139 variance (MANOV A), followed by element-specific ANOV As testing the global effects of tissue, 140 genotype, and interaction . Then, we used Welch’s t -test to evaluate differences in the ALR -141 transformed ratios of individual elements in single tissues (Welch, 1938) . Further, we used 142 Levene’s test for homogeneity of variance to evaluate differences in variance between genotypes 143 (WT vs KO) in individual elements within each tissue (Levene, 1960). All statistical analyses were 144 performed in statistical language R ver 4.5.2 (R Core Team, 2025) 145 .CC-BY-NC 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted January 30, 2026. ; https://doi.org/10.64898/2026.01.27.702117doi: bioRxiv preprint

146 Tissue-specific ionomes 147 The balance of 10-elements in the six tissue types differed significantly as evidenced by a 148 MANOV A on ALR-transformed ratios (Pillai’s Trace = 3.87; F 45,370 = 25.0; p < 0.0001; Figure 149 1A; Table S1). PCA revealed complete separation in PC space between feces, fur, and cecal 150 contents, but less separation between olfactory bulb, PMHTH, and striatum (Figure 1B). All 151 elements except potassium and sulfur loaded negatively (PC score > |0.2|) onto PC1 (Table S2). 152 Calcium, sulfur, and zinc loaded negatively onto PC2, and potassium and sodium loaded 153 positively onto PC2 (Table S2). 154 The relative abundance of all 10 elements also differed individually between tissues as 155 detected by ANOV A on ALR-transformed ratios (Table S2; raw mean + standard errors available 156 in Table S3). ANOV A also revealed global genotype (df = 1; F= 4.8; p = 0.03) and Tissue * 157 Genotype (df = 5; F = 2.9; p = 0.02) effects for zinc (Table S3). Tukey HSD post-hoc performed 158 on ALR-transformed ratios pooled at the tissue level (i.e., WT and KO) revealed many 159 significant differences among multiple tissues in all sampled elements (Table S4; Figures S2-160 S11). 161 Effects of Fmr1 knockout on tissue-specific ionomes 162 PMHTH was the only tissue which displayed significant differences in the balance of 10 163 elements between Fmr1-KO and WT mice (Pillai’s Trace = 0.99; F 10,3 = 43.0; p = 0.005; Figure 164 S12A; Table S5). The largest two principal components from the PCA comparing PMHTH 165 elements among genotypes poorly accounted for total variability observed in the input dataset 166 (55.8%), but shows near total overlap between Fmr1-KO and WT ellipses in these axes (Figure 167 S12B; Table S6). The third principal component explained a further 15.6% of variation from the 168 input dataset, but we observed weak evidence for differences between the PC3 scores of each 169 .CC-BY-NC 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted January 30, 2026. ; https://doi.org/10.64898/2026.01.27.702117doi: bioRxiv preprint genotype (t-test p = 0.08; Table S6, S7). The elements which loaded onto PC3 with a score > |0.2| 170 include iron, sulfur, and zinc in the positive direction, and calcium, manganese, and sodium in 171 the negative direction (Table S7). 172 Welch’s t-tests performed on the relative abundance of individual elements revealed that 173 Fmr1-KO did not affect mean ratios of any of the 10 elements in cecal contents, fur, PMHTH, or 174 striatum (Table 1). The mean relative abundances of magnesium (Fig. 2A) and zinc (Fig. 2B) in 175 feces of Fmr1-KO were significantly lower compared to WT (Table 1). Additionally, olfactory 176 bulbs of Fmr1-KO contained higher mean iron (Fig. 2C) compared to WT (Table 1). Relative 177 contribution to the measured total and absolute concentrations for each element in each tissue are 178 available for both genotypes in Tables S8 and S9, respectively. Plots showing ALR-transformed 179 and raw concentration values are available in Figure S13. 180 Levene’s tests performed on the relative abundance of individual elements revealed no 181 significant effects of Fmr1-KO on elemental variance of all 10 elements in cecal contents, fur, or 182 feces (Table 2). The variance in relative abundance of copper (Fig. 3A) and sodium (Fig. 3B) in 183 olfactory bulbs of Fmr1-KO were significantly higher than WT (Table 2). In PMHTH, variance 184 of sodium was higher in WT than Fmr1-KO (Fig. 3C). Finally, the variance in potassium was 185 higher in WT than Fmr1-KO (Fig. 3D). Plots showing ALR-transformed and raw concentration 186 values are available in Figure S14. 187 .CC-BY-NC 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted January 30, 2026. ; https://doi.org/10.64898/2026.01.27.702117doi: bioRxiv preprint 188 Figure 1. A) Composition plot of % elements showing striking differences in the relative 189 abundance of elements in each tissue. Values show average (+ SE) measured percentage of 190 original sample mass (i.e., 100 – Fv). Relative elemental contributions are available in Table S1. 191 B) PCA on ALR-transformed data showing unique positions of tissues in multidimensional 192 space. Elemental loadings available in Table S2. 193 .CC-BY-NC 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted January 30, 2026. ; https://doi.org/10.64898/2026.01.27.702117doi: bioRxiv preprint 194 Figure 2. Element concentrations (µg/mg) where the mean ALR-transformed ratio significantly 195 differed between Fmr1-KO and WT genotypes according to Welch’s t-test (see Table 1). A) Mg 196 in feces, C) Zn in feces, C) Fe in olfactory bulb. 197 .CC-BY-NC 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted January 30, 2026. ; https://doi.org/10.64898/2026.01.27.702117doi: bioRxiv preprint 198 199 Figure 3. Elemental concentrations (µg/mg) where variance of ALR-transformed ratio 200 significantly differed between Fmr1-KO and WT genotypes according to Levene’s test (see Table 201 2). A) Cu in olfactory bulb, B) Na in olfactory bulb, C) Na in PMHTH, D) K in striatum. 202 .CC-BY-NC 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted January 30, 2026. ; https://doi.org/10.64898/2026.01.27.702117doi: bioRxiv preprint Table 1. Effects of Fmr1-KO on tissue-specific elemental ratios. Welch’s t-tests on each element 203 were performed on ALR-transformed concentrations. Significant differences are represented in 204 bold. 205 Tissue Element Estimate (KO–WT) t Ratio DF p-value Cecal Contents Ca 0.136 -1.400 9.5 0.2 Cu 0.047 -0.679 11.6 0.5 Fe 0.138 -1.508 11.2 0.2 K 0.100 -1.602 11.4 0.1 Mg 0.077 -0.848 11.4 0.4 Mn 0.033 -0.353 10.4 0.7 Na 0.067 -0.724 9.0 0.5 P 0.034 -0.715 9.2 0.5 S -0.014 0.254 11.2 0.8 Zn 0.160 -2.074 7.3 0.08 Feces Ca -0.184 1.452 12.0 0.2 Cu -0.192 1.658 10.6 0.1 Fe -0.153 1.713 10.6 0.1 K 0.234 -0.539 6.2 0.6 Mg -0.347 2.508 8.7 0.03 Mn -0.118 0.719 11.4 0.5 Na -0.515 1.763 11.9 0.1 P -0.141 1.140 11.7 0.3 S -0.042 0.383 8.9 0.7 Zn -0.306 2.600 11.8 0.02 Fur Ca 0.787 -0.569 12.0 0.6 Cu -0.004 0.035 6.6 1.0 Fe -0.040 0.082 8.1 0.9 K -0.465 2.060 11.5 0.06 Mg -0.097 1.189 11.9 0.3 Mn -0.036 0.209 11.8 0.8 Na -0.367 0.851 10.8 0.4 P -0.066 0.087 11.6 0.9 S -0.382 0.388 5.8 0.7 Zn 0.438 -1.376 8.2 0.2 Olfactory Bulb Ca 0.912 -0.445 9.3 0.7 Cu -0.007 0.157 8.2 0.9 .CC-BY-NC 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted January 30, 2026. ; https://doi.org/10.64898/2026.01.27.702117doi: bioRxiv preprint Tissue Element Estimate (KO–WT) t Ratio DF p-value Fe 0.171 -2.384 9.8 0.04 K -0.064 1.005 7.1 0.3 Mg 0.190 -2.180 5.4 0.08 Mn 0.093 -1.343 7.6 0.2 Na -0.007 0.108 6.0 0.9 P 0.099 -0.065 9.9 0.9 S 0.191 -0.205 11.6 0.8 Zn 0.311 -1.561 8.0 0.2 PMHTH Ca -1.348 0.869 7.6 0.4 Cu -0.008 0.156 12.0 0.9 Fe 0.087 -1.824 7.9 0.1 K -0.005 0.091 12.0 0.9 Mg -0.099 1.294 7.5 0.2 Mn 0.016 -0.453 9.1 0.7 Na 0.004 -0.091 10.5 0.9 P -1.013 0.728 6.4 0.5 S 0.334 -0.352 8.2 0.7 Zn 1.607 -2.118 9.7 0.06 Striatum Ca 0.002 -0.008 11.2 1.0 Cu 0.009 -0.091 7.4 0.9 Fe 0.105 -1.630 12.0 0.1 K 0.058 -0.886 8.4 0.4 Mg 0.060 -0.264 11.2 0.8 Mn -0.036 0.593 11.7 0.6 Na 0.083 -0.947 10.6 0.4 P 0.042 -0.825 11.3 0.4 S 0.022 -0.368 11.9 0.7 Zn -0.097 0.972 12.0 0.4 206 .CC-BY-NC 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted January 30, 2026. ; https://doi.org/10.64898/2026.01.27.702117doi: bioRxiv preprint Table 2. Effects of Fmr1-KO on tissue-specific elemental ratios. Levene’s test for homogeneity 207 of variances was performed on ALR-transformed elemental concentrations. Significant 208 differences are represented in bold. 209 Tissue Element F df1 df2 p-value Cecal Contents Ca 1.292 1 12 0.3 Cu 0.162 1 12 0.7 Fe 1.242 1 12 0.3 K 1.556 1 12 0.2 Mg 0.033 1 12 0.9 Mn 1.929 1 12 0.2 Na 1.198 1 12 0.3 P 2.563 1 12 0.1 S 1.314 1 12 0.3 Zn 0.095 1 12 0.8 Feces Ca 0.257 1 12 0.6 Cu 0.780 1 12 0.4 Fe 1.279 1 12 0.3 K 4.474 1 12 0.1 Mg 2.179 1 12 0.2 Mn 0.003 1 12 1.0 Na 0.068 1 12 0.8 P 0.210 1 12 0.7 S 0.396 1 12 0.5 Zn 0.131 1 12 0.7 Fur Ca 0.118 1 12 0.7 Cu 0.254 1 12 0.6 Fe 0.310 1 12 0.6 K 0.665 1 12 0.4 Mg 0.064 1 12 0.8 Mn 0.806 1 12 0.4 Na 0.023 1 12 0.9 P 0.288 1 12 0.6 S 0.529 1 12 0.5 Zn 1.048 1 12 0.3 Olfactory Bulb Ca 0.274 1 12 0.6 Cu 5.396 1 12 0.04 Fe 0.033 1 12 0.9 K 1.309 1 12 0.3 Mg 3.477 1 12 0.1 Mn 0.034 1 12 0.9 Na 7.040 1 12 0.02 P 0.041 1 12 0.8 .CC-BY-NC 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted January 30, 2026. ; https://doi.org/10.64898/2026.01.27.702117doi: bioRxiv preprint Tissue Element F df1 df2 p-value S 0.043 1 12 0.8 Zn 1.391 1 12 0.3 PMHTH Ca 0.411 1 12 0.5 Cu 0.301 1 12 0.6 Fe 1.737 1 12 0.2 K 0.607 1 12 0.5 Mg 0.876 1 12 0.4 Mn 0.078 1 12 0.8 Na 4.913 1 12 0.05 P 1.097 1 12 0.3 S 0.037 1 12 0.9 Zn 4.059 1 12 0.1 Striatum Ca 0.085 1 12 0.8 Cu 1.734 1 12 0.2 Fe 0.341 1 12 0.6 K 8.264 1 12 0.01 Mg 0.298 1 12 0.6 Mn 0.113 1 12 0.7 Na 0.066 1 12 0.8 P 0.048 1 12 0.8 S 0.333 1 12 0.6 Zn 0.101 1 12 0.8 210 .CC-BY-NC 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted January 30, 2026. ; https://doi.org/10.64898/2026.01.27.702117doi: bioRxiv preprint

211 The results of this work provided limited support for our hypothesis that FXS affects the 212 compositional ionome of ten elements (Ca, Cu, Fe, K, Mg, Mn, Na, S, P, and Zn) in neural and 213 somatic tissues. We discovered differences in overall elemental composition between Fmr1-214 knockout and wild-type neural tissue (e.g., in PMHTH). Yet, it remains unclear which elements 215 drive differences in overall composition between Fmr1-KO and WT PMHTH. Within several 216 tissues, we observed differences between genotypes in the mean and variability of the relative 217 contributions of individual elements. Overall, these differences support previous observations 218 that Fmr1-KO influences neural and gut function through electrolyte (e.g., Na, K, Mg) and metal 219 ion (e.g., Cu, Fe, Zn) dysregulation (Cheng et al., 2021; D’Antoni et al., 2024; Deng and 220 Klyachko, 2021; Napoli et al., 2016). 221 Neural tissues 222 Elemental analysis through compositional measurements (PCA) highlighted several 223 important features of this dataset. Clustering of neural tissue ellipses in PCA space indicated 224 ionomic similarity between genotypes, although PMHTH and striatum ellipses did not overlap. 225 Indeed, the PMHTH ellipse further demonstrated relatively higher ionomic variability than 226 olfactory bulb and striatum in both PC axes and Fmr1-KO significantly affected overall 227 elemental balance (MANOV A) of PMHTH but neither PCA nor means comparison of individual 228 elements detected significant differences. However, KO PMHTH had lower Na variance than 229 WT. The differences in PMHTH relative to other brain regions and tissues could be due to the 230 heterogeneity of the tissue (several regions combined), when clearly there are differences in 231 ionomic composition between tissues (Fig. 2). Similarly, FXS research often presents variable 232 .CC-BY-NC 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted January 30, 2026. ; https://doi.org/10.64898/2026.01.27.702117doi: bioRxiv preprint phenotypes between humans and mouse models, genetic strains of rodents models, and potential 233 increased variability as an overall phenotype (Kooy, 2003). 234 When examining individual elemental composition in tissues between genotypes, we 235 observed genotype-level differences in variance of either sodium or potassium in all neural 236 tissues. For example, we found that Fmr1-KO PMHTH had lower Na variance than WT, KO 237 Olfactory bulb had higher Na variance than WT, and KO striatum had lower K variance than 238 WT. This observation aligns with previous literature which demonstrates FMRP deficiency 239 promotes misregulation and mislocalization of ion channels (Deng and Klyachko, 2021). Given 240 the role in FMRP in mRNA transport and translation regulation, FXS pathologies may also be 241 expected to influence metal ion composition (e.g., for metalloenzymes; D’Antoni et al., 2024; 242 Napoli et al., 2016). 243 Other elements also showed significant differences in mean or variance contribution 244 between genotypes in the olfactory bulb. Specifically the mean contribution of iron and the 245 variance for copper were higher in Fmr1-KO animals. Previous work has shown increased iron 246 accumulation in the putamen of people with fragile X-associated tremor/ataxia, another FXS 247 premutation disorder due to a deficit in proteins responsible for eliminating iron from cells 248 (Ariza et al., 2017). It is possible that a similar mechanism underlies olfactory bulb iron increases 249 in the current study, but further studies are necessary. Copper is an important trace element for 250 neural signaling (D’Ambrosi and Rossi, 2015), and high levels of copper have been shown in the 251 serum of people with ASD (Li et al., 2014), consistent with our findings in the olfactory bulb. 252 Indeed, the olfactory bulb is one of the regions of the brain with the highest levels of copper 253 where it has an important role in neuronal excitability (Horning and Trombley, 2001). Examining 254 .CC-BY-NC 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted January 30, 2026. ; https://doi.org/10.64898/2026.01.27.702117doi: bioRxiv preprint tissue and region level effects of individual elements can provide greater insight into specific 255 mechanisms that may be altered in FXS contributing to global symptology. 256 Somatic Tissues 257 Consistent with results seen in neural tissues, we failed to detect differences in the 258 balance of 10 elements between genotypes in cecal contents, feces, and fur. However, we 259 detected a global effect of genotype and the interaction between tissue:genotype on zinc 260 contribution, but mean zinc contribution differed between Fmr1-KO and WT only in feces. Thus, 261 our results align with previous work which shows a lack of general pattern in Zn between tissues 262 of people with and without ASD (Do Nascimento et al., 2023). 263 Interestingly, feces of WT contained relatively more Mg and Zn than Fmr1-KO mice, 264 suggesting altered excretion of these elements (Prakash et al., 2015), which may include those 265 with zinc-binding motifs that interfere with FMRP-regulated proteins and pathways (Edbauer et 266 al., 2010). Previous work further showed children with ASD self-select diets higher in Mg and 267 lower in protein and Ca than children without ASD, and that selective diets were more likely to 268

in substantial deficiency of at least one nutrient (Zimmer et al., 2012). Interestingly, 269 evidence from children with ASD suggested Mg supplementation might alleviate neuro-270 behavioral symptoms (M Mousain-Bosc et al., 2006). Although this remains to be rigorously 271 tested, previous authors advocate for the relevance of understanding Mg action in FMR1 272 pathways (Mousain-Bosc, 2011). 273

and Future Directions 274 In conclusion, we found differences in ionomic composition between tissues in all 10 individual 275 elements; although, we found genotype-level differences in overall composition and individual 276 .CC-BY-NC 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted January 30, 2026. ; https://doi.org/10.64898/2026.01.27.702117doi: bioRxiv preprint elements only in some tissues. Yet, several limitations must be acknowledged. First, our study 277 did not use mechanistic assays to link elemental changes directly to FMRP function or 278 downstream molecular pathways. Additionally, other elements contribute to tissue-level ionomes 279 and are required for cellular processes and structures (e.g., carbon, nitrogen, silicon, etc.) which 280 we did not measure here. As a result, the total percentage of original sample mass measured was 281 in most cases quite small. The application of ALR ratios relative to Fv as (100 – total percentage) 282 may thus also obscure the true concentration-based shifts for elemental compositional networks 283 which were close to detection limits, and may mask or exaggerate differences in measured 284 concentration. 285 Nevertheless, analyzing multi-tissue, multi-elemental data allowed us to identify 286 systematic and genotype-dependent ionomic shifts in this Fmr1 knockout male mouse model. 287 The combined use of additive log-ratio (ALR) transformation and compositional data analysis 288 provided a robust framework for handling the constrained and interdependent nature of multi-289 elemental data (Greenacre, 2021). Beyond descriptive comparisons, ionomics provides a 290 powerful reverse-genetic lens by linking gene perturbations to coordinated shifts in elemental 291 balance across tissues, an approach that has proven effective in large-scale genetic screens 292 (Huang and Salt, 2016). Genome-wide RNAi ionomics screens in human cells have further 293 demonstrated that multielemental profiles can reveal previously unrecognized genes and 294 regulatory networks governing trace element metabolism (Malinouski et al., 2014). Because 295 elemental ratios integrate the net outcomes of transport, storage, and metabolic regulation, they 296 serve as sensitive system-level phenotypes that can help identify functional regulatory pathways, 297 effectively locating mechanistic “needles” within complex genomic “haystacks” (Elser and 298 Hamilton, 2007). Future work should integrate ionomic patterns with other biochemical systems 299 .CC-BY-NC 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted January 30, 2026. ; https://doi.org/10.64898/2026.01.27.702117doi: bioRxiv preprint (e.g., transcriptome, proteome) to identify mechanistic pathways underlying loss of FMRP and 300 disrupted elemental homeostasis. Validation of tissue-specific elemental signatures (e.g., Mg and 301 Zn in feces; Cu, Fe, and electrolytes in brain regions) may further enable biomarker discovery 302 and the development of ion-targeted therapeutic strategies for Fragile X Syndrome. 303

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